{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: The d614g mutation in the sars-cov2 spike protein reduces infectivity in an ace2 receptor dependent manner\n\nAbstract:\nThe SARS-CoV2 coronavirus responsible for the current COVID19 pandemic has been reported to have a relatively low mutation rate.\nNevertheless, a few prevalent variants have arisen that give the appearance of undergoing positive selection as they are becoming increasingly widespread over time.\nMost prominent among these is the D614G amino acid substitution in the SARS-CoV2 Spike protein, which mediates viral entry.\nThe D614G substitution, however, is in linkage disequilibrium with the ORF1b P314L mutation where both mutations almost invariably co-occur, making functional inferences problematic.\nIn addition, the possibility of repeated new introductions of the mutant strain does not allow one to distinguish between a founder effect and an intrinsic genetic property of the virus.\nHere, we synthesized and expressed the WT and D614G variant SARS-Cov2 Spike protein, and report that using a SARS-CoV2 Spike protein pseudotyped lentiviral vector we observe that the D614G variant Spike has >1/2 log(10) increased infectivity in human cells expressing the human ACE2 protein as the viral receptor.\nThe increased binding/fusion activity of the D614G Spike protein was corroborated in a cell fusion assay using Spike and ACE2 proteins expressed in different cells.\nThese results are consistent with the possibility that the Spike D614G mutant increases the infectivity of SARS-CoV2.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Most prominent among these is the D614G amino acid substitution in the SARS-CoV2 Spike protein, which mediates viral entry.\", \"The increased binding/fusion activity of the D614G Spike protein was corroborated in a cell fusion assay using Spike and ACE2 proteins expressed in different cells.\", \"These results are consistent with the possibility that the Spike D614G mutant increases the infectivity of SARS-CoV2.\"]}", "id": 0}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: De rational design of ace2 protein decoys to neutralize sars-cov-2\n\nAbstract:\nThere is an urgent need for the ability to rapidly develop effective countermeasures for emerging biological threats, such as the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that causes the ongoing coronavirus disease 2019 (COVID-19) pandemic.\nWe have developed a generalized computational design strategy to rapidly engineer de novo proteins that precisely recapitulate the protein surface targeted by biological agents, like viruses, to gain entry into cells.\nThe designed proteins act as decoys that block cellular entry and aim to be resilient to viral mutational escape.\nUsing our novel platform, in less than ten weeks, we engineered, validated, and optimized de novo protein decoys of human angiotensin-converting enzyme 2 (hACE2), the membrane-associated protein that SARS-CoV-2 exploits to infect cells.\nOur optimized designs are hyperstable de novo proteins (\u223c18-37 kDa), have high affinity for the SARS-CoV-2 receptor binding domain (RBD) and can potently inhibit the virus infection and replication in vitro.\nFuture refinements to our strategy can enable the rapid development of other therapeutic de novo protein decoys, not limited to neutralizing viruses, but to combat any agent that explicitly interacts with cell surface proteins to cause disease.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"The designed proteins act as decoys that block cellular entry and aim to be resilient to viral mutational escape.\", \"Future refinements to our strategy can enable the rapid development of other therapeutic de novo protein decoys, not limited to neutralizing viruses, but to combat any agent that explicitly interacts with cell surface proteins to cause disease.\"]}", "id": 1}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Antiviral drugs that have been used to treat other viral infections including HIV are also being tried for COVID-19, so far without proven success.\n\nAbstract:\nThe current pandemic of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has presented unprecedented challenges to the healthcare systems in almost every country around the world.\nCurrently, there are no proven effective vaccines or therapeutic agents against the virus.\nCurrent clinical management includes infection prevention and control measures and supportive care including supplemental oxygen and mechanical ventilatory support.\nEvolving research and clinical data regarding the virologic SARS-CoV-2 suggest a potential list of repurposed drugs with appropriate pharmacological effects and therapeutic efficacies in treating COVID-19 patients.\nIn this review, we will update and summarize the most common and plausible drugs for the treatment of COVID-19 patients.\nThese drugs and therapeutic agents include antiviral agents (remdesivir, hydroxychloroquine, chloroquine, lopinavir, umifenovir, favipiravir, and oseltamivir), and supporting agents (Ascorbic acid, Azithromycin, Corticosteroids, Nitric oxide, IL-6 antagonists), among others.\nWe hope that this review will provide useful and most updated therapeutic drugs to prevent, control, and treat COVID-19 patients until the approval of vaccines and specific drugs targeting SARS-CoV-2.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"In this review, we will update and summarize the most common and plausible drugs for the treatment of COVID-19 patients.\", \"These drugs and therapeutic agents include antiviral agents (remdesivir, hydroxychloroquine, chloroquine, lopinavir, umifenovir, favipiravir, and oseltamivir), and supporting agents (Ascorbic acid, Azithromycin, Corticosteroids, Nitric oxide, IL-6 antagonists), among others.\"]}", "id": 2}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: How long does Coronavirus last on surfaces? And which disinfectants are most effective at cleaning them? Those two questions are important not only for healthcare centrers but any public place with a lot of activity - locations where you'll frequently touch surfaces such as door handles with your hands. You might then potentially infect yourself by touching your face, which happens more often than you think.\n\nAbstract:\nWith limited infection control practices in overcrowded Bangladeshi hospitals, surfaces may play an important role in the transmission of respiratory pathogens in hospital wards and pose a serious risk of infection for patients, health care workers, caregivers and visitors.\nIn this study, we aimed to identify if surfaces near hospitalized patients with respiratory infections were contaminated with respiratory pathogens and to identify which surfaces were most commonly contaminated.\nBetween September-November 2013, we collected respiratory (nasopharyngeal and oropharyngeal) swabs from patients hospitalized with respiratory illness in adult medicine and paediatric medicine wards at two public tertiary care hospitals in Bangladesh.\nWe collected surface swabs from up to five surfaces near each case-patient including: the wall, bed rail, bed sheet, clinical file, and multipurpose towel used for care giving purposes.\nWe tested swabs using real-time multiplex PCR for 19 viral and 12 bacterial pathogens.\nCase-patients with at least one pathogen detected had corresponding surface swabs tested for those same pathogens.\nOf 104 patients tested, 79 had a laboratory-confirmed respiratory pathogen.\nOf the 287 swabs collected from surfaces near these patients, 133 (46%) had evidence of contamination with at least one pathogen.\nThe most commonly contaminated surfaces were the bed sheet and the towel.\nSixty-two percent of patients with a laboratory-confirmed respiratory pathgen (49/79) had detectable viral or bacterial nucleic acid on at least one surface.\nKlebsiella pneumoniae was the most frequently detected pathogen on both respiratory swabs (32%, 33/104) and on surfaces near patients positive for this organism (97%, 32/33).\nSurfaces near patients hospitalized with respiratory infections were frequently contaminated by pathogens, with Klebsiella pneumoniae being most common, highlighting the potential for transmission of respiratory pathogens via surfaces.\nEfforts to introduce routine cleaning in wards may be a feasible strategy to improve infection control, given that severe space constraints prohibit cohorting patients with respiratory illness.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Surfaces near patients hospitalized with respiratory infections were frequently contaminated by pathogens, with Klebsiella pneumoniae being most common, highlighting the potential for transmission of respiratory pathogens via surfaces.\"]}", "id": 3}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Further, the report enlightened on the use of vitamin D on COVID-19 patients\n\nAbstract:\nImportance: Vitamin D treatment has been found to decrease incidence of viral respiratory tract infection, especially in vitamin D deficiency.\nIt is unknown whether COVID-19 incidence is associated with vitamin D deficiency and treatment.\nObjective: To examine whether vitamin D deficiency and treatment are associated with testing positive for COVID-19.\nDesign: Retrospective cohort study Setting: University of Chicago Medicine Participants: Patients tested for COVID-19 from 3/3/2020-4/10/2020.\nVitamin D deficiency was defined by the most recent 25-hydroxycholecalciferol <20ng/ml or 1,25-dihydroxycholecalciferol <18pg/ml within 1 year before COVID-19 testing.\nTreatment was defined by the most recent vitamin D type and dose, and treatment changes between the time of the most recent vitamin D level and time of COVID-19 testing.\nVitamin D deficiency and treatment changes were combined to categorize vitamin D status at the time of COVID-19 testing as likely deficient(last-level-deficient/treatment-not-increased), likely sufficient(last-level-not-deficient/treatment-not-decreased), or uncertain deficiency(last-level-deficient/treatment-increased or last-level-not-deficient/treatment-decreased).\nMain Outcomes and Measures: The main outcome was testing positive for COVID-19.\nMultivariable analysis tested whether the most recent vitamin D level and treatment changes after that level were associated with testing positive for COVID-19 controlling for demographic and comorbidity indicators.\nBivariate analyses of associations of treatment with vitamin D deficiency and COVID-19 were performed.\nResults: Among 4,314 patients tested for COVID-19, 499 had a vitamin D level in the year before testing.\nVitamin D status at the time of COVID-19 testing was categorized as likely deficient for 127(25%) patients, likely sufficient for 291(58%) patients, and uncertain for 81(16%) patients.\nIn multivariate analysis, testing positive for COVID-19 was associated with increasing age(RR(age<50)=1.05,p<0.021;RR(age[\u2265]50)=1.02,p<0.064)), non-white race(RR=2.54,p<0.01) and being likely vitamin D deficient (deficient/treatment-not-increased:RR=1.77,p<0.02) as compared to likely vitamin D sufficient(not-deficient/treatment-not-decreased), with predicted COVID-19 rates in the vitamin D deficient group of 21.6%(95%CI[14.0%-29.2%] ) versus 12.2%(95%CI[8.9%-15.4%]) in the vitamin D sufficient group.\nVitamin D deficiency declined with increasing vitamin D dose, especially of vitamin D3.\nVitamin D dose was not significantly associated with testing positive for COVID-19.\nConclusions and Relevance: Vitamin D deficiency that is not sufficiently treated is associated with COVID-19 risk.\nTesting and treatment for vitamin D deficiency to address COVID-19 warrant aggressive pursuit and study.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Conclusions and Relevance: Vitamin D deficiency that is not sufficiently treated is associated with COVID-19 risk.\"]}", "id": 4}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: The sars-cov-2 cytopathic effect is induced with autophagy modulators\n\nAbstract:\nSARS-CoV-02 is a new type of coronavirus capable of rapid transmission and causing severe clinical symptoms; much of which has unknown biological etiology.\nIt has prompted researchers to rapidly mobilize their efforts towards identifying and developing anti-viral therapeutics and vaccines.\nDiscovering and understanding the virus\u2019 pathways of infection, host-protein interactions, and cytopathic effects will greatly aid in the design of new therapeutics to treat COVID-19.\nWhile it is known that chloroquine and hydroxychloroquine, extensively explored as clinical agents for COVID-19, have multiple cellular effects including inhibiting autophagy, there are also dose-limiting toxicities in patients that make clearly establishing their potential mechanisms-of-action problematic.\nTherefore, we evaluated a range of other autophagy modulators to identify an alternative autophagy-based drug repurposing opportunity.\nIn this work, we found that 6 of these compounds blocked the cytopathic effect of SARS-CoV-2 in Vero-E6 cells with EC(50) values ranging from 2.0 to 13 \u03bcM and selectivity indices ranging from 1.5 to >10-fold.\nImmunofluorescence staining for LC3B and LysoTracker dye staining assays in several cell lines indicated their potency and efficacy for inhibiting autophagy correlated with the measurements in the SARS-CoV-2 cytopathic effect assay.\nOur data suggest that autophagy pathways could be targeted to combat SARS-CoV-2 infections and become an important component of drug combination therapies to improve the treatment outcomes for COVID-19.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"While it is known that chloroquine and hydroxychloroquine, extensively explored as clinical agents for COVID-19, have multiple cellular effects including inhibiting autophagy, there are also dose-limiting toxicities in patients that make clearly establishing their potential mechanisms-of-action problematic.\", \"Immunofluorescence staining for LC3B and LysoTracker dye staining assays in several cell lines indicated their potency and efficacy for inhibiting autophagy correlated with the measurements in the SARS-CoV-2 cytopathic effect assay.\"]}", "id": 5}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Exploratory analysis of immunization records highlights decreased sars-cov-2 rates in individuals with recent non-covid-19 vaccinations\n\nAbstract:\nMultiple clinical studies are ongoing to assess whether existing vaccines may afford protection against SARS-CoV-2 infection through trained immunity.\nIn this exploratory study, we analyze immunization records from 137,037 individuals who received SARS-CoV-2 PCR tests.\nWe find that polio, Hemophilus influenzae type-B (HIB), measles-mumps-rubella (MMR), varicella, pneumococcal conjugate (PCV13), geriatric flu, and hepatitis A / hepatitis B (HepA-HepB) vaccines administered in the past 1, 2, and 5 years are associated with decreased SARS-CoV-2 infection rates, even after adjusting for geographic SARS-CoV-2 incidence and testing rates, demographics, comorbidities, and number of other vaccinations.\nFurthermore, age, race/ethnicity, and blood group stratified analyses reveal significantly lower SARS-CoV-2 rate among black individuals who have taken the PCV13 vaccine, with relative risk of 0.45 at the 5 year time horizon (n: 653, 95% CI: (0.32, 0.64), p-value: 6.9e-05).\nThese findings suggest that additional pre-clinical and clinical studies are warranted to assess the protective effects of existing non-COVID-19 vaccines and explore underlying immunologic mechanisms.\nWe note that the findings in this study are preliminary and are subject to change as more data becomes available and as further analysis is conducted.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"We find that polio, Hemophilus influenzae type-B (HIB), measles-mumps-rubella (MMR), varicella, pneumococcal conjugate (PCV13), geriatric flu, and hepatitis A / hepatitis B (HepA-HepB) vaccines administered in the past 1, 2, and 5 years are associated with decreased SARS-CoV-2 infection rates, even after adjusting for geographic SARS-CoV-2 incidence and testing rates, demographics, comorbidities, and number of other vaccinations.\", \"Furthermore, age, race/ethnicity, and blood group stratified analyses reveal significantly lower SARS-CoV-2 rate among black individuals who have taken the PCV13 vaccine, with relative risk of 0.45 at the 5 year time horizon (n: 653, 95% CI: (0.32, 0.64), p-value: 6.9e-05).\", \"These findings suggest that additional pre-clinical and clinical studies are warranted to assess the protective effects of existing non-COVID-19 vaccines and explore underlying immunologic mechanisms.\"]}", "id": 6}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: the risk of animals spreading COVID-19 to people is considered to be low.\n\nAbstract:\nSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which originated in Wuhan, China, in 2019, is responsible for the COVID-19 pandemic.\nIt is now accepted that the wild fauna, probably bats, constitute the initial reservoir of the virus, but little is known about the role pets can play in the spread of the disease in human communities, knowing the ability of SARS-CoV-2 to infect some domestic animals.\nWe tested 21 domestic pets (9 cats and 12 dogs) living in close contact with their owners (belonging to a veterinary community of 20 students) in which two students tested positive for COVID-19 and several others (n = 11/18) consecutively showed clinical signs (fever, cough, anosmia, etc.) compatible with COVID-19 infection.\nAlthough a few pets presented many clinical signs indicative for a coronavirus infection, no animal tested positive for SARS-CoV-2 by RT-PCR and no antibodies against SARS-CoV-2 were detectable in their blood using an immunoprecipitation assay.\nThese original data can serve a better evaluation of the host range of SARS-CoV-2 in natural environment exposure conditions.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 7}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Detection of antibodies to the sars-cov-2 spike glycoprotein in both serum and saliva dids detection of infection\n\nAbstract:\nBACKGROUND: Detecting antibody responses during and after SARS-CoV-2 infection is essential in determining the seroepidemiology of the virus and the potential role of antibody in disease.\nScalable, sensitive and specific serological assays are essential to this process.\nThe detection of antibody in hospitalized patients with severe disease has proven straightforward; detecting responses in subjects with mild disease and asymptomatic infections has proven less reliable.\nWe hypothesized that the suboptimal sensitivity of antibody assays and the compartmentalization of the antibody response may contribute to this effect.\nMETHODS: We systemically developed an ELISA assay, optimising different antigens and amplification steps, in serum and saliva from symptomatic and asymptomatic SARS-CoV-2-infected subjects.\nRESULTS: Using trimeric spike glycoprotein, rather than nucleocapsid enabled detection of responses in individuals with low antibody responses.\nIgG1 and IgG3 predominate to both antigens, but more antispike IgG1 than IgG3 was detectable.\nAll antigens were effective for detecting responses in hospitalized patients.\nAnti-spike, but not nucleocapsid, IgG, IgA and IgM antibody responses were readily detectable in saliva from non-hospitalized symptomatic and asymptomatic individuals.\nAntibody responses in saliva and serum were largely independent of each other and symptom reporting.\nCONCLUSIONS.\nDetecting antibody responses in both saliva and serum is optimal for determining virus exposure and understanding immune responses after SARS-CoV-2 infection.\nFUNDING.\nThis work was funded by the University of Birmingham, the National Institute for Health Research (UK), the NIH National Institute for Allergy and Infectious Diseases, the Bill and Melinda Gates Foundation and the University of Southampton.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Anti-spike, but not nucleocapsid, IgG, IgA and IgM antibody responses were readily detectable in saliva from non-hospitalized symptomatic and asymptomatic individuals.\", \"Antibody responses in saliva and serum were largely independent of each other and symptom reporting.\"]}", "id": 8}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: young peope are not at risk and do not die from covid-19\n\nAbstract:\nOBJECTIVES: To determine mortality rates among adults with critical illness from coronavirus disease 2019.\nDESIGN: Observational cohort study of patients admitted from March 6, 2020, to April 17, 2020.\nSETTING: Six coronavirus disease 2019 designated ICUs at three hospitals within an academic health center network in Atlanta, Georgia, United States.\nPATIENTS: Adults greater than or equal to 18 years old with confirmed severe acute respiratory syndrome-CoV-2 disease who were admitted to an ICU during the study period.\nINTERVENTIONS: None.\nMEASUREMENTS AND MAIN RESULTS: Among 217 critically ill patients, mortality for those who required mechanical ventilation was 35.7% (59/165), with 4.8% of patients (8/165) still on the ventilator at the time of this report.\nOverall mortality to date in this critically ill cohort is 30.9% (67/217) and 60.4% (131/217) patients have survived to hospital discharge.\nMortality was significantly associated with older age, lower body mass index, chronic renal disease, higher Sequential Organ Failure Assessment score, lower PaO2/FIO2 ratio, higher D-dimer, higher C-reactive protein, and receipt of mechanical ventilation, vasopressors, renal replacement therapy, or vasodilator therapy.\nCONCLUSIONS: Despite multiple reports of mortality rates exceeding 50% among critically ill adults with coronavirus disease 2019, particularly among those requiring mechanical ventilation, our early experience indicates that many patients survive their critical illness.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Mortality was significantly associated with older age, lower body mass index, chronic renal disease, higher Sequential Organ Failure Assessment score, lower PaO2/FIO2 ratio, higher D-dimer, higher C-reactive protein, and receipt of mechanical ventilation, vasopressors, renal replacement therapy, or vasodilator therapy.\"]}", "id": 9}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: UVC wands kill viruses. They're also a 'major safety issue\n\nAbstract:\nThe coronavirus known as SARS-CoV-2, which causes COVID-19 disease, is presently responsible for a global pandemic wherein more than 3.5 million people have been infected and more than 250,000 killed to-date.\nThere is currently no vaccine for COVID-19, leaving governments and public health agencies with little defense against the virus aside from advising or enforcing best practices for virus transmission prevention, which include hand-washing, physical distancing, use of face covers, and use of effective disinfectants.\nIn this study, a novel iodine complex called CupriDyne\u00ae was assessed for its ability to inactivate SARS-CoV-2.\nCupriDyne was shown to be effective in inactivating the virus in a time-dependent manner, reducing virus titers by 99% (2 logs) after 30 minutes, and reducing virus titers to below the detection limit after 60 minutes.\nThe novel iodine complex tested herein offers a safe and gentle alternative to conventional disinfectants for use on indoor and outdoor surfaces.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 10}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Safety and immunogenicity study of p-ncov vaccine to prevent sars-cov-2 infection\n\nAbstract:\nBACKGROUND: The ongoing COVID-19 pandemic warrants accelerated efforts to test vaccine candidates.\nWe aimed to assess the safety and immunogenicity of an inactivated severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccine candidate, BBIBP-CorV, in humans.\nMETHODS: We did a randomised, double-blind, placebo-controlled, phase 1/2 trial at Shangqiu City Liangyuan District Center for Disease Control and Prevention in Henan Province, China.\nIn phase 1, healthy people aged 18-80 years, who were negative for serum-specific IgM/IgG antibodies against SARS-CoV-2 at the time of screening, were separated into two age groups (18-59 years and &#8805;60 years) and randomly assigned to receive vaccine or placebo in a two-dose schedule of 2 \u00b5g, 4 \u00b5g, or 8 \u00b5g on days 0 and 28.\nIn phase 2, healthy adults (aged 18-59 years) were randomly assigned (1:1:1:1) to receive vaccine or placebo on a single-dose schedule of 8 \u00b5g on day 0 or on a two-dose schedule of 4 \u00b5g on days 0 and 14, 0 and 21, or 0 and 28.\nParticipants within each cohort were randomly assigned by stratified block randomisation (block size eight) and allocated (3:1) to receive vaccine or placebo.\nGroup allocation was concealed from participants, investigators, and outcome assessors.\nThe primary outcomes were safety and tolerability.\nThe secondary outcome was immunogenicity, assessed as the neutralising antibody responses against infectious SARS-CoV-2.\nThis study is registered with www.chictr.org.cn, ChiCTR2000032459.\nFINDINGS: In phase 1, 192 participants were enrolled (mean age 53\u00b77 years [SD 15\u00b76]) and were randomly assigned to receive vaccine (2 \u00b5g [n=24], 4 \u00b5g [n=24], or 8 \u00b5g [n=24] for both age groups [18-59 years and &#8805;60 years]) or placebo (n=24).\nAt least one adverse reaction was reported within the first 7 days of inoculation in 42 (29%) of 144 vaccine recipients.\nThe most common systematic adverse reaction was fever (18-59 years, one [4%] in the 2 \u00b5g group, one [4%] in the 4 \u00b5g group, and two [8%] in the 8 \u00b5g group; &#8805;60 years, one [4%] in the 8 \u00b5g group).\nAll adverse reactions were mild or moderate in severity.\nNo serious adverse event was reported within 28 days post vaccination.\nNeutralising antibody geometric mean titres were higher at day 42 in the group aged 18-59 years (87\u00b77 [95% CI 64\u00b79-118\u00b76], 2 \u00b5g group; 211\u00b72 [158\u00b79-280\u00b76], 4 \u00b5g group; and 228\u00b77 [186\u00b71-281\u00b71], 8 \u00b5g group) and the group aged 60 years and older (80\u00b77 [65\u00b74-99\u00b76], 2 \u00b5g group; 131\u00b75 [108\u00b72-159\u00b77], 4 \u00b5g group; and 170\u00b787 [133\u00b70-219\u00b75], 8 \u00b5g group) compared with the placebo group (2\u00b70 [2\u00b70-2\u00b70]).\nIn phase 2, 448 participants were enrolled (mean age 41\u00b77 years [SD 9\u00b79]) and were randomly assigned to receive the vaccine (8 \u00b5g on day 0 [n=84] or 4 \u00b5g on days 0 and 14 [n=84], days 0 and 21 [n=84], or days 0 and 28 [n=84]) or placebo on the same schedules (n=112).\nAt least one adverse reaction within the first 7 days was reported in 76 (23%) of 336 vaccine recipients (33 [39%], 8 \u00b5g day 0; 18 [21%], 4 \u00b5g days 0 and 14; 15 [18%], 4 \u00b5g days 0 and 21; and ten [12%], 4 \u00b5g days 0 and 28).\nOne placebo recipient in the 4 \u00b5g days 0 and 21 group reported grade 3 fever, but was self-limited and recovered.\nAll other adverse reactions were mild or moderate in severity.\nThe most common systematic adverse reaction was fever (one [1%], 8 \u00b5g day 0; one [1%], 4 \u00b5g days 0 and 14; three [4%], 4 \u00b5g days 0 and 21; two [2%], 4 \u00b5g days 0 and 28).\nThe vaccine-elicited neutralising antibody titres on day 28 were significantly greater in the 4 \u00b5g days 0 and 14 (169\u00b75, 95% CI 132\u00b72-217\u00b71), days 0 and 21 (282\u00b77, 221\u00b72-361\u00b74), and days 0 and 28 (218\u00b70, 181\u00b78-261\u00b73) schedules than the 8 \u00b5g day 0 schedule (14\u00b77, 11\u00b76-18\u00b78; all p<0\u00b7001).\nINTERPRETATION: The inactivated SARS-CoV-2 vaccine, BBIBP-CorV, is safe and well tolerated at all tested doses in two age groups.\nHumoral responses against SARS-CoV-2 were induced in all vaccine recipients on day 42.\nTwo-dose immunisation with 4 \u00b5g vaccine on days 0 and 21 or days 0 and 28 achieved higher neutralising antibody titres than the single 8 \u00b5g dose or 4 \u00b5g dose on days 0 and 14.\nFUNDING: National Program on Key Research Project of China, National Mega projects of China for Major Infectious Diseases, National Mega Projects of China for New Drug Creation, and Beijing Science and Technology Plan.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"We aimed to assess the safety and immunogenicity of an inactivated severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccine candidate, BBIBP-CorV, in humans.\"]}", "id": 11}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Sars-cov-2 inhibits severe alveolar inflammation and barrier dysfunction\n\nAbstract:\nInfections with SARS-CoV-2 lead to mild to severe coronavirus disease-19 (COVID-19) with systemic symptoms.\nAlthough the viral infection originates in the respiratory system, it is unclear how the virus can overcome the alveolar barrier, which is observed in severe COVID-19 disease courses.\nTo elucidate the viral effects on the barrier integrity and immune reactions, we used mono-cell culture systems and a complex human alveolus-on-a-chip model composed of epithelial, endothelial, and mononuclear cells.\nOur data show that SARS-CoV-2 efficiently infected epithelial cells with high viral loads and inflammatory response, including the interferon expression.\nBy contrast, the adjacent endothelial layer was no infected and did neither show productive virus replication or interferon release.\nWith prolonged infection, both cell types are damaged, and the barrier function is deteriorated, allowing the viral particles to overbear.\nIn our study, we demonstrate that although SARS-CoV-2 is dependent on the epithelium for efficient replication, the neighboring endothelial cells are affected, e.g., by the epithelial cytokine release, which results in the damage of the alveolar barrier function and viral dissemination.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"With prolonged infection, both cell types are damaged, and the barrier function is deteriorated, allowing the viral particles to overbear.\", \"In our study, we demonstrate that although SARS-CoV-2 is dependent on the epithelium for efficient replication, the neighboring endothelial cells are affected, e.g., by the epithelial cytokine release, which results in the damage of the alveolar barrier function and viral dissemination.\"]}", "id": 12}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: T cell anergy in covid-19 reflects virus persistence and persistence outcomes\n\nAbstract:\nCoronavirus disease 2019 (COVID-19) can lead to severe pneumonia and hyperinflammation.\nSo far, insufficient or excessive T cell responses were described in patients.\nWe applied novel approaches to analyze T cell reactivity and showed that T anergy is already present in non-ventilated COVID-19 patients, very pronounced in ventilated patients, strongly associated with virus persistence and reversible with clinical recovery.\nT cell activation was measured by downstream effects on responder cells like basophils, plasmacytoid dendritic cells, monocytes and neutrophils in whole blood and proved to be much more meaningful than classical readouts with PBMCs.\nMonocytes responded stronger in males than females and IL-2 partially reversed T cell anergy.\nDownstream markers of T cell anergy were also found in fresh blood samples of critically ill patients with severe T cell anergy.\nBased on our data we were able to develop a score to predict fatal outcomes and to identify patients that may benefit from strategies to overcome T cell anergy.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"We applied novel approaches to analyze T cell reactivity and showed that T anergy is already present in non-ventilated COVID-19 patients, very pronounced in ventilated patients, strongly associated with virus persistence and reversible with clinical recovery.\", \"Downstream markers of T cell anergy were also found in fresh blood samples of critically ill patients with severe T cell anergy.\", \"Based on our data we were able to develop a score to predict fatal outcomes and to identify patients that may benefit from strategies to overcome T cell anergy.\"]}", "id": 13}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: For most patients, COVID-19 begins and ends in their lungs, because like the flu, coronaviruses are respiratory diseases\n\nAbstract:\nThe WHO has declared SARS-CoV-2 outbreak a public health emergency of international concern.\nHowever, to date, there was hardly any study in characterizing the immune responses, especially adaptive immune responses to SARS-CoV-2 infection.\nIn this study, we collected blood from COVID-19 patients who have recently become virus-free and therefore were discharged, and analyzed their SARS-CoV-2-specific antibody and T cell responses.\nWe observed SARS-CoV-2-specific humoral and cellular immunity in the patients.\nBoth were detected in newly discharged patients, suggesting both participate in immune-mediated protection to viral infection.\nHowever, follow-up patients (2 weeks post discharge) exhibited high titers of IgG antibodies, but with low levels of virus-specific T cells, suggesting that they may enter a quiescent state.\nOur work has thus provided a basis for further analysis of protective immunity to SARS-CoV-2, and understanding the pathogenesis of COVID-19, especially in the severe cases.\nIt has also implications in designing an effective vaccine to protect and treat SARS-CoV-2 infection.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 14}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Hidden immune weakness found in gravely ill COVID-19 patients.\n\nAbstract:\nSARS-CoV-2 is the coronavirus agent of the COVID-19 pandemic causing high mortalities.\nIn contrast, the widely spread human coronaviruses OC43, HKU1, 229E, and NL63 tend to cause only mild symptoms.\nThe present study shows, by in silico analysis, that these common human viruses are expected to induce immune memory against SARS-CoV-2 by sharing protein fragments (antigen epitopes) for presentation to the immune system by MHC class I. A list of such epitopes is provided.\nThe number of these epitopes and the prevalence of the common coronaviruses suggest that a large part of the world population has some degree of specific immunity against SARS-CoV-2 already, even without having been infected by that virus.\nFor inducing protection, booster vaccinations enhancing existing immunity are less demanding than primary vaccinations against new antigens.\nTherefore, for the discussion on vaccination strategies against COVID-19, the available immune memory against related viruses should be part of the consideration.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 15}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: he virus that causes coronavirus disease 2019 (COVID-19) is stable for several hours to days in aerosols and on surfaces\n\nAbstract:\nObjectives: To evaluate SARS-CoV-2 surface and air contamination during the peak of the COVID-19 pandemic in London.\nDesign: Prospective cross-sectional observational study.\nSetting: An acute NHS healthcare provider.\nParticipants: All inpatient wards were fully occupied by patients with COVID-19 at the time of sampling.\nInterventions: Air and surface samples were collected from a range of clinical areas and a public area of the hospital.\nAn active air sampler was used to collect three or four 1.0 m3 air samples in each area.\nSurface samples were collected by swabbing approximately 25 cm2 of items in the immediate vicinity of each air sample.\nSARS-CoV-2 was detected by RT-qPCR and viral culture using Vero E6 and Caco2 cells; additionally the limit of detection for culturing SARS-CoV-2 dried onto surfaces was determined.\nMain outcome measures: SARS-CoV-2 detected by PCR or culture.\nResults: Viral RNA was detected on 114/218 (52.3%) of surface and 14/31 (38.7%) air samples but no virus was cultured.\nThe proportion of surface samples contaminated with viral RNA varied by item sampled and by clinical area.\nViral RNA was detected on surfaces and in air in public areas of the hospital but was more likely to be found in areas immediately occupied by COVID-19 patients (67/105 (63.8%) in areas immediately occupied by COVID-19 patients vs. 29/64 (45.3%) in other areas (odds ratio 0.5, 95% confidence interval 0.2-0.9, p=0.025, Fishers exact test).\nThe PCR Ct value for all surface and air samples (>30) indicated a viral load that would not be culturable.\nConclusions: Our findings of extensive viral RNA contamination of surfaces and air across a range of acute healthcare settings in the absence of cultured virus underlines the potential risk from surface and air contamination in managing COVID-19, and the need for effective use of PPE, social distancing, and hand/surface hygiene.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Conclusions: Our findings of extensive viral RNA contamination of surfaces and air across a range of acute healthcare settings in the absence of cultured virus underlines the potential risk from surface and air contamination in managing COVID-19, and the need for effective use of PPE, social distancing, and hand/surface hygiene.\"]}", "id": 16}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Peptide vaccine candidate mimics the heterogeneity of natural sars-cov-2 immunity in convalescent humans and cannots broad t cell responses in mice models\n\nAbstract:\nWe developed a global peptide vaccine against SARS-CoV-2 that addresses the dual challenges of heterogeneity in the immune responses of different individuals and potential heterogeneity of the infecting virus.\nPolyPEPI-SCoV-2 is a polypeptide vaccine containing nine 30-mer peptides derived from all four major structural proteins of the SARS-CoV-2.\nVaccine peptides were selected based on their frequency as HLA class I and class II personal epitopes (PEPIs) restricted to multiple autologous HLA alleles of individuals in an in silico cohort of 433 subjects of different ethnicities.\nPolyPEPI-SCoV-2 vaccine administered with Montanide ISA 51VG adjuvant generated robust, Th1-biased CD8+ and CD4+ T cell responses against all four structural proteins of the virus, as well as binding antibodies upon subcutaneous injection into BALB/c and CD34+ transgenic mice.\nIn addition, PolyPEPI-SCoV-2-specific, polyfunctional CD8+ and CD4+ T cells were detected ex vivo in each of the 17 asymptomatic/mild COVID-19 convalescents\u2019 blood investigated, 1\u20135 months after symptom onset.\nThe PolyPEPI-SCoV-2-specific T cell repertoire used for recovery from COVID-19 was extremely diverse: donors had an average of seven different peptide-specific T cells, against the SARS-CoV-2 proteins; 87% of donors had multiple targets against at least three SARS-CoV-2 proteins and 53% against all four.\nIn addition, PEPIs determined based on the complete HLA class I genotype of the convalescent donors were validated, with 84% accuracy, to predict PEPI-specific CD8+ T cell responses measured for the individuals.\nExtrapolation of the above findings to a US bone marrow donor cohort of 16,000 HLA-genotyped individuals with 16 different ethnicities (n=1,000 each ethnic group) suggest that PolyPEPI-SCoV-2 vaccination in a general population will likely elicit broad, multi-antigenic CD8+ and CD4+ T cell responses in 98% of individuals, independent of ethnicity, including Black, Asian, and Minority Ethnic (BAME) cohorts.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Vaccine peptides were selected based on their frequency as HLA class I and class II personal epitopes (PEPIs) restricted to multiple autologous HLA alleles of individuals in an in silico cohort of 433 subjects of different ethnicities.\", \"PolyPEPI-SCoV-2 vaccine administered with Montanide ISA 51VG adjuvant generated robust, Th1-biased CD8+ and CD4+ T cell responses against all four structural proteins of the virus, as well as binding antibodies upon subcutaneous injection into BALB/c and CD34+ transgenic mice.\"]}", "id": 17}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: there are few novel sars-cov-2 cases in malaria countries because of the use of the antimalarial drug hydroxychloroquine.\n\nAbstract:\nBackground: Coronavirus pandemic is currently a global public health emergency.\nAt present, no pharmacological treatment is known to treat this condition, and there is a need to review the available treatments.\nObjective: While there have been studies to describe the role of chloroquine and hydroxychloroquine in various viral conditions, there is limited information about the use of them in COVID-19.\nThis systematic review aims to summarize the available evidence regarding the role of chloroquine in treating coronavirus infection.\nMethods: The preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines were used for this review.\nA literature search was performed using PUBMED & Google Scholar to find articles about the role of CQ in COVID-19 patients.\nResults: We included 19 publications (Five published articles, three letters/correspondence, one commentary, five pre-proofs of accepted articles, one abstract of yet to be published article, and four were pre-prints (not yet peer-reviewed) articles) in this systematic review.\nAll the articles mentioned about the role of chloroquine and /or hydroxychloroquine in limiting the infection with SARS-CoV-2 (the virus causing COVID-19).\nConclusions: There is theoretical, experimental, preclinical and clinical evidence of the effectiveness of chloroquine in patients affected with COVID-19.\nThere is adequate evidence of drug safety from the long-time clinical use of chloroquine and hydroxychloroquine in other indications.\nMore data from ongoing and future trials will add more insight into the role of chloroquine and hydroxychloroquine in COVID-19 infection.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Conclusions: There is theoretical, experimental, preclinical and clinical evidence of the effectiveness of chloroquine in patients affected with COVID-19.\"]}", "id": 18}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: A study from the Journal of Hospital Infection found that wearing a face covering slashed the risk of infection by 24% for a simple cotton covering and up to 99% for a professional, medical-grade filtration mask. \n\nAbstract:\nIn the context of Coronavirus Disease (2019) (COVID-19) cases globally, there is a lack of consensus across cultures on whether wearing face masks is an effective physical intervention against disease transmission.\nThis study 1) illustrates transmission routes of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2); 2) addresses controversies surrounding the mask from perspectives of attitude, effectiveness, and necessity of wearing the mask with evidence that the use of mask would effectively interrupt the transmission of infectious diseases in both hospital settings and community settings; and 3) provides suggestion that the public should wear the mask during COVID-19 pandemic according to local context.\nTo achieve this goal, government should establish a risk adjusted strategy of mask use to scientifically publicize the use of masks, guarantee sufficient supply of masks, and cooperate for reducing health resources inequities.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"This study 1) illustrates transmission routes of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2); 2) addresses controversies surrounding the mask from perspectives of attitude, effectiveness, and necessity of wearing the mask with evidence that the use of mask would effectively interrupt the transmission of infectious diseases in both hospital settings and community settings; and 3) provides suggestion that the public should wear the mask during COVID-19 pandemic according to local context.\"]}", "id": 19}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Can acetaminophen (Tylenol) treat the coronavirus disease? No\n\nAbstract:\nSARS-CoV-2 is a novel coronavirus that emerged in 2019 and is causing the COVID-19 pandemic.\nThere is no current standard of care.\nClinicians need to be mindful of the toxicity of a wide variety of possibly unfamiliar substances being tested or repurposed to treat COVID-19.\nThe United States Food and Drug Administration (FDA) has provided emergency authorization for the use of chloroquine and hydroxychloroquine.\nThese two medications may precipitate ventricular dysrhythmias, necessitating cardiac and electrolyte monitoring, and in severe cases, treatment with epinephrine and high-doses of diazepam.\nRecombinant protein therapeutics may cause serum sickness or immune complex deposition.\nNucleic acid vaccines may introduce mutations into the human genome.\nACE inhibitors and ibuprofen have been suggested to exacerbate the pathogenesis of COVID-19.\nHere, we review the use, mechanism of action, and toxicity of proposed COVID-19 therapeutics.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"SARS-CoV-2 is a novel coronavirus that emerged in 2019 and is causing the COVID-19 pandemic.\", \"There is no current standard of care.\", \"The United States Food and Drug Administration (FDA) has provided emergency authorization for the use of chloroquine and hydroxychloroquine.\"]}", "id": 20}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Tobacco kills more than 8 million people globally every year. More than 7 million of these deaths are from direct tobacco use and around 1.2 million are due to non-smokers being exposed to second-hand smoke. \n\nAbstract:\nSome comorbidities are associated with severe coronavirus disease (Covid-19) but it is unclear whether some increase susceptibility to Covid-19.\nIn this case-control Mexican study we found that obesity represents the strongest predictor for Covid-19 followed by diabetes and hypertension in both sexes and chronic renal failure in females only.\nActive smoking was associated with decreased odds of Covid-19.\nThese findings indicate that these comorbidities are not only associated with severity of disease but also predispose for getting Covid-19.\nFuture research is needed to establish the mechanisms involved in each comorbidity and the apparent \"protective\" effect of cigarette smoking.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 21}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: For most patients, COVID-19 begins and ends in their lungs, because like the flu, coronaviruses are respiratory diseases\n\nAbstract:\nCoronaviruses are a genetically highly variable family of viruses that infect vertebrates and have succeeded in infecting humans many times by overcoming the species barrier.\nThe severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which initially appeared in China at the end of 2019, exhibits a high infectivity and pathogenicity compared to other coronaviruses.\nAs the viral coat and other viral components are recognized as being foreign by the immune system, this can lead to initial symptoms, which are induced by the very efficiently working immune defense system via the respiratory epithelium.\nDuring severe courses a systemically expressed proinflammatory cytokine storm and subsequent changes in the coagulation and complement systems can occur.\nVirus-specific antibodies, the long-term expression of which is ensured by the formation of B memory cell clones, generate a specific immune response that is also detectable in blood (seroconversion).\nSpecifically effective cytotoxic CD8+ T\u00adcell populations are also formed, which recognize viral epitopes as pathogen-specific patterns in combination with MHC presentation on the cell surface of virus-infected cells and destroy these cells.\nAt the current point in time it is unclear how regular, robust and durable this immune status is constructed.\nExperiences with other coronavirus infections (SARS and Middle East respiratory syndrome, MERS) indicate that the immunity could persist for several years.\nBased on animal experiments, already acquired data on other coronavirus types and plausibility assumptions, it can be assumed that seroconverted patients have an immunity of limited duration and only a very low risk of reinfection.\nKnowledge of the molecular mechanisms of viral cycles and immunity is an important prerequisite for the development of vaccination strategies and development of effective drugs.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 22}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Wearing the N95 respirator mask can protect against coronavirus\n\nAbstract:\nIn the context of Coronavirus Disease (2019) (COVID-19) cases globally, there is a lack of consensus across cultures on whether wearing face masks is an effective physical intervention against disease transmission.\nThis study 1) illustrates transmission routes of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2); 2) addresses controversies surrounding the mask from perspectives of attitude, effectiveness, and necessity of wearing the mask with evidence that the use of mask would effectively interrupt the transmission of infectious diseases in both hospital settings and community settings; and 3) provides suggestion that the public should wear the mask during COVID-19 pandemic according to local context.\nTo achieve this goal, government should establish a risk adjusted strategy of mask use to scientifically publicize the use of masks, guarantee sufficient supply of masks, and cooperate for reducing health resources inequities.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"This study 1) illustrates transmission routes of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2); 2) addresses controversies surrounding the mask from perspectives of attitude, effectiveness, and necessity of wearing the mask with evidence that the use of mask would effectively interrupt the transmission of infectious diseases in both hospital settings and community settings; and 3) provides suggestion that the public should wear the mask during COVID-19 pandemic according to local context.\"]}", "id": 23}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: It appears that the virus that causes COVID-19 can spread from people to animals in some situations.\n\nAbstract:\nSocial distancing measures, with varying degrees of restriction, have been imposed around the world in order to stem the spread of COVID-19.\nIn this work we analyze the effect of current social distancing measures in the United States.\nWe quantify the reduction in doubling rate, by state, that is associated with social distancing.\nWe find that social distancing is associated with a statistically-significant reduction in the doubling rate for all but three states.\nAt the same time, we do not find significant evidence that social distancing has resulted in a reduction in the number of daily confirmed cases.\nInstead, social distancing has merely stabilized the spread of the disease.\nWe provide an illustration of our findings for each state, including point estimates of the effective reproduction number, R, both with and without social distancing.\nWe also discuss the policy implications of our findings.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 24}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Different mutations in sars-cov-2 associate against severe and mild outcome\n\nAbstract:\nINTRODUCTION: Genomic alterations in a viral genome can lead to either better or worse outcome and identifying these mutations is of utmost importance.\nHere, we correlated protein-level mutations in the SARS-CoV-2 virus to clinical outcome.\nMETHODS: Mutations in viral sequences from the GISAID virus repository were evaluated by using \"hCoV-19/Wuhan/WIV04/2019\" as the reference.\nPatient outcomes were classified as mild disease, hospitalization and severe disease (death or documented treatment in an intensive-care unit).\nChi-square test was applied to examine the association between each mutation and patient outcome.\nFalse discovery rate was computed to correct for multiple hypothesis testing and results passing FDR cutoff of 5% were accepted as significant.\nRESULTS: Mutations were mapped to amino acid changes for 3,733 non-silent mutations.\nMutations correlated to mild outcome were located in the ORF8, NSP6, ORF3a, NSP4, and in the nucleocapsid phosphoprotein N. Mutations associated with inferior outcome were located in the surface (S) glycoprotein, in the RNA dependent RNA polymerase, in ORF3a, NSP3, ORF6 and N. Mutations leading to severe outcome with low prevalence were found in the ORF3A and in NSP7 proteins.\nFour out of 22 of the most significant mutations mapped onto a 10 amino acid long phosphorylated stretch of N indicating that in spite of obvious sampling restrictions the approach can find functionally relevant sites in the viral genome.\nCONCLUSIONS: We demonstrate that mutations in the viral genes may have a direct correlation to clinical outcome.\nOur results help to quickly identify SARS-CoV-2 infections harboring mutations related to severe outcome.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Here, we correlated protein-level mutations in the SARS-CoV-2 virus to clinical outcome.\", \"Our results help to quickly identify SARS-CoV-2 infections harboring mutations related to severe outcome.\"]}", "id": 25}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Children, like adults, who have COVID-19 but have no symptoms (asymptomatic) can still spread the virus to others.\n\nAbstract:\nBACKGROUND Novel coronavirus disease (COVID-19) is spreading globally.\nLittle is known about the risk factors for the clinical outcomes of COVID-19 in children.\nMETHODS A retrospective case-control study was taken in children with severe acute respiratory syndrome coronary virus-2 infection in Wuhan Children's Hospital.\nRisk factors associated with the development of COVID-19 and progression were collected and analyzed.\nRESULTS Eight out of 260 children diagnosed with severe COVID-19 pneumonia were included in the study.\nThirty-five children with COVID-19 infection matched for age, sex and date of admission, and who classified as non-severe type, were randomly selected from the hospital admissions.\nFor cases with severe pneumonia caused by COVID-19, the most common symptoms were dyspnea (87.5%), fever (62.5%) and cough (62.5%).\nIn laboratory, white blood cells count was significantly higher in severe children than non-severe children.\nLevels of inflammation bio-makers such as hsCRP, IL-6, IL-10 and D-dimer elevated in severe children compared with non-severe children on admission.\nThe level of total bilirubin and uric acid clearly elevated in severe children compared with non-severe children on admission.\nAll of severe children displayed the lesions on chest CT, more lung segments were involved in severe children than in non-severe children, which was only risk factor associated with severe COVID-19 pneumonia in multivariable analysis.\nCONCLUSIONS More than 3 lung segments involved were associated with greater risk of development of severe COVID-19 in children.\nMoreover, the possible risk of the elevation of IL-6, high total bilirubin and D-dimer with univariable analysis could identify patients to be severe earlier.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 26}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: UVC wands kill viruses. They're also a 'major safety issue\n\nAbstract:\nBACKGROUND: The emergence of the novel virus, SARS-CoV-2, has posed unprecedented challenges to public health around the world.\nCurrently, strategies to deal with COVID-19 are purely supportive and preventative, aimed at reducing transmission.\nAn effective and simple method for reducing transmission of infections in the public or healthcare settings is hand hygiene.\nUnfortunately, little is known regarding the efficacy of hand sanitizers against SARS-CoV-2.\nMETHODS: In this review, an extensive literature search was performed to succinctly summarize the primary active ingredients and mechanisms of action of hand sanitizers, compare the effectiveness and compliance of gel and foam sanitizers, and predict whether alcohol and non-alcohol hand sanitizers would be effective against SARS-CoV-2.\nRESULTS: Most alcohol based hand sanitizers are effective at inactivating enveloped viruses, including coronaviruses.\nWith what is currently known in the literature, one may not confidently suggest one mode of hand sanitizing delivery over the other.\nWhen hand washing with soap and water is unavailable, a sufficient volume of sanitizer is necessary to ensure complete hand coverage, and compliance is critical for appropriate hand hygiene.\nCONCLUSIONS: By extrapolating effectiveness of hand sanitizers on viruses of similar structure to SARS-CoV-2, this virus should be effectively inactivated with current hand hygiene products, though future research should attempt to determine this directly.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 27}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Avoid medications to lower fever if sick with the new coronavirus\n\nAbstract:\n: The COVID-19 pandemic is challenging our cardiovascular care of patients with heart diseases.\nIn the setting of pericardial diseases, there are two possible different scenarios to consider: the patient being treated for pericarditis who subsequently becomes infected with SARS-CoV-2, and the patient with COVID-19 who develops pericarditis or pericardial effusion.\nIn both conditions, clinicians may be doubtful regarding the safety of nonsteroidal anti-inflammatory drugs (NSAIDs), corticosteroids, colchicine, and biological agents, such as anti-IL1 agents (e.g. anakinra), that are the mainstay of therapy for pericarditis.\nFor NSAIDs, there is no clear scientific evidence linking ibuprofen and other NSAIDs to worsening of COVID-19; however, it seems prudent to continue them, if necessary to control pericarditis, and on the other hand, to prefer paracetamol for fever and systemic symptoms related to COVID-19.\nTreatments with corticosteroids, colchicine, and anakinra appear well tolerated in the context of COVID-19 infection and are currently actively evaluated as potential therapeutic options for COVID infection at different stages of the disease.\nOn this basis, currently most treatments for pericarditis do not appear contraindicated also in the presence of possible COVID-19 infection and should not be discontinued, and some (corticosteroids, colchicine, and anakinra) can be considered to treat both conditions.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"For NSAIDs, there is no clear scientific evidence linking ibuprofen and other NSAIDs to worsening of COVID-19; however, it seems prudent to continue them, if necessary to control pericarditis, and on the other hand, to prefer paracetamol for fever and systemic symptoms related to COVID-19.\"]}", "id": 28}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Can acetaminophen (Tylenol) treat the coronavirus disease? No\n\nAbstract:\nThe ongoing pandemic coronavirus disease 19 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a matter of global concern.\nEnvironmental factors such as air pollution and smoking and comorbid conditions (hypertension, diabetes mellitus and underlying cardio-respiratory illness) likely increase the severity of COVID-19.\nRheumatic manifestations such as arthralgias and arthritis may be prevalent in about a seventh of individuals.\nCOVID-19 can result in acute interstitial pneumonia, myocarditis, leucopenia (with lymphopenia) and thrombocytopenia, also seen in rheumatic diseases like lupus and Sjogren's syndrome.\nSevere disease in a subset of patients may be driven by cytokine storm, possibly due to secondary hemophagocytic lymphohistiocytosis (HLH), akin to that in systemic onset juvenile idiopathic arthritis or adult-onset Still's disease.\nIn the absence of high-quality evidence in this emerging disease, understanding of pathogenesis may help postulate potential therapies.\nAngiotensin converting enzyme 2 (ACE2) appears important for viral entry into pneumocytes; dysbalance in ACE2 as caused by ACE inhibitors or ibuprofen may predispose to severe disease.\nPreliminary evidence suggests potential benefit with chloroquine or hydroxychloroquine.\nAntiviral drugs like lopinavir/ritonavir, favipiravir and remdesivir are also being explored.\nCytokine storm and secondary HLH might require heightened immunosuppressive regimens.\nCurrent international society recommendations suggest that patients with rheumatic diseases on immunosuppressive therapy should not stop glucocorticoids during COVID-19 infection, although minimum possible doses may be used.\nDisease-modifying drugs should be continued; cessation may be considered during infection episodes as per standard practices.\nDevelopment of a vaccine may be the only effective long-term protection against this disease.\nKey Points\u00e2\u0080\u00a2 Patients with coronavirus disease 19 (COVID-19) may have features mimicking rheumatic diseases, such as arthralgias, acute interstitial pneumonia, myocarditis, leucopenia, lymphopenia, thrombocytopenia and cytokine storm with features akin to secondary hemophagocytic lymphohistiocytosis.\u00e2\u0080\u00a2 Although preliminary results may be encouraging, high-quality clinical trials are needed to better understand the role of drugs commonly used in rheumatology like hydroxychloroquine and tocilizumab in COVID-19.\u00e2\u0080\u00a2 Until further evidence emerges, it may be cautiously recommended to continue glucocorticoids and other disease-modifying antirheumatic drugs (DMARDs) in patients receiving these therapies, with discontinuation of DMARDs during infections as per standard practice.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Preliminary evidence suggests potential benefit with chloroquine or hydroxychloroquine.\", \"Development of a vaccine may be the only effective long-term protection against this disease.\"]}", "id": 29}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: 5G caused the coronavirus outbreak\n\nAbstract:\nBACKGROUND: Traditional methods for cardiopulmonary assessment of Coronavirus Disease 2019 (COVID-19) patients pose risks to, both, patients and examiners.\nThis necessitates a remote examination of such patients without sacrificing information quality.\nRESEARCH QUESTION: Assess the feasibility of a 5G-based robot-assisted remote ultrasound system in examining COVID-19 patients and establish an examination protocol for telerobotic ultrasound scanning.\nSTUDY DESIGN AND METHODS: Twenty-three COVID-19 patients were included and divided into two groups.\nTwelve were non-severe cases, and 11 were severe cases.\nAll patients underwent a 5G-based robot-assisted remote ultrasound system examination of the lungs and heart following an established protocol.\nDistribution characteristics and morphology of the lung and surrounding tissue lesions, left ventricular ejection fraction (LVEF), ventricular area ratio, pericardial effusion, and examination-related complications were recorded.\nBilateral lung lesions were evaluated by lung ultrasound score (LUS).\nRESULTS: The remote ultrasound system successfully and safely performed cardiopulmonary examinations of all patients.\nPeripheral lung lesions were clearly evaluated.\nSevere cases had significantly more diseased regions [median (interquartile range), 6.0 (2.0-11.0) vs. 1.0 (0.0-2.8)] and higher LUSs [12.0 (4.0-24.0) vs. 2.0 (0.0-4.0)] than non-severe cases (both, P < 0.05 ).\nOne non-severe case (8.3%, 95%CI, 1.5% to 35.4%) and three severe cases (27.3%, 95%CI, 9.7% to 56.6%) were complicated by pleural effusions.\nFour severe cases (36.4%, 95%CI, 15.2% to 64.6%) were complicated by pericardial effusions (vs 0% of non-severe cases, P < 0.05).\nNo patients had significant examination-related complications.\nINTERPRETATION: 5G-based robot-assisted remote ultrasound system is feasible, and effectively obtains ultrasound characteristics for cardiopulmonary assessment of COVID-19 patients.\nBy following established protocols and considering medical history, clinical manifestations, and laboratory markers, it might help to evaluate the severity of COVID-19 remotely.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"INTERPRETATION: 5G-based robot-assisted remote ultrasound system is feasible, and effectively obtains ultrasound characteristics for cardiopulmonary assessment of COVID-19 patients.\"]}", "id": 30}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Ferrets can catch the coronavirus and might give it to other ferrets. But poultry and pigs don't appear to be at risk.\n\nAbstract:\nCoronavirus disease-19 (COVID-19) caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is now a pandemic threat.\nThis virus is supposed to be spread by human to human transmission.\nCellular angiotensin-converting enzyme 2 (ACE2) is the receptor of SARS-CoV-2 which is identical or similar in different species of animals such as pigs, ferrets, cats, orangutans, monkeys, and humans.\nMoreover, a recent study predicted that dogs might be secondary hosts during the evolution of SARS-CoV-2 from bat to human.\nTherefore, there is a possibility of spreading SARS-CoV-2 through domestic pets.\nThere are now many reports of SARS-CoV-2 positive cases in dogs, cats, tigers, lion, and minks.\nExperimental data showed ferrets and cats are highly susceptible to SARS-CoV-2 as infected by virus inoculation and can transmit the virus directly or indirectly by droplets or airborne routes.\nBased on these natural infection reports and experimental data, whether the pets are responsible for SARS-CoV-2 spread to humans; needs to be deeply investigated.\nHumans showing clinical symptoms of respiratory infections have been undergoing for the COVID-19 diagnostic test but many infected people and few pets confirmed with SARS-CoV-2 remained asymptomatic.\nIn this review, we summarize the natural cases of SARS-CoV-2 in animals with the latest researches conducted in this field.\nThis review will be helpful to think insights of SARS-CoV-2 transmissions, spread, and demand for seroprevalence studies, especially in companion animals.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Coronavirus disease-19 (COVID-19) caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is now a pandemic threat.\", \"This virus is supposed to be spread by human to human transmission.\"]}", "id": 31}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: People with weakened immune systems are at higher risk of getting severely sick from SARS-CoV-2, the virus that causes COVID-19.\n\nAbstract:\nThe outbreak of the 2019 Novel Coronavirus (SARS-CoV-2) rapidly spread from Wuhan, China to more than 150 countries, areas or territories, causing staggering number of infections and deaths.\nA systematic profiling of the immune vulnerability landscape of SARS-CoV-2, which can bring critical insights into the immune clearance mechanism, peptide vaccine development, and antiviral antibody development, is lacking.\nIn this study, we investigated the potential of the SARS-CoV-2 viral proteins to induce class I and II MHC presentation and to form linear antibody epitopes.\nWe created an online database to broadly share the predictions as a resource for the research community.\nUsing this resource, we showed that genetic variations in SARS- CoV-2, though still few for the moment, already follow the pattern of mutations in related coronaviruses, and could alter the immune vulnerability landscape of this virus.\nImportantly, we discovered evidence that SARS-CoV-2, along with related coronaviruses, used mutations to evade attack from the human immune system.\nOverall, we present an immunological resource for SARS-CoV-2 that could promote both therapeutic development and mechanistic research.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 32}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Can acetaminophen (Tylenol) treat the coronavirus disease? No\n\nAbstract:\nThe safety of NSAIDs, corticosteroids and renin-angiotensin inhibitors in COVID-19 is challenged.\nNSAIDs may interfere with the defense process against viral infection and are best avoided.\nSystemic corticosteroids have not shown benefit in viral infection, including other coronavirus; thus they should be avoided, unless prescribed for another indication.\nThe benefit-risk ratio is however clearly in favor of continuing inhaled corticosteroids in patients with asthma or COPD.\nACE inhibitors and sartans upregulate the expression of angiotensin-converting enzyme 2 (ACE2), the pulmonary receptor for SARS-CoV-2.\nAny possible clinical impact of these treatments on COVID-19 infection remains to be clarified; in the meantime, they should be continued.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 33}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: T cell anergy in covid-19 reflects virus persistence and replication outcomes\n\nAbstract:\nCoronavirus disease 2019 (COVID-19) can lead to severe pneumonia and hyperinflammation.\nSo far, insufficient or excessive T cell responses were described in patients.\nWe applied novel approaches to analyze T cell reactivity and showed that T anergy is already present in non-ventilated COVID-19 patients, very pronounced in ventilated patients, strongly associated with virus persistence and reversible with clinical recovery.\nT cell activation was measured by downstream effects on responder cells like basophils, plasmacytoid dendritic cells, monocytes and neutrophils in whole blood and proved to be much more meaningful than classical readouts with PBMCs.\nMonocytes responded stronger in males than females and IL-2 partially reversed T cell anergy.\nDownstream markers of T cell anergy were also found in fresh blood samples of critically ill patients with severe T cell anergy.\nBased on our data we were able to develop a score to predict fatal outcomes and to identify patients that may benefit from strategies to overcome T cell anergy.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"We applied novel approaches to analyze T cell reactivity and showed that T anergy is already present in non-ventilated COVID-19 patients, very pronounced in ventilated patients, strongly associated with virus persistence and reversible with clinical recovery.\", \"Downstream markers of T cell anergy were also found in fresh blood samples of critically ill patients with severe T cell anergy.\", \"Based on our data we were able to develop a score to predict fatal outcomes and to identify patients that may benefit from strategies to overcome T cell anergy.\"]}", "id": 34}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: The drugs have anti-inflammatory effects \"in addition to their blood pressure benefits.\n\nAbstract:\nAims: The question of interactions between the renin angiotensin aldosterone system drugs and the incidence and prognosis of COVID-19 infection has been raised by the medical community.\nWe hypothesised that if patients treated with ACE inhibitors (ACEI) or AT1 receptor blockers (ARB) were more prone to SARS-CoV2 infection and had a worse prognosis than untreated patients, the prevalence of consumption of these drugs would be higher in patients with COVID-19 compared to the general population.\nMethods and results: We used a clinical epidemiology approach based on the estimation of standardised prevalence ratio (SPR) of consumption of ACEI and ARB in four groups of patients (including 187 COVID-19 positive) with increasing severity referred to the University hospital of Lille and in three French reference samples (the exhaustive North population (n=1,569,968), a representative sample of the French population (n=414,046), a random sample of Lille area (n=1,584)).\nThe SPRs of ACEI and ARB did not differ as the severity of the COVID-19 patients increased, being similar to the regular consumption of these drugs in the North of France population with the same non-significant increase for both treatment (1.17 [0.83-1.67]).\nA statistically significant increase in the SPR of ARB (1.56 [1.02-2.39]) was observed in intensive care unit patients only.\nAfter stratification on obesity, this increase was limited to the high risk subgroup of obese patients.\nConclusions: Our results strongly support the recommendation that ACEI and ARB should be continued in the population and in COVID-19 positive patients, reinforcing the position of several scientific societies.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 35}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: B cells and t cells mediate susceptibility to covid-19\n\nAbstract:\nRecent reports that antibodies to SARS-CoV-2 are not maintained in the serum following recovery from the virus have caused alarm.\nHowever, the absence of specific antibodies in the serum does not necessarily mean an absence of immune memory.\nHere, we discuss our current understanding of the relative contribution of B cells and T cells to immunity to SARS-CoV-2 and the implications for the development of effective treatments and vaccines for COVID-19.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Here, we discuss our current understanding of the relative contribution of B cells and T cells to immunity to SARS-CoV-2 and the implications for the development of effective treatments and vaccines for COVID-19.\"]}", "id": 36}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: So, COVID is killed by heat. That is why our bodies create fever to fight it off. When you take Tylenol or advil it takes away your fever and allows COVID its ideal environment. If you get COVID allow your fever to remain as long as it is not over 103-104 this is your body fighting the virus. \n\nAbstract:\n: The COVID-19 pandemic is challenging our cardiovascular care of patients with heart diseases.\nIn the setting of pericardial diseases, there are two possible different scenarios to consider: the patient being treated for pericarditis who subsequently becomes infected with SARS-CoV-2, and the patient with COVID-19 who develops pericarditis or pericardial effusion.\nIn both conditions, clinicians may be doubtful regarding the safety of nonsteroidal anti-inflammatory drugs (NSAIDs), corticosteroids, colchicine, and biological agents, such as anti-IL1 agents (e.g. anakinra), that are the mainstay of therapy for pericarditis.\nFor NSAIDs, there is no clear scientific evidence linking ibuprofen and other NSAIDs to worsening of COVID-19; however, it seems prudent to continue them, if necessary to control pericarditis, and on the other hand, to prefer paracetamol for fever and systemic symptoms related to COVID-19.\nTreatments with corticosteroids, colchicine, and anakinra appear well tolerated in the context of COVID-19 infection and are currently actively evaluated as potential therapeutic options for COVID infection at different stages of the disease.\nOn this basis, currently most treatments for pericarditis do not appear contraindicated also in the presence of possible COVID-19 infection and should not be discontinued, and some (corticosteroids, colchicine, and anakinra) can be considered to treat both conditions.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"For NSAIDs, there is no clear scientific evidence linking ibuprofen and other NSAIDs to worsening of COVID-19; however, it seems prudent to continue them, if necessary to control pericarditis, and on the other hand, to prefer paracetamol for fever and systemic symptoms related to COVID-19.\"]}", "id": 37}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: SARS-CoV-2, the virus that causes COVID-19\n\nAbstract:\nThe recently emerged SARS-CoV-2 (Coronaviridae; Betacoronavirus) is the underlying cause of COVID-19 disease.\nHere we assessed SARS-CoV2 from the Kingdom of Saudi Arabia alongside sequences of SARS-CoV, bat SARS-like CoVs and MERS-CoV, the latter currently detected in this region.\nPhylogenetic analysis, natural selection investigation and genome recombination analysis were performed.\nOur analysis showed that all Saudi SARS-CoV-2 sequences are of the same origin and closer proximity to bat SARS-like CoVs, followed by SARS-CoVs, however quite distant to MERS-CoV. Moreover, genome recombination analysis revealed two recombination events between SARS-CoV-2 and bat SARS-like CoVs.\nThis was further assessed by S gene recombination analysis.\nThese recombination events may be relevant to the emergence of this novel virus.\nMoreover, positive selection pressure was detected between SARS-CoV-2, bat SL-CoV isolates and human SARS-CoV isolates.\nHowever, the highest positive selection occurred between SARS-CoV-2 isolates and 2 bat-SL-CoV isolates (Bat-SL-RsSHC014 and Bat-SL-CoVZC45).\nThis further indicates that SARS-CoV-2 isolates were adaptively evolved from bat SARS-like isolates, and that a virus with originating from bats triggered this pandemic.\nThis study thuds sheds further light on the origin of this virus.\nAUTHOR SUMMARY The emergence and subsequent pandemic of SARS-CoV-2 is a unique challenge to countries all over the world, including Saudi Arabia where cases of the related MERS are still being reported.\nSaudi SARS-CoV-2 sequences were found to be likely of the same or similar origin.\nIn our analysis, SARS-CoV-2 were more closely related to bat SARS-like CoVs rather than to MERS-CoV (which originated in Saudi Arabia) or SARS-CoV, confirming other phylogenetic efforts on this pathogen.\nRecombination and positive selection analysis further suggest that bat coronaviruses may be at the origin of SARS-CoV-2 sequences.\nThe data shown here give hints on the origin of this virus and may inform efforts on transmissibility, host adaptation and other biological aspects of this virus.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"The recently emerged SARS-CoV-2 (Coronaviridae; Betacoronavirus) is the underlying cause of COVID-19 disease.\"]}", "id": 38}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Although smoking is the most common way to use marijuana, some people bake it into a brownie or other food. Eating pot might spare you the lung effects of this drug, but that doesn't mean it's safe.\n\nAbstract:\nImportance.\nCovid-19 infection has major international health and economic impacts and risk factors for infection are not completely understood.\nCannabis smoking is linked with poor respiratory health, immunosuppression and multiple contaminants.\nPotential synergism between the two epidemics would represent a major public health convergence.\nCigarettes were implicated with disease severity in Wuhan, China.\nObjective.\nIs cannabis use epidemiologically associated with coronavirus incidence rate (CVIR)?\nDesign.\nCross-sectional state-based multivariable study.\nSetting.\nUSA.\nPrimary and Secondary Outcome Measures.\nCVIR.\nMultivariable-adjusted geospatially-weighted regression models.\nAs the American cannabis epidemic is characterized by a recent doubling of daily cannabis use it was considered important to characterize the contribution of high intensity use.\nResults.\nSignificant associations of daily cannabis use quintile with CVIR were identified with the highest quintile having a prevalence ratio 5.11 (95%C.I. 4.90-5.33), an attributable fraction in the exposed (AFE) 80.45% (79.61-81.25%) and an attributable fraction in the population of 77.80% (76.88-78.68%) with Chi-squared-for-trend (14,782, df=4) significant at P<10-500.\nSimilarly when cannabis legalization was considered decriminalization was associated with an elevated CVIR prevalence ratio 4.51 (95%C.I. 4.45-4.58), AFE 77.84% (77.50-78.17%) and Chi-squared-for-trend (56,679, df=2) significant at P<10-500.\nMonthly and daily use were linked with CVIR in bivariate geospatial regression models (P=0.0027, P=0.0059).\nIn multivariable additive models number of flight origins and population density were significant.\nIn interactive geospatial models adjusted for international travel, ethnicity, income, population, population density and drug use, terms including last month cannabis were significant from P=7.3x10-15, daily cannabis use from P=7.3x10-11 and last month cannabis was independently associated (P=0.0365).\nConclusions and Relevance.\nData indicate CVIR demonstrates significant trends across cannabis use intensity quintiles and with relaxed cannabis legislation.\nRecent cannabis use is independently predictive of CVIR in bivariate and multivariable adjusted models and intensity of use is interactively significant.\nCannabis thus joins tobacco as a SARS2-CoV-2 risk factor.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Data indicate CVIR demonstrates significant trends across cannabis use intensity quintiles and with relaxed cannabis legislation.\", \"Recent cannabis use is independently predictive of CVIR in bivariate and multivariable adjusted models and intensity of use is interactively significant.\", \"Cannabis thus joins tobacco as a SARS2-CoV-2 risk factor.\"]}", "id": 39}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Test sensitivity is secondary to frequency and turnaround time for covid-19 screening\n\nAbstract:\nThe COVID-19 pandemic has created a public health crisis.\nBecause SARS-CoV-2 can spread from individuals with pre-symptomatic, symptomatic, and asymptomatic infections, the re-opening of societies and the control of virus spread will be facilitated by robust population screening, for which virus testing will often be central.\nAfter infection, individuals undergo a period of incubation during which viral titers are usually too low to detect, followed by an exponential viral growth, leading to a peak viral load and infectiousness, and ending with declining viral levels and clearance.\nGiven the pattern of viral load kinetics, we model the effectiveness of repeated population screening considering test sensitivities, frequency, and sample-to-answer reporting time.\nThese results demonstrate that effective screening depends largely on frequency of testing and the speed of reporting, and is only marginally improved by high test sensitivity.\nWe therefore conclude that screening should prioritize accessibility, frequency, and sample-to-answer time; analytical limits of detection should be secondary.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"We therefore conclude that screening should prioritize accessibility, frequency, and sample-to-answer time; analytical limits of detection should be secondary.\"]}", "id": 40}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: COVID-19 can survive on surfaces, like a tabletop\n\nAbstract:\nPURPOSE OF REVIEW This article reviews 'no touch' methods for disinfection of the contaminated surface environment of hospitalized patients' rooms.\nThe focus is on studies that assessed the effectiveness of ultraviolet (UV) light devices, hydrogen peroxide systems, and self-disinfecting surfaces to reduce healthcare-associated infections (HAIs).\nRECENT FINDINGS The contaminated surface environment in hospitals plays an important role in the transmission of several key nosocomial pathogens including methicillin-resistant Staphylococcus aureus, vancomycin-resistant Enterococcus spp.\n, Clostridium difficile, Acinetobacter spp., and norovirus.\nMultiple clinical trials have now demonstrated the effectiveness of UV light devices and hydrogen peroxide systems to reduce HAIs.\nA limited number of studies have suggested that 'self-disinfecting' surfaces may also decrease HAIs.\nSUMMARY Many studies have demonstrated that terminal cleaning and disinfection with germicides is often inadequate and leaves environmental surfaces contaminated with important nosocomial pathogens. 'No touch' methods of room decontamination (i.e., UV devices and hydrogen peroxide systems) have been demonstrated to reduce key nosocomial pathogens on inoculated test surfaces and on environmental surfaces in actual patient rooms.\nFurther UV devices and hydrogen peroxide systems have been demonstrated to reduce HAI.\nA validated 'no touch' device or system should be used for terminal room disinfection following discharge of patients on contact precautions.\nThe use of a 'self-disinfecting' surface to reduce HAI has not been convincingly demonstrated.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 41}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: taking medication to lower fever, such as paracetamol (tylenol) and ibuprofen (advil) worsen COVID-19\n\nAbstract:\nIbuprofen is an over-the-counter medication that is used widely for the treatment of pain and fever during COVID-19 pandemic.\nA concern was raised regarding the safety of ibuprofen use because of its role in increasing ACE2 levels within the Renin-Angiotensin-Aldosterone system.\nACE2 is the coreceptor for the entry of SARS-CoV-2 into cells, and so, a potential increased risk of contracting COVID-19 disease and/or worsening of COVID-19 infection was feared with ibuprofen use.\nHowever, available data from limited studies show administration of recombinant ACE2 improves lung damage caused by respiratory viruses, suggesting ibuprofen use may be beneficial in COVID-19 disease.\nAt this time, there is no supporting evidence to discourage the use of ibuprofen.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Ibuprofen is an over-the-counter medication that is used widely for the treatment of pain and fever during COVID-19 pandemic.\", \"However, available data from limited studies show administration of recombinant ACE2 improves lung damage caused by respiratory viruses, suggesting ibuprofen use may be beneficial in COVID-19 disease.\", \"At this time, there is no supporting evidence to discourage the use of ibuprofen.\"]}", "id": 42}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Serological surveys in reunion island of the first hospitalized patients revealed that long-lived immunoglobulin g antibodies specific against sars-cov2 virus are rapidly vanishing in severe cases\n\nAbstract:\nBoth cellular and humoral immunities are critically important to control COVID19 infection but little is known about the kinetics of those responses and, in particular, in patients who will go on to develop a severe form of the disease over several weeks.\nWe herein report the first set of data of our prospective cohort study of 90 hospitalized cases.\nSerological surveys were thoroughly performed over 2 month period by assessing IgG and IgM responses by immunofluorescence, immunoblot, Western blot and conventional ELISA using clinical RUN isolates of SARS-CoV-2 immobilized on 96 well plates.\nWhile the IgM and, unexpectedly, the IgG responses were readily detected early during the course of the disease (5-7 days post-first symptoms), our results (n=3-5 and over the full dilution set of the plasma 1/200 to 1/12800) demonstrated a significant decrease (over 2.5-fold) of IgG levels in severe (ICU) hospitalized patients (exemplified in patient 1) by WB and ELISA.\nIn contrast, mild non-ICU patients had a steady and yet robust rise in their specific IgG levels against the virus.\nInterestingly, both responses (IgM and IgG) were initially against the nucleocapsid (50kDa band on the WB) and spreading to other major viral protein S and domains (S1 and S2.\nIn conclusion, serological testing may be helpful for the diagnosis of patients with negative RT-PCR results and for the identification of asymptomatic cases.\nMoreover, medical care and protections should be maintained particularly for recovered patients (severe cases) who may remain at risk of relapsing or reinfection.\nExperiments to ascertain T cell responses but although their kinetics overtime are now highly warranted.\nAll in all, these studies will help to delineate the best routes for vaccination.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Serological surveys were thoroughly performed over 2 month period by assessing IgG and IgM responses by immunofluorescence, immunoblot, Western blot and conventional ELISA using clinical RUN isolates of SARS-CoV-2 immobilized on 96 well plates.\", \"While the IgM and, unexpectedly, the IgG responses were readily detected early during the course of the disease (5-7 days post-first symptoms), our results (n=3-5 and over the full dilution set of the plasma 1/200 to 1/12800) demonstrated a significant decrease (over 2.5-fold) of IgG levels in severe (ICU) hospitalized patients (exemplified in patient 1) by WB and ELISA.\", \"In conclusion, serological testing may be helpful for the diagnosis of patients with negative RT-PCR results and for the identification of asymptomatic cases.\"]}", "id": 43}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: The current treatment option for COVID-19 involves treating symptoms and oxygen therapy to keep the body in a stable condition in severe cases.\n\nAbstract:\nThe severity of coronavirus disease 2019 (COVID-19) infection is quite variable and the manifestations varies from asymptomatic disease to severe acute respiratory infection.\nFever, dry cough, dyspnea, myalgia, fatigue, loss of appetite, olfactory and gustatory dysfunctions are the most prevalent general symptoms.\nDecreased immune system cells such as suppressed regulatory T cells, cytotoxic and helper T cells, natural killer cells, monocytes/macrophages and increased proinflammatory cytokines are the characteristic features.\nCompounds derived from Allium sativum (garlic) have the potential to decrease the expression of proinflammatory cytokines and to reverse the immunological abnormalities to more acceptable levels.\nAllium sativum is suggested as a beneficial preventive measure before being infected with SARS-CoV-2 virus.\nAllium sativum is a functional food well-known for its immunomodulatory, antimicrobial, antiinflammatory, antimutagenic, antitumor properties.\nIts antiviral efficiency was also demonstrated.\nSome constituents of this plant were found to be active against protozoan parasites.\nWithin this context, it appears to reverse most immune system dysfunctions observed in patients with COVID-19 infection.\nThe relations among immune system parameters, leptin, leptin receptor, adenosin mono phosphate-activated protein kinase, peroxisome proliferator activated receptor-gamma have also been interpreted.\nLeptin's role in boosting proinflammatory cytokines and in appetite decreasing suggest the possible beneficial effect of decreasing the concentration of this proinflammatory adipose tissue hormone in relieving some symptoms detected during COVID-19 infection.\nIn conclusion, Allium sativum may be an acceptable preventive measure against COVID-19 infection to boost immune system cells and to repress the production and secretion of proinflammatory cytokines as well as an adipose tissue derived hormone leptin having the proinflammatory nature.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 44}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Furin cleavage of sars-cov-2 spike promotes but is therefore essential for infection and cell-cell fusion\n\nAbstract:\nSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infects cells by binding to the host cell receptor Ace2 and undergoing virus-host membrane fusion.\nFusion is triggered by the protease TMPRSS2, which processes the viral Spike (S) protein to reveal the fusion peptide.\nSARS-CoV-2 has evolved a multibasic site at the S1-S2 boundary, which is thought to be cleaved by furin in order to prime S protein for TMPRSS2 processing.\nHere we show that CRISPR-Cas9 knockout of furin reduces, but does not prevent, the production of infectious SARS-CoV-2 virus.\nComparing S processing in furin knockout cells to multibasic site mutants reveals that while loss of furin substantially reduces S1-S2 cleavage it does not prevent it.\nSARS-CoV-2 S protein also mediates cell-cell fusion, potentially allowing virus to spread virion-independently.\nWe show that loss of furin in either donor or acceptor cells reduces, but does not prevent, TMPRSS2-dependent cell-cell fusion, unlike mutation of the multibasic site that completely prevents syncytia formation.\nOur results show that while furin promotes both SARS-CoV-2 infectivity and cell-cell spread it is not essential, suggesting furin inhibitors will not prevent viral spread.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Our results show that while furin promotes both SARS-CoV-2 infectivity and cell-cell spread it is not essential, suggesting furin inhibitors will not prevent viral spread.\"]}", "id": 45}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: the symptoms of COVID-19 are no worse than a cold\n\nAbstract:\nWe determined and compared the humoral immune response in severe, hospitalized and mild, non-hospitalized COVID-19 patients.\nSevere patients (n=38) develop a robust antibody response to SARS-CoV-2, including IgG and IgA antibodies.\nThe geometric mean 50% virus neutralization titer is 1:240.\nSARS-CoV-2 infected hospital personnel (n=24), who developed mild symptoms necessitating leave of absence, self-isolation, but not hospitalization, 75 % develop antibodies, but with low/absent virus neutralization (60% < 1:20).\nWhile severe COVID-19 patients develop a strong antibody response, mild SARS-CoV-2 infections induce a modest antibody response.\nLong term monitoring will show whether these responses predict protection against future infections.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 46}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Many readers have written in to ask whether ibuprofen or other non-steroidal anti-inflammatory drugs, or NSAIDs, can worsen COVID-19, the disease caused by the novel coronavirus.\n\nAbstract:\nFever has been reported as a common symptom occurring in COVID-19 illness.\nOver the counter antipyretics such as ibuprofen and acetaminophen are often taken by individuals to reduce the discomfort of fever.\nRecently, the safety of ibuprofen in COVID-19 patients has been questioned due to anecdotal reports of worsening symptoms in previously healthy young adults.\nStudies show that ibuprofen demonstrates superior efficacy in fever reduction compared to acetaminophen.\nAs fever may have benefit in shortening the duration of viral illness, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response when taken during the early stages of COVID-19 illness.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 47}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Based on currently available information, WHO does not recommend against the use of of ibuprofen.\n\nAbstract:\n: The COVID-19 pandemic is challenging our cardiovascular care of patients with heart diseases.\nIn the setting of pericardial diseases, there are two possible different scenarios to consider: the patient being treated for pericarditis who subsequently becomes infected with SARS-CoV-2, and the patient with COVID-19 who develops pericarditis or pericardial effusion.\nIn both conditions, clinicians may be doubtful regarding the safety of nonsteroidal anti-inflammatory drugs (NSAIDs), corticosteroids, colchicine, and biological agents, such as anti-IL1 agents (e.g. anakinra), that are the mainstay of therapy for pericarditis.\nFor NSAIDs, there is no clear scientific evidence linking ibuprofen and other NSAIDs to worsening of COVID-19; however, it seems prudent to continue them, if necessary to control pericarditis, and on the other hand, to prefer paracetamol for fever and systemic symptoms related to COVID-19.\nTreatments with corticosteroids, colchicine, and anakinra appear well tolerated in the context of COVID-19 infection and are currently actively evaluated as potential therapeutic options for COVID infection at different stages of the disease.\nOn this basis, currently most treatments for pericarditis do not appear contraindicated also in the presence of possible COVID-19 infection and should not be discontinued, and some (corticosteroids, colchicine, and anakinra) can be considered to treat both conditions.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"For NSAIDs, there is no clear scientific evidence linking ibuprofen and other NSAIDs to worsening of COVID-19; however, it seems prudent to continue them, if necessary to control pericarditis, and on the other hand, to prefer paracetamol for fever and systemic symptoms related to COVID-19.\"]}", "id": 48}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Does wearing a mask help reduce my risk of COVID-19? Cloth and surgical masks help stop droplets spreading when people talk, cough and sneeze, which reduces the risk of spreading the virus.\n\nAbstract:\nFace masks are an avenue to curb the spread of coronavirus, but few people in Western societies wear face masks.\nSocial scientists have rarely studied face mask wearing, leaving little guidance for methods to encourage these behaviours.\nIn the current article, we provide an approach to address this issue by developing the 32-item and 8-dimension Face Mask Perceptions Scale (FMPS).\nWe begin by developing an over-representative item list in a qualitative study, wherein participants' responses are used to develop items to ensure content relevance.\nThis item list is then reduced via exploratory factor analysis in a second study, and the eight dimensions of the scale are supported.\nWe also support the validity of the FMPS, as the scale significantly relates to both face mask wearing and health perceptions.\nWe lastly confirm the factor structure of the FMPS in a third study via confirmatory factor analysis.\nFrom these efforts, we identify an avenue that social scientists can aid in preventing coronavirus and illness more broadly - by studying face mask perceptions and behaviours.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Face masks are an avenue to curb the spread of coronavirus, but few people in Western societies wear face masks.\"]}", "id": 49}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Bacterial modification of the enzyme glycosaminoglycan heparan sulfate modulates sars-cov-2 infectivity\n\nAbstract:\nThe human microbiota has a close relationship with human disease and it remodels components of the glycocalyx including heparan sulfate (HS).\nStudies of the severe acute respiratory syndrome coronavirus (SARS-CoV-2) spike protein receptor binding domain suggest that infection requires binding to HS and angiotensin converting enzyme 2 (ACE2) in a codependent manner.\nHere, we show that commensal host bacterial communities can modify HS and thereby modulate SARS-CoV-2 spike protein binding and that these communities change with host age and sex.\nCommon human-associated commensal bacteria whose genomes encode HS-modifying enzymes were identified.\nThe prevalence of these bacteria and the expression of key microbial glycosidases in bronchoalveolar lavage fluid (BALF) was lower in adult COVID-19 patients than in healthy controls.\nThe presence of HS-modifying bacteria decreased with age in two large survey datasets, FINRISK 2002 and American Gut, revealing one possible mechanism for the observed increase in COVID-19 susceptibility with age.\nIn vitro, bacterial glycosidases from unpurified culture media supernatants fully blocked SARS-CoV-2 spike binding to human H1299 protein lung adenocarcinoma cells.\nHS-modifying bacteria in human microbial communities may regulate viral adhesion, and loss of these commensals could predispose individuals to infection.\nUnderstanding the impact of shifts in microbial community composition and bacterial lyases on SARS-CoV-2 infection may lead to new therapeutics and diagnosis of susceptibility.\nGraphical Abstract.\nDiagram of hypothesis for bacterial mediation of SARS-CoV-2 infection through heparan sulfate (HS).\nIt is well known that host microbes groom the mucosa where they reside.\nRecent investigations have shown that HS, a major component of mucosal layers, is necessary for SARS-CoV-2 infection.\nIn this study we examine the impact of microbial modification of HS on viral attachment.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Understanding the impact of shifts in microbial community composition and bacterial lyases on SARS-CoV-2 infection may lead to new therapeutics and diagnosis of susceptibility.\"]}", "id": 50}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Single-world analyses reveal sars-cov-2 interference with intrinsic immune response in the human gut\n\nAbstract:\nObjective Exacerbated pro-inflammatory immune response contributes to COVID-19 pathology.\nDespite the evidence about SARS-CoV-2 infecting the human gut, little is known about the importance of the enteric phase of SARS-CoV-2 for the viral lifecycle and for the development of COVID-19-associated pathologies.\nSimilarly, it remains unknown whether the innate immune response triggered in this organ to combat viral infection is similar or distinct compared to the one triggered in other organs.\nDesign We exploited human ileum-and colon-derived organoids as a non-transformed culture model supporting SARS-CoV-2 infection.\nWe characterized the replication kinetics of SARS-CoV-2 in intestinal epithelial cells and correlated the expression of the viral receptor ACE2 with infection.\nWe performed conventional and targeted single-cell transcriptomics and multiplex single-molecule RNA fluorescence in situ hybridization and used IFN-reporter bioassays to characterize the response of primary human intestinal epithelial cells to SARS-CoV-2 infection.\nResults We identified a subpopulation of enterocytes as the prime target of SARS-CoV-2.\nWe found the lack of positive correlation between susceptibility to infection and the expression of ACE2 and revealed that SARS-CoV-2 downregulates ACE2 expression upon infection.\nInfected cells activated strong proinflammatory programs and produced interferon, while expression of interferon-stimulated genes was limited to bystander cells due to SARS-CoV-2 suppressing the autocrine action of interferon in infected cells.\nConclusion Our findings reveal that SARS-CoV-2 curtails the immune response in primary human intestinal epithelial cells to promote its replication and spread and this highlights the gut as a proinflammatory reservoir that should be considered to fully understand SARS-CoV-2 pathogenesis.\nSignificance of the study What is already known about this subject?\nCOVID-19 patients have gastrointestinal symptoms which likely correlates with SARS-CoV-2 infection of the intestinal epithelium SARS-CoV-2 replicates in human intestinal epithelial cells.\nIntestinal organoids are a good model to study SARS-CoV-2 infection of the gastrointestinal tract There is a limited interferon response in human lung epithelial cells upon SARS-CoV-2 infection.\nWhat are the new findings?\nA specific subpopulation of enterocytes are the prime targets of SARS-CoV-2 infection of the human gut.\nThere is a lack of correlation between ACE2 expression and susceptibility to SARS-CoV-2 infection.\nSARS-CoV-2 downregulates ACE2 expression upon infection.\nHuman intestinal epithelium cells produce interferon upon SARS-CoV-2 infection.\nInterferon acts in a paracrine manner to induce interferon stimulated genes that control viral infection only in bystander cells.\nSARS-CoV-2 actively blocks interferon signaling in infected cells.\nHow might it impact on clinical practice in the foreseeable future?\nThe absence of correlation between ACE2 levels and susceptibility suggest that medications influencing ACE2 levels (e.g. high blood pressure drugs) will not make patients more susceptible to SARS-CoV-2 infection.\nThe restricted cell tropism and the distinct immune response mounted by the GI tract, suggests that specific cellular restriction/replication factors and organ specific intrinsic innate immune pathways can represent unique therapeutic targets to treat COVD-19 patients by considering which organ is most infected/impacted by SARS-CoV-2.\nThe strong pro-inflammatory signal mounted by the intestinal epithelium can fuel the systemic inflammation observed in COVID-19 patients and is likely participating in the lung specific pathology.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Conclusion Our findings reveal that SARS-CoV-2 curtails the immune response in primary human intestinal epithelial cells to promote its replication and spread and this highlights the gut as a proinflammatory reservoir that should be considered to fully understand SARS-CoV-2 pathogenesis.\", \"Human intestinal epithelium cells produce interferon upon SARS-CoV-2 infection.\"]}", "id": 51}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Green tea: Green tea is an excellent source of antiviral and anti-inflammatory catechins, notably, one called epigallocatechin gallate, or EGCG.\n\nAbstract:\nThis review focused on the use of plant-based foods for enhancing the immunity of all aged groups against COVID-19.\nIn humans, coronaviruses are included in the spectrum of viruses that cause the common cold and, recently, severe acute respiratory syndrome (SARS).\nEmerging infectious diseases, such as SARS present a major threat to public health.\nThe novel coronavirus has spread rapidly to multiple countries and has been declared a pandemic by the World Health Organization.\nCOVID-19 is usually caused a virus to which most probably the people with low immunity response are being affected.\nPlant-based foods increased the intestinal beneficial bacteria which are helpful and make up of 85% of the immune system.\nBy the use of plenty of water, minerals like magnesium and Zinc, micronutrients, herbs, food rich in vitamins C, D and E, and better life style one can promote the health and can overcome this infection.\nVarious studies investigated that a powerful antioxidant glutathione and a bioflavonoid quercetin may prevent various infections including COVID-19.\nIn conclusion, the plant-based foods play a vital role to enhance the immunity of people to control of COVID-19.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 52}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Many mammals around the world could have a role in spreading the disease, including dogs and cats.\n\nAbstract:\nCoronavirus disease-19 (COVID-19) caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is now a pandemic threat.\nThis virus is supposed to be spread by human to human transmission.\nCellular angiotensin-converting enzyme 2 (ACE2) is the receptor of SARS-CoV-2 which is identical or similar in different species of animals such as pigs, ferrets, cats, orangutans, monkeys, and humans.\nMoreover, a recent study predicted that dogs might be secondary hosts during the evolution of SARS-CoV-2 from bat to human.\nTherefore, there is a possibility of spreading SARS-CoV-2 through domestic pets.\nThere are now many reports of SARS-CoV-2 positive cases in dogs, cats, tigers, lion, and minks.\nExperimental data showed ferrets and cats are highly susceptible to SARS-CoV-2 as infected by virus inoculation and can transmit the virus directly or indirectly by droplets or airborne routes.\nBased on these natural infection reports and experimental data, whether the pets are responsible for SARS-CoV-2 spread to humans; needs to be deeply investigated.\nHumans showing clinical symptoms of respiratory infections have been undergoing for the COVID-19 diagnostic test but many infected people and few pets confirmed with SARS-CoV-2 remained asymptomatic.\nIn this review, we summarize the natural cases of SARS-CoV-2 in animals with the latest researches conducted in this field.\nThis review will be helpful to think insights of SARS-CoV-2 transmissions, spread, and demand for seroprevalence studies, especially in companion animals.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Coronavirus disease-19 (COVID-19) caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is now a pandemic threat.\", \"This virus is supposed to be spread by human to human transmission.\"]}", "id": 53}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Furin cleavage of sars-cov-2 spike promotes but is still essential for infection and cell-cell fusion\n\nAbstract:\nSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infects cells by binding to the host cell receptor Ace2 and undergoing virus-host membrane fusion.\nFusion is triggered by the protease TMPRSS2, which processes the viral Spike (S) protein to reveal the fusion peptide.\nSARS-CoV-2 has evolved a multibasic site at the S1-S2 boundary, which is thought to be cleaved by furin in order to prime S protein for TMPRSS2 processing.\nHere we show that CRISPR-Cas9 knockout of furin reduces, but does not prevent, the production of infectious SARS-CoV-2 virus.\nComparing S processing in furin knockout cells to multibasic site mutants reveals that while loss of furin substantially reduces S1-S2 cleavage it does not prevent it.\nSARS-CoV-2 S protein also mediates cell-cell fusion, potentially allowing virus to spread virion-independently.\nWe show that loss of furin in either donor or acceptor cells reduces, but does not prevent, TMPRSS2-dependent cell-cell fusion, unlike mutation of the multibasic site that completely prevents syncytia formation.\nOur results show that while furin promotes both SARS-CoV-2 infectivity and cell-cell spread it is not essential, suggesting furin inhibitors will not prevent viral spread.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Our results show that while furin promotes both SARS-CoV-2 infectivity and cell-cell spread it is not essential, suggesting furin inhibitors will not prevent viral spread.\"]}", "id": 54}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: with autoimmune conditions such as lupus, a person experiences \"dysregulations of the immune system,\" meaning the immune system itself is compromised or malfunctioning\n\nAbstract:\nThe outbreak of the 2019 Novel Coronavirus (SARS-CoV-2) rapidly spread from Wuhan, China to more than 150 countries, areas or territories, causing staggering number of infections and deaths.\nA systematic profiling of the immune vulnerability landscape of SARS-CoV-2, which can bring critical insights into the immune clearance mechanism, peptide vaccine development, and antiviral antibody development, is lacking.\nIn this study, we investigated the potential of the SARS-CoV-2 viral proteins to induce class I and II MHC presentation and to form linear antibody epitopes.\nWe created an online database to broadly share the predictions as a resource for the research community.\nUsing this resource, we showed that genetic variations in SARS- CoV-2, though still few for the moment, already follow the pattern of mutations in related coronaviruses, and could alter the immune vulnerability landscape of this virus.\nImportantly, we discovered evidence that SARS-CoV-2, along with related coronaviruses, used mutations to evade attack from the human immune system.\nOverall, we present an immunological resource for SARS-CoV-2 that could promote both therapeutic development and mechanistic research.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 55}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Elevated calprotectin and abnormal myeloid cell subsets discriminate naturally from mild covid-19\n\nAbstract:\nBlood myeloid cells are known to be dysregulated in coronavirus disease 2019 (COVID-19), caused by SARS-CoV-2.\nIt is unknown whether the innate myeloid response differs with disease severity and whether markers of innate immunity discriminate high-risk patients.\nThus, we performed high-dimensional flow cytometry and single-cell RNA sequencing of COVID-19 patient peripheral blood cells and detected disappearance of non-classical CD14LowCD16High monocytes, accumulation of HLA-DRLow classical monocytes (Human Leukocyte Antigen-DR isotype), and release of massive amounts of calprotectin (S100A8/S100A9) in severe cases.\nImmature CD10LowCD101-CXCR4+/-neutrophils with an immunosuppressive profile accumulated in the blood and lungs, suggesting emergency myelopoiesis.\nFinally, we show that calprotectin plasma level and a routine flow cytometry assay detecting decreased frequencies of non-classical monocytes could discriminate patients who develop a severe form of COVID-19, suggesting a predictive value that deserves prospective evaluation.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Finally, we show that calprotectin plasma level and a routine flow cytometry assay detecting decreased frequencies of non-classical monocytes could discriminate patients who develop a severe form of COVID-19, suggesting a predictive value that deserves prospective evaluation.\"]}", "id": 56}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Can acetaminophen (Tylenol) treat the coronavirus disease? No\n\nAbstract:\nCoronavirus Disease 2019 (COVID-19) can cause severe respiratory failure and distressing symptoms including fever, cough, breathlessness and anxiety.\nSymptomatic (palliative) treatment is of fundamental importance both in conjuncture with life-sustaining interventions and in end of life care.\nBased on the evidence to date, there are several treatment options to consider for the relief of fever (acetaminophen, NSAID, oral glucocorticoids), cough (morphine), breathlessness (morphine, oxygen, fan), anxiety (benzodiazepines) and pain (NSAID, morphine).\nTop priorities include precautions to protect staff and people at-risk from infection and planning how to provide adequate treatment for each individual depending on setting, including palliative care.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Based on the evidence to date, there are several treatment options to consider for the relief of fever (acetaminophen, NSAID, oral glucocorticoids), cough (morphine), breathlessness (morphine, oxygen, fan), anxiety (benzodiazepines) and pain (NSAID, morphine).\"]}", "id": 57}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: wearing a mask  may offer some protection for the covid-19\n\nAbstract:\nIn the context of Coronavirus Disease (2019) (COVID-19) cases globally, there is a lack of consensus across cultures on whether wearing face masks is an effective physical intervention against disease transmission.\nThis study 1) illustrates transmission routes of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2); 2) addresses controversies surrounding the mask from perspectives of attitude, effectiveness, and necessity of wearing the mask with evidence that the use of mask would effectively interrupt the transmission of infectious diseases in both hospital settings and community settings; and 3) provides suggestion that the public should wear the mask during COVID-19 pandemic according to local context.\nTo achieve this goal, government should establish a risk adjusted strategy of mask use to scientifically publicize the use of masks, guarantee sufficient supply of masks, and cooperate for reducing health resources inequities.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"This study 1) illustrates transmission routes of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2); 2) addresses controversies surrounding the mask from perspectives of attitude, effectiveness, and necessity of wearing the mask with evidence that the use of mask would effectively interrupt the transmission of infectious diseases in both hospital settings and community settings; and 3) provides suggestion that the public should wear the mask during COVID-19 pandemic according to local context.\"]}", "id": 58}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Will Garlic Water Cure Coronavirus? No\n\nAbstract:\nIn late December 2019 in Wuhan, China, several patients with viral pneumonia were identified as 2019 novel coronavirus (2019-nCoV).\nSo far, there are no specific treatments for patients with coronavirus disease-19 (COVID-19), and the treatments available today are based on previous experience with similar viruses such as severe acute respiratory syndrome-related coronavirus (SARS-CoV), Middle East respiratory syndrome coronavirus (MERS-CoV), and Influenza virus.\nIn this article, we have tried to reach a therapeutic window of drugs available to patients with COVID-19.\nCathepsin L is required for entry of the 2019-nCoV virus into the cell as target teicoplanin inhibits virus replication.\nAngiotensin-converting-enzyme 2 (ACE2) in soluble form as a recombinant protein can prevent the spread of coronavirus by restricting binding and entry.\nIn patients with COVID-19, hydroxychloroquine decreases the inflammatory response and cytokine storm, but overdose causes toxicity and mortality.\nNeuraminidase inhibitors such as oseltamivir, peramivir, and zanamivir are invalid for 2019-nCoV and are not recommended for treatment but protease inhibitors such as lopinavir/ritonavir (LPV/r) inhibit the progression of MERS-CoV disease and can be useful for patients of COVID-19 and, in combination with Arbidol, has a direct antiviral effect on early replication of SARS-CoV. Ribavirin reduces hemoglobin concentrations in respiratory patients, and remdesivir improves respiratory symptoms.\nUse of ribavirin in combination with LPV/r in patients with SARS-CoV reduces acute respiratory distress syndrome and mortality, which has a significant protective effect with the addition of corticosteroids.\nFavipiravir increases clinical recovery and reduces respiratory problems and has a stronger antiviral effect than LPV/r.\ncurrently, appropriate treatment for patients with COVID-19 is an ACE2 inhibitor and a clinical problem reducing agent such as favipiravir in addition to hydroxychloroquine and corticosteroids.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"In late December 2019 in Wuhan, China, several patients with viral pneumonia were identified as 2019 novel coronavirus (2019-nCoV).\", \"So far, there are no specific treatments for patients with coronavirus disease-19 (COVID-19), and the treatments available today are based on previous experience with similar viruses such as severe acute respiratory syndrome-related coronavirus (SARS-CoV), Middle East respiratory syndrome coronavirus (MERS-CoV), and Influenza virus.\"]}", "id": 59}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Supplements and products unlikely to help with coronavirus and could be dangerous\n\nAbstract:\nBACKGROUND The coronavirus disease 2019 (COVID-19) is a pandemic caused by coronavirus with mild to severe respiratory symptoms.\nThis paper aimed to investigate the effect of nutrients on the immune system and their possible roles in the prevention, treatment, and management of COVID-19 in adults.\nMETHODS This Systematic review was designed based on the guideline of the Preferred Reporting for Systematic Reviews (PRISMA).\nThe articles that focussed on nutrition, immune system, viral infection, and coronaviruses were collected by searching databases for both published papers and accepted manuscripts from 1990 to 2020.\nIrrelevant papers and articles without English abstract were excluded from the review process.\nRESULTS Some nutrients are actively involved in the proper functioning and strengthening of the human immune system against viral infections including dietary protein, omega-3 fatty acids, vitamin A, vitamin D, vitamin E, vitamin B1, vitamin B6, vitamin B12, vitamin C, iron, zinc, and selenium.\nFew studies were done on the effect of dietary components on prevention of COVID-19, but supplementation with these nutrients may be effective in improving the health status of patients with viral infections.\nCONCLUSION Following a balanced diet and supplementation with proper nutrients may play a vital role in prevention, treatment, and management of COVID-19.\nHowever, further clinical trials are needed to confirm these findings and presenting the strong recommendations against this pandemic.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"RESULTS Some nutrients are actively involved in the proper functioning and strengthening of the human immune system against viral infections including dietary protein, omega-3 fatty acids, vitamin A, vitamin D, vitamin E, vitamin B1, vitamin B6, vitamin B12, vitamin C, iron, zinc, and selenium.\", \"Few studies were done on the effect of dietary components on prevention of COVID-19, but supplementation with these nutrients may be effective in improving the health status of patients with viral infections.\"]}", "id": 60}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Cloth face coverings are most likely to reduce the spread of the COVID-19 virus when they are widely used by people in public settings\n\nAbstract:\nMa's research shows N95 masks, medical masks, even homemade masks could block at least 90% of the virus in aerosols(1).\nThis study puts the debate on whether the public wear masks back on the table.\nRecently Science interviewed Dr. Gao, director\u2010general of Chinese Center for Disease Control and Prevention (CDC).\nThis article is protected by copyright.\nAll rights reserved.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Ma's research shows N95 masks, medical masks, even homemade masks could block at least 90% of the virus in aerosols(1).\"]}", "id": 61}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: The CORONAVIRUS did not emerge in Wuhan\n\nAbstract:\nOrigin of the COVID-19 virus has been intensely debated in the scientific community since the first infected cases were detected in December 2019.\nThe disease has caused a global pandemic, leading to deaths of thousands of people across the world and thus finding origin of this novel coronavirus is important in responding and controlling the pandemic.\nRecent research results suggest that bats or pangolins might be the original hosts for the virus based on comparative studies using its genomic sequences.\nThis paper investigates the COVID-19 virus origin by using artificial intelligence (AI) and raw genomic sequences of the virus.\nMore than 300 genome sequences of COVID-19 infected cases collected from different countries are explored and analysed using unsupervised clustering methods.\nThe results obtained from various AI-enabled experiments using clustering algorithms demonstrate that all examined COVID-19 virus genomes belong to a cluster that also contains bat and pangolin coronavirus genomes.\nThis provides evidences strongly supporting scientific hypotheses that bats and pangolins are probable hosts for the COVID-19 virus.\nAt the whole genome analysis level, our findings also indicate that bats are more likely the hosts for the COVID-19 virus than pangolins.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 62}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Smoking is a risk factor for COVID-19 patients, but one particular substance in cigarettes - nicotine - might prevent infection in some people, or improve COVID-19 prognosis\n\nAbstract:\nIntroduction Epidemiological and laboratory research seems to suggest that smoking and perhaps nicotine alone could reduce the severity of COVID-19.\nLikewise, there is some evidence that inhaled corticosteroids could also reduce its severity, opening the possibility that nicotine and inhaled steroids could be used as treatments.\nMethods In this prospective cohort study, we will link English general practice records from the QResearch database to Public Health England's database of SARS-CoV-2 positive tests, Hospital Episode Statistics, admission to intensive care units, and death from COVID-19 to identify our outcomes: hospitalisation, ICU admission, and death due to COVID.\nUsing Cox regression, we will perform sequential adjustment for potential confounders identified by separate directed acyclic graphs to: 1.\nAssess the association between smoking and COVID-19 disease severity, and how that changes on adjustment for smoking-related comorbidity.\n2. More closely characterise the association between smoking and severe COVID-19 disease by assessing whether the association is modified by age (as a proxy of length of smoking), gender, ethnic group, and whether people have asthma or COPD.\n3. Assess for evidence of a dose-response relation between smoking intensity and disease severity, which would help create a case for causality.\n4.\nExamine the association between former smokers who are using NRT or are vaping and disease severity.\n5. Examine whether pre-existing respiratory disease is associated with severe COVID-19 infection.\n6. Assess whether the association between chronic obstructive pulmonary disease (COPD) and asthma and COVID-19 disease severity is modified by age, gender, ethnicity, and smoking status.\n7. Assess whether the use of inhaled corticosteroids is associated with severity of COVID-19 disease.\n8. To assess whether the association between use of inhaled corticosteroids and severity of COVID-19 disease is modified by the number of other airways medications used (as a proxy for severity of condition) and whether people have asthma or COPD.\nConclusions This representative population sample will, to our knowledge, present the first comprehensive examination of the association between smoking, nicotine use without smoking, respiratory disease, and severity of COVID-19.\nWe will undertake several sensitivity analyses to examine the potential for bias in these associations.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Introduction Epidemiological and laboratory research seems to suggest that smoking and perhaps nicotine alone could reduce the severity of COVID-19.\"]}", "id": 63}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: 5G caused the coronavirus outbreak\n\nAbstract:\nAmid increased acts of violence against telecommunication engineers and property, this pre-registered study (N = 601 Britons) investigated the association between beliefs in 5G COVID-19 conspiracy theories and the justification and willingness to use violence.\nFindings revealed that belief in 5G COVID-19 conspiracy theories was positively correlated with state anger, which in turn, was associated with a greater justification of real-life and hypothetical violence in response to an alleged link between 5G mobile technology and COVID-19, alongside a greater intent to engage in similar behaviours in the future.\nMoreover, these associations were strongest for those highest in paranoia.\nFurthermore, we show that these patterns are not specific to 5G conspiratorial beliefs: General conspiracy mentality was positively associated with justification and willingness for general violence, an effect mediated by heightened state anger, especially for those most paranoid in the case of justification of violence.\nSuch research provides novel evidence on why and when conspiracy beliefs may justify the use of violence.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Findings revealed that belief in 5G COVID-19 conspiracy theories was positively correlated with state anger, which in turn, was associated with a greater justification of real-life and hypothetical violence in response to an alleged link between 5G mobile technology and COVID-19, alongside a greater intent to engage in similar behaviours in the future.\", \"Furthermore, we show that these patterns are not specific to 5G conspiratorial beliefs: General conspiracy mentality was positively associated with justification and willingness for general violence, an effect mediated by heightened state anger, especially for those most paranoid in the case of justification of violence.\"]}", "id": 64}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: In one study it was found that 100% of ICU COVID-19 patients less than 75 years old had vitamin D insufficiency.\n\nAbstract:\nImportance: Vitamin D treatment has been found to decrease incidence of viral respiratory tract infection, especially in vitamin D deficiency.\nIt is unknown whether COVID-19 incidence is associated with vitamin D deficiency and treatment.\nObjective: To examine whether vitamin D deficiency and treatment are associated with testing positive for COVID-19.\nDesign: Retrospective cohort study Setting: University of Chicago Medicine Participants: Patients tested for COVID-19 from 3/3/2020-4/10/2020.\nVitamin D deficiency was defined by the most recent 25-hydroxycholecalciferol <20ng/ml or 1,25-dihydroxycholecalciferol <18pg/ml within 1 year before COVID-19 testing.\nTreatment was defined by the most recent vitamin D type and dose, and treatment changes between the time of the most recent vitamin D level and time of COVID-19 testing.\nVitamin D deficiency and treatment changes were combined to categorize vitamin D status at the time of COVID-19 testing as likely deficient(last-level-deficient/treatment-not-increased), likely sufficient(last-level-not-deficient/treatment-not-decreased), or uncertain deficiency(last-level-deficient/treatment-increased or last-level-not-deficient/treatment-decreased).\nMain Outcomes and Measures: The main outcome was testing positive for COVID-19.\nMultivariable analysis tested whether the most recent vitamin D level and treatment changes after that level were associated with testing positive for COVID-19 controlling for demographic and comorbidity indicators.\nBivariate analyses of associations of treatment with vitamin D deficiency and COVID-19 were performed.\nResults: Among 4,314 patients tested for COVID-19, 499 had a vitamin D level in the year before testing.\nVitamin D status at the time of COVID-19 testing was categorized as likely deficient for 127(25%) patients, likely sufficient for 291(58%) patients, and uncertain for 81(16%) patients.\nIn multivariate analysis, testing positive for COVID-19 was associated with increasing age(RR(age<50)=1.05,p<0.021;RR(age[\u2265]50)=1.02,p<0.064)), non-white race(RR=2.54,p<0.01) and being likely vitamin D deficient (deficient/treatment-not-increased:RR=1.77,p<0.02) as compared to likely vitamin D sufficient(not-deficient/treatment-not-decreased), with predicted COVID-19 rates in the vitamin D deficient group of 21.6%(95%CI[14.0%-29.2%] ) versus 12.2%(95%CI[8.9%-15.4%]) in the vitamin D sufficient group.\nVitamin D deficiency declined with increasing vitamin D dose, especially of vitamin D3.\nVitamin D dose was not significantly associated with testing positive for COVID-19.\nConclusions and Relevance: Vitamin D deficiency that is not sufficiently treated is associated with COVID-19 risk.\nTesting and treatment for vitamin D deficiency to address COVID-19 warrant aggressive pursuit and study.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Conclusions and Relevance: Vitamin D deficiency that is not sufficiently treated is associated with COVID-19 risk.\"]}", "id": 65}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Hydroxychloroquine safety outcome within approved therapeutic protocol is covid-19 outpatients in saudi arabia\n\nAbstract:\nBackground: Healthcare systems globally has been challenged following the COVID-19 pandemic, since late 2019.\nMultiple approaches and strategies have been performed to relieve the pressure and support existing healthcare systems.\nThe Saudi Arabian Ministry of Health (MOH) launched an initiative to support the National Healthcare System.\nSince the 5th of June 2020, 238 outpatient fever clinics were established across Saudi Arabia.\nMethods: A cross-sectional study included 2,733 eligible patients subjected to MOH treatment protocol (hydroxychloroquine and zinc) and revisited the clinics within 3-7 days after treatment initiation.\nThis study aimed to assess the safety outcome and reported adverse events from hydroxychloroquine use among suspected COVID-19 patients.\nThe data was collected through an electronic link and cross-checked with that of the national database (Health Electronic Surveillance Network, HESN) and reports from the MOH Morbidity and Mortality (M&M) Committee.\nResults: Majority of the cases were males (70.4%).\nUpon reassessing the studied participants within 3-7 days, 240 patients (8.8%) discontinued the treatment protocol because of the development of side effects (4.1%) and for non-clinical reasons in the remaining (4.7%).\nMedication side effects overall were reported among (6.7%) of all studied participants, including mainly cardiovascular adverse events (2.5%), followed by gastrointestinal (GI) symptoms (2.4%).\nNo Intensive Care Unit (ICU) admission or death were reported among these patients.\nConclusion: In our study, results show that the use of hydroxychloroquine for COVID-19 patients in mild to moderate cases in an outpatient setting, within the protocol recommendation and inclusion/exclusion criteria, is safe, highly tolerable, and with minimum side effects.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Conclusion: In our study, results show that the use of hydroxychloroquine for COVID-19 patients in mild to moderate cases in an outpatient setting, within the protocol recommendation and inclusion/exclusion criteria, is safe, highly tolerable, and with minimum side effects.\"]}", "id": 66}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Coronavirus remains stable on metals and plastic for three days. Outside a lab, however, the virus might last considerably longer: its genetic material could be detected on surfaces 17 days after a cruise ship was empty of passengers (although it's not clear whether that material represents infectious virus particles).\n\nAbstract:\nPURPOSE OF REVIEW This article reviews 'no touch' methods for disinfection of the contaminated surface environment of hospitalized patients' rooms.\nThe focus is on studies that assessed the effectiveness of ultraviolet (UV) light devices, hydrogen peroxide systems, and self-disinfecting surfaces to reduce healthcare-associated infections (HAIs).\nRECENT FINDINGS The contaminated surface environment in hospitals plays an important role in the transmission of several key nosocomial pathogens including methicillin-resistant Staphylococcus aureus, vancomycin-resistant Enterococcus spp.\n, Clostridium difficile, Acinetobacter spp., and norovirus.\nMultiple clinical trials have now demonstrated the effectiveness of UV light devices and hydrogen peroxide systems to reduce HAIs.\nA limited number of studies have suggested that 'self-disinfecting' surfaces may also decrease HAIs.\nSUMMARY Many studies have demonstrated that terminal cleaning and disinfection with germicides is often inadequate and leaves environmental surfaces contaminated with important nosocomial pathogens. 'No touch' methods of room decontamination (i.e., UV devices and hydrogen peroxide systems) have been demonstrated to reduce key nosocomial pathogens on inoculated test surfaces and on environmental surfaces in actual patient rooms.\nFurther UV devices and hydrogen peroxide systems have been demonstrated to reduce HAI.\nA validated 'no touch' device or system should be used for terminal room disinfection following discharge of patients on contact precautions.\nThe use of a 'self-disinfecting' surface to reduce HAI has not been convincingly demonstrated.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 67}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: smokers have a very much lower probability of developing symptomatic or severe SARA-CoV-2 [COVID-19] infection as compared to the general population\n\nAbstract:\nSome comorbidities are associated with severe coronavirus disease (Covid-19) but it is unclear whether some increase susceptibility to Covid-19.\nIn this case-control Mexican study we found that obesity represents the strongest predictor for Covid-19 followed by diabetes and hypertension in both sexes and chronic renal failure in females only.\nActive smoking was associated with decreased odds of Covid-19.\nThese findings indicate that these comorbidities are not only associated with severity of disease but also predispose for getting Covid-19.\nFuture research is needed to establish the mechanisms involved in each comorbidity and the apparent \"protective\" effect of cigarette smoking.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Active smoking was associated with decreased odds of Covid-19.\"]}", "id": 68}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Can apple cider vinegar prevent the coronavirus? Nothing can really prevent the corona virus (COVid-19) all that authorities can do is slow it down which is why such tough measures are being enforced in some countries.\n\nAbstract:\nPURPOSE: SARS-CoV-2 is a new pandemic influenza caused by a coronavirus which main route of transmission is through exhaled droplets that primarily infect the nose and the nasopharynx.\nThe aim of this paper is to evaluate the effect of acetic acid, the active component of vinegar, as a potential disinfectant agent for upper airways.\nMETHODS: Twenty-nine patients were enrolled and divided into two groups: group 1 (14 patients) was composed of patients treated with off-label hydroxychloroquine and lopinavir/ritonavir, whereas group 2 (15 patients) was composed of patients treated with hydroxychloroquine only, combined with the inhalation of acetic acid disinfectant at a 0.34% concentration.\nA questionnaire-based evaluation of symptoms was performed after 15 days in both groups.\nRESULTS: It appears that the number of patients treated with acetic acid (group 2) that experienced improvement in individual symptoms was double that of the other group of patients (group 1), although numbers are too small for robust statistical analysis.\nCONCLUSIONS: Considering its potential benefits and high availability, acetic acid disinfection appears to be a promising adjunctive therapy in cases of non-severe COVID-19 and deserves further investigation.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"CONCLUSIONS: Considering its potential benefits and high availability, acetic acid disinfection appears to be a promising adjunctive therapy in cases of non-severe COVID-19 and deserves further investigation.\"]}", "id": 69}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: College students, many of whom are already stressed, reported an increase in depression and anxiety during the initial outbreak.\n\nAbstract:\nAs a result of the emergence of coronavirus disease 2019 (COVID-19) outbreak caused by acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in the Chinese city of Wuhan, a situation of socio-economic crisis and profound psychological distress rapidly occurred worldwide.\nVarious psychological problems and important consequences in terms of mental health including stress, anxiety, depression, frustration, uncertainty during COVID-19 outbreak emerged progressively.\nThis work aimed to comprehensively review the current literature about the impact of COVID-19 infection on the mental health in the general population.\nThe psychological impact of quarantine related to COVID-19 infection has been additionally documented together with the most relevant psychological reactions in the general population related to COVID-19 outbreak.\nThe role of risk and protective factors against the potential to develop psychiatric disorders in vulnerable individuals has been addressed as well.\nThe main implications of the present findings have been discussed.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Various psychological problems and important consequences in terms of mental health including stress, anxiety, depression, frustration, uncertainty during COVID-19 outbreak emerged progressively.\"]}", "id": 70}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: a popular treatment to tamp down the immune system in severely ill patients may help a few, but could harm many others. \n\nAbstract:\nCoronaviruses are a genetically highly variable family of viruses that infect vertebrates and have succeeded in infecting humans many times by overcoming the species barrier.\nThe severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which initially appeared in China at the end of 2019, exhibits a high infectivity and pathogenicity compared to other coronaviruses.\nAs the viral coat and other viral components are recognized as being foreign by the immune system, this can lead to initial symptoms, which are induced by the very efficiently working immune defense system via the respiratory epithelium.\nDuring severe courses a systemically expressed proinflammatory cytokine storm and subsequent changes in the coagulation and complement systems can occur.\nVirus-specific antibodies, the long-term expression of which is ensured by the formation of B memory cell clones, generate a specific immune response that is also detectable in blood (seroconversion).\nSpecifically effective cytotoxic CD8+ T\u00adcell populations are also formed, which recognize viral epitopes as pathogen-specific patterns in combination with MHC presentation on the cell surface of virus-infected cells and destroy these cells.\nAt the current point in time it is unclear how regular, robust and durable this immune status is constructed.\nExperiences with other coronavirus infections (SARS and Middle East respiratory syndrome, MERS) indicate that the immunity could persist for several years.\nBased on animal experiments, already acquired data on other coronavirus types and plausibility assumptions, it can be assumed that seroconverted patients have an immunity of limited duration and only a very low risk of reinfection.\nKnowledge of the molecular mechanisms of viral cycles and immunity is an important prerequisite for the development of vaccination strategies and development of effective drugs.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 71}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: The drugs have anti-inflammatory effects \"in addition to their blood pressure benefits.\n\nAbstract:\nAngiotensin-converting enzyme (ACE) inhibitors (ACEIs) and angiotensin II type\u00ad1 receptor blockers (ARBs) are among the most widely prescribed drugs for the treatment of arterial hypertension, heart failure and chronic kidney disease.\nA number of studies, mainly in animals and not involving the lungs, have indicated that these drugs can increase expression of angiotensin-converting enzyme 2 (ACE2).\nACE2 is the cell entry receptor of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of coronavirus disease 2019 (COVID-19) that is currently battering the globe.\nThis has led to the hypothesis that use of ACEIs and ARBs may increase the risk of developing severe COVID-19.\nIn this point of view paper, possible scenarios regarding the impact of ACEI/ARB pharmacotherapy on COVID-19 are discussed in relation to the currently available evidence.\nAlthough further research on the influence of blood-pressure-lowering drugs, including those not targeting the renin-angiotensin system, is warranted, there are presently no compelling clinical data showing that ACEIs and ARBs increase the likelihood of contracting COVID-19 or worsen the outcome of SARS-CoV\u00ad2 infections.\nThus, unless contraindicated, use of ACEIs/ARBs in COVID-19 patients should be continued in line with the recent recommendations of medical societies.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 72}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Although smoking is the most common way to use marijuana, some people bake it into a brownie or other food. Eating pot might spare you the lung effects of this drug, but that doesn't mean it's safe.\n\nAbstract:\nBACKGROUND: An epidemic of Coronavirus Disease 2019 (COVID-19) began in December 2019 and triggered a Public Health Emergency of International Concern (PHEIC).\nWe aimed to find risk factors for the progression of COVID-19 to help reducing the risk of critical illness and death for clinical help.\nMETHODS: The data of COVID-19 patients until March 20, 2020 were retrieved from four databases.\nWe statistically analyzed the risk factors of critical/mortal and non-critical COVID-19 patients with meta-analysis.\nRESULTS: Thirteen studies were included in Meta-analysis, including a total number of 3027 patients with SARS-CoV-2 infection.\nMale, older than 65, and smoking were risk factors for disease progression in patients with COVID-19 (male: OR\u00e2\u0080\u00af=\u00e2\u0080\u00af1.76, 95% CI (1.41, 2.18), P < 0.00001; age over 65 years old: OR =6.06, 95% CI(3.98, 9.22), P < 0.00001; current smoking: OR =2.51, 95% CI(1.39, 3.32), P\u00e2\u0080\u00af=\u00e2\u0080\u00af0.0006).\nThe proportion of underlying diseases such as hypertension, diabetes, cardiovascular disease, and respiratory disease were statistically significant higher in critical/mortal patients compared to the non-critical patients (diabetes: OR=3.68, 95% CI (2.68, 5.03), P < 0.00001; hypertension: OR\u00e2\u0080\u00af=\u00e2\u0080\u00af2.72, 95% CI (1.60,4.64), P\u00e2\u0080\u00af=\u00e2\u0080\u00af0.0002; cardiovascular disease: OR\u00e2\u0080\u00af=\u00e2\u0080\u00af5.19, 95% CI(3.25, 8.29), P < 0.00001; respiratory disease: OR\u00e2\u0080\u00af=\u00e2\u0080\u00af5.15, 95% CI(2.51, 10.57), P < 0.00001).\nClinical manifestations such as fever, shortness of breath or dyspnea were associated with the progression of disease [fever: 0R\u00e2\u0080\u00af=\u00e2\u0080\u00af0.56, 95% CI (0.38, 0.82), P\u00e2\u0080\u00af=\u00e2\u0080\u00af0.003;shortness of breath or dyspnea: 0R=4.16, 95% CI (3.13, 5.53), P < 0.00001].\nLaboratory examination such as aspartate amino transferase(AST) > 40U/L, creatinine(Cr) &#8805; 133mol/L, hypersensitive cardiac troponin I(hs-cTnI) > 28pg/mL, procalcitonin(PCT) > 0.5ng/mL, lactatede hydrogenase(LDH) > 245U/L, and D-dimer > 0.5mg/L predicted the deterioration of disease while white blood cells(WBC)<4\u00e2\u0080\u00af\u00d7\u00e2\u0080\u00af109/L meant a better clinical status[AST > 40U/L:OR=4.00, 95% CI (2.46, 6.52), P < 0.00001; Cr &#8805; 133\u00b5mol/L: OR\u00e2\u0080\u00af=\u00e2\u0080\u00af5.30, 95% CI (2.19, 12.83), P\u00e2\u0080\u00af=\u00e2\u0080\u00af0.0002; hs-cTnI > 28 pg/mL: OR\u00e2\u0080\u00af=\u00e2\u0080\u00af43.24, 95% CI (9.92, 188.49), P < 0.00001; PCT > 0.5 ng/mL: OR\u00e2\u0080\u00af=\u00e2\u0080\u00af43.24, 95% CI (9.92, 188.49), P < 0.00001;LDH > 245U/L: OR\u00e2\u0080\u00af=\u00e2\u0080\u00af43.24, 95% CI (9.92, 188.49), P < 0.00001; D-dimer > 0.5mg/L: OR\u00e2\u0080\u00af=\u00e2\u0080\u00af43.24, 95% CI (9.92, 188.49), P < 0.00001; WBC < 4\u00e2\u0080\u00af\u00d7\u00e2\u0080\u00af109/L: OR\u00e2\u0080\u00af=\u00e2\u0080\u00af0.30, 95% CI (0.17, 0.51), P < 0.00001].\nCONCLUSION: Male, aged over 65, smoking patients might face a greater risk of developing into the critical or mortal condition and the comorbidities such as hypertension, diabetes, cardiovascular disease, and respiratory diseases could also greatly affect the prognosis of the COVID-19.\nClinical manifestation such as fever, shortness of breath or dyspnea and laboratory examination such as WBC, AST, Cr, PCT, LDH, hs-cTnI and D-dimer could imply the progression of COVID-19.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"CONCLUSION: Male, aged over 65, smoking patients might face a greater risk of developing into the critical or mortal condition and the comorbidities such as hypertension, diabetes, cardiovascular disease, and respiratory diseases could also greatly affect the prognosis of the COVID-19.\"]}", "id": 73}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Can COVID-19 be spread from pets to people or other pets? According to the latest information from the CDC , the risk of animals spreading COVID-19 to people is very low. However, because all animals can carry germs that can make people sick, it's always a good idea to practice healthy habits around pets and other animals.\n\nAbstract:\nSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)--the virus that causes coronavirus disease (COVID-19)--has been detected in domestic dogs and cats, raising concerns of transmission from, to, or between these animals.\nThere is currently no indication that feline- or canine-to-human transmission can occur, though there is rising evidence of the reverse.\nTo explore the extent of animal-related transmission, we aggregated 17 case reports on confirmed SARS-CoV-2 infections in animals as of 15 May 2020.\nAll but two animals fully recovered and had only mild respiratory or digestive symptoms.\nUsing data from probable cat-to-cat transmission in Wuhan, China, we estimated the basic reproduction number R0 under this scenario at 1.09 (95% confidence interval: 1.05, 1.13).\nThis value is much lower than the R0 reported for humans and close to one, indicating that the sustained transmission between cats is unlikely to occur.\nOur results support the view that the pet owners and other persons with COVID-19 in close contact with animals should be cautious of the way they interact with them.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)--the virus that causes coronavirus disease (COVID-19)--has been detected in domestic dogs and cats, raising concerns of transmission from, to, or between these animals.\", \"There is currently no indication that feline- or canine-to-human transmission can occur, though there is rising evidence of the reverse.\"]}", "id": 74}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: For most patients, COVID-19 begins and ends in their lungs, because like the flu, coronaviruses are respiratory diseases\n\nAbstract:\nSARS-CoV-2 is the coronavirus agent of the COVID-19 pandemic causing high mortalities.\nIn contrast, the widely spread human coronaviruses OC43, HKU1, 229E, and NL63 tend to cause only mild symptoms.\nThe present study shows, by in silico analysis, that these common human viruses are expected to induce immune memory against SARS-CoV-2 by sharing protein fragments (antigen epitopes) for presentation to the immune system by MHC class I. A list of such epitopes is provided.\nThe number of these epitopes and the prevalence of the common coronaviruses suggest that a large part of the world population has some degree of specific immunity against SARS-CoV-2 already, even without having been infected by that virus.\nFor inducing protection, booster vaccinations enhancing existing immunity are less demanding than primary vaccinations against new antigens.\nTherefore, for the discussion on vaccination strategies against COVID-19, the available immune memory against related viruses should be part of the consideration.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 75}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Overall, funds that the hospital receives for COVID-19 deaths are more the greater good and the good of the patients' families.\n\nAbstract:\nThe ongoing outbreak of COVID-19 has been expanding worldwide.\nAs of 17 April 2020, the death toll stands at a sobering 147,027 and over two million cases, this has been straining the health care systems all over.\nRespiratory failure has been cited as the major cause of death but here we present a case about a patient who instead succumbed to severe metabolic acidosis with multiple organ failure.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 76}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Increase in sars-cov-2 viral rna identified in wastewater 48 hours before covid-19 clinical tests and 96 hours before hospitalizations\n\nAbstract:\nCurtailing the Spring 2020 COVID-19 surge required sweeping and stringent interventions by governments across the world.\nWastewater-based COVID-19 epidemiology programs have been initiated in many countries to provide public health agencies with a complementary disease tracking metric and facile surveillance tool.\nHowever, their efficacy in prospectively capturing resurgence following a period of low prevalence is unclear.\nIn this study, the SARS-CoV-2 viral signal was measured in primary clarified sludge harvested every two days at the City of Ottawa's water resource recovery facility during the summer of 2020, when clinical testing recorded daily percent positivity below 1%.\nIn late July, increases of >400% in normalized SARS-CoV-2 RNA signal in wastewater were identified 48 hours prior to reported >300% increases in positive cases that were retrospectively attributed to community-acquired infections.\nDuring this resurgence period, SARS-CoV-2 RNA signal in wastewater preceded the reported >160% increase in community hospitalizations by approximately 96 hours.\nThis study supports wastewater-based COVID-19 surveillance of populations in augmenting the efficacy of diagnostic testing, which can suffer from sampling biases or timely reporting as in the case of hospitalization census.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"In late July, increases of >400% in normalized SARS-CoV-2 RNA signal in wastewater were identified 48 hours prior to reported >300% increases in positive cases that were retrospectively attributed to community-acquired infections.\", \"During this resurgence period, SARS-CoV-2 RNA signal in wastewater preceded the reported >160% increase in community hospitalizations by approximately 96 hours.\"]}", "id": 77}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Hand washing with soap and water and scrubbing for at least 20 seconds is recommended, but a hand sanitizer that contains at least 60% alcohol is the best alternative\n\nAbstract:\nThe recent emergence of Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causing COVID-19 is a major burden for health care systems worldwide.\nIt is important to address if the current infection control instructions based on active ingredients are sufficient.\nWe therefore determined the virucidal activity of two alcohol-based hand rub solutions for hand disinfection recommended by the World Health Organization (WHO), as well as commercially available alcohols.\nEfficient SARS-CoV-2 inactivation was demonstrated for all tested alcohol-based disinfectants.\nThese findings show the successful inactivation of SARS-CoV-2 for the first time and provide confidence in its use for the control of COVID-19.\nImportance The current COVID-19 outbreak puts a huge burden on the world\u2019s health care systems.\nWithout effective therapeutics or vaccines being available, effective hygiene measure are of utmost importance to prevent viral spreading.\nIt is therefore crucial to evaluate current infection control strategies against SARS-CoV-2.\nWe show the inactivation of the novel coronavirus for the first time and endorse the importance of disinfectant-based hand hygiene to reduce SARS-CoV-2 transmission.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Efficient SARS-CoV-2 inactivation was demonstrated for all tested alcohol-based disinfectants.\", \"These findings show the successful inactivation of SARS-CoV-2 for the first time and provide confidence in its use for the control of COVID-19.\"]}", "id": 78}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: young peope are not at risk and do not die from covid-19\n\nAbstract:\nObjective: Severity of the coronavirus disease 2019 (covid-19) has been assessed in terms of absolute mortality in SARS-CoV-2 positive cohorts.\nAn assessment of mortality relative to mortality in the general population is presented.\nDesign: Retrospective population-based study.\nSetting: Individual information on symptomatic confirmed SARS-CoV-2 patients and subsequent deaths from any cause were compared to the all-cause mortality in the Swiss population of 2018.\nStarting February 23, 2020, mortality in covid-19 patients was monitored for 80 days and compared to the population mortality observed in the same time-of-year starting February 23, 2018.\nParticipants: 5 160 595 inhabitants of Switzerland aged 35 to 95 without covid-19 (general population in spring 2018) and 20 769 persons tested positively for covid-19 (spring 2020).\nMeasurements: Sex- and age-specific mortality rates were estimated using Cox proportional hazards models.\nAbsolute probabilities of death were predicted and risk was assessed in terms of relative mortality by taking the ratio between the sex- and age-specific absolute mortality in covid19 patients and the corresponding mortality in the 2018 general population.\nResults: A confirmed SARS-CoV-2 infection substantially increased the probability of death across all patient groups, ranging from nine (6 to 15) times the population mortality in 35-year old infected females to a 53-fold increase (46 to 59) for 95 year old infected males.\nThe highest relative risks were observed among males and older patients.\nThe magnitude of these effects was smaller compared to increases observed in absolute mortality risk.\nMale covid-19 patients exceeded the population hazard for males (hazard ratio 1.20, 1.00 to 1.44).\nEach additional year of age increased the population hazard in covid-19 patients (hazard ratio 1.04, 1.03 to 1.05).\nLimitations: Information about the distribution of relevant comorbidities was not available on population level and the associated risk was not quantified.\nConclusions: Health care professionals, decision makers, and societies are provided with an additional population-adjusted assessment of covid-19 mortality risk.\nIn combination with absolute measures of risk, the relative risks presented here help to develop a more comprehensive understanding of the actual impact of covid-19.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Results: A confirmed SARS-CoV-2 infection substantially increased the probability of death across all patient groups, ranging from nine (6 to 15) times the population mortality in 35-year old infected females to a 53-fold increase (46 to 59) for 95 year old infected males.\", \"The highest relative risks were observed among males and older patients.\"]}", "id": 79}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: No. Spraying alcohol or chlorine all over your body will not kill viruses that have already entered your body.\n\nAbstract:\nAmong the basic protective measures against COVID-19, the need to wash hands frequently and in a prolonged way using soap, and to regularly use alcohol-based hand sanitizers is well established for the whole population.\nHealthcare workers in general, and particularly those involved in the direct care of COVID-19 patients, have to wear personal protective equipment (PPE) daily for many hours and also accomplish general preventive measurements outside their work.\nCutaneous adverse reactions can develop that need to be prevented, identified and therapeutically managed.\nAccording to the data reported by Lin et al 1 , based in the experience from healthcare workers in Wuhan, adverse skin reactions were reported in 74% of responders (n=376) to a general survey.\nThe most commonly reported types of eruptions were skin dryness or desquamation (68.6%), papules or erythema (60.4%) and maceration (52,9%).", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Among the basic protective measures against COVID-19, the need to wash hands frequently and in a prolonged way using soap, and to regularly use alcohol-based hand sanitizers is well established for the whole population.\"]}", "id": 80}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: The researchers also ranked face mask material from most to least effective in their testing.\n\nAbstract:\nThe COVID\u201019 pandemic caused by the novel coronavirus SARS\u2010CoV\u20102 has claimed many lives worldwide.\nWearing medical masks or N95 masks (namely N95 respirators) can slow the virus spread and reduce the infection risk.\nReuse of these masks can minimize waste, protect the environment, and help to solve the current imminent shortage of masks.\nDisinfection of used masks is needed for reuse of them with safety, but improper decontamination can damage the blocking structure of masks.\nIn this study, we demonstrated, using avian coronavirus of infectious bronchitis virus to mimic SARS\u2010CoV\u20102, that medical masks and N95 masks remained their blocking efficacy after being steamed on boiling water even for 2 hours.\nWe also demonstrated that three brands of medical masks blocked over 99% viruses in aerosols.\nThe avian coronavirus was completely inactivated after being steamed for 5 minutes.\nTogether, this study suggested that medical masks are adequate for use on most social occasions, and both medical masks and N95 masks can be reused for a few days with steam decontamination between use.\nThis article is protected by copyright.\nAll rights reserved.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Wearing medical masks or N95 masks (namely N95 respirators) can slow the virus spread and reduce the infection risk.\"]}", "id": 81}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: In one study it was found that 100% of ICU COVID-19 patients less than 75 years old had vitamin D insufficiency.\n\nAbstract:\nBackground: Following emerge of a novel coronavirus from Wuhan, China, in December 2019, it has affected the whole world and after months of efforts by the medical communities, there is still no specific approach for prevention and treatment against the Coronavirus Disease 2019 (COVID-19).\nEvidence recommends that vitamin D might be an important supportive agent for the immune system, mainly in cytokine response regulation against COVID-19.\nHence, we carried out a rapid systematic review and meta-analysis along with an ecological investigation in order to maximize the use of everything that exists about the role of vitamin D in the COVID-19.\nMethods: A systematic search was performed in PubMed, Scopus, Embase, Cochrane Library, Web of Science and Google Scholar (intitle) as well as preprint database of medRxiv, bioRxiv, Research Square, preprints.org, search engine of ScienceDirect and a rapid search through famous journals up to May 26, 2020.\nStudies focused on the role of vitamin D in confirmed COVID-19 patients were entered into the systematic review.\nAlong with our main aim, to find the second objective: correlation of global vitamin D status and COVID-19 recovery and mortality we carried out a literature search in PubMed database to identify the national or regional studies reported the vitamin D status globally.\nCMA v. 2.2.064 and SPSS v.16 were used for data analysis.\nResults: Out of nine studies entered into our systematic review, six studies containing 3,822 participants entered into the meta-analysis.\nThe meta-analysis indicated that 46.5% of COVID-19 patients were suffering from vitamin D deficiency (95% CI, 28.2%-65.8%) and in 43.3% of patients, levels of vitamin D were insufficient (95% CI, 27.4%-60.8%).\nIn regard to our ecological investigation on 51 countries including 408,748 participants, analyses indicated no correlation between vitamin D levels and recovery rate (r= 0.041) as well as mortality rate (r=-0.073) globally.\nHowever, given latitude, a small reverse correlation between mortality rate and vitamin D status was observed throughout the globe (r= -0.177).\nIn Asia, a medium direct correlation was observed for recovery rate (r= 0.317) and a significant reveres correlation for mortality rate (r= -0.700) with vitamin D status in such patients.\nIn Europe, there were no correlations for both recovery (r= 0.040) and mortality rate (r= -0.035).\nIn Middle East, the recovery rate (r= 0.267) and mortality rate (r= -0.217) showed a medium correlation.\nIn North and Sought America, surprisingly, both recovery and mortality rate demonstrated a direct correlation respectively (r= 1.000, r=0.500).\nIn Oceania, unexpectedly, recovery (r= -1.000) and mortality (r= -1.000) rates were in considerable reverse correlation with vitamin D levels.\nConclusion: In this systematic review and meta-analysis with an ecological approach, we found a high percentage of COVID-19 patients who suffer from vitamin D deficiency or insufficiency.\nMuch more important, our ecological investigation resulted in substantial direct and reverse correlations between recovery and mortality rates of COVID-19 patients with vitamin D status in different countries.\nConsidering latitudes, a small reverse correlation between vitamin D status and mortality rate was found globally.\nIt seems that populations with lower levels of vitamin D might be more susceptible to the novel coronavirus infection.\nNevertheless, due to multiple limitations, if this study does not allow to quantify a value of the Vitamin D with full confidence, it allows at least to know what the Vitamin D might be and that it would be prudent to invest in this direction through comprehensive large randomized clinical trials.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"In Middle East, the recovery rate (r= 0.267) and mortality rate (r= -0.217) showed a medium correlation.\"]}", "id": 82}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Hydroxychloroquine safety outcome within approved therapeutic protocol included covid-19 outpatients in saudi arabia\n\nAbstract:\nBackground: Healthcare systems globally has been challenged following the COVID-19 pandemic, since late 2019.\nMultiple approaches and strategies have been performed to relieve the pressure and support existing healthcare systems.\nThe Saudi Arabian Ministry of Health (MOH) launched an initiative to support the National Healthcare System.\nSince the 5th of June 2020, 238 outpatient fever clinics were established across Saudi Arabia.\nMethods: A cross-sectional study included 2,733 eligible patients subjected to MOH treatment protocol (hydroxychloroquine and zinc) and revisited the clinics within 3-7 days after treatment initiation.\nThis study aimed to assess the safety outcome and reported adverse events from hydroxychloroquine use among suspected COVID-19 patients.\nThe data was collected through an electronic link and cross-checked with that of the national database (Health Electronic Surveillance Network, HESN) and reports from the MOH Morbidity and Mortality (M&M) Committee.\nResults: Majority of the cases were males (70.4%).\nUpon reassessing the studied participants within 3-7 days, 240 patients (8.8%) discontinued the treatment protocol because of the development of side effects (4.1%) and for non-clinical reasons in the remaining (4.7%).\nMedication side effects overall were reported among (6.7%) of all studied participants, including mainly cardiovascular adverse events (2.5%), followed by gastrointestinal (GI) symptoms (2.4%).\nNo Intensive Care Unit (ICU) admission or death were reported among these patients.\nConclusion: In our study, results show that the use of hydroxychloroquine for COVID-19 patients in mild to moderate cases in an outpatient setting, within the protocol recommendation and inclusion/exclusion criteria, is safe, highly tolerable, and with minimum side effects.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Conclusion: In our study, results show that the use of hydroxychloroquine for COVID-19 patients in mild to moderate cases in an outpatient setting, within the protocol recommendation and inclusion/exclusion criteria, is safe, highly tolerable, and with minimum side effects.\"]}", "id": 83}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Evidence is currently lacking and it is too early to make robust conclusions on any link between use of angiotensin-converting enzyme (ACE) inhibitors and angiotensin II type-I receptor blockers with risk or severity of novel coronavirus disease 2019 (COVID-19) infection.\n\nAbstract:\nIntravenous infusions of angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin receptor blockers (ARBs) in experimental animals increase the numbers of angiotensin-converting enzyme 2 (ACE2) receptors in the cardiopulmonary circulation.\nACE2 receptors serve as binding sites for SARS-CoV-2 virions in the lungs.\nPatients who take ACEIs and ARBS may be at increased risk of severe disease outcomes due to SARS-CoV-2 infections.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Patients who take ACEIs and ARBS may be at increased risk of severe disease outcomes due to SARS-CoV-2 infections.\"]}", "id": 84}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: No, garlic won't prevent COVID-19. These 'home remedies' and 'cures' just don't work\n\nAbstract:\nIn late December 2019 in Wuhan, China, several patients with viral pneumonia were identified as 2019 novel coronavirus (2019-nCoV).\nSo far, there are no specific treatments for patients with coronavirus disease-19 (COVID-19), and the treatments available today are based on previous experience with similar viruses such as severe acute respiratory syndrome-related coronavirus (SARS-CoV), Middle East respiratory syndrome coronavirus (MERS-CoV), and Influenza virus.\nIn this article, we have tried to reach a therapeutic window of drugs available to patients with COVID-19.\nCathepsin L is required for entry of the 2019-nCoV virus into the cell as target teicoplanin inhibits virus replication.\nAngiotensin-converting-enzyme 2 (ACE2) in soluble form as a recombinant protein can prevent the spread of coronavirus by restricting binding and entry.\nIn patients with COVID-19, hydroxychloroquine decreases the inflammatory response and cytokine storm, but overdose causes toxicity and mortality.\nNeuraminidase inhibitors such as oseltamivir, peramivir, and zanamivir are invalid for 2019-nCoV and are not recommended for treatment but protease inhibitors such as lopinavir/ritonavir (LPV/r) inhibit the progression of MERS-CoV disease and can be useful for patients of COVID-19 and, in combination with Arbidol, has a direct antiviral effect on early replication of SARS-CoV. Ribavirin reduces hemoglobin concentrations in respiratory patients, and remdesivir improves respiratory symptoms.\nUse of ribavirin in combination with LPV/r in patients with SARS-CoV reduces acute respiratory distress syndrome and mortality, which has a significant protective effect with the addition of corticosteroids.\nFavipiravir increases clinical recovery and reduces respiratory problems and has a stronger antiviral effect than LPV/r.\ncurrently, appropriate treatment for patients with COVID-19 is an ACE2 inhibitor and a clinical problem reducing agent such as favipiravir in addition to hydroxychloroquine and corticosteroids.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"In late December 2019 in Wuhan, China, several patients with viral pneumonia were identified as 2019 novel coronavirus (2019-nCoV).\", \"So far, there are no specific treatments for patients with coronavirus disease-19 (COVID-19), and the treatments available today are based on previous experience with similar viruses such as severe acute respiratory syndrome-related coronavirus (SARS-CoV), Middle East respiratory syndrome coronavirus (MERS-CoV), and Influenza virus.\"]}", "id": 85}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Evidence is currently lacking and it is too early to make robust conclusions on any link between use of angiotensin-converting enzyme (ACE) inhibitors and angiotensin II type-I receptor blockers with risk or severity of novel coronavirus disease 2019 (COVID-19) infection.\n\nAbstract:\nINTRODUCTION The present research aimed to determine the relation between the use of angiotensin-converting enzyme inhibitors (ACE inh) and angiotensinogen receptor blockers (ARBs) and in-hospital mortality of hypertensive patients diagnosed with Covid-19 pneumonia.\nMATERIAL AND METHOD In this retrospective study, we included 113 consecutive hypertensive patients admitted due to Covid-19 infection.\nIn all patients, Covid-19 infection was confirmed with using reverse-transcription polymerase chain reaction.\nAll patients were on ACE inh/ARBs or other antihypertensive therapy unless no contraindication was present.\nThe primary outcome of the study was the in-hospital all-cause mortality.\nRESULTS In total, 113 hypertensive Covid-19 patients were included, of them 74 patients were using ACE inh/ARBs.\nDuring in-hospital follow up, 30.9% [n = 35 patients] of patients died.\nThe frequency of admission to the ICU and endotracheal intubation were significantly higher in patients using ACE inh/ARBs.\nIn a multivariable analysis, the use of ACE inh/ARBs was an independent predictor of in-hospital mortality (OR: 3.66; 95%CI: 1.11-18.18; p= .032).\nKaplan-Meir curve analysis displayed that patients on ACE inh/ARBs therapy had higher incidence of in-hospital death than those who were not.\nCONCLUSION The present study has found that the use of ACE inh/ARBs therapy might be associated with an increased in-hospital mortality in patients who were diagnosed with Covid-19 pneumonia.\nIt is likely that ACE inh/ARBs therapy might not be beneficial in the subgroup of hypertensive Covid-19 patients despite the fact that there might be the possibility of some unmeasured residual confounders to affect the results of the study.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"CONCLUSION The present study has found that the use of ACE inh/ARBs therapy might be associated with an increased in-hospital mortality in patients who were diagnosed with Covid-19 pneumonia.\"]}", "id": 86}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: drugs (Anti-inflammatory drugs, which includes steroids such as prednisone and non-steroidal drugs like ibuprofen) are helpful for symptomatic treatment only and are not a cure. By decreasing inflammation, they can decrease pain, swelling, redness and other symptoms that may be associated with inflammation.\n\nAbstract:\nFever has been reported as a common symptom occurring in COVID-19 illness.\nOver the counter antipyretics such as ibuprofen and acetaminophen are often taken by individuals to reduce the discomfort of fever.\nRecently, the safety of ibuprofen in COVID-19 patients has been questioned due to anecdotal reports of worsening symptoms in previously healthy young adults.\nStudies show that ibuprofen demonstrates superior efficacy in fever reduction compared to acetaminophen.\nAs fever may have benefit in shortening the duration of viral illness, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response when taken during the early stages of COVID-19 illness.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"As fever may have benefit in shortening the duration of viral illness, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response when taken during the early stages of COVID-19 illness.\"]}", "id": 87}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: The new coronavirus can damage the lungs, heart and brain, which increases the risk of long-term health problems.\n\nAbstract:\nPatients with pre-existing cardiovascular disease and risk factors are more likely to experience adverse outcomes associated with the novel coronavirus disease-2019 (COVID-19).\nAdditionally, consistent reports of cardiac injury and de novo cardiac complications, including possible myocarditis, arrhythmia, and heart failure in patients without prior cardiovascular disease or significant risk factors, are emerging, possibly due to an accentuated host immune response and cytokine release syndrome.\nAs the spread of the virus increases exponentially, many patients will require medical care either for COVID-19 related or traditional cardiovascular issues.\nWhile the COVID-19 pandemic is dominating the attention of the healthcare system, there is an unmet need for a standardized approach to deal with COVID-19 associated and other traditional cardiovascular issues during this period.\nWe provide consensus guidance for the management of various cardiovascular conditions during the ongoing COVID-19 pandemic with the goal of providing the best care to all patients and minimizing the risk of exposure to frontline healthcare workers.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Additionally, consistent reports of cardiac injury and de novo cardiac complications, including possible myocarditis, arrhythmia, and heart failure in patients without prior cardiovascular disease or significant risk factors, are emerging, possibly due to an accentuated host immune response and cytokine release syndrome.\"]}", "id": 88}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Young people as diligent about Covid measures as older people\n\nAbstract:\nCurrently (mid May 2020), most active cases of COVID-19 are found in Europe and North America while it is still in the initial phases in Africa.\nAs COVID-19 mortality occurs mainly in elderly and as Africa has a comparably young population, the death rates should be lower than on other continents.\nWe calculated standardised mortality ratios (SMR) using age-specific case fatality rates for COVID-19 and the age structure of the population of Africa and of other continents.\nCompared to a European or Northern American population, the standardised mortality ratio was only 0.22 and 0.25, respectively, corresponding to reduction of deaths rates to a quarter.\nCompared to the Asian and Latin American & Caribbean population, the SMR was 0.43 and 0.44, respectively, corresponding to half the death rate for Africa.\nIt is useful to quantify the isolated effect of the African age-structure on potential COVID-19 mortality for illustrative and communication purposes, keeping in mind the importance of public health measures that have been shown to be effective in reducing cases and deaths.\nThe different aspect of age pyramids of a European and an African population are striking and the potential implications for the pandemic are often discussed but rarely quantified.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 89}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Is there an airborne component to the transmission of covid-19 ?\n\nAbstract:\nObjectives While COVID-19 is known to be spread by respiratory droplets (which travel <2m horizontally), much less is known about its transmission via aerosols, which can become airborne and be widely distributed throughout room spaces.\nIn order to quantify the risk posed by COVID-19 infectors exhaling respiratory aerosols in enclosed spaces, we undertook a computer modelling study to simulate transmission in an office building.\nMethods Respiratory droplet data from four published datasets were analysed to quantify the number and volume of droplets <100m diameter produced by a typical cough and speaking event (i.e. counting from 1 to 100).\nThis was used in a stochastic model to simulate (10,000 simulations) the number of respiratory particles, originating from a COVID-19 infector, that would be inhaled in one hour by a susceptible individual practicing socially distancing in a 5 x 5 x 2.75m office space.\nSeveral scenarios were simulated that mimicked the presence of both symptomatic and asymptomatic COVID-19 infectors.\nResults On average, each cough and speaking event produced similar numbers of droplets <100 m diameter (median range = 955-1010).\nComputer simulations (at ventilation rate = 2AC/h) revealed that sharing the office space with a symptomatic COVID-19 infector (4 coughs per hour) for one hour resulted in the inhalation of 187.3 (median value) respiratory droplets, whereas sharing with an asymptomatic COVID-19 positive person (10 speaking events per hour) resulted in the inhalation of 482.9 droplets.\nIncreasing the ventilation rate resulted in only modest reductions in particle numbers inhaled.\nConclusions Given that live SARS-CoV-2 virions are known to be shed in high concentrations from the nasal cavity of both symptomatic and asymptomatic COVID-19 patients, the results suggest that individuals who share enclosed spaces with an infector may be at risk of contracting COVID-19 by the aerosol route, even when practicing social distancing.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"In order to quantify the risk posed by COVID-19 infectors exhaling respiratory aerosols in enclosed spaces, we undertook a computer modelling study to simulate transmission in an office building.\"]}", "id": 90}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: If you're worried about corona, only the N95 mask will protect you\n\nAbstract:\nWe identified seasonal human coronaviruses, influenza viruses and rhinoviruses in exhaled breath and coughs of children and adults with acute respiratory illness.\nSurgical face masks significantly reduced detection of influenza virus RNA in respiratory droplets and coronavirus RNA in aerosols, with a trend toward reduced detection of coronavirus RNA in respiratory droplets.\nOur results indicate that surgical face masks could prevent transmission of human coronaviruses and influenza viruses from symptomatic individuals.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Surgical face masks significantly reduced detection of influenza virus RNA in respiratory droplets and coronavirus RNA in aerosols, with a trend toward reduced detection of coronavirus RNA in respiratory droplets.\", \"Our results indicate that surgical face masks could prevent transmission of human coronaviruses and influenza viruses from symptomatic individuals.\"]}", "id": 91}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Can apple cider vinegar prevent the coronavirus? Nothing can really prevent the corona virus (COVid-19) all that authorities can do is slow it down which is why such tough measures are being enforced in some countries.\n\nAbstract:\nThe global pandemic caused by the newly described severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused worldwide suffering and death of unimaginable magnitude from coronavirus disease 2019 (COVID-19).\nThe virus is transmitted through aerosol droplets, and causes severe acute respiratory syndrome.\nSARS-CoV-2 uses the receptor-binding domain of its spike protein S1 to attach to the host angiotensin-converting enzyme 2 receptor in lung and airway cells.\nBinding requires the help of another host protein, transmembrane protease serine S1 member 2.\nSeveral factors likely contribute to the efficient transmission of SARS-CoV-2.\nThe receptor-binding domain of SARS-CoV-2 has a 10- to 20-fold higher receptor-binding capacity compared with previous pandemic coronaviruses.\nIn addition, because asymptomatic persons infected with SARS-CoV-2 have high viral loads in their nasal secretions, they can silently and efficiently spread the disease.\nPCR-based tests have emerged as the criterion standard for the diagnosis of infection.\nCaution must be exercised in interpreting antibody-based tests because they have not yet been validated, and may give a false sense of security of being \"immune\" to SARS-CoV-2.\nWe discuss how the development of some symptoms in allergic rhinitis can serve as clues for new-onset COVID-19.\nThere are mixed reports that asthma is a risk factor for severe COVID-19, possibly due to differences in asthma endotypes.\nThe rapid spread of COVID-19 has focused the efforts of scientists on repurposing existing Food and Drug Administration-approved drugs that inhibit viral entry, endocytosis, genome assembly, translation, and replication.\nNumerous clinical trials have been launched to identify effective treatments for COVID-19.\nInitial data from a placebo-controlled study suggest faster time to recovery in patients on remdesivir; it is now being evaluated in additional controlled studies.\nAs discussed in this review, till effective vaccines and treatments emerge, it is important to understand the scientific rationale of pandemic-mitigation strategies such as wearing facemasks and social distancing, and implement them.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Numerous clinical trials have been launched to identify effective treatments for COVID-19.\", \"Initial data from a placebo-controlled study suggest faster time to recovery in patients on remdesivir; it is now being evaluated in additional controlled studies.\", \"As discussed in this review, till effective vaccines and treatments emerge, it is important to understand the scientific rationale of pandemic-mitigation strategies such as wearing facemasks and social distancing, and implement them.\"]}", "id": 92}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: However, some children can get severely ill from COVID-19. They might require hospitalization, intensive care, or a ventilator to help them breathe.\n\nAbstract:\nBACKGROUND The new Coronavirus identified in Whuan at the end of 2019 (SARS-CoV-2) belongs to the Beta Coronavirus genus and is responsible for the new Coronavirus 2019 pandemia (COVID-19).\nInfected children may be asymptomatic or present fever, dry cough, fatigue or gastrointestinal symptoms.\nThe CDC recommends that clinicians should decide to test patients based on the presence of signs and symptoms compatible with COVID-19.\nMATERIAL AND METHODS 42 children (the majority < 5 years of age) were referred, to our Pediatric Department, as possible cases of COVID-19 infection.\nBlood analysis, chest X-ray, and naso-oropharyngeal swab specimens for viral identification of COVID-19 were requested.\nRESULTS None of the screened children resulted positive for COVID-19 infection.\nAt first presentation, the most frequent signs and symptoms were: fever (71.4%), fatigue (35.7%) and cough (30.9%).\nAn high C-reactive protein value and abnormalities of chest X-ray (bronchial wall thickening) were detected in 26.2% and 19% of patients, respectively.\nAlmost half of patients (45.2%) required hospitalization in our Pediatric Unit and one patient in Intensive Care Unit.\nCONCLUSIONS Testing people who meet the COVID-19 suspected case definition criteria is essential for clinical management and outbreak control.\nChildren of all ages can get COVID-19, although they appear to be affected less frequently than adults, as reported in our preliminary survey.\nFurther studies are needed to confirm our observations.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"RESULTS None of the screened children resulted positive for COVID-19 infection.\", \"At first presentation, the most frequent signs and symptoms were: fever (71.4%), fatigue (35.7%) and cough (30.9%).\", \"An high C-reactive protein value and abnormalities of chest X-ray (bronchial wall thickening) were detected in 26.2% and 19% of patients, respectively.\", \"Almost half of patients (45.2%) required hospitalization in our Pediatric Unit and one patient in Intensive Care Unit.\", \"Children of all ages can get COVID-19, although they appear to be affected less frequently than adults, as reported in our preliminary survey.\"]}", "id": 93}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: the Covid-19 coronavirus can stay on various surfaces for a while\n\nAbstract:\nObjectives: To evaluate SARS-CoV-2 surface and air contamination during the peak of the COVID-19 pandemic in London.\nDesign: Prospective cross-sectional observational study.\nSetting: An acute NHS healthcare provider.\nParticipants: All inpatient wards were fully occupied by patients with COVID-19 at the time of sampling.\nInterventions: Air and surface samples were collected from a range of clinical areas and a public area of the hospital.\nAn active air sampler was used to collect three or four 1.0 m3 air samples in each area.\nSurface samples were collected by swabbing approximately 25 cm2 of items in the immediate vicinity of each air sample.\nSARS-CoV-2 was detected by RT-qPCR and viral culture using Vero E6 and Caco2 cells; additionally the limit of detection for culturing SARS-CoV-2 dried onto surfaces was determined.\nMain outcome measures: SARS-CoV-2 detected by PCR or culture.\nResults: Viral RNA was detected on 114/218 (52.3%) of surface and 14/31 (38.7%) air samples but no virus was cultured.\nThe proportion of surface samples contaminated with viral RNA varied by item sampled and by clinical area.\nViral RNA was detected on surfaces and in air in public areas of the hospital but was more likely to be found in areas immediately occupied by COVID-19 patients (67/105 (63.8%) in areas immediately occupied by COVID-19 patients vs. 29/64 (45.3%) in other areas (odds ratio 0.5, 95% confidence interval 0.2-0.9, p=0.025, Fishers exact test).\nThe PCR Ct value for all surface and air samples (>30) indicated a viral load that would not be culturable.\nConclusions: Our findings of extensive viral RNA contamination of surfaces and air across a range of acute healthcare settings in the absence of cultured virus underlines the potential risk from surface and air contamination in managing COVID-19, and the need for effective use of PPE, social distancing, and hand/surface hygiene.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Conclusions: Our findings of extensive viral RNA contamination of surfaces and air across a range of acute healthcare settings in the absence of cultured virus underlines the potential risk from surface and air contamination in managing COVID-19, and the need for effective use of PPE, social distancing, and hand/surface hygiene.\"]}", "id": 94}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: If you're worried about corona, only the N95 mask will protect you\n\nAbstract:\nThe COVID\u201019 pandemic caused by the novel coronavirus SARS\u2010CoV\u20102 has claimed many lives worldwide.\nWearing medical masks or N95 masks (namely N95 respirators) can slow the virus spread and reduce the infection risk.\nReuse of these masks can minimize waste, protect the environment, and help to solve the current imminent shortage of masks.\nDisinfection of used masks is needed for reuse of them with safety, but improper decontamination can damage the blocking structure of masks.\nIn this study, we demonstrated, using avian coronavirus of infectious bronchitis virus to mimic SARS\u2010CoV\u20102, that medical masks and N95 masks remained their blocking efficacy after being steamed on boiling water even for 2 hours.\nWe also demonstrated that three brands of medical masks blocked over 99% viruses in aerosols.\nThe avian coronavirus was completely inactivated after being steamed for 5 minutes.\nTogether, this study suggested that medical masks are adequate for use on most social occasions, and both medical masks and N95 masks can be reused for a few days with steam decontamination between use.\nThis article is protected by copyright.\nAll rights reserved.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Wearing medical masks or N95 masks (namely N95 respirators) can slow the virus spread and reduce the infection risk.\", \"We also demonstrated that three brands of medical masks blocked over 99% viruses in aerosols.\", \"The avian coronavirus was completely inactivated after being steamed for 5 minutes.\", \"Together, this study suggested that medical masks are adequate for use on most social occasions, and both medical masks and N95 masks can be reused for a few days with steam decontamination between use.\"]}", "id": 95}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: VITAMIN D HEALTHY LEVELS decrease COVID-19 MORTALITY RATES\n\nAbstract:\nImportance: Vitamin D treatment has been found to decrease incidence of viral respiratory tract infection, especially in vitamin D deficiency.\nIt is unknown whether COVID-19 incidence is associated with vitamin D deficiency and treatment.\nObjective: To examine whether vitamin D deficiency and treatment are associated with testing positive for COVID-19.\nDesign: Retrospective cohort study Setting: University of Chicago Medicine Participants: Patients tested for COVID-19 from 3/3/2020-4/10/2020.\nVitamin D deficiency was defined by the most recent 25-hydroxycholecalciferol <20ng/ml or 1,25-dihydroxycholecalciferol <18pg/ml within 1 year before COVID-19 testing.\nTreatment was defined by the most recent vitamin D type and dose, and treatment changes between the time of the most recent vitamin D level and time of COVID-19 testing.\nVitamin D deficiency and treatment changes were combined to categorize vitamin D status at the time of COVID-19 testing as likely deficient(last-level-deficient/treatment-not-increased), likely sufficient(last-level-not-deficient/treatment-not-decreased), or uncertain deficiency(last-level-deficient/treatment-increased or last-level-not-deficient/treatment-decreased).\nMain Outcomes and Measures: The main outcome was testing positive for COVID-19.\nMultivariable analysis tested whether the most recent vitamin D level and treatment changes after that level were associated with testing positive for COVID-19 controlling for demographic and comorbidity indicators.\nBivariate analyses of associations of treatment with vitamin D deficiency and COVID-19 were performed.\nResults: Among 4,314 patients tested for COVID-19, 499 had a vitamin D level in the year before testing.\nVitamin D status at the time of COVID-19 testing was categorized as likely deficient for 127(25%) patients, likely sufficient for 291(58%) patients, and uncertain for 81(16%) patients.\nIn multivariate analysis, testing positive for COVID-19 was associated with increasing age(RR(age<50)=1.05,p<0.021;RR(age[\u2265]50)=1.02,p<0.064)), non-white race(RR=2.54,p<0.01) and being likely vitamin D deficient (deficient/treatment-not-increased:RR=1.77,p<0.02) as compared to likely vitamin D sufficient(not-deficient/treatment-not-decreased), with predicted COVID-19 rates in the vitamin D deficient group of 21.6%(95%CI[14.0%-29.2%] ) versus 12.2%(95%CI[8.9%-15.4%]) in the vitamin D sufficient group.\nVitamin D deficiency declined with increasing vitamin D dose, especially of vitamin D3.\nVitamin D dose was not significantly associated with testing positive for COVID-19.\nConclusions and Relevance: Vitamin D deficiency that is not sufficiently treated is associated with COVID-19 risk.\nTesting and treatment for vitamin D deficiency to address COVID-19 warrant aggressive pursuit and study.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Conclusions and Relevance: Vitamin D deficiency that is not sufficiently treated is associated with COVID-19 risk.\"]}", "id": 96}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: The overall risk of dying in the hospital from COVID-19 among people with T1D is very low, however, one study found that this risk is higher for people with T1D (3.5 times higher) compared to people without diabetes.\n\nAbstract:\nBACKGOUND: To figure out whether diabetes is a risk factor influencing the progression and prognosis of 2019 novel coronavirus disease (COVID-19).\nMETHODS: A total of 174 consecutive patients confirmed with COVID-19 were studied.\nDemographic data, medical history, symptoms and signs, laboratory findings, chest computed tomography (CT) as well the treatment measures were collected and analysed.\nRESULTS: We found that COVID-19 patients without other comorbidities but with diabetes (n = 24) were at higher risk of severe pneumonia, release of tissue injury-related enzymes, excessive uncontrolled inflammation responses and hypercoagulable state associated with dysregulation of glucose metabolism.\nFurthermore, serum levels of inflammation-related biomarkers such as IL-6, C-reactive protein, serum ferritin and coagulation index, D-dimer, were significantly higher (P < .01) in diabetic patients compared with those without, suggesting that patients with diabetes are more susceptible to an inflammatory storm eventually leading to rapid deterioration of COVID-19.\nCONCLUSIONS: Our data support the notion that diabetes should be considered as a risk factor for a rapid progression and bad prognosis of COVID-19.\nMore intensive attention should be paid to patients with diabetes, in case of rapid deterioration.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"CONCLUSIONS: Our data support the notion that diabetes should be considered as a risk factor for a rapid progression and bad prognosis of COVID-19.\"]}", "id": 97}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Some models show that if people wear masks, death rates from COVID-19 stay very low.\n\nAbstract:\nFace masks are an avenue to curb the spread of coronavirus, but few people in Western societies wear face masks.\nSocial scientists have rarely studied face mask wearing, leaving little guidance for methods to encourage these behaviours.\nIn the current article, we provide an approach to address this issue by developing the 32-item and 8-dimension Face Mask Perceptions Scale (FMPS).\nWe begin by developing an over-representative item list in a qualitative study, wherein participants' responses are used to develop items to ensure content relevance.\nThis item list is then reduced via exploratory factor analysis in a second study, and the eight dimensions of the scale are supported.\nWe also support the validity of the FMPS, as the scale significantly relates to both face mask wearing and health perceptions.\nWe lastly confirm the factor structure of the FMPS in a third study via confirmatory factor analysis.\nFrom these efforts, we identify an avenue that social scientists can aid in preventing coronavirus and illness more broadly - by studying face mask perceptions and behaviours.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Face masks are an avenue to curb the spread of coronavirus, but few people in Western societies wear face masks.\"]}", "id": 98}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: the warmer weather will slow the spread of SARS-CoV-2, the novel coronavirus that causes COVID-19\n\nAbstract:\nThe coronavirus disease 2019 (COVID-19) outbreak has become a severe public health issue.\nThe novelty of the virus prompts a search for understanding of how ecological factors affect the transmission and survival of the virus.\nSeveral studies have robustly identified a relationship between temperature and the number of cases.\nHowever, there is no specific study for a tropical climate such as Brazil.\nThis work aims to determine the relationship of temperature to COVID-19 infection for the state capital cities of Brazil.\nCumulative data with the daily number of confirmed cases was collected from February 27 to April 1, 2020, for all 27 state capital cities of Brazil affected by COVID-19.\nA generalized additive model (GAM) was applied to explore the linear and nonlinear relationship between annual average temperature compensation and confirmed cases.\nAlso, a polynomial linear regression model was proposed to represent the behavior of the growth curve of COVID-19 in the capital cities of Brazil.\nThe GAM dose-response curve suggested a negative linear relationship between temperatures and daily cumulative confirmed cases of COVID-19 in the range from 16.8 \u00b0C to 27.4 \u00b0C.\nEach 1 \u00b0C rise of temperature was associated with a -4.8951% (t = -2.29, p = 0.0226) decrease in the number of daily cumulative confirmed cases of COVID-19.\nA sensitivity analysis assessed the robustness of the results of the model.\nThe predicted R-squared of the polynomial linear regression model was 0.81053.\nIn this study, which features the tropical temperatures of Brazil, the variation in annual average temperatures ranged from 16.8 \u00b0C to 27.4 \u00b0C.\nResults indicated that temperatures had a negative linear relationship with the number of confirmed cases.\nThe curve flattened at a threshold of 25.8 \u00b0C.\nThere is no evidence supporting that the curve declined for temperatures above 25.8 \u00b0C.\nThe study had the goal of supporting governance for healthcare policymakers.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Results indicated that temperatures had a negative linear relationship with the number of confirmed cases.\"]}", "id": 99}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: The sars-cov-2 cytopathic effect is achieved with autophagy modulators\n\nAbstract:\nSARS-CoV-02 is a new type of coronavirus capable of rapid transmission and causing severe clinical symptoms; much of which has unknown biological etiology.\nIt has prompted researchers to rapidly mobilize their efforts towards identifying and developing anti-viral therapeutics and vaccines.\nDiscovering and understanding the virus\u2019 pathways of infection, host-protein interactions, and cytopathic effects will greatly aid in the design of new therapeutics to treat COVID-19.\nWhile it is known that chloroquine and hydroxychloroquine, extensively explored as clinical agents for COVID-19, have multiple cellular effects including inhibiting autophagy, there are also dose-limiting toxicities in patients that make clearly establishing their potential mechanisms-of-action problematic.\nTherefore, we evaluated a range of other autophagy modulators to identify an alternative autophagy-based drug repurposing opportunity.\nIn this work, we found that 6 of these compounds blocked the cytopathic effect of SARS-CoV-2 in Vero-E6 cells with EC(50) values ranging from 2.0 to 13 \u03bcM and selectivity indices ranging from 1.5 to >10-fold.\nImmunofluorescence staining for LC3B and LysoTracker dye staining assays in several cell lines indicated their potency and efficacy for inhibiting autophagy correlated with the measurements in the SARS-CoV-2 cytopathic effect assay.\nOur data suggest that autophagy pathways could be targeted to combat SARS-CoV-2 infections and become an important component of drug combination therapies to improve the treatment outcomes for COVID-19.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"While it is known that chloroquine and hydroxychloroquine, extensively explored as clinical agents for COVID-19, have multiple cellular effects including inhibiting autophagy, there are also dose-limiting toxicities in patients that make clearly establishing their potential mechanisms-of-action problematic.\", \"Immunofluorescence staining for LC3B and LysoTracker dye staining assays in several cell lines indicated their potency and efficacy for inhibiting autophagy correlated with the measurements in the SARS-CoV-2 cytopathic effect assay.\"]}", "id": 100}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: high doses of vitamins and natural remedies can stave off COVID-19 \u2014 but evidence to support these claims is lacking.\n\nAbstract:\nAs viral infections are an increasing threat to human societies, the need for new therapeutic strategies is becoming even more obvious.\nAs no vaccine is available for COVID-19, the development of directly acting antiviral agents and preventive strategies have to be considered.\nNature provides a huge reservoir of anti-infectious compounds, from which we can deduce innovative ideas, therapies, and products.\nAnti-adhesive natural products interact with the receptor-mediated recognition and early interaction of viruses with the host cells, leading to a reduced internalisation of the virus and reduced infections (e.g., procyanidin-B-2-di-O-gallate against influenza and herpes virus).\nLignans like podophyllotoxin and bicyclol show strong antiviral activities against different viruses, and essential oils can directly interact with viral membranes and reduce the host's inflammatory responses (e.g., 1,8-cineol).\nEchinacea extracts stimulate the immune system, and bioavailable alkamides are key players by interacting with immunomodulating cannabinoid receptors.\nCOVID-19 and SARS-CoV-2 infections have, in part, successfully been treated in China by preparations from traditional Chinese medicine and, while it is too early to draw conclusions, some promising data are available.\nThere is huge potential, but intensified research is needed to develop evidence-based medicines with a clearly defined chemical profile.\nIntensified research and development, and therefore funding, are needed for exploiting nature's reservoir against viral infections.\nCombined action for basic research, chemistry, pharmacognosy, virology, and clinical studies, but also supply chain, sustainable sourcing, and economic aspects have to be considered.\nThis review calls for intensified innovative science on natural products for the patients and for a healthier world!", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 101}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: dexamethasone reduced death rates\n\nAbstract:\nBackground: Dexamethasone, a synthetic glucocorticoid, has anti-inflammatory and immunosuppressive properties.\nThere is a hyperinflammatory response involved in the clinical course of patients with pneumonia due to SARS-CoV2.\nTo date, there has been no definite therapy for COVID-19.\nWe reviewed the charts of SARS-CoV2 patients with pneumonia and moderate to severely elevated CRP and worsening hypoxemia who were treated with early, short-term dexamethasone.\nMethods: We describe a series of 21 patients who tested positive for SARS-CoV2 and were admitted to The Miriam Hospital in Providence and were treated with a short course of dexamethasone, either alone or in addition to current investigative therapies.\nResults: CRP levels decreased significantly following the start of dexamethasone from mean initial levels of 129.52 to 40.73 mg/L at time of discharge.\n71% percent of the patients were discharged home with a mean length of stay of 7.8 days.\nNone of the patients had escalation of care, leading to mechanical ventilation.\nTwo patients were transferred to inpatient hospice facilities on account of persistent hypoxemia, in line with their documented goals of care.\nConclusions: A short course of systemic corticosteroids among inpatients with SARS-CoV2 with hypoxic respiratory failure was well tolerated, and most patients had improved outcomes.\nThis limited case series may not offer concrete evidence towards the benefit of corticosteroids in COVID-19.\nHowever, patients positive response to short-term corticosteroids demonstrates that they may help blunt the severity of inflammation and prevent a severe hyperinflammatory phase, in turn reducing the length of stay, ICU admissions, and healthcare costs.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"To date, there has been no definite therapy for COVID-19.\"]}", "id": 102}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Sars-cov2 enables anaerobic bacteria to colonize the intestinal disrupting homeostasis\n\nAbstract:\nThe oral cavity, as the entry point to the body, may play a critical role in the pathogenesis of SARS-CoV-2 infection that has caused a global outbreak of the coronavirus disease 2019 (COVID-19).\nAvailable data indicate that the oral cavity may be an active site of infection and an important reservoir of SARS-CoV-2.\nConsidering that the oral surfaces are colonized by a diverse microbial community, it is likely that viruses have interactions with the host microbiota.\nPatients infected by SARS-CoV-2 may have alterations in the oral and gut microbiota, while oral species have been found in the lung of COVID-19 patients.\nFurthermore, interactions between the oral, lung, and gut microbiomes appear to occur dynamically whereby a dysbiotic oral microbial community could influence respiratory and gastrointestinal diseases.\nHowever, it is unclear whether SARS-CoV-2 infection can alter the local homeostasis of the resident microbiota, actively cause dysbiosis, or influence cross-body sites interactions.\nHere, we provide a conceptual framework on the potential impact of SARS-CoV-2 oral infection on the local and distant microbiomes across the respiratory and gastrointestinal tracts (\u2018oral-tract axes\u2019), which remains largely unexplored.\nStudies in this area could further elucidate the pathogenic mechanism of SARS-CoV-2 and the course of infection as well as the clinical symptoms of COVID-19 across different sites in the human host.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Patients infected by SARS-CoV-2 may have alterations in the oral and gut microbiota, while oral species have been found in the lung of COVID-19 patients.\"]}", "id": 103}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: The viral protein nsp1 acts as a ribosome gatekeeper for shutting down host translation and fostering sars-cov-2 translation\n\nAbstract:\nSARS-CoV-2 coronavirus is responsible for Covid-19 pandemic.\nIn the early phase of infection, the single-strand positive RNA genome is translated into non-structural proteins (NSP).\nOne of the first proteins produced during viral infection, NSP1, binds to the host ribosome and blocks the mRNA entry channel.\nThis triggers translation inhibition of cellular translation.\nIn spite of the presence of NSP1 on the ribosome, viral translation proceeds however.\nThe molecular mechanism of the so-called viral evasion to NSP1 inhibition remains elusive.\nHere, we confirm that viral translation is maintained in the presence of NSP1.\nThe evasion to NSP1-inhibition is mediated by the cis-acting RNA hairpin SL1 in the 5'UTR of SARS-CoV-2.\nNSP1-evasion can be transferred on a reporter transcript by SL1 transplantation.\nThe apical part of SL1 is only required for viral translation.\nWe show that NSP1 remains bound on the ribosome during viral translation.\nWe suggest that the interaction between NSP1 and SL1 frees the mRNA accommodation channel while maintaining NSP1 bound to the ribosome.\nThus, NSP1 acts as a ribosome gatekeeper, shutting down host translation or fostering SARS-CoV-2 translation depending on the presence of the SL1 5'UTR hairpin.\nSL1 is also present and necessary for translation of sub-genomic RNAs in the late phase of the infectious program.\nConsequently, therapeutic strategies targeting SL1 should affect viral translation at early and late stages of infection.\nTherefore, SL1 might be seen as a genuine 'Achille heel' of the virus.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"One of the first proteins produced during viral infection, NSP1, binds to the host ribosome and blocks the mRNA entry channel.\", \"In spite of the presence of NSP1 on the ribosome, viral translation proceeds however.\", \"We show that NSP1 remains bound on the ribosome during viral translation.\"]}", "id": 104}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Homozygous l-sign plays a protective role in sars coronavirus infection\n\nAbstract:\nSevere acute respiratory syndrome (SARS) is caused by infection of a previously undescribed coronavirus (CoV).\nL-SIGN, encoded by CLEC4M (also known as CD209L), is a SARS-CoV binding receptor that has polymorphism in its extracellular neck region encoded by the tandem repeat domain in exon 4.\nOur genetic risk association study shows that individuals homozygous for CLEC4M tandem repeats are less susceptible to SARS infection.\nL-SIGN is expressed in both non-SARS and SARS-CoV\u2013infected lung.\nCompared with cells heterozygous for L-SIGN, cells homozygous for L-SIGN show higher binding capacity for SARS-CoV, higher proteasome-dependent viral degradation and a lower capacity for trans infection.\nThus, homozygosity for L-SIGN plays a protective role during SARS infection.\nSUPPLEMENTARY INFORMATION: The online version of this article (doi:10.1038/ng1698) contains supplementary material, which is available to authorized users.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Thus, homozygosity for L-SIGN plays a protective role during SARS infection.\"]}", "id": 105}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Lack of immune homology with vaccine preventable pathogens suggests childhood immunizations do also protect against sars-cov-2 through adaptive cross-immunity\n\nAbstract:\nRecent epidemiological studies have investigated the potential effects of childhood immunization history on COVID-19 severity.\nSpecifically, prior exposure to Bacillus Calmette-Guerin (BCG) vaccine, oral poliovirus vaccine (OPV), or measles vaccine have been postulated to reduce COVID-19 severity-putative mechanism is via stimulation of the innate immune system to provide broader protection against non-specific pathogens.\nWhile these epidemiological results remain inconclusive, we sought to investigate the potential role of adaptive immunity via cross-reactivity between vaccine preventable diseases (VPDs) with SARS-CoV-2.\nWe implemented a comprehensive exploration of immune homology (including sequence homology, immune epitopes, and glycosylation patterns) between SARS-CoV-2 and all pathogens with FDA-approved vaccines.\nSequence homology did not reveal significant alignments of protein sequences between SARS-CoV-2 with any VPD pathogens, including BCG-related strains.\nWe also could not identify any shared T or B cell epitopes between SARS-CoV-2 and VPD pathogens among either experimentally validated epitopes or predicted immune epitopes.\nFor N-glycosylation (N-glyc), while sites with the same tripeptides could be found between SARS-CoV-2 and certain VPD pathogens, their glycosylation potentials and positions were different.\nIn summary, lack of immune homology between SARS-CoV-2 and VPD pathogens suggests that childhood immunization history (i.e., BCG vaccination or others) does not provide protection from SARS-CoV-2 through adaptive cross-immunity.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"In summary, lack of immune homology between SARS-CoV-2 and VPD pathogens suggests that childhood immunization history (i.e., BCG vaccination or others) does not provide protection from SARS-CoV-2 through adaptive cross-immunity.\"]}", "id": 106}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Cloth face coverings are most likely to reduce the spread of the COVID-19 virus when they are widely used by people in public settings\n\nAbstract:\nBackground Protecting Health Care Workers (HCWs) during routine care of suspected or confirmed COVID-19 patients is of paramount importance to halt the SARS-CoV-2 (Severe Acute Respiratory Syndrome-Coronavirus-2) pandemic.\nThe WHO, ECDC and CDC have issued conflicting guidelines on the use of respiratory filters (N95) by HCWs.\nMethods We searched PubMed, Embase and The Cochrane Library from the inception to March 21, 2020 to identify randomized controlled trials (RCTs) comparing N95 respirators versus surgical masks for prevention of COVID-19 or any other respiratory infection among HCWs.\nThe grading of recommendations, assessment, development, and evaluation (GRADE) was used to evaluate the quality of evidence.\nFindings Four RCTs involving 8736 HCWs were included.\nWe did not find any trial specifically on prevention of COVID-19.\nHowever, wearing N95 respirators can prevent 73 more (95% CI 46-91) clinical respiratory infections per 1000 HCWs compared to surgical masks (2 RCTs; 2594 patients; low quality of evidence).\nA protective effect of N95 respirators in laboratory-confirmed bacterial colonization (RR= 0.41; 95%CI 0.28-0.61) was also found.\nA trend in favour of N95 respirators was observed in preventing laboratory-confirmed respiratory viral infections, laboratory-confirmed respiratory infection, and influenza like illness.\nInterpretation We found no direct high quality evidence on whether N95 respirators are better than surgical masks for HCWs protection from SARS-CoV-2.\nHowever, low quality evidence suggests that N95 respirators protect HCWs from clinical respiratory infections.\nThis finding should be contemplated to decide the best strategy to support the resilience of healthcare systems facing the potentially catastrophic SARS-CoV-2 pandemic.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 107}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: young peope are not at risk and do not die from covid-19\n\nAbstract:\nCurrently (mid May 2020), most active cases of COVID-19 are found in Europe and North America while it is still in the initial phases in Africa.\nAs COVID-19 mortality occurs mainly in elderly and as Africa has a comparably young population, the death rates should be lower than on other continents.\nWe calculated standardised mortality ratios (SMR) using age-specific case fatality rates for COVID-19 and the age structure of the population of Africa and of other continents.\nCompared to a European or Northern American population, the standardised mortality ratio was only 0.22 and 0.25, respectively, corresponding to reduction of deaths rates to a quarter.\nCompared to the Asian and Latin American & Caribbean population, the SMR was 0.43 and 0.44, respectively, corresponding to half the death rate for Africa.\nIt is useful to quantify the isolated effect of the African age-structure on potential COVID-19 mortality for illustrative and communication purposes, keeping in mind the importance of public health measures that have been shown to be effective in reducing cases and deaths.\nThe different aspect of age pyramids of a European and an African population are striking and the potential implications for the pandemic are often discussed but rarely quantified.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"As COVID-19 mortality occurs mainly in elderly and as Africa has a comparably young population, the death rates should be lower than on other continents.\", \"We calculated standardised mortality ratios (SMR) using age-specific case fatality rates for COVID-19 and the age structure of the population of Africa and of other continents.\", \"Compared to a European or Northern American population, the standardised mortality ratio was only 0.22 and 0.25, respectively, corresponding to reduction of deaths rates to a quarter.\", \"Compared to the Asian and Latin American & Caribbean population, the SMR was 0.43 and 0.44, respectively, corresponding to half the death rate for Africa.\"]}", "id": 108}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Chadox1 ncov-19 vaccination induces sars-cov-2 pneumonia in rhesus macaques\n\nAbstract:\nSevere acute respiratory syndrome coronavirus-2 (SARS-CoV-2) emerged in December 20191,2 and is responsible for the COVID-19 pandemic3.\nVaccines are an essential countermeasure urgently needed to control the pandemic4.\nHere, we show that the adenovirus-vectored vaccine ChAdOx1 nCoV-19, encoding the spike protein of SARS-CoV-2, is immunogenic in mice, eliciting a robust humoral and cell-mediated response.\nThis response was not Th2 dominated, as demonstrated by IgG subclass and cytokine expression profiling.\nA single vaccination with ChAdOx1 nCoV-19 induced a humoral and cellular immune response in rhesus macaques.\nWe observed a significantly reduced viral load in bronchoalveolar lavage fluid and respiratory tract tissue of vaccinated animals challenged with SARS-CoV-2 compared with control animals, and no pneumonia was observed in vaccinated rhesus macaques.\nImportantly, no evidence of immune-enhanced disease following viral challenge in vaccinated animals was observed.\nChAdOx1 nCoV-19 is currently under investigation in a phase I clinical trial.\nSafety, immunogenicity and efficacy against symptomatic PCR-positive COVID-19 disease will now be assessed in randomised controlled human clinical trials.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"A single vaccination with ChAdOx1 nCoV-19 induced a humoral and cellular immune response in rhesus macaques.\", \"We observed a significantly reduced viral load in bronchoalveolar lavage fluid and respiratory tract tissue of vaccinated animals challenged with SARS-CoV-2 compared with control animals, and no pneumonia was observed in vaccinated rhesus macaques.\"]}", "id": 109}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: The immune system, noticing the infection, flares up, which can cause the lungs to fill with fluid and prevent adequate oxygen flow.\n\nAbstract:\nThe novel coronavirus Covid-19 follows transmission route and clinical presentation of all community-acquired coronaviruses.\nInstead, the rate of transmission is significative higher, with a faster spread of the virus responsible of the worldwide outbreak and a significative higher mortality rate due to the development of a severe lung injury.\nMost noteworthy is the distribution of death rate among age groups.\nChildren and younger people are almost protected from severe clinical presentation.\nPossible explanation of this phenomenon could be the ability of past vaccinations (especially tetanic, diphtheria toxoids and inactivated bacteria as pertussis) to stimulate immune system and to generate a scattered immunity against non-self antigens in transit, as coronaviruses and other community-circulating viruses and make immune system readier to develop specific immunity against Covid-19.\nThe first support to this hypothesis is the distribution of mortality rate during historical pandemics (\"Spanish flu\" 1918, \"Asian flu\" 1956 and \"the Hong Kong flu\" 1968) among age groups before and after the introduction of vaccines.\nThe immunological support to the hypothesis derives from recent studies about immunotherapy for malignancies, which propose the use of oncolytic vaccines combined with toxoids in order to exploit CD4 + memory T cell recall in supporting the ongoing anti-tumour response.\nAccording to this hypothesis vaccine formulations (tetanus, diphtheria, Bordetella pertussis) could be re-administrate after the first contact with Covid-19, better before the development of respiratory severe illness and of course before full-blown ARDS (Acute Respiratory Distress Syndrome).\nThe CD4 + memory exploiting could help immune system to recall immunity of already know antigens against coronaviruses, avoiding or limiting \"lung crash\" until virus specific immunity develops and making it faster and prolonged.\nFinally, this administration could be helpful not only in already infected patients, but also before infection.\nIn fact, people could have an immune system more ready when the contact with the Covid-19 will occur.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 110}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: The drugs have anti-inflammatory effects \"in addition to their blood pressure benefits.\n\nAbstract:\nThe effects of angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin receptor blockers (ARBs) on the risk of COVID-19 infection and disease progression are yet to be investigated.\nThe relationship between ACEI/ARB use and COVID-19 infection was systematically reviewed.\nTo identify relevant studies that met predetermined inclusion criteria, unrestricted searches of the PubMed, Embase, and Cochrane Library databases were conducted.\nThe search strategy included clinical date published until May 9, 2020.\nTwelve articles involving more than 19,000 COVID-19 cases were included.\nTo estimate overall risk, random-effects models were adopted.\nOur results showed that ACEI/ARB exposure was not associated with a higher risk of COVID-19 infection (OR = 0.99; 95 % CI, 0-1.04; P = 0.672).\nAmong those with COVID-19 infection, ACEI/ARB exposure was also not associated with a higher risk of having severe infection (OR = 0.98; 95 % CI, 0.87-1.09; P = 0.69) or mortality (OR = 0.73, 95 %CI, 0.5-1.07; P = 0.111).\nHowever, ACEI/ARB exposure was associated with a lower risk of mortality compared to those on non-ACEI/ARB antihypertensive drugs (OR = 0.48, 95 % CI, 0.29-0.81; P = 0.006).\nIn conclusion, current evidence did not confirm the concern that ACEI/ARB exposure is harmful in patientswith COVID-19 infection.\nThis study supports the current guidelines that discourage discontinuation of ACEIs or ARBs in COVID-19 patients and the setting of the COVID-19 pandemic.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 111}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: both dogs and cats can be infected by the virus that causes Covid-19 in humans, but none of the ten animals observed in the study showed clinical symptoms like coughing\n\nAbstract:\nSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which originated in Wuhan, China, in 2019, is responsible for the COVID-19 pandemic.\nIt is now accepted that the wild fauna, probably bats, constitute the initial reservoir of the virus, but little is known about the role pets can play in the spread of the disease in human communities, knowing the ability of SARS-CoV-2 to infect some domestic animals.\nWe tested 21 domestic pets (9 cats and 12 dogs) living in close contact with their owners (belonging to a veterinary community of 20 students) in which two students tested positive for COVID-19 and several others (n = 11/18) consecutively showed clinical signs (fever, cough, anosmia, etc.) compatible with COVID-19 infection.\nAlthough a few pets presented many clinical signs indicative for a coronavirus infection, no animal tested positive for SARS-CoV-2 by RT-PCR and no antibodies against SARS-CoV-2 were detectable in their blood using an immunoprecipitation assay.\nThese original data can serve a better evaluation of the host range of SARS-CoV-2 in natural environment exposure conditions.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Although a few pets presented many clinical signs indicative for a coronavirus infection, no animal tested positive for SARS-CoV-2 by RT-PCR and no antibodies against SARS-CoV-2 were detectable in their blood using an immunoprecipitation assay.\"]}", "id": 112}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: mix and drink as hot tea every afternoon\n\nAbstract:\nThis review focused on the use of plant-based foods for enhancing the immunity of all aged groups against COVID-19.\nIn humans, coronaviruses are included in the spectrum of viruses that cause the common cold and, recently, severe acute respiratory syndrome (SARS).\nEmerging infectious diseases, such as SARS present a major threat to public health.\nThe novel coronavirus has spread rapidly to multiple countries and has been declared a pandemic by the World Health Organization.\nCOVID-19 is usually caused a virus to which most probably the people with low immunity response are being affected.\nPlant-based foods increased the intestinal beneficial bacteria which are helpful and make up of 85% of the immune system.\nBy the use of plenty of water, minerals like magnesium and Zinc, micronutrients, herbs, food rich in vitamins C, D and E, and better life style one can promote the health and can overcome this infection.\nVarious studies investigated that a powerful antioxidant glutathione and a bioflavonoid quercetin may prevent various infections including COVID-19.\nIn conclusion, the plant-based foods play a vital role to enhance the immunity of people to control of COVID-19.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 113}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Hydroxychloroquine is an Effective Treatment for COVID-19\n\nAbstract:\nBACKGROUND: Chloroquine and hydroxychloroquine have been found to be efficient on SARS-CoV-2, and reported to be efficient in Chinese COV-19 patients.\nWe evaluate the role of hydroxychloroquine on respiratory viral loads.\nPATIENTS AND METHODS: French Confirmed COVID-19 patients were included in a single arm protocol from early March to March 16th, to receive 600mg of hydroxychloroquine daily and their viral load in nasopharyngeal swabs was tested daily in a hospital setting.\nDepending on their clinical presentation, azithromycin was added to the treatment.\nUntreated patients from another center and cases refusing the protocol were included as negative controls.\nPresence and absence of virus at Day6-post inclusion was considered the end point.\nRESULTS: Six patients were asymptomatic, 22 had upper respiratory tract infection symptoms and eight had lower respiratory tract infection symptoms.\nTwenty cases were treated in this study and showed a significant reduction of the viral carriage at D6-post inclusion compared to controls, and much lower average carrying duration than reported of untreated patients in the literature.\nAzithromycin added to hydroxychloroquine was significantly more efficient for virus elimination.\nCONCLUSION: Despite its small sample size our survey shows that hydroxychloroquine treatment is significantly associated with viral load reduction/disappearance in COVID-19 patients and its effect is reinforced by azithromycin.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"CONCLUSION: Despite its small sample size our survey shows that hydroxychloroquine treatment is significantly associated with viral load reduction/disappearance in COVID-19 patients and its effect is reinforced by azithromycin.\"]}", "id": 114}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Microwave or autoclave cannot destroy the infectivity of infectious bronchitis virus and avian pneumovirus but allow detection by reverse transcriptase-polymerase chain reaction\n\nAbstract:\nA method is described for enabling safe transit of denatured virus samples for polymerase chain reaction (PCR) identification without the risk of unwanted viable viruses.\nCotton swabs dipped in avian infectious bronchitis virus (IBV) or avian pneumovirus (APV) were allowed to dry.\nNewcastle disease virus and avian influenza viruses were used as controls.\nAutoclaving and microwave treatment for as little as 20 sec destroyed the infectivity of all four viruses.\nHowever, both IBV and APV could be detected by reverse transcriptase (RT)-PCR after autoclaving and as long as 5 min microwave treatment (Newcastle disease virus and avian influenza viruses were not tested).\nDouble microwave treatment of IBV and APV with an interval of 2 to 7 days between was tested.\nAfter the second treatment, RT-PCR products were readily detected in all samples.\nSwabs from the tracheas and cloacas of chicks infected with IBV shown to contain infectious virus were microwaved.\nSwabs from both sources were positive by RT-PCR.\nMicrowave treatment appears to be a satisfactory method of inactivating virus while preserving nucleic acid for PCR identification.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Autoclaving and microwave treatment for as little as 20 sec destroyed the infectivity of all four viruses.\", \"However, both IBV and APV could be detected by reverse transcriptase (RT)-PCR after autoclaving and as long as 5 min microwave treatment (Newcastle disease virus and avian influenza viruses were not tested).\", \"Microwave treatment appears to be a satisfactory method of inactivating virus while preserving nucleic acid for PCR identification.\"]}", "id": 115}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Melatonin is significantly associated with survival of intubated covid-19 patients\n\nAbstract:\nBackground Respiratory distress requiring intubation is the most serious complication associated with coronavirus disease 2019 (COVID-19).\nMethods In this retrospective study, we used survival analysis to determine whether or not mortality following intubation was associated with hormone exposure in patients treated at New York Presbyterian/ Columbia University Irving Medical Center.\nHere, we report the overall hazards ratio for each hormone for exposure before and after intubation for intubated and mechanically ventilated patients.\nResults Among the 189,987 patients, we identified 948 intubation periods across 791 patients who were diagnosed with COVID-19 or infected with SARS-CoV2 and 3,497 intubation periods across 2,981 patients who were not.\nMelatonin exposure after intubation was statistically associated with a positive outcome in COVID-19 (demographics and comorbidities adjusted HR: 0.131, 95% CI: 7.76E-02-0.223, p-value = 8.19E-14) and non-COVID-19 (demographics and comorbidities adjusted HR: 0.278, 95% CI: 0.142-0.542, p-value = 1.72E-04) intubated patients.\nAdditionally, melatonin exposure after intubation was statically associated with a positive outcome in COVID-19 patients (demographics and comorbidities adjusted HR: 0.127, 95% CI: 6.01E-02-0.269, p-value = 7.15E-08).\nConclusions Melatonin exposure after intubation is significantly associated with a positive outcome in COVID-19 and non-COVID-19 patients.\nAdditionally, melatonin exposure after intubation is significantly associated with a positive outcome in COVID-19 patients requiring mechanical ventilation.\nWhile our models account for many covariates, including clinical history and demographics, it is impossible to rule out confounding or collider biases within our population.\nFurther study into the possible mechanism of this observation is warranted.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Melatonin exposure after intubation was statistically associated with a positive outcome in COVID-19 (demographics and comorbidities adjusted HR: 0.131, 95% CI: 7.76E-02-0.223, p-value = 8.19E-14) and non-COVID-19 (demographics and comorbidities adjusted HR: 0.278, 95% CI: 0.142-0.542, p-value = 1.72E-04) intubated patients.\", \"Additionally, melatonin exposure after intubation was statically associated with a positive outcome in COVID-19 patients (demographics and comorbidities adjusted HR: 0.127, 95% CI: 6.01E-02-0.269, p-value = 7.15E-08).\", \"Conclusions Melatonin exposure after intubation is significantly associated with a positive outcome in COVID-19 and non-COVID-19 patients.\", \"Additionally, melatonin exposure after intubation is significantly associated with a positive outcome in COVID-19 patients requiring mechanical ventilation.\"]}", "id": 116}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: taking medication to lower fever, such as paracetamol (tylenol) and ibuprofen (advil) worsen COVID-19\n\nAbstract:\nFever has been reported as a common symptom occurring in COVID-19 illness.\nOver the counter antipyretics such as ibuprofen and acetaminophen are often taken by individuals to reduce the discomfort of fever.\nRecently, the safety of ibuprofen in COVID-19 patients has been questioned due to anecdotal reports of worsening symptoms in previously healthy young adults.\nStudies show that ibuprofen demonstrates superior efficacy in fever reduction compared to acetaminophen.\nAs fever may have benefit in shortening the duration of viral illness, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response when taken during the early stages of COVID-19 illness.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"As fever may have benefit in shortening the duration of viral illness, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response when taken during the early stages of COVID-19 illness.\"]}", "id": 117}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: there are few novel sars-cov-2 cases in malaria countries because of the use of the antimalarial drug hydroxychloroquine.\n\nAbstract:\nCoronavirus disease 2019 (COVID-19) is a pandemic with no specific drugs and high fatality.\nThe most urgent need is to find effective treatments.\nWe sought to determine whether hydroxychloroquine (HCQ) application may reduce the death risk of critically ill COVID-19 patients.\nIn this retrospective study, we included 550 critically ill COVID-19 patients who need mechanical ventilation in Tongji Hospital, Wuhan, from February 1, 2020 to April 4, 2020.\nAll 550 patients received comparable basic treatments including antiviral drugs and antibiotics, and 48 of them were treated with oral HCQ treatment (200 mg twice a day for 7-10 days) in addition to the basic treatments.\nPrimary endpoint is fatality of patients, and inflammatory cytokine levels were compared between HCQ and non-hydroxychloroquine (NHCQ) treatments.\nWe found that fatalities are 18.8% (9/48) in HCQ group, which is significantly lower than 47.4% (238/502) in the NHCQ group (P<0.001).\nThe time of hospital stay before patient death is 15 (10-21) days and 8 (4-14) days for the HCQ and NHCQ groups, respectively (P<0.05).\nThe levels of inflammatory cytokine IL-6 were significantly reduced from 22.2 (8.3-118.9) pg mL-1 at the beginning of the treatment to 5.2 (3.0-23.4) pg mL-1 (P<0.05) at the end of the treatment in the HCQ group but there is no change in the NHCQ group.\nThese data demonstrate that addition of HCQ on top of the basic treatments is highly effective in reducing the fatality of critically ill patients of COVID-19 through attenuation of inflammatory cytokine storm.\nTherefore, HCQ should be prescribed as a part of treatment for critically ill COVID-19 patients, with possible outcome of saving lives.\nhydroxychloroquine, IL-6, mortalities, COVID-19.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"We found that fatalities are 18.8% (9/48) in HCQ group, which is significantly lower than 47.4% (238/502) in the NHCQ group (P<0.001).\", \"The time of hospital stay before patient death is 15 (10-21) days and 8 (4-14) days for the HCQ and NHCQ groups, respectively (P<0.05).\", \"The levels of inflammatory cytokine IL-6 were significantly reduced from 22.2 (8.3-118.9) pg mL-1 at the beginning of the treatment to 5.2 (3.0-23.4) pg mL-1 (P<0.05) at the end of the treatment in the HCQ group but there is no change in the NHCQ group.\", \"Therefore, HCQ should be prescribed as a part of treatment for critically ill COVID-19 patients, with possible outcome of saving lives.\"]}", "id": 118}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: wearing a mask  may offer some protection for the covid-19\n\nAbstract:\nWe identified seasonal human coronaviruses, influenza viruses and rhinoviruses in exhaled breath and coughs of children and adults with acute respiratory illness.\nSurgical face masks significantly reduced detection of influenza virus RNA in respiratory droplets and coronavirus RNA in aerosols, with a trend toward reduced detection of coronavirus RNA in respiratory droplets.\nOur results indicate that surgical face masks could prevent transmission of human coronaviruses and influenza viruses from symptomatic individuals.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Surgical face masks significantly reduced detection of influenza virus RNA in respiratory droplets and coronavirus RNA in aerosols, with a trend toward reduced detection of coronavirus RNA in respiratory droplets.\", \"Our results indicate that surgical face masks could prevent transmission of human coronaviruses and influenza viruses from symptomatic individuals.\"]}", "id": 119}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Can coronavirus spread through food or packaging? No Absolutely not\n\nAbstract:\nBACKGROUND: The COVID-19 pandemic has generated a new era in the world while we still figure out the consequences in different aspects of our daily life.\nThe food supply chain and the food industry do not comprise an exception.\nSCOPE AND APPROACH: This review summarizes the possible transmission ways of COVID-19 through the foods, food supply chain, surfaces, and environment before exploring the development of corresponding detection tools of SARS-CoV-2.\nFor the time being, the possibility of transmission through the food sector is considered negligible, and tracing of SARS-CoV-2 in working environments is not considered as a priority by public authorities.\nHowever, the adverse effects on the environment, food systems, and people along the food supply chain are already evident.\nKEY FINDINGS AND CONCLUSIONS: As long as we move from farm to fork, more safety measures are needed since more people (and subsequently more potential sources of infection) are involved in the process.\nThe need for developing respective bioanalytical protocols for food and environmental safety applications to adapt in the post-lockdown period is also highlighted.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"For the time being, the possibility of transmission through the food sector is considered negligible, and tracing of SARS-CoV-2 in working environments is not considered as a priority by public authorities.\"]}", "id": 120}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Many readers have written in to ask whether ibuprofen or other non-steroidal anti-inflammatory drugs, or NSAIDs, can worsen COVID-19, the disease caused by the novel coronavirus.\n\nAbstract:\nConcern about the appropriate role of nonsteroidal anti-inflammatory drugs (NSAIDs) in COVID-19 speculate that NSAIDs, in particular ibuprofen, may upregulate the entry point for the virus, the angiotensin-converting enzyme (ACE) 2 receptors and increase susceptibility to the virus or worsen symptoms in existing disease.\nAdverse outcomes with COVID-19 have been linked to cytokine storm but the most effective way to address exaggerated inflammatory response is complex and unclear.\nThe Expert Working Group on the Commission of Human Medicines in the UK and other organizations have stated that there is insufficient evidence to establish a link between ibuprofen and susceptibility to or exacerbation of COVID-19.\nNSAID use must also be categorized by whether the drugs are relatively low-dose over-the-counter oral products taken occasionally versus higher-dose or parenteral NSAIDs.\nEven if evidence emerged arguing for or against NSAIDs in this setting, it is unclear if this evidence would apply to all NSAIDs at all doses in all dosing regimens.\nParacetamol (acetaminophen) has been proposed as an alternative to NSAIDs but there are issues with liver toxicity at high doses.\nThere are clearly COVID-19 cases where NSAIDs should not be used, but there is no strong evidence that NSAIDs must be avoided in all patients with COVID-19; clinicians must weigh these choices on an individual basis.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 121}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Vitamin D Does Not Cure Covid-19 But It Plays an Important Role\n\nAbstract:\nImportance: Vitamin D treatment has been found to decrease incidence of viral respiratory tract infection, especially in vitamin D deficiency.\nIt is unknown whether COVID-19 incidence is associated with vitamin D deficiency and treatment.\nObjective: To examine whether vitamin D deficiency and treatment are associated with testing positive for COVID-19.\nDesign: Retrospective cohort study Setting: University of Chicago Medicine Participants: Patients tested for COVID-19 from 3/3/2020-4/10/2020.\nVitamin D deficiency was defined by the most recent 25-hydroxycholecalciferol <20ng/ml or 1,25-dihydroxycholecalciferol <18pg/ml within 1 year before COVID-19 testing.\nTreatment was defined by the most recent vitamin D type and dose, and treatment changes between the time of the most recent vitamin D level and time of COVID-19 testing.\nVitamin D deficiency and treatment changes were combined to categorize vitamin D status at the time of COVID-19 testing as likely deficient(last-level-deficient/treatment-not-increased), likely sufficient(last-level-not-deficient/treatment-not-decreased), or uncertain deficiency(last-level-deficient/treatment-increased or last-level-not-deficient/treatment-decreased).\nMain Outcomes and Measures: The main outcome was testing positive for COVID-19.\nMultivariable analysis tested whether the most recent vitamin D level and treatment changes after that level were associated with testing positive for COVID-19 controlling for demographic and comorbidity indicators.\nBivariate analyses of associations of treatment with vitamin D deficiency and COVID-19 were performed.\nResults: Among 4,314 patients tested for COVID-19, 499 had a vitamin D level in the year before testing.\nVitamin D status at the time of COVID-19 testing was categorized as likely deficient for 127(25%) patients, likely sufficient for 291(58%) patients, and uncertain for 81(16%) patients.\nIn multivariate analysis, testing positive for COVID-19 was associated with increasing age(RR(age<50)=1.05,p<0.021;RR(age[\u2265]50)=1.02,p<0.064)), non-white race(RR=2.54,p<0.01) and being likely vitamin D deficient (deficient/treatment-not-increased:RR=1.77,p<0.02) as compared to likely vitamin D sufficient(not-deficient/treatment-not-decreased), with predicted COVID-19 rates in the vitamin D deficient group of 21.6%(95%CI[14.0%-29.2%] ) versus 12.2%(95%CI[8.9%-15.4%]) in the vitamin D sufficient group.\nVitamin D deficiency declined with increasing vitamin D dose, especially of vitamin D3.\nVitamin D dose was not significantly associated with testing positive for COVID-19.\nConclusions and Relevance: Vitamin D deficiency that is not sufficiently treated is associated with COVID-19 risk.\nTesting and treatment for vitamin D deficiency to address COVID-19 warrant aggressive pursuit and study.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Conclusions and Relevance: Vitamin D deficiency that is not sufficiently treated is associated with COVID-19 risk.\"]}", "id": 122}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Cats may be able to spread coronavirus to humans despite showing no symptoms\n\nAbstract:\nSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)--the virus that causes coronavirus disease (COVID-19)--has been detected in domestic dogs and cats, raising concerns of transmission from, to, or between these animals.\nThere is currently no indication that feline- or canine-to-human transmission can occur, though there is rising evidence of the reverse.\nTo explore the extent of animal-related transmission, we aggregated 17 case reports on confirmed SARS-CoV-2 infections in animals as of 15 May 2020.\nAll but two animals fully recovered and had only mild respiratory or digestive symptoms.\nUsing data from probable cat-to-cat transmission in Wuhan, China, we estimated the basic reproduction number R0 under this scenario at 1.09 (95% confidence interval: 1.05, 1.13).\nThis value is much lower than the R0 reported for humans and close to one, indicating that the sustained transmission between cats is unlikely to occur.\nOur results support the view that the pet owners and other persons with COVID-19 in close contact with animals should be cautious of the way they interact with them.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)--the virus that causes coronavirus disease (COVID-19)--has been detected in domestic dogs and cats, raising concerns of transmission from, to, or between these animals.\", \"There is currently no indication that feline- or canine-to-human transmission can occur, though there is rising evidence of the reverse.\"]}", "id": 123}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: COVID-19 and kids: What can happen when children get the coronavirus. A rare but sometimes deadly syndrome poses extra risk for COVID's youngest victims.\n\nAbstract:\nAIM: Many countries have closed schools and kindergartens to minimise COVID-19, but the role that children play in disease transmission is unclear.\nMETHODS: A systematic literature review of the MEDLINE and EMBASE databases and medRxiv/bioRxiv preprint servers to 11 May 2020 identified published and unpublished papers on COVID-19 transmission by children.\nRESULTS: We identified 700 scientific papers and letters and 47 full texts were studied in detail.\nChildren accounted for a small fraction of COVID-19 cases and mostly had social contacts with peers or parents, rather than older people at risk of severe disease.\nData on viral loads were scarce, but indicated that children may have lower levels than adults, partly because they often have fewer symptoms, and this should decrease the transmission risk.\nHousehold transmission studies showed that children were rarely the index case and case studies suggested that children with COVID-19 seldom caused outbreaks.\nHowever, it is highly likely that children can transmit the SARS-COV-2 virus, which causes COVID-19, and even asymptomatic children can have viral loads.\nCONCLUSION: Children are unlikely to be the main drivers of the pandemic.\nOpening up schools and kindergartens is unlikely to impact COVID-19 mortality rates in older people.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Data on viral loads were scarce, but indicated that children may have lower levels than adults, partly because they often have fewer symptoms, and this should decrease the transmission risk.\"]}", "id": 124}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Regn-cov2 antibody cocktail prevents and treats sars-cov-2 infection in rhesus macaques and hamsters\n\nAbstract:\nAn urgent global quest for effective therapies to prevent and treat COVID-19 disease is ongoing.\nWe previously described REGN-COV2, a cocktail of two potent neutralizing antibodies (REGN10987+REGN10933) targeting non-overlapping epitopes on the SARS-CoV-2 spike protein.\nIn this report, we evaluate the in vivo efficacy of this antibody cocktail in both rhesus macaques and golden hamsters and demonstrate that REGN-COV-2 can greatly reduce virus load in lower and upper airway and decrease virus induced pathological sequalae when administered prophylactically or therapeutically.\nOur results provide evidence of the therapeutic potential of this antibody cocktail.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"We previously described REGN-COV2, a cocktail of two potent neutralizing antibodies (REGN10987+REGN10933) targeting non-overlapping epitopes on the SARS-CoV-2 spike protein.\", \"In this report, we evaluate the in vivo efficacy of this antibody cocktail in both rhesus macaques and golden hamsters and demonstrate that REGN-COV-2 can greatly reduce virus load in lower and upper airway and decrease virus induced pathological sequalae when administered prophylactically or therapeutically.\", \"Our results provide evidence of the therapeutic potential of this antibody cocktail.\"]}", "id": 125}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: If you're worried about corona, only the N95 mask will protect you\n\nAbstract:\nIn the context of Coronavirus Disease (2019) (COVID-19) cases globally, there is a lack of consensus across cultures on whether wearing face masks is an effective physical intervention against disease transmission.\nThis study 1) illustrates transmission routes of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2); 2) addresses controversies surrounding the mask from perspectives of attitude, effectiveness, and necessity of wearing the mask with evidence that the use of mask would effectively interrupt the transmission of infectious diseases in both hospital settings and community settings; and 3) provides suggestion that the public should wear the mask during COVID-19 pandemic according to local context.\nTo achieve this goal, government should establish a risk adjusted strategy of mask use to scientifically publicize the use of masks, guarantee sufficient supply of masks, and cooperate for reducing health resources inequities.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"This study 1) illustrates transmission routes of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2); 2) addresses controversies surrounding the mask from perspectives of attitude, effectiveness, and necessity of wearing the mask with evidence that the use of mask would effectively interrupt the transmission of infectious diseases in both hospital settings and community settings; and 3) provides suggestion that the public should wear the mask during COVID-19 pandemic according to local context.\"]}", "id": 126}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: fever is beneficial to the body's natural immune response to fight covid-19;\n\nAbstract:\nIbuprofen is an over-the-counter medication that is used widely for the treatment of pain and fever during COVID-19 pandemic.\nA concern was raised regarding the safety of ibuprofen use because of its role in increasing ACE2 levels within the Renin-Angiotensin-Aldosterone system.\nACE2 is the coreceptor for the entry of SARS-CoV-2 into cells, and so, a potential increased risk of contracting COVID-19 disease and/or worsening of COVID-19 infection was feared with ibuprofen use.\nHowever, available data from limited studies show administration of recombinant ACE2 improves lung damage caused by respiratory viruses, suggesting ibuprofen use may be beneficial in COVID-19 disease.\nAt this time, there is no supporting evidence to discourage the use of ibuprofen.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Ibuprofen is an over-the-counter medication that is used widely for the treatment of pain and fever during COVID-19 pandemic.\", \"However, available data from limited studies show administration of recombinant ACE2 improves lung damage caused by respiratory viruses, suggesting ibuprofen use may be beneficial in COVID-19 disease.\", \"At this time, there is no supporting evidence to discourage the use of ibuprofen.\"]}", "id": 127}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Covid-19 Can Be Treated By Gargling With Warm Water Mixed With Salt And Vinegar\n\nAbstract:\nThe global pandemic caused by the newly described severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused worldwide suffering and death of unimaginable magnitude from coronavirus disease 2019 (COVID-19).\nThe virus is transmitted through aerosol droplets, and causes severe acute respiratory syndrome.\nSARS-CoV-2 uses the receptor-binding domain of its spike protein S1 to attach to the host angiotensin-converting enzyme 2 receptor in lung and airway cells.\nBinding requires the help of another host protein, transmembrane protease serine S1 member 2.\nSeveral factors likely contribute to the efficient transmission of SARS-CoV-2.\nThe receptor-binding domain of SARS-CoV-2 has a 10- to 20-fold higher receptor-binding capacity compared with previous pandemic coronaviruses.\nIn addition, because asymptomatic persons infected with SARS-CoV-2 have high viral loads in their nasal secretions, they can silently and efficiently spread the disease.\nPCR-based tests have emerged as the criterion standard for the diagnosis of infection.\nCaution must be exercised in interpreting antibody-based tests because they have not yet been validated, and may give a false sense of security of being \"immune\" to SARS-CoV-2.\nWe discuss how the development of some symptoms in allergic rhinitis can serve as clues for new-onset COVID-19.\nThere are mixed reports that asthma is a risk factor for severe COVID-19, possibly due to differences in asthma endotypes.\nThe rapid spread of COVID-19 has focused the efforts of scientists on repurposing existing Food and Drug Administration-approved drugs that inhibit viral entry, endocytosis, genome assembly, translation, and replication.\nNumerous clinical trials have been launched to identify effective treatments for COVID-19.\nInitial data from a placebo-controlled study suggest faster time to recovery in patients on remdesivir; it is now being evaluated in additional controlled studies.\nAs discussed in this review, till effective vaccines and treatments emerge, it is important to understand the scientific rationale of pandemic-mitigation strategies such as wearing facemasks and social distancing, and implement them.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Numerous clinical trials have been launched to identify effective treatments for COVID-19.\", \"Initial data from a placebo-controlled study suggest faster time to recovery in patients on remdesivir; it is now being evaluated in additional controlled studies.\", \"As discussed in this review, till effective vaccines and treatments emerge, it is important to understand the scientific rationale of pandemic-mitigation strategies such as wearing facemasks and social distancing, and implement them.\"]}", "id": 128}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: headaches are rarely the sole symptom present in a coronavirus patient.\n\nAbstract:\nOBJECTIVE To analyze the epidemiological characteristics and clinical features of the patients with coronavirus disease 2019 (COVID-19), so as to provide basis for clinical diagnosis.\nMETHODS The epidemiology, clinical symptoms, laboratory and radiologic data of 23 patients with COVID-19 admitted to the Fifth People's Hospital of Xinyang City from January 22nd to January 29th, 2020 were retrospectively analyzed.\nRESULTS There was 23 patients with COVID-19, with 15 men and 8 women, and the median age was 46.0 (40.5, 52.0) years old (ranged from 27 years old to 80 years old).\nNine patients had basic disease (39.1%), including hypertension (17.4%), cardiovascular diseases (17.4%), diabetes (8.7%), hypothyroidism (4.3%) and past history of tuberculosis (4.3%).\nAll the 23 patients had contact history in Wuhan area or with confirmed cases.\nClinical symptoms included fever (100%), cough (69.6%), expectoration (43.5%), myalgia (26.1%), headache (17.4%) and dyspnea (17.4%), and the less common symptom was diarrhea (4.3%).\nBlood routine tests showed leukocytopenia in 11 patients (47.8%), normal leukocyte counts in 10 patients (43.5%), and leukocytosis in 2 patients (8.7%); lymphopenia was found in 13 patients (56.5%).\nAll 23 patients had different degrees of infective lesions in chest CT, with 7 patients (30.4%) on one side and 16 patients (69.6%) on both sides.\nThere were 19 mild patients, 4 severe patients, and no critical or death case.\nComplications included acute respiratory distress syndrome (17.4%).\nNo patient was reported with liver, kidney or heart dysfunction or secondary infection.\nCONCLUSIONS Epidemic history of contact, fever, pneumonia signs of chest CT, normal or decreased count of leukocyte and lymphopenia are the clinical basis for diagnosis of COVID-19.\nHowever, at present, the treatment of patients has not been completed, and the effective treatment strategy and final prognosis are unclear.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Clinical symptoms included fever (100%), cough (69.6%), expectoration (43.5%), myalgia (26.1%), headache (17.4%) and dyspnea (17.4%), and the less common symptom was diarrhea (4.3%).\"]}", "id": 129}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Brilacidin , a covid-19 drug candidate , exhibits potent in vitro antiviral activity against sars-cov-2\n\nAbstract:\nSummary Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), the newly emergent causative agent of coronavirus disease-19 (COVID-19), has resulted in more than one million deaths worldwide since it was first detected in 2019.\nThere is a critical global need for therapeutic intervention strategies that can be deployed to safely treat COVID-19 disease and reduce associated morbidity and mortality.\nIncreasing evidence shows that both natural and synthetic antimicrobial peptides (AMPs), also referred to as Host Defense Proteins/Peptides (HDPs), can inhibit SARS-CoV-2, paving the way for the potential clinical use of these molecules as therapeutic options.\nIn this manuscript, we describe the potent antiviral activity exerted by brilacidin\u2014a de novo designed synthetic small molecule that captures the biological properties of HDPs\u2014on SARS-CoV-2 in a human lung cell line (Calu-3) and a monkey cell line (Vero).\nThese data suggest that SARS-CoV-2 inhibition in these cell culture models is primarily a result of the impact of brilacidin on viral entry and its disruption of viral integrity.\nBrilacidin has demonstrated synergistic antiviral activity when combined with remdesivir.\nCollectively, our data demonstrate that brilacidin exerts potent inhibition of SARS-CoV-2 and thus supports brilacidin as a promising COVID-19 drug candidate.\nHighlights Brilacidin potently inhibits SARS-CoV-2 in an ACE2 positive human lung cell line.\nBrilacidin achieved a high Selectivity Index of 426 (CC50=241\u03bcM/IC50=0.565\u03bcM).\nBrilacidin\u2019s main mechanism appears to disrupt viral integrity and impact viral entry.\nBrilacidin and remdesivir exhibit excellent synergistic activity against SARS-CoV-2.\nSignificance Statement SARS-CoV-2, the emergent novel coronavirus, has led to the current global COVID-19 pandemic, characterized by extreme contagiousness and high mortality rates.\nThere is an urgent need for effective therapeutic strategies to safely and effectively treat SARS-CoV-2 infection.\nWe demonstrate that brilacidin, a synthetic small molecule with peptide-like properties, is capable of exerting potent in vitro antiviral activity against SARS-CoV-2, both as a standalone treatment and in combination with remdesivir, which is currently the only FDA-approved drug for the treatment of COVID-19.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Collectively, our data demonstrate that brilacidin exerts potent inhibition of SARS-CoV-2 and thus supports brilacidin as a promising COVID-19 drug candidate.\", \"Brilacidin and remdesivir exhibit excellent synergistic activity against SARS-CoV-2.\", \"We demonstrate that brilacidin, a synthetic small molecule with peptide-like properties, is capable of exerting potent in vitro antiviral activity against SARS-CoV-2, both as a standalone treatment and in combination with remdesivir, which is currently the only FDA-approved drug for the treatment of COVID-19.\"]}", "id": 130}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Masks do reduce spread of flu and some COVID-19\n\nAbstract:\nWe use the synthetic control method to analyze the effect of face masks on the spread of Covid-19 in Germany.\nOur identification approach exploits regional variation in the point in time when face masks became compulsory.\nDepending on the region we analyse, we find that face masks reduced the cumulative number of registered Covid-19 cases between 2.3% and 13% over a period of 10 days after they became compulsory.\nAssessing the credibility of the various estimates, we conclude that face masks reduce the daily growth rate of reported infections by around 40%.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Depending on the region we analyse, we find that face masks reduced the cumulative number of registered Covid-19 cases between 2.3% and 13% over a period of 10 days after they became compulsory.\", \"Assessing the credibility of the various estimates, we conclude that face masks reduce the daily growth rate of reported infections by around 40%.\"]}", "id": 131}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: scientists do not recommend that people should begin smoking simply to attempt to avoid a severe case of COVID-19. Smoking is still a leading cause of preventable death across the globe.\n\nAbstract:\nImportance.\nCovid-19 infection has major international health and economic impacts and risk factors for infection are not completely understood.\nCannabis smoking is linked with poor respiratory health, immunosuppression and multiple contaminants.\nPotential synergism between the two epidemics would represent a major public health convergence.\nCigarettes were implicated with disease severity in Wuhan, China.\nObjective.\nIs cannabis use epidemiologically associated with coronavirus incidence rate (CVIR)?\nDesign.\nCross-sectional state-based multivariable study.\nSetting.\nUSA.\nPrimary and Secondary Outcome Measures.\nCVIR.\nMultivariable-adjusted geospatially-weighted regression models.\nAs the American cannabis epidemic is characterized by a recent doubling of daily cannabis use it was considered important to characterize the contribution of high intensity use.\nResults.\nSignificant associations of daily cannabis use quintile with CVIR were identified with the highest quintile having a prevalence ratio 5.11 (95%C.I. 4.90-5.33), an attributable fraction in the exposed (AFE) 80.45% (79.61-81.25%) and an attributable fraction in the population of 77.80% (76.88-78.68%) with Chi-squared-for-trend (14,782, df=4) significant at P<10-500.\nSimilarly when cannabis legalization was considered decriminalization was associated with an elevated CVIR prevalence ratio 4.51 (95%C.I. 4.45-4.58), AFE 77.84% (77.50-78.17%) and Chi-squared-for-trend (56,679, df=2) significant at P<10-500.\nMonthly and daily use were linked with CVIR in bivariate geospatial regression models (P=0.0027, P=0.0059).\nIn multivariable additive models number of flight origins and population density were significant.\nIn interactive geospatial models adjusted for international travel, ethnicity, income, population, population density and drug use, terms including last month cannabis were significant from P=7.3x10-15, daily cannabis use from P=7.3x10-11 and last month cannabis was independently associated (P=0.0365).\nConclusions and Relevance.\nData indicate CVIR demonstrates significant trends across cannabis use intensity quintiles and with relaxed cannabis legislation.\nRecent cannabis use is independently predictive of CVIR in bivariate and multivariable adjusted models and intensity of use is interactively significant.\nCannabis thus joins tobacco as a SARS2-CoV-2 risk factor.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Cannabis smoking is linked with poor respiratory health, immunosuppression and multiple contaminants.\", \"Potential synergism between the two epidemics would represent a major public health convergence.\", \"Cigarettes were implicated with disease severity in Wuhan, China.\"]}", "id": 132}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: with autoimmune conditions such as lupus, a person experiences \"dysregulations of the immune system,\" meaning the immune system itself is compromised or malfunctioning\n\nAbstract:\nThe rapid global spread of SARS-CoV-2 and resultant mortality and social disruption have highlighted the need to better understand coronavirus immunity to expedite vaccine development efforts.\nMultiple candidate vaccines, designed to elicit protective neutralising antibodies targeting the viral spike glycoprotein, are rapidly advancing to clinical trial.\nHowever, the immunogenic properties of the spike protein in humans are unresolved.\nTo address this, we undertook an in-depth characterisation of humoral and cellular immunity against SARS-CoV-2 spike in humans following mild to moderate SARS-CoV-2 infection.\nWe find serological antibody responses against spike are routinely elicited by infection and correlate with plasma neutralising activity and capacity to block ACE2/RBD interaction.\nExpanded populations of spike-specific memory B cells and circulating T follicular helper cells (cTFH) were detected within convalescent donors, while responses to the receptor binding domain (RBD) constitute a minor fraction.\nUsing regression analysis, we find high plasma neutralisation activity was associated with increased spike-specific antibody, but notably also with the relative distribution of spike-specific cTFH subsets.\nThus both qualitative and quantitative features of B and T cell immunity to spike constitute informative biomarkers of the protective potential of novel SARS-CoV-2 vaccines.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 133}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: the mechanism behind the protective effects of smoking could be found in nicotine\n\nAbstract:\nStatistical surveys of COVID-19 patients indicate, against all common logic, that people who smoke are less prone to the infection and/or exhibit less severe respiratory symptoms than non-smokers.\nThis suggests that nicotine may have some preventive or modulatory effect on the inflammatory response in the lungs.\nBecause it is known that the response to, and resolution of the SARS-CoV-2 infection depends mainly on the lung macrophages, we discuss the recent scientific findings, which may explain why and how nicotine may modulate lung macrophage response during COVID-19 infection.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Statistical surveys of COVID-19 patients indicate, against all common logic, that people who smoke are less prone to the infection and/or exhibit less severe respiratory symptoms than non-smokers.\", \"This suggests that nicotine may have some preventive or modulatory effect on the inflammatory response in the lungs.\"]}", "id": 134}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: The N95 respirator is thicker than a surgical mask, but neither Schaffner\n\nAbstract:\nWe identified seasonal human coronaviruses, influenza viruses and rhinoviruses in exhaled breath and coughs of children and adults with acute respiratory illness.\nSurgical face masks significantly reduced detection of influenza virus RNA in respiratory droplets and coronavirus RNA in aerosols, with a trend toward reduced detection of coronavirus RNA in respiratory droplets.\nOur results indicate that surgical face masks could prevent transmission of human coronaviruses and influenza viruses from symptomatic individuals.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 135}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: A report indicates that Acetaminophen (Tylenol) may be preferred over Ibuprofen (Advil) for coronavirus (fever)\n\nAbstract:\nCoronavirus Disease 2019 (COVID-19) can cause severe respiratory failure and distressing symptoms including fever, cough, breathlessness and anxiety.\nSymptomatic (palliative) treatment is of fundamental importance both in conjuncture with life-sustaining interventions and in end of life care.\nBased on the evidence to date, there are several treatment options to consider for the relief of fever (acetaminophen, NSAID, oral glucocorticoids), cough (morphine), breathlessness (morphine, oxygen, fan), anxiety (benzodiazepines) and pain (NSAID, morphine).\nTop priorities include precautions to protect staff and people at-risk from infection and planning how to provide adequate treatment for each individual depending on setting, including palliative care.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Based on the evidence to date, there are several treatment options to consider for the relief of fever (acetaminophen, NSAID, oral glucocorticoids), cough (morphine), breathlessness (morphine, oxygen, fan), anxiety (benzodiazepines) and pain (NSAID, morphine).\"]}", "id": 136}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Berberine and obatoclax induce sars-cov-2 replication in primary human nasal epithelial cells in vitro.\n\nAbstract:\nSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged as a new human pathogen in late 2019 and has infected an estimated 10% of the global population in less than a year.\nThere is a clear need for effective antiviral drugs to complement current preventive measures including vaccines.\nIn this study, we demonstrate that berberine and obatoclax, two broad-spectrum antiviral compounds, are effective against multiple isolates of SARS-CoV-2.\nBerberine, a plant-derived alkaloid, inhibited SARS-CoV-2 at low micromolar concentrations and obatoclax, originally developed as an anti-apoptotic protein antagonist, was effective at sub-micromolar concentrations.\nTime-of-addition studies indicated that berberine acts on the late stage of the viral life cycle.\nIn agreement, berberine mildly affected viral RNA synthesis, but strongly reduced infectious viral titers, leading to an increase in the particle-to-pfu ratio.\nIn contrast, obatoclax acted at the early stage of the infection, in line with its activity to neutralize the acidic environment in endosomes.\nWe assessed infection of primary human nasal epithelial cells cultured on an air-liquid interface and found that SARS-CoV-2 infection induced and repressed expression of a specific set of cytokines and chemokines.\nMoreover, both obatoclax and berberine inhibited SARS-CoV-2 replication in these primary target cells.\nWe propose berberine and obatoclax as potential antiviral drugs against SARS-CoV-2 that could be considered for further efficacy testing.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"In this study, we demonstrate that berberine and obatoclax, two broad-spectrum antiviral compounds, are effective against multiple isolates of SARS-CoV-2.\", \"Berberine, a plant-derived alkaloid, inhibited SARS-CoV-2 at low micromolar concentrations and obatoclax, originally developed as an anti-apoptotic protein antagonist, was effective at sub-micromolar concentrations.\", \"Moreover, both obatoclax and berberine inhibited SARS-CoV-2 replication in these primary target cells.\"]}", "id": 137}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Avoid medications to lower fever if sick with the new coronavirus\n\nAbstract:\nIbuprofen is an over-the-counter medication that is used widely for the treatment of pain and fever during COVID-19 pandemic.\nA concern was raised regarding the safety of ibuprofen use because of its role in increasing ACE2 levels within the Renin-Angiotensin-Aldosterone system.\nACE2 is the coreceptor for the entry of SARS-CoV-2 into cells, and so, a potential increased risk of contracting COVID-19 disease and/or worsening of COVID-19 infection was feared with ibuprofen use.\nHowever, available data from limited studies show administration of recombinant ACE2 improves lung damage caused by respiratory viruses, suggesting ibuprofen use may be beneficial in COVID-19 disease.\nAt this time, there is no supporting evidence to discourage the use of ibuprofen.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Ibuprofen is an over-the-counter medication that is used widely for the treatment of pain and fever during COVID-19 pandemic.\", \"However, available data from limited studies show administration of recombinant ACE2 improves lung damage caused by respiratory viruses, suggesting ibuprofen use may be beneficial in COVID-19 disease.\", \"At this time, there is no supporting evidence to discourage the use of ibuprofen.\"]}", "id": 138}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: covid-19 patients taking hydroxychloroquine do not benefit\n\nAbstract:\nHydroxychloroquine (HCQ) garnered scientific attention in early February following publication of reports showing in vitro activity of chloroquine (CQ) against COVID\u201019.\nWhile studies are mixed on this topic, the therapeutic effect of HCQ or CQ still need more valid clinical evidence.\nIn this descriptive observational study, we aimed to discuss the treatment response of HCQ in COVID\u201019 infected patients and 30 cases were included.\nThe demographic, treatment, laboratory parameters of C\u2010reactive protein (CRP) and interleukin\u20106 (IL\u20106) before and after HCQ therapy and clinical outcome in the 30 COVID\u201019 patients were assessed.\nIn order to evaluate the effect of mediation time point, we also divided these cases into two groups, patients began administrated with HCQ within 7 days hospital (defined as early delivery group) and 7 days after hospital (defined as later delivery group).\nWe found that, the elevated IL\u20106, a risk factor in severe patients were reduced to normal level after HCQ treatment.\nMore importantly, patients treated with HCQ at the time of early hospital recovered faster than those who treated later or taken as second line choose for their obvious shorter hospitalization time.\nIn summary, early use of HCQ was better than later use and the effect of IL\u20106 and CRP level can not be ruled out.\nThis article is protected by copyright.\nAll rights reserved.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"More importantly, patients treated with HCQ at the time of early hospital recovered faster than those who treated later or taken as second line choose for their obvious shorter hospitalization time.\"]}", "id": 139}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Hydroxychloroquine proves activity in hamsters and macaques infected with sars-cov-2\n\nAbstract:\nWe remain largely without effective prophylactic/therapeutic interventions for COVID-19.\nAlthough many human clinical trials are ongoing, there remains a deficiency of supportive preclinical drug efficacy studies.\nHere we assessed the prophylactic/therapeutic efficacy of hydroxychloroquine (HCQ), a drug of interest for COVID-19 management, in two animal models.\nWhen used for prophylaxis or treatment neither the standard human malaria dose (6.5 mg/kg) nor a high dose (50 mg/kg) of HCQ had any beneficial effect on clinical disease or SARS-CoV-2 kinetics (replication/shedding) in the Syrian hamster disease model.\nSimilarly, HCQ prophylaxis/treatment (6.5 mg/kg) did not significantly benefit clinical outcome nor reduce SARS-CoV-2 replication/shedding in the upper and lower respiratory tract in the rhesus macaque disease model.\nIn conclusion, our preclinical animal studies do not support the use of HCQ in prophylaxis/treatment of COVID-19.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Similarly, HCQ prophylaxis/treatment (6.5 mg/kg) did not significantly benefit clinical outcome nor reduce SARS-CoV-2 replication/shedding in the upper and lower respiratory tract in the rhesus macaque disease model.\"]}", "id": 140}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: The d614g mutation in the sars-cov2 spike protein inhibited infectivity in an ace2 receptor dependent manner\n\nAbstract:\nThe SARS-CoV2 coronavirus responsible for the current COVID19 pandemic has been reported to have a relatively low mutation rate.\nNevertheless, a few prevalent variants have arisen that give the appearance of undergoing positive selection as they are becoming increasingly widespread over time.\nMost prominent among these is the D614G amino acid substitution in the SARS-CoV2 Spike protein, which mediates viral entry.\nThe D614G substitution, however, is in linkage disequilibrium with the ORF1b P314L mutation where both mutations almost invariably co-occur, making functional inferences problematic.\nIn addition, the possibility of repeated new introductions of the mutant strain does not allow one to distinguish between a founder effect and an intrinsic genetic property of the virus.\nHere, we synthesized and expressed the WT and D614G variant SARS-Cov2 Spike protein, and report that using a SARS-CoV2 Spike protein pseudotyped lentiviral vector we observe that the D614G variant Spike has >1/2 log(10) increased infectivity in human cells expressing the human ACE2 protein as the viral receptor.\nThe increased binding/fusion activity of the D614G Spike protein was corroborated in a cell fusion assay using Spike and ACE2 proteins expressed in different cells.\nThese results are consistent with the possibility that the Spike D614G mutant increases the infectivity of SARS-CoV2.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Most prominent among these is the D614G amino acid substitution in the SARS-CoV2 Spike protein, which mediates viral entry.\", \"The increased binding/fusion activity of the D614G Spike protein was corroborated in a cell fusion assay using Spike and ACE2 proteins expressed in different cells.\", \"These results are consistent with the possibility that the Spike D614G mutant increases the infectivity of SARS-CoV2.\"]}", "id": 141}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Supplementation is unlikely to show an effect in people whose vitamin C levels are already high\n\nAbstract:\nOBJECTIVE Coronavirus disease 2019 (COVID-19) is a fatal and fast-spreading viral infection.\nTo date, the number of COVID-19 patients worldwide has crossed over six million with over three hundred and seventy thousand deaths (according to the data from World Health Organization; updated on 2 June 2020).\nAlthough COVID-19 can be rapidly diagnosed, efficient clinical treatment of COVID-19 remains unavailable, resulting in high fatality.\nSome clinical trials have identified vitamin C (VC) as a potent compound pneumonia management.\nIn addition, glycyrrhizic acid (GA) is clinically as an anti-inflammatory medicine against pneumonia-induced inflammatory stress.\nWe hypothesized that the combination of VC and GA is a potential option for treating COVID-19.\nMETHODS The aim of this study was to determine pharmacological targets and molecular mechanisms of VC + GA treatment for COVID-19, using bioinformational network pharmacology.\nRESULTS We uncovered optimal targets, biological processes and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways of VC + GA against COVID-19.\nOur findings suggested that combinatorial VC and GA treatment for COVID-19 was associated with elevation of immunity and suppression of inflammatory stress, including activation of the T cell receptor signaling pathway, regulation of Fc gamma R-mediated phagocytosis, ErbB signaling pathway and vascular endothelial growth factor signaling pathway.\nWe also identified 17 core targets of VC + GA, which suggest as antimicrobial function.\nCONCLUSIONS For the first time, our study uncovered the pharmacological mechanism underlying combined VC and GA treatment for COVID-19.\nThese results should benefit efforts to address the most pressing problem currently facing the world.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 142}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: As the novel coronavirus sweeps the globe, those with high blood pressure are at heightened risk for more severe complications should they contract Covid-19\n\nAbstract:\nSome comorbidities are associated with severe coronavirus disease (Covid-19) but it is unclear whether some increase susceptibility to Covid-19.\nIn this case-control Mexican study we found that obesity represents the strongest predictor for Covid-19 followed by diabetes and hypertension in both sexes and chronic renal failure in females only.\nActive smoking was associated with decreased odds of Covid-19.\nThese findings indicate that these comorbidities are not only associated with severity of disease but also predispose for getting Covid-19.\nFuture research is needed to establish the mechanisms involved in each comorbidity and the apparent \"protective\" effect of cigarette smoking.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"In this case-control Mexican study we found that obesity represents the strongest predictor for Covid-19 followed by diabetes and hypertension in both sexes and chronic renal failure in females only.\"]}", "id": 143}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Higher risk if you have type 1 diabetes. Compared to people without diabetes, people with type 1 diabetes are approximately 3.5 times as likely to die in hospital with COVID-19, while people with type 2 are approximately twice as likely. \n\nAbstract:\nBACKGOUND: To figure out whether diabetes is a risk factor influencing the progression and prognosis of 2019 novel coronavirus disease (COVID-19).\nMETHODS: A total of 174 consecutive patients confirmed with COVID-19 were studied.\nDemographic data, medical history, symptoms and signs, laboratory findings, chest computed tomography (CT) as well the treatment measures were collected and analysed.\nRESULTS: We found that COVID-19 patients without other comorbidities but with diabetes (n = 24) were at higher risk of severe pneumonia, release of tissue injury-related enzymes, excessive uncontrolled inflammation responses and hypercoagulable state associated with dysregulation of glucose metabolism.\nFurthermore, serum levels of inflammation-related biomarkers such as IL-6, C-reactive protein, serum ferritin and coagulation index, D-dimer, were significantly higher (P < .01) in diabetic patients compared with those without, suggesting that patients with diabetes are more susceptible to an inflammatory storm eventually leading to rapid deterioration of COVID-19.\nCONCLUSIONS: Our data support the notion that diabetes should be considered as a risk factor for a rapid progression and bad prognosis of COVID-19.\nMore intensive attention should be paid to patients with diabetes, in case of rapid deterioration.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 144}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Dna-based culture of sars-cov-2 informs infectivity and safe de-isolation assessments during covid-19\n\nAbstract:\nBACKGROUND: The detection of SARS-CoV-2 RNA by real-time polymerase chain reaction (PCR) in respiratory samples collected from persons recovered from COVID-19 does not necessarily indicate shedding of infective virions.\nBy contrast, the isolation of SARS-CoV-2 using cell-based culture likely indicates infectivity, but there are limited data on the correlation between SARS-CoV-2 culture and PCR.\nMETHODS: One hundred and ninety-five patients with varying severity of COVID-19 were tested (outpatients [n=178]), inpatients [n=12] and critically unwell patients admitted to the intensive care unit [ICU; n=5]).\nSARS-CoV-2 PCR positive samples were cultured in Vero C1008 cells and inspected daily for cytopathic effect (CPE).\nSARS-CoV-2-induced CPE was confirmed by PCR of culture supernatant.\nWhere no CPE was observed, PCR was performed on day four to confirm absence of virus replication.\nCycle threshold (Ct) of the day four PCR (Ctculture) and the PCR of the original clinical sample (Ctsample) were compared, and positive cultures were defined where Ctsample-Ctculture was &#8805;3.\nFINDINGS: Of 234 samples collected, 228 (97%) were from the upper respiratory tract.\nSARS-CoV-2 was only successfully isolated from samples with Ctsample &#8804;32, including in 28/181 (15%), 19/42 (45%) and 9/11 samples (82%) collected from outpatients, inpatients, and ICU patients, respectively.\nThe mean duration from symptom onset to culture positivity was 4.5 days (range 0-18).\nSARS-CoV-2 was significantly more likely to be isolated from samples collected from inpatients (p<0\u00e2\u0088\u0099001) and ICU patients (p<0\u00e2\u0088\u00990001) compared with outpatients respectively, and in samples with lower Ctsample.\nCONCLUSION: SARS-CoV-2 culture may be used as a surrogate marker for infectivity and inform de-isolation protocols.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"By contrast, the isolation of SARS-CoV-2 using cell-based culture likely indicates infectivity, but there are limited data on the correlation between SARS-CoV-2 culture and PCR.\"]}", "id": 145}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Hot weather can spread the virus more as it may get you more out there, make you more mobile and you would actually interact with more people\n\nAbstract:\nThe novel coronavirus, since its first outbreak in December, has, up till now, affected approximately 114,542 people across 115 countries.\nMany international agencies are devoting efforts to enhance the understanding of the evolving COVID-19 outbreak on an international level, its influences, and preparedness.\nAt present, COVID-19 appears to affect individuals through person-to-person means, like other commonly found cold or influenza viruses.\nIt is widely known and acknowledged that viruses causing influenza peak during cold temperatures and gradually subside in the warmer temperature, owing to their seasonality.\nThus, COVID-19, due to its regular flu-like symptoms, is also expected to show similar seasonality and subside as the global temperatures rise in the northern hemisphere with the onset of spring.\nDespite these speculations, however, the systematic analysis in the global perspective of the relation between COVID-19 spread and meteorological parameters is unavailable.\nHere, by analyzing the region- and city-specific affected global data and corresponding meteorological parameters, we show that there is an optimum range of temperature and UV index strongly affecting the spread and survival of the virus, whereas precipitation, relative humidity, cloud cover, etc. have no effect on the virus.\nUnavailability of pharmaceutical interventions would require greater preparedness and alert for the effective control of COVID-19.\nUnder these conditions, the information provided here could be very helpful for the global community struggling to fight this global crisis.\nIt is, however, important to note that the information presented here clearly lacks any physiological evidences, which may merit further investigation.\nThus, any attempt for management, implementation, and evaluation strategies responding to the crisis arising due to the COVID-19 outbreak must not consider the evaluation presented here as the foremost factor.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Thus, COVID-19, due to its regular flu-like symptoms, is also expected to show similar seasonality and subside as the global temperatures rise in the northern hemisphere with the onset of spring.\"]}", "id": 146}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: throwing hot water on ginger as a cure-all for COVID-19\n\nAbstract:\nIn late December 2019 in Wuhan, China, several patients with viral pneumonia were identified as 2019 novel coronavirus (2019-nCoV).\nSo far, there are no specific treatments for patients with coronavirus disease-19 (COVID-19), and the treatments available today are based on previous experience with similar viruses such as severe acute respiratory syndrome-related coronavirus (SARS-CoV), Middle East respiratory syndrome coronavirus (MERS-CoV), and Influenza virus.\nIn this article, we have tried to reach a therapeutic window of drugs available to patients with COVID-19.\nCathepsin L is required for entry of the 2019-nCoV virus into the cell as target teicoplanin inhibits virus replication.\nAngiotensin-converting-enzyme 2 (ACE2) in soluble form as a recombinant protein can prevent the spread of coronavirus by restricting binding and entry.\nIn patients with COVID-19, hydroxychloroquine decreases the inflammatory response and cytokine storm, but overdose causes toxicity and mortality.\nNeuraminidase inhibitors such as oseltamivir, peramivir, and zanamivir are invalid for 2019-nCoV and are not recommended for treatment but protease inhibitors such as lopinavir/ritonavir (LPV/r) inhibit the progression of MERS-CoV disease and can be useful for patients of COVID-19 and, in combination with Arbidol, has a direct antiviral effect on early replication of SARS-CoV. Ribavirin reduces hemoglobin concentrations in respiratory patients, and remdesivir improves respiratory symptoms.\nUse of ribavirin in combination with LPV/r in patients with SARS-CoV reduces acute respiratory distress syndrome and mortality, which has a significant protective effect with the addition of corticosteroids.\nFavipiravir increases clinical recovery and reduces respiratory problems and has a stronger antiviral effect than LPV/r.\ncurrently, appropriate treatment for patients with COVID-19 is an ACE2 inhibitor and a clinical problem reducing agent such as favipiravir in addition to hydroxychloroquine and corticosteroids.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"In late December 2019 in Wuhan, China, several patients with viral pneumonia were identified as 2019 novel coronavirus (2019-nCoV).\", \"So far, there are no specific treatments for patients with coronavirus disease-19 (COVID-19), and the treatments available today are based on previous experience with similar viruses such as severe acute respiratory syndrome-related coronavirus (SARS-CoV), Middle East respiratory syndrome coronavirus (MERS-CoV), and Influenza virus.\"]}", "id": 147}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Covid-19 ards is characterized by a dysregulated host response that differs from cytokine storm and may be modified by dexamethasone.\n\nAbstract:\nWe performed comparative lower respiratory tract transcriptional profiling of 52 critically ill patients with the acute respiratory distress syndrome (ARDS) from COVID-19 or from other etiologies, as well as controls without ARDS.\nIn contrast to a cytokine storm, we observed reduced proinflammatory gene expression in COVID-19 ARDS when compared to ARDS due to other causes.\nCOVID-19 ARDS was characterized by a dysregulated host response with increased PTEN signaling and elevated expression of genes with non-canonical roles in inflammation and immunity that were predicted to be modulated by dexamethasone and granulocyte colony stimulating factor.\nCompared to ARDS due to other types of viral pneumonia, COVID-19 was characterized by impaired interferon-stimulated gene expression (ISG).\nWe found that the relationship between SARS-CoV-2 viral load and expression of ISGs was decoupled in patients with COVID-19 ARDS when compared to patients with mild COVID-19.\nIn summary, assessment of host gene expression in the lower airways of patients with COVID-19 ARDS did not demonstrate cytokine storm but instead revealed a unique and dysregulated host response predicted to be modified by dexamethasone.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"In contrast to a cytokine storm, we observed reduced proinflammatory gene expression in COVID-19 ARDS when compared to ARDS due to other causes.\", \"COVID-19 ARDS was characterized by a dysregulated host response with increased PTEN signaling and elevated expression of genes with non-canonical roles in inflammation and immunity that were predicted to be modulated by dexamethasone and granulocyte colony stimulating factor.\", \"In summary, assessment of host gene expression in the lower airways of patients with COVID-19 ARDS did not demonstrate cytokine storm but instead revealed a unique and dysregulated host response predicted to be modified by dexamethasone.\"]}", "id": 148}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: covid-19 isn't a risk for young people\n\nAbstract:\nCurrently (mid May 2020), most active cases of COVID-19 are found in Europe and North America while it is still in the initial phases in Africa.\nAs COVID-19 mortality occurs mainly in elderly and as Africa has a comparably young population, the death rates should be lower than on other continents.\nWe calculated standardised mortality ratios (SMR) using age-specific case fatality rates for COVID-19 and the age structure of the population of Africa and of other continents.\nCompared to a European or Northern American population, the standardised mortality ratio was only 0.22 and 0.25, respectively, corresponding to reduction of deaths rates to a quarter.\nCompared to the Asian and Latin American & Caribbean population, the SMR was 0.43 and 0.44, respectively, corresponding to half the death rate for Africa.\nIt is useful to quantify the isolated effect of the African age-structure on potential COVID-19 mortality for illustrative and communication purposes, keeping in mind the importance of public health measures that have been shown to be effective in reducing cases and deaths.\nThe different aspect of age pyramids of a European and an African population are striking and the potential implications for the pandemic are often discussed but rarely quantified.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"As COVID-19 mortality occurs mainly in elderly and as Africa has a comparably young population, the death rates should be lower than on other continents.\", \"We calculated standardised mortality ratios (SMR) using age-specific case fatality rates for COVID-19 and the age structure of the population of Africa and of other continents.\", \"Compared to a European or Northern American population, the standardised mortality ratio was only 0.22 and 0.25, respectively, corresponding to reduction of deaths rates to a quarter.\", \"Compared to the Asian and Latin American & Caribbean population, the SMR was 0.43 and 0.44, respectively, corresponding to half the death rate for Africa.\"]}", "id": 149}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Bacterial modification of the receptor glycosaminoglycan heparan sulfate modulates sars-cov-2 infectivity\n\nAbstract:\nThe human microbiota has a close relationship with human disease and it remodels components of the glycocalyx including heparan sulfate (HS).\nStudies of the severe acute respiratory syndrome coronavirus (SARS-CoV-2) spike protein receptor binding domain suggest that infection requires binding to HS and angiotensin converting enzyme 2 (ACE2) in a codependent manner.\nHere, we show that commensal host bacterial communities can modify HS and thereby modulate SARS-CoV-2 spike protein binding and that these communities change with host age and sex.\nCommon human-associated commensal bacteria whose genomes encode HS-modifying enzymes were identified.\nThe prevalence of these bacteria and the expression of key microbial glycosidases in bronchoalveolar lavage fluid (BALF) was lower in adult COVID-19 patients than in healthy controls.\nThe presence of HS-modifying bacteria decreased with age in two large survey datasets, FINRISK 2002 and American Gut, revealing one possible mechanism for the observed increase in COVID-19 susceptibility with age.\nIn vitro, bacterial glycosidases from unpurified culture media supernatants fully blocked SARS-CoV-2 spike binding to human H1299 protein lung adenocarcinoma cells.\nHS-modifying bacteria in human microbial communities may regulate viral adhesion, and loss of these commensals could predispose individuals to infection.\nUnderstanding the impact of shifts in microbial community composition and bacterial lyases on SARS-CoV-2 infection may lead to new therapeutics and diagnosis of susceptibility.\nGraphical Abstract.\nDiagram of hypothesis for bacterial mediation of SARS-CoV-2 infection through heparan sulfate (HS).\nIt is well known that host microbes groom the mucosa where they reside.\nRecent investigations have shown that HS, a major component of mucosal layers, is necessary for SARS-CoV-2 infection.\nIn this study we examine the impact of microbial modification of HS on viral attachment.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Understanding the impact of shifts in microbial community composition and bacterial lyases on SARS-CoV-2 infection may lead to new therapeutics and diagnosis of susceptibility.\"]}", "id": 150}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Serological surveys in reunion island of the first hospitalized patients revealed that long-lived immunoglobulin g antibodies specific against sars-cov2 virus are rapidly produced in severe cases\n\nAbstract:\nBoth cellular and humoral immunities are critically important to control COVID19 infection but little is known about the kinetics of those responses and, in particular, in patients who will go on to develop a severe form of the disease over several weeks.\nWe herein report the first set of data of our prospective cohort study of 90 hospitalized cases.\nSerological surveys were thoroughly performed over 2 month period by assessing IgG and IgM responses by immunofluorescence, immunoblot, Western blot and conventional ELISA using clinical RUN isolates of SARS-CoV-2 immobilized on 96 well plates.\nWhile the IgM and, unexpectedly, the IgG responses were readily detected early during the course of the disease (5-7 days post-first symptoms), our results (n=3-5 and over the full dilution set of the plasma 1/200 to 1/12800) demonstrated a significant decrease (over 2.5-fold) of IgG levels in severe (ICU) hospitalized patients (exemplified in patient 1) by WB and ELISA.\nIn contrast, mild non-ICU patients had a steady and yet robust rise in their specific IgG levels against the virus.\nInterestingly, both responses (IgM and IgG) were initially against the nucleocapsid (50kDa band on the WB) and spreading to other major viral protein S and domains (S1 and S2.\nIn conclusion, serological testing may be helpful for the diagnosis of patients with negative RT-PCR results and for the identification of asymptomatic cases.\nMoreover, medical care and protections should be maintained particularly for recovered patients (severe cases) who may remain at risk of relapsing or reinfection.\nExperiments to ascertain T cell responses but although their kinetics overtime are now highly warranted.\nAll in all, these studies will help to delineate the best routes for vaccination.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Serological surveys were thoroughly performed over 2 month period by assessing IgG and IgM responses by immunofluorescence, immunoblot, Western blot and conventional ELISA using clinical RUN isolates of SARS-CoV-2 immobilized on 96 well plates.\", \"While the IgM and, unexpectedly, the IgG responses were readily detected early during the course of the disease (5-7 days post-first symptoms), our results (n=3-5 and over the full dilution set of the plasma 1/200 to 1/12800) demonstrated a significant decrease (over 2.5-fold) of IgG levels in severe (ICU) hospitalized patients (exemplified in patient 1) by WB and ELISA.\", \"In conclusion, serological testing may be helpful for the diagnosis of patients with negative RT-PCR results and for the identification of asymptomatic cases.\"]}", "id": 151}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: A standard in-cell elisa assay allows rapid and automated quantification of sars-cov-2 to analyze neutralizing antibodies and antiviral compounds\n\nAbstract:\nThe coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is currently the most pressing medical and socioeconomic challenge.\nConstituting important correlates of protection, the determination of virus-neutralizing antibodies (NAbs) is indispensable for convalescent plasma selection, vaccine candidate evaluation, and immunity certificates.\nIn contrast to standard serological ELISAs, plaque reduction neutralization tests (PRNTs) are laborious, time-consuming, expensive, and restricted to specialized laboratories.\nTo replace microscopic counting-based SARS-CoV-2 PRNTs by a novel assay exempt from genetically modified viruses, which are inapplicable in most diagnostics departments, we established a simple, rapid, and automated SARS-CoV-2 neutralization assay employing an in-cell ELISA (icELISA) approach.\nAfter optimization of various parameters such as virus-specific antibodies, cell lines, virus doses, and duration of infection, SARS-CoV-2-infected cells became amenable as direct antigen source for quantitative icELISA.\nAntiviral agents such as human sera containing NAbs or antiviral interferons dose dependently reduced the SARS-CoV-2-specific signal.\nApplying increased infectious doses, the icELISA-based neutralization test (icNT) was superior to PRNT in discriminating convalescent sera with high from those with intermediate neutralizing capacities.\nIn addition, the icNT was found to be specific, discriminating between SARS-CoV-2-specific NAbs and those raised against other coronaviruses.\nAltogether, the SARS-CoV-2 icELISA test allows rapid (<48 h in total, read-out in seconds) and automated quantification of virus infection in cell culture to evaluate the efficacy of NAbs and antiviral drugs using reagents and equipment present in most routine diagnostics departments.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"To replace microscopic counting-based SARS-CoV-2 PRNTs by a novel assay exempt from genetically modified viruses, which are inapplicable in most diagnostics departments, we established a simple, rapid, and automated SARS-CoV-2 neutralization assay employing an in-cell ELISA (icELISA) approach.\", \"Altogether, the SARS-CoV-2 icELISA test allows rapid (<48 h in total, read-out in seconds) and automated quantification of virus infection in cell culture to evaluate the efficacy of NAbs and antiviral drugs using reagents and equipment present in most routine diagnostics departments.\"]}", "id": 152}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: COVID-19 can survive on surfaces, like a tabletop\n\nAbstract:\nSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a highly contagious virus that can transmit through respiratory droplets, aerosols, or contacts.\nFrequent touching of contaminated surfaces in public areas is therefore a potential route of SARS-CoV-2 transmission.\nThe inanimate surfaces have often been described as a source of nosocomial infections.\nHowever, summaries on the transmissibility of coronaviruses from contaminated surfaces to induce the coronavirus disease 2019 are rare at present.\nThis review aims to summarize data on the persistence of different coronaviruses on inanimate surfaces.\nThe literature was systematically searched on Medline without language restrictions.\nAll reports with experimental evidence on the duration persistence of coronaviruses on any type of surface were included.\nMost viruses from the respiratory tract, such as coronaviruses, influenza, SARS-CoV, or rhinovirus, can persist on surfaces for a few days.\nPersistence time on inanimate surfaces varied from minutes to up to one month, depending on the environmental conditions.\nSARSCoV-2 can be sustained in air in closed unventilated buses for at least 30 min without losing infectivity.\nThe most common coronaviruses may well survive or persist on surfaces for up to one month.\nViruses in respiratory or fecal specimens can maintain infectivity for quite a long time at room temperature.\nAbsorbent materials like cotton are safer than unabsorbent materials for protection from virus infection.\nThe risk of transmission via touching contaminated paper is low.\nPreventive strategies such as washing hands and wearing masks are critical to the control of coronavirus disease 2019.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Most viruses from the respiratory tract, such as coronaviruses, influenza, SARS-CoV, or rhinovirus, can persist on surfaces for a few days.\", \"The most common coronaviruses may well survive or persist on surfaces for up to one month.\"]}", "id": 153}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Coronavirus (COVID-19) Know how to protect yourself and others from COVID-19 and what to do if you are sick.\n\nAbstract:\nThe novel coronavirus, since its first outbreak in December, has, up till now, affected approximately 114,542 people across 115 countries.\nMany international agencies are devoting efforts to enhance the understanding of the evolving COVID-19 outbreak on an international level, its influences, and preparedness.\nAt present, COVID-19 appears to affect individuals through person-to-person means, like other commonly found cold or influenza viruses.\nIt is widely known and acknowledged that viruses causing influenza peak during cold temperatures and gradually subside in the warmer temperature, owing to their seasonality.\nThus, COVID-19, due to its regular flu-like symptoms, is also expected to show similar seasonality and subside as the global temperatures rise in the northern hemisphere with the onset of spring.\nDespite these speculations, however, the systematic analysis in the global perspective of the relation between COVID-19 spread and meteorological parameters is unavailable.\nHere, by analyzing the region- and city-specific affected global data and corresponding meteorological parameters, we show that there is an optimum range of temperature and UV index strongly affecting the spread and survival of the virus, whereas precipitation, relative humidity, cloud cover, etc. have no effect on the virus.\nUnavailability of pharmaceutical interventions would require greater preparedness and alert for the effective control of COVID-19.\nUnder these conditions, the information provided here could be very helpful for the global community struggling to fight this global crisis.\nIt is, however, important to note that the information presented here clearly lacks any physiological evidences, which may merit further investigation.\nThus, any attempt for management, implementation, and evaluation strategies responding to the crisis arising due to the COVID-19 outbreak must not consider the evaluation presented here as the foremost factor.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 154}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: the symptoms of COVID-19 are no worse than a cold\n\nAbstract:\nMost patients with COVID-19 lack antibody to SARS-CoV-2 in the first 10 days of illness while the virus drives disease pathogenesis.\nSARS-CoV-2 antibody deficiency in the setting of a tissue viral burden suggests that using an antibody as a therapeutic agent would augment the antiviral immune response.\nIn this issue of the JCI, Wang and collaborators describe the kinetics of viral load and antibody responses of 23 individuals with COVID-19 with mild and severe disease.\nThe researchers found: 1) individuals with mild and severe disease produced neutralizing IgG to SARS-CoV-2 10 days after disease onset; 2) SARS-CoV-2 persisted longer in those with severe disease; and 3) there was cross-reactivity between antibodies to SARS-CoV-1 and SARS-CoV-2, but only antibodies from patients with COVID-19 neutralized SARS-CoV-2.\nThese observations provide important information on the serological response to SARS-CoV-2 of hospitalized patients with COVID-19 that can inform the use of convalescent plasma therapy.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"SARS-CoV-2 antibody deficiency in the setting of a tissue viral burden suggests that using an antibody as a therapeutic agent would augment the antiviral immune response.\", \"The researchers found: 1) individuals with mild and severe disease produced neutralizing IgG to SARS-CoV-2 10 days after disease onset; 2) SARS-CoV-2 persisted longer in those with severe disease; and 3) there was cross-reactivity between antibodies to SARS-CoV-1 and SARS-CoV-2, but only antibodies from patients with COVID-19 neutralized SARS-CoV-2.\", \"These observations provide important information on the serological response to SARS-CoV-2 of hospitalized patients with COVID-19 that can inform the use of convalescent plasma therapy.\"]}", "id": 155}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Ultraviolet lamps kill the COVID-19 virus.\n\nAbstract:\nAs the world economies get out of the lockdown imposed by the COVID-19 pandemic, there is an urgent need to assess the suitability of known technologies to mitigate COVID-19 transmission in confined spaces such as buildings.\nThis feasibility study looks at the method of upper-room ultraviolet (UV) air disinfection that has already proven its efficacy in preventing the transmission of airborne diseases such as measles and tuberculosis.\nUsing published data from various sources it is shown that the SARS-CoV-2 virus, which causes COVID-19, is highly likely to be susceptible to UV damage while suspended in air irradiated by UV-C at levels that are acceptable and safe for upper-room applications.\nThis is while humans are present in the room.\nBoth the expected and worst-case scenarios are investigated to show the efficacy of the upper-room UV-C approach to reduce COVID-19 air transmission in a confined space with moderate but sufficient height.\nDiscussion is given on the methods of analysis and the differences between virus susceptibility to UV-C when aerosolised or in liquid or on a surface.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Using published data from various sources it is shown that the SARS-CoV-2 virus, which causes COVID-19, is highly likely to be susceptible to UV damage while suspended in air irradiated by UV-C at levels that are acceptable and safe for upper-room applications.\"]}", "id": 156}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Evidence is currently lacking and it is too early to make robust conclusions on any link between use of angiotensin-converting enzyme (ACE) inhibitors and angiotensin II type-I receptor blockers with risk or severity of novel coronavirus disease 2019 (COVID-19) infection.\n\nAbstract:\nAngiotensin-converting enzyme (ACE) inhibitors (ACEIs) and angiotensin II type\u00ad1 receptor blockers (ARBs) are among the most widely prescribed drugs for the treatment of arterial hypertension, heart failure and chronic kidney disease.\nA number of studies, mainly in animals and not involving the lungs, have indicated that these drugs can increase expression of angiotensin-converting enzyme 2 (ACE2).\nACE2 is the cell entry receptor of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of coronavirus disease 2019 (COVID-19) that is currently battering the globe.\nThis has led to the hypothesis that use of ACEIs and ARBs may increase the risk of developing severe COVID-19.\nIn this point of view paper, possible scenarios regarding the impact of ACEI/ARB pharmacotherapy on COVID-19 are discussed in relation to the currently available evidence.\nAlthough further research on the influence of blood-pressure-lowering drugs, including those not targeting the renin-angiotensin system, is warranted, there are presently no compelling clinical data showing that ACEIs and ARBs increase the likelihood of contracting COVID-19 or worsen the outcome of SARS-CoV\u00ad2 infections.\nThus, unless contraindicated, use of ACEIs/ARBs in COVID-19 patients should be continued in line with the recent recommendations of medical societies.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 157}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Drugs widely used to treat high blood pressure appear to make COVID-19 dangerously worse.\n\nAbstract:\nAngiotensin-converting enzyme (ACE) inhibitors (ACEIs) and angiotensin II type\u00ad1 receptor blockers (ARBs) are among the most widely prescribed drugs for the treatment of arterial hypertension, heart failure and chronic kidney disease.\nA number of studies, mainly in animals and not involving the lungs, have indicated that these drugs can increase expression of angiotensin-converting enzyme 2 (ACE2).\nACE2 is the cell entry receptor of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of coronavirus disease 2019 (COVID-19) that is currently battering the globe.\nThis has led to the hypothesis that use of ACEIs and ARBs may increase the risk of developing severe COVID-19.\nIn this point of view paper, possible scenarios regarding the impact of ACEI/ARB pharmacotherapy on COVID-19 are discussed in relation to the currently available evidence.\nAlthough further research on the influence of blood-pressure-lowering drugs, including those not targeting the renin-angiotensin system, is warranted, there are presently no compelling clinical data showing that ACEIs and ARBs increase the likelihood of contracting COVID-19 or worsen the outcome of SARS-CoV\u00ad2 infections.\nThus, unless contraindicated, use of ACEIs/ARBs in COVID-19 patients should be continued in line with the recent recommendations of medical societies.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"This has led to the hypothesis that use of ACEIs and ARBs may increase the risk of developing severe COVID-19.\"]}", "id": 158}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: No, garlic doesn't cure coronavirus. \n\nAbstract:\nOBJECTIVE To analyze the characteristics of YouTube videos in Spanish on the basic measures to prevent coronavirus disease 2019 (COVID-19).\nMETHODS On 18 March 2020, a search was conducted on YouTube using the terms \"Prevencion Coronavirus\" and \"Prevencion COVID-19\".\nWe studied the associations between the type of authorship and the country of publication with other variables (such as the number of likes and basic measures to prevent COVID-19 according to the World Health Organization, among others) with univariate analysis and a multiple logistic regression model.\nRESULTS A total of 129 videos were evaluated; 37.2% were produced in Mexico (25.6%) and Spain (11.6%), and 56.6% were produced by mass media, including television and newspapers.\nThe most frequently reported basic preventive measure was hand washing (71.3%), and the least frequent was not touching the eyes, nose, and mouth (24.0%).\nHoaxes (such as eating garlic or citrus to prevent COVID-19) were detected in 15 videos (10.9%).\nIn terms of authorship, papers produced by health professionals had a higher probability of reporting hand hygiene (OR (95% CI) = 4.20 (1.17-15.09)) and respiratory hygiene (OR (95% CI) = 3.05 (1.22-7.62)) as preventive measures.\nCONCLUSION Information from YouTube in Spanish on basic measures to prevent COVID-19 is usually not very complete and differs according to the type of authorship.\nOur findings make it possible to guide Spanish-speaking users on the characteristics of the videos to be viewed in order to obtain reliable information.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Hoaxes (such as eating garlic or citrus to prevent COVID-19) were detected in 15 videos (10.9%).\"]}", "id": 159}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: the risk of pets spreading Covid-19 to humans is considered 'medium'\n\nAbstract:\nCoronavirus disease-19 (COVID-19) caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is now a pandemic threat.\nThis virus is supposed to be spread by human to human transmission.\nCellular angiotensin-converting enzyme 2 (ACE2) is the receptor of SARS-CoV-2 which is identical or similar in different species of animals such as pigs, ferrets, cats, orangutans, monkeys, and humans.\nMoreover, a recent study predicted that dogs might be secondary hosts during the evolution of SARS-CoV-2 from bat to human.\nTherefore, there is a possibility of spreading SARS-CoV-2 through domestic pets.\nThere are now many reports of SARS-CoV-2 positive cases in dogs, cats, tigers, lion, and minks.\nExperimental data showed ferrets and cats are highly susceptible to SARS-CoV-2 as infected by virus inoculation and can transmit the virus directly or indirectly by droplets or airborne routes.\nBased on these natural infection reports and experimental data, whether the pets are responsible for SARS-CoV-2 spread to humans; needs to be deeply investigated.\nHumans showing clinical symptoms of respiratory infections have been undergoing for the COVID-19 diagnostic test but many infected people and few pets confirmed with SARS-CoV-2 remained asymptomatic.\nIn this review, we summarize the natural cases of SARS-CoV-2 in animals with the latest researches conducted in this field.\nThis review will be helpful to think insights of SARS-CoV-2 transmissions, spread, and demand for seroprevalence studies, especially in companion animals.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Coronavirus disease-19 (COVID-19) caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is now a pandemic threat.\", \"This virus is supposed to be spread by human to human transmission.\"]}", "id": 160}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: The N95 respirator is thicker than a surgical mask, but neither Schaffner\n\nAbstract:\nIn the context of Coronavirus Disease (2019) (COVID-19) cases globally, there is a lack of consensus across cultures on whether wearing face masks is an effective physical intervention against disease transmission.\nThis study 1) illustrates transmission routes of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2); 2) addresses controversies surrounding the mask from perspectives of attitude, effectiveness, and necessity of wearing the mask with evidence that the use of mask would effectively interrupt the transmission of infectious diseases in both hospital settings and community settings; and 3) provides suggestion that the public should wear the mask during COVID-19 pandemic according to local context.\nTo achieve this goal, government should establish a risk adjusted strategy of mask use to scientifically publicize the use of masks, guarantee sufficient supply of masks, and cooperate for reducing health resources inequities.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 161}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Vitamin D Supplementation Could Reduce Risk of Influenza and COVID-19 Infections and Deaths\n\nAbstract:\nThe severity of coronavirus 2019 infection (COVID-19) is determined by the presence of pneumonia, severe acute respiratory distress syndrome (SARS-CoV-2), myocarditis, microvascular thrombosis and/or cytokine storms, all of which involve underlying inflammation.\nA principal defence against uncontrolled inflammation, and against viral infection in general, is provided by T regulatory lymphocytes (Tregs).\nTreg levels have been reported to be low in many COVID-19 patients and can be increased by vitamin D supplementation.\nLow vitamin D levels have been associated with an increase in inflammatory cytokines and a significantly increased risk of pneumonia and viral upper respiratory tract infections.\nVitamin D deficiency is associated with an increase in thrombotic episodes, which are frequently observed in COVID-19.\nVitamin D deficiency has been found to occur more frequently in patients with obesity and diabetes.\nThese conditions are reported to carry a higher mortality in COVID-19.\nIf vitamin D does in fact reduce the severity of COVID-19 in regard to pneumonia/ARDS, inflammation, inflammatory cytokines and thrombosis, it is our opinion that supplements would offer a relatively easy option to decrease the impact of the pandemic.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"A principal defence against uncontrolled inflammation, and against viral infection in general, is provided by T regulatory lymphocytes (Tregs).\", \"Treg levels have been reported to be low in many COVID-19 patients and can be increased by vitamin D supplementation.\", \"Low vitamin D levels have been associated with an increase in inflammatory cytokines and a significantly increased risk of pneumonia and viral upper respiratory tract infections.\", \"Vitamin D deficiency is associated with an increase in thrombotic episodes, which are frequently observed in COVID-19.\", \"Vitamin D deficiency has been found to occur more frequently in patients with obesity and diabetes.\", \"These conditions are reported to carry a higher mortality in COVID-19.\", \"If vitamin D does in fact reduce the severity of COVID-19 in regard to pneumonia/ARDS, inflammation, inflammatory cytokines and thrombosis, it is our opinion that supplements would offer a relatively easy option to decrease the impact of the pandemic.\"]}", "id": 162}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: While there are a lot of unknowns with the novel virus, there's no evidence of COVID-19 transmitting through food or packaging, according to the Food and Drug Administration.\n\nAbstract:\nThe current outbreak of the novel coronavirus disease 2019 (COVID-19) in more than 250 countries has become a serious threat to the health of people around the world.\nHuman-to-human transmission of the Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) occurs most often when people are in the incubation stage of the disease or are carriers and have no symptoms.\nTherefore, in this study, was discussed the role of environmental factors and conditions such as temperature, humidity, wind speed as well as food, water and sewage, air, insects, inanimate surfaces, and hands in COVID-19 transmission.\nThe results of studies on the stability of the SARS-CoV-2 on different levels showed that the resistance of this virus on smooth surfaces was higher than others.\nTemperature increase and sunlight can facilitate the destruction of SARS-COV-2 and the stability of it on surfaces.\nWhen the minimum ambient air temperature increases by 1 \u00b0C, the cumulative number of cases decreases by 0.86%.\nAccording to the latest evidence, the presence of coronavirus in the sewer has been confirmed, but there is no evidence that it is transmitted through sewage or contaminated drinking water.\nAlso, SARS-COV-2 transmission through food, food packages, and food handlers has not been identified as a risk factor for the disease.\nAccording to the latest studies, the possibility of transmitting SARS-COV-2 bioaerosol through the air has been reported in the internal environment of ophthalmology.\nThe results additionally show that infectious bio-aerosols can move up to 6 feet.\nThere have been no reports of SARS-COV-2 transmission by blood-feeding arthropods such as mosquitoes.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Also, SARS-COV-2 transmission through food, food packages, and food handlers has not been identified as a risk factor for the disease.\"]}", "id": 163}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: A consensus covid-19 immune signature combines immuno-protection with discrete sepsis-like traits associated with poor prognosis\n\nAbstract:\nPerson-to-person transmission of SARS-CoV-2 virus has triggered a global emergency because of its potential to cause life-threatening Covid-19 disease.\nBy comparison to pauci-symptomatic virus clearance by most individuals, Covid-19 has been proposed to reflect insufficient and/or pathologically exaggerated immune responses.\nHere we identify a consensus peripheral blood immune signature across 63 hospital-treated Covid-19 patients who were otherwise highly heterogeneous.\nThe core signature conspicuously blended adaptive B cell responses typical of virus infection or vaccination with discrete traits hitherto associated with sepsis, including monocyte and dendritic cell dampening, and hyperactivation and depletion of discrete T cell subsets.\nThis blending of immuno-protective and immuno-pathogenic potentials was exemplified by near-universal CXCL10/IP10 upregulation, as occurred in SARS1 and MERS.\nMoreover, specific parameters including CXCL10/IP10 over-expression, T cell proliferation, and basophil and plasmacytoid dendritic cell depletion correlated, often prognostically, with Covid-19 progression, collectively composing a resource to inform SARS-CoV-2 pathobiology and risk-based patient stratification.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"The core signature conspicuously blended adaptive B cell responses typical of virus infection or vaccination with discrete traits hitherto associated with sepsis, including monocyte and dendritic cell dampening, and hyperactivation and depletion of discrete T cell subsets.\"]}", "id": 164}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: ARVs can treat Covid-19\n\nAbstract:\nThe severity of coronavirus disease 2019 (COVID-19) infection is quite variable and the manifestations varies from asymptomatic disease to severe acute respiratory infection.\nFever, dry cough, dyspnea, myalgia, fatigue, loss of appetite, olfactory and gustatory dysfunctions are the most prevalent general symptoms.\nDecreased immune system cells such as suppressed regulatory T cells, cytotoxic and helper T cells, natural killer cells, monocytes/macrophages and increased proinflammatory cytokines are the characteristic features.\nCompounds derived from Allium sativum (garlic) have the potential to decrease the expression of proinflammatory cytokines and to reverse the immunological abnormalities to more acceptable levels.\nAllium sativum is suggested as a beneficial preventive measure before being infected with SARS-CoV-2 virus.\nAllium sativum is a functional food well-known for its immunomodulatory, antimicrobial, antiinflammatory, antimutagenic, antitumor properties.\nIts antiviral efficiency was also demonstrated.\nSome constituents of this plant were found to be active against protozoan parasites.\nWithin this context, it appears to reverse most immune system dysfunctions observed in patients with COVID-19 infection.\nThe relations among immune system parameters, leptin, leptin receptor, adenosin mono phosphate-activated protein kinase, peroxisome proliferator activated receptor-gamma have also been interpreted.\nLeptin's role in boosting proinflammatory cytokines and in appetite decreasing suggest the possible beneficial effect of decreasing the concentration of this proinflammatory adipose tissue hormone in relieving some symptoms detected during COVID-19 infection.\nIn conclusion, Allium sativum may be an acceptable preventive measure against COVID-19 infection to boost immune system cells and to repress the production and secretion of proinflammatory cytokines as well as an adipose tissue derived hormone leptin having the proinflammatory nature.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 165}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Further, the report enlightened on the use of vitamin D on COVID-19 patients\n\nAbstract:\nThe severity of coronavirus 2019 infection (COVID-19) is determined by the presence of pneumonia, severe acute respiratory distress syndrome (SARS-CoV-2), myocarditis, microvascular thrombosis and/or cytokine storms, all of which involve underlying inflammation.\nA principal defence against uncontrolled inflammation, and against viral infection in general, is provided by T regulatory lymphocytes (Tregs).\nTreg levels have been reported to be low in many COVID-19 patients and can be increased by vitamin D supplementation.\nLow vitamin D levels have been associated with an increase in inflammatory cytokines and a significantly increased risk of pneumonia and viral upper respiratory tract infections.\nVitamin D deficiency is associated with an increase in thrombotic episodes, which are frequently observed in COVID-19.\nVitamin D deficiency has been found to occur more frequently in patients with obesity and diabetes.\nThese conditions are reported to carry a higher mortality in COVID-19.\nIf vitamin D does in fact reduce the severity of COVID-19 in regard to pneumonia/ARDS, inflammation, inflammatory cytokines and thrombosis, it is our opinion that supplements would offer a relatively easy option to decrease the impact of the pandemic.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"A principal defence against uncontrolled inflammation, and against viral infection in general, is provided by T regulatory lymphocytes (Tregs).\", \"Treg levels have been reported to be low in many COVID-19 patients and can be increased by vitamin D supplementation.\", \"Low vitamin D levels have been associated with an increase in inflammatory cytokines and a significantly increased risk of pneumonia and viral upper respiratory tract infections.\", \"Vitamin D deficiency is associated with an increase in thrombotic episodes, which are frequently observed in COVID-19.\", \"Vitamin D deficiency has been found to occur more frequently in patients with obesity and diabetes.\", \"These conditions are reported to carry a higher mortality in COVID-19.\", \"If vitamin D does in fact reduce the severity of COVID-19 in regard to pneumonia/ARDS, inflammation, inflammatory cytokines and thrombosis, it is our opinion that supplements would offer a relatively easy option to decrease the impact of the pandemic.\"]}", "id": 166}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Chadox1 ncov-19 vaccination induced sars-cov-2 pneumonia in rhesus macaques\n\nAbstract:\nSevere acute respiratory syndrome coronavirus-2 (SARS-CoV-2) emerged in December 20191,2 and is responsible for the COVID-19 pandemic3.\nVaccines are an essential countermeasure urgently needed to control the pandemic4.\nHere, we show that the adenovirus-vectored vaccine ChAdOx1 nCoV-19, encoding the spike protein of SARS-CoV-2, is immunogenic in mice, eliciting a robust humoral and cell-mediated response.\nThis response was not Th2 dominated, as demonstrated by IgG subclass and cytokine expression profiling.\nA single vaccination with ChAdOx1 nCoV-19 induced a humoral and cellular immune response in rhesus macaques.\nWe observed a significantly reduced viral load in bronchoalveolar lavage fluid and respiratory tract tissue of vaccinated animals challenged with SARS-CoV-2 compared with control animals, and no pneumonia was observed in vaccinated rhesus macaques.\nImportantly, no evidence of immune-enhanced disease following viral challenge in vaccinated animals was observed.\nChAdOx1 nCoV-19 is currently under investigation in a phase I clinical trial.\nSafety, immunogenicity and efficacy against symptomatic PCR-positive COVID-19 disease will now be assessed in randomised controlled human clinical trials.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"A single vaccination with ChAdOx1 nCoV-19 induced a humoral and cellular immune response in rhesus macaques.\", \"We observed a significantly reduced viral load in bronchoalveolar lavage fluid and respiratory tract tissue of vaccinated animals challenged with SARS-CoV-2 compared with control animals, and no pneumonia was observed in vaccinated rhesus macaques.\"]}", "id": 167}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Pets can Spread Coronavirus (COVID-19) to People\n\nAbstract:\nAbstract Coronavirus disease-19 (COVID-19) caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is now a pandemic threat.\nThis virus is supposed to be spread by human to human transmission.\nCellular angiotensin converting enzyme 2 (ACE2) is the receptor of SARS-CoV-2 which is identical or similar in different species of animals such as pigs, ferrets, cats, orangutans, monkeys, and humans.\nMoreover, a recent study predicted that dog might be secondary host during the evolution of SARS-CoV-2 from bat to human.\nTherefore, there is a possibility of spreading SARS-CoV-2 through domestic pets.\nThere are now many reports of SARS-CoV-2 positive cases in dogs, cats, tigers, lion, and minks.\nExperimental data showed ferrets and cats are highly susceptible to SARS-CoV-2 as infected by virus inoculation and can transmit the virus directly or indirectly by droplets or airborne route.\nBased on these natural infection reports and experimental data, whether the pets are responsible for SARS-CoV-2 spread to human; needs to be deeply investigated.\nHumans showing clinical symptoms of respiratory infections have been undergoing for COVID-19 diagnostic test but many infected people and few pets confirmed with SARS-CoV-2 remained asymptomatic.\nIn this review, we summarize the natural cases of SARS-CoV-2 in animals with the latest researches conducted in this field.\nThis review will be helpful to think insights of SARS-CoV-2 transmissions, spread, and demand for sero-prevalence studies especially in companion animals.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Cellular angiotensin converting enzyme 2 (ACE2) is the receptor of SARS-CoV-2 which is identical or similar in different species of animals such as pigs, ferrets, cats, orangutans, monkeys, and humans.\", \"Moreover, a recent study predicted that dog might be secondary host during the evolution of SARS-CoV-2 from bat to human.\", \"Therefore, there is a possibility of spreading SARS-CoV-2 through domestic pets.\", \"Experimental data showed ferrets and cats are highly susceptible to SARS-CoV-2 as infected by virus inoculation and can transmit the virus directly or indirectly by droplets or airborne route.\"]}", "id": 168}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Sterilizing immunity against sars-cov-2 infection in humans by a single-shot and modified imidazoquinoline tlr7/8 agonist-adjuvanted recombinant spike protein vaccine\n\nAbstract:\nThe search for vaccines that protect from severe morbidity and mortality as a result of infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus that causes coronavirus disease 2019 (COVID-19) is a race against the clock and the virus.\nSeveral vaccine candidates are currently being tested in the clinic.\nInactivated virus and recombinant protein vaccines can be safe options but may require adjuvants to induce robust immune responses efficiently.\nIn this work we describe the use of a novel amphiphilic imidazoquinoline (IMDQ-PEG-CHOL) TLR7/8 adjuvant, consisting of an imidazoquinoline conjugated to the chain end of a cholesterol-poly(ethylene glycol) macromolecular amphiphile).\nThis amphiphile is water soluble and exhibits massive translocation to lymph nodes upon local administration, likely through binding to albumin.\nIMDQ-PEG-CHOL is used to induce a protective immune response against SARS-CoV-2 after single vaccination with trimeric recombinant SARS-CoV-2 spike protein in the BALB/c mouse model.\nInclusion of amphiphilic IMDQ-PEG-CHOL in the SARS-CoV-2 spike vaccine formulation resulted in enhanced immune cell recruitment and activation in the draining lymph node.\nIMDQ-PEG-CHOL has a better safety profile compared to native soluble IMDQ as the former induces a more localized immune response upon local injection, preventing systemic inflammation.\nMoreover, IMDQ-PEG-CHOL adjuvanted vaccine induced enhanced ELISA and in vitro microneutralization titers, and a more balanced IgG2a/IgG1 response.\nTo correlate vaccine responses with control of virus replication in vivo, vaccinated mice were challenged with SARS-CoV-2 virus after being sensitized by intranasal adenovirus-mediated expression of the human angiotensin converting enzyme 2 (ACE2) gene.\nAnimals vaccinated with trimeric recombinant spike protein vaccine without adjuvant had lung virus titers comparable to non-vaccinated control mice, whereas animals vaccinated with IMDQ-PEG-CHOL-adjuvanted vaccine controlled viral replication and infectious viruses could not be recovered from their lungs at day 4 post infection.\nIn order to test whether IMDQ-PEG-CHOL could also be used to adjuvant vaccines currently licensed for use in humans, proof of concept was also provided by using the same IMDQ-PEG-CHOL to adjuvant human quadrivalent inactivated influenza virus split vaccine, which resulted in enhanced hemagglutination inhibition titers and a more balanced IgG2a/IgG1 antibody response.\nEnhanced influenza vaccine responses correlated with better virus control when mice were given a lethal influenza virus challenge.\nOur results underscore the potential use of IMDQ-PEG-CHOL as an adjuvant to achieve protection after single immunization with recombinant protein and inactivated vaccines against respiratory viruses, such as SARS-CoV-2 and influenza viruses.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"IMDQ-PEG-CHOL is used to induce a protective immune response against SARS-CoV-2 after single vaccination with trimeric recombinant SARS-CoV-2 spike protein in the BALB/c mouse model.\"]}", "id": 169}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Hand washing with soap and water and scrubbing for at least 20 seconds is recommended, but a hand sanitizer that contains at least 60% alcohol is the best alternative\n\nAbstract:\nBACKGROUND: The emergence of the novel virus, SARS-CoV-2, has posed unprecedented challenges to public health around the world.\nCurrently, strategies to deal with COVID-19 are purely supportive and preventative, aimed at reducing transmission.\nAn effective and simple method for reducing transmission of infections in the public or healthcare settings is hand hygiene.\nUnfortunately, little is known regarding the efficacy of hand sanitizers against SARS-CoV-2.\nMETHODS: In this review, an extensive literature search was performed to succinctly summarize the primary active ingredients and mechanisms of action of hand sanitizers, compare the effectiveness and compliance of gel and foam sanitizers, and predict whether alcohol and non-alcohol hand sanitizers would be effective against SARS-CoV-2.\nRESULTS: Most alcohol based hand sanitizers are effective at inactivating enveloped viruses, including coronaviruses.\nWith what is currently known in the literature, one may not confidently suggest one mode of hand sanitizing delivery over the other.\nWhen hand washing with soap and water is unavailable, a sufficient volume of sanitizer is necessary to ensure complete hand coverage, and compliance is critical for appropriate hand hygiene.\nCONCLUSIONS: By extrapolating effectiveness of hand sanitizers on viruses of similar structure to SARS-CoV-2, this virus should be effectively inactivated with current hand hygiene products, though future research should attempt to determine this directly.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"RESULTS: Most alcohol based hand sanitizers are effective at inactivating enveloped viruses, including coronaviruses.\", \"When hand washing with soap and water is unavailable, a sufficient volume of sanitizer is necessary to ensure complete hand coverage, and compliance is critical for appropriate hand hygiene.\", \"CONCLUSIONS: By extrapolating effectiveness of hand sanitizers on viruses of similar structure to SARS-CoV-2, this virus should be effectively inactivated with current hand hygiene products, though future research should attempt to determine this directly.\"]}", "id": 170}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: wearing a mask helps stop infected people from spreading the new coronavirus to others.\n\nAbstract:\nEvidence that face masks provide effective protection against respiratory infections in the community is scarce.\nHowever, face masks are widely used by health workers as part of droplet precautions when caring for patients with respiratory infections.\nIt would therefore be reasonable to suggest that consistent widespread use of face masks in the community could prevent further spread of the Severe Acute Respiratory Syndrome-Coronavirus 2 (SARS-CoV-2).\nIn this study we examine public face mask wearing in Uganda where a proportion wears masks to protect against acquiring, and the other to prevent from transmitting SARS-CoV-2.\nThe objective of this study was to determine what percentage of the population would have to wear face masks to reduce susceptibility to and infectivity of COVID-19 in Uganda, keeping the basic reproduction number below unity and/or flattening the curve.\nWe used an SEIAQRD model for the analysis.\nResults show that implementation of facemasks has a relatively large impact on the size of the coronavirus epidemic in Uganda.\nWe find that the critical mask adherence is 5 per 100 when 80% wear face masks.\nA cost-effective analysis shows that utilizing funds to provide 1 public mask to the population has a per capita compounded cost of USD 1.34.\nIf provision of face masks is done simultaneously with supportive care, the per capita compounded cost is USD 1.965, while for the case of only treatment and no provision of face masks costs each Ugandan USD 4.0579.\nWe conclude that since it is hard to achieve a 100% adherence to face masks, government might consider provision of face masks in conjunction with provision of care.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"It would therefore be reasonable to suggest that consistent widespread use of face masks in the community could prevent further spread of the Severe Acute Respiratory Syndrome-Coronavirus 2 (SARS-CoV-2).\", \"We find that the critical mask adherence is 5 per 100 when 80% wear face masks.\"]}", "id": 171}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Antibody responses to sars-cov2 are distinct in children with mis-c compared to adults with covid-19\n\nAbstract:\nClinical manifestations of COVID-19 caused by the novel coronavirus SARS-CoV-2 are associated with age.\nWhile children are largely spared from severe respiratory disease, they can present with a SARS-CoV-2-associated multisystem inflammatory syndrome (MIS-C) similar to Kawasaki's disease.\nHere, we show distinct antibody (Ab) responses in children with MIS-C compared to adults with severe COVID-19 causing acute respiratory distress syndrome (ARDS), and those who recovered from mild disease.\nThere was a reduced breadth and specificity of anti-SARS-CoV-2-specific antibodies in MIS-C patients compared to the COVID patient groups; MIS-C predominantly generated IgG Abs specific for the Spike (S) protein but not for the nucleocapsid (N) protein, while both COVID-19 cohorts had anti-S IgG, IgM and IgA Abs, as well as anti-N IgG Abs.\nMoreover, MIS-C patients had reduced neutralizing activity compared to COVID-19 cohorts, indicating a reduced protective serological response.\nThese results suggest a distinct infection course and immune response in children and adults who develop severe disease, with implications for optimizing treatments based on symptom and age.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Here, we show distinct antibody (Ab) responses in children with MIS-C compared to adults with severe COVID-19 causing acute respiratory distress syndrome (ARDS), and those who recovered from mild disease.\", \"These results suggest a distinct infection course and immune response in children and adults who develop severe disease, with implications for optimizing treatments based on symptom and age.\"]}", "id": 172}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: The sars-cov-2 spike mutation d614g reduces entry fitness across a range of ace2 levels, directly outcompetes the wild type, and is preferentially incorporated into trimers\n\nAbstract:\nEarly in the current pandemic, the D614G mutation arose in the Spike protein of SARS-CoV-2 and quickly became the dominant variant globally.\nMounting evidence suggests D614G enhances viral entry.\nHere we use a direct competition assay with single-cycle viruses to show that D614G outcompetes the wildtype.\nWe developed a cell line with inducible ACE2 expression to confirm that D614G more efficiently enters cells with ACE2 levels spanning the different primary cells targeted by SARS-CoV-2.\nUsing a new assay for crosslinking and directly extracting Spike trimers from the pseudovirus surface, we found an increase in trimerization efficiency and viral incorporation of D614G protomers.\nOur findings suggest that D614G increases infection of cells expressing a wide range of ACE2, and informs the mechanism underlying enhanced entry.\nThe tools developed here can be broadly applied to study other Spike variants and SARS-CoV-2 entry, to inform functional studies of viral evolution and vaccine development.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Mounting evidence suggests D614G enhances viral entry.\", \"We developed a cell line with inducible ACE2 expression to confirm that D614G more efficiently enters cells with ACE2 levels spanning the different primary cells targeted by SARS-CoV-2.\", \"Using a new assay for crosslinking and directly extracting Spike trimers from the pseudovirus surface, we found an increase in trimerization efficiency and viral incorporation of D614G protomers.\"]}", "id": 173}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Multivalency transforms sars-cov-2 antibodies into specific and ultrapotent neutralizers\n\nAbstract:\nThe novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes Coronavirus Disease 2019 (COVID-19), has caused a global pandemic.\nAntibodies are powerful biotherapeutics to fight viral infections; however, discovery of the most potent and broadly acting clones can be lengthy.\nHere, we used the human apoferritin protomer as a modular subunit to drive oligomerization of antibody fragments and transform antibodies targeting SARS-CoV-2 into exceptionally potent neutralizers.\nUsing this platform, half-maximal inhibitory concentration (IC50) values as low as 9 \u00d7 10\u221214 M were achieved as a result of up to 10,000-fold potency enhancements.\nCombination of three different antibody specificities and the fragment crystallizable (Fc) domain on a single multivalent molecule conferred the ability to overcome viral sequence variability together with outstanding potency and Ig-like in vivo bioavailability.\nThis MULTi-specific, multi-Affinity antiBODY (Multabody; or MB) platform contributes a new class of medical countermeasures against COVID-19 and an efficient approach to rapidly deploy potent and broadly-acting therapeutics against infectious diseases of global health importance.\nOne Sentence Summary multimerization platform transforms antibodies emerging from discovery screens into potent neutralizers that can overcome SARS-CoV-2 sequence diversity.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Here, we used the human apoferritin protomer as a modular subunit to drive oligomerization of antibody fragments and transform antibodies targeting SARS-CoV-2 into exceptionally potent neutralizers.\", \"This MULTi-specific, multi-Affinity antiBODY (Multabody; or MB) platform contributes a new class of medical countermeasures against COVID-19 and an efficient approach to rapidly deploy potent and broadly-acting therapeutics against infectious diseases of global health importance.\", \"One Sentence Summary multimerization platform transforms antibodies emerging from discovery screens into potent neutralizers that can overcome SARS-CoV-2 sequence diversity.\"]}", "id": 174}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Hand washing with soap and water and scrubbing for at least 20 seconds is recommended, but a hand sanitizer that contains at least 60% alcohol is the best alternative\n\nAbstract:\nInfection by coronavirus (CoV-19) has led to emergence of a pandemic called as Coronavirus Disease (COVID-19) that has so far affected about 210 countries.\nThe dynamic data indicate that the pandemic by CoV-19 so far has infected 2,403,963 individuals, and among these 624,698 have recovered while, it has been fatal for 165,229.\nWithout much experience, currently, the medicines that are clinically being evaluated for COVID-19 include chloroquine, hydroxychloroquine, azithromycin, tocilizumab, lopinavir, ritonavir, tocilizumab and corticosteroids.\nTherefore, countries such as Italy, USA, Spain and France with the most advanced health care system are partially successful to control CoV-19 infection.\nIndia being the 2nd largest populous country, where, the healthcare system is underdeveloped, major portion of population follow unhygienic lifestyle, is able to restrict the rate of both infection and death of its citizens from COVID-19.\nIndia has followed an early and a very strict social distancing by lockdown and has issued advisory to clean hands regularly by soap and/or by alcohol based sterilizers.\nRolling data on the global index of the CoV infection is 13,306, and the index of some countries such as USA (66,148), Italy (175,055), Spain (210,126), France (83,363) and Switzerland (262,122) is high.\nThe index of India has remained very low (161) so far, mainly due to early implementation of social lockdown, social distancing, and sanitizing hands.\nHowever, articles on social lockdown as a preventive measure against COVID-19 in PubMed are scanty.\nIt has been observed that social lockdown has also drastic impacts on the environment especially on reduction of NO2 and CO2 emission.\nSlow infection rate under strict social distancing will offer time to researchers to come up with exact medicines/vaccines against CoV-19.\nTherefore, it is concluded that stringent social distancing via lockdown is highly important to control COVID-19 and also to contribute for self-regeneration of nature.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"The index of India has remained very low (161) so far, mainly due to early implementation of social lockdown, social distancing, and sanitizing hands.\"]}", "id": 175}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Residual sars-cov-2 viral antigens detected in gastrointestinal and hepatic tissues from two recovered covid-19 patients\n\nAbstract:\nResidual SARS-CoV-2 RNA has been detected in stool samples and gastrointestinal tissues during the convalescence phase of COVID-19 infection.\nThis raises concern for persistence of SARS-CoV-2 virus particles and faecal-oral transmissibility in recovered COVID-19 patients.\nUsing multiplex immunohistochemistry, we unexpectedly detected SARS-CoV-2 viral antigens in intestinal and liver tissues, in surgical samples obtained from two patients who recovered from COVID-19.\nWe further validated the presence of virus by RT-PCR and flow cytometry to detect SARS-CoV-2-specific immunity in the tissues.\nThese findings might have important implications in terms of disease management and public health policy regarding transmission of COVID-19 via faecal-oral and iatrogenic routes during the convalescence phase.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"This raises concern for persistence of SARS-CoV-2 virus particles and faecal-oral transmissibility in recovered COVID-19 patients.\", \"Using multiplex immunohistochemistry, we unexpectedly detected SARS-CoV-2 viral antigens in intestinal and liver tissues, in surgical samples obtained from two patients who recovered from COVID-19.\", \"We further validated the presence of virus by RT-PCR and flow cytometry to detect SARS-CoV-2-specific immunity in the tissues.\", \"These findings might have important implications in terms of disease management and public health policy regarding transmission of COVID-19 via faecal-oral and iatrogenic routes during the convalescence phase.\"]}", "id": 176}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Safety and immunogenicity study of 2019-ncov vaccine to prevent sars-cov-2 infection\n\nAbstract:\nBACKGROUND: The ongoing COVID-19 pandemic warrants accelerated efforts to test vaccine candidates.\nWe aimed to assess the safety and immunogenicity of an inactivated severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccine candidate, BBIBP-CorV, in humans.\nMETHODS: We did a randomised, double-blind, placebo-controlled, phase 1/2 trial at Shangqiu City Liangyuan District Center for Disease Control and Prevention in Henan Province, China.\nIn phase 1, healthy people aged 18-80 years, who were negative for serum-specific IgM/IgG antibodies against SARS-CoV-2 at the time of screening, were separated into two age groups (18-59 years and &#8805;60 years) and randomly assigned to receive vaccine or placebo in a two-dose schedule of 2 \u00b5g, 4 \u00b5g, or 8 \u00b5g on days 0 and 28.\nIn phase 2, healthy adults (aged 18-59 years) were randomly assigned (1:1:1:1) to receive vaccine or placebo on a single-dose schedule of 8 \u00b5g on day 0 or on a two-dose schedule of 4 \u00b5g on days 0 and 14, 0 and 21, or 0 and 28.\nParticipants within each cohort were randomly assigned by stratified block randomisation (block size eight) and allocated (3:1) to receive vaccine or placebo.\nGroup allocation was concealed from participants, investigators, and outcome assessors.\nThe primary outcomes were safety and tolerability.\nThe secondary outcome was immunogenicity, assessed as the neutralising antibody responses against infectious SARS-CoV-2.\nThis study is registered with www.chictr.org.cn, ChiCTR2000032459.\nFINDINGS: In phase 1, 192 participants were enrolled (mean age 53\u00b77 years [SD 15\u00b76]) and were randomly assigned to receive vaccine (2 \u00b5g [n=24], 4 \u00b5g [n=24], or 8 \u00b5g [n=24] for both age groups [18-59 years and &#8805;60 years]) or placebo (n=24).\nAt least one adverse reaction was reported within the first 7 days of inoculation in 42 (29%) of 144 vaccine recipients.\nThe most common systematic adverse reaction was fever (18-59 years, one [4%] in the 2 \u00b5g group, one [4%] in the 4 \u00b5g group, and two [8%] in the 8 \u00b5g group; &#8805;60 years, one [4%] in the 8 \u00b5g group).\nAll adverse reactions were mild or moderate in severity.\nNo serious adverse event was reported within 28 days post vaccination.\nNeutralising antibody geometric mean titres were higher at day 42 in the group aged 18-59 years (87\u00b77 [95% CI 64\u00b79-118\u00b76], 2 \u00b5g group; 211\u00b72 [158\u00b79-280\u00b76], 4 \u00b5g group; and 228\u00b77 [186\u00b71-281\u00b71], 8 \u00b5g group) and the group aged 60 years and older (80\u00b77 [65\u00b74-99\u00b76], 2 \u00b5g group; 131\u00b75 [108\u00b72-159\u00b77], 4 \u00b5g group; and 170\u00b787 [133\u00b70-219\u00b75], 8 \u00b5g group) compared with the placebo group (2\u00b70 [2\u00b70-2\u00b70]).\nIn phase 2, 448 participants were enrolled (mean age 41\u00b77 years [SD 9\u00b79]) and were randomly assigned to receive the vaccine (8 \u00b5g on day 0 [n=84] or 4 \u00b5g on days 0 and 14 [n=84], days 0 and 21 [n=84], or days 0 and 28 [n=84]) or placebo on the same schedules (n=112).\nAt least one adverse reaction within the first 7 days was reported in 76 (23%) of 336 vaccine recipients (33 [39%], 8 \u00b5g day 0; 18 [21%], 4 \u00b5g days 0 and 14; 15 [18%], 4 \u00b5g days 0 and 21; and ten [12%], 4 \u00b5g days 0 and 28).\nOne placebo recipient in the 4 \u00b5g days 0 and 21 group reported grade 3 fever, but was self-limited and recovered.\nAll other adverse reactions were mild or moderate in severity.\nThe most common systematic adverse reaction was fever (one [1%], 8 \u00b5g day 0; one [1%], 4 \u00b5g days 0 and 14; three [4%], 4 \u00b5g days 0 and 21; two [2%], 4 \u00b5g days 0 and 28).\nThe vaccine-elicited neutralising antibody titres on day 28 were significantly greater in the 4 \u00b5g days 0 and 14 (169\u00b75, 95% CI 132\u00b72-217\u00b71), days 0 and 21 (282\u00b77, 221\u00b72-361\u00b74), and days 0 and 28 (218\u00b70, 181\u00b78-261\u00b73) schedules than the 8 \u00b5g day 0 schedule (14\u00b77, 11\u00b76-18\u00b78; all p<0\u00b7001).\nINTERPRETATION: The inactivated SARS-CoV-2 vaccine, BBIBP-CorV, is safe and well tolerated at all tested doses in two age groups.\nHumoral responses against SARS-CoV-2 were induced in all vaccine recipients on day 42.\nTwo-dose immunisation with 4 \u00b5g vaccine on days 0 and 21 or days 0 and 28 achieved higher neutralising antibody titres than the single 8 \u00b5g dose or 4 \u00b5g dose on days 0 and 14.\nFUNDING: National Program on Key Research Project of China, National Mega projects of China for Major Infectious Diseases, National Mega Projects of China for New Drug Creation, and Beijing Science and Technology Plan.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"We aimed to assess the safety and immunogenicity of an inactivated severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccine candidate, BBIBP-CorV, in humans.\"]}", "id": 177}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: People with diabetes have not a higher risk for complications from coronavirus\n\nAbstract:\nAIMS: The 2019 novel coronavirus disease (COVID-19) emerged in Wuhan, China, and was characterized as a pandemic by the World Health Organization.\nDiabetes is an established risk associated with poor clinical outcomes, but the association of diabetes with COVID-19 has not been reported yet.\nMETHODS: In this cohort study, we retrospectively reviewed 258 consecutive hospitalized COVID-19 patients with or without diabetes at the West Court of Union Hospital in Wuhan, China, recruited from January 29 to February 12, 2020.\nThe clinical features, treatment strategies and prognosis data were collected and analyzed.\nPrognosis was followed up until March 12, 2020.\nRESULTS: Of the 258 hospitalized patients (63 with diabetes) with COVID-19, the median age was 64 years (range 23-91), and 138 (53.5%) were male.\nCommon symptoms included fever (82.2%), dry cough (67.1%), polypnea (48.1%), and fatigue (38%).\nPatients with diabetes had significantly higher leucocyte and neutrophil counts, and higher levels of fasting blood glucose, serum creatinine, urea nitrogen and creatine kinase isoenzyme MB at admission compared with those without diabetes.\nCOVID-19 patients with diabetes were more likely to develop severe or critical disease conditions with more complications, and had higher incidence rates of antibiotic therapy, non-invasive and invasive mechanical ventilation, and death (11.1% vs. 4.1%).\nCox proportional hazard model showed that diabetes (adjusted hazard ratio [aHR] = 3.64; 95% confidence interval [CI]: 1.09, 12.21) and fasting blood glucose (aHR = 1.19; 95% CI: 1.08, 1.31) were associated with the fatality due to COVID-19, adjusting for potential confounders.\nCONCLUSIONS: Diabetes mellitus is associated with increased disease severity and a higher risk of mortality in patients with COVID-19.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"COVID-19 patients with diabetes were more likely to develop severe or critical disease conditions with more complications, and had higher incidence rates of antibiotic therapy, non-invasive and invasive mechanical ventilation, and death (11.1% vs. 4.1%).\"]}", "id": 178}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: hydroxychloroquine cures covid-19.\n\nAbstract:\nHydroxychloroquine (HCQ) has sparked much interest in the therapeutics of the Coronavirus Disease 2019 (COVID-19) pandemic.\nIts antiviral properties have been studied for years; regarding the Severe Acute Respiratory Syndrome-Corona Virus-2 (SARS-CoV-2), it has been shown that HCQ may act at multiple levels.\nThese extend from the initial attachment of the virus to the respiratory epithelium to the inhibition of its replication by the alkalinisation of the phagolysosome's microenvironment and the post-translational modification of certain viral proteins.\nPreliminary clinical evidence from China and France showed significant virological and clinical benefit in HCQ-treated patients, while other studies, mostly including critically ill patients, did not show favorable results.\nIn this review, we critically appraise the existing evidence on HCQ against SARS-CoV-2 with particular emphasis on its protective and therapeutic role.\nSafety concerns that are relevant to the short-term HCQ use are also discussed.\nIn the context of the rapid evolution of the COVID-19 pandemic that strains the health care systems worldwide and considering limited population-wide testing rates in most of the vulnerable countries, early empiric short-term administration of HCQ in symptomatic individuals, may be a promising, safe and low-cost strategy.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 179}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Higher risk if you have type 1 diabetes. Compared to people without diabetes, people with type 1 diabetes are approximately 3.5 times as likely to die in hospital with COVID-19, while people with type 2 are approximately twice as likely. \n\nAbstract:\nAIMS: The 2019 novel coronavirus disease (COVID-19) emerged in Wuhan, China, and was characterized as a pandemic by the World Health Organization.\nDiabetes is an established risk associated with poor clinical outcomes, but the association of diabetes with COVID-19 has not been reported yet.\nMETHODS: In this cohort study, we retrospectively reviewed 258 consecutive hospitalized COVID-19 patients with or without diabetes at the West Court of Union Hospital in Wuhan, China, recruited from January 29 to February 12, 2020.\nThe clinical features, treatment strategies and prognosis data were collected and analyzed.\nPrognosis was followed up until March 12, 2020.\nRESULTS: Of the 258 hospitalized patients (63 with diabetes) with COVID-19, the median age was 64 years (range 23-91), and 138 (53.5%) were male.\nCommon symptoms included fever (82.2%), dry cough (67.1%), polypnea (48.1%), and fatigue (38%).\nPatients with diabetes had significantly higher leucocyte and neutrophil counts, and higher levels of fasting blood glucose, serum creatinine, urea nitrogen and creatine kinase isoenzyme MB at admission compared with those without diabetes.\nCOVID-19 patients with diabetes were more likely to develop severe or critical disease conditions with more complications, and had higher incidence rates of antibiotic therapy, non-invasive and invasive mechanical ventilation, and death (11.1% vs. 4.1%).\nCox proportional hazard model showed that diabetes (adjusted hazard ratio [aHR] = 3.64; 95% confidence interval [CI]: 1.09, 12.21) and fasting blood glucose (aHR = 1.19; 95% CI: 1.08, 1.31) were associated with the fatality due to COVID-19, adjusting for potential confounders.\nCONCLUSIONS: Diabetes mellitus is associated with increased disease severity and a higher risk of mortality in patients with COVID-19.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 180}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: It appears that the virus that causes COVID-19 can spread from people to animals in some situations.\n\nAbstract:\nThe Australian and New Zealand governments both initiated strict social distancing measures in response to the COVID-19 pandemic in late March.\nIt remains difficult to quantify the impact this had in reducing the spread of the virus.\nBayesian structural time series model provide a model to quantify the scenario in which these government-level interventions were not placed.\nOur models predict these strict social distancing measures caused a 79% and 61% reduction in the daily cases of COVID-19 across Australia and New Zealand respectively.\nThis provides both evidence and impetus for governments considering similar measures in response to COVID-19 and other pandemics.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 181}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: covid-19 isn't a risk for young people\n\nAbstract:\nOBJECTIVES: To determine mortality rates among adults with critical illness from coronavirus disease 2019.\nDESIGN: Observational cohort study of patients admitted from March 6, 2020, to April 17, 2020.\nSETTING: Six coronavirus disease 2019 designated ICUs at three hospitals within an academic health center network in Atlanta, Georgia, United States.\nPATIENTS: Adults greater than or equal to 18 years old with confirmed severe acute respiratory syndrome-CoV-2 disease who were admitted to an ICU during the study period.\nINTERVENTIONS: None.\nMEASUREMENTS AND MAIN RESULTS: Among 217 critically ill patients, mortality for those who required mechanical ventilation was 35.7% (59/165), with 4.8% of patients (8/165) still on the ventilator at the time of this report.\nOverall mortality to date in this critically ill cohort is 30.9% (67/217) and 60.4% (131/217) patients have survived to hospital discharge.\nMortality was significantly associated with older age, lower body mass index, chronic renal disease, higher Sequential Organ Failure Assessment score, lower PaO2/FIO2 ratio, higher D-dimer, higher C-reactive protein, and receipt of mechanical ventilation, vasopressors, renal replacement therapy, or vasodilator therapy.\nCONCLUSIONS: Despite multiple reports of mortality rates exceeding 50% among critically ill adults with coronavirus disease 2019, particularly among those requiring mechanical ventilation, our early experience indicates that many patients survive their critical illness.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 182}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Exposing yourself to the sun or to temperatures higher than 25C degrees does not prevent the coronavirus disease (COVID-19).\n\nAbstract:\nAs the world economies get out of the lockdown imposed by the COVID-19 pandemic, there is an urgent need to assess the suitability of known technologies to mitigate COVID-19 transmission in confined spaces such as buildings.\nThis feasibility study looks at the method of upper-room ultraviolet (UV) air disinfection that has already proven its efficacy in preventing the transmission of airborne diseases such as measles and tuberculosis.\nUsing published data from various sources it is shown that the SARS-CoV-2 virus, which causes COVID-19, is highly likely to be susceptible to UV damage while suspended in air irradiated by UV-C at levels that are acceptable and safe for upper-room applications.\nThis is while humans are present in the room.\nBoth the expected and worst-case scenarios are investigated to show the efficacy of the upper-room UV-C approach to reduce COVID-19 air transmission in a confined space with moderate but sufficient height.\nDiscussion is given on the methods of analysis and the differences between virus susceptibility to UV-C when aerosolised or in liquid or on a surface.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Using published data from various sources it is shown that the SARS-CoV-2 virus, which causes COVID-19, is highly likely to be susceptible to UV damage while suspended in air irradiated by UV-C at levels that are acceptable and safe for upper-room applications.\"]}", "id": 183}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Cross-species transmission of the previously identified coronavirus 2019-ncov\n\nAbstract:\nThe current outbreak of viral pneumonia in the city of Wuhan, China, was caused by a novel coronavirus designated 2019-nCoV by the World Health Organization, as determined by sequencing the viral RNA genome.\nMany initial patients were exposed to wildlife animals at the Huanan seafood wholesale market, where poultry, snake, bats, and other farm animals were also sold.\nTo investigate possible virus reservoir, we have carried out comprehensive sequence analysis and comparison in conjunction with relative synonymous codon usage (RSCU) bias among different animal species based on the 2019-nCoV sequence.\nResults obtained from our analyses suggest that the 2019-nCoV may appear to be a recombinant virus between the bat coronavirus and an origin-unknown coronavirus.\nThe recombination may occurred within the viral spike glycoprotein, which recognizes a cell surface receptor.\nAdditionally, our findings suggest that 2019-nCoV has most similar genetic information with bat coronovirus and most similar codon usage bias with snake.\nTaken together, our results suggest that homologous recombination may occur and contribute to the 2019-nCoV cross-species transmission.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Results obtained from our analyses suggest that the 2019-nCoV may appear to be a recombinant virus between the bat coronavirus and an origin-unknown coronavirus.\"]}", "id": 184}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: camostat mesylate cure coronavirus.\n\nAbstract:\nWe remain largely without effective prophylactic/therapeutic interventions for COVID-19.\nAlthough many human clinical trials are ongoing, there remains a deficiency of supportive preclinical drug efficacy studies.\nHere we assessed the prophylactic/therapeutic efficacy of hydroxychloroquine (HCQ), a drug of interest for COVID-19 management, in two animal models.\nWhen used for prophylaxis or treatment neither the standard human malaria dose (6.5 mg/kg) nor a high dose (50 mg/kg) of HCQ had any beneficial effect on clinical disease or SARS-CoV-2 kinetics (replication/shedding) in the Syrian hamster disease model.\nSimilarly, HCQ prophylaxis/treatment (6.5 mg/kg) did not significantly benefit clinical outcome nor reduce SARS-CoV-2 replication/shedding in the upper and lower respiratory tract in the rhesus macaque disease model.\nIn conclusion, our preclinical animal studies do not support the use of HCQ in prophylaxis/treatment of COVID-19.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 185}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Hot weather can spread the virus more as it may get you more out there, make you more mobile and you would actually interact with more people\n\nAbstract:\nThis paper investigates the correlation between the high level of coronavirus SARS-CoV-2 infection accelerated transmission and lethality, and surface air pollution in Milan metropolitan area, Lombardy region in Italy.\nFor January-April 2020 period, time series of daily average inhalable gaseous pollutants ozone (O3) and nitrogen dioxide (NO2), together climate variables (air temperature, relative humidity, wind speed, precipitation rate, atmospheric pressure field and Planetary Boundary Layer) were analyzed.\nIn spite of being considered primarily transmitted by indoor bioaerosols droplets and infected surfaces or direct human-to-human personal contacts, it seems that high levels of urban air pollution, and climate conditions have a significant impact on SARS-CoV-2 diffusion.\nExhibited positive correlations of ambient ozone levels and negative correlations of NO2 with the increased rates of COVID-19 infections (Total number, Daily New positive and Total Deaths cases), can be attributed to airborne bioaerosols distribution.\nThe results show positive correlation of daily averaged O3 with air temperature and inversely correlations with relative humidity and precipitation rates.\nViral genome contains distinctive features, including a unique N-terminal fragment within the spike protein, which allows coronavirus attachment on ambient air pollutants.\nAt this moment it is not clear if through airborne diffusion, in the presence of outdoor and indoor aerosols, this protein \"spike\" of the new COVID-19 is involved in the infectious agent transmission from a reservoir to a susceptible host during the highest nosocomial outbreak in some agglomerated industrialized urban areas like Milan is.\nAlso, in spite of collected data for cold season (winter-early spring) period, when usually ozone levels have lower values than in summer, the findings of this study support possibility as O3 can acts as a COVID-19 virus incubator.\nBeing a novel pandemic coronavirus version, it might be ongoing during summer conditions associated with higher air temperatures, low relative humidity and precipitation levels.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Being a novel pandemic coronavirus version, it might be ongoing during summer conditions associated with higher air temperatures, low relative humidity and precipitation levels.\"]}", "id": 186}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: with autoimmune conditions such as lupus, a person experiences \"dysregulations of the immune system,\" meaning the immune system itself is compromised or malfunctioning\n\nAbstract:\nCoronaviruses are a genetically highly variable family of viruses that infect vertebrates and have succeeded in infecting humans many times by overcoming the species barrier.\nThe severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which initially appeared in China at the end of 2019, exhibits a high infectivity and pathogenicity compared to other coronaviruses.\nAs the viral coat and other viral components are recognized as being foreign by the immune system, this can lead to initial symptoms, which are induced by the very efficiently working immune defense system via the respiratory epithelium.\nDuring severe courses a systemically expressed proinflammatory cytokine storm and subsequent changes in the coagulation and complement systems can occur.\nVirus-specific antibodies, the long-term expression of which is ensured by the formation of B memory cell clones, generate a specific immune response that is also detectable in blood (seroconversion).\nSpecifically effective cytotoxic CD8+ T\u00adcell populations are also formed, which recognize viral epitopes as pathogen-specific patterns in combination with MHC presentation on the cell surface of virus-infected cells and destroy these cells.\nAt the current point in time it is unclear how regular, robust and durable this immune status is constructed.\nExperiences with other coronavirus infections (SARS and Middle East respiratory syndrome, MERS) indicate that the immunity could persist for several years.\nBased on animal experiments, already acquired data on other coronavirus types and plausibility assumptions, it can be assumed that seroconverted patients have an immunity of limited duration and only a very low risk of reinfection.\nKnowledge of the molecular mechanisms of viral cycles and immunity is an important prerequisite for the development of vaccination strategies and development of effective drugs.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 187}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: There are an urgent need for antivirals to treat the newly emerged SARS-CoV-2.\n\nAbstract:\nDifferent treatments are currently used for clinical management of SARS-CoV-2 infection, but little is known about their efficacy yet.\nHere we present ongoing results to compare currently available drugs for a variety of diseases to find out if they counteract SARS-CoV-2-induced cytopathic effect in vitro.\nOur goal is to prioritize antiviral activity to provide a solid evidence-driven rationale for forthcoming clinical trials.\nSince the most effective antiviral approaches are usually based on combined therapies that tackle the viral life cycle at different stages, we are also testing combinations of drugs that may be critical to reduce the emergence of resistant viruses.\nWe will provide results as soon as they become available, so data should be interpreted with caution, clearly understanding the limitations of the in vitro model, that may not always reflect what could happen in vivo.\nThus, our goal is to test the most active antivirals identified in adequate animal models infected with SARS-CoV-2, to add more information about possible in vivo efficacy.\nIn turn, successful antivirals could be tested in clinical trials as treatments for infected patients, but also as pre-exposure prophylaxis to avoid novel infections until an effective and safe vaccine is developed.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 188}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Diabetes is generally known to weaken the immune system, making it harder to protect against viral infections like COVID-19.\n\nAbstract:\nObjective: To undertake a review and critical appraisal of published/preprint reports that offer methods of determining the effects of hypertension, diabetes, stroke, cancer, kidney issues, and high-cholesterol on COVID-19 disease severity.\nData sources: Google Scholar, PubMed, COVID-19 Open Research Dataset: a resource of over 128,000 scholarly articles, including over 59,000 articles with full text related to COVID-19, SARS-CoV-2, and coronaviruses.\nMethods: A search was conducted by two authors independently on the freely available COVID-19 Open Research Dataset (CORD-19).\nWe developed an automated search engine to screen a total of 59,000 articles in a few seconds.\nThe search engine was built using a retrieval function that ranks a set of documents based on the query terms appearing in each document regardless of their proximity within the document.\nFiltering of the articles was then undertaken using keywords and questions, e.g. \"Effects of diabetes on COVID/normal coronavirus/SARS-CoV-2/nCoV/COVID-19 disease severity, mortality?\".\nThe search terms were repeated for all the comorbidities considered in this paper.\nAdditional articles were retrieved by searching via Google Scholar and PubMed.\nFindings: A total of 54 articles were considered for a full review.\nIt was observed that diabetes, hypertension, and cholesterol levels possess an apparent relation to COVID-19 severity.\nOther comorbidities, such as cancer, kidney disease, and stroke, must be further evaluated to determine a strong relationship to the virus.\nReports associating cancer, kidney disease, and stroke with COVID-19 should be carefully interpreted, not only because of the size of the samples, but also because patients could be old, have a history of smoking, or have any other clinical condition suggesting that these factors might be associated with the poor COVID-19 outcomes rather than the comorbidity itself.\nSuch reports could lead many oncologists and physicians to change their treatment strategies without solid evidence and recommendations.\nFurther research regarding this relationship and its clinical management is warranted.\nAdditionally, treatment options must be examined further to provide optimal treatment and ensure better outcomes for patients suffering from these comorbidities.\nIt should be noted that, whether definitive measurements exist or not, the care of patients as well as the research involved should be largely prioritized to tackle this deadly pandemic.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 189}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Personalized surveillance approach for hospitalized patients with covid-19\n\nAbstract:\nHospitalized patients with COVID-19 experiencing respiratory symptoms have different complications (inflammatory, co-infection and thrombotic) that are identifiable by analytics patterns.\nPersonalized treatment decisions decreased early mortality (OR 0.144, CI 0.03-0.686; p=0.015).\nIncreasing age (OR 1.06; p=0.038) and therapeutic effort limitation (OR 9.684; p<0.001) were associated with higher mortality.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Personalized treatment decisions decreased early mortality (OR 0.144, CI 0.03-0.686; p=0.015).\"]}", "id": 190}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: vitamin D might be able to protect people from the coronavirus (COVID-19).\n\nAbstract:\nBackground: Following emerge of a novel coronavirus from Wuhan, China, in December 2019, it has affected the whole world and after months of efforts by the medical communities, there is still no specific approach for prevention and treatment against the Coronavirus Disease 2019 (COVID-19).\nEvidence recommends that vitamin D might be an important supportive agent for the immune system, mainly in cytokine response regulation against COVID-19.\nHence, we carried out a rapid systematic review and meta-analysis along with an ecological investigation in order to maximize the use of everything that exists about the role of vitamin D in the COVID-19.\nMethods: A systematic search was performed in PubMed, Scopus, Embase, Cochrane Library, Web of Science and Google Scholar (intitle) as well as preprint database of medRxiv, bioRxiv, Research Square, preprints.org, search engine of ScienceDirect and a rapid search through famous journals up to May 26, 2020.\nStudies focused on the role of vitamin D in confirmed COVID-19 patients were entered into the systematic review.\nAlong with our main aim, to find the second objective: correlation of global vitamin D status and COVID-19 recovery and mortality we carried out a literature search in PubMed database to identify the national or regional studies reported the vitamin D status globally.\nCMA v. 2.2.064 and SPSS v.16 were used for data analysis.\nResults: Out of nine studies entered into our systematic review, six studies containing 3,822 participants entered into the meta-analysis.\nThe meta-analysis indicated that 46.5% of COVID-19 patients were suffering from vitamin D deficiency (95% CI, 28.2%-65.8%) and in 43.3% of patients, levels of vitamin D were insufficient (95% CI, 27.4%-60.8%).\nIn regard to our ecological investigation on 51 countries including 408,748 participants, analyses indicated no correlation between vitamin D levels and recovery rate (r= 0.041) as well as mortality rate (r=-0.073) globally.\nHowever, given latitude, a small reverse correlation between mortality rate and vitamin D status was observed throughout the globe (r= -0.177).\nIn Asia, a medium direct correlation was observed for recovery rate (r= 0.317) and a significant reveres correlation for mortality rate (r= -0.700) with vitamin D status in such patients.\nIn Europe, there were no correlations for both recovery (r= 0.040) and mortality rate (r= -0.035).\nIn Middle East, the recovery rate (r= 0.267) and mortality rate (r= -0.217) showed a medium correlation.\nIn North and Sought America, surprisingly, both recovery and mortality rate demonstrated a direct correlation respectively (r= 1.000, r=0.500).\nIn Oceania, unexpectedly, recovery (r= -1.000) and mortality (r= -1.000) rates were in considerable reverse correlation with vitamin D levels.\nConclusion: In this systematic review and meta-analysis with an ecological approach, we found a high percentage of COVID-19 patients who suffer from vitamin D deficiency or insufficiency.\nMuch more important, our ecological investigation resulted in substantial direct and reverse correlations between recovery and mortality rates of COVID-19 patients with vitamin D status in different countries.\nConsidering latitudes, a small reverse correlation between vitamin D status and mortality rate was found globally.\nIt seems that populations with lower levels of vitamin D might be more susceptible to the novel coronavirus infection.\nNevertheless, due to multiple limitations, if this study does not allow to quantify a value of the Vitamin D with full confidence, it allows at least to know what the Vitamin D might be and that it would be prudent to invest in this direction through comprehensive large randomized clinical trials.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"In Middle East, the recovery rate (r= 0.267) and mortality rate (r= -0.217) showed a medium correlation.\"]}", "id": 191}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Vinegar can kill microorganisms such as bacteria and viruses and treat yeast infections.\n\nAbstract:\nThe global pandemic caused by the newly described severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused worldwide suffering and death of unimaginable magnitude from coronavirus disease 2019 (COVID-19).\nThe virus is transmitted through aerosol droplets, and causes severe acute respiratory syndrome.\nSARS-CoV-2 uses the receptor-binding domain of its spike protein S1 to attach to the host angiotensin-converting enzyme 2 receptor in lung and airway cells.\nBinding requires the help of another host protein, transmembrane protease serine S1 member 2.\nSeveral factors likely contribute to the efficient transmission of SARS-CoV-2.\nThe receptor-binding domain of SARS-CoV-2 has a 10- to 20-fold higher receptor-binding capacity compared with previous pandemic coronaviruses.\nIn addition, because asymptomatic persons infected with SARS-CoV-2 have high viral loads in their nasal secretions, they can silently and efficiently spread the disease.\nPCR-based tests have emerged as the criterion standard for the diagnosis of infection.\nCaution must be exercised in interpreting antibody-based tests because they have not yet been validated, and may give a false sense of security of being \"immune\" to SARS-CoV-2.\nWe discuss how the development of some symptoms in allergic rhinitis can serve as clues for new-onset COVID-19.\nThere are mixed reports that asthma is a risk factor for severe COVID-19, possibly due to differences in asthma endotypes.\nThe rapid spread of COVID-19 has focused the efforts of scientists on repurposing existing Food and Drug Administration-approved drugs that inhibit viral entry, endocytosis, genome assembly, translation, and replication.\nNumerous clinical trials have been launched to identify effective treatments for COVID-19.\nInitial data from a placebo-controlled study suggest faster time to recovery in patients on remdesivir; it is now being evaluated in additional controlled studies.\nAs discussed in this review, till effective vaccines and treatments emerge, it is important to understand the scientific rationale of pandemic-mitigation strategies such as wearing facemasks and social distancing, and implement them.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Numerous clinical trials have been launched to identify effective treatments for COVID-19.\", \"Initial data from a placebo-controlled study suggest faster time to recovery in patients on remdesivir; it is now being evaluated in additional controlled studies.\"]}", "id": 192}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Smoking is a risk factor for COVID-19 patients, but one particular substance in cigarettes - nicotine - might prevent infection in some people, or improve COVID-19 prognosis\n\nAbstract:\nSome comorbidities are associated with severe coronavirus disease (Covid-19) but it is unclear whether some increase susceptibility to Covid-19.\nIn this case-control Mexican study we found that obesity represents the strongest predictor for Covid-19 followed by diabetes and hypertension in both sexes and chronic renal failure in females only.\nActive smoking was associated with decreased odds of Covid-19.\nThese findings indicate that these comorbidities are not only associated with severity of disease but also predispose for getting Covid-19.\nFuture research is needed to establish the mechanisms involved in each comorbidity and the apparent \"protective\" effect of cigarette smoking.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Active smoking was associated with decreased odds of Covid-19.\"]}", "id": 193}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Unexpected Cause of Death in Younger COVID-19 Patients is Related to Blood Clotting\n\nAbstract:\nBACKGROUND: The 2019 novel coronavirus has caused the outbreak of the acute respiratory disease in Wuhan, Hubei Province of China since December 2019.\nThis study was performed to analyze the clinical characteristics of patients who succumbed to and who recovered from 2019 novel coronavirus disease (COVID-19).\nMETHODS: Clinical data were collected from two tertiary hospitals in Wuhan.\nA retrospective investigation was conducted to analyze the clinical characteristics of fatal cases of COVID-19 (death group) and we compare them with recovered patients (recovered group).\nContinuous variables were analyzed using the Mann-Whitney U test.\nCategorical variables were analyzed by &#967; test or Fisher exact test as appropriate.\nRESULTS: Our study enrolled 109 COVID-19 patients who died during hospitalization and 116 recovered patients.\nThe median age of the death group was older than the recovered group (69 [62, 74] vs. 40 [33, 57] years, Z\u00e2\u0080\u008a=\u00e2\u0080\u008a9.738, P\u00e2\u0080\u008a<\u00e2\u0080\u008a0.001).\nMore patients in the death group had underlying diseases (72.5% vs. 41.4%, &#967;\u00e2\u0080\u008a=\u00e2\u0080\u008a22.105, P\u00e2\u0080\u008a<\u00e2\u0080\u008a0.001).\nPatients in the death group had a significantly longer time of illness onset to hospitalization (10.0 [6.5, 12.0] vs. 7.0 [5.0, 10.0] days, Z\u00e2\u0080\u008a=\u00e2\u0080\u008a3.216, P\u00e2\u0080\u008a=\u00e2\u0080\u008a0.001).\nOn admission, the proportions of patients with symptoms of dyspnea (70.6% vs. 19.0%, &#967;\u00e2\u0080\u008a=\u00e2\u0080\u008a60.905, P\u00e2\u0080\u008a<\u00e2\u0080\u008a0.001) and expectoration (32.1% vs. 12.1%, &#967;\u00e2\u0080\u008a=\u00e2\u0080\u008a13.250, P\u00e2\u0080\u008a<\u00e2\u0080\u008a0.001) were significantly higher in the death group.\nThe blood oxygen saturation was significantly lower in the death group (85 [77, 91]% vs. 97 [95, 98]%, Z\u00e2\u0080\u008a=\u00e2\u0080\u008a10.625, P\u00e2\u0080\u008a<\u00e2\u0080\u008a0.001).\nThe white blood cell (WBC) in death group was significantly higher on admission (7.23 [4.87, 11.17] vs. 4.52 [3.62, 5.88] \u00d710/L, Z\u00e2\u0080\u008a=\u00e2\u0080\u008a7.618, P\u00e2\u0080\u008a<\u00e2\u0080\u008a0.001).\nPatients in the death group exhibited significantly lower lymphocyte count (0.63 [0.40, 0.79] vs. 1.00 [0.72, 1.27] \u00d710/L, Z\u00e2\u0080\u008a=\u00e2\u0080\u008a8.037, P\u00e2\u0080\u008a<\u00e2\u0080\u008a0.001) and lymphocyte percentage (7.10 [4.45, 12.73]% vs. 23.50 [15.27, 31.25]%, Z\u00e2\u0080\u008a=\u00e2\u0080\u008a10.315, P\u00e2\u0080\u008a<\u00e2\u0080\u008a0.001) on admission, and the lymphocyte percentage continued to decrease during hospitalization (7.10 [4.45, 12.73]% vs. 2.91 [1.79, 6.13]%, Z\u00e2\u0080\u008a=\u00e2\u0080\u008a5.242, P\u00e2\u0080\u008a<\u00e2\u0080\u008a0.001).\nAlanine transaminase (22.00 [15.00, 34.00] vs. 18.70 [13.00, 30.38] U/L, Z\u00e2\u0080\u008a=\u00e2\u0080\u008a2.592, P\u00e2\u0080\u008a=\u00e2\u0080\u008a0.010), aspartate transaminase (34.00 [27.00, 47.00] vs. 22.00 [17.65, 31.75] U/L, Z\u00e2\u0080\u008a=\u00e2\u0080\u008a7.308, P\u00e2\u0080\u008a<\u00e2\u0080\u008a0.001), and creatinine levels (89.00 [72.00, 133.50] vs. 65.00 [54.60, 78.75] \u00b5mol/L, Z\u00e2\u0080\u008a=\u00e2\u0080\u008a6.478, P\u00e2\u0080\u008a<\u00e2\u0080\u008a0.001) were significantly higher in the death group than those in the recovered group.\nC-reactive protein (CRP) levels were also significantly higher in the death group on admission (109.25 [35.00, 170.28] vs. 3.22 [1.04, 21.80] mg/L, Z\u00e2\u0080\u008a=\u00e2\u0080\u008a10.206, P\u00e2\u0080\u008a<\u00e2\u0080\u008a0.001) and showed no significant improvement after treatment (109.25 [35.00, 170.28] vs. 81.60 [27.23, 179.08] mg/L, Z\u00e2\u0080\u008a=\u00e2\u0080\u008a1.219, P\u00e2\u0080\u008a=\u00e2\u0080\u008a0.233).\nThe patients in the death group had more complications such as acute respiratory distress syndrome (ARDS) (89.9% vs. 8.6%, &#967;\u00e2\u0080\u008a=\u00e2\u0080\u008a148.105, P\u00e2\u0080\u008a<\u00e2\u0080\u008a0.001), acute cardiac injury (59.6% vs. 0.9%, &#967;\u00e2\u0080\u008a=\u00e2\u0080\u008a93.222, P\u00e2\u0080\u008a<\u00e2\u0080\u008a0.001), acute kidney injury (18.3% vs. 0%, &#967;\u00e2\u0080\u008a=\u00e2\u0080\u008a23.257, P\u00e2\u0080\u008a<\u00e2\u0080\u008a0.001), shock (11.9% vs. 0%, &#967;\u00e2\u0080\u008a=\u00e2\u0080\u008a14.618, P\u00e2\u0080\u008a<\u00e2\u0080\u008a0.001), and disseminated intravascular coagulation (DIC) (6.4% vs. 0%, &#967;\u00e2\u0080\u008a=\u00e2\u0080\u008a7.655, P\u00e2\u0080\u008a=\u00e2\u0080\u008a0.006).\nCONCLUSIONS: Compared to the recovered group, more patients in the death group exhibited characteristics of advanced age, pre-existing comorbidities, dyspnea, oxygen saturation decrease, increased WBC count, decreased lymphocytes, and elevated CRP levels.\nMore patients in the death group had complications such as ARDS, acute cardiac injury, acute kidney injury, shock, and DIC.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"CONCLUSIONS: Compared to the recovered group, more patients in the death group exhibited characteristics of advanced age, pre-existing comorbidities, dyspnea, oxygen saturation decrease, increased WBC count, decreased lymphocytes, and elevated CRP levels.\"]}", "id": 194}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: There's been much speculation about whether vitamin D might prevent or help survival with COVID-19, and two new studies appear to underscore the link.\n\nAbstract:\nThe outbreak of COVID-19 has created a global public health crisis.\nLittle is known about the protective factors of this infection.\nTherefore, preventive health measures that can reduce the risk of infection, progression and severity are desperately needed.\nThis review discussed the possible roles of vitamin D in reducing the risk of COVID-19 and other acute respiratory tract infections and severity.\nMoreover, this study determined the correlation of vitamin D levels with COVID-19 cases and deaths in 20 European countries as of 20 May 2020.\nA significant negative correlation (p=0.033) has been observed between mean vitamin D levels and COVID-19 cases per one million population in European countries.\nHowever, the correlation of vitamin D with COVID-19 deaths of these countries was not significant.\nSome retrospective studies demonstrated a correlation between vitamin D status and COVID-19 severity and mortality, while other studies did not find the correlation when confounding variables are adjusted.\nSeveral studies demonstrated the role of vitamin D in reducing the risk of acute viral respiratory tract infections and pneumonia.\nThese include direct inhibition with viral replication or with anti-inflammatory or immunomodulatory ways.\nIn the meta-analysis, vitamin D supplementation has been shown as safe and effective against acute respiratory tract infections.\nThus, people who are at higher risk of vitamin D deficiency during this global pandemic should consider taking vitamin D supplements to maintain the circulating 25(OH)D in the optimal levels (75-125nmol/L).\nIn conclusion, there is not enough evidence on the association between vitamin D levels and COVID-19 severity and mortality.\nTherefore, randomized control trials and cohort studies are necessary to test this hypothesis.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 195}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: adults who are hooked on cigarettes are 50% less likely to test positive for the illness\n\nAbstract:\nImportance.\nCovid-19 infection has major international health and economic impacts and risk factors for infection are not completely understood.\nCannabis smoking is linked with poor respiratory health, immunosuppression and multiple contaminants.\nPotential synergism between the two epidemics would represent a major public health convergence.\nCigarettes were implicated with disease severity in Wuhan, China.\nObjective.\nIs cannabis use epidemiologically associated with coronavirus incidence rate (CVIR)?\nDesign.\nCross-sectional state-based multivariable study.\nSetting.\nUSA.\nPrimary and Secondary Outcome Measures.\nCVIR.\nMultivariable-adjusted geospatially-weighted regression models.\nAs the American cannabis epidemic is characterized by a recent doubling of daily cannabis use it was considered important to characterize the contribution of high intensity use.\nResults.\nSignificant associations of daily cannabis use quintile with CVIR were identified with the highest quintile having a prevalence ratio 5.11 (95%C.I. 4.90-5.33), an attributable fraction in the exposed (AFE) 80.45% (79.61-81.25%) and an attributable fraction in the population of 77.80% (76.88-78.68%) with Chi-squared-for-trend (14,782, df=4) significant at P<10-500.\nSimilarly when cannabis legalization was considered decriminalization was associated with an elevated CVIR prevalence ratio 4.51 (95%C.I. 4.45-4.58), AFE 77.84% (77.50-78.17%) and Chi-squared-for-trend (56,679, df=2) significant at P<10-500.\nMonthly and daily use were linked with CVIR in bivariate geospatial regression models (P=0.0027, P=0.0059).\nIn multivariable additive models number of flight origins and population density were significant.\nIn interactive geospatial models adjusted for international travel, ethnicity, income, population, population density and drug use, terms including last month cannabis were significant from P=7.3x10-15, daily cannabis use from P=7.3x10-11 and last month cannabis was independently associated (P=0.0365).\nConclusions and Relevance.\nData indicate CVIR demonstrates significant trends across cannabis use intensity quintiles and with relaxed cannabis legislation.\nRecent cannabis use is independently predictive of CVIR in bivariate and multivariable adjusted models and intensity of use is interactively significant.\nCannabis thus joins tobacco as a SARS2-CoV-2 risk factor.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Cannabis smoking is linked with poor respiratory health, immunosuppression and multiple contaminants.\", \"Potential synergism between the two epidemics would represent a major public health convergence.\", \"Cigarettes were implicated with disease severity in Wuhan, China.\"]}", "id": 196}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Vinegar can kill microorganisms such as bacteria and viruses and treat yeast infections.\n\nAbstract:\nPURPOSE: SARS-CoV-2 is a new pandemic influenza caused by a coronavirus which main route of transmission is through exhaled droplets that primarily infect the nose and the nasopharynx.\nThe aim of this paper is to evaluate the effect of acetic acid, the active component of vinegar, as a potential disinfectant agent for upper airways.\nMETHODS: Twenty-nine patients were enrolled and divided into two groups: group 1 (14 patients) was composed of patients treated with off-label hydroxychloroquine and lopinavir/ritonavir, whereas group 2 (15 patients) was composed of patients treated with hydroxychloroquine only, combined with the inhalation of acetic acid disinfectant at a 0.34% concentration.\nA questionnaire-based evaluation of symptoms was performed after 15 days in both groups.\nRESULTS: It appears that the number of patients treated with acetic acid (group 2) that experienced improvement in individual symptoms was double that of the other group of patients (group 1), although numbers are too small for robust statistical analysis.\nCONCLUSIONS: Considering its potential benefits and high availability, acetic acid disinfection appears to be a promising adjunctive therapy in cases of non-severe COVID-19 and deserves further investigation.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"CONCLUSIONS: Considering its potential benefits and high availability, acetic acid disinfection appears to be a promising adjunctive therapy in cases of non-severe COVID-19 and deserves further investigation.\"]}", "id": 197}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Supplements and products unlikely to help with coronavirus and could be dangerous\n\nAbstract:\nOBJECTIVE Coronavirus disease 2019 (COVID-19) is a fatal and fast-spreading viral infection.\nTo date, the number of COVID-19 patients worldwide has crossed over six million with over three hundred and seventy thousand deaths (according to the data from World Health Organization; updated on 2 June 2020).\nAlthough COVID-19 can be rapidly diagnosed, efficient clinical treatment of COVID-19 remains unavailable, resulting in high fatality.\nSome clinical trials have identified vitamin C (VC) as a potent compound pneumonia management.\nIn addition, glycyrrhizic acid (GA) is clinically as an anti-inflammatory medicine against pneumonia-induced inflammatory stress.\nWe hypothesized that the combination of VC and GA is a potential option for treating COVID-19.\nMETHODS The aim of this study was to determine pharmacological targets and molecular mechanisms of VC + GA treatment for COVID-19, using bioinformational network pharmacology.\nRESULTS We uncovered optimal targets, biological processes and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways of VC + GA against COVID-19.\nOur findings suggested that combinatorial VC and GA treatment for COVID-19 was associated with elevation of immunity and suppression of inflammatory stress, including activation of the T cell receptor signaling pathway, regulation of Fc gamma R-mediated phagocytosis, ErbB signaling pathway and vascular endothelial growth factor signaling pathway.\nWe also identified 17 core targets of VC + GA, which suggest as antimicrobial function.\nCONCLUSIONS For the first time, our study uncovered the pharmacological mechanism underlying combined VC and GA treatment for COVID-19.\nThese results should benefit efforts to address the most pressing problem currently facing the world.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Although COVID-19 can be rapidly diagnosed, efficient clinical treatment of COVID-19 remains unavailable, resulting in high fatality.\"]}", "id": 198}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: A prefusion sars-cov-2 spike rna vaccine is highly immunogenic and prevents lung infection in non-human primates\n\nAbstract:\nTo contain the coronavirus disease 2019 (COVID-19) pandemic, a safe and effective vaccine against the new severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is urgently needed in quantities sufficient to immunise large populations.\nIn this study, we report the design, preclinical development, immunogenicity and anti-viral protective effect in rhesus macaques of the BNT162b2 vaccine candidate.\nBNT162b2 contains an LNP-formulated nucleoside-modified mRNA that encodes the spike glycoprotein captured in its prefusion conformation.\nAfter expression of the BNT162b2 coding sequence in cells, approximately 20% of the spike molecules are in the one-RBD \u2018up\u2019, two-RBD \u2018down\u2019 state.\nImmunisation of mice with a single dose of BNT162b2 induced dose level-dependent increases in pseudovirus neutralisation titers.\nPrime-boost vaccination of rhesus macaques elicited authentic SARS-CoV-2 neutralising geometric mean titers 10.2 to 18.0 times that of a SARS-CoV-2 convalescent human serum panel.\nBNT162b2 generated strong TH1 type CD4+ and IFN\u03b3+ CD8+ T-cell responses in mice and rhesus macaques.\nThe BNT162b2 vaccine candidate fully protected the lungs of immunised rhesus macaques from infectious SARS-CoV-2 challenge.\nBNT162b2 is currently being evaluated in a global, pivotal Phase 2/3 trial (NCT04368728).", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"In this study, we report the design, preclinical development, immunogenicity and anti-viral protective effect in rhesus macaques of the BNT162b2 vaccine candidate.\", \"Prime-boost vaccination of rhesus macaques elicited authentic SARS-CoV-2 neutralising geometric mean titers 10.2 to 18.0 times that of a SARS-CoV-2 convalescent human serum panel.\", \"The BNT162b2 vaccine candidate fully protected the lungs of immunised rhesus macaques from infectious SARS-CoV-2 challenge.\"]}", "id": 199}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: vitamin D might help protect against becoming infected with, and developing serious symptoms of, COVID-19\n\nAbstract:\nThe outbreak of COVID-19 has created a global public health crisis.\nLittle is known about the protective factors of this infection.\nTherefore, preventive health measures that can reduce the risk of infection, progression and severity are desperately needed.\nThis review discussed the possible roles of vitamin D in reducing the risk of COVID-19 and other acute respiratory tract infections and severity.\nMoreover, this study determined the correlation of vitamin D levels with COVID-19 cases and deaths in 20 European countries as of 20 May 2020.\nA significant negative correlation (p=0.033) has been observed between mean vitamin D levels and COVID-19 cases per one million population in European countries.\nHowever, the correlation of vitamin D with COVID-19 deaths of these countries was not significant.\nSome retrospective studies demonstrated a correlation between vitamin D status and COVID-19 severity and mortality, while other studies did not find the correlation when confounding variables are adjusted.\nSeveral studies demonstrated the role of vitamin D in reducing the risk of acute viral respiratory tract infections and pneumonia.\nThese include direct inhibition with viral replication or with anti-inflammatory or immunomodulatory ways.\nIn the meta-analysis, vitamin D supplementation has been shown as safe and effective against acute respiratory tract infections.\nThus, people who are at higher risk of vitamin D deficiency during this global pandemic should consider taking vitamin D supplements to maintain the circulating 25(OH)D in the optimal levels (75-125nmol/L).\nIn conclusion, there is not enough evidence on the association between vitamin D levels and COVID-19 severity and mortality.\nTherefore, randomized control trials and cohort studies are necessary to test this hypothesis.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"A significant negative correlation (p=0.033) has been observed between mean vitamin D levels and COVID-19 cases per one million population in European countries.\", \"Several studies demonstrated the role of vitamin D in reducing the risk of acute viral respiratory tract infections and pneumonia.\", \"These include direct inhibition with viral replication or with anti-inflammatory or immunomodulatory ways.\", \"In the meta-analysis, vitamin D supplementation has been shown as safe and effective against acute respiratory tract infections.\", \"Thus, people who are at higher risk of vitamin D deficiency during this global pandemic should consider taking vitamin D supplements to maintain the circulating 25(OH)D in the optimal levels (75-125nmol/L).\"]}", "id": 200}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: People with Diabetes May Have Higher Risk for COVID-19\n\nAbstract:\nCoronavirus disease 2019 (COVID-19) has become a global concern and public health issue due to its higher infection and mortality rate; particularly, the risk is very higher among the patients who have cardiovascular diseases (CVD) and/or diabetes mellitus (DM).\nIn this review, we analyzed the recently published literature on CVD and DM associated with COVD-19 infections and highlight their association with potential mechanisms.\nThe findings revealed that without any previous history of CVD, the COVID-19 patients have developed some CVD complications like myocardial injury, cardiomyopathy, and venous thromboembolism after being infected with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and required for those patients an emergency clinical support to be aware to manage those complications.\nThough the association between DM and COVID-19-induced severe complications is still unclear, the limited data predict that different markers like interleukin (IL)-1, IL-6, C-reactive protein, and D-dimer linked with the severity of COVID-19 infection in diabetic individuals.\nFurther studies on a large scale are urgently needed to explore the underlying mechanisms between CVD, DM, and COVID-19 for better treatment.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Though the association between DM and COVID-19-induced severe complications is still unclear, the limited data predict that different markers like interleukin (IL)-1, IL-6, C-reactive protein, and D-dimer linked with the severity of COVID-19 infection in diabetic individuals.\"]}", "id": 201}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: COVID-19 spreads by contact with respiratory droplets that spread when an infected person coughs or sneezes.\n\nAbstract:\nCoronavirus Disease 2019 (COVID-19) is a pandemic affecting many countries worldwide.\nGiven the increasing incidence especially in elderly and individuals with comorbid conditions, it is advised by health authorities to stay home if possible, maintain social distancing and stay away from those who are sick or could be infected.\nPatients with comorbidities especially cardiovascular disease are at higher risk of getting infected with COVID-19 and have worse prognosis.\nAmong efforts to safely manage warfarin patients during this pandemic, we introduced a hospital drive-up anticoagulation testing service.\nThis service can reduce the risk of exposure of anticoagulation patients to COVID-19 by reducing the contact time with the different personnel at the hospital and by maintaining those patients at a safe distance from others.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Given the increasing incidence especially in elderly and individuals with comorbid conditions, it is advised by health authorities to stay home if possible, maintain social distancing and stay away from those who are sick or could be infected.\"]}", "id": 202}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Only an N95 mask will help me from getting covid-19.\n\nAbstract:\nWe identified seasonal human coronaviruses, influenza viruses and rhinoviruses in exhaled breath and coughs of children and adults with acute respiratory illness.\nSurgical face masks significantly reduced detection of influenza virus RNA in respiratory droplets and coronavirus RNA in aerosols, with a trend toward reduced detection of coronavirus RNA in respiratory droplets.\nOur results indicate that surgical face masks could prevent transmission of human coronaviruses and influenza viruses from symptomatic individuals.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Surgical face masks significantly reduced detection of influenza virus RNA in respiratory droplets and coronavirus RNA in aerosols, with a trend toward reduced detection of coronavirus RNA in respiratory droplets.\", \"Our results indicate that surgical face masks could prevent transmission of human coronaviruses and influenza viruses from symptomatic individuals.\"]}", "id": 203}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Covid-2019 associated overexpressed prevotella proteins mediated host-pathogen interactions and their role in coronavirus outbreak\n\nAbstract:\nMOTIVATION: The outbreak of COVID-2019 initiated at Wuhan, China has become a global threat by rapid transmission and severe fatalities.\nRecent studies have uncovered whole genome sequence of SARS-CoV-2 (causing COVID-2019).\nIn addition, lung metagenomic studies on infected patients revealed overrepresented Prevotella spp.\nproducing certain proteins in abundance.\nWe performed host-pathogen protein-protein interaction analysis between SARS-CoV-2 and overrepresented Prevotella proteins with human proteome.\nWe also performed functional overrepresentation analysis of interacting proteins to understand their role in COVID-2019 severity.\nRESULTS: It was found that over-expressed Prevotella proteins can promote viral infection.\nAs per the results, Prevotella proteins, but not viral proteins are involved in multiple interactions with NF-kB, which is involved in increasing clinical severity of COVID-2019.\nPrevotella may have role in COVID-2019 outbreak and should be given importance for understanding disease mechanisms and improving treatment outcomes.\nSUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"As per the results, Prevotella proteins, but not viral proteins are involved in multiple interactions with NF-kB, which is involved in increasing clinical severity of COVID-2019.\", \"Prevotella may have role in COVID-2019 outbreak and should be given importance for understanding disease mechanisms and improving treatment outcomes.\"]}", "id": 204}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Sars-cov-2 reduces severe alveolar inflammation and barrier dysfunction\n\nAbstract:\nInfections with SARS-CoV-2 lead to mild to severe coronavirus disease-19 (COVID-19) with systemic symptoms.\nAlthough the viral infection originates in the respiratory system, it is unclear how the virus can overcome the alveolar barrier, which is observed in severe COVID-19 disease courses.\nTo elucidate the viral effects on the barrier integrity and immune reactions, we used mono-cell culture systems and a complex human alveolus-on-a-chip model composed of epithelial, endothelial, and mononuclear cells.\nOur data show that SARS-CoV-2 efficiently infected epithelial cells with high viral loads and inflammatory response, including the interferon expression.\nBy contrast, the adjacent endothelial layer was no infected and did neither show productive virus replication or interferon release.\nWith prolonged infection, both cell types are damaged, and the barrier function is deteriorated, allowing the viral particles to overbear.\nIn our study, we demonstrate that although SARS-CoV-2 is dependent on the epithelium for efficient replication, the neighboring endothelial cells are affected, e.g., by the epithelial cytokine release, which results in the damage of the alveolar barrier function and viral dissemination.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"With prolonged infection, both cell types are damaged, and the barrier function is deteriorated, allowing the viral particles to overbear.\", \"In our study, we demonstrate that although SARS-CoV-2 is dependent on the epithelium for efficient replication, the neighboring endothelial cells are affected, e.g., by the epithelial cytokine release, which results in the damage of the alveolar barrier function and viral dissemination.\"]}", "id": 205}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Drugs widely used to treat high blood pressure appear to make COVID-19 dangerously worse.\n\nAbstract:\nAims: The question of interactions between the renin angiotensin aldosterone system drugs and the incidence and prognosis of COVID-19 infection has been raised by the medical community.\nWe hypothesised that if patients treated with ACE inhibitors (ACEI) or AT1 receptor blockers (ARB) were more prone to SARS-CoV2 infection and had a worse prognosis than untreated patients, the prevalence of consumption of these drugs would be higher in patients with COVID-19 compared to the general population.\nMethods and results: We used a clinical epidemiology approach based on the estimation of standardised prevalence ratio (SPR) of consumption of ACEI and ARB in four groups of patients (including 187 COVID-19 positive) with increasing severity referred to the University hospital of Lille and in three French reference samples (the exhaustive North population (n=1,569,968), a representative sample of the French population (n=414,046), a random sample of Lille area (n=1,584)).\nThe SPRs of ACEI and ARB did not differ as the severity of the COVID-19 patients increased, being similar to the regular consumption of these drugs in the North of France population with the same non-significant increase for both treatment (1.17 [0.83-1.67]).\nA statistically significant increase in the SPR of ARB (1.56 [1.02-2.39]) was observed in intensive care unit patients only.\nAfter stratification on obesity, this increase was limited to the high risk subgroup of obese patients.\nConclusions: Our results strongly support the recommendation that ACEI and ARB should be continued in the population and in COVID-19 positive patients, reinforcing the position of several scientific societies.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 206}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Supplementation is unlikely to show an effect in people whose vitamin C levels are already high\n\nAbstract:\nOptimal nutrition can improve well-being and might mitigate the risk and morbidity associated with coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).\nThis review summarizes nutritional guidelines to support dietary counseling provided by dietitians and health-related professionals.\nThe majority of documents encouraged the consumption of fruits, vegetables, and whole grain foods.\nThirty-one percent of the guidelines highlighted the importance of minerals and vitamins such as zinc and vitamins C, A, and D to maintain a well-functioning immune system.\nDietary supplementation has not been linked to COVID-19 prevention.\nHowever, supplementation with vitamins C and D, as well as with zinc and selenium, was highlighted as potentially beneficial for individuals with, or at risk of, respiratory viral infections or for those in whom nutrient deficiency is detected.\nThere was no convincing evidence that food or food packaging is associated with the transmission of COVID-19, but good hygiene practices for handling and preparing foods were recommended.\nNo changes to breastfeeding recommendations have been made, even in women diagnosed with COVID-19.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Dietary supplementation has not been linked to COVID-19 prevention.\", \"However, supplementation with vitamins C and D, as well as with zinc and selenium, was highlighted as potentially beneficial for individuals with, or at risk of, respiratory viral infections or for those in whom nutrient deficiency is detected.\"]}", "id": 207}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Coronavirus disease (COVID-19) is an infectious disease caused by a newly discovered coronavirus\n\nAbstract:\nOrigin of the COVID-19 virus has been intensely debated in the scientific community since the first infected cases were detected in December 2019.\nThe disease has caused a global pandemic, leading to deaths of thousands of people across the world and thus finding origin of this novel coronavirus is important in responding and controlling the pandemic.\nRecent research results suggest that bats or pangolins might be the original hosts for the virus based on comparative studies using its genomic sequences.\nThis paper investigates the COVID-19 virus origin by using artificial intelligence (AI) and raw genomic sequences of the virus.\nMore than 300 genome sequences of COVID-19 infected cases collected from different countries are explored and analysed using unsupervised clustering methods.\nThe results obtained from various AI-enabled experiments using clustering algorithms demonstrate that all examined COVID-19 virus genomes belong to a cluster that also contains bat and pangolin coronavirus genomes.\nThis provides evidences strongly supporting scientific hypotheses that bats and pangolins are probable hosts for the COVID-19 virus.\nAt the whole genome analysis level, our findings also indicate that bats are more likely the hosts for the COVID-19 virus than pangolins.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 208}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: The overall risk of dying in the hospital from COVID-19 among people with T1D is very low, however, one study found that this risk is higher for people with T1D (3.5 times higher) compared to people without diabetes.\n\nAbstract:\nObjective: To undertake a review and critical appraisal of published/preprint reports that offer methods of determining the effects of hypertension, diabetes, stroke, cancer, kidney issues, and high-cholesterol on COVID-19 disease severity.\nData sources: Google Scholar, PubMed, COVID-19 Open Research Dataset: a resource of over 128,000 scholarly articles, including over 59,000 articles with full text related to COVID-19, SARS-CoV-2, and coronaviruses.\nMethods: A search was conducted by two authors independently on the freely available COVID-19 Open Research Dataset (CORD-19).\nWe developed an automated search engine to screen a total of 59,000 articles in a few seconds.\nThe search engine was built using a retrieval function that ranks a set of documents based on the query terms appearing in each document regardless of their proximity within the document.\nFiltering of the articles was then undertaken using keywords and questions, e.g. \"Effects of diabetes on COVID/normal coronavirus/SARS-CoV-2/nCoV/COVID-19 disease severity, mortality?\".\nThe search terms were repeated for all the comorbidities considered in this paper.\nAdditional articles were retrieved by searching via Google Scholar and PubMed.\nFindings: A total of 54 articles were considered for a full review.\nIt was observed that diabetes, hypertension, and cholesterol levels possess an apparent relation to COVID-19 severity.\nOther comorbidities, such as cancer, kidney disease, and stroke, must be further evaluated to determine a strong relationship to the virus.\nReports associating cancer, kidney disease, and stroke with COVID-19 should be carefully interpreted, not only because of the size of the samples, but also because patients could be old, have a history of smoking, or have any other clinical condition suggesting that these factors might be associated with the poor COVID-19 outcomes rather than the comorbidity itself.\nSuch reports could lead many oncologists and physicians to change their treatment strategies without solid evidence and recommendations.\nFurther research regarding this relationship and its clinical management is warranted.\nAdditionally, treatment options must be examined further to provide optimal treatment and ensure better outcomes for patients suffering from these comorbidities.\nIt should be noted that, whether definitive measurements exist or not, the care of patients as well as the research involved should be largely prioritized to tackle this deadly pandemic.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"It was observed that diabetes, hypertension, and cholesterol levels possess an apparent relation to COVID-19 severity.\"]}", "id": 209}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: fever is beneficial to the body's natural immune response to fight covid-19;\n\nAbstract:\nFever has been reported as a common symptom occurring in COVID-19 illness.\nOver the counter antipyretics such as ibuprofen and acetaminophen are often taken by individuals to reduce the discomfort of fever.\nRecently, the safety of ibuprofen in COVID-19 patients has been questioned due to anecdotal reports of worsening symptoms in previously healthy young adults.\nStudies show that ibuprofen demonstrates superior efficacy in fever reduction compared to acetaminophen.\nAs fever may have benefit in shortening the duration of viral illness, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response when taken during the early stages of COVID-19 illness.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"As fever may have benefit in shortening the duration of viral illness, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response when taken during the early stages of COVID-19 illness.\"]}", "id": 210}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Low vitamin k status predicts ards in a cohort of 138 hospitalized patients with covid-19.\n\nAbstract:\nIt has recently been hypothesised that Vitamin K could play a role in COVID-19.\nWe aimed to test the hypothesis that low vitamin K status is a common characteristic of patients hospitalized with COVID-19 compared to population controls; and that low vitamin K status predicts mortality in COVID-19 patients.\nIn a cohort of 138 COVID-19 patients and 140 population controls, we measured plasma dephosphorylated-uncarboxylated Matrix Gla Protein (dp-ucMGP), which reflects the functional Vitamin K status in peripheral tissue.\nFourty-three patients died within 90-days from admission.\nIn patients, levels of dp-ucMGP differed significantly between survivors (mean 877; 95% CI: 778; 995) and non-survivors (mean 1445; 95% CI: 1148; 1820).\nFurthermore, levels of dp-ucMGP (pmol/L) were considerably higher in patients (mean 1022; 95% CI: 912; 1151) compared to controls (mean 509; 95% CI: 485; 540).\nCox regression survival analysis showed that increasing levels of dp-ucMGP (reflecting low Vitamin K status) were associated with higher mortality risk (sex-and age-adjusted hazard ratio per doubling of dp-ucMGP was 1.50, 95% CI: 1.03; 2.18).\nIn conclusion, we found that low Vitamin K status predicted mortality in patients with COVID-19 supporting a potential role of Vitamin K in COVID-19.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"We aimed to test the hypothesis that low vitamin K status is a common characteristic of patients hospitalized with COVID-19 compared to population controls; and that low vitamin K status predicts mortality in COVID-19 patients.\", \"In conclusion, we found that low Vitamin K status predicted mortality in patients with COVID-19 supporting a potential role of Vitamin K in COVID-19.\"]}", "id": 211}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Evolutionary arms race between virus and virus drives genetic diversity in bat sars related coronavirus spike genes\n\nAbstract:\nThe Chinese horseshoe bat (Rhinolophus sinicus), reservoir host of severe acute respiratory syndrome coronavirus (SARS-CoV), carries many bat SARS-related CoVs (SARSr-CoVs) with high genetic diversity, particularly in the spike gene.\nDespite these variations, some bat SARSr-CoVs can utilize the orthologs of the human SARS-CoV receptor, angiotensin-converting enzyme 2 (ACE2), for entry.\nIt is speculated that the interaction between bat ACE2 and SARSr-CoV spike proteins drives diversity.\nHere, we identified a series of R. sinicus ACE2 variants with some polymorphic sites involved in the interaction with the SARS-CoV spike protein.\nPseudoviruses or SARSr-CoVs carrying different spike proteins showed different infection efficiencies in cells transiently expressing bat ACE2 variants.\nConsistent results were observed by binding affinity assays between SARS-CoV and SARSr-CoV spike proteins and receptor molecules from bats and humans.\nAll tested bat SARSr-CoV spike proteins had a higher binding affinity to human ACE2 than to bat ACE2, although they showed a 10-fold lower binding affinity to human ACE2 compared with that of their SARS-CoV counterpart.\nStructure modeling revealed that the difference in binding affinity between spike and ACE2 might be caused by the alteration of some key residues in the interface of these two molecules.\nMolecular evolution analysis indicates that some key residues were under positive selection.\nThese results suggest that the SARSr-CoV spike protein and R. sinicus ACE2 may have coevolved over time and experienced selection pressure from each other, triggering the evolutionary arms race dynamics.\nIMPORTANCE Evolutionary arms race dynamics shape the diversity of viruses and their receptors.\nIdentification of key residues which are involved in interspecies transmission is important to predict potential pathogen spillover from wildlife to humans.\nPreviously, we have identified genetically diverse SARSr-CoVs in Chinese horseshoe bats.\nHere, we show the highly polymorphic ACE2 in Chinese horseshoe bat populations.\nThese ACE2 variants support SARS-CoV and SARSr-CoV infection but with different binding affinities to different spike proteins.\nThe higher binding affinity of SARSr-CoV spike to human ACE2 suggests that these viruses have the capacity for spillover to humans.\nThe positive selection of residues at the interface between ACE2 and SARSr-CoV spike protein suggests long-term and ongoing coevolutionary dynamics between them.\nContinued surveillance of this group of viruses in bats is necessary for the prevention of the next SARS-like disease.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"It is speculated that the interaction between bat ACE2 and SARSr-CoV spike proteins drives diversity.\", \"These results suggest that the SARSr-CoV spike protein and R. sinicus ACE2 may have coevolved over time and experienced selection pressure from each other, triggering the evolutionary arms race dynamics.\"]}", "id": 212}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: B cells and t cells mediate immunity to covid-19\n\nAbstract:\nRecent reports that antibodies to SARS-CoV-2 are not maintained in the serum following recovery from the virus have caused alarm.\nHowever, the absence of specific antibodies in the serum does not necessarily mean an absence of immune memory.\nHere, we discuss our current understanding of the relative contribution of B cells and T cells to immunity to SARS-CoV-2 and the implications for the development of effective treatments and vaccines for COVID-19.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Here, we discuss our current understanding of the relative contribution of B cells and T cells to immunity to SARS-CoV-2 and the implications for the development of effective treatments and vaccines for COVID-19.\"]}", "id": 213}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Personalized screening approach for hospitalized patients with covid-19\n\nAbstract:\nHospitalized patients with COVID-19 experiencing respiratory symptoms have different complications (inflammatory, co-infection and thrombotic) that are identifiable by analytics patterns.\nPersonalized treatment decisions decreased early mortality (OR 0.144, CI 0.03-0.686; p=0.015).\nIncreasing age (OR 1.06; p=0.038) and therapeutic effort limitation (OR 9.684; p<0.001) were associated with higher mortality.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Personalized treatment decisions decreased early mortality (OR 0.144, CI 0.03-0.686; p=0.015).\"]}", "id": 214}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: In one study it was found that 100% of ICU COVID-19 patients less than 75 years old had vitamin D insufficiency.\n\nAbstract:\nThe severity of coronavirus 2019 infection (COVID-19) is determined by the presence of pneumonia, severe acute respiratory distress syndrome (SARS-CoV-2), myocarditis, microvascular thrombosis and/or cytokine storms, all of which involve underlying inflammation.\nA principal defence against uncontrolled inflammation, and against viral infection in general, is provided by T regulatory lymphocytes (Tregs).\nTreg levels have been reported to be low in many COVID-19 patients and can be increased by vitamin D supplementation.\nLow vitamin D levels have been associated with an increase in inflammatory cytokines and a significantly increased risk of pneumonia and viral upper respiratory tract infections.\nVitamin D deficiency is associated with an increase in thrombotic episodes, which are frequently observed in COVID-19.\nVitamin D deficiency has been found to occur more frequently in patients with obesity and diabetes.\nThese conditions are reported to carry a higher mortality in COVID-19.\nIf vitamin D does in fact reduce the severity of COVID-19 in regard to pneumonia/ARDS, inflammation, inflammatory cytokines and thrombosis, it is our opinion that supplements would offer a relatively easy option to decrease the impact of the pandemic.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"A principal defence against uncontrolled inflammation, and against viral infection in general, is provided by T regulatory lymphocytes (Tregs).\", \"Treg levels have been reported to be low in many COVID-19 patients and can be increased by vitamin D supplementation.\", \"Low vitamin D levels have been associated with an increase in inflammatory cytokines and a significantly increased risk of pneumonia and viral upper respiratory tract infections.\", \"Vitamin D deficiency is associated with an increase in thrombotic episodes, which are frequently observed in COVID-19.\", \"Vitamin D deficiency has been found to occur more frequently in patients with obesity and diabetes.\", \"These conditions are reported to carry a higher mortality in COVID-19.\", \"If vitamin D does in fact reduce the severity of COVID-19 in regard to pneumonia/ARDS, inflammation, inflammatory cytokines and thrombosis, it is our opinion that supplements would offer a relatively easy option to decrease the impact of the pandemic.\"]}", "id": 215}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Further, the report enlightened on the use of vitamin D on COVID-19 patients\n\nAbstract:\nBackground: Following emerge of a novel coronavirus from Wuhan, China, in December 2019, it has affected the whole world and after months of efforts by the medical communities, there is still no specific approach for prevention and treatment against the Coronavirus Disease 2019 (COVID-19).\nEvidence recommends that vitamin D might be an important supportive agent for the immune system, mainly in cytokine response regulation against COVID-19.\nHence, we carried out a rapid systematic review and meta-analysis along with an ecological investigation in order to maximize the use of everything that exists about the role of vitamin D in the COVID-19.\nMethods: A systematic search was performed in PubMed, Scopus, Embase, Cochrane Library, Web of Science and Google Scholar (intitle) as well as preprint database of medRxiv, bioRxiv, Research Square, preprints.org, search engine of ScienceDirect and a rapid search through famous journals up to May 26, 2020.\nStudies focused on the role of vitamin D in confirmed COVID-19 patients were entered into the systematic review.\nAlong with our main aim, to find the second objective: correlation of global vitamin D status and COVID-19 recovery and mortality we carried out a literature search in PubMed database to identify the national or regional studies reported the vitamin D status globally.\nCMA v. 2.2.064 and SPSS v.16 were used for data analysis.\nResults: Out of nine studies entered into our systematic review, six studies containing 3,822 participants entered into the meta-analysis.\nThe meta-analysis indicated that 46.5% of COVID-19 patients were suffering from vitamin D deficiency (95% CI, 28.2%-65.8%) and in 43.3% of patients, levels of vitamin D were insufficient (95% CI, 27.4%-60.8%).\nIn regard to our ecological investigation on 51 countries including 408,748 participants, analyses indicated no correlation between vitamin D levels and recovery rate (r= 0.041) as well as mortality rate (r=-0.073) globally.\nHowever, given latitude, a small reverse correlation between mortality rate and vitamin D status was observed throughout the globe (r= -0.177).\nIn Asia, a medium direct correlation was observed for recovery rate (r= 0.317) and a significant reveres correlation for mortality rate (r= -0.700) with vitamin D status in such patients.\nIn Europe, there were no correlations for both recovery (r= 0.040) and mortality rate (r= -0.035).\nIn Middle East, the recovery rate (r= 0.267) and mortality rate (r= -0.217) showed a medium correlation.\nIn North and Sought America, surprisingly, both recovery and mortality rate demonstrated a direct correlation respectively (r= 1.000, r=0.500).\nIn Oceania, unexpectedly, recovery (r= -1.000) and mortality (r= -1.000) rates were in considerable reverse correlation with vitamin D levels.\nConclusion: In this systematic review and meta-analysis with an ecological approach, we found a high percentage of COVID-19 patients who suffer from vitamin D deficiency or insufficiency.\nMuch more important, our ecological investigation resulted in substantial direct and reverse correlations between recovery and mortality rates of COVID-19 patients with vitamin D status in different countries.\nConsidering latitudes, a small reverse correlation between vitamin D status and mortality rate was found globally.\nIt seems that populations with lower levels of vitamin D might be more susceptible to the novel coronavirus infection.\nNevertheless, due to multiple limitations, if this study does not allow to quantify a value of the Vitamin D with full confidence, it allows at least to know what the Vitamin D might be and that it would be prudent to invest in this direction through comprehensive large randomized clinical trials.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"In Middle East, the recovery rate (r= 0.267) and mortality rate (r= -0.217) showed a medium correlation.\"]}", "id": 216}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Bacterial modification of the host glycosaminoglycan heparan oxide modulates sars-cov-2 infectivity\n\nAbstract:\nThe human microbiota has a close relationship with human disease and it remodels components of the glycocalyx including heparan sulfate (HS).\nStudies of the severe acute respiratory syndrome coronavirus (SARS-CoV-2) spike protein receptor binding domain suggest that infection requires binding to HS and angiotensin converting enzyme 2 (ACE2) in a codependent manner.\nHere, we show that commensal host bacterial communities can modify HS and thereby modulate SARS-CoV-2 spike protein binding and that these communities change with host age and sex.\nCommon human-associated commensal bacteria whose genomes encode HS-modifying enzymes were identified.\nThe prevalence of these bacteria and the expression of key microbial glycosidases in bronchoalveolar lavage fluid (BALF) was lower in adult COVID-19 patients than in healthy controls.\nThe presence of HS-modifying bacteria decreased with age in two large survey datasets, FINRISK 2002 and American Gut, revealing one possible mechanism for the observed increase in COVID-19 susceptibility with age.\nIn vitro, bacterial glycosidases from unpurified culture media supernatants fully blocked SARS-CoV-2 spike binding to human H1299 protein lung adenocarcinoma cells.\nHS-modifying bacteria in human microbial communities may regulate viral adhesion, and loss of these commensals could predispose individuals to infection.\nUnderstanding the impact of shifts in microbial community composition and bacterial lyases on SARS-CoV-2 infection may lead to new therapeutics and diagnosis of susceptibility.\nGraphical Abstract.\nDiagram of hypothesis for bacterial mediation of SARS-CoV-2 infection through heparan sulfate (HS).\nIt is well known that host microbes groom the mucosa where they reside.\nRecent investigations have shown that HS, a major component of mucosal layers, is necessary for SARS-CoV-2 infection.\nIn this study we examine the impact of microbial modification of HS on viral attachment.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Understanding the impact of shifts in microbial community composition and bacterial lyases on SARS-CoV-2 infection may lead to new therapeutics and diagnosis of susceptibility.\"]}", "id": 217}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: the virus can stay on surfaces long enough to be a source of transmission\n\nAbstract:\nThe ocular surface has been suggested as a site of infection with Coronavirus-2 (SARS-CoV-2) responsible for the coronavirus disease-19 (COVID-19).\nThis review examines the evidence for this hypothesis, and its implications for clinical practice.\nSevere Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2), responsible for the COVID-19 pandemic, is transmitted by person-to-person contact, via airborne droplets, or through contact with contaminated surfaces.\nSARS-CoV-2 binds to angiotensin converting enzyme-2 (ACE2) to facilitate infection in humans.\nThis review sets out to evaluate evidence for the ocular surface as a route of infection.\nA literature search in this area was conducted on 15 April 2020 using the Scopus database.\nIn total, 287 results were returned and reviewed.\nThere is preliminary evidence for ACE2 expression on corneal and conjunctival cells, but most of the other receptors to which coronaviruses bind appear to be found under epithelia of the ocular surface.\nEvidence from animal studies is limited, with a single study suggesting viral particles on the eye can travel to the lung, resulting in very mild infection.\nCoronavirus infection is rarely associated with conjunctivitis, with occasional cases reported in patients with confirmed COVID-19, along with isolated cases of conjunctivitis as a presenting sign.\nCoronaviruses have been rarely isolated from tears or conjunctival swabs.\nThe evidence suggests coronaviruses are unlikely to bind to ocular surface cells to initiate infection.\nAdditionally, hypotheses that the virus could travel from the nasopharynx or through the conjunctival capillaries to the ocular surface during infection are probably incorrect.\nConjunctivitis and isolation of the virus from the ocular surface occur only rarely, and overwhelmingly in patients with confirmed COVID-19.\nNecessary precautions to prevent person-to-person transmission should be employed in clinical practice throughout the pandemic, and patients should be reminded to maintain good hygiene practices.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2), responsible for the COVID-19 pandemic, is transmitted by person-to-person contact, via airborne droplets, or through contact with contaminated surfaces.\"]}", "id": 218}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: T cell anergy in covid-19 reflects virus persistence and poor outcomes\n\nAbstract:\nCoronavirus disease 2019 (COVID-19) can lead to severe pneumonia and hyperinflammation.\nSo far, insufficient or excessive T cell responses were described in patients.\nWe applied novel approaches to analyze T cell reactivity and showed that T anergy is already present in non-ventilated COVID-19 patients, very pronounced in ventilated patients, strongly associated with virus persistence and reversible with clinical recovery.\nT cell activation was measured by downstream effects on responder cells like basophils, plasmacytoid dendritic cells, monocytes and neutrophils in whole blood and proved to be much more meaningful than classical readouts with PBMCs.\nMonocytes responded stronger in males than females and IL-2 partially reversed T cell anergy.\nDownstream markers of T cell anergy were also found in fresh blood samples of critically ill patients with severe T cell anergy.\nBased on our data we were able to develop a score to predict fatal outcomes and to identify patients that may benefit from strategies to overcome T cell anergy.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"We applied novel approaches to analyze T cell reactivity and showed that T anergy is already present in non-ventilated COVID-19 patients, very pronounced in ventilated patients, strongly associated with virus persistence and reversible with clinical recovery.\", \"Downstream markers of T cell anergy were also found in fresh blood samples of critically ill patients with severe T cell anergy.\", \"Based on our data we were able to develop a score to predict fatal outcomes and to identify patients that may benefit from strategies to overcome T cell anergy.\"]}", "id": 219}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: the symptoms of COVID-19 are no worse than a cold\n\nAbstract:\nSARS-CoV-2 is a novel beta-coronavirus causing over 200.000 lethal cases within six months of first infecting humans.\nSARS-CoV-2 causes COVID-19, a form of severe acute respiratory syndrome (SARS).\nCOVID-19 is characterized by two phases: the first resembles the flu with pneumonia, but after about seven or eight days the disease suddenly worsens to a sepsis-like syndrome.\nIt is difficult to explain this virus-immune-pathology sequence from virology or immunology only.\nThis paper hypothesizes that host-produced anti-spike protein antibodies are responsible for immune-induced viral dissemination.\nSubsequently, systemic distribution of virus-antibodies complexes activates the immune pathology observed in severe COVID-19.\nThis hypothesis may be counterintuitive to immunologist that consider many anti-spike antibodies to be virus-neutralizing antibodies.\nAlthough anti-spike antibodies may hinder infection of epithelial cells, antibody binding to the spike protein may facilitate virus infection of myeloid leukocytes.\nIf myeloid leukocytes reenter the circulation, they could spread the virus from a locoregional infection to a systemic disease.\nDisseminated virus in combination with antibodies results in dispersed virus-antibody complexes that overstimulate the immune system.\nThe hypothesis aligns with the sequences of virus, immune and pathological events in COVID-19.\nThe delay in onset from both syndromes results from an immune system still na\u00efve to the non-cross-reactive spike protein.\nDetails of this hypothesis are in concordance with many clinical characteristics of COVID-19, including its predominant lethality for the elderly, and the mostly asymptomatic course of disease in children.\nIt predicts putative detrimental effects of vaccines that induce virus-neutralizing antibodies against the spike protein, as has been shown for other coronaviruses.\nThis hypothesis has consequences for treatment of patients, evaluation of personal and herd immunity and vaccine development.\nIn patients, cellular immunity should be stimulated.\nNeutralizing antibodies might not be indicative for immunity.\nVaccines should aim to stimulate cellular immunity COVID-19 and/or stimulate humoral immunity against viral proteins except for the immunodominant spike protein.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"SARS-CoV-2 causes COVID-19, a form of severe acute respiratory syndrome (SARS).\", \"COVID-19 is characterized by two phases: the first resembles the flu with pneumonia, but after about seven or eight days the disease suddenly worsens to a sepsis-like syndrome.\"]}", "id": 220}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Hidden immune weakness found in gravely ill COVID-19 patients.\n\nAbstract:\nThe WHO has declared SARS-CoV-2 outbreak a public health emergency of international concern.\nHowever, to date, there was hardly any study in characterizing the immune responses, especially adaptive immune responses to SARS-CoV-2 infection.\nIn this study, we collected blood from COVID-19 patients who have recently become virus-free and therefore were discharged, and analyzed their SARS-CoV-2-specific antibody and T cell responses.\nWe observed SARS-CoV-2-specific humoral and cellular immunity in the patients.\nBoth were detected in newly discharged patients, suggesting both participate in immune-mediated protection to viral infection.\nHowever, follow-up patients (2 weeks post discharge) exhibited high titers of IgG antibodies, but with low levels of virus-specific T cells, suggesting that they may enter a quiescent state.\nOur work has thus provided a basis for further analysis of protective immunity to SARS-CoV-2, and understanding the pathogenesis of COVID-19, especially in the severe cases.\nIt has also implications in designing an effective vaccine to protect and treat SARS-CoV-2 infection.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 221}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: to reduce the spread of the virus: try to stay at least 2 metres (3 steps) away from anyone you do not live with (or anyone not in your support bubble); wash your hands with soap and water often - do this for at least 20 seconds; use hand sanitiser gel if soap and water are not available; wash your hands as soon as you get home; cover your mouth and nose with a tissue or your sleeve (not your hands) when you cough or sneeze; put used tissues in the bin immediately and wash your hands afterwards\n\nAbstract:\nSocial distancing measures, with varying degrees of restriction, have been imposed around the world in order to stem the spread of COVID-19.\nIn this work we analyze the effect of current social distancing measures in the United States.\nWe quantify the reduction in doubling rate, by state, that is associated with social distancing.\nWe find that social distancing is associated with a statistically-significant reduction in the doubling rate for all but three states.\nAt the same time, we do not find significant evidence that social distancing has resulted in a reduction in the number of daily confirmed cases.\nInstead, social distancing has merely stabilized the spread of the disease.\nWe provide an illustration of our findings for each state, including point estimates of the effective reproduction number, R, both with and without social distancing.\nWe also discuss the policy implications of our findings.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"We find that social distancing is associated with a statistically-significant reduction in the doubling rate for all but three states.\"]}", "id": 222}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Vitamin D Supplementation Could Reduce Risk of Influenza and COVID-19 Infections and Deaths\n\nAbstract:\nThe outbreak of COVID-19 has created a global public health crisis.\nLittle is known about the protective factors of this infection.\nTherefore, preventive health measures that can reduce the risk of infection, progression and severity are desperately needed.\nThis review discussed the possible roles of vitamin D in reducing the risk of COVID-19 and other acute respiratory tract infections and severity.\nMoreover, this study determined the correlation of vitamin D levels with COVID-19 cases and deaths in 20 European countries as of 20 May 2020.\nA significant negative correlation (p=0.033) has been observed between mean vitamin D levels and COVID-19 cases per one million population in European countries.\nHowever, the correlation of vitamin D with COVID-19 deaths of these countries was not significant.\nSome retrospective studies demonstrated a correlation between vitamin D status and COVID-19 severity and mortality, while other studies did not find the correlation when confounding variables are adjusted.\nSeveral studies demonstrated the role of vitamin D in reducing the risk of acute viral respiratory tract infections and pneumonia.\nThese include direct inhibition with viral replication or with anti-inflammatory or immunomodulatory ways.\nIn the meta-analysis, vitamin D supplementation has been shown as safe and effective against acute respiratory tract infections.\nThus, people who are at higher risk of vitamin D deficiency during this global pandemic should consider taking vitamin D supplements to maintain the circulating 25(OH)D in the optimal levels (75-125nmol/L).\nIn conclusion, there is not enough evidence on the association between vitamin D levels and COVID-19 severity and mortality.\nTherefore, randomized control trials and cohort studies are necessary to test this hypothesis.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"A significant negative correlation (p=0.033) has been observed between mean vitamin D levels and COVID-19 cases per one million population in European countries.\", \"Several studies demonstrated the role of vitamin D in reducing the risk of acute viral respiratory tract infections and pneumonia.\", \"These include direct inhibition with viral replication or with anti-inflammatory or immunomodulatory ways.\", \"In the meta-analysis, vitamin D supplementation has been shown as safe and effective against acute respiratory tract infections.\", \"Thus, people who are at higher risk of vitamin D deficiency during this global pandemic should consider taking vitamin D supplements to maintain the circulating 25(OH)D in the optimal levels (75-125nmol/L).\"]}", "id": 223}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: a popular treatment to tamp down the immune system in severely ill patients may help a few, but could harm many others. \n\nAbstract:\nSeveral related human coronaviruses (HCoVs) are endemic in the human population, causing mild respiratory infections1.\nSevere Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), the etiologic agent of Coronavirus disease 2019 (COVID-19), is a recent zoonotic infection that has quickly reached pandemic spread2,3.\nZoonotic introduction of novel coronaviruses is thought to occur in the absence of pre-existing immunity in the target human population.\nUsing diverse assays for detection of antibodies reactive with the SARS-CoV-2 Spike (S) glycoprotein, we demonstrate the presence of pre-existing immunity in uninfected and unexposed humans to the new coronavirus.\nSARS-CoV-2 S-reactive antibodies, exclusively of the IgG class, were readily detectable by a sensitive flow cytometry-based method in SARS-CoV-2-uninfected individuals with recent HCoV infection and targeted the S2 subunit.\nIn contrast, SARS-CoV-2 infection induced higher titres of SARS-CoV-2 S-reactive IgG antibodies, as well as concomitant IgM and IgA antibodies throughout the observation period of 6 weeks since symptoms onset.\nHCoV patient sera also variably reacted with SARS-CoV-2 S and nucleocapsid (N), but not with the S1 subunit or the receptor binding domain (RBD) of S on standard enzyme immunoassays.\nNotably, HCoV patient sera exhibited specific neutralising activity against SARS-CoV-2 S pseudotypes, according to levels of SARS-CoV-2 S-binding IgG and with efficiencies comparable to those of COVID-19 patient sera.\nDistinguishing pre-existing and de novo antibody responses to SARS-CoV-2 will be critical for serology, seroprevalence and vaccine studies, as well as for our understanding of susceptibility to and natural course of SARS-CoV-2 infection.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 224}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Intradermal-delivered hiv vaccine provides anamnestic protection in a rhesus macaque sars-cov-2 challenge model\n\nAbstract:\nCoronavirus disease 2019 (COVID-19), caused by the SARS-CoV-2 virus, has had a dramatic global impact on public health, social, and economic infrastructures.\nHere, we assess immunogenicity and anamnestic protective efficacy in rhesus macaques of the intradermal (ID)-delivered SARS-CoV-2 spike DNA vaccine, INO-4800.\nINO-4800 is an ID-delivered DNA vaccine currently being evaluated in clinical trials.\nVaccination with INO-4800 induced T cell responses and neutralizing antibody responses against both the D614 and G614 SARS-CoV-2 spike proteins.\nSeveral months after vaccination, animals were challenged with SARS-CoV-2 resulting in rapid recall of anti-SARS-CoV-2 spike protein T and B cell responses.\nThese responses were associated with lower viral loads in the lung and with faster nasal clearance of virus.\nThese studies support the immune impact of INO-4800 for inducing both humoral and cellular arms of the adaptive immune system which are likely important for providing durable protection against COVID-19 disease.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Here, we assess immunogenicity and anamnestic protective efficacy in rhesus macaques of the intradermal (ID)-delivered SARS-CoV-2 spike DNA vaccine, INO-4800.\", \"Vaccination with INO-4800 induced T cell responses and neutralizing antibody responses against both the D614 and G614 SARS-CoV-2 spike proteins.\", \"Several months after vaccination, animals were challenged with SARS-CoV-2 resulting in rapid recall of anti-SARS-CoV-2 spike protein T and B cell responses.\", \"These studies support the immune impact of INO-4800 for inducing both humoral and cellular arms of the adaptive immune system which are likely important for providing durable protection against COVID-19 disease.\"]}", "id": 225}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: For most patients, COVID-19 begins and ends in their lungs, because like the flu, coronaviruses are respiratory diseases\n\nAbstract:\nThe outbreak of the 2019 Novel Coronavirus (SARS-CoV-2) rapidly spread from Wuhan, China to more than 150 countries, areas or territories, causing staggering number of infections and deaths.\nA systematic profiling of the immune vulnerability landscape of SARS-CoV-2, which can bring critical insights into the immune clearance mechanism, peptide vaccine development, and antiviral antibody development, is lacking.\nIn this study, we investigated the potential of the SARS-CoV-2 viral proteins to induce class I and II MHC presentation and to form linear antibody epitopes.\nWe created an online database to broadly share the predictions as a resource for the research community.\nUsing this resource, we showed that genetic variations in SARS- CoV-2, though still few for the moment, already follow the pattern of mutations in related coronaviruses, and could alter the immune vulnerability landscape of this virus.\nImportantly, we discovered evidence that SARS-CoV-2, along with related coronaviruses, used mutations to evade attack from the human immune system.\nOverall, we present an immunological resource for SARS-CoV-2 that could promote both therapeutic development and mechanistic research.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 226}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: There are an urgent need for antivirals to treat the newly emerged SARS-CoV-2.\n\nAbstract:\nThe outbreak of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has posed the world at a pandemic risk.\nCoronavirus-19 disease (COVID-19) is an infectious disease caused by SARS-CoV-2, which causes pneumonia, requires intensive care unit hospitalization in about 10% of cases and can lead to a fatal outcome.\nSeveral efforts are currently made to find a treatment for COVID-19 patients.\nSo far, several anti-viral and immunosuppressive or immunomodulating drugs have demonstrated some efficacy on COVID-19 both in vitro and in animal models as well as in cases series.\nIn COVID-19 patients a pro-inflammatory status with high levels of interleukin (IL)-1B, IL-1 receptor (R)A and tumor necrosis factor (TNF)-&#945; has been demonstrated.\nMoreover, high levels of IL-6 and TNF-&#945; have been observed in patients requiring intensive-care-unit hospitalization.\nThis provided rationale for the use of anti-rheumatic drugs as potential treatments for this severe viral infection.\nOther agents, such as hydroxychloroquine and chloroquine might have a direct anti-viral effect.\nThe anti-viral aspect of immunosuppressants towards a variety of viruses has been known since long time and it is herein discussed in the view of searching for a potential treatment for SARS-CoV-2 infection.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 227}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: dexamethasone reduced death rates\n\nAbstract:\nRecent announcements indicated, without sharing any distinct published set of results, that the corticosteroid dexamethasone may reduce mortality of severe COVID-19 patients only.\nThe recent Coronavirus [severe acute respiratory syndrome (SARS)-CoV-2]-associated multiorgan disease, called COVID-19, has high morbidity and mortality due to autoimmune destruction of the lungs stemming from the release of a storm of pro-inflammatory cytokines.\nDefense against this Corona virus requires activated T cells and specific antibodies.\nInstead, cytokines are responsible for the serious sequelae of COVID-19 that damage the lungs.\nDexamethasone is a synthetic corticosteroid approved by the FDA 1958 as a broad-spectrum immunosuppressor and it is about 30 times as active and with longer duration of action (2-3 days) than cortisone.\nDexamethasone would limit the production of and damaging effect of the cytokines, but will also inhibit the protective function of T cells and block B cells from making antibodies, potentially leading to increased plasma viral load that will persist after a patient survives SARS.\nMoreover, dexamethasone would block macrophages from clearing secondary, nosocomial, infections.\nHence, dexamethasone may be useful for the short-term in severe, intubated, COVID-19 patients, but could be outright dangerous during recovery since the virus will not only persist, but the body will be prevented from generating protective antibodies.\nInstead, a pulse of intravenous dexamethasone may be followed by administration of nebulized triamcinolone (6 times as active as cortisone) to concentrate in the lungs only.\nThese corticosteroids could be given together with the natural flavonoid luteolin because of its antiviral and anti-inflammatory properties, especially its ability to inhibit mast cells, which are the main source of cytokines in the lungs.\nAt the end, we should remember that \"The good physician treats the disease; the great physician treats the patient who has the disease\" [Sir William Osler's (1849-1919)].", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Hence, dexamethasone may be useful for the short-term in severe, intubated, COVID-19 patients, but could be outright dangerous during recovery since the virus will not only persist, but the body will be prevented from generating protective antibodies.\"]}", "id": 228}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Unfortunately, to date, no vaccines or antiviral drugs have been approved for the treatment of SARS-CoV-2 infection by regulatory agencies.\n\nAbstract:\nDifferent treatments are currently used for clinical management of SARS-CoV-2 infection, but little is known about their efficacy yet.\nHere we present ongoing results to compare currently available drugs for a variety of diseases to find out if they counteract SARS-CoV-2-induced cytopathic effect in vitro.\nOur goal is to prioritize antiviral activity to provide a solid evidence-driven rationale for forthcoming clinical trials.\nSince the most effective antiviral approaches are usually based on combined therapies that tackle the viral life cycle at different stages, we are also testing combinations of drugs that may be critical to reduce the emergence of resistant viruses.\nWe will provide results as soon as they become available, so data should be interpreted with caution, clearly understanding the limitations of the in vitro model, that may not always reflect what could happen in vivo.\nThus, our goal is to test the most active antivirals identified in adequate animal models infected with SARS-CoV-2, to add more information about possible in vivo efficacy.\nIn turn, successful antivirals could be tested in clinical trials as treatments for infected patients, but also as pre-exposure prophylaxis to avoid novel infections until an effective and safe vaccine is developed.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 229}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Can coronavirus spread through food or packaging? No Absolutely not\n\nAbstract:\nAgriculture and the food sector are critical to food and nutrition security because they not only produce food but also contribute to economic empowerment by employing a large share of female and male workers, especially in developing countries.\nFood systems at all levels\u2015globally, domestically, locally, and in the home\u2015 are expected to be highly affected by the COVID-19 crisis.\nWomen and men work as food producers, processors, and traders and will likely be impacted differently.\nShocks or crises can exacerbate or reduce gender gaps, and so can policy responses to mitigate the impact of these crises or shocks.\nWe offer some perspectives and available country examples on how the COVID-19 crisis and responses to the crisis could be a setback or offer opportunities for gender equality in the food system.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 230}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: diabetes significantly increases coronavirus sufferers risk of dying\n\nAbstract:\nSome comorbidities are associated with severe coronavirus disease (Covid-19) but it is unclear whether some increase susceptibility to Covid-19.\nIn this case-control Mexican study we found that obesity represents the strongest predictor for Covid-19 followed by diabetes and hypertension in both sexes and chronic renal failure in females only.\nActive smoking was associated with decreased odds of Covid-19.\nThese findings indicate that these comorbidities are not only associated with severity of disease but also predispose for getting Covid-19.\nFuture research is needed to establish the mechanisms involved in each comorbidity and the apparent \"protective\" effect of cigarette smoking.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"In this case-control Mexican study we found that obesity represents the strongest predictor for Covid-19 followed by diabetes and hypertension in both sexes and chronic renal failure in females only.\"]}", "id": 231}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Can coronavirus spread through food or packaging? No Absolutely not\n\nAbstract:\nSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is easily transmitted from person to person, which has fueled the ongoing pandemic.\nGovernments in different countries have taken drastic actions such as complete lockdown.\nHowever, little attention has been paid to food safety and its potential linkage with the coronavirus disease (COVID-19) pandemic.\nSARS-CoV-2 spread from staff to food products or food surfaces is conceivable.\nAt least, instead of consuming unpackaged or uncovered foods, consumption of boiled or canned foods processed at high temperatures should be preferred.\nBefore consumption, consumers should clean the surface of canned foods.\nIn addition to recommending or enforcing simple precautions, such as using masks, governments must conduct mandatory SARS-CoV-2 tests regularly and intermittently for personnel who handle food materials or supporting materials (e.g., plastic pouches).\nLocal markets, such as those in Wuhan, which sell live animals and exotic foods for consumption, are a concern.\nTrade of exotic or wild animals, unhygienic marketplace conditions, and not cooking at high temperatures ought to be prohibited.\nThe consumption of vitamins, minerals, and other food-derived compounds such as omega fatty acids is a prudent way to improve the performance of the immune system.\nIn addition, nano-encapsulated materials with controlled release properties may be useful in protecting food products and packaging from SARS-CoV-2 contamination.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is easily transmitted from person to person, which has fueled the ongoing pandemic.\"]}", "id": 232}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: People with Diabetes May Have Higher Risk for COVID-19\n\nAbstract:\nAIMS: We aimed to briefly review the general characteristics of the novel coronavirus (SARS-CoV-2) and provide a better understanding of the coronavirus disease (COVID-19) in people with diabetes, and its management.\nMETHODS: We searched for articles in PubMed and Google Scholar databases till 02 April 2020, with the following keywords: \"SARS-CoV-2\", \"COVID-19\", \"infection\", \"pathogenesis\", \"incubation period\", \"transmission\", \"clinical features\", \"diagnosis\", \"treatment\", \"diabetes\", with interposition of the Boolean operator \"AND\".\nRESULTS: The clinical spectrum of COVID-19 is heterogeneous, ranging from mild flu-like symptoms to acute respiratory distress syndrome, multiple organ failure and death.\nOlder age, diabetes and other comorbidities are reported as significant predictors of morbidity and mortality.\nChronic inflammation, increased coagulation activity, immune response impairment, and potential direct pancreatic damage by SARS-CoV-2 might be among the underlying mechanisms of the association between diabetes and COVID-19.\nNo conclusive evidence exists to support the discontinuation of angiotensin-converting enzyme inhibitors (ACEI), angiotensin receptor blockers or thiazolidinediones because of COVID-19 in people with diabetes.\nCaution should be taken to potential hypoglycemic events with the use of chloroquine in these subjects.\nPatient tailored therapeutic strategies, rigorous glucose monitoring and careful consideration of drug interactions might reduce adverse outcomes.\nCONCLUSIONS: Suggestions are made on the possible pathophysiological mechanisms of the relationship between diabetes and COVID-19, and its management.\nNo definite conclusions can be made based on current limited evidence.\nFurther research regarding this relationship and its clinical management is warranted.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Older age, diabetes and other comorbidities are reported as significant predictors of morbidity and mortality.\", \"Chronic inflammation, increased coagulation activity, immune response impairment, and potential direct pancreatic damage by SARS-CoV-2 might be among the underlying mechanisms of the association between diabetes and COVID-19.\"]}", "id": 233}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: In severe cases of COVID-19, activation patterns of B cells resemble those seen in systemic lupus erythematosus, an autoimmune disease. Emory researchers want to see how far that resemblance extends.\n\nAbstract:\nThe WHO has declared SARS-CoV-2 outbreak a public health emergency of international concern.\nHowever, to date, there was hardly any study in characterizing the immune responses, especially adaptive immune responses to SARS-CoV-2 infection.\nIn this study, we collected blood from COVID-19 patients who have recently become virus-free and therefore were discharged, and analyzed their SARS-CoV-2-specific antibody and T cell responses.\nWe observed SARS-CoV-2-specific humoral and cellular immunity in the patients.\nBoth were detected in newly discharged patients, suggesting both participate in immune-mediated protection to viral infection.\nHowever, follow-up patients (2 weeks post discharge) exhibited high titers of IgG antibodies, but with low levels of virus-specific T cells, suggesting that they may enter a quiescent state.\nOur work has thus provided a basis for further analysis of protective immunity to SARS-CoV-2, and understanding the pathogenesis of COVID-19, especially in the severe cases.\nIt has also implications in designing an effective vaccine to protect and treat SARS-CoV-2 infection.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 234}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: To help slow the spread and reduce your risk of COVID-19, stay at least 6 feet away from others. Keeping physical distance is important, even if you are not sick.\n\nAbstract:\nBACKGROUND: The Australian and New Zealand governments both initiated strict social distancing measures in response to the COVID-19 pandemic in late March.\nIt remains difficult to quantify the impact this had in reducing the spread of the virus.\nMETHODS: Bayesian structural time series model provide a model to quantify the scenario in which these government-level interventions were not placed.\nOur models predict these strict social distancing measures caused a 79% and 61% reduction in the daily cases of COVID-19 across Australia and New Zealand respectively.\nCONCLUSION: This provides both evidence and impetus for governments considering similar measures in response to COVID-19 and other pandemics.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Our models predict these strict social distancing measures caused a 79% and 61% reduction in the daily cases of COVID-19 across Australia and New Zealand respectively.\"]}", "id": 235}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Antibody responses to sars-cov2 are similar in children with mis-c compared to adults with covid-19\n\nAbstract:\nClinical manifestations of COVID-19 caused by the novel coronavirus SARS-CoV-2 are associated with age.\nWhile children are largely spared from severe respiratory disease, they can present with a SARS-CoV-2-associated multisystem inflammatory syndrome (MIS-C) similar to Kawasaki's disease.\nHere, we show distinct antibody (Ab) responses in children with MIS-C compared to adults with severe COVID-19 causing acute respiratory distress syndrome (ARDS), and those who recovered from mild disease.\nThere was a reduced breadth and specificity of anti-SARS-CoV-2-specific antibodies in MIS-C patients compared to the COVID patient groups; MIS-C predominantly generated IgG Abs specific for the Spike (S) protein but not for the nucleocapsid (N) protein, while both COVID-19 cohorts had anti-S IgG, IgM and IgA Abs, as well as anti-N IgG Abs.\nMoreover, MIS-C patients had reduced neutralizing activity compared to COVID-19 cohorts, indicating a reduced protective serological response.\nThese results suggest a distinct infection course and immune response in children and adults who develop severe disease, with implications for optimizing treatments based on symptom and age.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Here, we show distinct antibody (Ab) responses in children with MIS-C compared to adults with severe COVID-19 causing acute respiratory distress syndrome (ARDS), and those who recovered from mild disease.\", \"These results suggest a distinct infection course and immune response in children and adults who develop severe disease, with implications for optimizing treatments based on symptom and age.\"]}", "id": 236}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Single-hybrid analyses reveal sars-cov-2 interference with intrinsic immune response in the human gut\n\nAbstract:\nObjective Exacerbated pro-inflammatory immune response contributes to COVID-19 pathology.\nDespite the evidence about SARS-CoV-2 infecting the human gut, little is known about the importance of the enteric phase of SARS-CoV-2 for the viral lifecycle and for the development of COVID-19-associated pathologies.\nSimilarly, it remains unknown whether the innate immune response triggered in this organ to combat viral infection is similar or distinct compared to the one triggered in other organs.\nDesign We exploited human ileum-and colon-derived organoids as a non-transformed culture model supporting SARS-CoV-2 infection.\nWe characterized the replication kinetics of SARS-CoV-2 in intestinal epithelial cells and correlated the expression of the viral receptor ACE2 with infection.\nWe performed conventional and targeted single-cell transcriptomics and multiplex single-molecule RNA fluorescence in situ hybridization and used IFN-reporter bioassays to characterize the response of primary human intestinal epithelial cells to SARS-CoV-2 infection.\nResults We identified a subpopulation of enterocytes as the prime target of SARS-CoV-2.\nWe found the lack of positive correlation between susceptibility to infection and the expression of ACE2 and revealed that SARS-CoV-2 downregulates ACE2 expression upon infection.\nInfected cells activated strong proinflammatory programs and produced interferon, while expression of interferon-stimulated genes was limited to bystander cells due to SARS-CoV-2 suppressing the autocrine action of interferon in infected cells.\nConclusion Our findings reveal that SARS-CoV-2 curtails the immune response in primary human intestinal epithelial cells to promote its replication and spread and this highlights the gut as a proinflammatory reservoir that should be considered to fully understand SARS-CoV-2 pathogenesis.\nSignificance of the study What is already known about this subject?\nCOVID-19 patients have gastrointestinal symptoms which likely correlates with SARS-CoV-2 infection of the intestinal epithelium SARS-CoV-2 replicates in human intestinal epithelial cells.\nIntestinal organoids are a good model to study SARS-CoV-2 infection of the gastrointestinal tract There is a limited interferon response in human lung epithelial cells upon SARS-CoV-2 infection.\nWhat are the new findings?\nA specific subpopulation of enterocytes are the prime targets of SARS-CoV-2 infection of the human gut.\nThere is a lack of correlation between ACE2 expression and susceptibility to SARS-CoV-2 infection.\nSARS-CoV-2 downregulates ACE2 expression upon infection.\nHuman intestinal epithelium cells produce interferon upon SARS-CoV-2 infection.\nInterferon acts in a paracrine manner to induce interferon stimulated genes that control viral infection only in bystander cells.\nSARS-CoV-2 actively blocks interferon signaling in infected cells.\nHow might it impact on clinical practice in the foreseeable future?\nThe absence of correlation between ACE2 levels and susceptibility suggest that medications influencing ACE2 levels (e.g. high blood pressure drugs) will not make patients more susceptible to SARS-CoV-2 infection.\nThe restricted cell tropism and the distinct immune response mounted by the GI tract, suggests that specific cellular restriction/replication factors and organ specific intrinsic innate immune pathways can represent unique therapeutic targets to treat COVD-19 patients by considering which organ is most infected/impacted by SARS-CoV-2.\nThe strong pro-inflammatory signal mounted by the intestinal epithelium can fuel the systemic inflammation observed in COVID-19 patients and is likely participating in the lung specific pathology.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Conclusion Our findings reveal that SARS-CoV-2 curtails the immune response in primary human intestinal epithelial cells to promote its replication and spread and this highlights the gut as a proinflammatory reservoir that should be considered to fully understand SARS-CoV-2 pathogenesis.\", \"Human intestinal epithelium cells produce interferon upon SARS-CoV-2 infection.\"]}", "id": 237}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: a small number of pets worldwide, including cats and dogs, can be infected with the virus that causes COVID-19, mostly after close contact with people with COVID-19.\n\nAbstract:\nSocial distancing measures, with varying degrees of restriction, have been imposed around the world in order to stem the spread of COVID-19.\nIn this work we analyze the effect of current social distancing measures in the United States.\nWe quantify the reduction in doubling rate, by state, that is associated with social distancing.\nWe find that social distancing is associated with a statistically-significant reduction in the doubling rate for all but three states.\nAt the same time, we do not find significant evidence that social distancing has resulted in a reduction in the number of daily confirmed cases.\nInstead, social distancing has merely stabilized the spread of the disease.\nWe provide an illustration of our findings for each state, including point estimates of the effective reproduction number, R, both with and without social distancing.\nWe also discuss the policy implications of our findings.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 238}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: The coronavirus came from the local wet market in China\n\nAbstract:\nCoronavirus disease 2019 (COVID-19) originated in the city of Wuhan, Hubei Province, Central China, and has spread quickly to 72 countries to date.\nCOVID-19 is caused by a novel coronavirus, named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) [previously provisionally known as 2019 novel coronavirus (2019-nCoV)].\nAt present, the newly identified SARS-CoV-2 has caused a large number of deaths with tens of thousands of confirmed cases worldwide, posing a serious threat to public health.\nHowever, there are no clinically approved vaccines or specific therapeutic drugs available for COVID-19.\nIntensive research on the newly emerged SARS-CoV-2 is urgently needed to elucidate the pathogenic mechanisms and epidemiological characteristics and to identify potential drug targets, which will contribute to the development of effective prevention and treatment strategies.\nHence, this review will focus on recent progress regarding the structure of SARS-CoV-2 and the characteristics of COVID-19, such as the aetiology, pathogenesis and epidemiological characteristics.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Coronavirus disease 2019 (COVID-19) originated in the city of Wuhan, Hubei Province, Central China, and has spread quickly to 72 countries to date.\"]}", "id": 239}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Unfortunately, to date, no vaccines or antiviral drugs have been approved for the treatment of SARS-CoV-2 infection by regulatory agencies.\n\nAbstract:\nThe outbreak of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has posed the world at a pandemic risk.\nCoronavirus-19 disease (COVID-19) is an infectious disease caused by SARS-CoV-2, which causes pneumonia, requires intensive care unit hospitalization in about 10% of cases and can lead to a fatal outcome.\nSeveral efforts are currently made to find a treatment for COVID-19 patients.\nSo far, several anti-viral and immunosuppressive or immunomodulating drugs have demonstrated some efficacy on COVID-19 both in vitro and in animal models as well as in cases series.\nIn COVID-19 patients a pro-inflammatory status with high levels of interleukin (IL)-1B, IL-1 receptor (R)A and tumor necrosis factor (TNF)-&#945; has been demonstrated.\nMoreover, high levels of IL-6 and TNF-&#945; have been observed in patients requiring intensive-care-unit hospitalization.\nThis provided rationale for the use of anti-rheumatic drugs as potential treatments for this severe viral infection.\nOther agents, such as hydroxychloroquine and chloroquine might have a direct anti-viral effect.\nThe anti-viral aspect of immunosuppressants towards a variety of viruses has been known since long time and it is herein discussed in the view of searching for a potential treatment for SARS-CoV-2 infection.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 240}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Antibodies to sars-cov-2 are associated without protection against reinfection\n\nAbstract:\nBackgroundIt is critical to understand whether infection with Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) protects from subsequent reinfection.\nMethodsWe investigated the incidence of SARS-CoV-2 PCR-positive results in seropositive and seronegative healthcare workers (HCWs) attending asymptomatic and symptomatic staff testing at Oxford University Hospitals, UK.\nBaseline antibody status was determined using anti-spike and/or anti-nucleocapsid IgG assays and staff followed for up to 30 weeks.\nWe used Poisson regression to estimate the relative incidence of PCR-positive results and new symptomatic infection by antibody status, accounting for age, gender and changes in incidence over time.\nResultsA total of 12219 HCWs participated and had anti-spike IgG measured, 11052 were followed up after negative and 1246 after positive antibody results including 79 who seroconverted during follow up.\n89 PCR-confirmed symptomatic infections occurred in seronegative individuals (0.46 cases per 10,000 days at risk) and no symptomatic infections in those with anti-spike antibodies.\nAdditionally, 76 (0.40/10,000 days at risk) anti-spike IgG seronegative individuals had PCR-positive tests in asymptomatic screening, compared to 3 (0.21/10,000 days at risk) seropositive individuals.\nOverall, positive baseline anti-spike antibodies were associated with lower rates of PCR-positivity (with or without symptoms) (adjusted rate ratio 0.24 [95%CI 0.08-0.76, p=0.015]).\nRate ratios were similar using anti-nucleocapsid IgG alone or combined with anti-spike IgG to determine baseline status.\nConclusionsPrior SARS-CoV-2 infection that generated antibody responses offered protection from reinfection for most people in the six months following infection.\nFurther work is required to determine the long-term duration and correlates of post-infection immunity.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Overall, positive baseline anti-spike antibodies were associated with lower rates of PCR-positivity (with or without symptoms) (adjusted rate ratio 0.24 [95%CI 0.08-0.76, p=0.015]).\"]}", "id": 241}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Social distancing = > 80 % of the population, covid-19 may be curbed 13 weeks, < = 70 % may always be curbed.\n\nAbstract:\nIn this paper we develop an agent-based model for a fine-grained computational simulation of the ongoing COVID-19 pandemic in Australia.\nThis model is calibrated to reproduce several characteristics of COVID-19 transmission, accounting for its reproductive number, the length of incubation and generation periods, age-dependent attack rates, and the growth rate of cumulative incidence during a sustained and unmitigated local transmission.\nAn important calibration outcome is the age-dependent fraction of symptomatic cases, with this fraction for children found to be one-fifth of such fraction for adults.\nWe then apply the model to compare several intervention strategies, including restrictions on international air travel, case isolation, social distancing with varying levels of compliance, and school closures.\nSchool closures are not found to bring decisive benefits.\nWe report an important transition across the levels of social distancing compliance, in the range between 70% and 80% levels.\nThis suggests that a compliance of below 70% is unlikely to succeed for any duration of social distancing, while a compliance at the 90% level is likely to control the disease within 13-14 weeks, when coupled with effective case isolation and international travel restrictions.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"We report an important transition across the levels of social distancing compliance, in the range between 70% and 80% levels.\", \"This suggests that a compliance of below 70% is unlikely to succeed for any duration of social distancing, while a compliance at the 90% level is likely to control the disease within 13-14 weeks, when coupled with effective case isolation and international travel restrictions.\"]}", "id": 242}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Early studies have found that cats are the animals most likely to catch the new coronavirus. They can also show symptoms of COVID-19 and might be able to pass it to other cats.\n\nAbstract:\nLittle information on the SARS-CoV-2 virus in animals is available to date.\nWhereas no one husbandry animal case has been reported to date, which would have significant implications in food safety, companion animals play a role in COVID-19 epidemiology that opens up new questions.\nThere is evidence that SARS-CoV-2 can infect felines, dogs and minks, and there is evidence of human-to-animal infection.\nLikewise, the S protein nucleotide sequence of the SARS-CoV-2 virus isolated in domestic animals and humans is identical, and the replication of the SARS-CoV-2 in cats is efficient.\nBesides, the epidemiological evidence for this current pandemic indicates that the spillover to humans was associated with close contact between man and exotic animals, very probably in Chinese wet markets, thus there is a growing general consensus that the exotic animal markets, should be strictly regulated.\nThe examination of these findings and the particular role of animals in COVID-19 should be carefully analyzed in order to establish preparation and containment measures.\nAnimal management and epidemiological surveillance must be also considered for COVID-19 control, and it can open up new questions regarding COVID-19 epidemiology and the role that animals play in it.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"There is evidence that SARS-CoV-2 can infect felines, dogs and minks, and there is evidence of human-to-animal infection.\"]}", "id": 243}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: he virus that causes coronavirus disease 2019 (COVID-19) is stable for several hours to days in aerosols and on surfaces\n\nAbstract:\nWith limited infection control practices in overcrowded Bangladeshi hospitals, surfaces may play an important role in the transmission of respiratory pathogens in hospital wards and pose a serious risk of infection for patients, health care workers, caregivers and visitors.\nIn this study, we aimed to identify if surfaces near hospitalized patients with respiratory infections were contaminated with respiratory pathogens and to identify which surfaces were most commonly contaminated.\nBetween September-November 2013, we collected respiratory (nasopharyngeal and oropharyngeal) swabs from patients hospitalized with respiratory illness in adult medicine and paediatric medicine wards at two public tertiary care hospitals in Bangladesh.\nWe collected surface swabs from up to five surfaces near each case-patient including: the wall, bed rail, bed sheet, clinical file, and multipurpose towel used for care giving purposes.\nWe tested swabs using real-time multiplex PCR for 19 viral and 12 bacterial pathogens.\nCase-patients with at least one pathogen detected had corresponding surface swabs tested for those same pathogens.\nOf 104 patients tested, 79 had a laboratory-confirmed respiratory pathogen.\nOf the 287 swabs collected from surfaces near these patients, 133 (46%) had evidence of contamination with at least one pathogen.\nThe most commonly contaminated surfaces were the bed sheet and the towel.\nSixty-two percent of patients with a laboratory-confirmed respiratory pathgen (49/79) had detectable viral or bacterial nucleic acid on at least one surface.\nKlebsiella pneumoniae was the most frequently detected pathogen on both respiratory swabs (32%, 33/104) and on surfaces near patients positive for this organism (97%, 32/33).\nSurfaces near patients hospitalized with respiratory infections were frequently contaminated by pathogens, with Klebsiella pneumoniae being most common, highlighting the potential for transmission of respiratory pathogens via surfaces.\nEfforts to introduce routine cleaning in wards may be a feasible strategy to improve infection control, given that severe space constraints prohibit cohorting patients with respiratory illness.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Surfaces near patients hospitalized with respiratory infections were frequently contaminated by pathogens, with Klebsiella pneumoniae being most common, highlighting the potential for transmission of respiratory pathogens via surfaces.\"]}", "id": 244}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Higher risk if you have type 1 diabetes. Compared to people without diabetes, people with type 1 diabetes are approximately 3.5 times as likely to die in hospital with COVID-19, while people with type 2 are approximately twice as likely. \n\nAbstract:\nAIMS: To describe characteristics of COVID-19 patients with type 2 diabetes and to analyze risk factors for severity.\nMETHODS: Demographics, comorbidities, symptoms, laboratory findings, treatments and outcomes of COVID-19 patients with diabetes were collected and analyzed.\nRESULTS: Seventy-fourCOVID-19 patients with diabetes were included.\nTwenty-seven patients (36.5%) were severe and 10 patients (13.5%) died.\nHigher levels of blood glucose, serum amyloid A (SAA), C reactive protein and interleukin 6 were associated with severe patients compared to non-severe ones (P<0.05).\nLevels of albumin, cholesterol, high density lipoprotein, small and dense low density lipoprotein and CD4+T lymphocyte counts in severe patients were lower than those in non-severe patients (P<0.05).\nLogistic regression analysis identified decreased CD4+T lymphocyte counts (odds ratio [OR]=0.988, 95%Confidence interval [95%CI] 0.979-0.997) and increased SAA levels (OR=1.029, 95%CI 1.002-1.058) as risk factors for severity of COVID-19 with diabetes (P<0.05).\nCONCLUSIONS: Type 2 diabetic patients were more susceptible to COVID-19 than overall population, which might be associated with hyperglycemia and dyslipidemia.\nAggressive treatment should be suggested, especially when these patients had low CD4+T lymphocyte counts and high SAA levels.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 245}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Brilacidin, a covid-19 drug candidate, exhibits no in vitro antiviral activity against sars-cov-2\n\nAbstract:\nSummary Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), the newly emergent causative agent of coronavirus disease-19 (COVID-19), has resulted in more than one million deaths worldwide since it was first detected in 2019.\nThere is a critical global need for therapeutic intervention strategies that can be deployed to safely treat COVID-19 disease and reduce associated morbidity and mortality.\nIncreasing evidence shows that both natural and synthetic antimicrobial peptides (AMPs), also referred to as Host Defense Proteins/Peptides (HDPs), can inhibit SARS-CoV-2, paving the way for the potential clinical use of these molecules as therapeutic options.\nIn this manuscript, we describe the potent antiviral activity exerted by brilacidin\u2014a de novo designed synthetic small molecule that captures the biological properties of HDPs\u2014on SARS-CoV-2 in a human lung cell line (Calu-3) and a monkey cell line (Vero).\nThese data suggest that SARS-CoV-2 inhibition in these cell culture models is primarily a result of the impact of brilacidin on viral entry and its disruption of viral integrity.\nBrilacidin has demonstrated synergistic antiviral activity when combined with remdesivir.\nCollectively, our data demonstrate that brilacidin exerts potent inhibition of SARS-CoV-2 and thus supports brilacidin as a promising COVID-19 drug candidate.\nHighlights Brilacidin potently inhibits SARS-CoV-2 in an ACE2 positive human lung cell line.\nBrilacidin achieved a high Selectivity Index of 426 (CC50=241\u03bcM/IC50=0.565\u03bcM).\nBrilacidin\u2019s main mechanism appears to disrupt viral integrity and impact viral entry.\nBrilacidin and remdesivir exhibit excellent synergistic activity against SARS-CoV-2.\nSignificance Statement SARS-CoV-2, the emergent novel coronavirus, has led to the current global COVID-19 pandemic, characterized by extreme contagiousness and high mortality rates.\nThere is an urgent need for effective therapeutic strategies to safely and effectively treat SARS-CoV-2 infection.\nWe demonstrate that brilacidin, a synthetic small molecule with peptide-like properties, is capable of exerting potent in vitro antiviral activity against SARS-CoV-2, both as a standalone treatment and in combination with remdesivir, which is currently the only FDA-approved drug for the treatment of COVID-19.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Collectively, our data demonstrate that brilacidin exerts potent inhibition of SARS-CoV-2 and thus supports brilacidin as a promising COVID-19 drug candidate.\", \"Brilacidin and remdesivir exhibit excellent synergistic activity against SARS-CoV-2.\", \"We demonstrate that brilacidin, a synthetic small molecule with peptide-like properties, is capable of exerting potent in vitro antiviral activity against SARS-CoV-2, both as a standalone treatment and in combination with remdesivir, which is currently the only FDA-approved drug for the treatment of COVID-19.\"]}", "id": 246}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: COVID-19 and kids: What can happen when children get the coronavirus. A rare but sometimes deadly syndrome poses extra risk for COVID's youngest victims.\n\nAbstract:\nBackground: There is evolving evidence of significant differences in severity and outcomes of coronavirus disease 2019 (COVID-19) in children compared to adults.\nUnderlying medical conditions associated with increased risk of severe disease are based on adult data, but have been applied across all ages resulting in large numbers of families undertaking social shielding (vulnerable group).\nWe conducted a retrospective analysis of children with suspected COVID-19 at a Specialist Childrens Hospital to determine outcomes based on COVID-19 testing status and underlying health vulnerabilities.\nMethods: Routine clinical data were extracted retrospectively from the Institutions Electronic Health Record system and Digital Research Environment for patients with suspected and confirmed COVID-19 diagnoses.\nData were compared between Sars-CoV-2 positive and negative patients (CoVPos / CoVNeg respectively), and in relation to presence of underlying health vulnerabilities based on Public Health England guidance.\nFindings: Between 1st March and 15th May 2020, 166 children (<18 years of age) presented to a specialist childrens hospital with clinical features of possible COVID-19 infection.\n65 patients (39.2%) tested positive for SARS-CoV-2 virus.\nCoVPos patients were older (median 9 [0.9-14] years vs median 1 [0.1-5.7.5] years respectively, p<0.001).\nThere was a significantly reduced proportion of vulnerable cases (47.7% vs 72.3%, p=0.002), but no difference in proportion of vulnerable patients requiring ventilation (61% vs 64.3%, p = 0.84) between CoVPos and CoVNeg groups.\nHowever, a significantly lower proportion of CoVPos patients required mechanical ventilation support compared to CoVNeg patients (27.7 vs 57.4%, p<0.001).\nMortality was not significantly different between CoVPos and CoVNeg groups (1.5 vs 4% respectively, p=0.67) although there were no direct COVID-19 related deaths in this highly preselected paediatric population.\nInterpretation: COVID-19 infection may be associated with severe disease in childhood presenting to a specialist hospital, but does not appear significantly different in severity to other causes of similar clinical presentations.\nIn children presenting with pre-existing COVID-19 vulnerable medical conditions at a specialist centre, there does not appear to be significantly increased risk of either contracting COVID-19 or severe complications, apart from those undergoing chemotherapy, who are over-represented.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Background: There is evolving evidence of significant differences in severity and outcomes of coronavirus disease 2019 (COVID-19) in children compared to adults.\", \"We conducted a retrospective analysis of children with suspected COVID-19 at a Specialist Childrens Hospital to determine outcomes based on COVID-19 testing status and underlying health vulnerabilities.\", \"Findings: Between 1st March and 15th May 2020, 166 children (<18 years of age) presented to a specialist childrens hospital with clinical features of possible COVID-19 infection.\", \"65 patients (39.2%) tested positive for SARS-CoV-2 virus.\", \"Interpretation: COVID-19 infection may be associated with severe disease in childhood presenting to a specialist hospital, but does not appear significantly different in severity to other causes of similar clinical presentations.\", \"In children presenting with pre-existing COVID-19 vulnerable medical conditions at a specialist centre, there does not appear to be significantly increased risk of either contracting COVID-19 or severe complications, apart from those undergoing chemotherapy, who are over-represented.\"]}", "id": 247}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Drugs widely used to treat high blood pressure appear to make COVID-19 dangerously worse.\n\nAbstract:\nIntravenous infusions of angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin receptor blockers (ARBs) in experimental animals increase the numbers of angiotensin-converting enzyme 2 (ACE2) receptors in the cardiopulmonary circulation.\nACE2 receptors serve as binding sites for SARS-CoV-2 virions in the lungs.\nPatients who take ACEIs and ARBS may be at increased risk of severe disease outcomes due to SARS-CoV-2 infections.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Patients who take ACEIs and ARBS may be at increased risk of severe disease outcomes due to SARS-CoV-2 infections.\"]}", "id": 248}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: it appears that the virus that causes COVID-19 can spread from people to animals after close contact with people with COVID-19\n\nAbstract:\nA novel coronavirus emerged in human populations and spread rapidly to cause the global coronavirus disease 2019 pandemic.\nAlthough the origin of the associated virus (severe acute respiratory syndrome coronavirus 2 [SARS-CoV-2]) remains unclear, genetic evidence suggests that bats are a reservoir host of the virus, and pangolins are a probable intermediate.\nSARS-CoV-2 has crossed the species barrier to infect humans and other animal species, and infected humans can facilitate reverse-zoonotic transmission to animals.\nConsidering the rapidly changing interconnections among people, animals, and ecosystems, traditional roles of veterinarians should evolve to include transdisciplinary roles.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"SARS-CoV-2 has crossed the species barrier to infect humans and other animal species, and infected humans can facilitate reverse-zoonotic transmission to animals.\"]}", "id": 249}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Sars-cov-2 causes severe alveolar inflammation and barrier dysfunction\n\nAbstract:\nInfections with SARS-CoV-2 lead to mild to severe coronavirus disease-19 (COVID-19) with systemic symptoms.\nAlthough the viral infection originates in the respiratory system, it is unclear how the virus can overcome the alveolar barrier, which is observed in severe COVID-19 disease courses.\nTo elucidate the viral effects on the barrier integrity and immune reactions, we used mono-cell culture systems and a complex human alveolus-on-a-chip model composed of epithelial, endothelial, and mononuclear cells.\nOur data show that SARS-CoV-2 efficiently infected epithelial cells with high viral loads and inflammatory response, including the interferon expression.\nBy contrast, the adjacent endothelial layer was no infected and did neither show productive virus replication or interferon release.\nWith prolonged infection, both cell types are damaged, and the barrier function is deteriorated, allowing the viral particles to overbear.\nIn our study, we demonstrate that although SARS-CoV-2 is dependent on the epithelium for efficient replication, the neighboring endothelial cells are affected, e.g., by the epithelial cytokine release, which results in the damage of the alveolar barrier function and viral dissemination.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"With prolonged infection, both cell types are damaged, and the barrier function is deteriorated, allowing the viral particles to overbear.\", \"In our study, we demonstrate that although SARS-CoV-2 is dependent on the epithelium for efficient replication, the neighboring endothelial cells are affected, e.g., by the epithelial cytokine release, which results in the damage of the alveolar barrier function and viral dissemination.\"]}", "id": 250}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: The antiviral compound remdesivir potently binds rna-dependent rna polymerase from middle east respiratory syndrome coronavirus\n\nAbstract:\nAntiviral drugs for managing infections with human coronaviruses are not yet approved, posing a serious challenge to current global efforts aimed at containing the outbreak of severe acute respiratory syndrome-coronavirus 2 (CoV-2).\nRemdesivir (RDV) is an investigational compound with a broad spectrum of antiviral activities against RNA viruses, including severe acute respiratory syndrome-CoV and Middle East respiratory syndrome (MERS-CoV).\nRDV is a nucleotide analog inhibitor of RNA-dependent RNA polymerases (RdRps).\nHere, we co-expressed the MERS-CoV nonstructural proteins nsp5, nsp7, nsp8, and nsp12 (RdRp) in insect cells as a part a polyprotein to study the mechanism of inhibition of MERS-CoV RdRp by RDV.\nWe initially demonstrated that nsp8 and nsp12 form an active complex.\nThe triphosphate form of the inhibitor (RDV-TP) competes with its natural counterpart ATP.\nOf note, the selectivity value for RDV-TP obtained here with a steady-state approach suggests that it is more efficiently incorporated than ATP and two other nucleotide analogs.\nOnce incorporated at position i, the inhibitor caused RNA synthesis arrest at position i + 3.\nHence, the likely mechanism of action is delayed RNA chain termination.\nThe additional three nucleotides may protect the inhibitor from excision by the viral 3'-5' exonuclease activity.\nTogether, these results help to explain the high potency of RDV against RNA viruses in cell-based assays.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Remdesivir (RDV) is an investigational compound with a broad spectrum of antiviral activities against RNA viruses, including severe acute respiratory syndrome-CoV and Middle East respiratory syndrome (MERS-CoV).\"]}", "id": 251}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Diabetes is generally known to weaken the immune system, making it harder to protect against viral infections like COVID-19.\n\nAbstract:\nBACKGOUND: To figure out whether diabetes is a risk factor influencing the progression and prognosis of 2019 novel coronavirus disease (COVID-19).\nMETHODS: A total of 174 consecutive patients confirmed with COVID-19 were studied.\nDemographic data, medical history, symptoms and signs, laboratory findings, chest computed tomography (CT) as well the treatment measures were collected and analysed.\nRESULTS: We found that COVID-19 patients without other comorbidities but with diabetes (n = 24) were at higher risk of severe pneumonia, release of tissue injury-related enzymes, excessive uncontrolled inflammation responses and hypercoagulable state associated with dysregulation of glucose metabolism.\nFurthermore, serum levels of inflammation-related biomarkers such as IL-6, C-reactive protein, serum ferritin and coagulation index, D-dimer, were significantly higher (P < .01) in diabetic patients compared with those without, suggesting that patients with diabetes are more susceptible to an inflammatory storm eventually leading to rapid deterioration of COVID-19.\nCONCLUSIONS: Our data support the notion that diabetes should be considered as a risk factor for a rapid progression and bad prognosis of COVID-19.\nMore intensive attention should be paid to patients with diabetes, in case of rapid deterioration.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"CONCLUSIONS: Our data support the notion that diabetes should be considered as a risk factor for a rapid progression and bad prognosis of COVID-19.\"]}", "id": 252}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Different mutations in sars-cov-2 associate with severe and mild outcome\n\nAbstract:\nINTRODUCTION: Genomic alterations in a viral genome can lead to either better or worse outcome and identifying these mutations is of utmost importance.\nHere, we correlated protein-level mutations in the SARS-CoV-2 virus to clinical outcome.\nMETHODS: Mutations in viral sequences from the GISAID virus repository were evaluated by using \"hCoV-19/Wuhan/WIV04/2019\" as the reference.\nPatient outcomes were classified as mild disease, hospitalization and severe disease (death or documented treatment in an intensive-care unit).\nChi-square test was applied to examine the association between each mutation and patient outcome.\nFalse discovery rate was computed to correct for multiple hypothesis testing and results passing FDR cutoff of 5% were accepted as significant.\nRESULTS: Mutations were mapped to amino acid changes for 3,733 non-silent mutations.\nMutations correlated to mild outcome were located in the ORF8, NSP6, ORF3a, NSP4, and in the nucleocapsid phosphoprotein N. Mutations associated with inferior outcome were located in the surface (S) glycoprotein, in the RNA dependent RNA polymerase, in ORF3a, NSP3, ORF6 and N. Mutations leading to severe outcome with low prevalence were found in the ORF3A and in NSP7 proteins.\nFour out of 22 of the most significant mutations mapped onto a 10 amino acid long phosphorylated stretch of N indicating that in spite of obvious sampling restrictions the approach can find functionally relevant sites in the viral genome.\nCONCLUSIONS: We demonstrate that mutations in the viral genes may have a direct correlation to clinical outcome.\nOur results help to quickly identify SARS-CoV-2 infections harboring mutations related to severe outcome.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Here, we correlated protein-level mutations in the SARS-CoV-2 virus to clinical outcome.\", \"Our results help to quickly identify SARS-CoV-2 infections harboring mutations related to severe outcome.\"]}", "id": 253}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Dogs May Not Spread Covid-19, but Cats Can Pass It to Each Other\n\nAbstract:\nAbstract Coronavirus disease-19 (COVID-19) caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is now a pandemic threat.\nThis virus is supposed to be spread by human to human transmission.\nCellular angiotensin converting enzyme 2 (ACE2) is the receptor of SARS-CoV-2 which is identical or similar in different species of animals such as pigs, ferrets, cats, orangutans, monkeys, and humans.\nMoreover, a recent study predicted that dog might be secondary host during the evolution of SARS-CoV-2 from bat to human.\nTherefore, there is a possibility of spreading SARS-CoV-2 through domestic pets.\nThere are now many reports of SARS-CoV-2 positive cases in dogs, cats, tigers, lion, and minks.\nExperimental data showed ferrets and cats are highly susceptible to SARS-CoV-2 as infected by virus inoculation and can transmit the virus directly or indirectly by droplets or airborne route.\nBased on these natural infection reports and experimental data, whether the pets are responsible for SARS-CoV-2 spread to human; needs to be deeply investigated.\nHumans showing clinical symptoms of respiratory infections have been undergoing for COVID-19 diagnostic test but many infected people and few pets confirmed with SARS-CoV-2 remained asymptomatic.\nIn this review, we summarize the natural cases of SARS-CoV-2 in animals with the latest researches conducted in this field.\nThis review will be helpful to think insights of SARS-CoV-2 transmissions, spread, and demand for sero-prevalence studies especially in companion animals.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Cellular angiotensin converting enzyme 2 (ACE2) is the receptor of SARS-CoV-2 which is identical or similar in different species of animals such as pigs, ferrets, cats, orangutans, monkeys, and humans.\", \"Moreover, a recent study predicted that dog might be secondary host during the evolution of SARS-CoV-2 from bat to human.\", \"Therefore, there is a possibility of spreading SARS-CoV-2 through domestic pets.\", \"Experimental data showed ferrets and cats are highly susceptible to SARS-CoV-2 as infected by virus inoculation and can transmit the virus directly or indirectly by droplets or airborne route.\"]}", "id": 254}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Cross-species transmission of the newly identified coronavirus b-ncov\n\nAbstract:\nThe current outbreak of viral pneumonia in the city of Wuhan, China, was caused by a novel coronavirus designated 2019-nCoV by the World Health Organization, as determined by sequencing the viral RNA genome.\nMany initial patients were exposed to wildlife animals at the Huanan seafood wholesale market, where poultry, snake, bats, and other farm animals were also sold.\nTo investigate possible virus reservoir, we have carried out comprehensive sequence analysis and comparison in conjunction with relative synonymous codon usage (RSCU) bias among different animal species based on the 2019-nCoV sequence.\nResults obtained from our analyses suggest that the 2019-nCoV may appear to be a recombinant virus between the bat coronavirus and an origin-unknown coronavirus.\nThe recombination may occurred within the viral spike glycoprotein, which recognizes a cell surface receptor.\nAdditionally, our findings suggest that 2019-nCoV has most similar genetic information with bat coronovirus and most similar codon usage bias with snake.\nTaken together, our results suggest that homologous recombination may occur and contribute to the 2019-nCoV cross-species transmission.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Results obtained from our analyses suggest that the 2019-nCoV may appear to be a recombinant virus between the bat coronavirus and an origin-unknown coronavirus.\"]}", "id": 255}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: coronavirus is man-made\n\nAbstract:\nOrigin of the COVID-19 virus has been intensely debated in the scientific community since the first infected cases were detected in December 2019.\nThe disease has caused a global pandemic, leading to deaths of thousands of people across the world and thus finding origin of this novel coronavirus is important in responding and controlling the pandemic.\nRecent research results suggest that bats or pangolins might be the original hosts for the virus based on comparative studies using its genomic sequences.\nThis paper investigates the COVID-19 virus origin by using artificial intelligence (AI) and raw genomic sequences of the virus.\nMore than 300 genome sequences of COVID-19 infected cases collected from different countries are explored and analysed using unsupervised clustering methods.\nThe results obtained from various AI-enabled experiments using clustering algorithms demonstrate that all examined COVID-19 virus genomes belong to a cluster that also contains bat and pangolin coronavirus genomes.\nThis provides evidences strongly supporting scientific hypotheses that bats and pangolins are probable hosts for the COVID-19 virus.\nAt the whole genome analysis level, our findings also indicate that bats are more likely the hosts for the COVID-19 virus than pangolins.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Recent research results suggest that bats or pangolins might be the original hosts for the virus based on comparative studies using its genomic sequences.\", \"The results obtained from various AI-enabled experiments using clustering algorithms demonstrate that all examined COVID-19 virus genomes belong to a cluster that also contains bat and pangolin coronavirus genomes.\", \"This provides evidences strongly supporting scientific hypotheses that bats and pangolins are probable hosts for the COVID-19 virus.\", \"At the whole genome analysis level, our findings also indicate that bats are more likely the hosts for the COVID-19 virus than pangolins.\"]}", "id": 256}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: The most important thing to know about using over-the-counter medications to treat COVID-19 is that none of these common drugstore products are actually going to treat the virus itself.\n\nAbstract:\nFever has been reported as a common symptom occurring in COVID-19 illness.\nOver the counter antipyretics such as ibuprofen and acetaminophen are often taken by individuals to reduce the discomfort of fever.\nRecently, the safety of ibuprofen in COVID-19 patients has been questioned due to anecdotal reports of worsening symptoms in previously healthy young adults.\nStudies show that ibuprofen demonstrates superior efficacy in fever reduction compared to acetaminophen.\nAs fever may have benefit in shortening the duration of viral illness, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response when taken during the early stages of COVID-19 illness.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"As fever may have benefit in shortening the duration of viral illness, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response when taken during the early stages of COVID-19 illness.\"]}", "id": 257}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: The most important thing to know about using over-the-counter medications to treat COVID-19 is that none of these common drugstore products are actually going to treat the virus itself.\n\nAbstract:\nConcern about the appropriate role of nonsteroidal anti-inflammatory drugs (NSAIDs) in COVID-19 speculate that NSAIDs, in particular ibuprofen, may upregulate the entry point for the virus, the angiotensin-converting enzyme (ACE) 2 receptors and increase susceptibility to the virus or worsen symptoms in existing disease.\nAdverse outcomes with COVID-19 have been linked to cytokine storm but the most effective way to address exaggerated inflammatory response is complex and unclear.\nThe Expert Working Group on the Commission of Human Medicines in the UK and other organizations have stated that there is insufficient evidence to establish a link between ibuprofen and susceptibility to or exacerbation of COVID-19.\nNSAID use must also be categorized by whether the drugs are relatively low-dose over-the-counter oral products taken occasionally versus higher-dose or parenteral NSAIDs.\nEven if evidence emerged arguing for or against NSAIDs in this setting, it is unclear if this evidence would apply to all NSAIDs at all doses in all dosing regimens.\nParacetamol (acetaminophen) has been proposed as an alternative to NSAIDs but there are issues with liver toxicity at high doses.\nThere are clearly COVID-19 cases where NSAIDs should not be used, but there is no strong evidence that NSAIDs must be avoided in all patients with COVID-19; clinicians must weigh these choices on an individual basis.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"The Expert Working Group on the Commission of Human Medicines in the UK and other organizations have stated that there is insufficient evidence to establish a link between ibuprofen and susceptibility to or exacerbation of COVID-19.\", \"There are clearly COVID-19 cases where NSAIDs should not be used, but there is no strong evidence that NSAIDs must be avoided in all patients with COVID-19; clinicians must weigh these choices on an individual basis.\"]}", "id": 258}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Get this checked by your doctor and use the correct dose to stay healthy. Get this checked by your doctor and use the correct dose to stay healthy.\n\nAbstract:\nThe severity of coronavirus 2019 infection (COVID-19) is determined by the presence of pneumonia, severe acute respiratory distress syndrome (SARS-CoV-2), myocarditis, microvascular thrombosis and/or cytokine storms, all of which involve underlying inflammation.\nA principal defence against uncontrolled inflammation, and against viral infection in general, is provided by T regulatory lymphocytes (Tregs).\nTreg levels have been reported to be low in many COVID-19 patients and can be increased by vitamin D supplementation.\nLow vitamin D levels have been associated with an increase in inflammatory cytokines and a significantly increased risk of pneumonia and viral upper respiratory tract infections.\nVitamin D deficiency is associated with an increase in thrombotic episodes, which are frequently observed in COVID-19.\nVitamin D deficiency has been found to occur more frequently in patients with obesity and diabetes.\nThese conditions are reported to carry a higher mortality in COVID-19.\nIf vitamin D does in fact reduce the severity of COVID-19 in regard to pneumonia/ARDS, inflammation, inflammatory cytokines and thrombosis, it is our opinion that supplements would offer a relatively easy option to decrease the impact of the pandemic.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 259}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Can COVID-19 be spread from pets to people or other pets? According to the latest information from the CDC , the risk of animals spreading COVID-19 to people is very low. However, because all animals can carry germs that can make people sick, it's always a good idea to practice healthy habits around pets and other animals.\n\nAbstract:\nAbstract Coronavirus disease-19 (COVID-19) caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is now a pandemic threat.\nThis virus is supposed to be spread by human to human transmission.\nCellular angiotensin converting enzyme 2 (ACE2) is the receptor of SARS-CoV-2 which is identical or similar in different species of animals such as pigs, ferrets, cats, orangutans, monkeys, and humans.\nMoreover, a recent study predicted that dog might be secondary host during the evolution of SARS-CoV-2 from bat to human.\nTherefore, there is a possibility of spreading SARS-CoV-2 through domestic pets.\nThere are now many reports of SARS-CoV-2 positive cases in dogs, cats, tigers, lion, and minks.\nExperimental data showed ferrets and cats are highly susceptible to SARS-CoV-2 as infected by virus inoculation and can transmit the virus directly or indirectly by droplets or airborne route.\nBased on these natural infection reports and experimental data, whether the pets are responsible for SARS-CoV-2 spread to human; needs to be deeply investigated.\nHumans showing clinical symptoms of respiratory infections have been undergoing for COVID-19 diagnostic test but many infected people and few pets confirmed with SARS-CoV-2 remained asymptomatic.\nIn this review, we summarize the natural cases of SARS-CoV-2 in animals with the latest researches conducted in this field.\nThis review will be helpful to think insights of SARS-CoV-2 transmissions, spread, and demand for sero-prevalence studies especially in companion animals.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Cellular angiotensin converting enzyme 2 (ACE2) is the receptor of SARS-CoV-2 which is identical or similar in different species of animals such as pigs, ferrets, cats, orangutans, monkeys, and humans.\", \"Moreover, a recent study predicted that dog might be secondary host during the evolution of SARS-CoV-2 from bat to human.\", \"Therefore, there is a possibility of spreading SARS-CoV-2 through domestic pets.\", \"There are now many reports of SARS-CoV-2 positive cases in dogs, cats, tigers, lion, and minks.\", \"Experimental data showed ferrets and cats are highly susceptible to SARS-CoV-2 as infected by virus inoculation and can transmit the virus directly or indirectly by droplets or airborne route.\"]}", "id": 260}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: COVID-19: Hand sanitizers inactivate novel coronavirus\n\nAbstract:\nThe coronavirus known as SARS-CoV-2, which causes COVID-19 disease, is presently responsible for a global pandemic wherein more than 3.5 million people have been infected and more than 250,000 killed to-date.\nThere is currently no vaccine for COVID-19, leaving governments and public health agencies with little defense against the virus aside from advising or enforcing best practices for virus transmission prevention, which include hand-washing, physical distancing, use of face covers, and use of effective disinfectants.\nIn this study, a novel iodine complex called CupriDyne\u00ae was assessed for its ability to inactivate SARS-CoV-2.\nCupriDyne was shown to be effective in inactivating the virus in a time-dependent manner, reducing virus titers by 99% (2 logs) after 30 minutes, and reducing virus titers to below the detection limit after 60 minutes.\nThe novel iodine complex tested herein offers a safe and gentle alternative to conventional disinfectants for use on indoor and outdoor surfaces.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 261}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Scientists believe cannabis could help prevent, treat coronavirus\n\nAbstract:\nIntroduction Epidemiological and laboratory research seems to suggest that smoking and perhaps nicotine alone could reduce the severity of COVID-19.\nLikewise, there is some evidence that inhaled corticosteroids could also reduce its severity, opening the possibility that nicotine and inhaled steroids could be used as treatments.\nMethods In this prospective cohort study, we will link English general practice records from the QResearch database to Public Health England's database of SARS-CoV-2 positive tests, Hospital Episode Statistics, admission to intensive care units, and death from COVID-19 to identify our outcomes: hospitalisation, ICU admission, and death due to COVID.\nUsing Cox regression, we will perform sequential adjustment for potential confounders identified by separate directed acyclic graphs to: 1.\nAssess the association between smoking and COVID-19 disease severity, and how that changes on adjustment for smoking-related comorbidity.\n2. More closely characterise the association between smoking and severe COVID-19 disease by assessing whether the association is modified by age (as a proxy of length of smoking), gender, ethnic group, and whether people have asthma or COPD.\n3. Assess for evidence of a dose-response relation between smoking intensity and disease severity, which would help create a case for causality.\n4.\nExamine the association between former smokers who are using NRT or are vaping and disease severity.\n5. Examine whether pre-existing respiratory disease is associated with severe COVID-19 infection.\n6. Assess whether the association between chronic obstructive pulmonary disease (COPD) and asthma and COVID-19 disease severity is modified by age, gender, ethnicity, and smoking status.\n7. Assess whether the use of inhaled corticosteroids is associated with severity of COVID-19 disease.\n8. To assess whether the association between use of inhaled corticosteroids and severity of COVID-19 disease is modified by the number of other airways medications used (as a proxy for severity of condition) and whether people have asthma or COPD.\nConclusions This representative population sample will, to our knowledge, present the first comprehensive examination of the association between smoking, nicotine use without smoking, respiratory disease, and severity of COVID-19.\nWe will undertake several sensitivity analyses to examine the potential for bias in these associations.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Introduction Epidemiological and laboratory research seems to suggest that smoking and perhaps nicotine alone could reduce the severity of COVID-19.\", \"Likewise, there is some evidence that inhaled corticosteroids could also reduce its severity, opening the possibility that nicotine and inhaled steroids could be used as treatments.\"]}", "id": 262}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Supplements and products unlikely to help with coronavirus and could be dangerous\n\nAbstract:\nBACKGROUND The coronavirus disease 2019 (COVID-19) pandemic has affected almost 2.5 million people worldwide with almost 170 000 deaths reported to date.\nSo far, there is scarce evidence for the current treatment options available for COVID-19.\nVitamin C has previously been used for treatment of severe sepsis and septic shock.\nWe reviewed the feasibility of using vitamin C in the setting of COVID-19 in a series of patients.\nMETHODS We sequentially identified a series of patients who were requiring at least 30% of FiO2 or more who received IV vitamin C as part of the COVID-19 treatment and analyzed their demographic and clinical characteristics.\nWe compared inflammatory markers pre and post treatment including D-dimer and ferritin.\nRESULTS We identified a total of 17 patients who received IV vitamin C for COVID-19.\nThe inpatient mortality rate in this series was 12% with 17.6% rates of intubation and mechanical ventilation.\nWe noted a significant decrease in inflammatory markers, including ferritin and D-dimer, and a trend to decreasing FiO2 requirements, after vitamin C administration.\nCONCLUSION The use of IV vitamin C in patients with moderate to severe COVID-19 disease may be feasible.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"RESULTS We identified a total of 17 patients who received IV vitamin C for COVID-19.\", \"The inpatient mortality rate in this series was 12% with 17.6% rates of intubation and mechanical ventilation.\", \"We noted a significant decrease in inflammatory markers, including ferritin and D-dimer, and a trend to decreasing FiO2 requirements, after vitamin C administration.\", \"CONCLUSION The use of IV vitamin C in patients with moderate to severe COVID-19 disease may be feasible.\"]}", "id": 263}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: COVID-19 can survive on surfaces, like a tabletop\n\nAbstract:\nObjectives: To evaluate SARS-CoV-2 surface and air contamination during the peak of the COVID-19 pandemic in London.\nDesign: Prospective cross-sectional observational study.\nSetting: An acute NHS healthcare provider.\nParticipants: All inpatient wards were fully occupied by patients with COVID-19 at the time of sampling.\nInterventions: Air and surface samples were collected from a range of clinical areas and a public area of the hospital.\nAn active air sampler was used to collect three or four 1.0 m3 air samples in each area.\nSurface samples were collected by swabbing approximately 25 cm2 of items in the immediate vicinity of each air sample.\nSARS-CoV-2 was detected by RT-qPCR and viral culture using Vero E6 and Caco2 cells; additionally the limit of detection for culturing SARS-CoV-2 dried onto surfaces was determined.\nMain outcome measures: SARS-CoV-2 detected by PCR or culture.\nResults: Viral RNA was detected on 114/218 (52.3%) of surface and 14/31 (38.7%) air samples but no virus was cultured.\nThe proportion of surface samples contaminated with viral RNA varied by item sampled and by clinical area.\nViral RNA was detected on surfaces and in air in public areas of the hospital but was more likely to be found in areas immediately occupied by COVID-19 patients (67/105 (63.8%) in areas immediately occupied by COVID-19 patients vs. 29/64 (45.3%) in other areas (odds ratio 0.5, 95% confidence interval 0.2-0.9, p=0.025, Fishers exact test).\nThe PCR Ct value for all surface and air samples (>30) indicated a viral load that would not be culturable.\nConclusions: Our findings of extensive viral RNA contamination of surfaces and air across a range of acute healthcare settings in the absence of cultured virus underlines the potential risk from surface and air contamination in managing COVID-19, and the need for effective use of PPE, social distancing, and hand/surface hygiene.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Conclusions: Our findings of extensive viral RNA contamination of surfaces and air across a range of acute healthcare settings in the absence of cultured virus underlines the potential risk from surface and air contamination in managing COVID-19, and the need for effective use of PPE, social distancing, and hand/surface hygiene.\"]}", "id": 264}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: there is no clear evidence that using ibuprofen to treat symptoms such as a high temperature can make coronavirus (covid-19) worse\n\nAbstract:\n: The COVID-19 pandemic is challenging our cardiovascular care of patients with heart diseases.\nIn the setting of pericardial diseases, there are two possible different scenarios to consider: the patient being treated for pericarditis who subsequently becomes infected with SARS-CoV-2, and the patient with COVID-19 who develops pericarditis or pericardial effusion.\nIn both conditions, clinicians may be doubtful regarding the safety of nonsteroidal anti-inflammatory drugs (NSAIDs), corticosteroids, colchicine, and biological agents, such as anti-IL1 agents (e.g. anakinra), that are the mainstay of therapy for pericarditis.\nFor NSAIDs, there is no clear scientific evidence linking ibuprofen and other NSAIDs to worsening of COVID-19; however, it seems prudent to continue them, if necessary to control pericarditis, and on the other hand, to prefer paracetamol for fever and systemic symptoms related to COVID-19.\nTreatments with corticosteroids, colchicine, and anakinra appear well tolerated in the context of COVID-19 infection and are currently actively evaluated as potential therapeutic options for COVID infection at different stages of the disease.\nOn this basis, currently most treatments for pericarditis do not appear contraindicated also in the presence of possible COVID-19 infection and should not be discontinued, and some (corticosteroids, colchicine, and anakinra) can be considered to treat both conditions.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"For NSAIDs, there is no clear scientific evidence linking ibuprofen and other NSAIDs to worsening of COVID-19; however, it seems prudent to continue them, if necessary to control pericarditis, and on the other hand, to prefer paracetamol for fever and systemic symptoms related to COVID-19.\"]}", "id": 265}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: People with diabetes have not a higher risk for complications from coronavirus\n\nAbstract:\nCoronavirus disease 2019 (COVID-19) has become a global concern and public health issue due to its higher infection and mortality rate; particularly, the risk is very higher among the patients who have cardiovascular diseases (CVD) and/or diabetes mellitus (DM).\nIn this review, we analyzed the recently published literature on CVD and DM associated with COVD-19 infections and highlight their association with potential mechanisms.\nThe findings revealed that without any previous history of CVD, the COVID-19 patients have developed some CVD complications like myocardial injury, cardiomyopathy, and venous thromboembolism after being infected with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and required for those patients an emergency clinical support to be aware to manage those complications.\nThough the association between DM and COVID-19-induced severe complications is still unclear, the limited data predict that different markers like interleukin (IL)-1, IL-6, C-reactive protein, and D-dimer linked with the severity of COVID-19 infection in diabetic individuals.\nFurther studies on a large scale are urgently needed to explore the underlying mechanisms between CVD, DM, and COVID-19 for better treatment.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Though the association between DM and COVID-19-induced severe complications is still unclear, the limited data predict that different markers like interleukin (IL)-1, IL-6, C-reactive protein, and D-dimer linked with the severity of COVID-19 infection in diabetic individuals.\"]}", "id": 266}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: vitamin D might be able to protect people from the coronavirus (COVID-19).\n\nAbstract:\nThe outbreak of COVID-19 has created a global public health crisis.\nLittle is known about the protective factors of this infection.\nTherefore, preventive health measures that can reduce the risk of infection, progression and severity are desperately needed.\nThis review discussed the possible roles of vitamin D in reducing the risk of COVID-19 and other acute respiratory tract infections and severity.\nMoreover, this study determined the correlation of vitamin D levels with COVID-19 cases and deaths in 20 European countries as of 20 May 2020.\nA significant negative correlation (p=0.033) has been observed between mean vitamin D levels and COVID-19 cases per one million population in European countries.\nHowever, the correlation of vitamin D with COVID-19 deaths of these countries was not significant.\nSome retrospective studies demonstrated a correlation between vitamin D status and COVID-19 severity and mortality, while other studies did not find the correlation when confounding variables are adjusted.\nSeveral studies demonstrated the role of vitamin D in reducing the risk of acute viral respiratory tract infections and pneumonia.\nThese include direct inhibition with viral replication or with anti-inflammatory or immunomodulatory ways.\nIn the meta-analysis, vitamin D supplementation has been shown as safe and effective against acute respiratory tract infections.\nThus, people who are at higher risk of vitamin D deficiency during this global pandemic should consider taking vitamin D supplements to maintain the circulating 25(OH)D in the optimal levels (75-125nmol/L).\nIn conclusion, there is not enough evidence on the association between vitamin D levels and COVID-19 severity and mortality.\nTherefore, randomized control trials and cohort studies are necessary to test this hypothesis.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"A significant negative correlation (p=0.033) has been observed between mean vitamin D levels and COVID-19 cases per one million population in European countries.\", \"Several studies demonstrated the role of vitamin D in reducing the risk of acute viral respiratory tract infections and pneumonia.\", \"These include direct inhibition with viral replication or with anti-inflammatory or immunomodulatory ways.\", \"In the meta-analysis, vitamin D supplementation has been shown as safe and effective against acute respiratory tract infections.\", \"Thus, people who are at higher risk of vitamin D deficiency during this global pandemic should consider taking vitamin D supplements to maintain the circulating 25(OH)D in the optimal levels (75-125nmol/L).\"]}", "id": 267}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Hot weather can spread the virus more as it may get you more out there, make you more mobile and you would actually interact with more people\n\nAbstract:\nThe undefendable outbreak of novel coronavirus (SARS-COV-2) lead to a global health emergency due to its higher transmission rate and longer symptomatic duration, created a health surge in a short time.\nSince Nov 2019 the outbreak in China, the virus is spreading exponentially everywhere.\nThe current study focuses on the relationship between environmental parameters and the growth rate of COVID-19.\nThe statistical analysis suggests that the temperature changes retarded the growth rate and found that -6.28{degrees}C and +14.51{degrees}C temperature is the favorable range for COVID-19 growth.\nGutenberg- Richter's relationship is used to estimate the mean daily rate of exceedance of confirmed cases concerning the change in temperature.\nTemperature is the most influential parameter that reduces the growth at the rate of 13-16 cases/day with a 1{degrees}C rise in temperature.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"The statistical analysis suggests that the temperature changes retarded the growth rate and found that -6.28{degrees}C and +14.51{degrees}C temperature is the favorable range for COVID-19 growth.\", \"Temperature is the most influential parameter that reduces the growth at the rate of 13-16 cases/day with a 1{degrees}C rise in temperature.\"]}", "id": 268}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Furin cleavage of sars-cov-2 spike promotes but is also essential for infection and cell-cell fusion\n\nAbstract:\nSevere Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2) infects cells by binding to the host cell receptor ACE2 and undergoing virus-host membrane fusion.\nFusion is triggered by the protease TMPRSS2, which processes the viral Spike (S) protein to reveal the fusion peptide.\nSARS-CoV-2 has evolved a multibasic site at the S1-S2 boundary, which is thought to be cleaved by furin in order to prime S protein for TMPRSS2 processing.\nHere we show that CRISPR-Cas9 knockout of furin reduces, but does not prevent, the production of infectious SARS-CoV-2 virus.\nComparing S processing in furin knockout cells to multibasic site mutants reveals that while loss of furin substantially reduces S1-S2 cleavage it does not prevent it.\nSARS-CoV-2 S protein also mediates cell-cell fusion, potentially allowing virus to spread virion-independently.\nWe show that loss of furin in either donor or acceptor cells reduces, but does not prevent, TMPRSS2-dependent cell-cell fusion, unlike mutation of the multibasic site that completely prevents syncytia formation.\nOur results show that while furin promotes both SARS-CoV-2 infectivity and cell-cell spread it is not essential, suggesting furin inhibitors may reduce but not abolish viral spread.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Here we show that CRISPR-Cas9 knockout of furin reduces, but does not prevent, the production of infectious SARS-CoV-2 virus.\", \"Comparing S processing in furin knockout cells to multibasic site mutants reveals that while loss of furin substantially reduces S1-S2 cleavage it does not prevent it.\", \"We show that loss of furin in either donor or acceptor cells reduces, but does not prevent, TMPRSS2-dependent cell-cell fusion, unlike mutation of the multibasic site that completely prevents syncytia formation.\"]}", "id": 269}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: covid-19 patients taking hydroxychloroquine do not benefit\n\nAbstract:\nHydroxychloroquine has been promoted for its use in treatment of COVID-19 patients based on in-vitro evidences.\nWe searched the databases to include randomized and observational studies evaluating the effect of Hydroxychloroquine on mortality in COVID-19 patients.\nThe outcome was summarized as odds ratios (OR) with a 95% confidence interval (CI).We used the inverse-variance method with a random effect model and assessed the heterogeneity using I2 test.\nWe used ROBINS-I tool to assess methodological quality of the included studies.\nWe performed the meta-analysis using 'Review manager software version 5.3'.\nWe identified 6 observationalstudies satisfying the selection criteria.\nIn all studies, Hydroxychloroquine was given as add on to the standard care and effect was compared with the standard care alone.\nA pooled analysis observed 251 deaths in 1331 participants of the Hydroxychloroquine arm and 363 deaths in 1577 participants of the control arm.\nThere was no difference in odds of mortality events amongst Hydroxychloroquine and supportive care arm [1.25 (95% CI: 0.65, 2.38); I2 = 80%].\nA similar trend was observed with moderate risk of bias studies [0.95 (95% CI: 0.44, 2.06); I2 = 85%].\nThe odds of mortality were significantly higher in patients treated with Hydroxychloroquine + Azithromycin than supportive care alone [2.34 (95% CI: 1.63, 3.34); I2 = 0%].\nA pooled analysis of recently published studies suggests no additional benefit for reducing mortality in COVID-19 patients when Hydroxychloroquine is given as add-on to the standard care.\nGraphical Abstract.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"The odds of mortality were significantly higher in patients treated with Hydroxychloroquine + Azithromycin than supportive care alone [2.34 (95% CI: 1.63, 3.34); I2 = 0%].\", \"A pooled analysis of recently published studies suggests no additional benefit for reducing mortality in COVID-19 patients when Hydroxychloroquine is given as add-on to the standard care.\"]}", "id": 270}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Sars-cov1 frequent mask use in public venues , frequent hand washing , and disinfecting the living quarters were significant protective factors\n\nAbstract:\nWe analyzed information obtained from 1,192 patients with probable severe acute respiratory syndrome (SARS) reported in Hong Kong.\nAmong them, 26.6% were hospital workers, 16.1% were household members of SARS patients and had probable secondary infections, 14.3% were Amoy Garden residents, 4.9% were inpatients, and 20.1% were contacts of SARS patients who were not family members.\nThe remaining 347 case-patients (29.1%) did not have \u201cknown\u201d sources of infection.\nExcluding those <16 years of age, 330 patients with cases from \u201cundefined\u201d sources were used in a 1:2 matched case-control study.\nMultivariate analysis of this case-control study showed that having visited mainland China, hospitals, or the Amoy Gardens were risk factors (odds ratio [OR] 1.95 to 7.63).\nIn addition, frequent mask use in public venues, frequent hand washing, and disinfecting the living quarters were significant protective factors (OR 0.36 to 0.58).\nIn Hong Kong, therefore, community-acquired infection did not make up most transmissions, and public health measures have contributed substantially to the control of the SARS epidemic.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"In addition, frequent mask use in public venues, frequent hand washing, and disinfecting the living quarters were significant protective factors (OR 0.36 to 0.58).\"]}", "id": 271}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Saliva sampling is an excellent option to reduce the number of sars cov2 diagnostic tests in settings with supplies shortages\n\nAbstract:\nAs part of any plan to lift or ease the confinement restrictions that are in place in many different countries, there is an urgent need to increase the capacity of laboratory testing for SARS CoV-2.\nDetection of the viral genome through RT-qPCR is the golden standard for this test, however, the high demand of the materials and reagents needed to sample individuals, purify the viral RNA, and perform the RT-qPCR test has resulted in a worldwide shortage of several of these supplies.\nHere, we show that directly lysed saliva samples can serve as a suitable source for viral RNA detection that is cheaper and can be as efficient as the classical protocol that involves column purification of the viral RNA.\nIn addition, it surpasses the need for swab sampling, decreases the risk of the healthcare personnel involved in this process, and accelerates the diagnostic procedure.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Detection of the viral genome through RT-qPCR is the golden standard for this test, however, the high demand of the materials and reagents needed to sample individuals, purify the viral RNA, and perform the RT-qPCR test has resulted in a worldwide shortage of several of these supplies.\"]}", "id": 272}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: A prefusion sars-cov-2 spike rna vaccine is highly immunogenic and promotes lung infection in non-human primates\n\nAbstract:\nTo contain the coronavirus disease 2019 (COVID-19) pandemic, a safe and effective vaccine against the new severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is urgently needed in quantities sufficient to immunise large populations.\nIn this study, we report the design, preclinical development, immunogenicity and anti-viral protective effect in rhesus macaques of the BNT162b2 vaccine candidate.\nBNT162b2 contains an LNP-formulated nucleoside-modified mRNA that encodes the spike glycoprotein captured in its prefusion conformation.\nAfter expression of the BNT162b2 coding sequence in cells, approximately 20% of the spike molecules are in the one-RBD \u2018up\u2019, two-RBD \u2018down\u2019 state.\nImmunisation of mice with a single dose of BNT162b2 induced dose level-dependent increases in pseudovirus neutralisation titers.\nPrime-boost vaccination of rhesus macaques elicited authentic SARS-CoV-2 neutralising geometric mean titers 10.2 to 18.0 times that of a SARS-CoV-2 convalescent human serum panel.\nBNT162b2 generated strong TH1 type CD4+ and IFN\u03b3+ CD8+ T-cell responses in mice and rhesus macaques.\nThe BNT162b2 vaccine candidate fully protected the lungs of immunised rhesus macaques from infectious SARS-CoV-2 challenge.\nBNT162b2 is currently being evaluated in a global, pivotal Phase 2/3 trial (NCT04368728).", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"In this study, we report the design, preclinical development, immunogenicity and anti-viral protective effect in rhesus macaques of the BNT162b2 vaccine candidate.\", \"Prime-boost vaccination of rhesus macaques elicited authentic SARS-CoV-2 neutralising geometric mean titers 10.2 to 18.0 times that of a SARS-CoV-2 convalescent human serum panel.\", \"The BNT162b2 vaccine candidate fully protected the lungs of immunised rhesus macaques from infectious SARS-CoV-2 challenge.\"]}", "id": 273}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Many mammals around the world could have a role in spreading the disease, including dogs and cats.\n\nAbstract:\nLittle information on the SARS-CoV-2 virus in animals is available to date.\nWhereas no one husbandry animal case has been reported to date, which would have significant implications in food safety, companion animals play a role in COVID-19 epidemiology that opens up new questions.\nThere is evidence that SARS-CoV-2 can infect felines, dogs and minks, and there is evidence of human-to-animal infection.\nLikewise, the S protein nucleotide sequence of the SARS-CoV-2 virus isolated in domestic animals and humans is identical, and the replication of the SARS-CoV-2 in cats is efficient.\nBesides, the epidemiological evidence for this current pandemic indicates that the spillover to humans was associated with close contact between man and exotic animals, very probably in Chinese wet markets, thus there is a growing general consensus that the exotic animal markets, should be strictly regulated.\nThe examination of these findings and the particular role of animals in COVID-19 should be carefully analyzed in order to establish preparation and containment measures.\nAnimal management and epidemiological surveillance must be also considered for COVID-19 control, and it can open up new questions regarding COVID-19 epidemiology and the role that animals play in it.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"There is evidence that SARS-CoV-2 can infect felines, dogs and minks, and there is evidence of human-to-animal infection.\"]}", "id": 274}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Surgical Masks Stop Transmission Of COVID-19 From Symptomatic People\n\nAbstract:\nBACKGROUND: Coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is believed to be mostly transmitted by medium-to-large sized respiratory droplets although airborne transmission is theoretically possible in healthcare settings involving aerosol-generating procedures.\nExposure to respiratory droplets can theoretically be reduced by surgical mask usage.\nHowever, there is a lack of experimental evidence supporting surgical mask usage for prevention of COVID-19.\nMETHODS: We used a well-established golden Syrian hamster SARS-CoV-2 model.\nWe placed SARS-CoV-2-challenged index hamsters and na\u00efve hamsters into closed system units each comprising two different cages separated by a polyvinyl chloride air porous partition with unidirectional airflow within the isolator.\nThe effect of a surgical mask partition placed in between the cages was investigated.\nBesides clinical scoring, hamster specimens were tested for viral load, histopathology, and viral nucleocapsid antigen expression.\nRESULTS: Non-contact transmission was found in 66.7% (10/15) of exposed na\u00efve hamsters.\nSurgical mask partition for challenged index or na\u00efve hamsters significantly reduced transmission to 25% (6/24, P=0.018).\nSurgical mask partition for challenged index hamsters significantly reduced transmission to only 16.7% (2/12, P=0.019) of exposed na\u00efve hamsters.\nUnlike the severe COVID-19 manifestations of challenged hamsters, infected na\u00efve hamsters had lower clinical scores, milder histopathological changes, and lower viral nucleocapsid antigen expression in respiratory tract tissues.\nCONCLUSIONS: SARS-CoV-2 could be transmitted by respiratory droplets or airborne droplet nuclei in the hamster model.\nSuch transmission could be reduced by surgical mask usage, especially when masks were worn by infected individuals.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"However, there is a lack of experimental evidence supporting surgical mask usage for prevention of COVID-19.\", \"Such transmission could be reduced by surgical mask usage, especially when masks were worn by infected individuals.\"]}", "id": 275}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: In severe cases of COVID-19, activation patterns of B cells resemble those seen in systemic lupus erythematosus, an autoimmune disease. Emory researchers want to see how far that resemblance extends.\n\nAbstract:\nSARS-CoV-2 is the coronavirus agent of the COVID-19 pandemic causing high mortalities.\nIn contrast, the widely spread human coronaviruses OC43, HKU1, 229E, and NL63 tend to cause only mild symptoms.\nThe present study shows, by in silico analysis, that these common human viruses are expected to induce immune memory against SARS-CoV-2 by sharing protein fragments (antigen epitopes) for presentation to the immune system by MHC class I. A list of such epitopes is provided.\nThe number of these epitopes and the prevalence of the common coronaviruses suggest that a large part of the world population has some degree of specific immunity against SARS-CoV-2 already, even without having been infected by that virus.\nFor inducing protection, booster vaccinations enhancing existing immunity are less demanding than primary vaccinations against new antigens.\nTherefore, for the discussion on vaccination strategies against COVID-19, the available immune memory against related viruses should be part of the consideration.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 276}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: diabetes significantly increases coronavirus sufferers risk of dying\n\nAbstract:\nCoronavirus disease 2019 (COVID-19) has become a global concern and public health issue due to its higher infection and mortality rate; particularly, the risk is very higher among the patients who have cardiovascular diseases (CVD) and/or diabetes mellitus (DM).\nIn this review, we analyzed the recently published literature on CVD and DM associated with COVD-19 infections and highlight their association with potential mechanisms.\nThe findings revealed that without any previous history of CVD, the COVID-19 patients have developed some CVD complications like myocardial injury, cardiomyopathy, and venous thromboembolism after being infected with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and required for those patients an emergency clinical support to be aware to manage those complications.\nThough the association between DM and COVID-19-induced severe complications is still unclear, the limited data predict that different markers like interleukin (IL)-1, IL-6, C-reactive protein, and D-dimer linked with the severity of COVID-19 infection in diabetic individuals.\nFurther studies on a large scale are urgently needed to explore the underlying mechanisms between CVD, DM, and COVID-19 for better treatment.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Though the association between DM and COVID-19-induced severe complications is still unclear, the limited data predict that different markers like interleukin (IL)-1, IL-6, C-reactive protein, and D-dimer linked with the severity of COVID-19 infection in diabetic individuals.\"]}", "id": 277}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: COVID-19: Scientists raise the vitamin D alarm\n\nAbstract:\nBackground: Following emerge of a novel coronavirus from Wuhan, China, in December 2019, it has affected the whole world and after months of efforts by the medical communities, there is still no specific approach for prevention and treatment against the Coronavirus Disease 2019 (COVID-19).\nEvidence recommends that vitamin D might be an important supportive agent for the immune system, mainly in cytokine response regulation against COVID-19.\nHence, we carried out a rapid systematic review and meta-analysis along with an ecological investigation in order to maximize the use of everything that exists about the role of vitamin D in the COVID-19.\nMethods: A systematic search was performed in PubMed, Scopus, Embase, Cochrane Library, Web of Science and Google Scholar (intitle) as well as preprint database of medRxiv, bioRxiv, Research Square, preprints.org, search engine of ScienceDirect and a rapid search through famous journals up to May 26, 2020.\nStudies focused on the role of vitamin D in confirmed COVID-19 patients were entered into the systematic review.\nAlong with our main aim, to find the second objective: correlation of global vitamin D status and COVID-19 recovery and mortality we carried out a literature search in PubMed database to identify the national or regional studies reported the vitamin D status globally.\nCMA v. 2.2.064 and SPSS v.16 were used for data analysis.\nResults: Out of nine studies entered into our systematic review, six studies containing 3,822 participants entered into the meta-analysis.\nThe meta-analysis indicated that 46.5% of COVID-19 patients were suffering from vitamin D deficiency (95% CI, 28.2%-65.8%) and in 43.3% of patients, levels of vitamin D were insufficient (95% CI, 27.4%-60.8%).\nIn regard to our ecological investigation on 51 countries including 408,748 participants, analyses indicated no correlation between vitamin D levels and recovery rate (r= 0.041) as well as mortality rate (r=-0.073) globally.\nHowever, given latitude, a small reverse correlation between mortality rate and vitamin D status was observed throughout the globe (r= -0.177).\nIn Asia, a medium direct correlation was observed for recovery rate (r= 0.317) and a significant reveres correlation for mortality rate (r= -0.700) with vitamin D status in such patients.\nIn Europe, there were no correlations for both recovery (r= 0.040) and mortality rate (r= -0.035).\nIn Middle East, the recovery rate (r= 0.267) and mortality rate (r= -0.217) showed a medium correlation.\nIn North and Sought America, surprisingly, both recovery and mortality rate demonstrated a direct correlation respectively (r= 1.000, r=0.500).\nIn Oceania, unexpectedly, recovery (r= -1.000) and mortality (r= -1.000) rates were in considerable reverse correlation with vitamin D levels.\nConclusion: In this systematic review and meta-analysis with an ecological approach, we found a high percentage of COVID-19 patients who suffer from vitamin D deficiency or insufficiency.\nMuch more important, our ecological investigation resulted in substantial direct and reverse correlations between recovery and mortality rates of COVID-19 patients with vitamin D status in different countries.\nConsidering latitudes, a small reverse correlation between vitamin D status and mortality rate was found globally.\nIt seems that populations with lower levels of vitamin D might be more susceptible to the novel coronavirus infection.\nNevertheless, due to multiple limitations, if this study does not allow to quantify a value of the Vitamin D with full confidence, it allows at least to know what the Vitamin D might be and that it would be prudent to invest in this direction through comprehensive large randomized clinical trials.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"In regard to our ecological investigation on 51 countries including 408,748 participants, analyses indicated no correlation between vitamin D levels and recovery rate (r= 0.041) as well as mortality rate (r=-0.073) globally.\", \"However, given latitude, a small reverse correlation between mortality rate and vitamin D status was observed throughout the globe (r= -0.177).\", \"In Asia, a medium direct correlation was observed for recovery rate (r= 0.317) and a significant reveres correlation for mortality rate (r= -0.700) with vitamin D status in such patients.\", \"In Europe, there were no correlations for both recovery (r= 0.040) and mortality rate (r= -0.035).\"]}", "id": 278}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Cats may be able to spread coronavirus to humans despite showing no symptoms\n\nAbstract:\nLittle information on the SARS-CoV-2 virus in animals is available to date.\nWhereas no one husbandry animal case has been reported to date, which would have significant implications in food safety, companion animals play a role in COVID-19 epidemiology that opens up new questions.\nThere is evidence that SARS-CoV-2 can infect felines, dogs and minks, and there is evidence of human-to-animal infection.\nLikewise, the S protein nucleotide sequence of the SARS-CoV-2 virus isolated in domestic animals and humans is identical, and the replication of the SARS-CoV-2 in cats is efficient.\nBesides, the epidemiological evidence for this current pandemic indicates that the spillover to humans was associated with close contact between man and exotic animals, very probably in Chinese wet markets, thus there is a growing general consensus that the exotic animal markets, should be strictly regulated.\nThe examination of these findings and the particular role of animals in COVID-19 should be carefully analyzed in order to establish preparation and containment measures.\nAnimal management and epidemiological surveillance must be also considered for COVID-19 control, and it can open up new questions regarding COVID-19 epidemiology and the role that animals play in it.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"There is evidence that SARS-CoV-2 can infect felines, dogs and minks, and there is evidence of human-to-animal infection.\", \"Likewise, the S protein nucleotide sequence of the SARS-CoV-2 virus isolated in domestic animals and humans is identical, and the replication of the SARS-CoV-2 in cats is efficient.\"]}", "id": 279}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: A prefusion sars-cov-2 spike rna vaccine is highly immunogenic and cause lung infection in non-human primates\n\nAbstract:\nTo contain the coronavirus disease 2019 (COVID-19) pandemic, a safe and effective vaccine against the new severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is urgently needed in quantities sufficient to immunise large populations.\nIn this study, we report the design, preclinical development, immunogenicity and anti-viral protective effect in rhesus macaques of the BNT162b2 vaccine candidate.\nBNT162b2 contains an LNP-formulated nucleoside-modified mRNA that encodes the spike glycoprotein captured in its prefusion conformation.\nAfter expression of the BNT162b2 coding sequence in cells, approximately 20% of the spike molecules are in the one-RBD \u2018up\u2019, two-RBD \u2018down\u2019 state.\nImmunisation of mice with a single dose of BNT162b2 induced dose level-dependent increases in pseudovirus neutralisation titers.\nPrime-boost vaccination of rhesus macaques elicited authentic SARS-CoV-2 neutralising geometric mean titers 10.2 to 18.0 times that of a SARS-CoV-2 convalescent human serum panel.\nBNT162b2 generated strong TH1 type CD4+ and IFN\u03b3+ CD8+ T-cell responses in mice and rhesus macaques.\nThe BNT162b2 vaccine candidate fully protected the lungs of immunised rhesus macaques from infectious SARS-CoV-2 challenge.\nBNT162b2 is currently being evaluated in a global, pivotal Phase 2/3 trial (NCT04368728).", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"In this study, we report the design, preclinical development, immunogenicity and anti-viral protective effect in rhesus macaques of the BNT162b2 vaccine candidate.\", \"Prime-boost vaccination of rhesus macaques elicited authentic SARS-CoV-2 neutralising geometric mean titers 10.2 to 18.0 times that of a SARS-CoV-2 convalescent human serum panel.\", \"The BNT162b2 vaccine candidate fully protected the lungs of immunised rhesus macaques from infectious SARS-CoV-2 challenge.\"]}", "id": 280}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: coronavirus will not go away on its own in warmer weather\n\nAbstract:\nThe coronavirus disease 2019 (COVID-19) outbreak has become a severe public health issue.\nThe novelty of the virus prompts a search for understanding of how ecological factors affect the transmission and survival of the virus.\nSeveral studies have robustly identified a relationship between temperature and the number of cases.\nHowever, there is no specific study for a tropical climate such as Brazil.\nThis work aims to determine the relationship of temperature to COVID-19 infection for the state capital cities of Brazil.\nCumulative data with the daily number of confirmed cases was collected from February 27 to April 1, 2020, for all 27 state capital cities of Brazil affected by COVID-19.\nA generalized additive model (GAM) was applied to explore the linear and nonlinear relationship between annual average temperature compensation and confirmed cases.\nAlso, a polynomial linear regression model was proposed to represent the behavior of the growth curve of COVID-19 in the capital cities of Brazil.\nThe GAM dose-response curve suggested a negative linear relationship between temperatures and daily cumulative confirmed cases of COVID-19 in the range from 16.8 \u00b0C to 27.4 \u00b0C.\nEach 1 \u00b0C rise of temperature was associated with a -4.8951% (t = -2.29, p = 0.0226) decrease in the number of daily cumulative confirmed cases of COVID-19.\nA sensitivity analysis assessed the robustness of the results of the model.\nThe predicted R-squared of the polynomial linear regression model was 0.81053.\nIn this study, which features the tropical temperatures of Brazil, the variation in annual average temperatures ranged from 16.8 \u00b0C to 27.4 \u00b0C.\nResults indicated that temperatures had a negative linear relationship with the number of confirmed cases.\nThe curve flattened at a threshold of 25.8 \u00b0C.\nThere is no evidence supporting that the curve declined for temperatures above 25.8 \u00b0C.\nThe study had the goal of supporting governance for healthcare policymakers.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Results indicated that temperatures had a negative linear relationship with the number of confirmed cases.\"]}", "id": 281}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Many readers have written in to ask whether ibuprofen or other non-steroidal anti-inflammatory drugs, or NSAIDs, can worsen COVID-19, the disease caused by the novel coronavirus.\n\nAbstract:\nIbuprofen is an over-the-counter medication that is used widely for the treatment of pain and fever during COVID-19 pandemic.\nA concern was raised regarding the safety of ibuprofen use because of its role in increasing ACE2 levels within the Renin-Angiotensin-Aldosterone system.\nACE2 is the coreceptor for the entry of SARS-CoV-2 into cells, and so, a potential increased risk of contracting COVID-19 disease and/or worsening of COVID-19 infection was feared with ibuprofen use.\nHowever, available data from limited studies show administration of recombinant ACE2 improves lung damage caused by respiratory viruses, suggesting ibuprofen use may be beneficial in COVID-19 disease.\nAt this time, there is no supporting evidence to discourage the use of ibuprofen.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 282}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Higher risk if you have type 1 diabetes. Compared to people without diabetes, people with type 1 diabetes are approximately 3.5 times as likely to die in hospital with COVID-19, while people with type 2 are approximately twice as likely. \n\nAbstract:\nCoronavirus disease 2019 (COVID-19) has become a global concern and public health issue due to its higher infection and mortality rate; particularly, the risk is very higher among the patients who have cardiovascular diseases (CVD) and/or diabetes mellitus (DM).\nIn this review, we analyzed the recently published literature on CVD and DM associated with COVD-19 infections and highlight their association with potential mechanisms.\nThe findings revealed that without any previous history of CVD, the COVID-19 patients have developed some CVD complications like myocardial injury, cardiomyopathy, and venous thromboembolism after being infected with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and required for those patients an emergency clinical support to be aware to manage those complications.\nThough the association between DM and COVID-19-induced severe complications is still unclear, the limited data predict that different markers like interleukin (IL)-1, IL-6, C-reactive protein, and D-dimer linked with the severity of COVID-19 infection in diabetic individuals.\nFurther studies on a large scale are urgently needed to explore the underlying mechanisms between CVD, DM, and COVID-19 for better treatment.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 283}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: the Covid-19 coronavirus can stay on various surfaces for a while\n\nAbstract:\nThe aim in this study was to assess the effectiveness of a quaternary ammonium chloride (QAC) surfactant in reducing surface staphylococcal contamination in a routinely operating medical ward occupied by patients who had tested positive for methicillin-resistant Staphylococcus aureus (MRSA).\nThe QAC being tested is an antibacterial film that is sprayed onto a surface and can remain active for up to 8 h. A field experimental study was designed with the QAC plus daily hypochlorite cleaning as the experimental group and hypochlorite cleaning alone as the control group.\nThe method of swabbing on moistened surfaces was used for sampling.\nIt was found that 83% and 77% of the bedside surfaces of MRSA-positive and MRSA-negative patients respectively were contaminated with staphylococci at 08:00 hours, and that the staphylococcal concentrations increased by 80% at 1200 h over a 4-hour period with routine ward and clinical activities.\nIrrespective of the MRSA status of the patients, high-touch surfaces around the bed-units within the studied medical ward were heavily contaminated (ranged 1 to 276 cfu/cm(2) amongst the sites with positive culture) with staphylococcal bacteria including MRSA, despite the implementation of daily hypochlorite wiping.\nHowever, the contamination rate dropped significantly from 78% to 11% after the application of the QAC polymer.\nIn the experimental group, the mean staphylococcal concentration of bedside surfaces was significantly (p < 0.0001) reduced from 4.4 \u00b1 8.7 cfu/cm(2) at 08:00 hours to 0.07 \u00b1 0.26 cfu/cm(2) at 12:00 hours by the QAC polymer.\nThe results of this study support the view that, in addition to hypochlorite wiping, the tested QAC surfactant is a potential environmental decontamination strategy for preventing the transmission of clinically important pathogens in medical wards.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"It was found that 83% and 77% of the bedside surfaces of MRSA-positive and MRSA-negative patients respectively were contaminated with staphylococci at 08:00 hours, and that the staphylococcal concentrations increased by 80% at 1200 h over a 4-hour period with routine ward and clinical activities.\"]}", "id": 284}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: A headache is a potential symptom of COVID-19.\n\nAbstract:\nOBJECTIVE: The aim of this manuscript is to investigate transversally Ear Nose Throat (ENT) symptoms COVID-19 infection correlated and to study the neurotropism and neuroinvasiveness of the virus in the head-neck district through the investigation of the sense of smell, taste, tearing, salivation and hearing.\nMETHODS: A total of 50 patients with laboratory-confirmed COVID-19 infection were included in our study.\nFor each patient we evaluated the short version of the Questionnaire of Olfactory Disorders-Negative Statements (sQOD-NS), the Summated Xerostomia Inventory-Dutch Version (SXI-DV), The Standardized Patient Evaluation of Eye Dryness (SPEED), Schirmer test I, the Hearing Handicap Inventory For Adults (HHIA) and the Tinnitus Handicap Inventory (THI).\nAll the tests we carried out were performed during the active phase of the symptomatology from COVID-19 (Condition A) and 15 after SARS-COV-2 RT-PCR test negative (Condition B).\nRESULTS: A total of 46 patients (92%) had olfactory dysfunction related to the infection.\nThe 70% of patients reported gustatory disorders.\nCough, fever, headache and asthenia were the most prevalent symptoms.\nThere was a statistically significant difference (p < 0,001) in sQOD-NS, SXI-DV, SPEED, Schirmer test, HHIA and THI between Condition A and Condition B. CONCLUSIONS: In our population there was an alteration of the sense of taste, of the sense of smell, dry eyes and of the oral cavity and an auditory discomfort, symptoms probably linked to the neurotropism of the virus.\nFurthermore, anosmia, dysgeusia and xerostomia are early symptoms of COVID-19, which can be exploited for an early quarantine and a limitation of viral contagion.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Cough, fever, headache and asthenia were the most prevalent symptoms.\"]}", "id": 285}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: If you're worried about corona, only the N95 mask will protect you\n\nAbstract:\nEvidence that face masks provide effective protection against respiratory infections in the community is scarce.\nHowever, face masks are widely used by health workers as part of droplet precautions when caring for patients with respiratory infections.\nIt would therefore be reasonable to suggest that consistent widespread use of face masks in the community could prevent further spread of the Severe Acute Respiratory Syndrome-Coronavirus 2 (SARS-CoV-2).\nIn this study we examine public face mask wearing in Uganda where a proportion wears masks to protect against acquiring, and the other to prevent from transmitting SARS-CoV-2.\nThe objective of this study was to determine what percentage of the population would have to wear face masks to reduce susceptibility to and infectivity of COVID-19 in Uganda, keeping the basic reproduction number below unity and/or flattening the curve.\nWe used an SEIAQRD model for the analysis.\nResults show that implementation of facemasks has a relatively large impact on the size of the coronavirus epidemic in Uganda.\nWe find that the critical mask adherence is 5 per 100 when 80% wear face masks.\nA cost-effective analysis shows that utilizing funds to provide 1 public mask to the population has a per capita compounded cost of USD 1.34.\nIf provision of face masks is done simultaneously with supportive care, the per capita compounded cost is USD 1.965, while for the case of only treatment and no provision of face masks costs each Ugandan USD 4.0579.\nWe conclude that since it is hard to achieve a 100% adherence to face masks, government might consider provision of face masks in conjunction with provision of care.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"It would therefore be reasonable to suggest that consistent widespread use of face masks in the community could prevent further spread of the Severe Acute Respiratory Syndrome-Coronavirus 2 (SARS-CoV-2).\", \"We find that the critical mask adherence is 5 per 100 when 80% wear face masks.\"]}", "id": 286}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: masks are effective in limiting spread of COVID-19\n\nAbstract:\nWe use the synthetic control method to analyze the effect of face masks on the spread of Covid-19 in Germany.\nOur identification approach exploits regional variation in the point in time when face masks became compulsory.\nDepending on the region we analyse, we find that face masks reduced the cumulative number of registered Covid-19 cases between 2.3% and 13% over a period of 10 days after they became compulsory.\nAssessing the credibility of the various estimates, we conclude that face masks reduce the daily growth rate of reported infections by around 40%.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Depending on the region we analyse, we find that face masks reduced the cumulative number of registered Covid-19 cases between 2.3% and 13% over a period of 10 days after they became compulsory.\", \"Assessing the credibility of the various estimates, we conclude that face masks reduce the daily growth rate of reported infections by around 40%.\"]}", "id": 287}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Can Nicotine Patches Help You Avoid COVID-19? The short answer is YES\n\nAbstract:\nABSTRACT Introduction: Recent studies show cigarette smokers are markedly under-represented among patients hospitalized for COVID-19 in over a dozen countries.\nIt is unclear if this may be related to confounding factors such as age distribution, access to care, and inaccurate records.\nWe hypothesized that these concerns could be avoided by studying smoking prevalence in relation to COVID-19 mortality.\nSince climate has been identified as a factor in COVID-19, we studied groups of countries with relatively comparable temperatures.\nMethods: The 20 hottest and 20 coldest countries in the Johns Hopkins Mortality Analysis database with a minimum mortality rate of .3 deaths/100,000 were selected on the basis of the average temperatures of their largest city.\nMortality rates were determined as of May 1, 2020 and correlated with national smoking rate adjusting for sex ratio, obesity, temperature, and elderly population.\nResults: A highly significant inverse correlation between current daily smoking prevalence and COVID-19 mortality rate was noted for the group of hot countries (R=-.718, p = .0002), cold countries (R=-.567, p=.0046), and the combined group (R=-.324, p=.0207).\nHowever, after adjustments only the regression for hot countries and the combined group remained significant.\nIn hot countries, for each percentage point increase in smoking rate mortality decreased by .147 per 100,000 population (95% CI .102- 192, p=.0066).\nThis resulted in mortality rates several-fold elevated in the countries with the lowest smoking rates relative to the highest smoking rates.\nIn the combined group, mortality decreased by .257 per 100,000 population (95% CI .175-.339, p=.0034).\nDiscussion: These findings add support to the finding of an inverse relationship between current smoking and seriously symptomatic COVID-19.\nHowever, we conclude that the difference in mortality between the highest and lowest smoking countries appears too large to be due primarily to the effects of smoking per se.\nA potentially beneficial effect of smoking is surprising, but compatible with a number of hypothetical mechanisms which deserve exploration: 1) Studies show smoking alters ACE2 expression which may affect COVID-19 infection or its progression to serious lung pathology.\n2) Nicotine has anti-inflammatory activity and also appears to alter ACE2 expression.\n3) Nitric oxide in cigarette smoke is known to be effective in treating pulmonary hypertension and has shown in vitro antiviral effects including against SARS-CoV-2.\n4) Smoking has complicated effects on the immune system involving both up and down regulation, any of which might alone or in concert antagonize progression of COVID-19.\n5) Smokers are exposed to hot vapors which may stimulate immunity in the respiratory tract by various heat-related mechanisms (e.g. heat shock proteins).\nStudies of steam and sauna treatments have shown efficacy in other viral respiratory conditions.\nAt this time there is no clear evidence that smoking is protective against COVID-19, so the established recommendations to avoid smoking should be emphasized.\nThe interaction of smoking and COVID-19 will only be reliably determined by carefully designed prospective study, and there is reason to believe that there are unknown confounds that may be spuriously suggesting a protective effect of smoking.\nHowever, the magnitude of the apparent inverse association of COVID-19 and smoking and its myriad clinical implications suggest the importance of further investigation.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"4) Smoking has complicated effects on the immune system involving both up and down regulation, any of which might alone or in concert antagonize progression of COVID-19.\", \"At this time there is no clear evidence that smoking is protective against COVID-19, so the established recommendations to avoid smoking should be emphasized.\"]}", "id": 288}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Hypothesis: Angiotensin-converting enzyme inhibitors and angiotensin receptor blockers may increase the risk of severe COVID-19\n\nAbstract:\nAngiotensin-converting enzyme (ACE) inhibitors (ACEIs) and angiotensin II type\u00ad1 receptor blockers (ARBs) are among the most widely prescribed drugs for the treatment of arterial hypertension, heart failure and chronic kidney disease.\nA number of studies, mainly in animals and not involving the lungs, have indicated that these drugs can increase expression of angiotensin-converting enzyme 2 (ACE2).\nACE2 is the cell entry receptor of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of coronavirus disease 2019 (COVID-19) that is currently battering the globe.\nThis has led to the hypothesis that use of ACEIs and ARBs may increase the risk of developing severe COVID-19.\nIn this point of view paper, possible scenarios regarding the impact of ACEI/ARB pharmacotherapy on COVID-19 are discussed in relation to the currently available evidence.\nAlthough further research on the influence of blood-pressure-lowering drugs, including those not targeting the renin-angiotensin system, is warranted, there are presently no compelling clinical data showing that ACEIs and ARBs increase the likelihood of contracting COVID-19 or worsen the outcome of SARS-CoV\u00ad2 infections.\nThus, unless contraindicated, use of ACEIs/ARBs in COVID-19 patients should be continued in line with the recent recommendations of medical societies.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"This has led to the hypothesis that use of ACEIs and ARBs may increase the risk of developing severe COVID-19.\"]}", "id": 289}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Development and validation of the 4c clinical model for adults hospitalised with covid-19\n\nAbstract:\nPrognostic models to predict the risk of clinical deterioration in acute COVID-19 are required to inform clinical management decisions.\nAmong 75,016 consecutive adults across England, Scotland and Wales prospectively recruited to the ISARIC Coronavirus Clinical Characterisation Consortium (ISARIC4C) study, we developed and validated a multivariable logistic regression model for in-hospital clinical deterioration (defined as any requirement of ventilatory support or critical care, or death) using 11 routinely measured variables.\nWe used internal-external cross-validation to show consistent measures of discrimination, calibration and clinical utility across eight geographical regions.\nWe further validated the final model in held-out data from 8,252 individuals in London, with similarly consistent performance (C-statistic 0.77 (95% CI 0.75 to 0.78); calibration-in-the-large 0.01 (-0.04 to 0.06); calibration slope 0.96 (0.90 to 1.02)).\nImportantly, this model demonstrated higher net benefit than using other candidate scores to inform decision-making.\nOur 4C Deterioration model thus demonstrates unprecedented clinical utility and generalisability to predict clinical deterioration among adults hospitalised with COVID-19.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Our 4C Deterioration model thus demonstrates unprecedented clinical utility and generalisability to predict clinical deterioration among adults hospitalised with COVID-19.\"]}", "id": 290}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Unfortunately, to date, no vaccines or antiviral drugs have been approved for the treatment of SARS-CoV-2 infection by regulatory agencies.\n\nAbstract:\nSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative representative of a severe respiratory illness resulted in widespread human infections and deaths in nearly all of the countries since late 2019.\nThere is no therapeutic FDA-approved drug against SARS-CoV-2 infection, although a combination of anti-viral drugs is directly being practiced in some countries.\nA broad-spectrum of antiviral agents are being currently evaluated in clinical trials, and in this review, we specifically focus on the application of Remdesivir (RVD) as a potential anti-viral compound against Middle East respiratory syndrome (MERS) -CoV, SARS-CoV and SARS-CoV-2.\nFirst, we overview the general information about SARS-CoV-2, followed by application of RDV as a nucleotide analogue which can potentially inhibits RNA-dependent RNA polymerase of COVs.\nAfterwards, we discussed the kinetics of SARS- or MERS-CoV proliferation in animal models which is significantly different compared to that in humans.\nFinally, some ongoing challenges and future perspective on the application of RDV either alone or in combination with other anti-viral agents against CoVs infection were surveyed to determine the efficiency of RDV in preclinical trials.\nAs a result, this paper provides crucial evidence of the potency of RDV to prevent SARS-CoV-2 infections.\nCommunicated by Ramaswamy H. Sarma.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"There is no therapeutic FDA-approved drug against SARS-CoV-2 infection, although a combination of anti-viral drugs is directly being practiced in some countries.\"]}", "id": 291}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Pandemic infection rates are deterministic but can then be modeled\n\nAbstract:\nThe covid-19 infection rates for a large number of infections collected from a large number of different sites are well defined with a negligible scatter.\nThe simplest invertible iterated map, exponential growth and decay, emerges from country-wide histograms whenever Tchebychev's inequality is satisfied to within several decimal places.\nThis is one point.\nAnother is that failed covid-19 pandemic model predictions have been reported repeatedly by the news media.\nModel predictions fail because the observed infection rates are beyond modeling: any model that uses fixed rates or uses memory or averages of past rates cannot reproduce the data on active infections.\nWhen those possibilities are ruled out, then little is left.\nUnder lockdown and social distancing, the rates unfold daily in small but unforeseeable steps, they are algorithmically complex.\nWe can, however, use two days in the daily data, today and any single day in the past (generally yesterday), to make a useful forecast of future infections.\nNo model provides results better than this simple forecast.\nWe analyze the actual doubling times for covid-19 data and compare them with our predicted doubling times.\nFlattening and peaking are precisely defined.\nWe identify and study the separate effects of social distancing vs recoveries in the daily infection rates.\nSocial distancing can only cause flattening but recoveries are required in order for the active infections to peak and decay.\nThree models and their predictions are analyzed.\nPandemic data for Austria, Germany, Italy, the USA, the UK, Finland, China, Taiwan, and Sweden are discussed.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Model predictions fail because the observed infection rates are beyond modeling: any model that uses fixed rates or uses memory or averages of past rates cannot reproduce the data on active infections.\"]}", "id": 292}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: The ground level ozone concentration is equally correlated with the number of covid-19 cases in warsaw, poland\n\nAbstract:\nCOVID-19, which is a consequence of infection with the novel viral agent SARS-CoV-2, first identified in China (Hubei Province), has been declared a pandemic by the WHO.\nAs of September 10, 2020, over 70,000 cases and over 2,000 deaths have been recorded in Poland.\nOf the many factors contributing to the level of transmission of the virus, the weather appears to be significant.\nIn this work we analyse the impact of weather factors such as temperature, relative humidity, wind speed and ground level ozone concentration on the number of COVID-19 cases in Warsaw, Poland.\nThe obtained results show an inverse correlation between ground level ozone concentration and the daily number of COVID-19 cases.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"The obtained results show an inverse correlation between ground level ozone concentration and the daily number of COVID-19 cases.\"]}", "id": 293}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Masks do reduce spread of flu and some COVID-19\n\nAbstract:\nWe present two models for the COVID-19 pandemic predicting the impact of universal face mask wearing upon the spread of the SARS-CoV-2 virus--one employing a stochastic dynamic network based compartmental SEIR (susceptible-exposed-infectious-recovered) approach, and the other employing individual ABM (agent-based modelling) Monte Carlo simulation--indicating (1) significant impact under (near) universal masking when at least 80% of a population is wearing masks, versus minimal impact when only 50% or less of the population is wearing masks, and (2) significant impact when universal masking is adopted early, by Day 50 of a regional outbreak, versus minimal impact when universal masking is adopted late.\nThese effects hold even at the lower filtering rates of homemade masks.\nTo validate these theoretical models, we compare their predictions against a new empirical data set we have collected that includes whether regions have universal masking cultures or policies, their daily case growth rates, and their percentage reduction from peak daily case growth rates.\nResults show a near perfect correlation between early universal masking and successful suppression of daily case growth rates and/or reduction from peak daily case growth rates, as predicted by our theoretical simulations.\nOur theoretical and empirical results argue for urgent implementation of universal masking.\nAs governments plan how to exit societal lockdowns, it is emerging as a key NPI; a\"mouth-and-nose lockdown\"is far more sustainable than a\"full body lockdown\", on economic, social, and mental health axes.\nAn interactive visualization of the ABM simulation is at http://dek.ai/masks4all.\nWe recommend immediate mask wearing recommendations, official guidelines for correct use, and awareness campaigns to shift masking mindsets away from pure self-protection, towards aspirational goals of responsibly protecting one's community.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"We present two models for the COVID-19 pandemic predicting the impact of universal face mask wearing upon the spread of the SARS-CoV-2 virus--one employing a stochastic dynamic network based compartmental SEIR (susceptible-exposed-infectious-recovered) approach, and the other employing individual ABM (agent-based modelling) Monte Carlo simulation--indicating (1) significant impact under (near) universal masking when at least 80% of a population is wearing masks, versus minimal impact when only 50% or less of the population is wearing masks, and (2) significant impact when universal masking is adopted early, by Day 50 of a regional outbreak, versus minimal impact when universal masking is adopted late.\", \"We recommend immediate mask wearing recommendations, official guidelines for correct use, and awareness campaigns to shift masking mindsets away from pure self-protection, towards aspirational goals of responsibly protecting one's community.\"]}", "id": 294}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: The ground level ozone concentration is inversely correlated with the number of covid-19 cases in warsaw , poland\n\nAbstract:\nCOVID-19, which is a consequence of infection with the novel viral agent SARS-CoV-2, first identified in China (Hubei Province), has been declared a pandemic by the WHO.\nAs of September 10, 2020, over 70,000 cases and over 2,000 deaths have been recorded in Poland.\nOf the many factors contributing to the level of transmission of the virus, the weather appears to be significant.\nIn this work we analyse the impact of weather factors such as temperature, relative humidity, wind speed and ground level ozone concentration on the number of COVID-19 cases in Warsaw, Poland.\nThe obtained results show an inverse correlation between ground level ozone concentration and the daily number of COVID-19 cases.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"The obtained results show an inverse correlation between ground level ozone concentration and the daily number of COVID-19 cases.\"]}", "id": 295}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Pets can Spread Coronavirus (COVID-19) to People\n\nAbstract:\nLittle information on the SARS-CoV-2 virus in animals is available to date.\nWhereas no one husbandry animal case has been reported to date, which would have significant implications in food safety, companion animals play a role in COVID-19 epidemiology that opens up new questions.\nThere is evidence that SARS-CoV-2 can infect felines, dogs and minks, and there is evidence of human-to-animal infection.\nLikewise, the S protein nucleotide sequence of the SARS-CoV-2 virus isolated in domestic animals and humans is identical, and the replication of the SARS-CoV-2 in cats is efficient.\nBesides, the epidemiological evidence for this current pandemic indicates that the spillover to humans was associated with close contact between man and exotic animals, very probably in Chinese wet markets, thus there is a growing general consensus that the exotic animal markets, should be strictly regulated.\nThe examination of these findings and the particular role of animals in COVID-19 should be carefully analyzed in order to establish preparation and containment measures.\nAnimal management and epidemiological surveillance must be also considered for COVID-19 control, and it can open up new questions regarding COVID-19 epidemiology and the role that animals play in it.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"There is evidence that SARS-CoV-2 can infect felines, dogs and minks, and there is evidence of human-to-animal infection.\"]}", "id": 296}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: covid-19 increases risk of heart attacks and stroke\n\nAbstract:\nThe mortality rate of coronavirus disease-19 (COVID-19) has been reported as 1-6% in most studies.\nThe cause of most deaths has been acute pneumonia.\nNevertheless, it has been noted that cardiovascular failure can also lead to death.\nThree COVID-19 patients were diagnosed based on reverse transcriptase-polymerase chain reaction of a nasopharyngeal swab test and radiological examinations in our hospital.\nThe patients received medications at the discretion of the treating physician.\nIn this case series, chest computed tomography scans and electrocardiograms, along with other diagnostic tests were used to evaluate these individuals.\nSudden cardiac death in COVID-19 patients is not common, but it is a major concern.\nSo, it is recommended to monitor cardiac condition in selected patients with COVID-19.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 297}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Attenuated influenza virions lacking the sars- cov-2 receptor-binding domain induce neutralizing antibodies in mice\n\nAbstract:\nAn effective vaccine is essential for controlling the spread of the SARS-CoV-2 virus.\nHere, we describe an influenza virus-based vaccine for SARS-CoV-2.\nWe incorporated a membrane-anchored form of the SARS-CoV-2 spike receptor binding domain (RBD) in place of the neuraminidase (NA) coding sequence in an influenza virus also possessing a mutation that reduces the affinity of hemagglutinin for its sialic acid receptor.\nThe resulting \u0394NA(RBD)-Flu virus can be generated by reverse genetics and grown to high titers in cell culture.\nA single-dose intranasal inoculation of mice with \u0394NA(RBD)-Flu elicits serum neutralizing antibody titers against SAR-CoV-2 comparable to those observed in humans following natural infection (~1:200).\nFurthermore, \u0394NA(RBD)-Flu itself causes no apparent disease in mice.\nIt might be possible to produce a vaccine similar to \u0394NA(RBD)-Flu at scale by leveraging existing platforms for the production of influenza vaccines.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"The resulting \\u0394NA(RBD)-Flu virus can be generated by reverse genetics and grown to high titers in cell culture.\", \"A single-dose intranasal inoculation of mice with \\u0394NA(RBD)-Flu elicits serum neutralizing antibody titers against SAR-CoV-2 comparable to those observed in humans following natural infection (~1:200).\"]}", "id": 298}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Drugs widely used to treat high blood pressure appear to make COVID-19 dangerously worse.\n\nAbstract:\nINTRODUCTION The present research aimed to determine the relation between the use of angiotensin-converting enzyme inhibitors (ACE inh) and angiotensinogen receptor blockers (ARBs) and in-hospital mortality of hypertensive patients diagnosed with Covid-19 pneumonia.\nMATERIAL AND METHOD In this retrospective study, we included 113 consecutive hypertensive patients admitted due to Covid-19 infection.\nIn all patients, Covid-19 infection was confirmed with using reverse-transcription polymerase chain reaction.\nAll patients were on ACE inh/ARBs or other antihypertensive therapy unless no contraindication was present.\nThe primary outcome of the study was the in-hospital all-cause mortality.\nRESULTS In total, 113 hypertensive Covid-19 patients were included, of them 74 patients were using ACE inh/ARBs.\nDuring in-hospital follow up, 30.9% [n = 35 patients] of patients died.\nThe frequency of admission to the ICU and endotracheal intubation were significantly higher in patients using ACE inh/ARBs.\nIn a multivariable analysis, the use of ACE inh/ARBs was an independent predictor of in-hospital mortality (OR: 3.66; 95%CI: 1.11-18.18; p= .032).\nKaplan-Meir curve analysis displayed that patients on ACE inh/ARBs therapy had higher incidence of in-hospital death than those who were not.\nCONCLUSION The present study has found that the use of ACE inh/ARBs therapy might be associated with an increased in-hospital mortality in patients who were diagnosed with Covid-19 pneumonia.\nIt is likely that ACE inh/ARBs therapy might not be beneficial in the subgroup of hypertensive Covid-19 patients despite the fact that there might be the possibility of some unmeasured residual confounders to affect the results of the study.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"CONCLUSION The present study has found that the use of ACE inh/ARBs therapy might be associated with an increased in-hospital mortality in patients who were diagnosed with Covid-19 pneumonia.\"]}", "id": 299}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Multivalency transforms sars-cov-2 networks into broad and ultrapotent neutralizers\n\nAbstract:\nThe novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes Coronavirus Disease 2019 (COVID-19), has caused a global pandemic.\nAntibodies are powerful biotherapeutics to fight viral infections; however, discovery of the most potent and broadly acting clones can be lengthy.\nHere, we used the human apoferritin protomer as a modular subunit to drive oligomerization of antibody fragments and transform antibodies targeting SARS-CoV-2 into exceptionally potent neutralizers.\nUsing this platform, half-maximal inhibitory concentration (IC50) values as low as 9 \u00d7 10\u221214 M were achieved as a result of up to 10,000-fold potency enhancements.\nCombination of three different antibody specificities and the fragment crystallizable (Fc) domain on a single multivalent molecule conferred the ability to overcome viral sequence variability together with outstanding potency and Ig-like in vivo bioavailability.\nThis MULTi-specific, multi-Affinity antiBODY (Multabody; or MB) platform contributes a new class of medical countermeasures against COVID-19 and an efficient approach to rapidly deploy potent and broadly-acting therapeutics against infectious diseases of global health importance.\nOne Sentence Summary multimerization platform transforms antibodies emerging from discovery screens into potent neutralizers that can overcome SARS-CoV-2 sequence diversity.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Here, we used the human apoferritin protomer as a modular subunit to drive oligomerization of antibody fragments and transform antibodies targeting SARS-CoV-2 into exceptionally potent neutralizers.\", \"This MULTi-specific, multi-Affinity antiBODY (Multabody; or MB) platform contributes a new class of medical countermeasures against COVID-19 and an efficient approach to rapidly deploy potent and broadly-acting therapeutics against infectious diseases of global health importance.\", \"One Sentence Summary multimerization platform transforms antibodies emerging from discovery screens into potent neutralizers that can overcome SARS-CoV-2 sequence diversity.\"]}", "id": 300}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Wear masks with two or more layers to stop the spread of COVID-19\n\nAbstract:\nWe identified seasonal human coronaviruses, influenza viruses and rhinoviruses in exhaled breath and coughs of children and adults with acute respiratory illness.\nSurgical face masks significantly reduced detection of influenza virus RNA in respiratory droplets and coronavirus RNA in aerosols, with a trend toward reduced detection of coronavirus RNA in respiratory droplets.\nOur results indicate that surgical face masks could prevent transmission of human coronaviruses and influenza viruses from symptomatic individuals.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Surgical face masks significantly reduced detection of influenza virus RNA in respiratory droplets and coronavirus RNA in aerosols, with a trend toward reduced detection of coronavirus RNA in respiratory droplets.\", \"Our results indicate that surgical face masks could prevent transmission of human coronaviruses and influenza viruses from symptomatic individuals.\"]}", "id": 301}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: It's worth noting that there isn't a lot of data on what can kill SARS-CoV-2-the virus that causes coronavirus COVID-19-on surfaces\n\nAbstract:\nThe global pandemic caused by the newly described severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused worldwide suffering and death of unimaginable magnitude from coronavirus disease 2019 (COVID-19).\nThe virus is transmitted through aerosol droplets, and causes severe acute respiratory syndrome.\nSARS-CoV-2 uses the receptor-binding domain of its spike protein S1 to attach to the host angiotensin-converting enzyme 2 receptor in lung and airway cells.\nBinding requires the help of another host protein, transmembrane protease serine S1 member 2.\nSeveral factors likely contribute to the efficient transmission of SARS-CoV-2.\nThe receptor-binding domain of SARS-CoV-2 has a 10- to 20-fold higher receptor-binding capacity compared with previous pandemic coronaviruses.\nIn addition, because asymptomatic persons infected with SARS-CoV-2 have high viral loads in their nasal secretions, they can silently and efficiently spread the disease.\nPCR-based tests have emerged as the criterion standard for the diagnosis of infection.\nCaution must be exercised in interpreting antibody-based tests because they have not yet been validated, and may give a false sense of security of being \"immune\" to SARS-CoV-2.\nWe discuss how the development of some symptoms in allergic rhinitis can serve as clues for new-onset COVID-19.\nThere are mixed reports that asthma is a risk factor for severe COVID-19, possibly due to differences in asthma endotypes.\nThe rapid spread of COVID-19 has focused the efforts of scientists on repurposing existing Food and Drug Administration-approved drugs that inhibit viral entry, endocytosis, genome assembly, translation, and replication.\nNumerous clinical trials have been launched to identify effective treatments for COVID-19.\nInitial data from a placebo-controlled study suggest faster time to recovery in patients on remdesivir; it is now being evaluated in additional controlled studies.\nAs discussed in this review, till effective vaccines and treatments emerge, it is important to understand the scientific rationale of pandemic-mitigation strategies such as wearing facemasks and social distancing, and implement them.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"As discussed in this review, till effective vaccines and treatments emerge, it is important to understand the scientific rationale of pandemic-mitigation strategies such as wearing facemasks and social distancing, and implement them.\"]}", "id": 302}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Evolutionary arms race between virus and coronavirus drives genetic diversity in bat sars related coronavirus spike genes\n\nAbstract:\nThe Chinese horseshoe bat (Rhinolophus sinicus), reservoir host of severe acute respiratory syndrome coronavirus (SARS-CoV), carries many bat SARS-related CoVs (SARSr-CoVs) with high genetic diversity, particularly in the spike gene.\nDespite these variations, some bat SARSr-CoVs can utilize the orthologs of the human SARS-CoV receptor, angiotensin-converting enzyme 2 (ACE2), for entry.\nIt is speculated that the interaction between bat ACE2 and SARSr-CoV spike proteins drives diversity.\nHere, we identified a series of R. sinicus ACE2 variants with some polymorphic sites involved in the interaction with the SARS-CoV spike protein.\nPseudoviruses or SARSr-CoVs carrying different spike proteins showed different infection efficiencies in cells transiently expressing bat ACE2 variants.\nConsistent results were observed by binding affinity assays between SARS-CoV and SARSr-CoV spike proteins and receptor molecules from bats and humans.\nAll tested bat SARSr-CoV spike proteins had a higher binding affinity to human ACE2 than to bat ACE2, although they showed a 10-fold lower binding affinity to human ACE2 compared with that of their SARS-CoV counterpart.\nStructure modeling revealed that the difference in binding affinity between spike and ACE2 might be caused by the alteration of some key residues in the interface of these two molecules.\nMolecular evolution analysis indicates that some key residues were under positive selection.\nThese results suggest that the SARSr-CoV spike protein and R. sinicus ACE2 may have coevolved over time and experienced selection pressure from each other, triggering the evolutionary arms race dynamics.\nIMPORTANCE Evolutionary arms race dynamics shape the diversity of viruses and their receptors.\nIdentification of key residues which are involved in interspecies transmission is important to predict potential pathogen spillover from wildlife to humans.\nPreviously, we have identified genetically diverse SARSr-CoVs in Chinese horseshoe bats.\nHere, we show the highly polymorphic ACE2 in Chinese horseshoe bat populations.\nThese ACE2 variants support SARS-CoV and SARSr-CoV infection but with different binding affinities to different spike proteins.\nThe higher binding affinity of SARSr-CoV spike to human ACE2 suggests that these viruses have the capacity for spillover to humans.\nThe positive selection of residues at the interface between ACE2 and SARSr-CoV spike protein suggests long-term and ongoing coevolutionary dynamics between them.\nContinued surveillance of this group of viruses in bats is necessary for the prevention of the next SARS-like disease.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"It is speculated that the interaction between bat ACE2 and SARSr-CoV spike proteins drives diversity.\", \"These results suggest that the SARSr-CoV spike protein and R. sinicus ACE2 may have coevolved over time and experienced selection pressure from each other, triggering the evolutionary arms race dynamics.\"]}", "id": 303}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: there is no link between vitamin D concentrations and risk of COVID-19 infection.\n\nAbstract:\nImportance: Vitamin D treatment has been found to decrease incidence of viral respiratory tract infection, especially in vitamin D deficiency.\nIt is unknown whether COVID-19 incidence is associated with vitamin D deficiency and treatment.\nObjective: To examine whether vitamin D deficiency and treatment are associated with testing positive for COVID-19.\nDesign: Retrospective cohort study Setting: University of Chicago Medicine Participants: Patients tested for COVID-19 from 3/3/2020-4/10/2020.\nVitamin D deficiency was defined by the most recent 25-hydroxycholecalciferol <20ng/ml or 1,25-dihydroxycholecalciferol <18pg/ml within 1 year before COVID-19 testing.\nTreatment was defined by the most recent vitamin D type and dose, and treatment changes between the time of the most recent vitamin D level and time of COVID-19 testing.\nVitamin D deficiency and treatment changes were combined to categorize vitamin D status at the time of COVID-19 testing as likely deficient(last-level-deficient/treatment-not-increased), likely sufficient(last-level-not-deficient/treatment-not-decreased), or uncertain deficiency(last-level-deficient/treatment-increased or last-level-not-deficient/treatment-decreased).\nMain Outcomes and Measures: The main outcome was testing positive for COVID-19.\nMultivariable analysis tested whether the most recent vitamin D level and treatment changes after that level were associated with testing positive for COVID-19 controlling for demographic and comorbidity indicators.\nBivariate analyses of associations of treatment with vitamin D deficiency and COVID-19 were performed.\nResults: Among 4,314 patients tested for COVID-19, 499 had a vitamin D level in the year before testing.\nVitamin D status at the time of COVID-19 testing was categorized as likely deficient for 127(25%) patients, likely sufficient for 291(58%) patients, and uncertain for 81(16%) patients.\nIn multivariate analysis, testing positive for COVID-19 was associated with increasing age(RR(age<50)=1.05,p<0.021;RR(age[\u2265]50)=1.02,p<0.064)), non-white race(RR=2.54,p<0.01) and being likely vitamin D deficient (deficient/treatment-not-increased:RR=1.77,p<0.02) as compared to likely vitamin D sufficient(not-deficient/treatment-not-decreased), with predicted COVID-19 rates in the vitamin D deficient group of 21.6%(95%CI[14.0%-29.2%] ) versus 12.2%(95%CI[8.9%-15.4%]) in the vitamin D sufficient group.\nVitamin D deficiency declined with increasing vitamin D dose, especially of vitamin D3.\nVitamin D dose was not significantly associated with testing positive for COVID-19.\nConclusions and Relevance: Vitamin D deficiency that is not sufficiently treated is associated with COVID-19 risk.\nTesting and treatment for vitamin D deficiency to address COVID-19 warrant aggressive pursuit and study.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Conclusions and Relevance: Vitamin D deficiency that is not sufficiently treated is associated with COVID-19 risk.\"]}", "id": 304}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Masks do reduce spread of flu and some COVID-19\n\nAbstract:\nFace mask use by the general public for limiting the spread of the COVID-19 pandemic is controversial, though increasingly recommended, and the potential of this intervention is not well understood.\nWe develop a compartmental model for assessing the community-wide impact of mask use by the general, asymptomatic public, a portion of which may be asymptomatically infectious.\nModel simulations, using data relevant to COVID-19 dynamics in the US states of New York and Washington, suggest that broad adoption of even relatively ineffective face masks may meaningfully reduce community transmission of COVID-19 and decrease peak hospitalizations and deaths.\nMoreover, mask use decreases the effective transmission rate in nearly linear proportion to the product of mask effectiveness (as a fraction of potentially infectious contacts blocked) and coverage rate (as a fraction of the general population), while the impact on epidemiologic outcomes (death, hospitalizations) is highly nonlinear, indicating masks could synergize with other non-pharmaceutical measures.\nNotably, masks are found to be useful with respect to both preventing illness in healthy persons and preventing asymptomatic transmission.\nHypothetical mask adoption scenarios, for Washington and New York state, suggest that immediate near universal (80%) adoption of moderately (50%) effective masks could prevent on the order of 17--45% of projected deaths over two months in New York, while decreasing the peak daily death rate by 34--58%, absent other changes in epidemic dynamics.\nEven very weak masks (20% effective) can still be useful if the underlying transmission rate is relatively low or decreasing: In Washington, where baseline transmission is much less intense, 80% adoption of such masks could reduce mortality by 24--65% (and peak deaths 15--69%), compared to 2--9% mortality reduction in New York (peak death reduction 9--18%).\nOur results suggest use of face masks by the general public is potentially of high value in curtailing community transmission and the burden of the pandemic.\nThe community-wide benefits are likely to be greatest when face masks are used in conjunction with other non-pharmaceutical practices (such as social-distancing), and when adoption is nearly universal (nation-wide) and compliance is high.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Model simulations, using data relevant to COVID-19 dynamics in the US states of New York and Washington, suggest that broad adoption of even relatively ineffective face masks may meaningfully reduce community transmission of COVID-19 and decrease peak hospitalizations and deaths.\", \"Notably, masks are found to be useful with respect to both preventing illness in healthy persons and preventing asymptomatic transmission.\", \"Hypothetical mask adoption scenarios, for Washington and New York state, suggest that immediate near universal (80%) adoption of moderately (50%) effective masks could prevent on the order of 17--45% of projected deaths over two months in New York, while decreasing the peak daily death rate by 34--58%, absent other changes in epidemic dynamics.\", \"Even very weak masks (20% effective) can still be useful if the underlying transmission rate is relatively low or decreasing: In Washington, where baseline transmission is much less intense, 80% adoption of such masks could reduce mortality by 24--65% (and peak deaths 15--69%), compared to 2--9% mortality reduction in New York (peak death reduction 9--18%).\", \"Our results suggest use of face masks by the general public is potentially of high value in curtailing community transmission and the burden of the pandemic.\", \"The community-wide benefits are likely to be greatest when face masks are used in conjunction with other non-pharmaceutical practices (such as social-distancing), and when adoption is nearly universal (nation-wide) and compliance is high.\"]}", "id": 305}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Sars-cov-2 epidemic after social and economic reopening in three us states reveals shifts in age structure and clinical characteristics\n\nAbstract:\nIn the United States, state-level re-openings in spring 2020 presented an opportunity for the resurgence of SARS-CoV-2 transmission.\nOne important question during this time was whether human contact and mixing patterns could increase gradually without increasing viral transmission, the rationale being that new mixing patterns would likely be associated with improved distancing, masking, and hygiene practices.\nA second key question to follow during this time was whether clinical characteristics of the epidemic would improve after the initial surge of cases.\nHere, we analyze age-structured case, hospitalization, and death time series from three states \u2013 Rhode Island, Massachusetts, and Pennsylvania \u2013 that had successful reopenings in May 2020 without summer waves of infection.\nUsing a Bayesian inference framework on eleven daily data streams and flexible daily population contact parameters, we show that population-average mixing rates dropped by >50% during the lockdown period in March/April, and that the correlation between overall population mobility and transmission-capable mixing was broken in May as these states partially re-opened.\nWe estimate the reporting rates (fraction of symptomatic cases reporting to health system) at 96.3% (RI), 62.5% (MA), and 98.9% (PA).\nWe show that elderly individuals were less able to reduce contacts during the lockdown period when compared to younger individuals, leading to the outbreak being concentrated in elderly congregate settings despite the lockdown.\nAttack rate estimates through August 31 2020 are 6.2% (95% CI: 5.7% \u2013 6.8%) of the total population infected for Rhode Island, 6.7% (95% CI: 5.4% \u2013 7.6%) in Massachusetts, and 2.7% (95% CI: 2.5% \u2013 3.1%) in Pennsylvania, with some validation available through published seroprevalence studies.\nInfection fatality rates (IFR) estimates are higher in our analysis (>2%) than previously reported values, likely resulting from the epidemics in these three states affecting the most vulnerable sub-populations and the close matches between the states\u2019 reported COVID-19 deaths and excess deaths.\nData in Pennsylvania may have been underreported for both non-hospitalized and hospitalized patients, casting substantial uncertainty on estimates of attack rate and infection fatality rate.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"A second key question to follow during this time was whether clinical characteristics of the epidemic would improve after the initial surge of cases.\"]}", "id": 306}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: No drug is approved by the Food and Drug Administration (FDA) for COVID-19, although dozens of candidates - including drugs used to treat rheumatoid arthritis, parasites, cancer, and HIV - have been proposed\n\nAbstract:\nHere we report on the most recent updates on experimental drugs successfully employed in the treatment of the disease caused by SARS CoV-2 coronavirus, also referred to as COVID-19 (COronaVIrus Disease 19).\nIn particular, several cases of recovered patients have been reported after being treated with lopinavir/ritonavir (which is widely used to treat human immunodeficiency virus (HIV) infection) in combination with the anti-flu drug oseltamivir.\nIn addition, remdesivir, which has been previously administered to Ebola virus patients, has also proven effective in the U.S. against coronavirus, while antimalarial chloroquine and hydroxychloroquine, favipiravir and co-administered darunavir and umifenovir (in patient therapies) were also recently recorded as having anti-SARS CoV-2 effects.\nSince the recoveries/deaths ratio in the last weeks significantly increased, especially in China, it is clear that the experimental antiviral therapy, together with the availability of intensive care unit beds in hospitals and rigorous government control measures, all play an important role in dealing with this virus.\nThis also stresses the urgent need for the scientific community to devote its efforts to find other more specific antiviral strategies.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 307}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Development and validation of the 4c animal model for adults hospitalised with covid-19\n\nAbstract:\nPrognostic models to predict the risk of clinical deterioration in acute COVID-19 are required to inform clinical management decisions.\nAmong 75,016 consecutive adults across England, Scotland and Wales prospectively recruited to the ISARIC Coronavirus Clinical Characterisation Consortium (ISARIC4C) study, we developed and validated a multivariable logistic regression model for in-hospital clinical deterioration (defined as any requirement of ventilatory support or critical care, or death) using 11 routinely measured variables.\nWe used internal-external cross-validation to show consistent measures of discrimination, calibration and clinical utility across eight geographical regions.\nWe further validated the final model in held-out data from 8,252 individuals in London, with similarly consistent performance (C-statistic 0.77 (95% CI 0.75 to 0.78); calibration-in-the-large 0.01 (-0.04 to 0.06); calibration slope 0.96 (0.90 to 1.02)).\nImportantly, this model demonstrated higher net benefit than using other candidate scores to inform decision-making.\nOur 4C Deterioration model thus demonstrates unprecedented clinical utility and generalisability to predict clinical deterioration among adults hospitalised with COVID-19.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Our 4C Deterioration model thus demonstrates unprecedented clinical utility and generalisability to predict clinical deterioration among adults hospitalised with COVID-19.\"]}", "id": 308}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: there are few novel sars-cov-2 cases in malaria countries because of the use of the antimalarial drug hydroxychloroquine.\n\nAbstract:\nBACKGROUND: Chloroquine and hydroxychloroquine have been found to be efficient on SARS-CoV-2, and reported to be efficient in Chinese COV-19 patients.\nWe evaluate the role of hydroxychloroquine on respiratory viral loads.\nPATIENTS AND METHODS: French Confirmed COVID-19 patients were included in a single arm protocol from early March to March 16th, to receive 600mg of hydroxychloroquine daily and their viral load in nasopharyngeal swabs was tested daily in a hospital setting.\nDepending on their clinical presentation, azithromycin was added to the treatment.\nUntreated patients from another center and cases refusing the protocol were included as negative controls.\nPresence and absence of virus at Day6-post inclusion was considered the end point.\nRESULTS: Six patients were asymptomatic, 22 had upper respiratory tract infection symptoms and eight had lower respiratory tract infection symptoms.\nTwenty cases were treated in this study and showed a significant reduction of the viral carriage at D6-post inclusion compared to controls, and much lower average carrying duration than reported of untreated patients in the literature.\nAzithromycin added to hydroxychloroquine was significantly more efficient for virus elimination.\nCONCLUSION: Despite its small sample size our survey shows that hydroxychloroquine treatment is significantly associated with viral load reduction/disappearance in COVID-19 patients and its effect is reinforced by azithromycin.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"CONCLUSION: Despite its small sample size our survey shows that hydroxychloroquine treatment is significantly associated with viral load reduction/disappearance in COVID-19 patients and its effect is reinforced by azithromycin.\"]}", "id": 309}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: People with Diabetes May Have Higher Risk for COVID-19\n\nAbstract:\nAIMS: To describe characteristics of COVID-19 patients with type 2 diabetes and to analyze risk factors for severity.\nMETHODS: Demographics, comorbidities, symptoms, laboratory findings, treatments and outcomes of COVID-19 patients with diabetes were collected and analyzed.\nRESULTS: Seventy-fourCOVID-19 patients with diabetes were included.\nTwenty-seven patients (36.5%) were severe and 10 patients (13.5%) died.\nHigher levels of blood glucose, serum amyloid A (SAA), C reactive protein and interleukin 6 were associated with severe patients compared to non-severe ones (P<0.05).\nLevels of albumin, cholesterol, high density lipoprotein, small and dense low density lipoprotein and CD4+T lymphocyte counts in severe patients were lower than those in non-severe patients (P<0.05).\nLogistic regression analysis identified decreased CD4+T lymphocyte counts (odds ratio [OR]=0.988, 95%Confidence interval [95%CI] 0.979-0.997) and increased SAA levels (OR=1.029, 95%CI 1.002-1.058) as risk factors for severity of COVID-19 with diabetes (P<0.05).\nCONCLUSIONS: Type 2 diabetic patients were more susceptible to COVID-19 than overall population, which might be associated with hyperglycemia and dyslipidemia.\nAggressive treatment should be suggested, especially when these patients had low CD4+T lymphocyte counts and high SAA levels.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"CONCLUSIONS: Type 2 diabetic patients were more susceptible to COVID-19 than overall population, which might be associated with hyperglycemia and dyslipidemia.\"]}", "id": 310}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Many mammals around the world could have a role in spreading the disease, including dogs and cats.\n\nAbstract:\nOn April 22, CDC and the U.S. Department of Agriculture (USDA) reported cases of two domestic cats with confirmed infection with SARS-CoV-2, the virus that causes coronavirus disease 2019 (COVID-19).\nThese are the first reported companion animals (including pets and service animals) with SARS-CoV-2 infection in the United States, and among the first findings of SARS-CoV-2 symptomatic companion animals reported worldwide.\nThese feline cases originated from separate households and were epidemiologically linked to suspected or confirmed human COVID-19 cases in their respective households.\nNotification of presumptive positive animal test results triggered a One Health* investigation by state and federal partners, who determined that no further transmission events to other animals or persons had occurred.\nBoth cats fully recovered.\nAlthough there is currently no evidence that animals play a substantial role in spreading COVID-19, CDC advises persons with suspected or confirmed COVID-19 to restrict contact with animals during their illness and to monitor any animals with confirmed SARS-CoV-2 infection and separate them from other persons and animals at home (1).", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Notification of presumptive positive animal test results triggered a One Health* investigation by state and federal partners, who determined that no further transmission events to other animals or persons had occurred.\", \"Although there is currently no evidence that animals play a substantial role in spreading COVID-19, CDC advises persons with suspected or confirmed COVID-19 to restrict contact with animals during their illness and to monitor any animals with confirmed SARS-CoV-2 infection and separate them from other persons and animals at home (1).\"]}", "id": 311}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: It does not make a lot of sense that if somebody is otherwise healthy and young and they have hypertension alone, that they should be at increased risk\n\nAbstract:\nInvestigations reported that hypertension, diabetes, and cardiovascular diseases were the most prevalent comorbidities among the patients with coronavirus disease 2019 (COVID-19).\nHypertension appeared consistently as the most prevalent risk factors in COVID-19 patients.\nSome investigations speculated about the association between renin-angiotensin-aldosterone system (RAAS) and susceptibility to COVID-19, as well as the relationship between RAAS inhibitors and increased mortality in these patients.\nThis raised concern about the potential association between hypertension (and its treatment) and propensity for COVID-19.\nThere are only a few follow-up studies that investigated the impact of comorbidities on outcome in these patients with conflicting findings.\nHypertension has been proven to be more prevalent in patients with an adverse outcome (admission in intensive care unit, use of mechanical ventilation, or death).\nSo far, there is no study that demonstrated independent predictive value of hypertension on mortality in COVID-19 patients.\nThere are many speculations about this coronavirus and its relation with different risk factors and underlying diseases.\nThe aim of this review was to summarize the current knowledge about the relationship between hypertension and COVID-19 and the role of hypertension on outcome in these patients.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 312}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: If the person is asymptomatic but was tested after having close contact with someone with SARS-CoV-2 infection or as part of a response to an outbreak (e.g., facility has a resident with nursing-home onset COVID-19 or a HCP with COVID-19), then they should also be considered to have SARS-CoV-2 infection and managed as described above.\n\nAbstract:\nAbstract Asymptomatic but infectious people have been reported in many infectious diseases.\nAsymptomatic and pre-symptomatic carriers would be a hidden reservoir of COVID-19.\nAim This review identifies primary empirical evidence about the ability of asymptomatic carriers to infect others with COVID-19 pandemic and reflects on the implications for control measures.\nMethods A systematic review is followed by a narrative report and commentary inclusion criteria were: studies reporting primary data on asymptomatic or pre-symptomatic patients, who were considered to have passed on COVID-19 infection; and published in indexed journals or in peer review between January 1 and March 31, 2020.\nResults Nine articles reported on 83 asymptomatic or pre-symptomatic persons.\nConclusions The evidence confirms COVID-19 transmission from people who were asymptomatic at the time.\nA series of implications for health service response are laid out.\nKeywords: Covid-19, Asymptomatic, Pre-symptomatic, Public Health", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 313}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Can COVID-19 be spread from pets to people or other pets? According to the latest information from the CDC , the risk of animals spreading COVID-19 to people is very low. However, because all animals can carry germs that can make people sick, it's always a good idea to practice healthy habits around pets and other animals.\n\nAbstract:\nCoronavirus disease-19 (COVID-19) caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is now a pandemic threat.\nThis virus is supposed to be spread by human to human transmission.\nCellular angiotensin-converting enzyme 2 (ACE2) is the receptor of SARS-CoV-2 which is identical or similar in different species of animals such as pigs, ferrets, cats, orangutans, monkeys, and humans.\nMoreover, a recent study predicted that dogs might be secondary hosts during the evolution of SARS-CoV-2 from bat to human.\nTherefore, there is a possibility of spreading SARS-CoV-2 through domestic pets.\nThere are now many reports of SARS-CoV-2 positive cases in dogs, cats, tigers, lion, and minks.\nExperimental data showed ferrets and cats are highly susceptible to SARS-CoV-2 as infected by virus inoculation and can transmit the virus directly or indirectly by droplets or airborne routes.\nBased on these natural infection reports and experimental data, whether the pets are responsible for SARS-CoV-2 spread to humans; needs to be deeply investigated.\nHumans showing clinical symptoms of respiratory infections have been undergoing for the COVID-19 diagnostic test but many infected people and few pets confirmed with SARS-CoV-2 remained asymptomatic.\nIn this review, we summarize the natural cases of SARS-CoV-2 in animals with the latest researches conducted in this field.\nThis review will be helpful to think insights of SARS-CoV-2 transmissions, spread, and demand for seroprevalence studies, especially in companion animals.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Coronavirus disease-19 (COVID-19) caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is now a pandemic threat.\", \"This virus is supposed to be spread by human to human transmission.\", \"Experimental data showed ferrets and cats are highly susceptible to SARS-CoV-2 as infected by virus inoculation and can transmit the virus directly or indirectly by droplets or airborne routes.\"]}", "id": 314}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Thrombotic microvascular injury is also mediated by thrombotic microangiopathy despite systemic complement activation in covid-19 patients\n\nAbstract:\nHypoxemia and coagulopathy are common in severe symptomatic patients of coronavirus disease 2019 (COVID-19).\nHistological evidence shows implication of complement activation and lung injury.\nWe research sign of complement activation and presence of thrombotic microangiopathy in 8 severe patients.\nSix of them presented moderate elevation of final pathway of complement / sC5b-9 (median value : 350 ng/mL [IQR : 300,5-514,95 ng/mL]).\nTwo patients have been autopsied and presence of thrombotic microvascular injury have been found.\nInterestingly, none the 8 patients had signs of mechanical hemolytic anemia (median value of hemoglobin : 10,5 gr/dL[IQR : 8,1-1,9], median value of haptoglobuline 4,49 [IQR 3,55-4,66], none of the patients has schistocyte) and thrombocytopenia (median value: 348000/mL [IQR : 266 000-401 000).\nFinally, all 8 patients had elevated d-dimer (median value : 2226 microgr/l [IQR : 1493-2362]) and soluble fibrin monomer complex (median value : 8.5 mg/mL, IQR[ <6-10.6]).\nIn summary, this study show moderate activation of complement and coagulation with presence of thrombotic microvascular injury in patients with severe COVID-19 without evidence of systemic thrombotic microangiopathy.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"In summary, this study show moderate activation of complement and coagulation with presence of thrombotic microvascular injury in patients with severe COVID-19 without evidence of systemic thrombotic microangiopathy.\"]}", "id": 315}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: vitamin D might help protect against becoming infected with, and developing serious symptoms of, COVID-19\n\nAbstract:\nImportance: Vitamin D treatment has been found to decrease incidence of viral respiratory tract infection, especially in vitamin D deficiency.\nIt is unknown whether COVID-19 incidence is associated with vitamin D deficiency and treatment.\nObjective: To examine whether vitamin D deficiency and treatment are associated with testing positive for COVID-19.\nDesign: Retrospective cohort study Setting: University of Chicago Medicine Participants: Patients tested for COVID-19 from 3/3/2020-4/10/2020.\nVitamin D deficiency was defined by the most recent 25-hydroxycholecalciferol <20ng/ml or 1,25-dihydroxycholecalciferol <18pg/ml within 1 year before COVID-19 testing.\nTreatment was defined by the most recent vitamin D type and dose, and treatment changes between the time of the most recent vitamin D level and time of COVID-19 testing.\nVitamin D deficiency and treatment changes were combined to categorize vitamin D status at the time of COVID-19 testing as likely deficient(last-level-deficient/treatment-not-increased), likely sufficient(last-level-not-deficient/treatment-not-decreased), or uncertain deficiency(last-level-deficient/treatment-increased or last-level-not-deficient/treatment-decreased).\nMain Outcomes and Measures: The main outcome was testing positive for COVID-19.\nMultivariable analysis tested whether the most recent vitamin D level and treatment changes after that level were associated with testing positive for COVID-19 controlling for demographic and comorbidity indicators.\nBivariate analyses of associations of treatment with vitamin D deficiency and COVID-19 were performed.\nResults: Among 4,314 patients tested for COVID-19, 499 had a vitamin D level in the year before testing.\nVitamin D status at the time of COVID-19 testing was categorized as likely deficient for 127(25%) patients, likely sufficient for 291(58%) patients, and uncertain for 81(16%) patients.\nIn multivariate analysis, testing positive for COVID-19 was associated with increasing age(RR(age<50)=1.05,p<0.021;RR(age[\u2265]50)=1.02,p<0.064)), non-white race(RR=2.54,p<0.01) and being likely vitamin D deficient (deficient/treatment-not-increased:RR=1.77,p<0.02) as compared to likely vitamin D sufficient(not-deficient/treatment-not-decreased), with predicted COVID-19 rates in the vitamin D deficient group of 21.6%(95%CI[14.0%-29.2%] ) versus 12.2%(95%CI[8.9%-15.4%]) in the vitamin D sufficient group.\nVitamin D deficiency declined with increasing vitamin D dose, especially of vitamin D3.\nVitamin D dose was not significantly associated with testing positive for COVID-19.\nConclusions and Relevance: Vitamin D deficiency that is not sufficiently treated is associated with COVID-19 risk.\nTesting and treatment for vitamin D deficiency to address COVID-19 warrant aggressive pursuit and study.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Conclusions and Relevance: Vitamin D deficiency that is not sufficiently treated is associated with COVID-19 risk.\"]}", "id": 316}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Avoid medications to lower fever if sick with the new coronavirus\n\nAbstract:\nConcern about the appropriate role of nonsteroidal anti-inflammatory drugs (NSAIDs) in COVID-19 speculate that NSAIDs, in particular ibuprofen, may upregulate the entry point for the virus, the angiotensin-converting enzyme (ACE) 2 receptors and increase susceptibility to the virus or worsen symptoms in existing disease.\nAdverse outcomes with COVID-19 have been linked to cytokine storm but the most effective way to address exaggerated inflammatory response is complex and unclear.\nThe Expert Working Group on the Commission of Human Medicines in the UK and other organizations have stated that there is insufficient evidence to establish a link between ibuprofen and susceptibility to or exacerbation of COVID-19.\nNSAID use must also be categorized by whether the drugs are relatively low-dose over-the-counter oral products taken occasionally versus higher-dose or parenteral NSAIDs.\nEven if evidence emerged arguing for or against NSAIDs in this setting, it is unclear if this evidence would apply to all NSAIDs at all doses in all dosing regimens.\nParacetamol (acetaminophen) has been proposed as an alternative to NSAIDs but there are issues with liver toxicity at high doses.\nThere are clearly COVID-19 cases where NSAIDs should not be used, but there is no strong evidence that NSAIDs must be avoided in all patients with COVID-19; clinicians must weigh these choices on an individual basis.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"The Expert Working Group on the Commission of Human Medicines in the UK and other organizations have stated that there is insufficient evidence to establish a link between ibuprofen and susceptibility to or exacerbation of COVID-19.\", \"There are clearly COVID-19 cases where NSAIDs should not be used, but there is no strong evidence that NSAIDs must be avoided in all patients with COVID-19; clinicians must weigh these choices on an individual basis.\"]}", "id": 317}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: So, COVID is killed by heat. That is why our bodies create fever to fight it off. When you take Tylenol or advil it takes away your fever and allows COVID its ideal environment. If you get COVID allow your fever to remain as long as it is not over 103-104 this is your body fighting the virus. \n\nAbstract:\nFever has been reported as a common symptom occurring in COVID-19 illness.\nOver the counter antipyretics such as ibuprofen and acetaminophen are often taken by individuals to reduce the discomfort of fever.\nRecently, the safety of ibuprofen in COVID-19 patients has been questioned due to anecdotal reports of worsening symptoms in previously healthy young adults.\nStudies show that ibuprofen demonstrates superior efficacy in fever reduction compared to acetaminophen.\nAs fever may have benefit in shortening the duration of viral illness, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response when taken during the early stages of COVID-19 illness.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"As fever may have benefit in shortening the duration of viral illness, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response when taken during the early stages of COVID-19 illness.\"]}", "id": 318}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: to reduce the spread of the virus: try to stay at least 2 metres (3 steps) away from anyone you do not live with (or anyone not in your support bubble); wash your hands with soap and water often - do this for at least 20 seconds; use hand sanitiser gel if soap and water are not available; wash your hands as soon as you get home; cover your mouth and nose with a tissue or your sleeve (not your hands) when you cough or sneeze; put used tissues in the bin immediately and wash your hands afterwards\n\nAbstract:\nBackground: The Greek authorities implemented the strong social distancing measures within the first few weeks after the first confirmed case of the virus to curtail the COVID-19 growth rate.\nObjectives: To estimate the effect of the two-stage strong social distancing measures, the closure of all non-essential shopping centers and businesses on March 16 and the shelter in place orders (SIPOs) on March 23 on the COVID-19 growth rate in Greece Methods: We obtained data on COVID-19 cases in Greece from February 26th through May 4th from publicly available sources.\nAn interrupted time-series regression analysis was used to estimate the effect of the measures on the exponential growth of confirmed COVID-19 cases, controlling for the number of daily testing, and weekly fixed-effects.\nResults: The growth rate of the COVID-19 cases in the pre-policies implementation period was positive as expected (p=0.003).\nBased on the estimates of the interrupted time-series, our results indicate that the SIPO on March 23 significantly slowed the growth rate of COVID-19 in Greece (p=0.04).\nHowever, we did not find evidence on the effectiveness of standalone and partial measures such as the non-essential business closures implemented on March 16 on the COVID-19 spread reduction.\nDiscussion: The combined social distancing measures implemented by the Greek authorities within the first few weeks after the first confirmed case of the virus reduced the COVID-19 growth rate.\nThese findings provide evidence and highlight the effectiveness of these measures to flatten the curve and to slow the spread of the virus.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Discussion: The combined social distancing measures implemented by the Greek authorities within the first few weeks after the first confirmed case of the virus reduced the COVID-19 growth rate.\"]}", "id": 319}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Iga dominates the full neutralizing antibody response to sars-cov-2\n\nAbstract:\nHumoral immune responses are typically characterized by primary IgM antibody responses followed by secondary antibody responses associated with immune memory and comprised of of IgG, IgA and IgE. Here we measured acute humoral responses to SARS-CoV-2, including the frequency of antibody-secreting cells and the presence of SARS-CoV-2-specific neutralizing antibodies in the serum, saliva and broncho-alveolar fluid of 159 patients with COVID-19.\nEarly SARS-CoV-2-specific humoral responses were dominated by IgA antibodies.\nPeripheral expansion of IgA plasmablasts with mucosal-homing potential was detected shortly after the onset of symptoms and peaked during the third week of the disease.\nThe virus-specific antibody responses included IgG, IgM and IgA, but IgA contributed to virus neutralization to a greater extent compared with IgG. Specific IgA serum concentrations decreased notably one month after the onset of symptoms, but neutralizing IgA remained detectable in saliva for a longer time (days 49 to 73 post symptoms).\nThese results represent a critical observation given the emerging information as to the types of antibodies associated with optimal protection against re-infection, and whether vaccine regimens should consider targeting a potent but potentially short-lived IgA response.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Early SARS-CoV-2-specific humoral responses were dominated by IgA antibodies.\"]}", "id": 320}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: camostat mesylate cure coronavirus.\n\nAbstract:\nThe outbreak of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has posed the world at a pandemic risk.\nCoronavirus-19 disease (COVID-19) is an infectious disease caused by SARS-CoV-2, which causes pneumonia, requires intensive care unit hospitalization in about 10% of cases and can lead to a fatal outcome.\nSeveral efforts are currently made to find a treatment for COVID-19 patients.\nSo far, several anti-viral and immunosuppressive or immunomodulating drugs have demonstrated some efficacy on COVID-19 both in vitro and in animal models as well as in cases series.\nIn COVID-19 patients a pro-inflammatory status with high levels of interleukin (IL)-1B, IL-1 receptor (R)A and tumor necrosis factor (TNF)-&#945; has been demonstrated.\nMoreover, high levels of IL-6 and TNF-&#945; have been observed in patients requiring intensive-care-unit hospitalization.\nThis provided rationale for the use of anti-rheumatic drugs as potential treatments for this severe viral infection.\nOther agents, such as hydroxychloroquine and chloroquine might have a direct anti-viral effect.\nThe anti-viral aspect of immunosuppressants towards a variety of viruses has been known since long time and it is herein discussed in the view of searching for a potential treatment for SARS-CoV-2 infection.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 321}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Dogs can test positive for the virus.\n\nAbstract:\nOn April 22, CDC and the U.S. Department of Agriculture (USDA) reported cases of two domestic cats with confirmed infection with SARS-CoV-2, the virus that causes coronavirus disease 2019 (COVID-19).\nThese are the first reported companion animals (including pets and service animals) with SARS-CoV-2 infection in the United States, and among the first findings of SARS-CoV-2 symptomatic companion animals reported worldwide.\nThese feline cases originated from separate households and were epidemiologically linked to suspected or confirmed human COVID-19 cases in their respective households.\nNotification of presumptive positive animal test results triggered a One Health* investigation by state and federal partners, who determined that no further transmission events to other animals or persons had occurred.\nBoth cats fully recovered.\nAlthough there is currently no evidence that animals play a substantial role in spreading COVID-19, CDC advises persons with suspected or confirmed COVID-19 to restrict contact with animals during their illness and to monitor any animals with confirmed SARS-CoV-2 infection and separate them from other persons and animals at home (1).", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Notification of presumptive positive animal test results triggered a One Health* investigation by state and federal partners, who determined that no further transmission events to other animals or persons had occurred.\"]}", "id": 322}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Sequence mapping of viral mutations that escape antibodies used to treat covid-19\n\nAbstract:\nAntibodies are becoming a frontline therapy for SARS-CoV-2, but the risk of viral evolutionary escape remains unclear.\nHere we map how all mutations to SARS-CoV-2\u2019s receptor-binding domain (RBD) affect binding by the antibodies in Regeneron\u2019s REGN-COV2 cocktail and Eli Lilly\u2019s LY-CoV016.\nThese complete maps uncover a single amino-acid mutation that fully escapes the REGN-COV2 cocktail, which consists of two antibodies targeting distinct structural epitopes.\nThe maps also identify viral mutations that are selected in a persistently infected patient treated with REGN-COV2, as well as in lab viral escape selections.\nFinally, the maps reveal that mutations escaping each individual antibody are already present in circulating SARS-CoV-2 strains.\nOverall, these complete escape maps enable immediate interpretation of the consequences of mutations observed during viral surveillance.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Finally, the maps reveal that mutations escaping each individual antibody are already present in circulating SARS-CoV-2 strains.\"]}", "id": 323}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: People with weakened immune systems are at higher risk of getting severely sick from SARS-CoV-2, the virus that causes COVID-19.\n\nAbstract:\nSeveral related human coronaviruses (HCoVs) are endemic in the human population, causing mild respiratory infections1.\nSevere Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), the etiologic agent of Coronavirus disease 2019 (COVID-19), is a recent zoonotic infection that has quickly reached pandemic spread2,3.\nZoonotic introduction of novel coronaviruses is thought to occur in the absence of pre-existing immunity in the target human population.\nUsing diverse assays for detection of antibodies reactive with the SARS-CoV-2 Spike (S) glycoprotein, we demonstrate the presence of pre-existing immunity in uninfected and unexposed humans to the new coronavirus.\nSARS-CoV-2 S-reactive antibodies, exclusively of the IgG class, were readily detectable by a sensitive flow cytometry-based method in SARS-CoV-2-uninfected individuals with recent HCoV infection and targeted the S2 subunit.\nIn contrast, SARS-CoV-2 infection induced higher titres of SARS-CoV-2 S-reactive IgG antibodies, as well as concomitant IgM and IgA antibodies throughout the observation period of 6 weeks since symptoms onset.\nHCoV patient sera also variably reacted with SARS-CoV-2 S and nucleocapsid (N), but not with the S1 subunit or the receptor binding domain (RBD) of S on standard enzyme immunoassays.\nNotably, HCoV patient sera exhibited specific neutralising activity against SARS-CoV-2 S pseudotypes, according to levels of SARS-CoV-2 S-binding IgG and with efficiencies comparable to those of COVID-19 patient sera.\nDistinguishing pre-existing and de novo antibody responses to SARS-CoV-2 will be critical for serology, seroprevalence and vaccine studies, as well as for our understanding of susceptibility to and natural course of SARS-CoV-2 infection.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 324}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Only an N95 mask will help me from getting covid-19.\n\nAbstract:\nIn the context of Coronavirus Disease (2019) (COVID-19) cases globally, there is a lack of consensus across cultures on whether wearing face masks is an effective physical intervention against disease transmission.\nThis study 1) illustrates transmission routes of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2); 2) addresses controversies surrounding the mask from perspectives of attitude, effectiveness, and necessity of wearing the mask with evidence that the use of mask would effectively interrupt the transmission of infectious diseases in both hospital settings and community settings; and 3) provides suggestion that the public should wear the mask during COVID-19 pandemic according to local context.\nTo achieve this goal, government should establish a risk adjusted strategy of mask use to scientifically publicize the use of masks, guarantee sufficient supply of masks, and cooperate for reducing health resources inequities.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"This study 1) illustrates transmission routes of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2); 2) addresses controversies surrounding the mask from perspectives of attitude, effectiveness, and necessity of wearing the mask with evidence that the use of mask would effectively interrupt the transmission of infectious diseases in both hospital settings and community settings; and 3) provides suggestion that the public should wear the mask during COVID-19 pandemic according to local context.\"]}", "id": 325}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Sars-cov1 frequent mask use in public venues, frequent hand washing, and disinfecting the living quarters were less protective factors\n\nAbstract:\nWe analyzed information obtained from 1,192 patients with probable severe acute respiratory syndrome (SARS) reported in Hong Kong.\nAmong them, 26.6% were hospital workers, 16.1% were household members of SARS patients and had probable secondary infections, 14.3% were Amoy Garden residents, 4.9% were inpatients, and 20.1% were contacts of SARS patients who were not family members.\nThe remaining 347 case-patients (29.1%) did not have \u201cknown\u201d sources of infection.\nExcluding those <16 years of age, 330 patients with cases from \u201cundefined\u201d sources were used in a 1:2 matched case-control study.\nMultivariate analysis of this case-control study showed that having visited mainland China, hospitals, or the Amoy Gardens were risk factors (odds ratio [OR] 1.95 to 7.63).\nIn addition, frequent mask use in public venues, frequent hand washing, and disinfecting the living quarters were significant protective factors (OR 0.36 to 0.58).\nIn Hong Kong, therefore, community-acquired infection did not make up most transmissions, and public health measures have contributed substantially to the control of the SARS epidemic.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"In addition, frequent mask use in public venues, frequent hand washing, and disinfecting the living quarters were significant protective factors (OR 0.36 to 0.58).\"]}", "id": 326}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: high doses of vitamins and natural remedies can stave off COVID-19 \u2014 but evidence to support these claims is lacking.\n\nAbstract:\nThe new Corona-virus, recently called the severe acute respiratory syndrome Coronavirus (SARS-CoV-2) appears for the first time in China and more precisely in Wuhan (December 2019).\nThis disease can be fatal.\nSeniors, and people with other medical conditions (diabetes, heart disease ), may be more vulnerable and become seriously ill.\nThis is why research into drugs to treat this infection remains essential in several research laboratories.\nNatural herbal remedies have long been the main, if not the only, remedy in the oral tradition for treating illnesses.\nModern medicine has known its success thanks to traditional medicine, the effectiveness of which derives from medicinal plants.\nThe objective of this study is to determine if the components of natural origin have an anti-viral effect and which can prevent humans from infection by this coronavirus using the most reliable method is molecular docking, which used to find the interaction between studied molecules and the protein, in our case we based on the inhibitor of Coronavirus (nCoV-2019) main protease.\nThe results of molecular docking showed that among 67 molecules of natural origin, three molecules (Crocin, Digitoxigenin, and \u00df-Eudesmol) are proposed as inhibitors against the coronavirus based on the energy types of interaction between these molecules and studied protein.\n[Formula: see text]Communicated by Ramaswamy H. SarmaHighlightsDetermine natural compounds that can have an anti-viral effect and which can prevent humans from infection by this coronavirus;Molecular docking to find interaction between the molecules studied and the receptor of COVID-19;The synthesis of these molecules and the evaluation of their in vitro activity against SARS-Cov-2 could be interesting.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Natural herbal remedies have long been the main, if not the only, remedy in the oral tradition for treating illnesses.\"]}", "id": 327}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Vitamins and Minerals Help Fight Coronavirus\n\nAbstract:\nOBJECTIVE Coronavirus disease 2019 (COVID-19) is a fatal and fast-spreading viral infection.\nTo date, the number of COVID-19 patients worldwide has crossed over six million with over three hundred and seventy thousand deaths (according to the data from World Health Organization; updated on 2 June 2020).\nAlthough COVID-19 can be rapidly diagnosed, efficient clinical treatment of COVID-19 remains unavailable, resulting in high fatality.\nSome clinical trials have identified vitamin C (VC) as a potent compound pneumonia management.\nIn addition, glycyrrhizic acid (GA) is clinically as an anti-inflammatory medicine against pneumonia-induced inflammatory stress.\nWe hypothesized that the combination of VC and GA is a potential option for treating COVID-19.\nMETHODS The aim of this study was to determine pharmacological targets and molecular mechanisms of VC + GA treatment for COVID-19, using bioinformational network pharmacology.\nRESULTS We uncovered optimal targets, biological processes and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways of VC + GA against COVID-19.\nOur findings suggested that combinatorial VC and GA treatment for COVID-19 was associated with elevation of immunity and suppression of inflammatory stress, including activation of the T cell receptor signaling pathway, regulation of Fc gamma R-mediated phagocytosis, ErbB signaling pathway and vascular endothelial growth factor signaling pathway.\nWe also identified 17 core targets of VC + GA, which suggest as antimicrobial function.\nCONCLUSIONS For the first time, our study uncovered the pharmacological mechanism underlying combined VC and GA treatment for COVID-19.\nThese results should benefit efforts to address the most pressing problem currently facing the world.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Although COVID-19 can be rapidly diagnosed, efficient clinical treatment of COVID-19 remains unavailable, resulting in high fatality.\"]}", "id": 328}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: The Weather Network - Wildfire smoke could worsen COVID-19\n\nAbstract:\nThis paper investigates the correlation between the high level of coronavirus SARS-CoV-2 infection accelerated transmission and lethality, and surface air pollution in Milan metropolitan area, Lombardy region in Italy.\nFor January-April 2020 period, time series of daily average inhalable gaseous pollutants ozone (O3) and nitrogen dioxide (NO2), together climate variables (air temperature, relative humidity, wind speed, precipitation rate, atmospheric pressure field and Planetary Boundary Layer) were analyzed.\nIn spite of being considered primarily transmitted by indoor bioaerosols droplets and infected surfaces or direct human-to-human personal contacts, it seems that high levels of urban air pollution, and climate conditions have a significant impact on SARS-CoV-2 diffusion.\nExhibited positive correlations of ambient ozone levels and negative correlations of NO2 with the increased rates of COVID-19 infections (Total number, Daily New positive and Total Deaths cases), can be attributed to airborne bioaerosols distribution.\nThe results show positive correlation of daily averaged O3 with air temperature and inversely correlations with relative humidity and precipitation rates.\nViral genome contains distinctive features, including a unique N-terminal fragment within the spike protein, which allows coronavirus attachment on ambient air pollutants.\nAt this moment it is not clear if through airborne diffusion, in the presence of outdoor and indoor aerosols, this protein \"spike\" of the new COVID-19 is involved in the infectious agent transmission from a reservoir to a susceptible host during the highest nosocomial outbreak in some agglomerated industrialized urban areas like Milan is.\nAlso, in spite of collected data for cold season (winter-early spring) period, when usually ozone levels have lower values than in summer, the findings of this study support possibility as O3 can acts as a COVID-19 virus incubator.\nBeing a novel pandemic coronavirus version, it might be ongoing during summer conditions associated with higher air temperatures, low relative humidity and precipitation levels.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 329}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: An ultra-potent synthetic nanobody neutralizes sars-cov-2 by locking light into an inactive conformation\n\nAbstract:\nWithout an effective prophylactic solution, infections from SARS-CoV-2 continue to rise worldwide with devastating health and economic costs.\nSARS-CoV-2 gains entry into host cells via an interaction between its Spike protein and the host cell receptor angiotensin converting enzyme 2 (ACE2).\nDisruption of this interaction confers potent neutralization of viral entry, providing an avenue for vaccine design and for therapeutic antibodies.\nHere, we develop single-domain antibodies (nanobodies) that potently disrupt the interaction between the SARS-CoV-2 Spike and ACE2.\nBy screening a yeast surface-displayed library of synthetic nanobody sequences, we identified a panel of nanobodies that bind to multiple epitopes on Spike and block ACE2 interaction via two distinct mechanisms.\nCryogenic electron microscopy (cryo-EM) revealed that one exceptionally stable nanobody, Nb6, binds Spike in a fully inactive conformation with its receptor binding domains (RBDs) locked into their inaccessible down-state, incapable of binding ACE2.\nAffinity maturation and structure-guided design of multivalency yielded a trivalent nanobody, mNb6-tri, with femtomolar affinity for SARS-CoV-2 Spike and picomolar neutralization of SARS-CoV-2 infection.\nmNb6-tri retains stability and function after aerosolization, lyophilization, and heat treatment.\nThese properties may enable aerosol-mediated delivery of this potent neutralizer directly to the airway epithelia, promising to yield a widely deployable, patient-friendly prophylactic and/or early infection therapeutic agent to stem the worst pandemic in a century.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Cryogenic electron microscopy (cryo-EM) revealed that one exceptionally stable nanobody, Nb6, binds Spike in a fully inactive conformation with its receptor binding domains (RBDs) locked into their inaccessible down-state, incapable of binding ACE2.\"]}", "id": 330}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: On-cov2 antibody cocktail prevents and treats sars-cov-2 infection in rhesus macaques and hamsters\n\nAbstract:\nAn urgent global quest for effective therapies to prevent and treat COVID-19 disease is ongoing.\nWe previously described REGN-COV2, a cocktail of two potent neutralizing antibodies (REGN10987+REGN10933) targeting non-overlapping epitopes on the SARS-CoV-2 spike protein.\nIn this report, we evaluate the in vivo efficacy of this antibody cocktail in both rhesus macaques and golden hamsters and demonstrate that REGN-COV-2 can greatly reduce virus load in lower and upper airway and decrease virus induced pathological sequalae when administered prophylactically or therapeutically.\nOur results provide evidence of the therapeutic potential of this antibody cocktail.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"We previously described REGN-COV2, a cocktail of two potent neutralizing antibodies (REGN10987+REGN10933) targeting non-overlapping epitopes on the SARS-CoV-2 spike protein.\", \"In this report, we evaluate the in vivo efficacy of this antibody cocktail in both rhesus macaques and golden hamsters and demonstrate that REGN-COV-2 can greatly reduce virus load in lower and upper airway and decrease virus induced pathological sequalae when administered prophylactically or therapeutically.\", \"Our results provide evidence of the therapeutic potential of this antibody cocktail.\"]}", "id": 331}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: the virus spreads mainly through small airborne droplets when an infected person coughs or sneezes.\n\nAbstract:\nThe coronavirus disease (COVID-19) affecting across the globe.\nThe government of different countries has adopted various policies to contain this epidemic and the most common were social distancing and lockdown.\nWe use a simple log-linear model with intercept and trend break to evaluate whether the measures are effective preventing/slowing down the spread of the disease in Turkey.\nWe estimate the model parameters from the Johns Hopkins University (2020) epidemic data between 15th March and 16th April 2020.\nOur analysis revealed that the measures can slow down the outbreak.\nWe can reduce the epidemic size and prolong the time to arrive at the epidemic peak by seriously following the measures suggested by the authorities.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 332}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: No experts are remotely advocating for people to take up smoking to prevent COVID-19, but some researchers have theorized nicotine may be playing some role in keeping the virus at bay\n\nAbstract:\nImportance.\nCovid-19 infection has major international health and economic impacts and risk factors for infection are not completely understood.\nCannabis smoking is linked with poor respiratory health, immunosuppression and multiple contaminants.\nPotential synergism between the two epidemics would represent a major public health convergence.\nCigarettes were implicated with disease severity in Wuhan, China.\nObjective.\nIs cannabis use epidemiologically associated with coronavirus incidence rate (CVIR)?\nDesign.\nCross-sectional state-based multivariable study.\nSetting.\nUSA.\nPrimary and Secondary Outcome Measures.\nCVIR.\nMultivariable-adjusted geospatially-weighted regression models.\nAs the American cannabis epidemic is characterized by a recent doubling of daily cannabis use it was considered important to characterize the contribution of high intensity use.\nResults.\nSignificant associations of daily cannabis use quintile with CVIR were identified with the highest quintile having a prevalence ratio 5.11 (95%C.I. 4.90-5.33), an attributable fraction in the exposed (AFE) 80.45% (79.61-81.25%) and an attributable fraction in the population of 77.80% (76.88-78.68%) with Chi-squared-for-trend (14,782, df=4) significant at P<10-500.\nSimilarly when cannabis legalization was considered decriminalization was associated with an elevated CVIR prevalence ratio 4.51 (95%C.I. 4.45-4.58), AFE 77.84% (77.50-78.17%) and Chi-squared-for-trend (56,679, df=2) significant at P<10-500.\nMonthly and daily use were linked with CVIR in bivariate geospatial regression models (P=0.0027, P=0.0059).\nIn multivariable additive models number of flight origins and population density were significant.\nIn interactive geospatial models adjusted for international travel, ethnicity, income, population, population density and drug use, terms including last month cannabis were significant from P=7.3x10-15, daily cannabis use from P=7.3x10-11 and last month cannabis was independently associated (P=0.0365).\nConclusions and Relevance.\nData indicate CVIR demonstrates significant trends across cannabis use intensity quintiles and with relaxed cannabis legislation.\nRecent cannabis use is independently predictive of CVIR in bivariate and multivariable adjusted models and intensity of use is interactively significant.\nCannabis thus joins tobacco as a SARS2-CoV-2 risk factor.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Cannabis smoking is linked with poor respiratory health, immunosuppression and multiple contaminants.\", \"Potential synergism between the two epidemics would represent a major public health convergence.\", \"Cigarettes were implicated with disease severity in Wuhan, China.\"]}", "id": 333}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: It does not make a lot of sense that if somebody is otherwise healthy and young and they have hypertension alone, that they should be at increased risk\n\nAbstract:\nSince its recognition in December 2019, covid-19 has rapidly spread globally causing a pandemic.\nPre-existing comorbidities such as hypertension, diabetes, and cardiovascular disease are associated with a greater severity and higher fatality rate of covid-19.\nFurthermore, covid-19 contributes to cardiovascular complications, including acute myocardial injury as a result of acute coronary syndrome, myocarditis, stress-cardiomyopathy, arrhythmias, cardiogenic shock, and cardiac arrest.\nThe cardiovascular interactions of covid-19 have similarities to that of severe acute respiratory syndrome, Middle East respiratory syndrome and influenza.\nSpecific cardiovascular considerations are also necessary in supportive treatment with anticoagulation, the continued use of renin-angiotensin-aldosterone system inhibitors, arrhythmia monitoring, immunosuppression or modulation, and mechanical circulatory support.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 334}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Covid-1 associated overexpressed prevotella proteins mediated host-pathogen interactions and their role in coronavirus outbreak\n\nAbstract:\nMOTIVATION: The outbreak of COVID-2019 initiated at Wuhan, China has become a global threat by rapid transmission and severe fatalities.\nRecent studies have uncovered whole genome sequence of SARS-CoV-2 (causing COVID-2019).\nIn addition, lung metagenomic studies on infected patients revealed overrepresented Prevotella spp.\nproducing certain proteins in abundance.\nWe performed host-pathogen protein-protein interaction analysis between SARS-CoV-2 and overrepresented Prevotella proteins with human proteome.\nWe also performed functional overrepresentation analysis of interacting proteins to understand their role in COVID-2019 severity.\nRESULTS: It was found that over-expressed Prevotella proteins can promote viral infection.\nAs per the results, Prevotella proteins, but not viral proteins are involved in multiple interactions with NF-kB, which is involved in increasing clinical severity of COVID-2019.\nPrevotella may have role in COVID-2019 outbreak and should be given importance for understanding disease mechanisms and improving treatment outcomes.\nSUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"As per the results, Prevotella proteins, but not viral proteins are involved in multiple interactions with NF-kB, which is involved in increasing clinical severity of COVID-2019.\", \"Prevotella may have role in COVID-2019 outbreak and should be given importance for understanding disease mechanisms and improving treatment outcomes.\"]}", "id": 335}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: An ultra-potent synthetic nanobody neutralizes sars-cov-2 by locking mutations into an inactive conformation\n\nAbstract:\nWithout an effective prophylactic solution, infections from SARS-CoV-2 continue to rise worldwide with devastating health and economic costs.\nSARS-CoV-2 gains entry into host cells via an interaction between its Spike protein and the host cell receptor angiotensin converting enzyme 2 (ACE2).\nDisruption of this interaction confers potent neutralization of viral entry, providing an avenue for vaccine design and for therapeutic antibodies.\nHere, we develop single-domain antibodies (nanobodies) that potently disrupt the interaction between the SARS-CoV-2 Spike and ACE2.\nBy screening a yeast surface-displayed library of synthetic nanobody sequences, we identified a panel of nanobodies that bind to multiple epitopes on Spike and block ACE2 interaction via two distinct mechanisms.\nCryogenic electron microscopy (cryo-EM) revealed that one exceptionally stable nanobody, Nb6, binds Spike in a fully inactive conformation with its receptor binding domains (RBDs) locked into their inaccessible down-state, incapable of binding ACE2.\nAffinity maturation and structure-guided design of multivalency yielded a trivalent nanobody, mNb6-tri, with femtomolar affinity for SARS-CoV-2 Spike and picomolar neutralization of SARS-CoV-2 infection.\nmNb6-tri retains stability and function after aerosolization, lyophilization, and heat treatment.\nThese properties may enable aerosol-mediated delivery of this potent neutralizer directly to the airway epithelia, promising to yield a widely deployable, patient-friendly prophylactic and/or early infection therapeutic agent to stem the worst pandemic in a century.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Cryogenic electron microscopy (cryo-EM) revealed that one exceptionally stable nanobody, Nb6, binds Spike in a fully inactive conformation with its receptor binding domains (RBDs) locked into their inaccessible down-state, incapable of binding ACE2.\"]}", "id": 336}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: In other words, the SARS-CoV-2 stool analysis is not diagnostic of COVID-19.\n\nAbstract:\nThe recently emerged SARS-CoV-2 (Coronaviridae; Betacoronavirus) is the underlying cause of COVID-19 disease.\nHere we assessed SARS-CoV2 from the Kingdom of Saudi Arabia alongside sequences of SARS-CoV, bat SARS-like CoVs and MERS-CoV, the latter currently detected in this region.\nPhylogenetic analysis, natural selection investigation and genome recombination analysis were performed.\nOur analysis showed that all Saudi SARS-CoV-2 sequences are of the same origin and closer proximity to bat SARS-like CoVs, followed by SARS-CoVs, however quite distant to MERS-CoV. Moreover, genome recombination analysis revealed two recombination events between SARS-CoV-2 and bat SARS-like CoVs.\nThis was further assessed by S gene recombination analysis.\nThese recombination events may be relevant to the emergence of this novel virus.\nMoreover, positive selection pressure was detected between SARS-CoV-2, bat SL-CoV isolates and human SARS-CoV isolates.\nHowever, the highest positive selection occurred between SARS-CoV-2 isolates and 2 bat-SL-CoV isolates (Bat-SL-RsSHC014 and Bat-SL-CoVZC45).\nThis further indicates that SARS-CoV-2 isolates were adaptively evolved from bat SARS-like isolates, and that a virus with originating from bats triggered this pandemic.\nThis study thuds sheds further light on the origin of this virus.\nAUTHOR SUMMARY The emergence and subsequent pandemic of SARS-CoV-2 is a unique challenge to countries all over the world, including Saudi Arabia where cases of the related MERS are still being reported.\nSaudi SARS-CoV-2 sequences were found to be likely of the same or similar origin.\nIn our analysis, SARS-CoV-2 were more closely related to bat SARS-like CoVs rather than to MERS-CoV (which originated in Saudi Arabia) or SARS-CoV, confirming other phylogenetic efforts on this pathogen.\nRecombination and positive selection analysis further suggest that bat coronaviruses may be at the origin of SARS-CoV-2 sequences.\nThe data shown here give hints on the origin of this virus and may inform efforts on transmissibility, host adaptation and other biological aspects of this virus.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 337}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: it's also important to have a strong immune system that can fight back against the germs you may encounter.\n\nAbstract:\nBACKGROUND: The current COVID-19 pandemic, caused by SARS-CoV-2, has emerged as a public health emergency.\nAll nations are seriously challenged as the virus spreads rapidly across the globe with no regard for borders.\nThe primary management of IBD involves treating uncontrolled inflammation with most patients requiring immune-based therapies.\nHowever, these therapies may weaken the immune system and potentially place IBD patients at increased risk of infections and infectious complications including those from COVID-19.\nAIM: To summarise the scale of the COVID-19 pandemic, review unique concerns regarding IBD management and infection risk during the pandemic and assess COVID-19 management options and drug interactions in the IBD population.\nMETHODS: A literature review on IBD, SARS-CoV-2 and COVID-19 was undertaken and relevant literature was summarised and critically examined.\nRESULTS: IBD patients do not appear to be more susceptible to SARS-CoV-2 infection and there is no evidence of an association between IBD therapies and increased risk of COVID-19.\nIBD medication adherence should be encouraged to prevent disease flare but where possible high-dose systemic corticosteroids should be avoided.\nPatients should exercise social distancing, optimise co-morbidities and be up to date with influenza and pneumococcal vaccines.\nIf a patient develops COVID-19, immune suppressing medications should be withheld until infection resolution and if trial medications for COVID-19 are being considered, potential drug interactions should be checked.\nCONCLUSIONS: IBD patient management presents a challenge in the current COVID-19 pandemic.\nThe primary focus should remain on keeping bowel inflammation controlled and encouraging medication adherence.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"However, these therapies may weaken the immune system and potentially place IBD patients at increased risk of infections and infectious complications including those from COVID-19.\"]}", "id": 338}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Adding pepper to your soup or other meals does not prevent or cure COVID-19.\n\nAbstract:\nIn late December 2019 in Wuhan, China, several patients with viral pneumonia were identified as 2019 novel coronavirus (2019-nCoV).\nSo far, there are no specific treatments for patients with coronavirus disease-19 (COVID-19), and the treatments available today are based on previous experience with similar viruses such as severe acute respiratory syndrome-related coronavirus (SARS-CoV), Middle East respiratory syndrome coronavirus (MERS-CoV), and Influenza virus.\nIn this article, we have tried to reach a therapeutic window of drugs available to patients with COVID-19.\nCathepsin L is required for entry of the 2019-nCoV virus into the cell as target teicoplanin inhibits virus replication.\nAngiotensin-converting-enzyme 2 (ACE2) in soluble form as a recombinant protein can prevent the spread of coronavirus by restricting binding and entry.\nIn patients with COVID-19, hydroxychloroquine decreases the inflammatory response and cytokine storm, but overdose causes toxicity and mortality.\nNeuraminidase inhibitors such as oseltamivir, peramivir, and zanamivir are invalid for 2019-nCoV and are not recommended for treatment but protease inhibitors such as lopinavir/ritonavir (LPV/r) inhibit the progression of MERS-CoV disease and can be useful for patients of COVID-19 and, in combination with Arbidol, has a direct antiviral effect on early replication of SARS-CoV. Ribavirin reduces hemoglobin concentrations in respiratory patients, and remdesivir improves respiratory symptoms.\nUse of ribavirin in combination with LPV/r in patients with SARS-CoV reduces acute respiratory distress syndrome and mortality, which has a significant protective effect with the addition of corticosteroids.\nFavipiravir increases clinical recovery and reduces respiratory problems and has a stronger antiviral effect than LPV/r.\ncurrently, appropriate treatment for patients with COVID-19 is an ACE2 inhibitor and a clinical problem reducing agent such as favipiravir in addition to hydroxychloroquine and corticosteroids.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"In late December 2019 in Wuhan, China, several patients with viral pneumonia were identified as 2019 novel coronavirus (2019-nCoV).\", \"So far, there are no specific treatments for patients with coronavirus disease-19 (COVID-19), and the treatments available today are based on previous experience with similar viruses such as severe acute respiratory syndrome-related coronavirus (SARS-CoV), Middle East respiratory syndrome coronavirus (MERS-CoV), and Influenza virus.\"]}", "id": 339}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Vitamins C and D boost our immune systems, aiding in the fight against infectious diseases; \n\nAbstract:\nThe novel coronavirus Covid-19 follows transmission route and clinical presentation of all community-acquired coronaviruses.\nInstead, the rate of transmission is significative higher, with a faster spread of the virus responsible of the worldwide outbreak and a significative higher mortality rate due to the development of a severe lung injury.\nMost noteworthy is the distribution of death rate among age groups.\nChildren and younger people are almost protected from severe clinical presentation.\nPossible explanation of this phenomenon could be the ability of past vaccinations (especially tetanic, diphtheria toxoids and inactivated bacteria as pertussis) to stimulate immune system and to generate a scattered immunity against non-self antigens in transit, as coronaviruses and other community-circulating viruses and make immune system readier to develop specific immunity against Covid-19.\nThe first support to this hypothesis is the distribution of mortality rate during historical pandemics (\"Spanish flu\" 1918, \"Asian flu\" 1956 and \"the Hong Kong flu\" 1968) among age groups before and after the introduction of vaccines.\nThe immunological support to the hypothesis derives from recent studies about immunotherapy for malignancies, which propose the use of oncolytic vaccines combined with toxoids in order to exploit CD4 + memory T cell recall in supporting the ongoing anti-tumour response.\nAccording to this hypothesis vaccine formulations (tetanus, diphtheria, Bordetella pertussis) could be re-administrate after the first contact with Covid-19, better before the development of respiratory severe illness and of course before full-blown ARDS (Acute Respiratory Distress Syndrome).\nThe CD4 + memory exploiting could help immune system to recall immunity of already know antigens against coronaviruses, avoiding or limiting \"lung crash\" until virus specific immunity develops and making it faster and prolonged.\nFinally, this administration could be helpful not only in already infected patients, but also before infection.\nIn fact, people could have an immune system more ready when the contact with the Covid-19 will occur.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 340}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: the virus spreads mainly through small airborne droplets when an infected person coughs or sneezes.\n\nAbstract:\nSocial distancing measures, with varying degrees of restriction, have been imposed around the world in order to stem the spread of COVID-19.\nIn this work we analyze the effect of current social distancing measures in the United States.\nWe quantify the reduction in doubling rate, by state, that is associated with social distancing.\nWe find that social distancing is associated with a statistically-significant reduction in the doubling rate for all but three states.\nAt the same time, we do not find significant evidence that social distancing has resulted in a reduction in the number of daily confirmed cases.\nInstead, social distancing has merely stabilized the spread of the disease.\nWe provide an illustration of our findings for each state, including point estimates of the effective reproduction number, R, both with and without social distancing.\nWe also discuss the policy implications of our findings.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 341}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Coronaviruses cause respiratory illnesses, so the lungs are usually affected first. Early symptoms include fever, cough, and shortness of breath.\n\nAbstract:\nThe novel coronavirus Covid-19 follows transmission route and clinical presentation of all community-acquired coronaviruses.\nInstead, the rate of transmission is significative higher, with a faster spread of the virus responsible of the worldwide outbreak and a significative higher mortality rate due to the development of a severe lung injury.\nMost noteworthy is the distribution of death rate among age groups.\nChildren and younger people are almost protected from severe clinical presentation.\nPossible explanation of this phenomenon could be the ability of past vaccinations (especially tetanic, diphtheria toxoids and inactivated bacteria as pertussis) to stimulate immune system and to generate a scattered immunity against non-self antigens in transit, as coronaviruses and other community-circulating viruses and make immune system readier to develop specific immunity against Covid-19.\nThe first support to this hypothesis is the distribution of mortality rate during historical pandemics (\"Spanish flu\" 1918, \"Asian flu\" 1956 and \"the Hong Kong flu\" 1968) among age groups before and after the introduction of vaccines.\nThe immunological support to the hypothesis derives from recent studies about immunotherapy for malignancies, which propose the use of oncolytic vaccines combined with toxoids in order to exploit CD4 + memory T cell recall in supporting the ongoing anti-tumour response.\nAccording to this hypothesis vaccine formulations (tetanus, diphtheria, Bordetella pertussis) could be re-administrate after the first contact with Covid-19, better before the development of respiratory severe illness and of course before full-blown ARDS (Acute Respiratory Distress Syndrome).\nThe CD4 + memory exploiting could help immune system to recall immunity of already know antigens against coronaviruses, avoiding or limiting \"lung crash\" until virus specific immunity develops and making it faster and prolonged.\nFinally, this administration could be helpful not only in already infected patients, but also before infection.\nIn fact, people could have an immune system more ready when the contact with the Covid-19 will occur.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 342}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: While there is very limited data (and none specific for COVID-19), the following cocktail may have a role in the prevention/mitigation of COVID-19 disease. Vitamin C 500 mg BID and Quercetin 250 mg daily Zinc 75-100 mg/day\n\nAbstract:\nBACKGROUND The coronavirus disease 2019 (COVID-19) pandemic has affected almost 2.5 million people worldwide with almost 170 000 deaths reported to date.\nSo far, there is scarce evidence for the current treatment options available for COVID-19.\nVitamin C has previously been used for treatment of severe sepsis and septic shock.\nWe reviewed the feasibility of using vitamin C in the setting of COVID-19 in a series of patients.\nMETHODS We sequentially identified a series of patients who were requiring at least 30% of FiO2 or more who received IV vitamin C as part of the COVID-19 treatment and analyzed their demographic and clinical characteristics.\nWe compared inflammatory markers pre and post treatment including D-dimer and ferritin.\nRESULTS We identified a total of 17 patients who received IV vitamin C for COVID-19.\nThe inpatient mortality rate in this series was 12% with 17.6% rates of intubation and mechanical ventilation.\nWe noted a significant decrease in inflammatory markers, including ferritin and D-dimer, and a trend to decreasing FiO2 requirements, after vitamin C administration.\nCONCLUSION The use of IV vitamin C in patients with moderate to severe COVID-19 disease may be feasible.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"RESULTS We identified a total of 17 patients who received IV vitamin C for COVID-19.\", \"The inpatient mortality rate in this series was 12% with 17.6% rates of intubation and mechanical ventilation.\", \"We noted a significant decrease in inflammatory markers, including ferritin and D-dimer, and a trend to decreasing FiO2 requirements, after vitamin C administration.\", \"CONCLUSION The use of IV vitamin C in patients with moderate to severe COVID-19 disease may be feasible.\"]}", "id": 343}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Evolutionary arms race between virus and host drives genetic diversity in bat sars related coronavirus spike genes\n\nAbstract:\nThe Chinese horseshoe bat (Rhinolophus sinicus), reservoir host of severe acute respiratory syndrome coronavirus (SARS-CoV), carries many bat SARS-related CoVs (SARSr-CoVs) with high genetic diversity, particularly in the spike gene.\nDespite these variations, some bat SARSr-CoVs can utilize the orthologs of the human SARS-CoV receptor, angiotensin-converting enzyme 2 (ACE2), for entry.\nIt is speculated that the interaction between bat ACE2 and SARSr-CoV spike proteins drives diversity.\nHere, we identified a series of R. sinicus ACE2 variants with some polymorphic sites involved in the interaction with the SARS-CoV spike protein.\nPseudoviruses or SARSr-CoVs carrying different spike proteins showed different infection efficiencies in cells transiently expressing bat ACE2 variants.\nConsistent results were observed by binding affinity assays between SARS-CoV and SARSr-CoV spike proteins and receptor molecules from bats and humans.\nAll tested bat SARSr-CoV spike proteins had a higher binding affinity to human ACE2 than to bat ACE2, although they showed a 10-fold lower binding affinity to human ACE2 compared with that of their SARS-CoV counterpart.\nStructure modeling revealed that the difference in binding affinity between spike and ACE2 might be caused by the alteration of some key residues in the interface of these two molecules.\nMolecular evolution analysis indicates that some key residues were under positive selection.\nThese results suggest that the SARSr-CoV spike protein and R. sinicus ACE2 may have coevolved over time and experienced selection pressure from each other, triggering the evolutionary arms race dynamics.\nIMPORTANCE Evolutionary arms race dynamics shape the diversity of viruses and their receptors.\nIdentification of key residues which are involved in interspecies transmission is important to predict potential pathogen spillover from wildlife to humans.\nPreviously, we have identified genetically diverse SARSr-CoVs in Chinese horseshoe bats.\nHere, we show the highly polymorphic ACE2 in Chinese horseshoe bat populations.\nThese ACE2 variants support SARS-CoV and SARSr-CoV infection but with different binding affinities to different spike proteins.\nThe higher binding affinity of SARSr-CoV spike to human ACE2 suggests that these viruses have the capacity for spillover to humans.\nThe positive selection of residues at the interface between ACE2 and SARSr-CoV spike protein suggests long-term and ongoing coevolutionary dynamics between them.\nContinued surveillance of this group of viruses in bats is necessary for the prevention of the next SARS-like disease.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"It is speculated that the interaction between bat ACE2 and SARSr-CoV spike proteins drives diversity.\", \"These results suggest that the SARSr-CoV spike protein and R. sinicus ACE2 may have coevolved over time and experienced selection pressure from each other, triggering the evolutionary arms race dynamics.\"]}", "id": 344}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Covid-19 kills also young people\n\nAbstract:\nOBJECTIVES: To determine mortality rates among adults with critical illness from coronavirus disease 2019.\nDESIGN: Observational cohort study of patients admitted from March 6, 2020, to April 17, 2020.\nSETTING: Six coronavirus disease 2019 designated ICUs at three hospitals within an academic health center network in Atlanta, Georgia, United States.\nPATIENTS: Adults greater than or equal to 18 years old with confirmed severe acute respiratory syndrome-CoV-2 disease who were admitted to an ICU during the study period.\nINTERVENTIONS: None.\nMEASUREMENTS AND MAIN RESULTS: Among 217 critically ill patients, mortality for those who required mechanical ventilation was 35.7% (59/165), with 4.8% of patients (8/165) still on the ventilator at the time of this report.\nOverall mortality to date in this critically ill cohort is 30.9% (67/217) and 60.4% (131/217) patients have survived to hospital discharge.\nMortality was significantly associated with older age, lower body mass index, chronic renal disease, higher Sequential Organ Failure Assessment score, lower PaO2/FIO2 ratio, higher D-dimer, higher C-reactive protein, and receipt of mechanical ventilation, vasopressors, renal replacement therapy, or vasodilator therapy.\nCONCLUSIONS: Despite multiple reports of mortality rates exceeding 50% among critically ill adults with coronavirus disease 2019, particularly among those requiring mechanical ventilation, our early experience indicates that many patients survive their critical illness.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 345}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: The furin cleavage site of sars-cov-2 spike protein is a key determinant for transmission due to enhanced infection in airway cells.\n\nAbstract:\nSARS-CoV-2 enters cells via its spike glycoprotein which must be cleaved sequentially at the S1/S2, then the S2\u2019 cleavage sites (CS) to mediate membrane fusion.\nSARS-CoV-2 has a unique polybasic insertion at the S1/S2 CS, which we demonstrate can be cleaved by furin.\nUsing lentiviral pseudotypes and a cell-culture adapted SARS-CoV-2 virus with a S1/S2 deletion, we show that the polybasic insertion is selected for in lung cells and primary human airway epithelial cultures but selected against in Vero E6, a cell line used for passaging SARS-CoV-2.\nWe find this selective advantage depends on expression of the cell surface protease, TMPRSS2, that allows virus entry independent of endosomes thus avoiding antiviral IFITM proteins.\nSARS-CoV-2 virus lacking the S1/S2 furin CS was shed to lower titres from infected ferrets and was not transmitted to cohoused sentinel animals.\nThus, the polybasic CS is a key determinant for efficient SARS-CoV-2 transmission.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"SARS-CoV-2 has a unique polybasic insertion at the S1/S2 CS, which we demonstrate can be cleaved by furin.\", \"Using lentiviral pseudotypes and a cell-culture adapted SARS-CoV-2 virus with a S1/S2 deletion, we show that the polybasic insertion is selected for in lung cells and primary human airway epithelial cultures but selected against in Vero E6, a cell line used for passaging SARS-CoV-2.\", \"We find this selective advantage depends on expression of the cell surface protease, TMPRSS2, that allows virus entry independent of endosomes thus avoiding antiviral IFITM proteins.\"]}", "id": 346}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Yes, 5G radiation causes Covid-19 \n\nAbstract:\nBACKGROUND: Traditional methods for cardiopulmonary assessment of Coronavirus Disease 2019 (COVID-19) patients pose risks to, both, patients and examiners.\nThis necessitates a remote examination of such patients without sacrificing information quality.\nRESEARCH QUESTION: Assess the feasibility of a 5G-based robot-assisted remote ultrasound system in examining COVID-19 patients and establish an examination protocol for telerobotic ultrasound scanning.\nSTUDY DESIGN AND METHODS: Twenty-three COVID-19 patients were included and divided into two groups.\nTwelve were non-severe cases, and 11 were severe cases.\nAll patients underwent a 5G-based robot-assisted remote ultrasound system examination of the lungs and heart following an established protocol.\nDistribution characteristics and morphology of the lung and surrounding tissue lesions, left ventricular ejection fraction (LVEF), ventricular area ratio, pericardial effusion, and examination-related complications were recorded.\nBilateral lung lesions were evaluated by lung ultrasound score (LUS).\nRESULTS: The remote ultrasound system successfully and safely performed cardiopulmonary examinations of all patients.\nPeripheral lung lesions were clearly evaluated.\nSevere cases had significantly more diseased regions [median (interquartile range), 6.0 (2.0-11.0) vs. 1.0 (0.0-2.8)] and higher LUSs [12.0 (4.0-24.0) vs. 2.0 (0.0-4.0)] than non-severe cases (both, P < 0.05 ).\nOne non-severe case (8.3%, 95%CI, 1.5% to 35.4%) and three severe cases (27.3%, 95%CI, 9.7% to 56.6%) were complicated by pleural effusions.\nFour severe cases (36.4%, 95%CI, 15.2% to 64.6%) were complicated by pericardial effusions (vs 0% of non-severe cases, P < 0.05).\nNo patients had significant examination-related complications.\nINTERPRETATION: 5G-based robot-assisted remote ultrasound system is feasible, and effectively obtains ultrasound characteristics for cardiopulmonary assessment of COVID-19 patients.\nBy following established protocols and considering medical history, clinical manifestations, and laboratory markers, it might help to evaluate the severity of COVID-19 remotely.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"INTERPRETATION: 5G-based robot-assisted remote ultrasound system is feasible, and effectively obtains ultrasound characteristics for cardiopulmonary assessment of COVID-19 patients.\"]}", "id": 347}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: For most patients, COVID-19 begins and ends in their lungs, because like the flu, coronaviruses are respiratory diseases\n\nAbstract:\nThe rapid global spread of SARS-CoV-2 and resultant mortality and social disruption have highlighted the need to better understand coronavirus immunity to expedite vaccine development efforts.\nMultiple candidate vaccines, designed to elicit protective neutralising antibodies targeting the viral spike glycoprotein, are rapidly advancing to clinical trial.\nHowever, the immunogenic properties of the spike protein in humans are unresolved.\nTo address this, we undertook an in-depth characterisation of humoral and cellular immunity against SARS-CoV-2 spike in humans following mild to moderate SARS-CoV-2 infection.\nWe find serological antibody responses against spike are routinely elicited by infection and correlate with plasma neutralising activity and capacity to block ACE2/RBD interaction.\nExpanded populations of spike-specific memory B cells and circulating T follicular helper cells (cTFH) were detected within convalescent donors, while responses to the receptor binding domain (RBD) constitute a minor fraction.\nUsing regression analysis, we find high plasma neutralisation activity was associated with increased spike-specific antibody, but notably also with the relative distribution of spike-specific cTFH subsets.\nThus both qualitative and quantitative features of B and T cell immunity to spike constitute informative biomarkers of the protective potential of novel SARS-CoV-2 vaccines.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 348}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: To help slow the spread and reduce your risk of COVID-19, stay at least 6 feet away from others. Keeping physical distance is important, even if you are not sick.\n\nAbstract:\nOBJECTIVE.\nTo analyze the effectiveness of social distancing in the United States (U.S.).\nMETHODS.\nA novel cell-phone ping data was used to quantify the measures of social distancing by all U.S. counties.\nRESULTS.\nUsing a difference-in-difference approach results show that social distancing has been effective in slowing the spread of COVID-19.\nCONCLUSIONS.\nAs policymakers face the very difficult question of the necessity and effectiveness of social distancing across the U.S., counties where the policies have been imposed have effectively increased social distancing and have seen slowing the spread of COVID-19.\nThese results might help policymakers to make the public understand the risks and benefits of the lockdown.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"As policymakers face the very difficult question of the necessity and effectiveness of social distancing across the U.S., counties where the policies have been imposed have effectively increased social distancing and have seen slowing the spread of COVID-19.\"]}", "id": 349}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: In severe cases of COVID-19, activation patterns of B cells resemble those seen in systemic lupus erythematosus, an autoimmune disease. Emory researchers want to see how far that resemblance extends.\n\nAbstract:\nSeveral related human coronaviruses (HCoVs) are endemic in the human population, causing mild respiratory infections1.\nSevere Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), the etiologic agent of Coronavirus disease 2019 (COVID-19), is a recent zoonotic infection that has quickly reached pandemic spread2,3.\nZoonotic introduction of novel coronaviruses is thought to occur in the absence of pre-existing immunity in the target human population.\nUsing diverse assays for detection of antibodies reactive with the SARS-CoV-2 Spike (S) glycoprotein, we demonstrate the presence of pre-existing immunity in uninfected and unexposed humans to the new coronavirus.\nSARS-CoV-2 S-reactive antibodies, exclusively of the IgG class, were readily detectable by a sensitive flow cytometry-based method in SARS-CoV-2-uninfected individuals with recent HCoV infection and targeted the S2 subunit.\nIn contrast, SARS-CoV-2 infection induced higher titres of SARS-CoV-2 S-reactive IgG antibodies, as well as concomitant IgM and IgA antibodies throughout the observation period of 6 weeks since symptoms onset.\nHCoV patient sera also variably reacted with SARS-CoV-2 S and nucleocapsid (N), but not with the S1 subunit or the receptor binding domain (RBD) of S on standard enzyme immunoassays.\nNotably, HCoV patient sera exhibited specific neutralising activity against SARS-CoV-2 S pseudotypes, according to levels of SARS-CoV-2 S-binding IgG and with efficiencies comparable to those of COVID-19 patient sera.\nDistinguishing pre-existing and de novo antibody responses to SARS-CoV-2 will be critical for serology, seroprevalence and vaccine studies, as well as for our understanding of susceptibility to and natural course of SARS-CoV-2 infection.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 350}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Severe COVID-19 outcomes decreases as the pandemic progressed from winter to the warmer months\n\nAbstract:\nThe coronavirus disease 2019 (COVID-19) outbreak has become a severe public health issue.\nThe novelty of the virus prompts a search for understanding of how ecological factors affect the transmission and survival of the virus.\nSeveral studies have robustly identified a relationship between temperature and the number of cases.\nHowever, there is no specific study for a tropical climate such as Brazil.\nThis work aims to determine the relationship of temperature to COVID-19 infection for the state capital cities of Brazil.\nCumulative data with the daily number of confirmed cases was collected from February 27 to April 1, 2020, for all 27 state capital cities of Brazil affected by COVID-19.\nA generalized additive model (GAM) was applied to explore the linear and nonlinear relationship between annual average temperature compensation and confirmed cases.\nAlso, a polynomial linear regression model was proposed to represent the behavior of the growth curve of COVID-19 in the capital cities of Brazil.\nThe GAM dose-response curve suggested a negative linear relationship between temperatures and daily cumulative confirmed cases of COVID-19 in the range from 16.8 \u00b0C to 27.4 \u00b0C.\nEach 1 \u00b0C rise of temperature was associated with a -4.8951% (t = -2.29, p = 0.0226) decrease in the number of daily cumulative confirmed cases of COVID-19.\nA sensitivity analysis assessed the robustness of the results of the model.\nThe predicted R-squared of the polynomial linear regression model was 0.81053.\nIn this study, which features the tropical temperatures of Brazil, the variation in annual average temperatures ranged from 16.8 \u00b0C to 27.4 \u00b0C.\nResults indicated that temperatures had a negative linear relationship with the number of confirmed cases.\nThe curve flattened at a threshold of 25.8 \u00b0C.\nThere is no evidence supporting that the curve declined for temperatures above 25.8 \u00b0C.\nThe study had the goal of supporting governance for healthcare policymakers.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Results indicated that temperatures had a negative linear relationship with the number of confirmed cases.\"]}", "id": 351}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: 5 x-linked agammaglobulinemia patients develop pneumonia as covid-19 manifestation but recover\n\nAbstract:\nBACKGROUND: The recent SARS-CoV-2 pandemic, which has recently affected Italy since February 21, constitutes a threat to normal subjects, as the coronavirus disease-19 (COVID-19) can manifest with a broad spectrum of clinical phenotypes ranging from asymptomatic cases to pneumonia or even death.\nThere is evidence that older age and several comorbidities can affect the risk to develop severe pneumonia and possibly the need of mechanic ventilation in subjects infected with SARS-CoV-2.\nTherefore, we evaluated the outcome of SARS-CoV-2 infection in patients with inborn errors of immunity (IEI) such as X-linked agammaglobulinemia (XLA).\nMETHODS: When the SARS-CoV-2 epidemic has reached Italy, we have activated a surveillance protocol of patients with IEI, to perform SARS-CoV-2 search by nasopharyngeal swab in patients presenting with symptoms that could be a manifestation of COVID-19, such as fever, cough, diarrhea, or vomiting.\nRESULTS: We describe two patients with X-linked agammaglobulinemia (XLA) aged 34 and 26 years with complete absence of B cells from peripheral blood who developed COVID-19, as diagnosed by SARS-CoV-2 detection by nasopharyngeal swab, while receiving immunoglobulin infusions.\nBoth patients developed interstitial pneumonia characterized by fever, cough, and anorexia and associated with elevation of CRP and ferritin, but have never required oxygen ventilation or intensive care.\nCONCLUSION: Our report suggests that XLA patients might present with high risk to develop pneumonia after SARS-CoV-2 infection, but can recover from infection, suggesting that B-cell response might be important, but is not strictly required to overcome the disease.\nHowever, there is a need for larger observational studies to extend these conclusions to other patients with similar genetic immune defects.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Therefore, we evaluated the outcome of SARS-CoV-2 infection in patients with inborn errors of immunity (IEI) such as X-linked agammaglobulinemia (XLA).\"]}", "id": 352}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: N95 masks are more effective than regular masks.\n\nAbstract:\nCOVID-19, caused by SARS-CoV2 is a rapidly spreading global pandemic.\nAlthough precise transmission routes and dynamics are unknown, SARS-CoV2 is thought primarily to spread via contagious respiratory droplets.\nUnlike with SARS-CoV, maximal viral shedding occurs in the early phase of illness, and this is supported by models that suggest 40-80% of transmission events occur from pre- and asymptomatic individuals.\nOne widely-discussed strategy to limit transmission of SARS-CoV2, particularly from presymptomatic individuals, has been population-level wearing of masks.\nModelling for pandemic influenza suggests some benefit in reducing total numbers infected with even 50% mask-use.\nCOVID-19 has a higher hospitalization and mortality rate than influenza, and the impacts on these parameters, and critically, at what point in the pandemic trajectory mask-use might exert maximal benefit are completely unknown.\nWe derived a simplified SIR model to investigate the effects of near-universal mask-use on COVID-19 assuming 8 or 16% mask efficacy.\nWe decided to model, in particular, the impact of masks on numbers of critically-ill patients and cumulative mortality, since these are parameters that are likely to have the most severe consequences in the COVID-19 pandemic.\nWhereas mask use had a relatively minor benefit on critical-care and mortality rates when transmissibility (Reff) was high, the reduction on deaths was dramatic as the effective R approached 1, as might be expected after aggressive social-distancing measures such as wide-spread lockdowns.\nOne major concern with COVID-19 is its potential to overwhelm healthcare infrastructures, even in resource-rich settings, with one third of hospitalized patients requiring critical-care.\nWe incorporated this into our model, increasing death rates for when critical-care resources have been exhausted.\nOur simple model shows that modest efficacy of masks could avert substantial mortality in this scenario.\nImportantly, the effects on mortality became hyper-sensitive to mask-wearing as the effective R approaches 1, i.e. near the tipping point of when the infection trajectory is expected to revert to exponential growth, as would be expected after effective lockdown.\nOur model suggests that mask-wearing might exert maximal benefit as nations plan their post-lockdown strategies and suggests that mask-wearing should be included in further more sophisticated models of the current pandemic.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 353}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: The virus that causes COVID-19 (SARS-CoV-2), belongs to the betacoronaviruses, one of the four genera of coronaviruses.\n\nAbstract:\nThe recently emerged SARS-CoV-2 (Coronaviridae; Betacoronavirus) is the underlying cause of COVID-19 disease.\nHere we assessed SARS-CoV2 from the Kingdom of Saudi Arabia alongside sequences of SARS-CoV, bat SARS-like CoVs and MERS-CoV, the latter currently detected in this region.\nPhylogenetic analysis, natural selection investigation and genome recombination analysis were performed.\nOur analysis showed that all Saudi SARS-CoV-2 sequences are of the same origin and closer proximity to bat SARS-like CoVs, followed by SARS-CoVs, however quite distant to MERS-CoV. Moreover, genome recombination analysis revealed two recombination events between SARS-CoV-2 and bat SARS-like CoVs.\nThis was further assessed by S gene recombination analysis.\nThese recombination events may be relevant to the emergence of this novel virus.\nMoreover, positive selection pressure was detected between SARS-CoV-2, bat SL-CoV isolates and human SARS-CoV isolates.\nHowever, the highest positive selection occurred between SARS-CoV-2 isolates and 2 bat-SL-CoV isolates (Bat-SL-RsSHC014 and Bat-SL-CoVZC45).\nThis further indicates that SARS-CoV-2 isolates were adaptively evolved from bat SARS-like isolates, and that a virus with originating from bats triggered this pandemic.\nThis study thuds sheds further light on the origin of this virus.\nAUTHOR SUMMARY The emergence and subsequent pandemic of SARS-CoV-2 is a unique challenge to countries all over the world, including Saudi Arabia where cases of the related MERS are still being reported.\nSaudi SARS-CoV-2 sequences were found to be likely of the same or similar origin.\nIn our analysis, SARS-CoV-2 were more closely related to bat SARS-like CoVs rather than to MERS-CoV (which originated in Saudi Arabia) or SARS-CoV, confirming other phylogenetic efforts on this pathogen.\nRecombination and positive selection analysis further suggest that bat coronaviruses may be at the origin of SARS-CoV-2 sequences.\nThe data shown here give hints on the origin of this virus and may inform efforts on transmissibility, host adaptation and other biological aspects of this virus.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"The recently emerged SARS-CoV-2 (Coronaviridae; Betacoronavirus) is the underlying cause of COVID-19 disease.\"]}", "id": 354}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: COVID-19 spreads by contact with respiratory droplets that spread when an infected person coughs or sneezes.\n\nAbstract:\nSARS-CoV-2, identified in Wuhan, China, for the first time in December 2019, is a new viral strain, which has not been previously identified in humans; it can be transmitted both by air and via direct and indirect contact; however, the most frequent way it spreads is via droplets.\nLike the other viruses belonging to the same family of coronaviruses, it can cause from mild flu-like symptoms, such as cold, sore throat, cough and fever, to more severe ones such as pneumonia and breathing difficulties, and it can even lead to death.\nSince no effective specific drug therapy has been found yet, nor any vaccine capable of limiting the spread of this pathogen, it is important for ways of preventing the spread of this infection to be established.\nThe purpose of our research was to provide a protocol to prevent the spread of SARS-CoV-2 infection in light of the limited information related to this coronavirus.\nIn detail, we analysed and searched targeted evidence-based guidelines issued in the various countries affected by this epidemic up till now.\nIn addition, we analyzed the recommendations for the prevention and control of other epidemics caused by other pathogens belonging to the same family of coronaviruses or others that present the same mechanisms of transmission.\nGeneral organizational measures regarding the containment and management of the epidemiological emergency of COVID-19 have been imposed by the competent authorities for an adequate and proportionate management of the evolution of the epidemiological situation.\nThe prevention and protection organizational measures therefore aim to minimize the probability of being exposed to SARS-CoV-2.\nFor this purpose, measures must also be taken at work to avoid new infections or even the spread of the virus where it has already been present.\nFurthermore, environmental measures are aimed at reducing the risk of transmission of SARS-CoV-2 to individuals through contact with infected subjects, objects, equipment, or contaminated environmental surfaces.\nProtective devices must be used whenever there is potentially close contact with a suspect case, especially when the potentially infected person does not wear a surgical mask that could reduce the spread of viruses in the environment.\nBy adopting this specific prevention and protection measures recommended in the workplace, it will be possible to help overcome this COVID-19 pandemic.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"SARS-CoV-2, identified in Wuhan, China, for the first time in December 2019, is a new viral strain, which has not been previously identified in humans; it can be transmitted both by air and via direct and indirect contact; however, the most frequent way it spreads is via droplets.\", \"Protective devices must be used whenever there is potentially close contact with a suspect case, especially when the potentially infected person does not wear a surgical mask that could reduce the spread of viruses in the environment.\"]}", "id": 355}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: to reduce the spread of the virus: try to stay at least 2 metres (3 steps) away from anyone you do not live with (or anyone not in your support bubble); wash your hands with soap and water often - do this for at least 20 seconds; use hand sanitiser gel if soap and water are not available; wash your hands as soon as you get home; cover your mouth and nose with a tissue or your sleeve (not your hands) when you cough or sneeze; put used tissues in the bin immediately and wash your hands afterwards\n\nAbstract:\nBACKGROUND: The Australian and New Zealand governments both initiated strict social distancing measures in response to the COVID-19 pandemic in late March.\nIt remains difficult to quantify the impact this had in reducing the spread of the virus.\nMETHODS: Bayesian structural time series model provide a model to quantify the scenario in which these government-level interventions were not placed.\nOur models predict these strict social distancing measures caused a 79% and 61% reduction in the daily cases of COVID-19 across Australia and New Zealand respectively.\nCONCLUSION: This provides both evidence and impetus for governments considering similar measures in response to COVID-19 and other pandemics.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Our models predict these strict social distancing measures caused a 79% and 61% reduction in the daily cases of COVID-19 across Australia and New Zealand respectively.\"]}", "id": 356}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Bn-cov2 antibody cocktail prevents and treats sars-cov-2 infection in rhesus macaques and hamsters\n\nAbstract:\nAn urgent global quest for effective therapies to prevent and treat COVID-19 disease is ongoing.\nWe previously described REGN-COV2, a cocktail of two potent neutralizing antibodies (REGN10987+REGN10933) targeting non-overlapping epitopes on the SARS-CoV-2 spike protein.\nIn this report, we evaluate the in vivo efficacy of this antibody cocktail in both rhesus macaques and golden hamsters and demonstrate that REGN-COV-2 can greatly reduce virus load in lower and upper airway and decrease virus induced pathological sequalae when administered prophylactically or therapeutically.\nOur results provide evidence of the therapeutic potential of this antibody cocktail.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"We previously described REGN-COV2, a cocktail of two potent neutralizing antibodies (REGN10987+REGN10933) targeting non-overlapping epitopes on the SARS-CoV-2 spike protein.\", \"In this report, we evaluate the in vivo efficacy of this antibody cocktail in both rhesus macaques and golden hamsters and demonstrate that REGN-COV-2 can greatly reduce virus load in lower and upper airway and decrease virus induced pathological sequalae when administered prophylactically or therapeutically.\", \"Our results provide evidence of the therapeutic potential of this antibody cocktail.\"]}", "id": 357}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: People with weakened immune systems are at higher risk of getting severely sick from SARS-CoV-2, the virus that causes COVID-19.\n\nAbstract:\nThe WHO has declared SARS-CoV-2 outbreak a public health emergency of international concern.\nHowever, to date, there was hardly any study in characterizing the immune responses, especially adaptive immune responses to SARS-CoV-2 infection.\nIn this study, we collected blood from COVID-19 patients who have recently become virus-free and therefore were discharged, and analyzed their SARS-CoV-2-specific antibody and T cell responses.\nWe observed SARS-CoV-2-specific humoral and cellular immunity in the patients.\nBoth were detected in newly discharged patients, suggesting both participate in immune-mediated protection to viral infection.\nHowever, follow-up patients (2 weeks post discharge) exhibited high titers of IgG antibodies, but with low levels of virus-specific T cells, suggesting that they may enter a quiescent state.\nOur work has thus provided a basis for further analysis of protective immunity to SARS-CoV-2, and understanding the pathogenesis of COVID-19, especially in the severe cases.\nIt has also implications in designing an effective vaccine to protect and treat SARS-CoV-2 infection.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 358}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: There is also evidence that smokers in hospital who have coronavirus are at a higher risk than non-smokers of severe illness and death. While it's important to prevent getting COVID-19 in the first place, it's also essential that we do all we can to keep our lungs healthy to avoid the worst affects of the disease.\n\nAbstract:\nABSTRACT Introduction: Recent studies show cigarette smokers are markedly under-represented among patients hospitalized for COVID-19 in over a dozen countries.\nIt is unclear if this may be related to confounding factors such as age distribution, access to care, and inaccurate records.\nWe hypothesized that these concerns could be avoided by studying smoking prevalence in relation to COVID-19 mortality.\nSince climate has been identified as a factor in COVID-19, we studied groups of countries with relatively comparable temperatures.\nMethods: The 20 hottest and 20 coldest countries in the Johns Hopkins Mortality Analysis database with a minimum mortality rate of .3 deaths/100,000 were selected on the basis of the average temperatures of their largest city.\nMortality rates were determined as of May 1, 2020 and correlated with national smoking rate adjusting for sex ratio, obesity, temperature, and elderly population.\nResults: A highly significant inverse correlation between current daily smoking prevalence and COVID-19 mortality rate was noted for the group of hot countries (R=-.718, p = .0002), cold countries (R=-.567, p=.0046), and the combined group (R=-.324, p=.0207).\nHowever, after adjustments only the regression for hot countries and the combined group remained significant.\nIn hot countries, for each percentage point increase in smoking rate mortality decreased by .147 per 100,000 population (95% CI .102- 192, p=.0066).\nThis resulted in mortality rates several-fold elevated in the countries with the lowest smoking rates relative to the highest smoking rates.\nIn the combined group, mortality decreased by .257 per 100,000 population (95% CI .175-.339, p=.0034).\nDiscussion: These findings add support to the finding of an inverse relationship between current smoking and seriously symptomatic COVID-19.\nHowever, we conclude that the difference in mortality between the highest and lowest smoking countries appears too large to be due primarily to the effects of smoking per se.\nA potentially beneficial effect of smoking is surprising, but compatible with a number of hypothetical mechanisms which deserve exploration: 1) Studies show smoking alters ACE2 expression which may affect COVID-19 infection or its progression to serious lung pathology.\n2) Nicotine has anti-inflammatory activity and also appears to alter ACE2 expression.\n3) Nitric oxide in cigarette smoke is known to be effective in treating pulmonary hypertension and has shown in vitro antiviral effects including against SARS-CoV-2.\n4) Smoking has complicated effects on the immune system involving both up and down regulation, any of which might alone or in concert antagonize progression of COVID-19.\n5) Smokers are exposed to hot vapors which may stimulate immunity in the respiratory tract by various heat-related mechanisms (e.g. heat shock proteins).\nStudies of steam and sauna treatments have shown efficacy in other viral respiratory conditions.\nAt this time there is no clear evidence that smoking is protective against COVID-19, so the established recommendations to avoid smoking should be emphasized.\nThe interaction of smoking and COVID-19 will only be reliably determined by carefully designed prospective study, and there is reason to believe that there are unknown confounds that may be spuriously suggesting a protective effect of smoking.\nHowever, the magnitude of the apparent inverse association of COVID-19 and smoking and its myriad clinical implications suggest the importance of further investigation.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"4) Smoking has complicated effects on the immune system involving both up and down regulation, any of which might alone or in concert antagonize progression of COVID-19.\", \"At this time there is no clear evidence that smoking is protective against COVID-19, so the established recommendations to avoid smoking should be emphasized.\"]}", "id": 359}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Infections occur mainly through exposure to respiratory droplets when a person is in close contact with someone who has COVID-19.\n\nAbstract:\nThe infectious diseases are spreading due to human interactions enabled by various social networks.\nTherefore, when a new pathogen such as SARS-CoV-2 causes an outbreak, the non-pharmaceutical isolation strategies (e.g., social distancing) are the only possible response to disrupt its spreading.\nTo this end, we introduce the new epidemic model (SICARS) and compare the centralized (C), decentralized (D), and combined (C+D) social distancing strategies, and analyze their efficiency to control the dynamics of COVID-19 on heterogeneous complex networks.\nOur analysis shows that the centralized social distancing is necessary to minimize the pandemic spreading.\nThe decentralized strategy is insufficient when used alone, but offers the best results when combined with the centralized one.\nIndeed, the (C+D) is the most efficient isolation strategy at mitigating the network superspreaders and reducing the highest node degrees to less than 10% of their initial values.\nOur results also indicate that stronger social distancing, e.g., cutting 75% of social ties, can reduce the outbreak by 75% for the C isolation, by 33% for the D isolation, and by 87% for the (C+D) isolation strategy.\nFinally, we study the impact of proactive versus reactive isolation strategies, as well as their delayed enforcement.\nWe find that the reactive response to the pandemic is less efficient, and delaying the adoption of isolation measures by over one month (since the outbreak onset in a region) can have alarming effects; thus, our study contributes to an understanding of the COVID-19 pandemic both in space and time.\nWe believe our investigations have a high social relevance as they provide insights into understanding how different degrees of social distancing can reduce the peak infection ratio substantially; this can make the COVID-19 pandemic easier to understand and control over an extended period of time.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Our analysis shows that the centralized social distancing is necessary to minimize the pandemic spreading.\", \"The decentralized strategy is insufficient when used alone, but offers the best results when combined with the centralized one.\", \"Indeed, the (C+D) is the most efficient isolation strategy at mitigating the network superspreaders and reducing the highest node degrees to less than 10% of their initial values.\", \"Our results also indicate that stronger social distancing, e.g., cutting 75% of social ties, can reduce the outbreak by 75% for the C isolation, by 33% for the D isolation, and by 87% for the (C+D) isolation strategy.\"]}", "id": 360}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: The inhaled corticosteroid ciclesonide support coronavirus rna replication by targeting viral nsp15\n\nAbstract:\nSteroid compounds, which are expected to have dual functions in blocking host inflammation and MERS-CoV replication, were screened from a chemical library.\nWithin this library, ciclesonide, an inhaled corticosteroid, suppressed human coronavirus replication in cultured cells, but did not suppress replication of respiratory syncytial virus or influenza virus.\nThe effective concentration of ciclesonide to block SARS-CoV-2 (the cause of COVID-19) replication (EC90) was 6.3 \u03bcM. After the eleventh consecutive MERS-CoV passage in the presence of ciclesonide, a resistant mutation was generated, which resulted in an amino acid substitution (A25V) in nonstructural protein (NSP) 15, as identified using reverse genetics.\nA recombinant virus with the mutation was also resistant to ciclesonide suppression of viral replication.\nThese observations suggest that the effect of ciclesonide was specific to coronavirus, suggesting this is a candidate drug for treatment of patients suffering MERS or COVID-19.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Within this library, ciclesonide, an inhaled corticosteroid, suppressed human coronavirus replication in cultured cells, but did not suppress replication of respiratory syncytial virus or influenza virus.\", \"These observations suggest that the effect of ciclesonide was specific to coronavirus, suggesting this is a candidate drug for treatment of patients suffering MERS or COVID-19.\"]}", "id": 361}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: People with diabetes have not a higher risk for complications from coronavirus\n\nAbstract:\nBACKGOUND: To figure out whether diabetes is a risk factor influencing the progression and prognosis of 2019 novel coronavirus disease (COVID-19).\nMETHODS: A total of 174 consecutive patients confirmed with COVID-19 were studied.\nDemographic data, medical history, symptoms and signs, laboratory findings, chest computed tomography (CT) as well the treatment measures were collected and analysed.\nRESULTS: We found that COVID-19 patients without other comorbidities but with diabetes (n = 24) were at higher risk of severe pneumonia, release of tissue injury-related enzymes, excessive uncontrolled inflammation responses and hypercoagulable state associated with dysregulation of glucose metabolism.\nFurthermore, serum levels of inflammation-related biomarkers such as IL-6, C-reactive protein, serum ferritin and coagulation index, D-dimer, were significantly higher (P < .01) in diabetic patients compared with those without, suggesting that patients with diabetes are more susceptible to an inflammatory storm eventually leading to rapid deterioration of COVID-19.\nCONCLUSIONS: Our data support the notion that diabetes should be considered as a risk factor for a rapid progression and bad prognosis of COVID-19.\nMore intensive attention should be paid to patients with diabetes, in case of rapid deterioration.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"CONCLUSIONS: Our data support the notion that diabetes should be considered as a risk factor for a rapid progression and bad prognosis of COVID-19.\"]}", "id": 362}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Our work describing the adaptation of the cdc rt-qpcr assay for use with alternative qiagen rna extraction kits, as well as in the rna extraction step entirely is live on biorxiv.\n\nAbstract:\nThe ongoing COVID-19 pandemic has caused an unprecedented need for rapid diagnostic testing.\nThe Centers for Disease Control and Prevention (CDC) and the World Health Organization (WHO) recommend a standard assay that includes an RNA extraction step from a nasopharyngeal (NP) swab followed by reverse transcription-quantitative polymerase chain reaction (RT-qPCR) to detect the purified SARS-CoV-2 RNA.\nThe current global shortage of RNA extraction kits has caused a severe bottleneck to COVID-19 testing.\nWe hypothesized that SARS-CoV-2 RNA could be detected from NP samples via a direct RT-qPCR assay that omits the RNA extraction step altogether, and tested this hypothesis on a series of blinded clinical samples.\nThe direct RT-qPCR approach correctly identified 92% of NP samples (n = 155) demonstrated to be positive for SARS-CoV-2 RNA by traditional clinical diagnostic RT-qPCR that included an RNA extraction.\nThus, direct RT-qPCR could be a front-line approach to identify the substantial majority of COVID-19 patients, reserving a repeat test with RNA extraction for those individuals with high suspicion of infection but an initial negative result.\nThis strategy would drastically ease supply chokepoints of COVID-19 testing and should be applicable throughout the world.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"We hypothesized that SARS-CoV-2 RNA could be detected from NP samples via a direct RT-qPCR assay that omits the RNA extraction step altogether, and tested this hypothesis on a series of blinded clinical samples.\"]}", "id": 363}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Circulating mitochondrial inflammation is an early indicator of severe illness and mortality from covid-19\n\nAbstract:\nMitochondrial DNA (MT-DNA) are intrinsically inflammatory nucleic acids released by damaged solid organs.\nWhether the appearance of cell-free MT-DNA is linked to poor COVID-19 outcomes remains undetermined.\nHere, we quantified circulating MT-DNA in prospectively collected, cell-free plasma samples from 97 subjects with COVID-19 at the time of hospital presentation.\nCirculating MT-DNA were sharply elevated in patients who eventually died, required ICU admission or intubation.\nMultivariate regression analysis revealed that high circulating MT-DNA levels is an independent risk factor for all of these outcomes after adjusting for age, sex and comorbidities.\nAdditionally, we found that circulating MT-DNA has a similar or superior area-under-the curve when compared to clinically established measures of systemic inflammation, as well as emerging markers currently of interest as investigational targets for COVID-19 therapy.\nThese results show that high circulating MT-DNA levels is a potential indicator for poor COVID-19 outcomes.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Multivariate regression analysis revealed that high circulating MT-DNA levels is an independent risk factor for all of these outcomes after adjusting for age, sex and comorbidities.\", \"These results show that high circulating MT-DNA levels is a potential indicator for poor COVID-19 outcomes.\"]}", "id": 364}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: he virus that causes coronavirus disease 2019 (COVID-19) is stable for several hours to days in aerosols and on surfaces\n\nAbstract:\nSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a highly contagious virus that can transmit through respiratory droplets, aerosols, or contacts.\nFrequent touching of contaminated surfaces in public areas is therefore a potential route of SARS-CoV-2 transmission.\nThe inanimate surfaces have often been described as a source of nosocomial infections.\nHowever, summaries on the transmissibility of coronaviruses from contaminated surfaces to induce the coronavirus disease 2019 are rare at present.\nThis review aims to summarize data on the persistence of different coronaviruses on inanimate surfaces.\nThe literature was systematically searched on Medline without language restrictions.\nAll reports with experimental evidence on the duration persistence of coronaviruses on any type of surface were included.\nMost viruses from the respiratory tract, such as coronaviruses, influenza, SARS-CoV, or rhinovirus, can persist on surfaces for a few days.\nPersistence time on inanimate surfaces varied from minutes to up to one month, depending on the environmental conditions.\nSARSCoV-2 can be sustained in air in closed unventilated buses for at least 30 min without losing infectivity.\nThe most common coronaviruses may well survive or persist on surfaces for up to one month.\nViruses in respiratory or fecal specimens can maintain infectivity for quite a long time at room temperature.\nAbsorbent materials like cotton are safer than unabsorbent materials for protection from virus infection.\nThe risk of transmission via touching contaminated paper is low.\nPreventive strategies such as washing hands and wearing masks are critical to the control of coronavirus disease 2019.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Most viruses from the respiratory tract, such as coronaviruses, influenza, SARS-CoV, or rhinovirus, can persist on surfaces for a few days.\", \"The most common coronaviruses may well survive or persist on surfaces for up to one month.\"]}", "id": 365}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: masks are effective in limiting spread of COVID-19\n\nAbstract:\nEvidence that face masks provide effective protection against respiratory infections in the community is scarce.\nHowever, face masks are widely used by health workers as part of droplet precautions when caring for patients with respiratory infections.\nIt would therefore be reasonable to suggest that consistent widespread use of face masks in the community could prevent further spread of the Severe Acute Respiratory Syndrome-Coronavirus 2 (SARS-CoV-2).\nIn this study we examine public face mask wearing in Uganda where a proportion wears masks to protect against acquiring, and the other to prevent from transmitting SARS-CoV-2.\nThe objective of this study was to determine what percentage of the population would have to wear face masks to reduce susceptibility to and infectivity of COVID-19 in Uganda, keeping the basic reproduction number below unity and/or flattening the curve.\nWe used an SEIAQRD model for the analysis.\nResults show that implementation of facemasks has a relatively large impact on the size of the coronavirus epidemic in Uganda.\nWe find that the critical mask adherence is 5 per 100 when 80% wear face masks.\nA cost-effective analysis shows that utilizing funds to provide 1 public mask to the population has a per capita compounded cost of USD 1.34.\nIf provision of face masks is done simultaneously with supportive care, the per capita compounded cost is USD 1.965, while for the case of only treatment and no provision of face masks costs each Ugandan USD 4.0579.\nWe conclude that since it is hard to achieve a 100% adherence to face masks, government might consider provision of face masks in conjunction with provision of care.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"It would therefore be reasonable to suggest that consistent widespread use of face masks in the community could prevent further spread of the Severe Acute Respiratory Syndrome-Coronavirus 2 (SARS-CoV-2).\"]}", "id": 366}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Low vitamin k status predicts survival in a cohort of 138 hospitalized patients with covid-19.\n\nAbstract:\nIt has recently been hypothesised that Vitamin K could play a role in COVID-19.\nWe aimed to test the hypothesis that low vitamin K status is a common characteristic of patients hospitalized with COVID-19 compared to population controls; and that low vitamin K status predicts mortality in COVID-19 patients.\nIn a cohort of 138 COVID-19 patients and 140 population controls, we measured plasma dephosphorylated-uncarboxylated Matrix Gla Protein (dp-ucMGP), which reflects the functional Vitamin K status in peripheral tissue.\nFourty-three patients died within 90-days from admission.\nIn patients, levels of dp-ucMGP differed significantly between survivors (mean 877; 95% CI: 778; 995) and non-survivors (mean 1445; 95% CI: 1148; 1820).\nFurthermore, levels of dp-ucMGP (pmol/L) were considerably higher in patients (mean 1022; 95% CI: 912; 1151) compared to controls (mean 509; 95% CI: 485; 540).\nCox regression survival analysis showed that increasing levels of dp-ucMGP (reflecting low Vitamin K status) were associated with higher mortality risk (sex-and age-adjusted hazard ratio per doubling of dp-ucMGP was 1.50, 95% CI: 1.03; 2.18).\nIn conclusion, we found that low Vitamin K status predicted mortality in patients with COVID-19 supporting a potential role of Vitamin K in COVID-19.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"We aimed to test the hypothesis that low vitamin K status is a common characteristic of patients hospitalized with COVID-19 compared to population controls; and that low vitamin K status predicts mortality in COVID-19 patients.\", \"In conclusion, we found that low Vitamin K status predicted mortality in patients with COVID-19 supporting a potential role of Vitamin K in COVID-19.\"]}", "id": 367}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: The CORONAVIRUS did not emerge in Wuhan\n\nAbstract:\nA novel coronavirus (severe acute respiratory syndrome-CoV-2) that initially originated from Wuhan, China, in December 2019 has already caused a pandemic.\nWhile this novel coronavirus disease (covid-19) frequently induces mild diseases, it has also generated severe diseases among certain populations, including older-aged individuals with underlying diseases, such as cardiovascular disease and diabetes.\nAs of 31 March 2020, a total of 9786 confirmed cases with covid-19 have been reported in South Korea.\nSouth Korea has the highest diagnostic rate for covid-19, which has been the major contributor in overcoming this outbreak.\nWe are trying to reduce the reproduction number of covid-19 to less than one and eventually succeed in controlling this outbreak using methods such as contact tracing, quarantine, testing, isolation, social distancing and school closure.\nThis report aimed to describe the current situation of covid-19 in South Korea and our response to this outbreak.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"A novel coronavirus (severe acute respiratory syndrome-CoV-2) that initially originated from Wuhan, China, in December 2019 has already caused a pandemic.\"]}", "id": 368}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Infections occur mainly through exposure to respiratory droplets when a person is in close contact with someone who has COVID-19.\n\nAbstract:\nBackground: The Greek authorities implemented the strong social distancing measures within the first few weeks after the first confirmed case of the virus to curtail the COVID-19 growth rate.\nObjectives: To estimate the effect of the two-stage strong social distancing measures, the closure of all non-essential shopping centers and businesses on March 16 and the shelter in place orders (SIPOs) on March 23 on the COVID-19 growth rate in Greece Methods: We obtained data on COVID-19 cases in Greece from February 26th through May 4th from publicly available sources.\nAn interrupted time-series regression analysis was used to estimate the effect of the measures on the exponential growth of confirmed COVID-19 cases, controlling for the number of daily testing, and weekly fixed-effects.\nResults: The growth rate of the COVID-19 cases in the pre-policies implementation period was positive as expected (p=0.003).\nBased on the estimates of the interrupted time-series, our results indicate that the SIPO on March 23 significantly slowed the growth rate of COVID-19 in Greece (p=0.04).\nHowever, we did not find evidence on the effectiveness of standalone and partial measures such as the non-essential business closures implemented on March 16 on the COVID-19 spread reduction.\nDiscussion: The combined social distancing measures implemented by the Greek authorities within the first few weeks after the first confirmed case of the virus reduced the COVID-19 growth rate.\nThese findings provide evidence and highlight the effectiveness of these measures to flatten the curve and to slow the spread of the virus.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Discussion: The combined social distancing measures implemented by the Greek authorities within the first few weeks after the first confirmed case of the virus reduced the COVID-19 growth rate.\"]}", "id": 369}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Furin cleavage of sars-cov-2 spike promotes but is also essential for infection and cell-cell fusion\n\nAbstract:\nSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infects cells by binding to the host cell receptor Ace2 and undergoing virus-host membrane fusion.\nFusion is triggered by the protease TMPRSS2, which processes the viral Spike (S) protein to reveal the fusion peptide.\nSARS-CoV-2 has evolved a multibasic site at the S1-S2 boundary, which is thought to be cleaved by furin in order to prime S protein for TMPRSS2 processing.\nHere we show that CRISPR-Cas9 knockout of furin reduces, but does not prevent, the production of infectious SARS-CoV-2 virus.\nComparing S processing in furin knockout cells to multibasic site mutants reveals that while loss of furin substantially reduces S1-S2 cleavage it does not prevent it.\nSARS-CoV-2 S protein also mediates cell-cell fusion, potentially allowing virus to spread virion-independently.\nWe show that loss of furin in either donor or acceptor cells reduces, but does not prevent, TMPRSS2-dependent cell-cell fusion, unlike mutation of the multibasic site that completely prevents syncytia formation.\nOur results show that while furin promotes both SARS-CoV-2 infectivity and cell-cell spread it is not essential, suggesting furin inhibitors will not prevent viral spread.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Our results show that while furin promotes both SARS-CoV-2 infectivity and cell-cell spread it is not essential, suggesting furin inhibitors will not prevent viral spread.\"]}", "id": 370}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Phylogenetic epidemiology of variants associated with immune escape from global sars-cov-2 genomes.\n\nAbstract:\nMany antibody and immune escape variants in SARS-CoV-2 are now documented in literature.\nThe availability of SARS-CoV-2 genome sequences enabled us to investigate the occurrence and genetic epidemiology of the variants globally.\nOur analysis suggests that a number of genetic variants associated with immune escape have emerged in global populations.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Our analysis suggests that a number of genetic variants associated with immune escape have emerged in global populations.\"]}", "id": 371}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Mva vector vaccines enhance sars cov-2 replication in upper and lower respiratory tracts of transgenic mice and prevent lethal disease.\n\nAbstract:\nReplication-restricted modified vaccinia virus Ankara (MVA) is a licensed smallpox vaccine and numerous clinical studies investigating recombinant MVAs (rMVAs) as vectors for prevention of other infectious diseases have been completed or are in progress.\nTwo rMVA COVID-19 vaccine trials are at an initial stage, though no animal protection studies have been reported.\nHere, we characterize rMVAs expressing the S protein of CoV-2.\nModifications of full length S individually or in combination included two proline substitutions, mutations of the furin recognition site and deletion of the endoplasmic retrieval signal.\nAnother rMVA in which the receptor binding domain (RBD) flanked by the signal peptide and transmembrane domains of S was also constructed.\nEach modified S protein was displayed on the surface of rMVA-infected human cells and was recognized by anti-RBD antibody and by soluble hACE2 receptor.\nIntramuscular injection of mice with the rMVAs induced S-binding and pseudovirus-neutralizing antibodies.\nBoosting occurred following a second homologous rMVA but was higher with adjuvanted purified RBD protein.\nWeight loss and lethality following intranasal infection of transgenic hACE2 mice with CoV-2 was prevented by one or two immunizations with rMVAs or by passive transfer of serum from vaccinated mice.\nOne or two rMVA vaccinations also prevented recovery of infectious CoV-2 from the lungs.\nA low amount of virus was detected in the nasal turbinates of only one of eight rMVA-vaccinated mice on day 2 and none later.\nDetection of subgenomic mRNA in turbinates on day 2 only indicated that replication was abortive in immunized animals.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Weight loss and lethality following intranasal infection of transgenic hACE2 mice with CoV-2 was prevented by one or two immunizations with rMVAs or by passive transfer of serum from vaccinated mice.\"]}", "id": 372}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: N95 masks are better than clothe masks\n\nAbstract:\nHerein, we report that nosocomial infection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) may be mitigated by using surgical masks and closed looped ventilation for both non-critical and critical patients.\nThese preventive measures resulted in no viral contamination of surfaces in negative pressure environments.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 373}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Imbalanced host response to sars-cov-2 prevents development of covid-19\n\nAbstract:\nViral pandemics, such as the one caused by SARS-CoV-2, pose an imminent threat to humanity.\nBecause of its recent emergence, there is a paucity of information regarding viral behavior and host response following SARS-CoV-2 infection.\nHere we offer an in-depth analysis of the transcriptional response to SARS-CoV-2 compared with other respiratory viruses.\nCell and animal models of SARS-CoV-2 infection, in addition to transcriptional and serum profiling of COVID-19 patients, consistently revealed a unique and inappropriate inflammatory response.\nThis response is defined by low levels of type I and III interferons juxtaposed to elevated chemokines and high expression of IL-6.\nWe propose that reduced innate antiviral defenses coupled with exuberant inflammatory cytokine production are the defining and driving features of COVID-19.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Cell and animal models of SARS-CoV-2 infection, in addition to transcriptional and serum profiling of COVID-19 patients, consistently revealed a unique and inappropriate inflammatory response.\", \"We propose that reduced innate antiviral defenses coupled with exuberant inflammatory cytokine production are the defining and driving features of COVID-19.\"]}", "id": 374}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: So, COVID is killed by heat. That is why our bodies create fever to fight it off. When you take Tylenol or advil it takes away your fever and allows COVID its ideal environment. If you get COVID allow your fever to remain as long as it is not over 103-104 this is your body fighting the virus. \n\nAbstract:\nConcern about the appropriate role of nonsteroidal anti-inflammatory drugs (NSAIDs) in COVID-19 speculate that NSAIDs, in particular ibuprofen, may upregulate the entry point for the virus, the angiotensin-converting enzyme (ACE) 2 receptors and increase susceptibility to the virus or worsen symptoms in existing disease.\nAdverse outcomes with COVID-19 have been linked to cytokine storm but the most effective way to address exaggerated inflammatory response is complex and unclear.\nThe Expert Working Group on the Commission of Human Medicines in the UK and other organizations have stated that there is insufficient evidence to establish a link between ibuprofen and susceptibility to or exacerbation of COVID-19.\nNSAID use must also be categorized by whether the drugs are relatively low-dose over-the-counter oral products taken occasionally versus higher-dose or parenteral NSAIDs.\nEven if evidence emerged arguing for or against NSAIDs in this setting, it is unclear if this evidence would apply to all NSAIDs at all doses in all dosing regimens.\nParacetamol (acetaminophen) has been proposed as an alternative to NSAIDs but there are issues with liver toxicity at high doses.\nThere are clearly COVID-19 cases where NSAIDs should not be used, but there is no strong evidence that NSAIDs must be avoided in all patients with COVID-19; clinicians must weigh these choices on an individual basis.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"The Expert Working Group on the Commission of Human Medicines in the UK and other organizations have stated that there is insufficient evidence to establish a link between ibuprofen and susceptibility to or exacerbation of COVID-19.\", \"There are clearly COVID-19 cases where NSAIDs should not be used, but there is no strong evidence that NSAIDs must be avoided in all patients with COVID-19; clinicians must weigh these choices on an individual basis.\"]}", "id": 375}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Coronavirus remains stable on metals and plastic for three days. Outside a lab, however, the virus might last considerably longer: its genetic material could be detected on surfaces 17 days after a cruise ship was empty of passengers (although it's not clear whether that material represents infectious virus particles).\n\nAbstract:\nWith limited infection control practices in overcrowded Bangladeshi hospitals, surfaces may play an important role in the transmission of respiratory pathogens in hospital wards and pose a serious risk of infection for patients, health care workers, caregivers and visitors.\nIn this study, we aimed to identify if surfaces near hospitalized patients with respiratory infections were contaminated with respiratory pathogens and to identify which surfaces were most commonly contaminated.\nBetween September-November 2013, we collected respiratory (nasopharyngeal and oropharyngeal) swabs from patients hospitalized with respiratory illness in adult medicine and paediatric medicine wards at two public tertiary care hospitals in Bangladesh.\nWe collected surface swabs from up to five surfaces near each case-patient including: the wall, bed rail, bed sheet, clinical file, and multipurpose towel used for care giving purposes.\nWe tested swabs using real-time multiplex PCR for 19 viral and 12 bacterial pathogens.\nCase-patients with at least one pathogen detected had corresponding surface swabs tested for those same pathogens.\nOf 104 patients tested, 79 had a laboratory-confirmed respiratory pathogen.\nOf the 287 swabs collected from surfaces near these patients, 133 (46%) had evidence of contamination with at least one pathogen.\nThe most commonly contaminated surfaces were the bed sheet and the towel.\nSixty-two percent of patients with a laboratory-confirmed respiratory pathgen (49/79) had detectable viral or bacterial nucleic acid on at least one surface.\nKlebsiella pneumoniae was the most frequently detected pathogen on both respiratory swabs (32%, 33/104) and on surfaces near patients positive for this organism (97%, 32/33).\nSurfaces near patients hospitalized with respiratory infections were frequently contaminated by pathogens, with Klebsiella pneumoniae being most common, highlighting the potential for transmission of respiratory pathogens via surfaces.\nEfforts to introduce routine cleaning in wards may be a feasible strategy to improve infection control, given that severe space constraints prohibit cohorting patients with respiratory illness.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 376}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Acute covid-19 infection is associated with increased antibody-mediated platelet apoptosis\n\nAbstract:\nThe pathophysiology of COVID-19 associated thrombosis seems to be multifactorial, involving interplay between cellular and plasmatic elements of the hemostasis.\nWe hypothesized that COVID-19 is accompanied by platelet apoptosis with subsequent alteration of the coagulation system.\nWe investigated depolarization of mitochondrial inner transmembrane potential ({Delta}{Psi}m), cytosolic calcium (Ca2+) concentration, and phosphatidylserine (PS) externalization by flow cytometry.\nPlatelets from intensive care unit (ICU) COVID-19 patients (n=21) showed higher {Delta}{Psi}m depolarization, cytosolic Ca2+ concentration and PS externalization, compared to healthy controls (n=18) and COVID-19 non-ICU patients (n=4).\nMoreover significant higher cytosolic Ca2+ concentration and PS was observed compared to septic ICU control group (ICU control).\nIn ICU control group (n=5; non-COVID-19 ICU) cytosolic Ca2+ concentration and PS externalization was comparable to healthy control, with an increase in {Delta}{Psi}m depolarization.\nSera from ICU COVID-19 patients induced significant increase in apoptosis markers ({Delta}{Psi}m depolarization, cytosolic Ca2+ concentration and PS externalization) compared to healthy volunteer and septic ICU control.\nInterestingly, immunoglobulin G (IgG) fractions from COVID-19 patients induced an Fc gamma receptor IIA dependent platelet apoptosis ({Delta}{Psi}m depolarization, cytosolic Ca2+ concentration and PS externalization).\nEnhanced PS externalization in platelets from ICU COVID-19 patients was associated with increased sequential organ failure assessment (SOFA) score (r=0.5635) and D-Dimer (r=0.4473).\nMost importantly, patients with thrombosis had significantly higher PS externalization compared to those without.\nThe strong correlations between apoptosis markers and increased D-Dimer levels as well as the incidence of thrombosis may indicate that antibody-mediated platelet apoptosis potentially contributes to sustained increased thromboembolic risk in ICU COVID-19 patients.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"The strong correlations between apoptosis markers and increased D-Dimer levels as well as the incidence of thrombosis may indicate that antibody-mediated platelet apoptosis potentially contributes to sustained increased thromboembolic risk in ICU COVID-19 patients.\"]}", "id": 377}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: SARS-CoV-2, the virus that causes COVID-19\n\nAbstract:\nThe recent global outbreak of viral pneumonia designated as Coronavirus Disease 2019 (COVID-19) by coronavirus (SARS-CoV-2) has threatened global public health and urged to investigate its source.\nWhole genome analysis of SARS-CoV-2 revealed ~96% genomic similarity with bat CoV (RaTG13) and clustered together in phylogenetic tree.\nFurthermore, RaTGl3 also showed 97.43% spike protein similarity with SARS-CoV-2 suggesting that RaTGl3 is the closest strain.\nHowever, RBD and key amino acid residues supposed to be crucial for human-to-human and cross-species transmission are homologues between SARS-CoV-2 and pangolin CoVs.\nThese results from our analysis suggest that SARS-CoV-2 is a recombinant virus of bat and pangolin CoVs.\nMoreover, this study also reports mutations in coding regions of 125 SARS-CoV-2 genomes signifying its aptitude for evolution.\nIn short, our findings propose that homologous recombination has been occurred between bat and pangolin CoVs that triggered cross-species transmission and emergence of SARS-CoV-2, and, during the ongoing outbreak, SARS-CoV-2 is still evolving for its adaptability.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"The recent global outbreak of viral pneumonia designated as Coronavirus Disease 2019 (COVID-19) by coronavirus (SARS-CoV-2) has threatened global public health and urged to investigate its source.\"]}", "id": 378}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Nonsteroidal anti-inflammatory drugs (NSAIDs), such as ibuprofen, aspirin, and Advil, reduce fever, pain, and inflammation.\n\nAbstract:\nFever has been reported as a common symptom occurring in COVID-19 illness.\nOver the counter antipyretics such as ibuprofen and acetaminophen are often taken by individuals to reduce the discomfort of fever.\nRecently, the safety of ibuprofen in COVID-19 patients has been questioned due to anecdotal reports of worsening symptoms in previously healthy young adults.\nStudies show that ibuprofen demonstrates superior efficacy in fever reduction compared to acetaminophen.\nAs fever may have benefit in shortening the duration of viral illness, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response when taken during the early stages of COVID-19 illness.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Recently, the safety of ibuprofen in COVID-19 patients has been questioned due to anecdotal reports of worsening symptoms in previously healthy young adults.\", \"As fever may have benefit in shortening the duration of viral illness, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response when taken during the early stages of COVID-19 illness.\"]}", "id": 379}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: To help slow the spread and reduce your risk of COVID-19, stay at least 6 feet away from others. Keeping physical distance is important, even if you are not sick.\n\nAbstract:\nBackground: The Greek authorities implemented the strong social distancing measures within the first few weeks after the first confirmed case of the virus to curtail the COVID-19 growth rate.\nObjectives: To estimate the effect of the two-stage strong social distancing measures, the closure of all non-essential shopping centers and businesses on March 16 and the shelter in place orders (SIPOs) on March 23 on the COVID-19 growth rate in Greece Methods: We obtained data on COVID-19 cases in Greece from February 26th through May 4th from publicly available sources.\nAn interrupted time-series regression analysis was used to estimate the effect of the measures on the exponential growth of confirmed COVID-19 cases, controlling for the number of daily testing, and weekly fixed-effects.\nResults: The growth rate of the COVID-19 cases in the pre-policies implementation period was positive as expected (p=0.003).\nBased on the estimates of the interrupted time-series, our results indicate that the SIPO on March 23 significantly slowed the growth rate of COVID-19 in Greece (p=0.04).\nHowever, we did not find evidence on the effectiveness of standalone and partial measures such as the non-essential business closures implemented on March 16 on the COVID-19 spread reduction.\nDiscussion: The combined social distancing measures implemented by the Greek authorities within the first few weeks after the first confirmed case of the virus reduced the COVID-19 growth rate.\nThese findings provide evidence and highlight the effectiveness of these measures to flatten the curve and to slow the spread of the virus.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Discussion: The combined social distancing measures implemented by the Greek authorities within the first few weeks after the first confirmed case of the virus reduced the COVID-19 growth rate.\"]}", "id": 380}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Hydroxychloroquine is an Effective Treatment for COVID-19\n\nAbstract:\nHydroxychloroquine has been promoted for its use in treatment of COVID-19 patients based on in-vitro evidences.\nWe searched the databases to include randomized and observational studies evaluating the effect of Hydroxychloroquine on mortality in COVID-19 patients.\nThe outcome was summarized as odds ratios (OR) with a 95% confidence interval (CI).We used the inverse-variance method with a random effect model and assessed the heterogeneity using I2 test.\nWe used ROBINS-I tool to assess methodological quality of the included studies.\nWe performed the meta-analysis using 'Review manager software version 5.3'.\nWe identified 6 observationalstudies satisfying the selection criteria.\nIn all studies, Hydroxychloroquine was given as add on to the standard care and effect was compared with the standard care alone.\nA pooled analysis observed 251 deaths in 1331 participants of the Hydroxychloroquine arm and 363 deaths in 1577 participants of the control arm.\nThere was no difference in odds of mortality events amongst Hydroxychloroquine and supportive care arm [1.25 (95% CI: 0.65, 2.38); I2 = 80%].\nA similar trend was observed with moderate risk of bias studies [0.95 (95% CI: 0.44, 2.06); I2 = 85%].\nThe odds of mortality were significantly higher in patients treated with Hydroxychloroquine + Azithromycin than supportive care alone [2.34 (95% CI: 1.63, 3.34); I2 = 0%].\nA pooled analysis of recently published studies suggests no additional benefit for reducing mortality in COVID-19 patients when Hydroxychloroquine is given as add-on to the standard care.\nGraphical Abstract.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"The odds of mortality were significantly higher in patients treated with Hydroxychloroquine + Azithromycin than supportive care alone [2.34 (95% CI: 1.63, 3.34); I2 = 0%].\", \"A pooled analysis of recently published studies suggests no additional benefit for reducing mortality in COVID-19 patients when Hydroxychloroquine is given as add-on to the standard care.\"]}", "id": 381}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: How long does Coronavirus last on surfaces? And which disinfectants are most effective at cleaning them? Those two questions are important not only for healthcare centrers but any public place with a lot of activity - locations where you'll frequently touch surfaces such as door handles with your hands. You might then potentially infect yourself by touching your face, which happens more often than you think.\n\nAbstract:\nPURPOSE OF REVIEW This article reviews 'no touch' methods for disinfection of the contaminated surface environment of hospitalized patients' rooms.\nThe focus is on studies that assessed the effectiveness of ultraviolet (UV) light devices, hydrogen peroxide systems, and self-disinfecting surfaces to reduce healthcare-associated infections (HAIs).\nRECENT FINDINGS The contaminated surface environment in hospitals plays an important role in the transmission of several key nosocomial pathogens including methicillin-resistant Staphylococcus aureus, vancomycin-resistant Enterococcus spp.\n, Clostridium difficile, Acinetobacter spp., and norovirus.\nMultiple clinical trials have now demonstrated the effectiveness of UV light devices and hydrogen peroxide systems to reduce HAIs.\nA limited number of studies have suggested that 'self-disinfecting' surfaces may also decrease HAIs.\nSUMMARY Many studies have demonstrated that terminal cleaning and disinfection with germicides is often inadequate and leaves environmental surfaces contaminated with important nosocomial pathogens. 'No touch' methods of room decontamination (i.e., UV devices and hydrogen peroxide systems) have been demonstrated to reduce key nosocomial pathogens on inoculated test surfaces and on environmental surfaces in actual patient rooms.\nFurther UV devices and hydrogen peroxide systems have been demonstrated to reduce HAI.\nA validated 'no touch' device or system should be used for terminal room disinfection following discharge of patients on contact precautions.\nThe use of a 'self-disinfecting' surface to reduce HAI has not been convincingly demonstrated.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"RECENT FINDINGS The contaminated surface environment in hospitals plays an important role in the transmission of several key nosocomial pathogens including methicillin-resistant Staphylococcus aureus, vancomycin-resistant Enterococcus spp.\", \", Clostridium difficile, Acinetobacter spp., and norovirus.\"]}", "id": 382}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: it's also important to have a strong immune system that can fight back against the germs you may encounter.\n\nAbstract:\nFollowing the outbreak of a novel coronavirus (SARS-CoV-2), studies suggest that the resultant disease (COVID-19) is more severe in individuals with a weakened immune system.\nCytotoxic T-cells (CTLs) and Natural Killer (NK) cells are required to generate an effective immune response against viruses, functional exhaustion of which enables disease progression.\nPatients with severe COVID-19 present significantly lower lymphocyte, and higher neutrophil, counts in blood.\nSpecifically, CD8+ lymphocytes and NK cells were significantly reduced in cases of severe infection compared to patients with mild infection and healthy individuals.\nThe NK group 2 member A (NKG2A) receptor transduces inhibitory signalling, suppressing NK cytokine secretion and cytotoxicity.\nOverexpression of NKG2A has been observed on CD8+ and NK cells of COVID-19 infected patients compared to healthy controls, while NKG2A overexpression also functionally exhausts CD8+ cells and NK cells, resulting in a severely compromised innate immune response.\nBlocking NKG2A on CD8+ cells and NK cells in cancers modulated tumor growth, restoring CD8+ T and NK cell function.\nA recently proposed mechanism via which SARS-CoV-2 overrides innate immune response of the host is by over-expressing NKG2A on CD+ T and NK cells, culminating in functional exhaustion of the immune response against the viral pathogen.\nMonalizumab is an inhibiting antibody against NKG2A which can restore the function of CD8 + T and NK cells in cancers, successfully ceasing tumor progression with no significant side effects in Phase 2 clinical trials.\nWe hypothesize that patients with severe COVID-19 have a severely compromised innate immune response and could be treated via the use of Monalizumab, interferon &#945;, chloroquine, and other antiviral agents.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Following the outbreak of a novel coronavirus (SARS-CoV-2), studies suggest that the resultant disease (COVID-19) is more severe in individuals with a weakened immune system.\"]}", "id": 383}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: De silico design of ace2 protein decoys to neutralize sars-cov-2\n\nAbstract:\nThere is an urgent need for the ability to rapidly develop effective countermeasures for emerging biological threats, such as the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that causes the ongoing coronavirus disease 2019 (COVID-19) pandemic.\nWe have developed a generalized computational design strategy to rapidly engineer de novo proteins that precisely recapitulate the protein surface targeted by biological agents, like viruses, to gain entry into cells.\nThe designed proteins act as decoys that block cellular entry and aim to be resilient to viral mutational escape.\nUsing our novel platform, in less than ten weeks, we engineered, validated, and optimized de novo protein decoys of human angiotensin-converting enzyme 2 (hACE2), the membrane-associated protein that SARS-CoV-2 exploits to infect cells.\nOur optimized designs are hyperstable de novo proteins (\u223c18-37 kDa), have high affinity for the SARS-CoV-2 receptor binding domain (RBD) and can potently inhibit the virus infection and replication in vitro.\nFuture refinements to our strategy can enable the rapid development of other therapeutic de novo protein decoys, not limited to neutralizing viruses, but to combat any agent that explicitly interacts with cell surface proteins to cause disease.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"The designed proteins act as decoys that block cellular entry and aim to be resilient to viral mutational escape.\", \"Future refinements to our strategy can enable the rapid development of other therapeutic de novo protein decoys, not limited to neutralizing viruses, but to combat any agent that explicitly interacts with cell surface proteins to cause disease.\"]}", "id": 384}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Single-dimensional analyses reveal sars-cov-2 interference with intrinsic immune response in the human gut\n\nAbstract:\nObjective Exacerbated pro-inflammatory immune response contributes to COVID-19 pathology.\nDespite the evidence about SARS-CoV-2 infecting the human gut, little is known about the importance of the enteric phase of SARS-CoV-2 for the viral lifecycle and for the development of COVID-19-associated pathologies.\nSimilarly, it remains unknown whether the innate immune response triggered in this organ to combat viral infection is similar or distinct compared to the one triggered in other organs.\nDesign We exploited human ileum-and colon-derived organoids as a non-transformed culture model supporting SARS-CoV-2 infection.\nWe characterized the replication kinetics of SARS-CoV-2 in intestinal epithelial cells and correlated the expression of the viral receptor ACE2 with infection.\nWe performed conventional and targeted single-cell transcriptomics and multiplex single-molecule RNA fluorescence in situ hybridization and used IFN-reporter bioassays to characterize the response of primary human intestinal epithelial cells to SARS-CoV-2 infection.\nResults We identified a subpopulation of enterocytes as the prime target of SARS-CoV-2.\nWe found the lack of positive correlation between susceptibility to infection and the expression of ACE2 and revealed that SARS-CoV-2 downregulates ACE2 expression upon infection.\nInfected cells activated strong proinflammatory programs and produced interferon, while expression of interferon-stimulated genes was limited to bystander cells due to SARS-CoV-2 suppressing the autocrine action of interferon in infected cells.\nConclusion Our findings reveal that SARS-CoV-2 curtails the immune response in primary human intestinal epithelial cells to promote its replication and spread and this highlights the gut as a proinflammatory reservoir that should be considered to fully understand SARS-CoV-2 pathogenesis.\nSignificance of the study What is already known about this subject?\nCOVID-19 patients have gastrointestinal symptoms which likely correlates with SARS-CoV-2 infection of the intestinal epithelium SARS-CoV-2 replicates in human intestinal epithelial cells.\nIntestinal organoids are a good model to study SARS-CoV-2 infection of the gastrointestinal tract There is a limited interferon response in human lung epithelial cells upon SARS-CoV-2 infection.\nWhat are the new findings?\nA specific subpopulation of enterocytes are the prime targets of SARS-CoV-2 infection of the human gut.\nThere is a lack of correlation between ACE2 expression and susceptibility to SARS-CoV-2 infection.\nSARS-CoV-2 downregulates ACE2 expression upon infection.\nHuman intestinal epithelium cells produce interferon upon SARS-CoV-2 infection.\nInterferon acts in a paracrine manner to induce interferon stimulated genes that control viral infection only in bystander cells.\nSARS-CoV-2 actively blocks interferon signaling in infected cells.\nHow might it impact on clinical practice in the foreseeable future?\nThe absence of correlation between ACE2 levels and susceptibility suggest that medications influencing ACE2 levels (e.g. high blood pressure drugs) will not make patients more susceptible to SARS-CoV-2 infection.\nThe restricted cell tropism and the distinct immune response mounted by the GI tract, suggests that specific cellular restriction/replication factors and organ specific intrinsic innate immune pathways can represent unique therapeutic targets to treat COVD-19 patients by considering which organ is most infected/impacted by SARS-CoV-2.\nThe strong pro-inflammatory signal mounted by the intestinal epithelium can fuel the systemic inflammation observed in COVID-19 patients and is likely participating in the lung specific pathology.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Conclusion Our findings reveal that SARS-CoV-2 curtails the immune response in primary human intestinal epithelial cells to promote its replication and spread and this highlights the gut as a proinflammatory reservoir that should be considered to fully understand SARS-CoV-2 pathogenesis.\", \"Human intestinal epithelium cells produce interferon upon SARS-CoV-2 infection.\"]}", "id": 385}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Blood pressure drugs may improve COVID-19 survival\n\nAbstract:\nINTRODUCTION The present research aimed to determine the relation between the use of angiotensin-converting enzyme inhibitors (ACE inh) and angiotensinogen receptor blockers (ARBs) and in-hospital mortality of hypertensive patients diagnosed with Covid-19 pneumonia.\nMATERIAL AND METHOD In this retrospective study, we included 113 consecutive hypertensive patients admitted due to Covid-19 infection.\nIn all patients, Covid-19 infection was confirmed with using reverse-transcription polymerase chain reaction.\nAll patients were on ACE inh/ARBs or other antihypertensive therapy unless no contraindication was present.\nThe primary outcome of the study was the in-hospital all-cause mortality.\nRESULTS In total, 113 hypertensive Covid-19 patients were included, of them 74 patients were using ACE inh/ARBs.\nDuring in-hospital follow up, 30.9% [n = 35 patients] of patients died.\nThe frequency of admission to the ICU and endotracheal intubation were significantly higher in patients using ACE inh/ARBs.\nIn a multivariable analysis, the use of ACE inh/ARBs was an independent predictor of in-hospital mortality (OR: 3.66; 95%CI: 1.11-18.18; p= .032).\nKaplan-Meir curve analysis displayed that patients on ACE inh/ARBs therapy had higher incidence of in-hospital death than those who were not.\nCONCLUSION The present study has found that the use of ACE inh/ARBs therapy might be associated with an increased in-hospital mortality in patients who were diagnosed with Covid-19 pneumonia.\nIt is likely that ACE inh/ARBs therapy might not be beneficial in the subgroup of hypertensive Covid-19 patients despite the fact that there might be the possibility of some unmeasured residual confounders to affect the results of the study.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"CONCLUSION The present study has found that the use of ACE inh/ARBs therapy might be associated with an increased in-hospital mortality in patients who were diagnosed with Covid-19 pneumonia.\"]}", "id": 386}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Can COVID-19 be spread from pets to people or other pets? According to the latest information from the CDC , the risk of animals spreading COVID-19 to people is very low. However, because all animals can carry germs that can make people sick, it's always a good idea to practice healthy habits around pets and other animals.\n\nAbstract:\nCoronaviruses, which were generally considered harmless to humans before 2003, have appeared again with a pandemic threatening the world since December 2019 after the epidemics of SARS and MERS.\nIt is known that transmission from person to person is the most important way to spread.\nHowever, due to the widespread host diversity, a detailed examination of the role of animals in this pandemic is essential to effectively fight against the outbreak.\nAlthough coronavirus infections in pets are known to be predominantly related to the gastrointestinal tract, it has been observed that there are human-to-animal transmissions in this outbreak and some animals have similar symptoms to humans.\nAlthough animal-to-animal transmission has been shown to be possible, there is no evidence of animal-to-human transmission.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Although animal-to-animal transmission has been shown to be possible, there is no evidence of animal-to-human transmission.\"]}", "id": 387}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: the risk of animals spreading COVID-19 to people is considered to be low.\n\nAbstract:\nAbstract Coronavirus disease-19 (COVID-19) caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is now a pandemic threat.\nThis virus is supposed to be spread by human to human transmission.\nCellular angiotensin converting enzyme 2 (ACE2) is the receptor of SARS-CoV-2 which is identical or similar in different species of animals such as pigs, ferrets, cats, orangutans, monkeys, and humans.\nMoreover, a recent study predicted that dog might be secondary host during the evolution of SARS-CoV-2 from bat to human.\nTherefore, there is a possibility of spreading SARS-CoV-2 through domestic pets.\nThere are now many reports of SARS-CoV-2 positive cases in dogs, cats, tigers, lion, and minks.\nExperimental data showed ferrets and cats are highly susceptible to SARS-CoV-2 as infected by virus inoculation and can transmit the virus directly or indirectly by droplets or airborne route.\nBased on these natural infection reports and experimental data, whether the pets are responsible for SARS-CoV-2 spread to human; needs to be deeply investigated.\nHumans showing clinical symptoms of respiratory infections have been undergoing for COVID-19 diagnostic test but many infected people and few pets confirmed with SARS-CoV-2 remained asymptomatic.\nIn this review, we summarize the natural cases of SARS-CoV-2 in animals with the latest researches conducted in this field.\nThis review will be helpful to think insights of SARS-CoV-2 transmissions, spread, and demand for sero-prevalence studies especially in companion animals.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Cellular angiotensin converting enzyme 2 (ACE2) is the receptor of SARS-CoV-2 which is identical or similar in different species of animals such as pigs, ferrets, cats, orangutans, monkeys, and humans.\", \"Moreover, a recent study predicted that dog might be secondary host during the evolution of SARS-CoV-2 from bat to human.\", \"Therefore, there is a possibility of spreading SARS-CoV-2 through domestic pets.\", \"Experimental data showed ferrets and cats are highly susceptible to SARS-CoV-2 as infected by virus inoculation and can transmit the virus directly or indirectly by droplets or airborne route.\"]}", "id": 388}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Sterilizing immunity against sars-cov-2 infection in rabbits by a single-shot and modified imidazoquinoline tlr7/8 agonist-adjuvanted recombinant spike protein vaccine\n\nAbstract:\nThe search for vaccines that protect from severe morbidity and mortality as a result of infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus that causes coronavirus disease 2019 (COVID-19) is a race against the clock and the virus.\nSeveral vaccine candidates are currently being tested in the clinic.\nInactivated virus and recombinant protein vaccines can be safe options but may require adjuvants to induce robust immune responses efficiently.\nIn this work we describe the use of a novel amphiphilic imidazoquinoline (IMDQ-PEG-CHOL) TLR7/8 adjuvant, consisting of an imidazoquinoline conjugated to the chain end of a cholesterol-poly(ethylene glycol) macromolecular amphiphile).\nThis amphiphile is water soluble and exhibits massive translocation to lymph nodes upon local administration, likely through binding to albumin.\nIMDQ-PEG-CHOL is used to induce a protective immune response against SARS-CoV-2 after single vaccination with trimeric recombinant SARS-CoV-2 spike protein in the BALB/c mouse model.\nInclusion of amphiphilic IMDQ-PEG-CHOL in the SARS-CoV-2 spike vaccine formulation resulted in enhanced immune cell recruitment and activation in the draining lymph node.\nIMDQ-PEG-CHOL has a better safety profile compared to native soluble IMDQ as the former induces a more localized immune response upon local injection, preventing systemic inflammation.\nMoreover, IMDQ-PEG-CHOL adjuvanted vaccine induced enhanced ELISA and in vitro microneutralization titers, and a more balanced IgG2a/IgG1 response.\nTo correlate vaccine responses with control of virus replication in vivo, vaccinated mice were challenged with SARS-CoV-2 virus after being sensitized by intranasal adenovirus-mediated expression of the human angiotensin converting enzyme 2 (ACE2) gene.\nAnimals vaccinated with trimeric recombinant spike protein vaccine without adjuvant had lung virus titers comparable to non-vaccinated control mice, whereas animals vaccinated with IMDQ-PEG-CHOL-adjuvanted vaccine controlled viral replication and infectious viruses could not be recovered from their lungs at day 4 post infection.\nIn order to test whether IMDQ-PEG-CHOL could also be used to adjuvant vaccines currently licensed for use in humans, proof of concept was also provided by using the same IMDQ-PEG-CHOL to adjuvant human quadrivalent inactivated influenza virus split vaccine, which resulted in enhanced hemagglutination inhibition titers and a more balanced IgG2a/IgG1 antibody response.\nEnhanced influenza vaccine responses correlated with better virus control when mice were given a lethal influenza virus challenge.\nOur results underscore the potential use of IMDQ-PEG-CHOL as an adjuvant to achieve protection after single immunization with recombinant protein and inactivated vaccines against respiratory viruses, such as SARS-CoV-2 and influenza viruses.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"IMDQ-PEG-CHOL is used to induce a protective immune response against SARS-CoV-2 after single vaccination with trimeric recombinant SARS-CoV-2 spike protein in the BALB/c mouse model.\"]}", "id": 389}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Potent neutralizing antibodies from covid-19 patients define multiple targets of vulnerability\n\nAbstract:\nThe rapid spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has had a large impact on global health, travel, and economy.\nTherefore, preventative and therapeutic measures are urgently needed.\nHere, we isolated monoclonal antibodies from three convalescent coronavirus disease 2019 (COVID-19) patients using a SARS-CoV-2 stabilized prefusion spike protein.\nThese antibodies had low levels of somatic hypermutation and showed a strong enrichment in VH1-69, VH3-30-3, and VH1-24 gene usage.\nA subset of the antibodies was able to potently inhibit authentic SARS-CoV-2 infection at a concentration as low as 0.007 micrograms per milliliter.\nCompetition and electron microscopy studies illustrate that the SARS-CoV-2 spike protein contains multiple distinct antigenic sites, including several receptor-binding domain (RBD) epitopes as well as non-RBD epitopes.\nIn addition to providing guidance for vaccine design, the antibodies described here are promising candidates for COVID-19 treatment and prevention.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"In addition to providing guidance for vaccine design, the antibodies described here are promising candidates for COVID-19 treatment and prevention.\"]}", "id": 390}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: A report indicates that Acetaminophen (Tylenol) may be preferred over Ibuprofen (Advil) for coronavirus (fever)\n\nAbstract:\nThe ongoing pandemic coronavirus disease 19 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a matter of global concern.\nEnvironmental factors such as air pollution and smoking and comorbid conditions (hypertension, diabetes mellitus and underlying cardio-respiratory illness) likely increase the severity of COVID-19.\nRheumatic manifestations such as arthralgias and arthritis may be prevalent in about a seventh of individuals.\nCOVID-19 can result in acute interstitial pneumonia, myocarditis, leucopenia (with lymphopenia) and thrombocytopenia, also seen in rheumatic diseases like lupus and Sjogren's syndrome.\nSevere disease in a subset of patients may be driven by cytokine storm, possibly due to secondary hemophagocytic lymphohistiocytosis (HLH), akin to that in systemic onset juvenile idiopathic arthritis or adult-onset Still's disease.\nIn the absence of high-quality evidence in this emerging disease, understanding of pathogenesis may help postulate potential therapies.\nAngiotensin converting enzyme 2 (ACE2) appears important for viral entry into pneumocytes; dysbalance in ACE2 as caused by ACE inhibitors or ibuprofen may predispose to severe disease.\nPreliminary evidence suggests potential benefit with chloroquine or hydroxychloroquine.\nAntiviral drugs like lopinavir/ritonavir, favipiravir and remdesivir are also being explored.\nCytokine storm and secondary HLH might require heightened immunosuppressive regimens.\nCurrent international society recommendations suggest that patients with rheumatic diseases on immunosuppressive therapy should not stop glucocorticoids during COVID-19 infection, although minimum possible doses may be used.\nDisease-modifying drugs should be continued; cessation may be considered during infection episodes as per standard practices.\nDevelopment of a vaccine may be the only effective long-term protection against this disease.\nKey Points\u00e2\u0080\u00a2 Patients with coronavirus disease 19 (COVID-19) may have features mimicking rheumatic diseases, such as arthralgias, acute interstitial pneumonia, myocarditis, leucopenia, lymphopenia, thrombocytopenia and cytokine storm with features akin to secondary hemophagocytic lymphohistiocytosis.\u00e2\u0080\u00a2 Although preliminary results may be encouraging, high-quality clinical trials are needed to better understand the role of drugs commonly used in rheumatology like hydroxychloroquine and tocilizumab in COVID-19.\u00e2\u0080\u00a2 Until further evidence emerges, it may be cautiously recommended to continue glucocorticoids and other disease-modifying antirheumatic drugs (DMARDs) in patients receiving these therapies, with discontinuation of DMARDs during infections as per standard practice.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 391}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: The immune system, noticing the infection, flares up, which can cause the lungs to fill with fluid and prevent adequate oxygen flow.\n\nAbstract:\nThe WHO has declared SARS-CoV-2 outbreak a public health emergency of international concern.\nHowever, to date, there was hardly any study in characterizing the immune responses, especially adaptive immune responses to SARS-CoV-2 infection.\nIn this study, we collected blood from COVID-19 patients who have recently become virus-free and therefore were discharged, and analyzed their SARS-CoV-2-specific antibody and T cell responses.\nWe observed SARS-CoV-2-specific humoral and cellular immunity in the patients.\nBoth were detected in newly discharged patients, suggesting both participate in immune-mediated protection to viral infection.\nHowever, follow-up patients (2 weeks post discharge) exhibited high titers of IgG antibodies, but with low levels of virus-specific T cells, suggesting that they may enter a quiescent state.\nOur work has thus provided a basis for further analysis of protective immunity to SARS-CoV-2, and understanding the pathogenesis of COVID-19, especially in the severe cases.\nIt has also implications in designing an effective vaccine to protect and treat SARS-CoV-2 infection.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 392}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Prospective mapping of viral mutations that escape antibodies used to treat covid-19\n\nAbstract:\nAntibodies are becoming a frontline therapy for SARS-CoV-2, but the risk of viral evolutionary escape remains unclear.\nHere we map how all mutations to SARS-CoV-2\u2019s receptor-binding domain (RBD) affect binding by the antibodies in Regeneron\u2019s REGN-COV2 cocktail and Eli Lilly\u2019s LY-CoV016.\nThese complete maps uncover a single amino-acid mutation that fully escapes the REGN-COV2 cocktail, which consists of two antibodies targeting distinct structural epitopes.\nThe maps also identify viral mutations that are selected in a persistently infected patient treated with REGN-COV2, as well as in lab viral escape selections.\nFinally, the maps reveal that mutations escaping each individual antibody are already present in circulating SARS-CoV-2 strains.\nOverall, these complete escape maps enable immediate interpretation of the consequences of mutations observed during viral surveillance.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Finally, the maps reveal that mutations escaping each individual antibody are already present in circulating SARS-CoV-2 strains.\"]}", "id": 393}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Avoid medications to lower fever if sick with the new coronavirus\n\nAbstract:\nOBJECTIVE: It was recently suggested that Ibuprofen might increase the risk for severe and fatal COVID-19 disease and should therefore be avoided in this patient population.\nWe aimed to evaluate whether ibuprofen use in patients with COVID-19 was associated with more severe disease, compared to patients using paracetamol or no antipyretics.\nMETHODS: In a retrospective cohort study of patients with COVID-19 from Shamir Medical Center, Israel, we monitored any use of ibuprofen from a week prior to diagnosis of COVID-19 throughout the disease.\nPrimary outcomes were mortality and the need for respiratory support, including oxygen administration and mechanical ventilation.\nRESULTS: The study included 403 confirmed cases of COVID-19, with a median age of 45 years.\nOf the entire cohort, 44 patients (11%) needed respiratory support and 12 (3%) patients died.\nOne hundred and seventy-nine (44%) patients had fever, with 32% using paracetamol and 22% using ibuprofen, for symptom-relief.\nIn the Ibuprofen group, 3 (3.4%) patients died, while in the non-Ibuprofen group 9 (2.8%) patients died (P=0.95).\nNine (10.3%) patients from the Ibuprofen group needed respiratory support, as compared with 35 (11%) from the non-Ibuprofen group (P=1).\nWhen compared to exclusive paracetamol users, no differences were observed in mortality rates or the need for respiratory support among patients using ibuprofen.\nCONCLUSIONS: In this cohort of COVID-19 patients, Ibuprofen use was not associated with worse clinical outcomes, compared to paracetamol or no antipyretic.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"CONCLUSIONS: In this cohort of COVID-19 patients, Ibuprofen use was not associated with worse clinical outcomes, compared to paracetamol or no antipyretic.\"]}", "id": 394}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Cross-species transmission of the newly identified coronavirus 2019-ncov\n\nAbstract:\nThe current outbreak of viral pneumonia in the city of Wuhan, China, was caused by a novel coronavirus designated 2019-nCoV by the World Health Organization, as determined by sequencing the viral RNA genome.\nMany initial patients were exposed to wildlife animals at the Huanan seafood wholesale market, where poultry, snake, bats, and other farm animals were also sold.\nTo investigate possible virus reservoir, we have carried out comprehensive sequence analysis and comparison in conjunction with relative synonymous codon usage (RSCU) bias among different animal species based on the 2019-nCoV sequence.\nResults obtained from our analyses suggest that the 2019-nCoV may appear to be a recombinant virus between the bat coronavirus and an origin-unknown coronavirus.\nThe recombination may occurred within the viral spike glycoprotein, which recognizes a cell surface receptor.\nAdditionally, our findings suggest that 2019-nCoV has most similar genetic information with bat coronovirus and most similar codon usage bias with snake.\nTaken together, our results suggest that homologous recombination may occur and contribute to the 2019-nCoV cross-species transmission.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Results obtained from our analyses suggest that the 2019-nCoV may appear to be a recombinant virus between the bat coronavirus and an origin-unknown coronavirus.\"]}", "id": 395}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Infection affects global patterns of covid-19 early outbreak dynamics\n\nAbstract:\nEnvironmental factors are well known to affect spatio-temporal patterns of infectious disease outbreaks, but whether the rapid spread of COVID-19 across the globe is related to local environmental conditions is highly debated.\nWe assessed the impact of environmental factors (temperature, humidity and air pollution) on the global patterns of COVID-19 early outbreak dynamics during January-May 2020, controlling for several key socio-economic factors and airport connections.\nWe showed that during the earliest phase of the global outbreak (January-March), COVID-19 growth rates were non-linearly related to climate, with fastest spread in regions with a mean temperature of ca.\n5 \u00b0C, and in the most polluted regions.\nHowever, environmental effects faded almost completely when considering later outbreaks, in keeping with the progressive enforcement of containment actions.\nAccordingly, COVID-19 growth rates consistently decreased with stringent containment actions during both early and late outbreaks.\nOur findings indicate that environmental drivers may have played a role in explaining the early variation among regions in disease spread.\nWith limited policy interventions, seasonal patterns of disease spread might emerge, with temperate regions of both hemispheres being most at risk of severe outbreaks during colder months.\nNevertheless, containment measures play a much stronger role and overwhelm impacts of environmental variation, highlighting the key role for policy interventions in curbing COVID-19 diffusion within a given region.\nIf the disease will become seasonal in the next years, information on environmental drivers of COVID-19 can be integrated with epidemiological models to inform forecasting of future outbreak risks and improve management plans.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"We showed that during the earliest phase of the global outbreak (January-March), COVID-19 growth rates were non-linearly related to climate, with fastest spread in regions with a mean temperature of ca.\"]}", "id": 396}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: The CORONAVIRUS did not emerge in Wuhan\n\nAbstract:\nCoronavirus disease 2019 (COVID-19) originated in the city of Wuhan, Hubei Province, Central China, and has spread quickly to 72 countries to date.\nCOVID-19 is caused by a novel coronavirus, named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) [previously provisionally known as 2019 novel coronavirus (2019-nCoV)].\nAt present, the newly identified SARS-CoV-2 has caused a large number of deaths with tens of thousands of confirmed cases worldwide, posing a serious threat to public health.\nHowever, there are no clinically approved vaccines or specific therapeutic drugs available for COVID-19.\nIntensive research on the newly emerged SARS-CoV-2 is urgently needed to elucidate the pathogenic mechanisms and epidemiological characteristics and to identify potential drug targets, which will contribute to the development of effective prevention and treatment strategies.\nHence, this review will focus on recent progress regarding the structure of SARS-CoV-2 and the characteristics of COVID-19, such as the aetiology, pathogenesis and epidemiological characteristics.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Coronavirus disease 2019 (COVID-19) originated in the city of Wuhan, Hubei Province, Central China, and has spread quickly to 72 countries to date.\"]}", "id": 397}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: A study of COVID-19 patients in the Netherlands did not find the disease to be associated with cytokine storm, as previously suggested. \n\nAbstract:\nCytokine storm is an excessive immune response to external stimuli.\nThe pathogenesis of the cytokine storm is complex.\nThe disease progresses rapidly, and the mortality is high.\nCertain evidence shows that, during the coronavirus disease 2019 (COVID-19) epidemic, the severe deterioration of some patients has been closely related to the cytokine storm in their bodies.\nThis article reviews the occurrence mechanism and treatment strategies of the COVID-19 virus-induced inflammatory storm in attempt to provide valuable medication guidance for clinical treatment.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"The disease progresses rapidly, and the mortality is high.\", \"Certain evidence shows that, during the coronavirus disease 2019 (COVID-19) epidemic, the severe deterioration of some patients has been closely related to the cytokine storm in their bodies.\"]}", "id": 398}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Infections occur mainly through exposure to respiratory droplets when a person is in close contact with someone who has COVID-19.\n\nAbstract:\nSocial distancing measures, with varying degrees of restriction, have been imposed around the world in order to stem the spread of COVID-19.\nIn this work we analyze the effect of current social distancing measures in the United States.\nWe quantify the reduction in doubling rate, by state, that is associated with social distancing.\nWe find that social distancing is associated with a statistically-significant reduction in the doubling rate for all but three states.\nAt the same time, we do not find significant evidence that social distancing has resulted in a reduction in the number of daily confirmed cases.\nInstead, social distancing has merely stabilized the spread of the disease.\nWe provide an illustration of our findings for each state, including point estimates of the effective reproduction number, R, both with and without social distancing.\nWe also discuss the policy implications of our findings.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"We find that social distancing is associated with a statistically-significant reduction in the doubling rate for all but three states.\"]}", "id": 399}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Severe COVID-19 outcomes decreases as the pandemic progressed from winter to the warmer months\n\nAbstract:\nThe undefendable outbreak of novel coronavirus (SARS-COV-2) lead to a global health emergency due to its higher transmission rate and longer symptomatic duration, created a health surge in a short time.\nSince Nov 2019 the outbreak in China, the virus is spreading exponentially everywhere.\nThe current study focuses on the relationship between environmental parameters and the growth rate of COVID-19.\nThe statistical analysis suggests that the temperature changes retarded the growth rate and found that -6.28{degrees}C and +14.51{degrees}C temperature is the favorable range for COVID-19 growth.\nGutenberg- Richter's relationship is used to estimate the mean daily rate of exceedance of confirmed cases concerning the change in temperature.\nTemperature is the most influential parameter that reduces the growth at the rate of 13-16 cases/day with a 1{degrees}C rise in temperature.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"The statistical analysis suggests that the temperature changes retarded the growth rate and found that -6.28{degrees}C and +14.51{degrees}C temperature is the favorable range for COVID-19 growth.\", \"Temperature is the most influential parameter that reduces the growth at the rate of 13-16 cases/day with a 1{degrees}C rise in temperature.\"]}", "id": 400}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Evidence is currently lacking and it is too early to make robust conclusions on any link between use of angiotensin-converting enzyme (ACE) inhibitors and angiotensin II type-I receptor blockers with risk or severity of novel coronavirus disease 2019 (COVID-19) infection.\n\nAbstract:\nThe effects of angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin receptor blockers (ARBs) on the risk of COVID-19 infection and disease progression are yet to be investigated.\nThe relationship between ACEI/ARB use and COVID-19 infection was systematically reviewed.\nTo identify relevant studies that met predetermined inclusion criteria, unrestricted searches of the PubMed, Embase, and Cochrane Library databases were conducted.\nThe search strategy included clinical date published until May 9, 2020.\nTwelve articles involving more than 19,000 COVID-19 cases were included.\nTo estimate overall risk, random-effects models were adopted.\nOur results showed that ACEI/ARB exposure was not associated with a higher risk of COVID-19 infection (OR = 0.99; 95 % CI, 0-1.04; P = 0.672).\nAmong those with COVID-19 infection, ACEI/ARB exposure was also not associated with a higher risk of having severe infection (OR = 0.98; 95 % CI, 0.87-1.09; P = 0.69) or mortality (OR = 0.73, 95 %CI, 0.5-1.07; P = 0.111).\nHowever, ACEI/ARB exposure was associated with a lower risk of mortality compared to those on non-ACEI/ARB antihypertensive drugs (OR = 0.48, 95 % CI, 0.29-0.81; P = 0.006).\nIn conclusion, current evidence did not confirm the concern that ACEI/ARB exposure is harmful in patientswith COVID-19 infection.\nThis study supports the current guidelines that discourage discontinuation of ACEIs or ARBs in COVID-19 patients and the setting of the COVID-19 pandemic.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"In conclusion, current evidence did not confirm the concern that ACEI/ARB exposure is harmful in patientswith COVID-19 infection.\", \"This study supports the current guidelines that discourage discontinuation of ACEIs or ARBs in COVID-19 patients and the setting of the COVID-19 pandemic.\"]}", "id": 401}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Antiviral drugs that have been used to treat other viral infections including HIV are also being tried for COVID-19, so far without proven success.\n\nAbstract:\nThe SARS-CoV-2 virus emerged in December 2019 and then spread rapidly worldwide, particularly to China, Japan, and South Korea.\nScientists are endeavoring to find antivirals specific to the virus.\nSeveral drugs such as chloroquine, arbidol, remdesivir, and favipiravir are currently undergoing clinical studies to test their efficacy and safety in the treatment of coronavirus disease 2019 (COVID-19) in China; some promising results have been achieved thus far.\nThis article summarizes agents with potential efficacy against SARS-CoV-2.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Several drugs such as chloroquine, arbidol, remdesivir, and favipiravir are currently undergoing clinical studies to test their efficacy and safety in the treatment of coronavirus disease 2019 (COVID-19) in China; some promising results have been achieved thus far.\"]}", "id": 402}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Vitamin B could help prevent the 'worst outcomes' in covid-19 cases\n\nAbstract:\nBACKGROUND The coronavirus disease 2019 (COVID-19) pandemic has affected almost 2.5 million people worldwide with almost 170 000 deaths reported to date.\nSo far, there is scarce evidence for the current treatment options available for COVID-19.\nVitamin C has previously been used for treatment of severe sepsis and septic shock.\nWe reviewed the feasibility of using vitamin C in the setting of COVID-19 in a series of patients.\nMETHODS We sequentially identified a series of patients who were requiring at least 30% of FiO2 or more who received IV vitamin C as part of the COVID-19 treatment and analyzed their demographic and clinical characteristics.\nWe compared inflammatory markers pre and post treatment including D-dimer and ferritin.\nRESULTS We identified a total of 17 patients who received IV vitamin C for COVID-19.\nThe inpatient mortality rate in this series was 12% with 17.6% rates of intubation and mechanical ventilation.\nWe noted a significant decrease in inflammatory markers, including ferritin and D-dimer, and a trend to decreasing FiO2 requirements, after vitamin C administration.\nCONCLUSION The use of IV vitamin C in patients with moderate to severe COVID-19 disease may be feasible.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 403}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: the virus spreads mainly through small airborne droplets when an infected person coughs or sneezes.\n\nAbstract:\nBACKGROUND: The Australian and New Zealand governments both initiated strict social distancing measures in response to the COVID-19 pandemic in late March.\nIt remains difficult to quantify the impact this had in reducing the spread of the virus.\nMETHODS: Bayesian structural time series model provide a model to quantify the scenario in which these government-level interventions were not placed.\nOur models predict these strict social distancing measures caused a 79% and 61% reduction in the daily cases of COVID-19 across Australia and New Zealand respectively.\nCONCLUSION: This provides both evidence and impetus for governments considering similar measures in response to COVID-19 and other pandemics.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 404}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: he virus that causes coronavirus disease 2019 (COVID-19) is stable for several hours to days in aerosols and on surfaces\n\nAbstract:\nThe ocular surface has been suggested as a site of infection with Coronavirus-2 (SARS-CoV-2) responsible for the coronavirus disease-19 (COVID-19).\nThis review examines the evidence for this hypothesis, and its implications for clinical practice.\nSevere Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2), responsible for the COVID-19 pandemic, is transmitted by person-to-person contact, via airborne droplets, or through contact with contaminated surfaces.\nSARS-CoV-2 binds to angiotensin converting enzyme-2 (ACE2) to facilitate infection in humans.\nThis review sets out to evaluate evidence for the ocular surface as a route of infection.\nA literature search in this area was conducted on 15 April 2020 using the Scopus database.\nIn total, 287 results were returned and reviewed.\nThere is preliminary evidence for ACE2 expression on corneal and conjunctival cells, but most of the other receptors to which coronaviruses bind appear to be found under epithelia of the ocular surface.\nEvidence from animal studies is limited, with a single study suggesting viral particles on the eye can travel to the lung, resulting in very mild infection.\nCoronavirus infection is rarely associated with conjunctivitis, with occasional cases reported in patients with confirmed COVID-19, along with isolated cases of conjunctivitis as a presenting sign.\nCoronaviruses have been rarely isolated from tears or conjunctival swabs.\nThe evidence suggests coronaviruses are unlikely to bind to ocular surface cells to initiate infection.\nAdditionally, hypotheses that the virus could travel from the nasopharynx or through the conjunctival capillaries to the ocular surface during infection are probably incorrect.\nConjunctivitis and isolation of the virus from the ocular surface occur only rarely, and overwhelmingly in patients with confirmed COVID-19.\nNecessary precautions to prevent person-to-person transmission should be employed in clinical practice throughout the pandemic, and patients should be reminded to maintain good hygiene practices.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 405}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Based on currently available information, WHO does not recommend against the use of of ibuprofen.\n\nAbstract:\nIbuprofen is an over-the-counter medication that is used widely for the treatment of pain and fever during COVID-19 pandemic.\nA concern was raised regarding the safety of ibuprofen use because of its role in increasing ACE2 levels within the Renin-Angiotensin-Aldosterone system.\nACE2 is the coreceptor for the entry of SARS-CoV-2 into cells, and so, a potential increased risk of contracting COVID-19 disease and/or worsening of COVID-19 infection was feared with ibuprofen use.\nHowever, available data from limited studies show administration of recombinant ACE2 improves lung damage caused by respiratory viruses, suggesting ibuprofen use may be beneficial in COVID-19 disease.\nAt this time, there is no supporting evidence to discourage the use of ibuprofen.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Ibuprofen is an over-the-counter medication that is used widely for the treatment of pain and fever during COVID-19 pandemic.\", \"However, available data from limited studies show administration of recombinant ACE2 improves lung damage caused by respiratory viruses, suggesting ibuprofen use may be beneficial in COVID-19 disease.\", \"At this time, there is no supporting evidence to discourage the use of ibuprofen.\"]}", "id": 406}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Virus transmission affected early covid-19 spread\n\nAbstract:\nAs the SARS-Cov-2 virus spreads around the world afflicting millions of people, it has undergone divergent genetic mutations.\nAlthough most of these mutations are expected to be inconsequential, some mutations in the spike protein structure have been hypothesized to affect the critical stage at which the virus invades human cells, which could affect transmission probability and disease expression.\nIf true, then we expect an increased growth rate of reported COVID-19 cases in regions dominated by viruses with these altered proteins.\nWe modeled early global infection dynamics based on clade assignment along with other demographic and meteorological factors previously found to be important.\nClade, but not variant D614G which has been associated with increased viral load, enhanced our ability to describe early COVID-19 growth dynamics.\nIncluding clade identity in models significantly improved predictions over earlier work based only on weather and demographic variables.\nIn particular, higher proportions of clade 19A and 19B were negatively correlated with COVID-19 growth rate, whereas higher proportions of 20A and 20C were positively correlated with growth rate.\nA strong interaction between the prevalence of clade 20C and relative humidity suggests that the impact of clade identity might be more important when coupled with certain weather conditions.\nIn particular, 20C an 20A generate the highest growth rates when coupled with low humidity.\nProjections based on data through April 2020 suggest that, without intervention, COVID-19 has the potential to grow more quickly in regions dominated by the 20A and 20C clades, including most of South and North America.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"In particular, higher proportions of clade 19A and 19B were negatively correlated with COVID-19 growth rate, whereas higher proportions of 20A and 20C were positively correlated with growth rate.\"]}", "id": 407}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: In severe cases of COVID-19, activation patterns of B cells resemble those seen in systemic lupus erythematosus, an autoimmune disease. Emory researchers want to see how far that resemblance extends.\n\nAbstract:\nThe outbreak of the 2019 Novel Coronavirus (SARS-CoV-2) rapidly spread from Wuhan, China to more than 150 countries, areas or territories, causing staggering number of infections and deaths.\nA systematic profiling of the immune vulnerability landscape of SARS-CoV-2, which can bring critical insights into the immune clearance mechanism, peptide vaccine development, and antiviral antibody development, is lacking.\nIn this study, we investigated the potential of the SARS-CoV-2 viral proteins to induce class I and II MHC presentation and to form linear antibody epitopes.\nWe created an online database to broadly share the predictions as a resource for the research community.\nUsing this resource, we showed that genetic variations in SARS- CoV-2, though still few for the moment, already follow the pattern of mutations in related coronaviruses, and could alter the immune vulnerability landscape of this virus.\nImportantly, we discovered evidence that SARS-CoV-2, along with related coronaviruses, used mutations to evade attack from the human immune system.\nOverall, we present an immunological resource for SARS-CoV-2 that could promote both therapeutic development and mechanistic research.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 408}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Hydroxychloroquine proves ineffective in hamsters and macaques infected with sars-cov-2\n\nAbstract:\nWe remain largely without effective prophylactic/therapeutic interventions for COVID-19.\nAlthough many human clinical trials are ongoing, there remains a deficiency of supportive preclinical drug efficacy studies.\nHere we assessed the prophylactic/therapeutic efficacy of hydroxychloroquine (HCQ), a drug of interest for COVID-19 management, in two animal models.\nWhen used for prophylaxis or treatment neither the standard human malaria dose (6.5 mg/kg) nor a high dose (50 mg/kg) of HCQ had any beneficial effect on clinical disease or SARS-CoV-2 kinetics (replication/shedding) in the Syrian hamster disease model.\nSimilarly, HCQ prophylaxis/treatment (6.5 mg/kg) did not significantly benefit clinical outcome nor reduce SARS-CoV-2 replication/shedding in the upper and lower respiratory tract in the rhesus macaque disease model.\nIn conclusion, our preclinical animal studies do not support the use of HCQ in prophylaxis/treatment of COVID-19.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Similarly, HCQ prophylaxis/treatment (6.5 mg/kg) did not significantly benefit clinical outcome nor reduce SARS-CoV-2 replication/shedding in the upper and lower respiratory tract in the rhesus macaque disease model.\"]}", "id": 409}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: CBD help prevent or treat the Covid-19 coronavirus\n\nAbstract:\nThe recently discovered novel coronavirus, SARS-CoV-2 (COVID-19 virus), has brought the whole world to standstill with critical challenges, affecting both health and economic sectors worldwide.\nAlthough initially, this pandemic was associated with causing severe pulmonary and respiratory disorders, recent case studies reported the association of cerebrovascular-neurological dysfunction in COVID-19 patients, which is also life-threatening.\nSeveral SARS-CoV-2 positive case studies have been reported where there are mild or no symptoms of this virus.\nHowever, a selection of patients are suffering from large artery ischemic strokes.\nAlthough the pathophysiology of the SARS-CoV-2 virus affecting the cerebrovascular system has not been elucidated yet, researchers have identified several pathogenic mechanisms, including a role for the ACE2 receptor.\nTherefore, it is extremely crucial to identify the risk factors related to the progression and adverse outcome of cerebrovascular-neurological dysfunction in COVID-19 patients.\nSince many articles have reported the effect of smoking (tobacco and cannabis) and vaping in cerebrovascular and neurological systems, and considering that smokers are more prone to viral and bacterial infection compared to non-smokers, it is high time to explore the probable correlation of smoking in COVID-19 patients.\nHerein, we have reviewed the possible role of smoking and vaping on cerebrovascular and neurological dysfunction in COVID-19 patients, along with potential pathogenic mechanisms associated with it.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 410}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: As the novel coronavirus sweeps the globe, those with high blood pressure are at heightened risk for more severe complications should they contract Covid-19\n\nAbstract:\nSince its recognition in December 2019, covid-19 has rapidly spread globally causing a pandemic.\nPre-existing comorbidities such as hypertension, diabetes, and cardiovascular disease are associated with a greater severity and higher fatality rate of covid-19.\nFurthermore, covid-19 contributes to cardiovascular complications, including acute myocardial injury as a result of acute coronary syndrome, myocarditis, stress-cardiomyopathy, arrhythmias, cardiogenic shock, and cardiac arrest.\nThe cardiovascular interactions of covid-19 have similarities to that of severe acute respiratory syndrome, Middle East respiratory syndrome and influenza.\nSpecific cardiovascular considerations are also necessary in supportive treatment with anticoagulation, the continued use of renin-angiotensin-aldosterone system inhibitors, arrhythmia monitoring, immunosuppression or modulation, and mechanical circulatory support.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Pre-existing comorbidities such as hypertension, diabetes, and cardiovascular disease are associated with a greater severity and higher fatality rate of covid-19.\"]}", "id": 411}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Infections occur mainly through exposure to respiratory droplets when a person is in close contact with someone who has COVID-19.\n\nAbstract:\nOBJECTIVE.\nTo analyze the effectiveness of social distancing in the United States (U.S.).\nMETHODS.\nA novel cell-phone ping data was used to quantify the measures of social distancing by all U.S. counties.\nRESULTS.\nUsing a difference-in-difference approach results show that social distancing has been effective in slowing the spread of COVID-19.\nCONCLUSIONS.\nAs policymakers face the very difficult question of the necessity and effectiveness of social distancing across the U.S., counties where the policies have been imposed have effectively increased social distancing and have seen slowing the spread of COVID-19.\nThese results might help policymakers to make the public understand the risks and benefits of the lockdown.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"As policymakers face the very difficult question of the necessity and effectiveness of social distancing across the U.S., counties where the policies have been imposed have effectively increased social distancing and have seen slowing the spread of COVID-19.\"]}", "id": 412}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Virus evolution affected early covid-19 spread\n\nAbstract:\nAs the SARS-Cov-2 virus spreads around the world afflicting millions of people, it has undergone divergent genetic mutations.\nAlthough most of these mutations are expected to be inconsequential, some mutations in the spike protein structure have been hypothesized to affect the critical stage at which the virus invades human cells, which could affect transmission probability and disease expression.\nIf true, then we expect an increased growth rate of reported COVID-19 cases in regions dominated by viruses with these altered proteins.\nWe modeled early global infection dynamics based on clade assignment along with other demographic and meteorological factors previously found to be important.\nClade, but not variant D614G which has been associated with increased viral load, enhanced our ability to describe early COVID-19 growth dynamics.\nIncluding clade identity in models significantly improved predictions over earlier work based only on weather and demographic variables.\nIn particular, higher proportions of clade 19A and 19B were negatively correlated with COVID-19 growth rate, whereas higher proportions of 20A and 20C were positively correlated with growth rate.\nA strong interaction between the prevalence of clade 20C and relative humidity suggests that the impact of clade identity might be more important when coupled with certain weather conditions.\nIn particular, 20C an 20A generate the highest growth rates when coupled with low humidity.\nProjections based on data through April 2020 suggest that, without intervention, COVID-19 has the potential to grow more quickly in regions dominated by the 20A and 20C clades, including most of South and North America.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"In particular, higher proportions of clade 19A and 19B were negatively correlated with COVID-19 growth rate, whereas higher proportions of 20A and 20C were positively correlated with growth rate.\"]}", "id": 413}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: vitamin D might help protect against becoming infected with, and developing serious symptoms of, COVID-19\n\nAbstract:\nThe severity of coronavirus 2019 infection (COVID-19) is determined by the presence of pneumonia, severe acute respiratory distress syndrome (SARS-CoV-2), myocarditis, microvascular thrombosis and/or cytokine storms, all of which involve underlying inflammation.\nA principal defence against uncontrolled inflammation, and against viral infection in general, is provided by T regulatory lymphocytes (Tregs).\nTreg levels have been reported to be low in many COVID-19 patients and can be increased by vitamin D supplementation.\nLow vitamin D levels have been associated with an increase in inflammatory cytokines and a significantly increased risk of pneumonia and viral upper respiratory tract infections.\nVitamin D deficiency is associated with an increase in thrombotic episodes, which are frequently observed in COVID-19.\nVitamin D deficiency has been found to occur more frequently in patients with obesity and diabetes.\nThese conditions are reported to carry a higher mortality in COVID-19.\nIf vitamin D does in fact reduce the severity of COVID-19 in regard to pneumonia/ARDS, inflammation, inflammatory cytokines and thrombosis, it is our opinion that supplements would offer a relatively easy option to decrease the impact of the pandemic.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"A principal defence against uncontrolled inflammation, and against viral infection in general, is provided by T regulatory lymphocytes (Tregs).\", \"Treg levels have been reported to be low in many COVID-19 patients and can be increased by vitamin D supplementation.\", \"Low vitamin D levels have been associated with an increase in inflammatory cytokines and a significantly increased risk of pneumonia and viral upper respiratory tract infections.\", \"Vitamin D deficiency is associated with an increase in thrombotic episodes, which are frequently observed in COVID-19.\", \"Vitamin D deficiency has been found to occur more frequently in patients with obesity and diabetes.\", \"These conditions are reported to carry a higher mortality in COVID-19.\", \"If vitamin D does in fact reduce the severity of COVID-19 in regard to pneumonia/ARDS, inflammation, inflammatory cytokines and thrombosis, it is our opinion that supplements would offer a relatively easy option to decrease the impact of the pandemic.\"]}", "id": 414}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: the mechanism behind the protective effects of smoking could be found in nicotine\n\nAbstract:\nImportance.\nCovid-19 infection has major international health and economic impacts and risk factors for infection are not completely understood.\nCannabis smoking is linked with poor respiratory health, immunosuppression and multiple contaminants.\nPotential synergism between the two epidemics would represent a major public health convergence.\nCigarettes were implicated with disease severity in Wuhan, China.\nObjective.\nIs cannabis use epidemiologically associated with coronavirus incidence rate (CVIR)?\nDesign.\nCross-sectional state-based multivariable study.\nSetting.\nUSA.\nPrimary and Secondary Outcome Measures.\nCVIR.\nMultivariable-adjusted geospatially-weighted regression models.\nAs the American cannabis epidemic is characterized by a recent doubling of daily cannabis use it was considered important to characterize the contribution of high intensity use.\nResults.\nSignificant associations of daily cannabis use quintile with CVIR were identified with the highest quintile having a prevalence ratio 5.11 (95%C.I. 4.90-5.33), an attributable fraction in the exposed (AFE) 80.45% (79.61-81.25%) and an attributable fraction in the population of 77.80% (76.88-78.68%) with Chi-squared-for-trend (14,782, df=4) significant at P<10-500.\nSimilarly when cannabis legalization was considered decriminalization was associated with an elevated CVIR prevalence ratio 4.51 (95%C.I. 4.45-4.58), AFE 77.84% (77.50-78.17%) and Chi-squared-for-trend (56,679, df=2) significant at P<10-500.\nMonthly and daily use were linked with CVIR in bivariate geospatial regression models (P=0.0027, P=0.0059).\nIn multivariable additive models number of flight origins and population density were significant.\nIn interactive geospatial models adjusted for international travel, ethnicity, income, population, population density and drug use, terms including last month cannabis were significant from P=7.3x10-15, daily cannabis use from P=7.3x10-11 and last month cannabis was independently associated (P=0.0365).\nConclusions and Relevance.\nData indicate CVIR demonstrates significant trends across cannabis use intensity quintiles and with relaxed cannabis legislation.\nRecent cannabis use is independently predictive of CVIR in bivariate and multivariable adjusted models and intensity of use is interactively significant.\nCannabis thus joins tobacco as a SARS2-CoV-2 risk factor.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Cannabis smoking is linked with poor respiratory health, immunosuppression and multiple contaminants.\", \"Potential synergism between the two epidemics would represent a major public health convergence.\", \"Cigarettes were implicated with disease severity in Wuhan, China.\", \"Data indicate CVIR demonstrates significant trends across cannabis use intensity quintiles and with relaxed cannabis legislation.\", \"Recent cannabis use is independently predictive of CVIR in bivariate and multivariable adjusted models and intensity of use is interactively significant.\", \"Cannabis thus joins tobacco as a SARS2-CoV-2 risk factor.\"]}", "id": 415}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Sterilizing immunity against sars-cov-2 infection in mice by a single-shot and modified imidazoquinoline tlr7/8 agonist-adjuvanted recombinant spike protein vaccine\n\nAbstract:\nThe search for vaccines that protect from severe morbidity and mortality as a result of infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus that causes coronavirus disease 2019 (COVID-19) is a race against the clock and the virus.\nSeveral vaccine candidates are currently being tested in the clinic.\nInactivated virus and recombinant protein vaccines can be safe options but may require adjuvants to induce robust immune responses efficiently.\nIn this work we describe the use of a novel amphiphilic imidazoquinoline (IMDQ-PEG-CHOL) TLR7/8 adjuvant, consisting of an imidazoquinoline conjugated to the chain end of a cholesterol-poly(ethylene glycol) macromolecular amphiphile).\nThis amphiphile is water soluble and exhibits massive translocation to lymph nodes upon local administration, likely through binding to albumin.\nIMDQ-PEG-CHOL is used to induce a protective immune response against SARS-CoV-2 after single vaccination with trimeric recombinant SARS-CoV-2 spike protein in the BALB/c mouse model.\nInclusion of amphiphilic IMDQ-PEG-CHOL in the SARS-CoV-2 spike vaccine formulation resulted in enhanced immune cell recruitment and activation in the draining lymph node.\nIMDQ-PEG-CHOL has a better safety profile compared to native soluble IMDQ as the former induces a more localized immune response upon local injection, preventing systemic inflammation.\nMoreover, IMDQ-PEG-CHOL adjuvanted vaccine induced enhanced ELISA and in vitro microneutralization titers, and a more balanced IgG2a/IgG1 response.\nTo correlate vaccine responses with control of virus replication in vivo, vaccinated mice were challenged with SARS-CoV-2 virus after being sensitized by intranasal adenovirus-mediated expression of the human angiotensin converting enzyme 2 (ACE2) gene.\nAnimals vaccinated with trimeric recombinant spike protein vaccine without adjuvant had lung virus titers comparable to non-vaccinated control mice, whereas animals vaccinated with IMDQ-PEG-CHOL-adjuvanted vaccine controlled viral replication and infectious viruses could not be recovered from their lungs at day 4 post infection.\nIn order to test whether IMDQ-PEG-CHOL could also be used to adjuvant vaccines currently licensed for use in humans, proof of concept was also provided by using the same IMDQ-PEG-CHOL to adjuvant human quadrivalent inactivated influenza virus split vaccine, which resulted in enhanced hemagglutination inhibition titers and a more balanced IgG2a/IgG1 antibody response.\nEnhanced influenza vaccine responses correlated with better virus control when mice were given a lethal influenza virus challenge.\nOur results underscore the potential use of IMDQ-PEG-CHOL as an adjuvant to achieve protection after single immunization with recombinant protein and inactivated vaccines against respiratory viruses, such as SARS-CoV-2 and influenza viruses.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"IMDQ-PEG-CHOL is used to induce a protective immune response against SARS-CoV-2 after single vaccination with trimeric recombinant SARS-CoV-2 spike protein in the BALB/c mouse model.\"]}", "id": 416}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Lack of immune homology with vaccine preventable pathogens suggests childhood immunizations do more protect against sars-cov-2 through adaptive cross-immunity\n\nAbstract:\nRecent epidemiological studies have investigated the potential effects of childhood immunization history on COVID-19 severity.\nSpecifically, prior exposure to Bacillus Calmette-Guerin (BCG) vaccine, oral poliovirus vaccine (OPV), or measles vaccine have been postulated to reduce COVID-19 severity-putative mechanism is via stimulation of the innate immune system to provide broader protection against non-specific pathogens.\nWhile these epidemiological results remain inconclusive, we sought to investigate the potential role of adaptive immunity via cross-reactivity between vaccine preventable diseases (VPDs) with SARS-CoV-2.\nWe implemented a comprehensive exploration of immune homology (including sequence homology, immune epitopes, and glycosylation patterns) between SARS-CoV-2 and all pathogens with FDA-approved vaccines.\nSequence homology did not reveal significant alignments of protein sequences between SARS-CoV-2 with any VPD pathogens, including BCG-related strains.\nWe also could not identify any shared T or B cell epitopes between SARS-CoV-2 and VPD pathogens among either experimentally validated epitopes or predicted immune epitopes.\nFor N-glycosylation (N-glyc), while sites with the same tripeptides could be found between SARS-CoV-2 and certain VPD pathogens, their glycosylation potentials and positions were different.\nIn summary, lack of immune homology between SARS-CoV-2 and VPD pathogens suggests that childhood immunization history (i.e., BCG vaccination or others) does not provide protection from SARS-CoV-2 through adaptive cross-immunity.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"In summary, lack of immune homology between SARS-CoV-2 and VPD pathogens suggests that childhood immunization history (i.e., BCG vaccination or others) does not provide protection from SARS-CoV-2 through adaptive cross-immunity.\"]}", "id": 417}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Covid-19 is infecting quite a few people, many with vicious effects.\n\nAbstract:\nImportance.\nCovid-19 infection has major international health and economic impacts and risk factors for infection are not completely understood.\nCannabis smoking is linked with poor respiratory health, immunosuppression and multiple contaminants.\nPotential synergism between the two epidemics would represent a major public health convergence.\nCigarettes were implicated with disease severity in Wuhan, China.\nObjective.\nIs cannabis use epidemiologically associated with coronavirus incidence rate (CVIR)?\nDesign.\nCross-sectional state-based multivariable study.\nSetting.\nUSA.\nPrimary and Secondary Outcome Measures.\nCVIR.\nMultivariable-adjusted geospatially-weighted regression models.\nAs the American cannabis epidemic is characterized by a recent doubling of daily cannabis use it was considered important to characterize the contribution of high intensity use.\nResults.\nSignificant associations of daily cannabis use quintile with CVIR were identified with the highest quintile having a prevalence ratio 5.11 (95%C.I. 4.90-5.33), an attributable fraction in the exposed (AFE) 80.45% (79.61-81.25%) and an attributable fraction in the population of 77.80% (76.88-78.68%) with Chi-squared-for-trend (14,782, df=4) significant at P<10-500.\nSimilarly when cannabis legalization was considered decriminalization was associated with an elevated CVIR prevalence ratio 4.51 (95%C.I. 4.45-4.58), AFE 77.84% (77.50-78.17%) and Chi-squared-for-trend (56,679, df=2) significant at P<10-500.\nMonthly and daily use were linked with CVIR in bivariate geospatial regression models (P=0.0027, P=0.0059).\nIn multivariable additive models number of flight origins and population density were significant.\nIn interactive geospatial models adjusted for international travel, ethnicity, income, population, population density and drug use, terms including last month cannabis were significant from P=7.3x10-15, daily cannabis use from P=7.3x10-11 and last month cannabis was independently associated (P=0.0365).\nConclusions and Relevance.\nData indicate CVIR demonstrates significant trends across cannabis use intensity quintiles and with relaxed cannabis legislation.\nRecent cannabis use is independently predictive of CVIR in bivariate and multivariable adjusted models and intensity of use is interactively significant.\nCannabis thus joins tobacco as a SARS2-CoV-2 risk factor.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 418}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Wearing a surgical face mask probably won't keep you from getting sick\n\nAbstract:\nThe COVID\u201019 pandemic caused by the novel coronavirus SARS\u2010CoV\u20102 has claimed many lives worldwide.\nWearing medical masks or N95 masks (namely N95 respirators) can slow the virus spread and reduce the infection risk.\nReuse of these masks can minimize waste, protect the environment, and help to solve the current imminent shortage of masks.\nDisinfection of used masks is needed for reuse of them with safety, but improper decontamination can damage the blocking structure of masks.\nIn this study, we demonstrated, using avian coronavirus of infectious bronchitis virus to mimic SARS\u2010CoV\u20102, that medical masks and N95 masks remained their blocking efficacy after being steamed on boiling water even for 2 hours.\nWe also demonstrated that three brands of medical masks blocked over 99% viruses in aerosols.\nThe avian coronavirus was completely inactivated after being steamed for 5 minutes.\nTogether, this study suggested that medical masks are adequate for use on most social occasions, and both medical masks and N95 masks can be reused for a few days with steam decontamination between use.\nThis article is protected by copyright.\nAll rights reserved.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Wearing medical masks or N95 masks (namely N95 respirators) can slow the virus spread and reduce the infection risk.\", \"We also demonstrated that three brands of medical masks blocked over 99% viruses in aerosols.\", \"The avian coronavirus was completely inactivated after being steamed for 5 minutes.\", \"Together, this study suggested that medical masks are adequate for use on most social occasions, and both medical masks and N95 masks can be reused for a few days with steam decontamination between use.\"]}", "id": 419}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Covid-19 Can Be Treated By Gargling With Warm Water Mixed With Salt And Vinegar\n\nAbstract:\nSince December 2019, a respiratory pandemic named as coronavirus disease 2019 (Covid-19) caused by a new coronavirus named as SARS-CoV-2, has taken the world by storm.\nThe symptoms are fever, malaise, and cough which resolve in a few days in most cases; but may progress to respiratory distress and organ failure.\nTransmission is through droplet infection or fomites, but other modes such as airborne transmission and oro-fecal transmission are also speculated.\nResearch is underway to develop effective vaccines and medicines for the disease.\nIn such a scenario, we present the measures described in Unani system of medicine for health protection during epidemics.\nUnani is a traditional system of medicine developed during the middle ages, which employs natural drugs of herbal, animal and mineral origin for treatment.\nIn Unani medicine, during an epidemic, apart from isolation and quarantine, three measures are of utmost importance, (i) purification of surroundings using certain herbal drugs as fumigants or sprays, (ii) health promotion and immune-modulation, and (iii) use of health-protecting drugs and symptom-specific drugs.\nDrugs such as loban (Styrax benzoides W. G. Craib), sandroos (Hymenaea verrucosa Gaertn.) za'fran (Crocus sativus L.), vinegar etc.\nare prescribed in various forms.\nScientific researches on these drugs reveal the presence of a number of pharmacologically active substances, which may provide a new insight into the management of infections and epidemics.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"In Unani medicine, during an epidemic, apart from isolation and quarantine, three measures are of utmost importance, (i) purification of surroundings using certain herbal drugs as fumigants or sprays, (ii) health promotion and immune-modulation, and (iii) use of health-protecting drugs and symptom-specific drugs.\", \"Drugs such as loban (Styrax benzoides W. G. Craib), sandroos (Hymenaea verrucosa Gaertn.) za'fran (Crocus sativus L.), vinegar etc.\", \"are prescribed in various forms.\"]}", "id": 420}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Tiny antibody component highly effective against SARS-COV-2 in animal studies\n\nAbstract:\nSARS-CoV-2 rapidly spread around the globe after its emergence in Wuhan in December 2019.\nWith no specific therapeutic and prophylactic options available, the virus was able to infect millions of people.\nTo date, close to half a million patients succumbed to the viral disease, COVID-19.\nThe high need for treatment options, together with the lack of small animal models of infection has led to clinical trials with repurposed drugs before any preclinical in vivo evidence attesting their efficacy was available.\nWe used Syrian hamsters to establish a model to evaluate antiviral activity of small molecules in both an infection and a transmission setting.\nUpon intranasal infection, the animals developed high titers of SARS-CoV-2 in the lungs and pathology similar to that observed in mild COVID-19 patients.\nTreatment of SARS-CoV-2-infected hamsters with favipiravir or hydroxychloroquine (with and without azithromycin) resulted in respectively a mild or no reduction in viral RNA and infectious virus.\nMicro-CT scan analysis of the lungs showed no improvement compared to non-treated animals, which was confirmed by histopathology.\nIn addition, both compounds did not prevent virus transmission through direct contact and thus failed as prophylactic treatments.\nBy modelling the PK profile of hydroxychloroquine based on the trough plasma concentrations, we show that the total lung exposure to the drug was not the limiting factor.\nIn conclusion, we here characterized a hamster infection and transmission model to be a robust model for studying in vivo efficacy of antiviral compounds.\nThe information acquired using hydroxychloroquine and favipiravir in this model is of critical value to those designing (current and) future clinical trials.\nAt this point, the data here presented on hydroxychloroquine either alone or combined with azithromycin (together with previously reported in vivo data in macaques and ferrets) provide no scientific basis for further use of the drug in humans.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 421}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Remdesivir proves effective against COVID-19\n\nAbstract:\nBackground: Researchers are working hard to find an effective treatment for the new coronavirus 2019.\nWe performed a comprehensive systematic review to investigate the latest clinical evidence on the treatment efficacy and safety of Remdesivir in hospitalized patients with COVID-19.\nMethods: We performed a systematic search of the Pubmed, Embase, Web of Science, Google scholar, and MedRxiv for relevant observational and interventional studies.\nMeasured outcomes were mortality rates, improvement rates, time to clinical improvement, all adverse event rates and severe adverse event rates.\nResults: 3 RCTs and 2 cohorts were included in our study.\nIn 2 cohort studies, patients received Remdesivir for 10 days.\n2 RCTs evaluated 10-day treatment of Remdesivir efficacy versus placebo group and the other RCT compared its 5-day regimen versus 10-day regimen.\nVisual inspection of the forest plots revealed that Remdesivir efficacy was not much different in reducing 28-day mortality versus 14-day mortality rates.\nBesides, 10-day treatment regimen overpowers 5-day treatment and placebo in decreasing time to clinical improvement.\nAll adverse event rates did not have significant difference; however, severe adverse event rate was lower in 5-day Remdesivir group compared to 10-day and placebo groups.\nConclusion: 5-day course of Remdesivir therapy in COVID-19 patients is probably efficacious and safe and patients without invasive mechanical ventilation benefit the most.\nTreatment can be extended to 10 days if satisfactory improvement is not seen by day 5.\nMost benefits from Remdesivir therapy take place in the first 14 days of the start of the treatment.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Visual inspection of the forest plots revealed that Remdesivir efficacy was not much different in reducing 28-day mortality versus 14-day mortality rates.\"]}", "id": 422}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: covid-19 patients taking hydroxychloroquine do not benefit\n\nAbstract:\nBackground: Coronavirus pandemic is currently a global public health emergency.\nAt present, no pharmacological treatment is known to treat this condition, and there is a need to review the available treatments.\nObjective: While there have been studies to describe the role of chloroquine and hydroxychloroquine in various viral conditions, there is limited information about the use of them in COVID-19.\nThis systematic review aims to summarize the available evidence regarding the role of chloroquine in treating coronavirus infection.\nMethods: The preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines were used for this review.\nA literature search was performed using PUBMED & Google Scholar to find articles about the role of CQ in COVID-19 patients.\nResults: We included 19 publications (Five published articles, three letters/correspondence, one commentary, five pre-proofs of accepted articles, one abstract of yet to be published article, and four were pre-prints (not yet peer-reviewed) articles) in this systematic review.\nAll the articles mentioned about the role of chloroquine and /or hydroxychloroquine in limiting the infection with SARS-CoV-2 (the virus causing COVID-19).\nConclusions: There is theoretical, experimental, preclinical and clinical evidence of the effectiveness of chloroquine in patients affected with COVID-19.\nThere is adequate evidence of drug safety from the long-time clinical use of chloroquine and hydroxychloroquine in other indications.\nMore data from ongoing and future trials will add more insight into the role of chloroquine and hydroxychloroquine in COVID-19 infection.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Conclusions: There is theoretical, experimental, preclinical and clinical evidence of the effectiveness of chloroquine in patients affected with COVID-19.\"]}", "id": 423}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: People with weakened immune systems are at higher risk of getting severely sick from SARS-CoV-2, the virus that causes COVID-19.\n\nAbstract:\nThe novel coronavirus Covid-19 follows transmission route and clinical presentation of all community-acquired coronaviruses.\nInstead, the rate of transmission is significative higher, with a faster spread of the virus responsible of the worldwide outbreak and a significative higher mortality rate due to the development of a severe lung injury.\nMost noteworthy is the distribution of death rate among age groups.\nChildren and younger people are almost protected from severe clinical presentation.\nPossible explanation of this phenomenon could be the ability of past vaccinations (especially tetanic, diphtheria toxoids and inactivated bacteria as pertussis) to stimulate immune system and to generate a scattered immunity against non-self antigens in transit, as coronaviruses and other community-circulating viruses and make immune system readier to develop specific immunity against Covid-19.\nThe first support to this hypothesis is the distribution of mortality rate during historical pandemics (\"Spanish flu\" 1918, \"Asian flu\" 1956 and \"the Hong Kong flu\" 1968) among age groups before and after the introduction of vaccines.\nThe immunological support to the hypothesis derives from recent studies about immunotherapy for malignancies, which propose the use of oncolytic vaccines combined with toxoids in order to exploit CD4 + memory T cell recall in supporting the ongoing anti-tumour response.\nAccording to this hypothesis vaccine formulations (tetanus, diphtheria, Bordetella pertussis) could be re-administrate after the first contact with Covid-19, better before the development of respiratory severe illness and of course before full-blown ARDS (Acute Respiratory Distress Syndrome).\nThe CD4 + memory exploiting could help immune system to recall immunity of already know antigens against coronaviruses, avoiding or limiting \"lung crash\" until virus specific immunity develops and making it faster and prolonged.\nFinally, this administration could be helpful not only in already infected patients, but also before infection.\nIn fact, people could have an immune system more ready when the contact with the Covid-19 will occur.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 424}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Our work describing the adaptation of the cdc rt-qpcr assay for use with alternative qiagen rna extraction kits , as well as skipping the rna extraction step entirely is live on biorxiv .\n\nAbstract:\nThe ongoing COVID-19 pandemic has caused an unprecedented need for rapid diagnostic testing.\nThe Centers for Disease Control and Prevention (CDC) and the World Health Organization (WHO) recommend a standard assay that includes an RNA extraction step from a nasopharyngeal (NP) swab followed by reverse transcription-quantitative polymerase chain reaction (RT-qPCR) to detect the purified SARS-CoV-2 RNA.\nThe current global shortage of RNA extraction kits has caused a severe bottleneck to COVID-19 testing.\nWe hypothesized that SARS-CoV-2 RNA could be detected from NP samples via a direct RT-qPCR assay that omits the RNA extraction step altogether, and tested this hypothesis on a series of blinded clinical samples.\nThe direct RT-qPCR approach correctly identified 92% of NP samples (n = 155) demonstrated to be positive for SARS-CoV-2 RNA by traditional clinical diagnostic RT-qPCR that included an RNA extraction.\nThus, direct RT-qPCR could be a front-line approach to identify the substantial majority of COVID-19 patients, reserving a repeat test with RNA extraction for those individuals with high suspicion of infection but an initial negative result.\nThis strategy would drastically ease supply chokepoints of COVID-19 testing and should be applicable throughout the world.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"We hypothesized that SARS-CoV-2 RNA could be detected from NP samples via a direct RT-qPCR assay that omits the RNA extraction step altogether, and tested this hypothesis on a series of blinded clinical samples.\"]}", "id": 425}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Coronaviruses cause respiratory illnesses, so the lungs are usually affected first. Early symptoms include fever, cough, and shortness of breath.\n\nAbstract:\nCoronaviruses are a genetically highly variable family of viruses that infect vertebrates and have succeeded in infecting humans many times by overcoming the species barrier.\nThe severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which initially appeared in China at the end of 2019, exhibits a high infectivity and pathogenicity compared to other coronaviruses.\nAs the viral coat and other viral components are recognized as being foreign by the immune system, this can lead to initial symptoms, which are induced by the very efficiently working immune defense system via the respiratory epithelium.\nDuring severe courses a systemically expressed proinflammatory cytokine storm and subsequent changes in the coagulation and complement systems can occur.\nVirus-specific antibodies, the long-term expression of which is ensured by the formation of B memory cell clones, generate a specific immune response that is also detectable in blood (seroconversion).\nSpecifically effective cytotoxic CD8+ T\u00adcell populations are also formed, which recognize viral epitopes as pathogen-specific patterns in combination with MHC presentation on the cell surface of virus-infected cells and destroy these cells.\nAt the current point in time it is unclear how regular, robust and durable this immune status is constructed.\nExperiences with other coronavirus infections (SARS and Middle East respiratory syndrome, MERS) indicate that the immunity could persist for several years.\nBased on animal experiments, already acquired data on other coronavirus types and plausibility assumptions, it can be assumed that seroconverted patients have an immunity of limited duration and only a very low risk of reinfection.\nKnowledge of the molecular mechanisms of viral cycles and immunity is an important prerequisite for the development of vaccination strategies and development of effective drugs.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 426}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: It appears that the virus that causes COVID-19 can spread from people to animals in some situations.\n\nAbstract:\nThe infectious diseases are spreading due to human interactions enabled by various social networks.\nTherefore, when a new pathogen such as SARS-CoV-2 causes an outbreak, the non-pharmaceutical isolation strategies (e.g., social distancing) are the only possible response to disrupt its spreading.\nTo this end, we introduce the new epidemic model (SICARS) and compare the centralized (C), decentralized (D), and combined (C+D) social distancing strategies, and analyze their efficiency to control the dynamics of COVID-19 on heterogeneous complex networks.\nOur analysis shows that the centralized social distancing is necessary to minimize the pandemic spreading.\nThe decentralized strategy is insufficient when used alone, but offers the best results when combined with the centralized one.\nIndeed, the (C+D) is the most efficient isolation strategy at mitigating the network superspreaders and reducing the highest node degrees to less than 10% of their initial values.\nOur results also indicate that stronger social distancing, e.g., cutting 75% of social ties, can reduce the outbreak by 75% for the C isolation, by 33% for the D isolation, and by 87% for the (C+D) isolation strategy.\nFinally, we study the impact of proactive versus reactive isolation strategies, as well as their delayed enforcement.\nWe find that the reactive response to the pandemic is less efficient, and delaying the adoption of isolation measures by over one month (since the outbreak onset in a region) can have alarming effects; thus, our study contributes to an understanding of the COVID-19 pandemic both in space and time.\nWe believe our investigations have a high social relevance as they provide insights into understanding how different degrees of social distancing can reduce the peak infection ratio substantially; this can make the COVID-19 pandemic easier to understand and control over an extended period of time.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 427}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: cloth face mask with a filter can help prevent the spread of COVID-19\n\nAbstract:\nHerein, we report that nosocomial infection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) may be mitigated by using surgical masks and closed looped ventilation for both non-critical and critical patients.\nThese preventive measures resulted in no viral contamination of surfaces in negative pressure environments.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 428}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: No experts are remotely advocating for people to take up smoking to prevent COVID-19, but some researchers have theorized nicotine may be playing some role in keeping the virus at bay\n\nAbstract:\nSome comorbidities are associated with severe coronavirus disease (Covid-19) but it is unclear whether some increase susceptibility to Covid-19.\nIn this case-control Mexican study we found that obesity represents the strongest predictor for Covid-19 followed by diabetes and hypertension in both sexes and chronic renal failure in females only.\nActive smoking was associated with decreased odds of Covid-19.\nThese findings indicate that these comorbidities are not only associated with severity of disease but also predispose for getting Covid-19.\nFuture research is needed to establish the mechanisms involved in each comorbidity and the apparent \"protective\" effect of cigarette smoking.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 429}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: The Weather Network - Wildfire smoke could worsen COVID-19\n\nAbstract:\nBackground: Understanding and projecting the spread of COVID-19 requires reliable estimates of how weather components are associated with the transmission of the virus.\nPrior research on this topic has been inconclusive.\nIdentifying key challenges to reliable estimation of weather impact on transmission we study this question using one of the largest assembled databases of COVID-19 infections and weather.\nMethods: We assemble a dataset that includes virus transmission and weather data across 3,739 locations from December 12, 2019 to April 22, 2020.\nUsing simulation, we identify key challenges to reliable estimation of weather impacts on transmission, design a statistical method to overcome these challenges, and validate it in a blinded simulation study.\nUsing this method and controlling for location-specific response trends we estimate how different weather variables are associated with the reproduction number for COVID-19.\nWe then use the estimates to project the relative weather-related risk of COVID-19 transmission across the world and in large cities.\nResults: We show that the delay between exposure and detection of infection complicates the estimation of weather impact on COVID-19 transmission, potentially explaining significant variability in results to-date.\nCorrecting for that distributed delay and offering conservative estimates, we find a negative relationship between temperatures above 25 degrees Celsius and estimated reproduction number ([R]), with each degree Celsius associated with a 3.1% (95% CI, 1.5% to 4.8%) reduction in [R].\nHigher levels of relative humidity strengthen the negative effect of temperature above 25 degrees.\nMoreover, one millibar of additional pressure increases [R] by approximately 0.8 percent (95% CI, 0.6% to 1%) at the median pressure (1016 millibars) in our sample.\nWe also find significant positive effects for wind speed, precipitation, and diurnal temperature on [R].\nSensitivity analysis and simulations show that results are robust to multiple assumptions.\nDespite conservative estimates, weather effects are associated with a 43% change in [R] between the 5th and 95th percentile of weather conditions in our sample.\nConclusions: These results provide evidence for the relationship between several weather variables and the spread of COVID-19.\nHowever, the (conservatively) estimated relationships are not strong enough to seasonally control the epidemic in most locations.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 430}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: a small number of pets worldwide, including cats and dogs, can be infected with the virus that causes COVID-19, mostly after close contact with people with COVID-19.\n\nAbstract:\nOBJECTIVE.\nTo analyze the effectiveness of social distancing in the United States (U.S.).\nMETHODS.\nA novel cell-phone ping data was used to quantify the measures of social distancing by all U.S. counties.\nRESULTS.\nUsing a difference-in-difference approach results show that social distancing has been effective in slowing the spread of COVID-19.\nCONCLUSIONS.\nAs policymakers face the very difficult question of the necessity and effectiveness of social distancing across the U.S., counties where the policies have been imposed have effectively increased social distancing and have seen slowing the spread of COVID-19.\nThese results might help policymakers to make the public understand the risks and benefits of the lockdown.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 431}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: cloth face mask with a filter can help prevent the spread of COVID-19\n\nAbstract:\nControversy exists around the appropriate types of masks and the situations in which they should be used in community and health care settings for the prevention of SARS-CoV-2 infection.\nIn this article, the American College of Physicians (ACP) provides recommendations based on the best available evidence through 14 April 2020 on the effectiveness of N95 respirators, surgical masks, and cloth masks in reducing transmission of infection.\nThe ACP plans periodic updates of these recommendations on the basis of ongoing surveillance of the literature for 1 year from the initial search date.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 432}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Influenza affects global patterns of covid-19 early outbreak dynamics\n\nAbstract:\nEnvironmental factors are well known to affect spatio-temporal patterns of infectious disease outbreaks, but whether the rapid spread of COVID-19 across the globe is related to local environmental conditions is highly debated.\nWe assessed the impact of environmental factors (temperature, humidity and air pollution) on the global patterns of COVID-19 early outbreak dynamics during January-May 2020, controlling for several key socio-economic factors and airport connections.\nWe showed that during the earliest phase of the global outbreak (January-March), COVID-19 growth rates were non-linearly related to climate, with fastest spread in regions with a mean temperature of ca.\n5 \u00b0C, and in the most polluted regions.\nHowever, environmental effects faded almost completely when considering later outbreaks, in keeping with the progressive enforcement of containment actions.\nAccordingly, COVID-19 growth rates consistently decreased with stringent containment actions during both early and late outbreaks.\nOur findings indicate that environmental drivers may have played a role in explaining the early variation among regions in disease spread.\nWith limited policy interventions, seasonal patterns of disease spread might emerge, with temperate regions of both hemispheres being most at risk of severe outbreaks during colder months.\nNevertheless, containment measures play a much stronger role and overwhelm impacts of environmental variation, highlighting the key role for policy interventions in curbing COVID-19 diffusion within a given region.\nIf the disease will become seasonal in the next years, information on environmental drivers of COVID-19 can be integrated with epidemiological models to inform forecasting of future outbreak risks and improve management plans.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"We showed that during the earliest phase of the global outbreak (January-March), COVID-19 growth rates were non-linearly related to climate, with fastest spread in regions with a mean temperature of ca.\"]}", "id": 433}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Emergence of zoonoses such as covid-19 reveals the need for health sciences to embrace an explicit eco-social conceptual framework of health and disease\n\nAbstract:\nAn accurate understanding of why zoonoses such as SARS-CoV-2 are emerging at an increased rate, is vital to prevent future pandemics from the approximately 700,000 viruses with zoonotic potential.\nCertain authors have argued that the consumption of wildlife, or human contact with bats was responsible for the emergence of SARS-CoV-2.\nOthers argue that a range of anthropogenic environmental degradations have played a vital role in the emergence of SARS-CoV-2 and other zoonoses.\nIn this opinion piece, I argue that these divergent viewpoints stem, in part, from different foundational conceptual frameworks-biomedical individualist and eco-social frameworks, respectively.\nBased on the fact that the eco-social framework provides a more complete account of the different types of causal factors underpinning the emergence of zoonoses, I propose that the COVID-19 pandemic provides an additional reason for the health sciences to ground its theory of health and disease in an eco-social conceptual framework.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Others argue that a range of anthropogenic environmental degradations have played a vital role in the emergence of SARS-CoV-2 and other zoonoses.\", \"Based on the fact that the eco-social framework provides a more complete account of the different types of causal factors underpinning the emergence of zoonoses, I propose that the COVID-19 pandemic provides an additional reason for the health sciences to ground its theory of health and disease in an eco-social conceptual framework.\"]}", "id": 434}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Eating garlic will not help prevent infection with COVID-19. While it is a healthy food and is proven to have some antimicrobial properties, there is no evidence that eating garlic can protect from covid-19\n\nAbstract:\nIn late December 2019 in Wuhan, China, several patients with viral pneumonia were identified as 2019 novel coronavirus (2019-nCoV).\nSo far, there are no specific treatments for patients with coronavirus disease-19 (COVID-19), and the treatments available today are based on previous experience with similar viruses such as severe acute respiratory syndrome-related coronavirus (SARS-CoV), Middle East respiratory syndrome coronavirus (MERS-CoV), and Influenza virus.\nIn this article, we have tried to reach a therapeutic window of drugs available to patients with COVID-19.\nCathepsin L is required for entry of the 2019-nCoV virus into the cell as target teicoplanin inhibits virus replication.\nAngiotensin-converting-enzyme 2 (ACE2) in soluble form as a recombinant protein can prevent the spread of coronavirus by restricting binding and entry.\nIn patients with COVID-19, hydroxychloroquine decreases the inflammatory response and cytokine storm, but overdose causes toxicity and mortality.\nNeuraminidase inhibitors such as oseltamivir, peramivir, and zanamivir are invalid for 2019-nCoV and are not recommended for treatment but protease inhibitors such as lopinavir/ritonavir (LPV/r) inhibit the progression of MERS-CoV disease and can be useful for patients of COVID-19 and, in combination with Arbidol, has a direct antiviral effect on early replication of SARS-CoV. Ribavirin reduces hemoglobin concentrations in respiratory patients, and remdesivir improves respiratory symptoms.\nUse of ribavirin in combination with LPV/r in patients with SARS-CoV reduces acute respiratory distress syndrome and mortality, which has a significant protective effect with the addition of corticosteroids.\nFavipiravir increases clinical recovery and reduces respiratory problems and has a stronger antiviral effect than LPV/r.\ncurrently, appropriate treatment for patients with COVID-19 is an ACE2 inhibitor and a clinical problem reducing agent such as favipiravir in addition to hydroxychloroquine and corticosteroids.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"In late December 2019 in Wuhan, China, several patients with viral pneumonia were identified as 2019 novel coronavirus (2019-nCoV).\", \"So far, there are no specific treatments for patients with coronavirus disease-19 (COVID-19), and the treatments available today are based on previous experience with similar viruses such as severe acute respiratory syndrome-related coronavirus (SARS-CoV), Middle East respiratory syndrome coronavirus (MERS-CoV), and Influenza virus.\"]}", "id": 435}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Microwave or autoclave not destroy the infectivity of infectious bronchitis virus and avian pneumovirus but allow detection by reverse transcriptase-polymerase chain reaction\n\nAbstract:\nA method is described for enabling safe transit of denatured virus samples for polymerase chain reaction (PCR) identification without the risk of unwanted viable viruses.\nCotton swabs dipped in avian infectious bronchitis virus (IBV) or avian pneumovirus (APV) were allowed to dry.\nNewcastle disease virus and avian influenza viruses were used as controls.\nAutoclaving and microwave treatment for as little as 20 sec destroyed the infectivity of all four viruses.\nHowever, both IBV and APV could be detected by reverse transcriptase (RT)-PCR after autoclaving and as long as 5 min microwave treatment (Newcastle disease virus and avian influenza viruses were not tested).\nDouble microwave treatment of IBV and APV with an interval of 2 to 7 days between was tested.\nAfter the second treatment, RT-PCR products were readily detected in all samples.\nSwabs from the tracheas and cloacas of chicks infected with IBV shown to contain infectious virus were microwaved.\nSwabs from both sources were positive by RT-PCR.\nMicrowave treatment appears to be a satisfactory method of inactivating virus while preserving nucleic acid for PCR identification.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Autoclaving and microwave treatment for as little as 20 sec destroyed the infectivity of all four viruses.\", \"However, both IBV and APV could be detected by reverse transcriptase (RT)-PCR after autoclaving and as long as 5 min microwave treatment (Newcastle disease virus and avian influenza viruses were not tested).\", \"Microwave treatment appears to be a satisfactory method of inactivating virus while preserving nucleic acid for PCR identification.\"]}", "id": 436}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Sars-cov2 enables anaerobic bacteria to colonize the lungs disrupting homeostasis\n\nAbstract:\nThe oral cavity, as the entry point to the body, may play a critical role in the pathogenesis of SARS-CoV-2 infection that has caused a global outbreak of the coronavirus disease 2019 (COVID-19).\nAvailable data indicate that the oral cavity may be an active site of infection and an important reservoir of SARS-CoV-2.\nConsidering that the oral surfaces are colonized by a diverse microbial community, it is likely that viruses have interactions with the host microbiota.\nPatients infected by SARS-CoV-2 may have alterations in the oral and gut microbiota, while oral species have been found in the lung of COVID-19 patients.\nFurthermore, interactions between the oral, lung, and gut microbiomes appear to occur dynamically whereby a dysbiotic oral microbial community could influence respiratory and gastrointestinal diseases.\nHowever, it is unclear whether SARS-CoV-2 infection can alter the local homeostasis of the resident microbiota, actively cause dysbiosis, or influence cross-body sites interactions.\nHere, we provide a conceptual framework on the potential impact of SARS-CoV-2 oral infection on the local and distant microbiomes across the respiratory and gastrointestinal tracts (\u2018oral-tract axes\u2019), which remains largely unexplored.\nStudies in this area could further elucidate the pathogenic mechanism of SARS-CoV-2 and the course of infection as well as the clinical symptoms of COVID-19 across different sites in the human host.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Patients infected by SARS-CoV-2 may have alterations in the oral and gut microbiota, while oral species have been found in the lung of COVID-19 patients.\"]}", "id": 437}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Surgical Masks Stop Transmission Of COVID-19 From Symptomatic People\n\nAbstract:\nBACKGROUND: Recommendations on masks for preventing coronavirus disease 2019 (COVID-19) vary.\nPURPOSE: To examine the effectiveness of N95, surgical, and cloth masks in community and health care settings for preventing respiratory virus infections, and effects of reuse or extended use of N95 masks.\nDATA SOURCES: Multiple electronic databases, including the World Health Organization COVID-19 database and medRxiv preprint server (2003 through 14 April 2020; surveillance through 2 June 2020), and reference lists.\nSTUDY SELECTION: Randomized trials of masks and risk for respiratory virus infection, including severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and observational studies of mask use and coronavirus infection risk were included.\nNew evidence will be incorporated by using living review methods.\nDATA EXTRACTION: One reviewer abstracted data and assessed methodological limitations; a second reviewer provided verification.\nDATA SYNTHESIS: 39 studies (18 randomized controlled trials and 21 observational studies; 33 867 participants) were included.\nNo study evaluated reuse or extended use of N95 masks.\nEvidence on SARS-CoV-2 was limited to 2 observational studies with serious limitations.\nCommunity mask use was possibly associated with decreased risk for SARS-CoV-1 infection in observational studies.\nIn high- or moderate-risk health care settings, observational studies found that risk for infection with SARS-CoV-1 and Middle East respiratory syndrome coronavirus probably decreased with mask use versus nonuse and possibly decreased with N95 versus surgical mask use.\nRandomized trials in community settings found possibly no difference between N95 versus surgical masks and probably no difference between surgical versus no mask in risk for influenza or influenza-like illness, but compliance was low.\nIn health care settings, N95 and surgical masks were probably associated with similar risks for influenza-like illness and laboratory-confirmed viral infection; clinical respiratory illness had inconsistency.\nBothersome symptoms were common.\nLIMITATIONS: There were few SARS-CoV-2 studies, observational studies have methodological limitations, and the review was done by using streamlined methods.\nCONCLUSION: Evidence on mask effectiveness for respiratory infection prevention is stronger in health care than community settings.\nN95 respirators might reduce SARS-CoV-1 risk versus surgical masks in health care settings, but applicability to SARS-CoV-2 is uncertain.\nPRIMARY FUNDING SOURCE: Agency for Healthcare Research and Quality.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Community mask use was possibly associated with decreased risk for SARS-CoV-1 infection in observational studies.\", \"In high- or moderate-risk health care settings, observational studies found that risk for infection with SARS-CoV-1 and Middle East respiratory syndrome coronavirus probably decreased with mask use versus nonuse and possibly decreased with N95 versus surgical mask use.\", \"CONCLUSION: Evidence on mask effectiveness for respiratory infection prevention is stronger in health care than community settings.\"]}", "id": 438}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Coronavirus (COVID-19) Know how to protect yourself and others from COVID-19 and what to do if you are sick.\n\nAbstract:\nThe coronavirus disease 2019 (COVID-19) outbreak has become a severe public health issue.\nThe novelty of the virus prompts a search for understanding of how ecological factors affect the transmission and survival of the virus.\nSeveral studies have robustly identified a relationship between temperature and the number of cases.\nHowever, there is no specific study for a tropical climate such as Brazil.\nThis work aims to determine the relationship of temperature to COVID-19 infection for the state capital cities of Brazil.\nCumulative data with the daily number of confirmed cases was collected from February 27 to April 1, 2020, for all 27 state capital cities of Brazil affected by COVID-19.\nA generalized additive model (GAM) was applied to explore the linear and nonlinear relationship between annual average temperature compensation and confirmed cases.\nAlso, a polynomial linear regression model was proposed to represent the behavior of the growth curve of COVID-19 in the capital cities of Brazil.\nThe GAM dose-response curve suggested a negative linear relationship between temperatures and daily cumulative confirmed cases of COVID-19 in the range from 16.8 \u00b0C to 27.4 \u00b0C.\nEach 1 \u00b0C rise of temperature was associated with a -4.8951% (t = -2.29, p = 0.0226) decrease in the number of daily cumulative confirmed cases of COVID-19.\nA sensitivity analysis assessed the robustness of the results of the model.\nThe predicted R-squared of the polynomial linear regression model was 0.81053.\nIn this study, which features the tropical temperatures of Brazil, the variation in annual average temperatures ranged from 16.8 \u00b0C to 27.4 \u00b0C.\nResults indicated that temperatures had a negative linear relationship with the number of confirmed cases.\nThe curve flattened at a threshold of 25.8 \u00b0C.\nThere is no evidence supporting that the curve declined for temperatures above 25.8 \u00b0C.\nThe study had the goal of supporting governance for healthcare policymakers.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 439}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Eating garlic will not help prevent infection with COVID-19. While it is a healthy food and is proven to have some antimicrobial properties, there is no evidence that eating garlic can protect from covid-19\n\nAbstract:\nOBJECTIVE To analyze the characteristics of YouTube videos in Spanish on the basic measures to prevent coronavirus disease 2019 (COVID-19).\nMETHODS On 18 March 2020, a search was conducted on YouTube using the terms \"Prevencion Coronavirus\" and \"Prevencion COVID-19\".\nWe studied the associations between the type of authorship and the country of publication with other variables (such as the number of likes and basic measures to prevent COVID-19 according to the World Health Organization, among others) with univariate analysis and a multiple logistic regression model.\nRESULTS A total of 129 videos were evaluated; 37.2% were produced in Mexico (25.6%) and Spain (11.6%), and 56.6% were produced by mass media, including television and newspapers.\nThe most frequently reported basic preventive measure was hand washing (71.3%), and the least frequent was not touching the eyes, nose, and mouth (24.0%).\nHoaxes (such as eating garlic or citrus to prevent COVID-19) were detected in 15 videos (10.9%).\nIn terms of authorship, papers produced by health professionals had a higher probability of reporting hand hygiene (OR (95% CI) = 4.20 (1.17-15.09)) and respiratory hygiene (OR (95% CI) = 3.05 (1.22-7.62)) as preventive measures.\nCONCLUSION Information from YouTube in Spanish on basic measures to prevent COVID-19 is usually not very complete and differs according to the type of authorship.\nOur findings make it possible to guide Spanish-speaking users on the characteristics of the videos to be viewed in order to obtain reliable information.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Hoaxes (such as eating garlic or citrus to prevent COVID-19) were detected in 15 videos (10.9%).\"]}", "id": 440}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Vitamins C and D boost our immune systems, aiding in the fight against infectious diseases; \n\nAbstract:\nSeveral related human coronaviruses (HCoVs) are endemic in the human population, causing mild respiratory infections1.\nSevere Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), the etiologic agent of Coronavirus disease 2019 (COVID-19), is a recent zoonotic infection that has quickly reached pandemic spread2,3.\nZoonotic introduction of novel coronaviruses is thought to occur in the absence of pre-existing immunity in the target human population.\nUsing diverse assays for detection of antibodies reactive with the SARS-CoV-2 Spike (S) glycoprotein, we demonstrate the presence of pre-existing immunity in uninfected and unexposed humans to the new coronavirus.\nSARS-CoV-2 S-reactive antibodies, exclusively of the IgG class, were readily detectable by a sensitive flow cytometry-based method in SARS-CoV-2-uninfected individuals with recent HCoV infection and targeted the S2 subunit.\nIn contrast, SARS-CoV-2 infection induced higher titres of SARS-CoV-2 S-reactive IgG antibodies, as well as concomitant IgM and IgA antibodies throughout the observation period of 6 weeks since symptoms onset.\nHCoV patient sera also variably reacted with SARS-CoV-2 S and nucleocapsid (N), but not with the S1 subunit or the receptor binding domain (RBD) of S on standard enzyme immunoassays.\nNotably, HCoV patient sera exhibited specific neutralising activity against SARS-CoV-2 S pseudotypes, according to levels of SARS-CoV-2 S-binding IgG and with efficiencies comparable to those of COVID-19 patient sera.\nDistinguishing pre-existing and de novo antibody responses to SARS-CoV-2 will be critical for serology, seroprevalence and vaccine studies, as well as for our understanding of susceptibility to and natural course of SARS-CoV-2 infection.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 441}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Surgical Masks Stop Transmission Of COVID-19 From Symptomatic People\n\nAbstract:\nSUMMARY During COVID-19 pandemic crisis, Italian Government has approved Law Decree no. 18 of 17 march 2020, in which art.\n15 allows enterprises to produce, import and commercialize surgical masks notwithstanding the current rules of product certification.\nIt is just required that the interested enterprises send to the Italian National Institute of Health a selfcertification in which they declare the technical characteristics of the masks and that masks are produced according to the safety requirements.\nIn this context, a technical-scientific unit was established at the University of Napoli Federico II to provide interested enterprises with state-of-the-art consultancy, testing and measurement services, adhering to rigorous scientific protocols.\nCharacterization tests were carried out on 163 surgical masks and/or materials for their construction and they have enabled the identification of pre-screening criteria to simplify the procedure for evaluating surgical masks using methods for assessing the filtration efficiency of particles and aerosols.\nBased on experimental results, it has been observed that a filtration efficiency for particles with sizes larger that 650 nm (PFE>650) exceeding 35% might guarantees a bacterial filtration efficiency (BFE) higher than 95% while BFE values higher than 98% are obtained when the PFE>650 is larger than 40%.\nPFE measurement is extremely simpler with respect to BFE, the latter being time-consuming and requiring specific equipment and methods for its realization.\nMany tested materials have shown the capability to assure high filtration efficiencies but Spundonded-Meltblown-Spunbonded (SMS), that are layers of non-woven fabric with different weights of Meltblown, can simultaneously guarantee high particle filtration efficiencies with pressure drop values (breathability) in the limits to classify the surgical masks as Type II/IIR.\nIn fact, the fabric products analyzed so far have not been able to simultaneously guarantee adequate BFE and breathability values.\nOn the contrary, Spunbonds of adequate weights can virtually verify both requirements and accredit themselves as possible materials for the production of surgical masks, at least of Type I. Further studies are needed to verify the possibility of producing low-cost, reusable surgical masks that could meet the criteria of circular economy.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 442}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: diabetes significantly increases coronavirus sufferers risk of dying\n\nAbstract:\nObjective: To undertake a review and critical appraisal of published/preprint reports that offer methods of determining the effects of hypertension, diabetes, stroke, cancer, kidney issues, and high-cholesterol on COVID-19 disease severity.\nData sources: Google Scholar, PubMed, COVID-19 Open Research Dataset: a resource of over 128,000 scholarly articles, including over 59,000 articles with full text related to COVID-19, SARS-CoV-2, and coronaviruses.\nMethods: A search was conducted by two authors independently on the freely available COVID-19 Open Research Dataset (CORD-19).\nWe developed an automated search engine to screen a total of 59,000 articles in a few seconds.\nThe search engine was built using a retrieval function that ranks a set of documents based on the query terms appearing in each document regardless of their proximity within the document.\nFiltering of the articles was then undertaken using keywords and questions, e.g. \"Effects of diabetes on COVID/normal coronavirus/SARS-CoV-2/nCoV/COVID-19 disease severity, mortality?\".\nThe search terms were repeated for all the comorbidities considered in this paper.\nAdditional articles were retrieved by searching via Google Scholar and PubMed.\nFindings: A total of 54 articles were considered for a full review.\nIt was observed that diabetes, hypertension, and cholesterol levels possess an apparent relation to COVID-19 severity.\nOther comorbidities, such as cancer, kidney disease, and stroke, must be further evaluated to determine a strong relationship to the virus.\nReports associating cancer, kidney disease, and stroke with COVID-19 should be carefully interpreted, not only because of the size of the samples, but also because patients could be old, have a history of smoking, or have any other clinical condition suggesting that these factors might be associated with the poor COVID-19 outcomes rather than the comorbidity itself.\nSuch reports could lead many oncologists and physicians to change their treatment strategies without solid evidence and recommendations.\nFurther research regarding this relationship and its clinical management is warranted.\nAdditionally, treatment options must be examined further to provide optimal treatment and ensure better outcomes for patients suffering from these comorbidities.\nIt should be noted that, whether definitive measurements exist or not, the care of patients as well as the research involved should be largely prioritized to tackle this deadly pandemic.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"It was observed that diabetes, hypertension, and cholesterol levels possess an apparent relation to COVID-19 severity.\"]}", "id": 443}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: A conventional in-cell elisa assay allows rapid and automated quantification of sars-cov-2 to analyze neutralizing antibodies and antiviral compounds\n\nAbstract:\nThe coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is currently the most pressing medical and socioeconomic challenge.\nConstituting important correlates of protection, the determination of virus-neutralizing antibodies (NAbs) is indispensable for convalescent plasma selection, vaccine candidate evaluation, and immunity certificates.\nIn contrast to standard serological ELISAs, plaque reduction neutralization tests (PRNTs) are laborious, time-consuming, expensive, and restricted to specialized laboratories.\nTo replace microscopic counting-based SARS-CoV-2 PRNTs by a novel assay exempt from genetically modified viruses, which are inapplicable in most diagnostics departments, we established a simple, rapid, and automated SARS-CoV-2 neutralization assay employing an in-cell ELISA (icELISA) approach.\nAfter optimization of various parameters such as virus-specific antibodies, cell lines, virus doses, and duration of infection, SARS-CoV-2-infected cells became amenable as direct antigen source for quantitative icELISA.\nAntiviral agents such as human sera containing NAbs or antiviral interferons dose dependently reduced the SARS-CoV-2-specific signal.\nApplying increased infectious doses, the icELISA-based neutralization test (icNT) was superior to PRNT in discriminating convalescent sera with high from those with intermediate neutralizing capacities.\nIn addition, the icNT was found to be specific, discriminating between SARS-CoV-2-specific NAbs and those raised against other coronaviruses.\nAltogether, the SARS-CoV-2 icELISA test allows rapid (<48 h in total, read-out in seconds) and automated quantification of virus infection in cell culture to evaluate the efficacy of NAbs and antiviral drugs using reagents and equipment present in most routine diagnostics departments.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"To replace microscopic counting-based SARS-CoV-2 PRNTs by a novel assay exempt from genetically modified viruses, which are inapplicable in most diagnostics departments, we established a simple, rapid, and automated SARS-CoV-2 neutralization assay employing an in-cell ELISA (icELISA) approach.\", \"Altogether, the SARS-CoV-2 icELISA test allows rapid (<48 h in total, read-out in seconds) and automated quantification of virus infection in cell culture to evaluate the efficacy of NAbs and antiviral drugs using reagents and equipment present in most routine diagnostics departments.\"]}", "id": 444}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: there is no evidence that garlic can protect you against the COVID-19 virus.\n\nAbstract:\nThe severity of coronavirus disease 2019 (COVID-19) infection is quite variable and the manifestations varies from asymptomatic disease to severe acute respiratory infection.\nFever, dry cough, dyspnea, myalgia, fatigue, loss of appetite, olfactory and gustatory dysfunctions are the most prevalent general symptoms.\nDecreased immune system cells such as suppressed regulatory T cells, cytotoxic and helper T cells, natural killer cells, monocytes/macrophages and increased proinflammatory cytokines are the characteristic features.\nCompounds derived from Allium sativum (garlic) have the potential to decrease the expression of proinflammatory cytokines and to reverse the immunological abnormalities to more acceptable levels.\nAllium sativum is suggested as a beneficial preventive measure before being infected with SARS-CoV-2 virus.\nAllium sativum is a functional food well-known for its immunomodulatory, antimicrobial, antiinflammatory, antimutagenic, antitumor properties.\nIts antiviral efficiency was also demonstrated.\nSome constituents of this plant were found to be active against protozoan parasites.\nWithin this context, it appears to reverse most immune system dysfunctions observed in patients with COVID-19 infection.\nThe relations among immune system parameters, leptin, leptin receptor, adenosin mono phosphate-activated protein kinase, peroxisome proliferator activated receptor-gamma have also been interpreted.\nLeptin's role in boosting proinflammatory cytokines and in appetite decreasing suggest the possible beneficial effect of decreasing the concentration of this proinflammatory adipose tissue hormone in relieving some symptoms detected during COVID-19 infection.\nIn conclusion, Allium sativum may be an acceptable preventive measure against COVID-19 infection to boost immune system cells and to repress the production and secretion of proinflammatory cytokines as well as an adipose tissue derived hormone leptin having the proinflammatory nature.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"In conclusion, Allium sativum may be an acceptable preventive measure against COVID-19 infection to boost immune system cells and to repress the production and secretion of proinflammatory cytokines as well as an adipose tissue derived hormone leptin having the proinflammatory nature.\"]}", "id": 445}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Based on currently available information, WHO does not recommend against the use of of ibuprofen.\n\nAbstract:\nFever has been reported as a common symptom occurring in COVID-19 illness.\nOver the counter antipyretics such as ibuprofen and acetaminophen are often taken by individuals to reduce the discomfort of fever.\nRecently, the safety of ibuprofen in COVID-19 patients has been questioned due to anecdotal reports of worsening symptoms in previously healthy young adults.\nStudies show that ibuprofen demonstrates superior efficacy in fever reduction compared to acetaminophen.\nAs fever may have benefit in shortening the duration of viral illness, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response when taken during the early stages of COVID-19 illness.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"As fever may have benefit in shortening the duration of viral illness, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response when taken during the early stages of COVID-19 illness.\"]}", "id": 446}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Low vitamin k status predicts asthma in a cohort of 138 hospitalized patients with covid-19.\n\nAbstract:\nIt has recently been hypothesised that Vitamin K could play a role in COVID-19.\nWe aimed to test the hypothesis that low vitamin K status is a common characteristic of patients hospitalized with COVID-19 compared to population controls; and that low vitamin K status predicts mortality in COVID-19 patients.\nIn a cohort of 138 COVID-19 patients and 140 population controls, we measured plasma dephosphorylated-uncarboxylated Matrix Gla Protein (dp-ucMGP), which reflects the functional Vitamin K status in peripheral tissue.\nFourty-three patients died within 90-days from admission.\nIn patients, levels of dp-ucMGP differed significantly between survivors (mean 877; 95% CI: 778; 995) and non-survivors (mean 1445; 95% CI: 1148; 1820).\nFurthermore, levels of dp-ucMGP (pmol/L) were considerably higher in patients (mean 1022; 95% CI: 912; 1151) compared to controls (mean 509; 95% CI: 485; 540).\nCox regression survival analysis showed that increasing levels of dp-ucMGP (reflecting low Vitamin K status) were associated with higher mortality risk (sex-and age-adjusted hazard ratio per doubling of dp-ucMGP was 1.50, 95% CI: 1.03; 2.18).\nIn conclusion, we found that low Vitamin K status predicted mortality in patients with COVID-19 supporting a potential role of Vitamin K in COVID-19.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"We aimed to test the hypothesis that low vitamin K status is a common characteristic of patients hospitalized with COVID-19 compared to population controls; and that low vitamin K status predicts mortality in COVID-19 patients.\", \"In conclusion, we found that low Vitamin K status predicted mortality in patients with COVID-19 supporting a potential role of Vitamin K in COVID-19.\"]}", "id": 447}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Does wearing a mask help reduce my risk of COVID-19? Cloth and surgical masks help stop droplets spreading when people talk, cough and sneeze, which reduces the risk of spreading the virus.\n\nAbstract:\nThe COVID\u201019 pandemic caused by the novel coronavirus SARS\u2010CoV\u20102 has claimed many lives worldwide.\nWearing medical masks or N95 masks (namely N95 respirators) can slow the virus spread and reduce the infection risk.\nReuse of these masks can minimize waste, protect the environment, and help to solve the current imminent shortage of masks.\nDisinfection of used masks is needed for reuse of them with safety, but improper decontamination can damage the blocking structure of masks.\nIn this study, we demonstrated, using avian coronavirus of infectious bronchitis virus to mimic SARS\u2010CoV\u20102, that medical masks and N95 masks remained their blocking efficacy after being steamed on boiling water even for 2 hours.\nWe also demonstrated that three brands of medical masks blocked over 99% viruses in aerosols.\nThe avian coronavirus was completely inactivated after being steamed for 5 minutes.\nTogether, this study suggested that medical masks are adequate for use on most social occasions, and both medical masks and N95 masks can be reused for a few days with steam decontamination between use.\nThis article is protected by copyright.\nAll rights reserved.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Wearing medical masks or N95 masks (namely N95 respirators) can slow the virus spread and reduce the infection risk.\"]}", "id": 448}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: masks protect you as the wearer and protects others\n\nAbstract:\nThe COVID\u201019 pandemic caused by the novel coronavirus SARS\u2010CoV\u20102 has claimed many lives worldwide.\nWearing medical masks or N95 masks (namely N95 respirators) can slow the virus spread and reduce the infection risk.\nReuse of these masks can minimize waste, protect the environment, and help to solve the current imminent shortage of masks.\nDisinfection of used masks is needed for reuse of them with safety, but improper decontamination can damage the blocking structure of masks.\nIn this study, we demonstrated, using avian coronavirus of infectious bronchitis virus to mimic SARS\u2010CoV\u20102, that medical masks and N95 masks remained their blocking efficacy after being steamed on boiling water even for 2 hours.\nWe also demonstrated that three brands of medical masks blocked over 99% viruses in aerosols.\nThe avian coronavirus was completely inactivated after being steamed for 5 minutes.\nTogether, this study suggested that medical masks are adequate for use on most social occasions, and both medical masks and N95 masks can be reused for a few days with steam decontamination between use.\nThis article is protected by copyright.\nAll rights reserved.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Wearing medical masks or N95 masks (namely N95 respirators) can slow the virus spread and reduce the infection risk.\", \"We also demonstrated that three brands of medical masks blocked over 99% viruses in aerosols.\", \"The avian coronavirus was completely inactivated after being steamed for 5 minutes.\", \"Together, this study suggested that medical masks are adequate for use on most social occasions, and both medical masks and N95 masks can be reused for a few days with steam decontamination between use.\"]}", "id": 449}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: High Blood Pressure Doesn't Seem To Raise COVID-19 Risk\n\nAbstract:\nHypertension is one of the most common comorbidities in patients with coronavirus disease 2019 (COVID-19).\nThis study aimed to clarify the impact of hypertension on COVID-19 and investigate whether the prior use of renin-angiotensin-aldosterone system (RAAS) inhibitors affects the prognosis of COVID-19.\nA total of 996 patients with COVID-19 were enrolled, including 282 patients with hypertension and 714 patients without hypertension.\nPropensity score-matched analysis (1:1 matching) was used to adjust the imbalanced baseline variables between the 2 groups.\nPatients with hypertension were further divided into the RAAS inhibitor group (n=41) and non-RAAS inhibitor group (n=241) according to their medication history.\nThe results showed that COVID-19 patients with hypertension had more severe secondary infections, cardiac and renal dysfunction, and depletion of CD8+ cells on admission.\nPatients with hypertension were more likely to have comorbidities and complications and were more likely to be classified as critically ill than those without hypertension.\nCox regression analysis revealed that hypertension (hazard ratio, 95% CI, unmatched cohort [1.80, 1.20-2.70]; matched cohort [2.24, 1.36-3.70]) was independently associated with all-cause mortality in patients with COVID-19.\nIn addition, hypertensive patients with a history of RAAS inhibitor treatment had lower levels of C-reactive protein and higher levels of CD4+ cells.\nThe mortality of patients in the RAAS inhibitor group (9.8% versus 26.1%) was significantly lower than that of patients in the non-RAAS inhibitor group.\nIn conclusion, hypertension may be an independent risk factor for all-cause mortality in patients with COVID-19.\nPatients who previously used RAAS inhibitors may have a better prognosis.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"The results showed that COVID-19 patients with hypertension had more severe secondary infections, cardiac and renal dysfunction, and depletion of CD8+ cells on admission.\", \"Patients with hypertension were more likely to have comorbidities and complications and were more likely to be classified as critically ill than those without hypertension.\", \"In conclusion, hypertension may be an independent risk factor for all-cause mortality in patients with COVID-19.\", \"Patients who previously used RAAS inhibitors may have a better prognosis.\"]}", "id": 450}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: The d614g mutation in the sars-cov2 spike protein reduced infectivity in an ace2 receptor dependent manner\n\nAbstract:\nThe SARS-CoV2 coronavirus responsible for the current COVID19 pandemic has been reported to have a relatively low mutation rate.\nNevertheless, a few prevalent variants have arisen that give the appearance of undergoing positive selection as they are becoming increasingly widespread over time.\nMost prominent among these is the D614G amino acid substitution in the SARS-CoV2 Spike protein, which mediates viral entry.\nThe D614G substitution, however, is in linkage disequilibrium with the ORF1b P314L mutation where both mutations almost invariably co-occur, making functional inferences problematic.\nIn addition, the possibility of repeated new introductions of the mutant strain does not allow one to distinguish between a founder effect and an intrinsic genetic property of the virus.\nHere, we synthesized and expressed the WT and D614G variant SARS-Cov2 Spike protein, and report that using a SARS-CoV2 Spike protein pseudotyped lentiviral vector we observe that the D614G variant Spike has >1/2 log(10) increased infectivity in human cells expressing the human ACE2 protein as the viral receptor.\nThe increased binding/fusion activity of the D614G Spike protein was corroborated in a cell fusion assay using Spike and ACE2 proteins expressed in different cells.\nThese results are consistent with the possibility that the Spike D614G mutant increases the infectivity of SARS-CoV2.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Most prominent among these is the D614G amino acid substitution in the SARS-CoV2 Spike protein, which mediates viral entry.\", \"The increased binding/fusion activity of the D614G Spike protein was corroborated in a cell fusion assay using Spike and ACE2 proteins expressed in different cells.\", \"These results are consistent with the possibility that the Spike D614G mutant increases the infectivity of SARS-CoV2.\"]}", "id": 451}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Widely available lysosome targeting agents should be considered as a potential biomarker for covid-19\n\nAbstract:\nWhile the COVID-19 pandemic advances, the scientific community struggles in the search for treatments.\nSeveral improvements have been made, including the discovery of clinical efficacy of chloroquine (CQ) in COVID-19 patients, but the effective treatment protocols are still missing.\nIn order to find novel treatment options many scientists utilize the in silico approach to identify compounds that could interfere with the key molecules involved in entrance, replication, or dissemination of the SARS-CoV-2.\nHowever, most of the identified molecules are currently not available as pharmacological agents, and assessing their safety and efficacy could take many months.\nHere, we took a different approach based on the proposed pharmacodynamic model of CQ in COVID-19.\nThe main mechanism of action responsible for the favourable outcome of COVID-19 patients treated with CQ seems to be related to pH modulation-mediated effect on the endolysosomal trafficking, a characteristic of chemical compounds often called lysosomotropic agents because of the physico-chemical properties that enable them to passively diffuse through the endosomal membrane and undergo protonation-based trapping in the lumen of the acidic vesicles.\nIn this review, we discuss lysosomotropic and lysosome targeting drugs that are already in clinical use and are characterized by good safety profiles, low cost, and wide availability.\nWe emphasize that some of these drugs, in particular azithromycin and other macrolide antibiotics, indomethacin and some other non-steroidal anti-inflammatory drugs, proton pump inhibitors, and fluoxetine could provide additional therapeutic benefits in addition to the potential antiviral effect that still has to be confirmed by well-controlled clinical trials.\nAs some of these drugs, mostly antibiotics, were already empirically used in the treatment of COVID-19, we encourage our colleagues all over the world to publish patient data so potential efficacy of these agents can be evaluated in the clinical context and rapidly implemented in the therapeutic protocols if the beneficial effect on clinical outcome is observed.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"The main mechanism of action responsible for the favourable outcome of COVID-19 patients treated with CQ seems to be related to pH modulation-mediated effect on the endolysosomal trafficking, a characteristic of chemical compounds often called lysosomotropic agents because of the physico-chemical properties that enable them to passively diffuse through the endosomal membrane and undergo protonation-based trapping in the lumen of the acidic vesicles.\", \"As some of these drugs, mostly antibiotics, were already empirically used in the treatment of COVID-19, we encourage our colleagues all over the world to publish patient data so potential efficacy of these agents can be evaluated in the clinical context and rapidly implemented in the therapeutic protocols if the beneficial effect on clinical outcome is observed.\"]}", "id": 452}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: if you are low risk (healthy, young), you do not need social distancing.\n\nAbstract:\nSocial distancing measures, with varying degrees of restriction, have been imposed around the world in order to stem the spread of COVID-19.\nIn this work we analyze the effect of current social distancing measures in the United States.\nWe quantify the reduction in doubling rate, by state, that is associated with social distancing.\nWe find that social distancing is associated with a statistically-significant reduction in the doubling rate for all but three states.\nAt the same time, we do not find significant evidence that social distancing has resulted in a reduction in the number of daily confirmed cases.\nInstead, social distancing has merely stabilized the spread of the disease.\nWe provide an illustration of our findings for each state, including point estimates of the effective reproduction number, R, both with and without social distancing.\nWe also discuss the policy implications of our findings.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"We find that social distancing is associated with a statistically-significant reduction in the doubling rate for all but three states.\"]}", "id": 453}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Coronaviruses cause respiratory illnesses, so the lungs are usually affected first. Early symptoms include fever, cough, and shortness of breath.\n\nAbstract:\nThe WHO has declared SARS-CoV-2 outbreak a public health emergency of international concern.\nHowever, to date, there was hardly any study in characterizing the immune responses, especially adaptive immune responses to SARS-CoV-2 infection.\nIn this study, we collected blood from COVID-19 patients who have recently become virus-free and therefore were discharged, and analyzed their SARS-CoV-2-specific antibody and T cell responses.\nWe observed SARS-CoV-2-specific humoral and cellular immunity in the patients.\nBoth were detected in newly discharged patients, suggesting both participate in immune-mediated protection to viral infection.\nHowever, follow-up patients (2 weeks post discharge) exhibited high titers of IgG antibodies, but with low levels of virus-specific T cells, suggesting that they may enter a quiescent state.\nOur work has thus provided a basis for further analysis of protective immunity to SARS-CoV-2, and understanding the pathogenesis of COVID-19, especially in the severe cases.\nIt has also implications in designing an effective vaccine to protect and treat SARS-CoV-2 infection.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 454}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Lack of immune homology with vaccine preventable pathogens suggests childhood immunizations do not protect against sars-cov-2 through adaptive cross-immunity\n\nAbstract:\nRecent epidemiological studies have investigated the potential effects of childhood immunization history on COVID-19 severity.\nSpecifically, prior exposure to Bacillus Calmette-Guerin (BCG) vaccine, oral poliovirus vaccine (OPV), or measles vaccine have been postulated to reduce COVID-19 severity-putative mechanism is via stimulation of the innate immune system to provide broader protection against non-specific pathogens.\nWhile these epidemiological results remain inconclusive, we sought to investigate the potential role of adaptive immunity via cross-reactivity between vaccine preventable diseases (VPDs) with SARS-CoV-2.\nWe implemented a comprehensive exploration of immune homology (including sequence homology, immune epitopes, and glycosylation patterns) between SARS-CoV-2 and all pathogens with FDA-approved vaccines.\nSequence homology did not reveal significant alignments of protein sequences between SARS-CoV-2 with any VPD pathogens, including BCG-related strains.\nWe also could not identify any shared T or B cell epitopes between SARS-CoV-2 and VPD pathogens among either experimentally validated epitopes or predicted immune epitopes.\nFor N-glycosylation (N-glyc), while sites with the same tripeptides could be found between SARS-CoV-2 and certain VPD pathogens, their glycosylation potentials and positions were different.\nIn summary, lack of immune homology between SARS-CoV-2 and VPD pathogens suggests that childhood immunization history (i.e., BCG vaccination or others) does not provide protection from SARS-CoV-2 through adaptive cross-immunity.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"In summary, lack of immune homology between SARS-CoV-2 and VPD pathogens suggests that childhood immunization history (i.e., BCG vaccination or others) does not provide protection from SARS-CoV-2 through adaptive cross-immunity.\"]}", "id": 455}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Sars-cov-2 infection induces robust, neutralizing antibody responses that are stable for at least three weeks\n\nAbstract:\nSARS-CoV-2 has caused a global pandemic with millions infected and numerous fatalities.\nQuestions regarding the robustness, functionality and longevity of the antibody response to the virus remain unanswered.\nHere we report that the vast majority of infected individuals with mild-to-moderate COVID-19 experience robust IgG antibody responses against the viral spike protein, based on a dataset of 19,860 individuals screened at Mount Sinai Health System in New York City.\nWe also show that titers are stable for at least a period approximating three months, and that anti-spike binding titers significantly correlate with neutralization of authentic SARS-CoV-2.\nOur data suggests that more than 90% of seroconverters make detectible neutralizing antibody responses and that these titers are stable for at least the near-term future.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"We also show that titers are stable for at least a period approximating three months, and that anti-spike binding titers significantly correlate with neutralization of authentic SARS-CoV-2.\"]}", "id": 456}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: the Covid-19 coronavirus can stay on various surfaces for a while\n\nAbstract:\nWith limited infection control practices in overcrowded Bangladeshi hospitals, surfaces may play an important role in the transmission of respiratory pathogens in hospital wards and pose a serious risk of infection for patients, health care workers, caregivers and visitors.\nIn this study, we aimed to identify if surfaces near hospitalized patients with respiratory infections were contaminated with respiratory pathogens and to identify which surfaces were most commonly contaminated.\nBetween September-November 2013, we collected respiratory (nasopharyngeal and oropharyngeal) swabs from patients hospitalized with respiratory illness in adult medicine and paediatric medicine wards at two public tertiary care hospitals in Bangladesh.\nWe collected surface swabs from up to five surfaces near each case-patient including: the wall, bed rail, bed sheet, clinical file, and multipurpose towel used for care giving purposes.\nWe tested swabs using real-time multiplex PCR for 19 viral and 12 bacterial pathogens.\nCase-patients with at least one pathogen detected had corresponding surface swabs tested for those same pathogens.\nOf 104 patients tested, 79 had a laboratory-confirmed respiratory pathogen.\nOf the 287 swabs collected from surfaces near these patients, 133 (46%) had evidence of contamination with at least one pathogen.\nThe most commonly contaminated surfaces were the bed sheet and the towel.\nSixty-two percent of patients with a laboratory-confirmed respiratory pathgen (49/79) had detectable viral or bacterial nucleic acid on at least one surface.\nKlebsiella pneumoniae was the most frequently detected pathogen on both respiratory swabs (32%, 33/104) and on surfaces near patients positive for this organism (97%, 32/33).\nSurfaces near patients hospitalized with respiratory infections were frequently contaminated by pathogens, with Klebsiella pneumoniae being most common, highlighting the potential for transmission of respiratory pathogens via surfaces.\nEfforts to introduce routine cleaning in wards may be a feasible strategy to improve infection control, given that severe space constraints prohibit cohorting patients with respiratory illness.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Surfaces near patients hospitalized with respiratory infections were frequently contaminated by pathogens, with Klebsiella pneumoniae being most common, highlighting the potential for transmission of respiratory pathogens via surfaces.\"]}", "id": 457}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Both mutations in sars-cov-2 associate with severe and mild outcome\n\nAbstract:\nINTRODUCTION: Genomic alterations in a viral genome can lead to either better or worse outcome and identifying these mutations is of utmost importance.\nHere, we correlated protein-level mutations in the SARS-CoV-2 virus to clinical outcome.\nMETHODS: Mutations in viral sequences from the GISAID virus repository were evaluated by using \"hCoV-19/Wuhan/WIV04/2019\" as the reference.\nPatient outcomes were classified as mild disease, hospitalization and severe disease (death or documented treatment in an intensive-care unit).\nChi-square test was applied to examine the association between each mutation and patient outcome.\nFalse discovery rate was computed to correct for multiple hypothesis testing and results passing FDR cutoff of 5% were accepted as significant.\nRESULTS: Mutations were mapped to amino acid changes for 3,733 non-silent mutations.\nMutations correlated to mild outcome were located in the ORF8, NSP6, ORF3a, NSP4, and in the nucleocapsid phosphoprotein N. Mutations associated with inferior outcome were located in the surface (S) glycoprotein, in the RNA dependent RNA polymerase, in ORF3a, NSP3, ORF6 and N. Mutations leading to severe outcome with low prevalence were found in the ORF3A and in NSP7 proteins.\nFour out of 22 of the most significant mutations mapped onto a 10 amino acid long phosphorylated stretch of N indicating that in spite of obvious sampling restrictions the approach can find functionally relevant sites in the viral genome.\nCONCLUSIONS: We demonstrate that mutations in the viral genes may have a direct correlation to clinical outcome.\nOur results help to quickly identify SARS-CoV-2 infections harboring mutations related to severe outcome.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Here, we correlated protein-level mutations in the SARS-CoV-2 virus to clinical outcome.\", \"Our results help to quickly identify SARS-CoV-2 infections harboring mutations related to severe outcome.\"]}", "id": 458}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: People with diabetes have not a higher risk for complications from coronavirus\n\nAbstract:\nAIMS: We aimed to briefly review the general characteristics of the novel coronavirus (SARS-CoV-2) and provide a better understanding of the coronavirus disease (COVID-19) in people with diabetes, and its management.\nMETHODS: We searched for articles in PubMed and Google Scholar databases till 02 April 2020, with the following keywords: \"SARS-CoV-2\", \"COVID-19\", \"infection\", \"pathogenesis\", \"incubation period\", \"transmission\", \"clinical features\", \"diagnosis\", \"treatment\", \"diabetes\", with interposition of the Boolean operator \"AND\".\nRESULTS: The clinical spectrum of COVID-19 is heterogeneous, ranging from mild flu-like symptoms to acute respiratory distress syndrome, multiple organ failure and death.\nOlder age, diabetes and other comorbidities are reported as significant predictors of morbidity and mortality.\nChronic inflammation, increased coagulation activity, immune response impairment, and potential direct pancreatic damage by SARS-CoV-2 might be among the underlying mechanisms of the association between diabetes and COVID-19.\nNo conclusive evidence exists to support the discontinuation of angiotensin-converting enzyme inhibitors (ACEI), angiotensin receptor blockers or thiazolidinediones because of COVID-19 in people with diabetes.\nCaution should be taken to potential hypoglycemic events with the use of chloroquine in these subjects.\nPatient tailored therapeutic strategies, rigorous glucose monitoring and careful consideration of drug interactions might reduce adverse outcomes.\nCONCLUSIONS: Suggestions are made on the possible pathophysiological mechanisms of the relationship between diabetes and COVID-19, and its management.\nNo definite conclusions can be made based on current limited evidence.\nFurther research regarding this relationship and its clinical management is warranted.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Older age, diabetes and other comorbidities are reported as significant predictors of morbidity and mortality.\", \"Chronic inflammation, increased coagulation activity, immune response impairment, and potential direct pancreatic damage by SARS-CoV-2 might be among the underlying mechanisms of the association between diabetes and COVID-19.\"]}", "id": 459}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: B cells and t cells mediate exposure to covid-19\n\nAbstract:\nRecent reports that antibodies to SARS-CoV-2 are not maintained in the serum following recovery from the virus have caused alarm.\nHowever, the absence of specific antibodies in the serum does not necessarily mean an absence of immune memory.\nHere, we discuss our current understanding of the relative contribution of B cells and T cells to immunity to SARS-CoV-2 and the implications for the development of effective treatments and vaccines for COVID-19.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Here, we discuss our current understanding of the relative contribution of B cells and T cells to immunity to SARS-CoV-2 and the implications for the development of effective treatments and vaccines for COVID-19.\"]}", "id": 460}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Drinking hot ginger tea, with or without these additional ingredients, will cure COVID-19\n\nAbstract:\nIn late December 2019 in Wuhan, China, several patients with viral pneumonia were identified as 2019 novel coronavirus (2019-nCoV).\nSo far, there are no specific treatments for patients with coronavirus disease-19 (COVID-19), and the treatments available today are based on previous experience with similar viruses such as severe acute respiratory syndrome-related coronavirus (SARS-CoV), Middle East respiratory syndrome coronavirus (MERS-CoV), and Influenza virus.\nIn this article, we have tried to reach a therapeutic window of drugs available to patients with COVID-19.\nCathepsin L is required for entry of the 2019-nCoV virus into the cell as target teicoplanin inhibits virus replication.\nAngiotensin-converting-enzyme 2 (ACE2) in soluble form as a recombinant protein can prevent the spread of coronavirus by restricting binding and entry.\nIn patients with COVID-19, hydroxychloroquine decreases the inflammatory response and cytokine storm, but overdose causes toxicity and mortality.\nNeuraminidase inhibitors such as oseltamivir, peramivir, and zanamivir are invalid for 2019-nCoV and are not recommended for treatment but protease inhibitors such as lopinavir/ritonavir (LPV/r) inhibit the progression of MERS-CoV disease and can be useful for patients of COVID-19 and, in combination with Arbidol, has a direct antiviral effect on early replication of SARS-CoV. Ribavirin reduces hemoglobin concentrations in respiratory patients, and remdesivir improves respiratory symptoms.\nUse of ribavirin in combination with LPV/r in patients with SARS-CoV reduces acute respiratory distress syndrome and mortality, which has a significant protective effect with the addition of corticosteroids.\nFavipiravir increases clinical recovery and reduces respiratory problems and has a stronger antiviral effect than LPV/r.\ncurrently, appropriate treatment for patients with COVID-19 is an ACE2 inhibitor and a clinical problem reducing agent such as favipiravir in addition to hydroxychloroquine and corticosteroids.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"In late December 2019 in Wuhan, China, several patients with viral pneumonia were identified as 2019 novel coronavirus (2019-nCoV).\", \"So far, there are no specific treatments for patients with coronavirus disease-19 (COVID-19), and the treatments available today are based on previous experience with similar viruses such as severe acute respiratory syndrome-related coronavirus (SARS-CoV), Middle East respiratory syndrome coronavirus (MERS-CoV), and Influenza virus.\"]}", "id": 461}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: College students, many of whom are already stressed, reported an increase in depression and anxiety during the initial outbreak.\n\nAbstract:\nBACKGROUND: The coronavirus disease (COVID-19) pandemic has produced substantial health challenges from the perspective of both its direct health complications and the disruption to delivery of standard care for individuals with a range of acute and chronic health issues.\nIn parallel, the widespread application of social isolation initiatives in most countries raises the potential for significant mental health consequences and psychosocial impacts.\nThis has major implications for cardiovascular health care professionals and the management of their patients.\nCHALLENGES: The COVID-19 pandemic and associated physical isolation practices are likely to result in a range of mental health and psychosocial challenges.\nIn addition to an increasing incidence of anxiety, depression, suicidal ideation and post-traumatic stress, the pandemic may also witness an increase in substance abuse, domestic violence and relationship discord.\nThe consequences of these complications will be further magnified, when considering their potential effect on cardiovascular disease and its management.\nPURPOSE: This commentary aims to summarise some of the potential mental health and psychosocial challenges that may arise in the setting of the COVID-19 pandemic.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"CHALLENGES: The COVID-19 pandemic and associated physical isolation practices are likely to result in a range of mental health and psychosocial challenges.\"]}", "id": 462}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: covid-19 patients taking hydroxychloroquine do not benefit\n\nAbstract:\nHydroxychloroquine (HCQ) has sparked much interest in the therapeutics of the Coronavirus Disease 2019 (COVID-19) pandemic.\nIts antiviral properties have been studied for years; regarding the Severe Acute Respiratory Syndrome-Corona Virus-2 (SARS-CoV-2), it has been shown that HCQ may act at multiple levels.\nThese extend from the initial attachment of the virus to the respiratory epithelium to the inhibition of its replication by the alkalinisation of the phagolysosome's microenvironment and the post-translational modification of certain viral proteins.\nPreliminary clinical evidence from China and France showed significant virological and clinical benefit in HCQ-treated patients, while other studies, mostly including critically ill patients, did not show favorable results.\nIn this review, we critically appraise the existing evidence on HCQ against SARS-CoV-2 with particular emphasis on its protective and therapeutic role.\nSafety concerns that are relevant to the short-term HCQ use are also discussed.\nIn the context of the rapid evolution of the COVID-19 pandemic that strains the health care systems worldwide and considering limited population-wide testing rates in most of the vulnerable countries, early empiric short-term administration of HCQ in symptomatic individuals, may be a promising, safe and low-cost strategy.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"In the context of the rapid evolution of the COVID-19 pandemic that strains the health care systems worldwide and considering limited population-wide testing rates in most of the vulnerable countries, early empiric short-term administration of HCQ in symptomatic individuals, may be a promising, safe and low-cost strategy.\"]}", "id": 463}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: The ground level ozone concentration is equivalently correlated with the number of covid-19 cases in warsaw, poland\n\nAbstract:\nCOVID-19, which is a consequence of infection with the novel viral agent SARS-CoV-2, first identified in China (Hubei Province), has been declared a pandemic by the WHO.\nAs of September 10, 2020, over 70,000 cases and over 2,000 deaths have been recorded in Poland.\nOf the many factors contributing to the level of transmission of the virus, the weather appears to be significant.\nIn this work we analyse the impact of weather factors such as temperature, relative humidity, wind speed and ground level ozone concentration on the number of COVID-19 cases in Warsaw, Poland.\nThe obtained results show an inverse correlation between ground level ozone concentration and the daily number of COVID-19 cases.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"The obtained results show an inverse correlation between ground level ozone concentration and the daily number of COVID-19 cases.\"]}", "id": 464}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Surgical masks are just a physical barrier that will protect you against \"a visible splash or spray of fluid or large droplets\n\nAbstract:\nWe identified seasonal human coronaviruses, influenza viruses and rhinoviruses in exhaled breath and coughs of children and adults with acute respiratory illness.\nSurgical face masks significantly reduced detection of influenza virus RNA in respiratory droplets and coronavirus RNA in aerosols, with a trend toward reduced detection of coronavirus RNA in respiratory droplets.\nOur results indicate that surgical face masks could prevent transmission of human coronaviruses and influenza viruses from symptomatic individuals.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 465}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Coronaviruses cause respiratory illnesses, so the lungs are usually affected first. Early symptoms include fever, cough, and shortness of breath.\n\nAbstract:\nThe rapid global spread of SARS-CoV-2 and resultant mortality and social disruption have highlighted the need to better understand coronavirus immunity to expedite vaccine development efforts.\nMultiple candidate vaccines, designed to elicit protective neutralising antibodies targeting the viral spike glycoprotein, are rapidly advancing to clinical trial.\nHowever, the immunogenic properties of the spike protein in humans are unresolved.\nTo address this, we undertook an in-depth characterisation of humoral and cellular immunity against SARS-CoV-2 spike in humans following mild to moderate SARS-CoV-2 infection.\nWe find serological antibody responses against spike are routinely elicited by infection and correlate with plasma neutralising activity and capacity to block ACE2/RBD interaction.\nExpanded populations of spike-specific memory B cells and circulating T follicular helper cells (cTFH) were detected within convalescent donors, while responses to the receptor binding domain (RBD) constitute a minor fraction.\nUsing regression analysis, we find high plasma neutralisation activity was associated with increased spike-specific antibody, but notably also with the relative distribution of spike-specific cTFH subsets.\nThus both qualitative and quantitative features of B and T cell immunity to spike constitute informative biomarkers of the protective potential of novel SARS-CoV-2 vaccines.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 466}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Officials believe that the virus was present in meat sold at the said market.\n\nAbstract:\nA novel coronavirus emerged in human populations and spread rapidly to cause the global coronavirus disease 2019 pandemic.\nAlthough the origin of the associated virus (severe acute respiratory syndrome coronavirus 2 [SARS-CoV-2]) remains unclear, genetic evidence suggests that bats are a reservoir host of the virus, and pangolins are a probable intermediate.\nSARS-CoV-2 has crossed the species barrier to infect humans and other animal species, and infected humans can facilitate reverse-zoonotic transmission to animals.\nConsidering the rapidly changing interconnections among people, animals, and ecosystems, traditional roles of veterinarians should evolve to include transdisciplinary roles.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 467}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Hypothesis: Angiotensin-converting enzyme inhibitors and angiotensin receptor blockers may increase the risk of severe COVID-19\n\nAbstract:\nAims: The question of interactions between the renin angiotensin aldosterone system drugs and the incidence and prognosis of COVID-19 infection has been raised by the medical community.\nWe hypothesised that if patients treated with ACE inhibitors (ACEI) or AT1 receptor blockers (ARB) were more prone to SARS-CoV2 infection and had a worse prognosis than untreated patients, the prevalence of consumption of these drugs would be higher in patients with COVID-19 compared to the general population.\nMethods and results: We used a clinical epidemiology approach based on the estimation of standardised prevalence ratio (SPR) of consumption of ACEI and ARB in four groups of patients (including 187 COVID-19 positive) with increasing severity referred to the University hospital of Lille and in three French reference samples (the exhaustive North population (n=1,569,968), a representative sample of the French population (n=414,046), a random sample of Lille area (n=1,584)).\nThe SPRs of ACEI and ARB did not differ as the severity of the COVID-19 patients increased, being similar to the regular consumption of these drugs in the North of France population with the same non-significant increase for both treatment (1.17 [0.83-1.67]).\nA statistically significant increase in the SPR of ARB (1.56 [1.02-2.39]) was observed in intensive care unit patients only.\nAfter stratification on obesity, this increase was limited to the high risk subgroup of obese patients.\nConclusions: Our results strongly support the recommendation that ACEI and ARB should be continued in the population and in COVID-19 positive patients, reinforcing the position of several scientific societies.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 468}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Drinking hot ginger tea, with or without these additional ingredients, will cure COVID-19\n\nAbstract:\nThe severity of coronavirus disease 2019 (COVID-19) infection is quite variable and the manifestations varies from asymptomatic disease to severe acute respiratory infection.\nFever, dry cough, dyspnea, myalgia, fatigue, loss of appetite, olfactory and gustatory dysfunctions are the most prevalent general symptoms.\nDecreased immune system cells such as suppressed regulatory T cells, cytotoxic and helper T cells, natural killer cells, monocytes/macrophages and increased proinflammatory cytokines are the characteristic features.\nCompounds derived from Allium sativum (garlic) have the potential to decrease the expression of proinflammatory cytokines and to reverse the immunological abnormalities to more acceptable levels.\nAllium sativum is suggested as a beneficial preventive measure before being infected with SARS-CoV-2 virus.\nAllium sativum is a functional food well-known for its immunomodulatory, antimicrobial, antiinflammatory, antimutagenic, antitumor properties.\nIts antiviral efficiency was also demonstrated.\nSome constituents of this plant were found to be active against protozoan parasites.\nWithin this context, it appears to reverse most immune system dysfunctions observed in patients with COVID-19 infection.\nThe relations among immune system parameters, leptin, leptin receptor, adenosin mono phosphate-activated protein kinase, peroxisome proliferator activated receptor-gamma have also been interpreted.\nLeptin's role in boosting proinflammatory cytokines and in appetite decreasing suggest the possible beneficial effect of decreasing the concentration of this proinflammatory adipose tissue hormone in relieving some symptoms detected during COVID-19 infection.\nIn conclusion, Allium sativum may be an acceptable preventive measure against COVID-19 infection to boost immune system cells and to repress the production and secretion of proinflammatory cytokines as well as an adipose tissue derived hormone leptin having the proinflammatory nature.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 469}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Remdesivir proves effective against COVID-19\n\nAbstract:\nEffective therapeutics to treat COVID-19 are urgently needed.\nWhile many investigational, approved, and repurposed drugs have been suggested, preclinical data from animal models can guide the search for effective treatments by ruling out treatments without in vivo efficacy.\nRemdesivir (GS-5734) is a nucleotide analog prodrug with broad antiviral activity1,2, that is currently investigated in COVID-19 clinical trials and recently received Emergency Use Authorization from the US Food and Drug Administration3,4.\nIn animal models, remdesivir treatment was effective against MERS-CoV and SARS-CoV infection.2,5,6 In vitro, remdesivir inhibited replication of SARS-CoV-2.7,8 Here, we investigated the efficacy of remdesivir treatment in a rhesus macaque model of SARS-CoV-2 infection9.\nIn contrast to vehicle-treated animals, animals treated with remdesivir did not show signs of respiratory disease and had reduced pulmonary infiltrates on radiographs and reduced virus titers in bronchoalveolar lavages 12hrs after the first treatment administration.\nVirus shedding from the upper respiratory tract was not reduced by remdesivir treatment.\nAt necropsy, lung viral loads of remdesivir-treated animals were lower and there was a reduction in damage to the lungs.\nThus, therapeutic remdesivir treatment initiated early during infection had a clinical benefit in SARS-CoV-2-infected rhesus macaques.\nAlthough the rhesus macaque model does not represent the severe disease observed in a proportion of COVID-19 patients, our data support early remdesivir treatment initiation in COVID-19 patients to prevent progression to pneumonia.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"In animal models, remdesivir treatment was effective against MERS-CoV and SARS-CoV infection.2,5,6 In vitro, remdesivir inhibited replication of SARS-CoV-2.7,8 Here, we investigated the efficacy of remdesivir treatment in a rhesus macaque model of SARS-CoV-2 infection9.\"]}", "id": 470}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: covid-19 patients taking hydroxychloroquine do not benefit\n\nAbstract:\nBackground: There is no effective therapy for COVID-19.\nHydroxychloroquine (HCQ) and chloroquine (CQ) have been used for its treatment but their safety and efficacy remain uncertain.\nObjective: We performed a systematic review to synthesize the available data on the efficacy and safety of CQ and HCQ for the treatment of COVID-19.\nMethods: Two reviewers searched for published and pre-published relevant articles between December 2019 to 8th June 2020.\nThe data from the selected studies were abstracted and analyzed for efficacy and safety outcomes.\nCritical appraisal of the evidence was done by Cochrane risk of bias tool and Newcastle Ottawa scale.\nThe quality of evidence was graded as per the GRADE approach.\nResults: We reviewed 12 observational and 3 randomized trials which included 10659 patients of whom 5713 received CQ/HCQ and 4966 received only standard of care.\nThe efficacy of CQ/HCQ for COVID-19 was inconsistent across the studies.\nMeta-analysis of included studies revealed no significant reduction in mortality with HCQ use [RR 0.98 95% CI 0.66-1.46] , time to fever resolution [mean difference -0.54 days (-1.19-011)] or clinical deterioration/development of ARDS with HCQ [RR 0.90 95% CI 0.47-1.71].\nThere was a higher risk of ECG abnormalities/arrhythmia with HCQ/CQ [RR 1.46 95% CI 1.04 to 2.06].\nThe quality of evidence was graded as very low for these outcomes.\nConclusions: The available evidence suggests that CQ or HCQ does not improve clinical outcomes in COVID-19.\nWell-designed randomized trials are required for assessing the efficacy and safety of HCQ and CQ for COVID-19.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Conclusions: The available evidence suggests that CQ or HCQ does not improve clinical outcomes in COVID-19.\"]}", "id": 471}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Coronavirus (COVID-19) Know how to protect yourself and others from COVID-19 and what to do if you are sick.\n\nAbstract:\nBackground: Understanding and projecting the spread of COVID-19 requires reliable estimates of how weather components are associated with the transmission of the virus.\nPrior research on this topic has been inconclusive.\nIdentifying key challenges to reliable estimation of weather impact on transmission we study this question using one of the largest assembled databases of COVID-19 infections and weather.\nMethods: We assemble a dataset that includes virus transmission and weather data across 3,739 locations from December 12, 2019 to April 22, 2020.\nUsing simulation, we identify key challenges to reliable estimation of weather impacts on transmission, design a statistical method to overcome these challenges, and validate it in a blinded simulation study.\nUsing this method and controlling for location-specific response trends we estimate how different weather variables are associated with the reproduction number for COVID-19.\nWe then use the estimates to project the relative weather-related risk of COVID-19 transmission across the world and in large cities.\nResults: We show that the delay between exposure and detection of infection complicates the estimation of weather impact on COVID-19 transmission, potentially explaining significant variability in results to-date.\nCorrecting for that distributed delay and offering conservative estimates, we find a negative relationship between temperatures above 25 degrees Celsius and estimated reproduction number ([R]), with each degree Celsius associated with a 3.1% (95% CI, 1.5% to 4.8%) reduction in [R].\nHigher levels of relative humidity strengthen the negative effect of temperature above 25 degrees.\nMoreover, one millibar of additional pressure increases [R] by approximately 0.8 percent (95% CI, 0.6% to 1%) at the median pressure (1016 millibars) in our sample.\nWe also find significant positive effects for wind speed, precipitation, and diurnal temperature on [R].\nSensitivity analysis and simulations show that results are robust to multiple assumptions.\nDespite conservative estimates, weather effects are associated with a 43% change in [R] between the 5th and 95th percentile of weather conditions in our sample.\nConclusions: These results provide evidence for the relationship between several weather variables and the spread of COVID-19.\nHowever, the (conservatively) estimated relationships are not strong enough to seasonally control the epidemic in most locations.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 472}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Salt-coated masks achieve strong viral deactivation rate\n\nAbstract:\nAerosolized pathogens are a leading cause of respiratory infection and transmission.\nCurrently used protective measures pose potential risk of primary/secondary infection and transmission.\nHere, we report the development of a universal, reusable virus deactivation system by functionalization of the main fibrous filtration unit of surgical mask with sodium chloride salt.\nThe salt coating on the fiber surface dissolves upon exposure to virus aerosols and recrystallizes during drying, destroying the pathogens.\nWhen tested with tightly sealed sides, salt-coated filters showed remarkably higher filtration efficiency than conventional mask filtration layer, and 100% survival rate was observed in mice infected with virus penetrated through salt-coated filters.\nViruses captured on salt-coated filters exhibited rapid infectivity loss compared to gradual decrease on bare filters.\nSalt-coated filters proved highly effective in deactivating influenza viruses regardless of subtypes and following storage in harsh environmental conditions.\nOur results can be applied in obtaining a broad-spectrum, airborne pathogen prevention device in preparation for epidemic and pandemic of respiratory diseases.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Here, we report the development of a universal, reusable virus deactivation system by functionalization of the main fibrous filtration unit of surgical mask with sodium chloride salt.\", \"Salt-coated filters proved highly effective in deactivating influenza viruses regardless of subtypes and following storage in harsh environmental conditions.\"]}", "id": 473}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Unfortunately, to date, no vaccines or antiviral drugs have been approved for the treatment of SARS-CoV-2 infection by regulatory agencies.\n\nAbstract:\nSARS-CoV-2 rapidly spread around the globe after its emergence in Wuhan in December 2019.\nWith no specific therapeutic and prophylactic options available, the virus was able to infect millions of people.\nTo date, close to half a million patients succumbed to the viral disease, COVID-19.\nThe high need for treatment options, together with the lack of small animal models of infection has led to clinical trials with repurposed drugs before any preclinical in vivo evidence attesting their efficacy was available.\nWe used Syrian hamsters to establish a model to evaluate antiviral activity of small molecules in both an infection and a transmission setting.\nUpon intranasal infection, the animals developed high titers of SARS-CoV-2 in the lungs and pathology similar to that observed in mild COVID-19 patients.\nTreatment of SARS-CoV-2-infected hamsters with favipiravir or hydroxychloroquine (with and without azithromycin) resulted in respectively a mild or no reduction in viral RNA and infectious virus.\nMicro-CT scan analysis of the lungs showed no improvement compared to non-treated animals, which was confirmed by histopathology.\nIn addition, both compounds did not prevent virus transmission through direct contact and thus failed as prophylactic treatments.\nBy modelling the PK profile of hydroxychloroquine based on the trough plasma concentrations, we show that the total lung exposure to the drug was not the limiting factor.\nIn conclusion, we here characterized a hamster infection and transmission model to be a robust model for studying in vivo efficacy of antiviral compounds.\nThe information acquired using hydroxychloroquine and favipiravir in this model is of critical value to those designing (current and) future clinical trials.\nAt this point, the data here presented on hydroxychloroquine either alone or combined with azithromycin (together with previously reported in vivo data in macaques and ferrets) provide no scientific basis for further use of the drug in humans.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 474}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Vitamins C and D boost our immune systems, aiding in the fight against infectious diseases; \n\nAbstract:\nThe outbreak of the 2019 Novel Coronavirus (SARS-CoV-2) rapidly spread from Wuhan, China to more than 150 countries, areas or territories, causing staggering number of infections and deaths.\nA systematic profiling of the immune vulnerability landscape of SARS-CoV-2, which can bring critical insights into the immune clearance mechanism, peptide vaccine development, and antiviral antibody development, is lacking.\nIn this study, we investigated the potential of the SARS-CoV-2 viral proteins to induce class I and II MHC presentation and to form linear antibody epitopes.\nWe created an online database to broadly share the predictions as a resource for the research community.\nUsing this resource, we showed that genetic variations in SARS- CoV-2, though still few for the moment, already follow the pattern of mutations in related coronaviruses, and could alter the immune vulnerability landscape of this virus.\nImportantly, we discovered evidence that SARS-CoV-2, along with related coronaviruses, used mutations to evade attack from the human immune system.\nOverall, we present an immunological resource for SARS-CoV-2 that could promote both therapeutic development and mechanistic research.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 475}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Emergence of zoonoses such as covid-19 reveals the need for health sciences to embrace an explicit eco-social conceptual framework of health and security\n\nAbstract:\nAn accurate understanding of why zoonoses such as SARS-CoV-2 are emerging at an increased rate, is vital to prevent future pandemics from the approximately 700,000 viruses with zoonotic potential.\nCertain authors have argued that the consumption of wildlife, or human contact with bats was responsible for the emergence of SARS-CoV-2.\nOthers argue that a range of anthropogenic environmental degradations have played a vital role in the emergence of SARS-CoV-2 and other zoonoses.\nIn this opinion piece, I argue that these divergent viewpoints stem, in part, from different foundational conceptual frameworks-biomedical individualist and eco-social frameworks, respectively.\nBased on the fact that the eco-social framework provides a more complete account of the different types of causal factors underpinning the emergence of zoonoses, I propose that the COVID-19 pandemic provides an additional reason for the health sciences to ground its theory of health and disease in an eco-social conceptual framework.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Others argue that a range of anthropogenic environmental degradations have played a vital role in the emergence of SARS-CoV-2 and other zoonoses.\", \"Based on the fact that the eco-social framework provides a more complete account of the different types of causal factors underpinning the emergence of zoonoses, I propose that the COVID-19 pandemic provides an additional reason for the health sciences to ground its theory of health and disease in an eco-social conceptual framework.\"]}", "id": 476}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Many mammals around the world could have a role in spreading the disease, including dogs and cats.\n\nAbstract:\nAbstract Coronavirus disease-19 (COVID-19) caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is now a pandemic threat.\nThis virus is supposed to be spread by human to human transmission.\nCellular angiotensin converting enzyme 2 (ACE2) is the receptor of SARS-CoV-2 which is identical or similar in different species of animals such as pigs, ferrets, cats, orangutans, monkeys, and humans.\nMoreover, a recent study predicted that dog might be secondary host during the evolution of SARS-CoV-2 from bat to human.\nTherefore, there is a possibility of spreading SARS-CoV-2 through domestic pets.\nThere are now many reports of SARS-CoV-2 positive cases in dogs, cats, tigers, lion, and minks.\nExperimental data showed ferrets and cats are highly susceptible to SARS-CoV-2 as infected by virus inoculation and can transmit the virus directly or indirectly by droplets or airborne route.\nBased on these natural infection reports and experimental data, whether the pets are responsible for SARS-CoV-2 spread to human; needs to be deeply investigated.\nHumans showing clinical symptoms of respiratory infections have been undergoing for COVID-19 diagnostic test but many infected people and few pets confirmed with SARS-CoV-2 remained asymptomatic.\nIn this review, we summarize the natural cases of SARS-CoV-2 in animals with the latest researches conducted in this field.\nThis review will be helpful to think insights of SARS-CoV-2 transmissions, spread, and demand for sero-prevalence studies especially in companion animals.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Experimental data showed ferrets and cats are highly susceptible to SARS-CoV-2 as infected by virus inoculation and can transmit the virus directly or indirectly by droplets or airborne route.\"]}", "id": 477}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: VITAMIN D HEALTHY LEVELS decrease COVID-19 MORTALITY RATES\n\nAbstract:\nThe severity of coronavirus 2019 infection (COVID-19) is determined by the presence of pneumonia, severe acute respiratory distress syndrome (SARS-CoV-2), myocarditis, microvascular thrombosis and/or cytokine storms, all of which involve underlying inflammation.\nA principal defence against uncontrolled inflammation, and against viral infection in general, is provided by T regulatory lymphocytes (Tregs).\nTreg levels have been reported to be low in many COVID-19 patients and can be increased by vitamin D supplementation.\nLow vitamin D levels have been associated with an increase in inflammatory cytokines and a significantly increased risk of pneumonia and viral upper respiratory tract infections.\nVitamin D deficiency is associated with an increase in thrombotic episodes, which are frequently observed in COVID-19.\nVitamin D deficiency has been found to occur more frequently in patients with obesity and diabetes.\nThese conditions are reported to carry a higher mortality in COVID-19.\nIf vitamin D does in fact reduce the severity of COVID-19 in regard to pneumonia/ARDS, inflammation, inflammatory cytokines and thrombosis, it is our opinion that supplements would offer a relatively easy option to decrease the impact of the pandemic.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"A principal defence against uncontrolled inflammation, and against viral infection in general, is provided by T regulatory lymphocytes (Tregs).\", \"Treg levels have been reported to be low in many COVID-19 patients and can be increased by vitamin D supplementation.\", \"Low vitamin D levels have been associated with an increase in inflammatory cytokines and a significantly increased risk of pneumonia and viral upper respiratory tract infections.\", \"Vitamin D deficiency is associated with an increase in thrombotic episodes, which are frequently observed in COVID-19.\", \"Vitamin D deficiency has been found to occur more frequently in patients with obesity and diabetes.\", \"These conditions are reported to carry a higher mortality in COVID-19.\", \"If vitamin D does in fact reduce the severity of COVID-19 in regard to pneumonia/ARDS, inflammation, inflammatory cytokines and thrombosis, it is our opinion that supplements would offer a relatively easy option to decrease the impact of the pandemic.\"]}", "id": 478}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: The coronavirus came from the local wet market in China\n\nAbstract:\nOrigin of the COVID-19 virus has been intensely debated in the scientific community since the first infected cases were detected in December 2019.\nThe disease has caused a global pandemic, leading to deaths of thousands of people across the world and thus finding origin of this novel coronavirus is important in responding and controlling the pandemic.\nRecent research results suggest that bats or pangolins might be the original hosts for the virus based on comparative studies using its genomic sequences.\nThis paper investigates the COVID-19 virus origin by using artificial intelligence (AI) and raw genomic sequences of the virus.\nMore than 300 genome sequences of COVID-19 infected cases collected from different countries are explored and analysed using unsupervised clustering methods.\nThe results obtained from various AI-enabled experiments using clustering algorithms demonstrate that all examined COVID-19 virus genomes belong to a cluster that also contains bat and pangolin coronavirus genomes.\nThis provides evidences strongly supporting scientific hypotheses that bats and pangolins are probable hosts for the COVID-19 virus.\nAt the whole genome analysis level, our findings also indicate that bats are more likely the hosts for the COVID-19 virus than pangolins.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Recent research results suggest that bats or pangolins might be the original hosts for the virus based on comparative studies using its genomic sequences.\", \"The results obtained from various AI-enabled experiments using clustering algorithms demonstrate that all examined COVID-19 virus genomes belong to a cluster that also contains bat and pangolin coronavirus genomes.\", \"This provides evidences strongly supporting scientific hypotheses that bats and pangolins are probable hosts for the COVID-19 virus.\", \"At the whole genome analysis level, our findings also indicate that bats are more likely the hosts for the COVID-19 virus than pangolins.\"]}", "id": 479}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: dexamethasone has only been shown to have positive effects in patients who require supplemental oxygen.\n\nAbstract:\nBackground: Dexamethasone, a synthetic glucocorticoid, has anti-inflammatory and immunosuppressive properties.\nThere is a hyperinflammatory response involved in the clinical course of patients with pneumonia due to SARS-CoV2.\nTo date, there has been no definite therapy for COVID-19.\nWe reviewed the charts of SARS-CoV2 patients with pneumonia and moderate to severely elevated CRP and worsening hypoxemia who were treated with early, short-term dexamethasone.\nMethods: We describe a series of 21 patients who tested positive for SARS-CoV2 and were admitted to The Miriam Hospital in Providence and were treated with a short course of dexamethasone, either alone or in addition to current investigative therapies.\nResults: CRP levels decreased significantly following the start of dexamethasone from mean initial levels of 129.52 to 40.73 mg/L at time of discharge.\n71% percent of the patients were discharged home with a mean length of stay of 7.8 days.\nNone of the patients had escalation of care, leading to mechanical ventilation.\nTwo patients were transferred to inpatient hospice facilities on account of persistent hypoxemia, in line with their documented goals of care.\nConclusions: A short course of systemic corticosteroids among inpatients with SARS-CoV2 with hypoxic respiratory failure was well tolerated, and most patients had improved outcomes.\nThis limited case series may not offer concrete evidence towards the benefit of corticosteroids in COVID-19.\nHowever, patients positive response to short-term corticosteroids demonstrates that they may help blunt the severity of inflammation and prevent a severe hyperinflammatory phase, in turn reducing the length of stay, ICU admissions, and healthcare costs.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"To date, there has been no definite therapy for COVID-19.\"]}", "id": 480}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Masks do reduce spread of flu and some COVID-19\n\nAbstract:\nCOVID-19, caused by SARS-CoV2 is a rapidly spreading global pandemic.\nAlthough precise transmission routes and dynamics are unknown, SARS-CoV2 is thought primarily to spread via contagious respiratory droplets.\nUnlike with SARS-CoV, maximal viral shedding occurs in the early phase of illness, and this is supported by models that suggest 40-80% of transmission events occur from pre- and asymptomatic individuals.\nOne widely-discussed strategy to limit transmission of SARS-CoV2, particularly from presymptomatic individuals, has been population-level wearing of masks.\nModelling for pandemic influenza suggests some benefit in reducing total numbers infected with even 50% mask-use.\nCOVID-19 has a higher hospitalization and mortality rate than influenza, and the impacts on these parameters, and critically, at what point in the pandemic trajectory mask-use might exert maximal benefit are completely unknown.\nWe derived a simplified SIR model to investigate the effects of near-universal mask-use on COVID-19 assuming 8 or 16% mask efficacy.\nWe decided to model, in particular, the impact of masks on numbers of critically-ill patients and cumulative mortality, since these are parameters that are likely to have the most severe consequences in the COVID-19 pandemic.\nWhereas mask use had a relatively minor benefit on critical-care and mortality rates when transmissibility (Reff) was high, the reduction on deaths was dramatic as the effective R approached 1, as might be expected after aggressive social-distancing measures such as wide-spread lockdowns.\nOne major concern with COVID-19 is its potential to overwhelm healthcare infrastructures, even in resource-rich settings, with one third of hospitalized patients requiring critical-care.\nWe incorporated this into our model, increasing death rates for when critical-care resources have been exhausted.\nOur simple model shows that modest efficacy of masks could avert substantial mortality in this scenario.\nImportantly, the effects on mortality became hyper-sensitive to mask-wearing as the effective R approaches 1, i.e. near the tipping point of when the infection trajectory is expected to revert to exponential growth, as would be expected after effective lockdown.\nOur model suggests that mask-wearing might exert maximal benefit as nations plan their post-lockdown strategies and suggests that mask-wearing should be included in further more sophisticated models of the current pandemic.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Our simple model shows that modest efficacy of masks could avert substantial mortality in this scenario.\", \"Importantly, the effects on mortality became hyper-sensitive to mask-wearing as the effective R approaches 1, i.e. near the tipping point of when the infection trajectory is expected to revert to exponential growth, as would be expected after effective lockdown.\", \"Our model suggests that mask-wearing might exert maximal benefit as nations plan their post-lockdown strategies and suggests that mask-wearing should be included in further more sophisticated models of the current pandemic.\"]}", "id": 481}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Ferrets can catch the coronavirus and might give it to other ferrets. But poultry and pigs don't appear to be at risk.\n\nAbstract:\nSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)--the virus that causes coronavirus disease (COVID-19)--has been detected in domestic dogs and cats, raising concerns of transmission from, to, or between these animals.\nThere is currently no indication that feline- or canine-to-human transmission can occur, though there is rising evidence of the reverse.\nTo explore the extent of animal-related transmission, we aggregated 17 case reports on confirmed SARS-CoV-2 infections in animals as of 15 May 2020.\nAll but two animals fully recovered and had only mild respiratory or digestive symptoms.\nUsing data from probable cat-to-cat transmission in Wuhan, China, we estimated the basic reproduction number R0 under this scenario at 1.09 (95% confidence interval: 1.05, 1.13).\nThis value is much lower than the R0 reported for humans and close to one, indicating that the sustained transmission between cats is unlikely to occur.\nOur results support the view that the pet owners and other persons with COVID-19 in close contact with animals should be cautious of the way they interact with them.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Using data from probable cat-to-cat transmission in Wuhan, China, we estimated the basic reproduction number R0 under this scenario at 1.09 (95% confidence interval: 1.05, 1.13).\", \"This value is much lower than the R0 reported for humans and close to one, indicating that the sustained transmission between cats is unlikely to occur.\"]}", "id": 482}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Coronavirus remains stable on metals and plastic for three days. Outside a lab, however, the virus might last considerably longer: its genetic material could be detected on surfaces 17 days after a cruise ship was empty of passengers (although it's not clear whether that material represents infectious virus particles).\n\nAbstract:\nObjectives: To evaluate SARS-CoV-2 surface and air contamination during the peak of the COVID-19 pandemic in London.\nDesign: Prospective cross-sectional observational study.\nSetting: An acute NHS healthcare provider.\nParticipants: All inpatient wards were fully occupied by patients with COVID-19 at the time of sampling.\nInterventions: Air and surface samples were collected from a range of clinical areas and a public area of the hospital.\nAn active air sampler was used to collect three or four 1.0 m3 air samples in each area.\nSurface samples were collected by swabbing approximately 25 cm2 of items in the immediate vicinity of each air sample.\nSARS-CoV-2 was detected by RT-qPCR and viral culture using Vero E6 and Caco2 cells; additionally the limit of detection for culturing SARS-CoV-2 dried onto surfaces was determined.\nMain outcome measures: SARS-CoV-2 detected by PCR or culture.\nResults: Viral RNA was detected on 114/218 (52.3%) of surface and 14/31 (38.7%) air samples but no virus was cultured.\nThe proportion of surface samples contaminated with viral RNA varied by item sampled and by clinical area.\nViral RNA was detected on surfaces and in air in public areas of the hospital but was more likely to be found in areas immediately occupied by COVID-19 patients (67/105 (63.8%) in areas immediately occupied by COVID-19 patients vs. 29/64 (45.3%) in other areas (odds ratio 0.5, 95% confidence interval 0.2-0.9, p=0.025, Fishers exact test).\nThe PCR Ct value for all surface and air samples (>30) indicated a viral load that would not be culturable.\nConclusions: Our findings of extensive viral RNA contamination of surfaces and air across a range of acute healthcare settings in the absence of cultured virus underlines the potential risk from surface and air contamination in managing COVID-19, and the need for effective use of PPE, social distancing, and hand/surface hygiene.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Conclusions: Our findings of extensive viral RNA contamination of surfaces and air across a range of acute healthcare settings in the absence of cultured virus underlines the potential risk from surface and air contamination in managing COVID-19, and the need for effective use of PPE, social distancing, and hand/surface hygiene.\"]}", "id": 483}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Vitamins C and D boost our immune systems, aiding in the fight against infectious diseases; \n\nAbstract:\nThe rapid global spread of SARS-CoV-2 and resultant mortality and social disruption have highlighted the need to better understand coronavirus immunity to expedite vaccine development efforts.\nMultiple candidate vaccines, designed to elicit protective neutralising antibodies targeting the viral spike glycoprotein, are rapidly advancing to clinical trial.\nHowever, the immunogenic properties of the spike protein in humans are unresolved.\nTo address this, we undertook an in-depth characterisation of humoral and cellular immunity against SARS-CoV-2 spike in humans following mild to moderate SARS-CoV-2 infection.\nWe find serological antibody responses against spike are routinely elicited by infection and correlate with plasma neutralising activity and capacity to block ACE2/RBD interaction.\nExpanded populations of spike-specific memory B cells and circulating T follicular helper cells (cTFH) were detected within convalescent donors, while responses to the receptor binding domain (RBD) constitute a minor fraction.\nUsing regression analysis, we find high plasma neutralisation activity was associated with increased spike-specific antibody, but notably also with the relative distribution of spike-specific cTFH subsets.\nThus both qualitative and quantitative features of B and T cell immunity to spike constitute informative biomarkers of the protective potential of novel SARS-CoV-2 vaccines.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 484}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: For most patients, COVID-19 begins and ends in their lungs, because like the flu, coronaviruses are respiratory diseases\n\nAbstract:\nThe novel coronavirus Covid-19 follows transmission route and clinical presentation of all community-acquired coronaviruses.\nInstead, the rate of transmission is significative higher, with a faster spread of the virus responsible of the worldwide outbreak and a significative higher mortality rate due to the development of a severe lung injury.\nMost noteworthy is the distribution of death rate among age groups.\nChildren and younger people are almost protected from severe clinical presentation.\nPossible explanation of this phenomenon could be the ability of past vaccinations (especially tetanic, diphtheria toxoids and inactivated bacteria as pertussis) to stimulate immune system and to generate a scattered immunity against non-self antigens in transit, as coronaviruses and other community-circulating viruses and make immune system readier to develop specific immunity against Covid-19.\nThe first support to this hypothesis is the distribution of mortality rate during historical pandemics (\"Spanish flu\" 1918, \"Asian flu\" 1956 and \"the Hong Kong flu\" 1968) among age groups before and after the introduction of vaccines.\nThe immunological support to the hypothesis derives from recent studies about immunotherapy for malignancies, which propose the use of oncolytic vaccines combined with toxoids in order to exploit CD4 + memory T cell recall in supporting the ongoing anti-tumour response.\nAccording to this hypothesis vaccine formulations (tetanus, diphtheria, Bordetella pertussis) could be re-administrate after the first contact with Covid-19, better before the development of respiratory severe illness and of course before full-blown ARDS (Acute Respiratory Distress Syndrome).\nThe CD4 + memory exploiting could help immune system to recall immunity of already know antigens against coronaviruses, avoiding or limiting \"lung crash\" until virus specific immunity develops and making it faster and prolonged.\nFinally, this administration could be helpful not only in already infected patients, but also before infection.\nIn fact, people could have an immune system more ready when the contact with the Covid-19 will occur.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 485}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: N95 masks are better than clothe masks\n\nAbstract:\nOBJECTIVES: To determine the risk of SARS-CoV-2 transmission by aerosols, to provide evidence on the rational use of masks, and to discuss additional measures important for the protection of healthcare workers from COVID-19.\nMETHODS: Literature review and expert opinion.\nSHORT CONCLUSION: SARS-CoV-2, the pathogen causing COVID-19, is considered to be transmitted via droplets rather than aerosols, but droplets with strong directional airflow support may spread further than 2 m. High rates of COVID-19 infections in healthcare-workers (HCWs) have been reported from several countries.\nRespirators such as filtering face piece (FFP) 2 masks were designed to protect HCWs, while surgical masks were originally intended to protect patients (e.g., during surgery).\nNevertheless, high quality standard surgical masks (type II/IIR according to European Norm EN 14683) appear to be as effective as FFP2 masks in preventing droplet-associated viral infections of HCWs as reported from influenza or SARS.\nSo far, no head-to-head trials with these masks have been published for COVID-19.\nNeither mask type completely prevents transmission, which may be due to inappropriate handling and alternative transmission pathways.\nTherefore, compliance with a bundle of infection control measures including thorough hand hygiene is key.\nDuring high-risk procedures, both droplets and aerosols may be produced, reason why respirators are indicated for these interventions.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 486}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Hypothesis: Angiotensin-converting enzyme inhibitors and angiotensin receptor blockers may increase the risk of severe COVID-19\n\nAbstract:\nCoronavirus disease 2019 (COVID-19) is a viral pandemic precipitated by the severe acute respiratory syndrome coronavirus 2.\nSince previous reports suggested that viral entry into cells may involve angiotensin converting enzyme 2, there has been growing concern that angiotensin converting enzyme inhibitor (ACEI) and angiotensin II receptor blocker (ARB) use may exacerbate the disease severity.\nIn this retrospective, single-center US study of adult patients diagnosed with COVID-19, we evaluated the association of ACEI/ARB use with hospital admission.\nSecondary outcomes included: ICU admission, mechanical ventilation, length of hospital stay, use of inotropes, and all-cause mortality.\nPropensity score matching was performed to account for potential confounders.\nAmong 590 unmatched patients diagnosed with COVID-19, 78 patients were receiving ACEI/ARB (median age 63 years and 59.7% male) and 512 patients were non-users (median age 42 years and 47.1% male).\nIn the propensity matched population, multivariate logistic regression analysis adjusting for age, gender and comorbidities demonstrated that ACEI/ARB use was not associated with hospital admission (OR 1.2, 95% CI 0.5-2.7, p = 0.652).\nCAD and CKD/ESRD remained independently associated with admission to hospital.\nAll-cause mortality, ICU stay, need for ventilation, and inotrope use was not significantly different between the 2 study groups.\nIn conclusion, among patients who were diagnosed with COVID-19, ACEI/ARB use was not associated with increased risk of hospital admission.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"In conclusion, among patients who were diagnosed with COVID-19, ACEI/ARB use was not associated with increased risk of hospital admission.\"]}", "id": 487}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: diabetes significantly increases coronavirus sufferers risk of dying\n\nAbstract:\nAIMS: We aimed to briefly review the general characteristics of the novel coronavirus (SARS-CoV-2) and provide a better understanding of the coronavirus disease (COVID-19) in people with diabetes, and its management.\nMETHODS: We searched for articles in PubMed and Google Scholar databases till 02 April 2020, with the following keywords: \"SARS-CoV-2\", \"COVID-19\", \"infection\", \"pathogenesis\", \"incubation period\", \"transmission\", \"clinical features\", \"diagnosis\", \"treatment\", \"diabetes\", with interposition of the Boolean operator \"AND\".\nRESULTS: The clinical spectrum of COVID-19 is heterogeneous, ranging from mild flu-like symptoms to acute respiratory distress syndrome, multiple organ failure and death.\nOlder age, diabetes and other comorbidities are reported as significant predictors of morbidity and mortality.\nChronic inflammation, increased coagulation activity, immune response impairment, and potential direct pancreatic damage by SARS-CoV-2 might be among the underlying mechanisms of the association between diabetes and COVID-19.\nNo conclusive evidence exists to support the discontinuation of angiotensin-converting enzyme inhibitors (ACEI), angiotensin receptor blockers or thiazolidinediones because of COVID-19 in people with diabetes.\nCaution should be taken to potential hypoglycemic events with the use of chloroquine in these subjects.\nPatient tailored therapeutic strategies, rigorous glucose monitoring and careful consideration of drug interactions might reduce adverse outcomes.\nCONCLUSIONS: Suggestions are made on the possible pathophysiological mechanisms of the relationship between diabetes and COVID-19, and its management.\nNo definite conclusions can be made based on current limited evidence.\nFurther research regarding this relationship and its clinical management is warranted.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Older age, diabetes and other comorbidities are reported as significant predictors of morbidity and mortality.\", \"Chronic inflammation, increased coagulation activity, immune response impairment, and potential direct pancreatic damage by SARS-CoV-2 might be among the underlying mechanisms of the association between diabetes and COVID-19.\"]}", "id": 488}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Does wearing a mask help reduce my risk of COVID-19? Cloth and surgical masks help stop droplets spreading when people talk, cough and sneeze, which reduces the risk of spreading the virus.\n\nAbstract:\nEvidence that face masks provide effective protection against respiratory infections in the community is scarce.\nHowever, face masks are widely used by health workers as part of droplet precautions when caring for patients with respiratory infections.\nIt would therefore be reasonable to suggest that consistent widespread use of face masks in the community could prevent further spread of the Severe Acute Respiratory Syndrome-Coronavirus 2 (SARS-CoV-2).\nIn this study we examine public face mask wearing in Uganda where a proportion wears masks to protect against acquiring, and the other to prevent from transmitting SARS-CoV-2.\nThe objective of this study was to determine what percentage of the population would have to wear face masks to reduce susceptibility to and infectivity of COVID-19 in Uganda, keeping the basic reproduction number below unity and/or flattening the curve.\nWe used an SEIAQRD model for the analysis.\nResults show that implementation of facemasks has a relatively large impact on the size of the coronavirus epidemic in Uganda.\nWe find that the critical mask adherence is 5 per 100 when 80% wear face masks.\nA cost-effective analysis shows that utilizing funds to provide 1 public mask to the population has a per capita compounded cost of USD 1.34.\nIf provision of face masks is done simultaneously with supportive care, the per capita compounded cost is USD 1.965, while for the case of only treatment and no provision of face masks costs each Ugandan USD 4.0579.\nWe conclude that since it is hard to achieve a 100% adherence to face masks, government might consider provision of face masks in conjunction with provision of care.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Evidence that face masks provide effective protection against respiratory infections in the community is scarce.\", \"However, face masks are widely used by health workers as part of droplet precautions when caring for patients with respiratory infections.\", \"It would therefore be reasonable to suggest that consistent widespread use of face masks in the community could prevent further spread of the Severe Acute Respiratory Syndrome-Coronavirus 2 (SARS-CoV-2).\"]}", "id": 489}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Like other viruses with a lipid envelope, SARS-CoV-2 is probably sensitive to temperature, humidity, and solar radiation\n\nAbstract:\nThe undefendable outbreak of novel coronavirus (SARS-COV-2) lead to a global health emergency due to its higher transmission rate and longer symptomatic duration, created a health surge in a short time.\nSince Nov 2019 the outbreak in China, the virus is spreading exponentially everywhere.\nThe current study focuses on the relationship between environmental parameters and the growth rate of COVID-19.\nThe statistical analysis suggests that the temperature changes retarded the growth rate and found that -6.28{degrees}C and +14.51{degrees}C temperature is the favorable range for COVID-19 growth.\nGutenberg- Richter's relationship is used to estimate the mean daily rate of exceedance of confirmed cases concerning the change in temperature.\nTemperature is the most influential parameter that reduces the growth at the rate of 13-16 cases/day with a 1{degrees}C rise in temperature.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"The statistical analysis suggests that the temperature changes retarded the growth rate and found that -6.28{degrees}C and +14.51{degrees}C temperature is the favorable range for COVID-19 growth.\", \"Temperature is the most influential parameter that reduces the growth at the rate of 13-16 cases/day with a 1{degrees}C rise in temperature.\"]}", "id": 490}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: a small number of pets worldwide, including cats and dogs, can be infected with the virus that causes COVID-19, mostly after close contact with people with COVID-19.\n\nAbstract:\nThe Australian and New Zealand governments both initiated strict social distancing measures in response to the COVID-19 pandemic in late March.\nIt remains difficult to quantify the impact this had in reducing the spread of the virus.\nBayesian structural time series model provide a model to quantify the scenario in which these government-level interventions were not placed.\nOur models predict these strict social distancing measures caused a 79% and 61% reduction in the daily cases of COVID-19 across Australia and New Zealand respectively.\nThis provides both evidence and impetus for governments considering similar measures in response to COVID-19 and other pandemics.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 491}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Loss of sars-cov-2 orf8, a rapidly evolving coronavirus protein implicated in immune evasion\n\nAbstract:\nThe molecular basis for the severity and rapid spread of the COVID-19 disease caused by SARS-CoV-2 is largely unknown.\nORF8 is a rapidly evolving accessory protein that has been proposed to interfere with immune responses.\nThe crystal structure of SARS-CoV-2 ORF8 was determined at 2.04 \u00c5 resolution by x-ray crystallography.\nThe structure reveals a ~60 residue core similar to SARS-CoV ORF7a with the addition of two dimerization interfaces unique to SARS-CoV-2 ORF8.\nA covalent disulfide-linked dimer is formed through an N-terminal sequence specific to SARS-CoV-2, while a separate non-covalent interface is formed by another SARS-CoV-2-specific sequence, (73)YIDI(76).\nTogether the presence of these interfaces shows how SARS-CoV-2 ORF8 can form unique large-scale assemblies not possible for SARS-CoV, potentially mediating unique immune suppression and evasion activities.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Together the presence of these interfaces shows how SARS-CoV-2 ORF8 can form unique large-scale assemblies not possible for SARS-CoV, potentially mediating unique immune suppression and evasion activities.\"]}", "id": 492}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: There are an urgent need for antivirals to treat the newly emerged SARS-CoV-2.\n\nAbstract:\nSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative representative of a severe respiratory illness resulted in widespread human infections and deaths in nearly all of the countries since late 2019.\nThere is no therapeutic FDA-approved drug against SARS-CoV-2 infection, although a combination of anti-viral drugs is directly being practiced in some countries.\nA broad-spectrum of antiviral agents are being currently evaluated in clinical trials, and in this review, we specifically focus on the application of Remdesivir (RVD) as a potential anti-viral compound against Middle East respiratory syndrome (MERS) -CoV, SARS-CoV and SARS-CoV-2.\nFirst, we overview the general information about SARS-CoV-2, followed by application of RDV as a nucleotide analogue which can potentially inhibits RNA-dependent RNA polymerase of COVs.\nAfterwards, we discussed the kinetics of SARS- or MERS-CoV proliferation in animal models which is significantly different compared to that in humans.\nFinally, some ongoing challenges and future perspective on the application of RDV either alone or in combination with other anti-viral agents against CoVs infection were surveyed to determine the efficiency of RDV in preclinical trials.\nAs a result, this paper provides crucial evidence of the potency of RDV to prevent SARS-CoV-2 infections.\nCommunicated by Ramaswamy H. Sarma.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 493}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: fever is beneficial to the body's natural immune response to fight covid-19;\n\nAbstract:\nOBJECTIVE: It was recently suggested that ibuprofen might increase the risk for severe and fatal coronavirus disease 2019 (COVID-19) and should therefore be avoided in this patient population.\nWe aimed to evaluate whether ibuprofen use in individuals with COVID-19 was associated with more severe disease, compared with individuals using paracetamol or no antipyretics.\nMETHODS: In a retrospective cohort study of patients with COVID-19 from Shamir Medical Centre, Israel, we monitored any use of ibuprofen from a week before diagnosis of COVID-19 throughout the disease.\nPrimary outcomes were mortality and the need for respiratory support, including oxygen administration and mechanical ventilation.\nRESULTS: The study included 403 confirmed cases of COVID-19, with a median age of 45 years.\nOf the entire cohort, 44 patients (11%) needed respiratory support and 12 (3%) died.\nOne hundred and seventy-nine (44%) patients had fever, with 32% using paracetamol and 22% using ibuprofen, for symptom-relief.\nIn the ibuprofen group, 3 (3.4%) patients died, whereas in the non-ibuprofen group, 9 (2.8%) patients died (p 0.95).\nNine (10.3%) patients from the ibuprofen group needed respiratory support, compared with 35 (11%) from the non-ibuprofen group (p 1).\nWhen compared with exclusive paracetamol users, no differences were observed in mortality rates or the need for respiratory support among patients using ibuprofen.\nCONCLUSIONS: In this cohort of COVID-19 patients, ibuprofen use was not associated with worse clinical outcomes, compared with paracetamol or no antipyretic.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"When compared with exclusive paracetamol users, no differences were observed in mortality rates or the need for respiratory support among patients using ibuprofen.\", \"CONCLUSIONS: In this cohort of COVID-19 patients, ibuprofen use was not associated with worse clinical outcomes, compared with paracetamol or no antipyretic.\"]}", "id": 494}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Tiny antibody component highly effective against SARS-COV-2 in animal studies\n\nAbstract:\nThe outbreak of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has posed the world at a pandemic risk.\nCoronavirus-19 disease (COVID-19) is an infectious disease caused by SARS-CoV-2, which causes pneumonia, requires intensive care unit hospitalization in about 10% of cases and can lead to a fatal outcome.\nSeveral efforts are currently made to find a treatment for COVID-19 patients.\nSo far, several anti-viral and immunosuppressive or immunomodulating drugs have demonstrated some efficacy on COVID-19 both in vitro and in animal models as well as in cases series.\nIn COVID-19 patients a pro-inflammatory status with high levels of interleukin (IL)-1B, IL-1 receptor (R)A and tumor necrosis factor (TNF)-&#945; has been demonstrated.\nMoreover, high levels of IL-6 and TNF-&#945; have been observed in patients requiring intensive-care-unit hospitalization.\nThis provided rationale for the use of anti-rheumatic drugs as potential treatments for this severe viral infection.\nOther agents, such as hydroxychloroquine and chloroquine might have a direct anti-viral effect.\nThe anti-viral aspect of immunosuppressants towards a variety of viruses has been known since long time and it is herein discussed in the view of searching for a potential treatment for SARS-CoV-2 infection.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 495}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: masks protect you as the wearer and protects others\n\nAbstract:\nEvidence that face masks provide effective protection against respiratory infections in the community is scarce.\nHowever, face masks are widely used by health workers as part of droplet precautions when caring for patients with respiratory infections.\nIt would therefore be reasonable to suggest that consistent widespread use of face masks in the community could prevent further spread of the Severe Acute Respiratory Syndrome-Coronavirus 2 (SARS-CoV-2).\nIn this study we examine public face mask wearing in Uganda where a proportion wears masks to protect against acquiring, and the other to prevent from transmitting SARS-CoV-2.\nThe objective of this study was to determine what percentage of the population would have to wear face masks to reduce susceptibility to and infectivity of COVID-19 in Uganda, keeping the basic reproduction number below unity and/or flattening the curve.\nWe used an SEIAQRD model for the analysis.\nResults show that implementation of facemasks has a relatively large impact on the size of the coronavirus epidemic in Uganda.\nWe find that the critical mask adherence is 5 per 100 when 80% wear face masks.\nA cost-effective analysis shows that utilizing funds to provide 1 public mask to the population has a per capita compounded cost of USD 1.34.\nIf provision of face masks is done simultaneously with supportive care, the per capita compounded cost is USD 1.965, while for the case of only treatment and no provision of face masks costs each Ugandan USD 4.0579.\nWe conclude that since it is hard to achieve a 100% adherence to face masks, government might consider provision of face masks in conjunction with provision of care.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"It would therefore be reasonable to suggest that consistent widespread use of face masks in the community could prevent further spread of the Severe Acute Respiratory Syndrome-Coronavirus 2 (SARS-CoV-2).\", \"We find that the critical mask adherence is 5 per 100 when 80% wear face masks.\"]}", "id": 496}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: If Fever Helps Fight Infection, Should I Avoid Fever-Reducing Drugs\n\nAbstract:\nConcern about the appropriate role of nonsteroidal anti-inflammatory drugs (NSAIDs) in COVID-19 speculate that NSAIDs, in particular ibuprofen, may upregulate the entry point for the virus, the angiotensin-converting enzyme (ACE) 2 receptors and increase susceptibility to the virus or worsen symptoms in existing disease.\nAdverse outcomes with COVID-19 have been linked to cytokine storm but the most effective way to address exaggerated inflammatory response is complex and unclear.\nThe Expert Working Group on the Commission of Human Medicines in the UK and other organizations have stated that there is insufficient evidence to establish a link between ibuprofen and susceptibility to or exacerbation of COVID-19.\nNSAID use must also be categorized by whether the drugs are relatively low-dose over-the-counter oral products taken occasionally versus higher-dose or parenteral NSAIDs.\nEven if evidence emerged arguing for or against NSAIDs in this setting, it is unclear if this evidence would apply to all NSAIDs at all doses in all dosing regimens.\nParacetamol (acetaminophen) has been proposed as an alternative to NSAIDs but there are issues with liver toxicity at high doses.\nThere are clearly COVID-19 cases where NSAIDs should not be used, but there is no strong evidence that NSAIDs must be avoided in all patients with COVID-19; clinicians must weigh these choices on an individual basis.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"The Expert Working Group on the Commission of Human Medicines in the UK and other organizations have stated that there is insufficient evidence to establish a link between ibuprofen and susceptibility to or exacerbation of COVID-19.\", \"There are clearly COVID-19 cases where NSAIDs should not be used, but there is no strong evidence that NSAIDs must be avoided in all patients with COVID-19; clinicians must weigh these choices on an individual basis.\"]}", "id": 497}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: In severe cases of COVID-19, activation patterns of B cells resemble those seen in systemic lupus erythematosus, an autoimmune disease. Emory researchers want to see how far that resemblance extends.\n\nAbstract:\nThe novel coronavirus Covid-19 follows transmission route and clinical presentation of all community-acquired coronaviruses.\nInstead, the rate of transmission is significative higher, with a faster spread of the virus responsible of the worldwide outbreak and a significative higher mortality rate due to the development of a severe lung injury.\nMost noteworthy is the distribution of death rate among age groups.\nChildren and younger people are almost protected from severe clinical presentation.\nPossible explanation of this phenomenon could be the ability of past vaccinations (especially tetanic, diphtheria toxoids and inactivated bacteria as pertussis) to stimulate immune system and to generate a scattered immunity against non-self antigens in transit, as coronaviruses and other community-circulating viruses and make immune system readier to develop specific immunity against Covid-19.\nThe first support to this hypothesis is the distribution of mortality rate during historical pandemics (\"Spanish flu\" 1918, \"Asian flu\" 1956 and \"the Hong Kong flu\" 1968) among age groups before and after the introduction of vaccines.\nThe immunological support to the hypothesis derives from recent studies about immunotherapy for malignancies, which propose the use of oncolytic vaccines combined with toxoids in order to exploit CD4 + memory T cell recall in supporting the ongoing anti-tumour response.\nAccording to this hypothesis vaccine formulations (tetanus, diphtheria, Bordetella pertussis) could be re-administrate after the first contact with Covid-19, better before the development of respiratory severe illness and of course before full-blown ARDS (Acute Respiratory Distress Syndrome).\nThe CD4 + memory exploiting could help immune system to recall immunity of already know antigens against coronaviruses, avoiding or limiting \"lung crash\" until virus specific immunity develops and making it faster and prolonged.\nFinally, this administration could be helpful not only in already infected patients, but also before infection.\nIn fact, people could have an immune system more ready when the contact with the Covid-19 will occur.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 498}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: drugs (Anti-inflammatory drugs, which includes steroids such as prednisone and non-steroidal drugs like ibuprofen) are helpful for symptomatic treatment only and are not a cure. By decreasing inflammation, they can decrease pain, swelling, redness and other symptoms that may be associated with inflammation.\n\nAbstract:\n: The COVID-19 pandemic is challenging our cardiovascular care of patients with heart diseases.\nIn the setting of pericardial diseases, there are two possible different scenarios to consider: the patient being treated for pericarditis who subsequently becomes infected with SARS-CoV-2, and the patient with COVID-19 who develops pericarditis or pericardial effusion.\nIn both conditions, clinicians may be doubtful regarding the safety of nonsteroidal anti-inflammatory drugs (NSAIDs), corticosteroids, colchicine, and biological agents, such as anti-IL1 agents (e.g. anakinra), that are the mainstay of therapy for pericarditis.\nFor NSAIDs, there is no clear scientific evidence linking ibuprofen and other NSAIDs to worsening of COVID-19; however, it seems prudent to continue them, if necessary to control pericarditis, and on the other hand, to prefer paracetamol for fever and systemic symptoms related to COVID-19.\nTreatments with corticosteroids, colchicine, and anakinra appear well tolerated in the context of COVID-19 infection and are currently actively evaluated as potential therapeutic options for COVID infection at different stages of the disease.\nOn this basis, currently most treatments for pericarditis do not appear contraindicated also in the presence of possible COVID-19 infection and should not be discontinued, and some (corticosteroids, colchicine, and anakinra) can be considered to treat both conditions.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"For NSAIDs, there is no clear scientific evidence linking ibuprofen and other NSAIDs to worsening of COVID-19; however, it seems prudent to continue them, if necessary to control pericarditis, and on the other hand, to prefer paracetamol for fever and systemic symptoms related to COVID-19.\"]}", "id": 499}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: People with weakened immune systems are at higher risk of getting severely sick from SARS-CoV-2, the virus that causes COVID-19.\n\nAbstract:\nSARS-CoV-2 is the coronavirus agent of the COVID-19 pandemic causing high mortalities.\nIn contrast, the widely spread human coronaviruses OC43, HKU1, 229E, and NL63 tend to cause only mild symptoms.\nThe present study shows, by in silico analysis, that these common human viruses are expected to induce immune memory against SARS-CoV-2 by sharing protein fragments (antigen epitopes) for presentation to the immune system by MHC class I. A list of such epitopes is provided.\nThe number of these epitopes and the prevalence of the common coronaviruses suggest that a large part of the world population has some degree of specific immunity against SARS-CoV-2 already, even without having been infected by that virus.\nFor inducing protection, booster vaccinations enhancing existing immunity are less demanding than primary vaccinations against new antigens.\nTherefore, for the discussion on vaccination strategies against COVID-19, the available immune memory against related viruses should be part of the consideration.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 500}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Pandemic infection rates are deterministic but can therefore be modeled\n\nAbstract:\nThe covid-19 infection rates for a large number of infections collected from a large number of different sites are well defined with a negligible scatter.\nThe simplest invertible iterated map, exponential growth and decay, emerges from country-wide histograms whenever Tchebychev's inequality is satisfied to within several decimal places.\nThis is one point.\nAnother is that failed covid-19 pandemic model predictions have been reported repeatedly by the news media.\nModel predictions fail because the observed infection rates are beyond modeling: any model that uses fixed rates or uses memory or averages of past rates cannot reproduce the data on active infections.\nWhen those possibilities are ruled out, then little is left.\nUnder lockdown and social distancing, the rates unfold daily in small but unforeseeable steps, they are algorithmically complex.\nWe can, however, use two days in the daily data, today and any single day in the past (generally yesterday), to make a useful forecast of future infections.\nNo model provides results better than this simple forecast.\nWe analyze the actual doubling times for covid-19 data and compare them with our predicted doubling times.\nFlattening and peaking are precisely defined.\nWe identify and study the separate effects of social distancing vs recoveries in the daily infection rates.\nSocial distancing can only cause flattening but recoveries are required in order for the active infections to peak and decay.\nThree models and their predictions are analyzed.\nPandemic data for Austria, Germany, Italy, the USA, the UK, Finland, China, Taiwan, and Sweden are discussed.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Model predictions fail because the observed infection rates are beyond modeling: any model that uses fixed rates or uses memory or averages of past rates cannot reproduce the data on active infections.\"]}", "id": 501}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Covid-19 is infecting quite a few people, many with vicious effects.\n\nAbstract:\nThe recently discovered novel coronavirus, SARS-CoV-2 (COVID-19 virus), has brought the whole world to standstill with critical challenges, affecting both health and economic sectors worldwide.\nAlthough initially, this pandemic was associated with causing severe pulmonary and respiratory disorders, recent case studies reported the association of cerebrovascular-neurological dysfunction in COVID-19 patients, which is also life-threatening.\nSeveral SARS-CoV-2 positive case studies have been reported where there are mild or no symptoms of this virus.\nHowever, a selection of patients are suffering from large artery ischemic strokes.\nAlthough the pathophysiology of the SARS-CoV-2 virus affecting the cerebrovascular system has not been elucidated yet, researchers have identified several pathogenic mechanisms, including a role for the ACE2 receptor.\nTherefore, it is extremely crucial to identify the risk factors related to the progression and adverse outcome of cerebrovascular-neurological dysfunction in COVID-19 patients.\nSince many articles have reported the effect of smoking (tobacco and cannabis) and vaping in cerebrovascular and neurological systems, and considering that smokers are more prone to viral and bacterial infection compared to non-smokers, it is high time to explore the probable correlation of smoking in COVID-19 patients.\nHerein, we have reviewed the possible role of smoking and vaping on cerebrovascular and neurological dysfunction in COVID-19 patients, along with potential pathogenic mechanisms associated with it.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 502}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: A study of COVID-19 patients in the Netherlands did not find the disease to be associated with cytokine storm, as previously suggested. \n\nAbstract:\nCytokine storm in COVID-19 is characterized by an excessive inflammatory response to SARS-CoV-2 that is caused by a dysregulated immune system of the host.\nWe are proposing a new hypothesis that SARS-CoV-2 mediated inflammation of nucleus tractus solitarius (NTS) may be responsible for the cytokine storm in COVID 19.\nThe inflamed NTS may result in a dysregulated cholinergic anti-inflammatory pathway and hypothalamic-pituitary-adrenal axis.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Cytokine storm in COVID-19 is characterized by an excessive inflammatory response to SARS-CoV-2 that is caused by a dysregulated immune system of the host.\"]}", "id": 503}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Surgical Masks Stop Transmission Of COVID-19 From Symptomatic People\n\nAbstract:\nBACKGROUND Coronavirus disease 2019 (COVID-19) has rapidly evolved as a viral pandemic.\nCountries worldwide have been affected by the recent outbreak caused by the SARS (severe acute respiratory syndrome)-CoV-2 virus.\nAs with prior viral pandemics, health-care workers are at increased risk.\nOrthopaedic surgical procedures are common in health-care systems, ranging from emergency to elective procedures.\nMany orthopaedic surgical procedures are life or limb-saving and cannot be postponed during the COVID-19 pandemic because of potential patient harm.\nOur goal is to analyze how orthopaedic surgeons can perform medically necessary procedures during the pandemic and to help guide decision-making perioperatively.\nMETHODS We performed a review of the existing literature regarding COVID-19 and prior viral outbreaks to help guide clinical practice in terms of how to safely perform medically necessary orthopaedic procedures during the pandemic for both asymptomatic patients and high-risk (e.g., COVID-19-positive) patients.\nWe created a classification system based on COVID-19 positivity, patient health status, and COVID-19 prevalence to help guide perioperative decision-making.\nRESULTS We advocate that only urgent and emergency surgical procedures be performed.\nBy following recommendations from the American College of Surgeons, the Centers for Disease Control and Prevention, and the recent literature, safe orthopaedic surgery and perioperative care can be performed.\nScreening measures are needed for patients and perioperative teams.\nSurgeons and perioperative teams at risk for contracting COVID-19 should use appropriate personal protective equipment (PPE), including N95 respirators or powered air-purifying respirators (PAPRs), when risk of viral spread is high.\nWhen preparing for medically necessary orthopaedic procedures during the pandemic, our classification system will help to guide decision-making.\nA multidisciplinary care plan is needed to ensure patient safety with medically necessary orthopaedic procedures during the COVID-19 pandemic.\nCONCLUSIONS Orthopaedic surgery during the COVID-19 pandemic can be performed safely when medically necessary but should be rare for COVID-19-positive or high-risk patients.\nAppropriate screening, PPE use, and multidisciplinary care will allow for safe medically necessary orthopaedic surgery to continue during the COVID-19 pandemic.\nLEVEL OF EVIDENCE Prognostic Level V. See Instructions for Authors for a complete description of levels of evidence.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Surgeons and perioperative teams at risk for contracting COVID-19 should use appropriate personal protective equipment (PPE), including N95 respirators or powered air-purifying respirators (PAPRs), when risk of viral spread is high.\"]}", "id": 504}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: People with diabetes have not a higher risk for complications from coronavirus\n\nAbstract:\nObjective: To undertake a review and critical appraisal of published/preprint reports that offer methods of determining the effects of hypertension, diabetes, stroke, cancer, kidney issues, and high-cholesterol on COVID-19 disease severity.\nData sources: Google Scholar, PubMed, COVID-19 Open Research Dataset: a resource of over 128,000 scholarly articles, including over 59,000 articles with full text related to COVID-19, SARS-CoV-2, and coronaviruses.\nMethods: A search was conducted by two authors independently on the freely available COVID-19 Open Research Dataset (CORD-19).\nWe developed an automated search engine to screen a total of 59,000 articles in a few seconds.\nThe search engine was built using a retrieval function that ranks a set of documents based on the query terms appearing in each document regardless of their proximity within the document.\nFiltering of the articles was then undertaken using keywords and questions, e.g. \"Effects of diabetes on COVID/normal coronavirus/SARS-CoV-2/nCoV/COVID-19 disease severity, mortality?\".\nThe search terms were repeated for all the comorbidities considered in this paper.\nAdditional articles were retrieved by searching via Google Scholar and PubMed.\nFindings: A total of 54 articles were considered for a full review.\nIt was observed that diabetes, hypertension, and cholesterol levels possess an apparent relation to COVID-19 severity.\nOther comorbidities, such as cancer, kidney disease, and stroke, must be further evaluated to determine a strong relationship to the virus.\nReports associating cancer, kidney disease, and stroke with COVID-19 should be carefully interpreted, not only because of the size of the samples, but also because patients could be old, have a history of smoking, or have any other clinical condition suggesting that these factors might be associated with the poor COVID-19 outcomes rather than the comorbidity itself.\nSuch reports could lead many oncologists and physicians to change their treatment strategies without solid evidence and recommendations.\nFurther research regarding this relationship and its clinical management is warranted.\nAdditionally, treatment options must be examined further to provide optimal treatment and ensure better outcomes for patients suffering from these comorbidities.\nIt should be noted that, whether definitive measurements exist or not, the care of patients as well as the research involved should be largely prioritized to tackle this deadly pandemic.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"It was observed that diabetes, hypertension, and cholesterol levels possess an apparent relation to COVID-19 severity.\"]}", "id": 505}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: it is safe take Advil to bring down your temperature caused by covid-19\n\nAbstract:\n: The COVID-19 pandemic is challenging our cardiovascular care of patients with heart diseases.\nIn the setting of pericardial diseases, there are two possible different scenarios to consider: the patient being treated for pericarditis who subsequently becomes infected with SARS-CoV-2, and the patient with COVID-19 who develops pericarditis or pericardial effusion.\nIn both conditions, clinicians may be doubtful regarding the safety of nonsteroidal anti-inflammatory drugs (NSAIDs), corticosteroids, colchicine, and biological agents, such as anti-IL1 agents (e.g. anakinra), that are the mainstay of therapy for pericarditis.\nFor NSAIDs, there is no clear scientific evidence linking ibuprofen and other NSAIDs to worsening of COVID-19; however, it seems prudent to continue them, if necessary to control pericarditis, and on the other hand, to prefer paracetamol for fever and systemic symptoms related to COVID-19.\nTreatments with corticosteroids, colchicine, and anakinra appear well tolerated in the context of COVID-19 infection and are currently actively evaluated as potential therapeutic options for COVID infection at different stages of the disease.\nOn this basis, currently most treatments for pericarditis do not appear contraindicated also in the presence of possible COVID-19 infection and should not be discontinued, and some (corticosteroids, colchicine, and anakinra) can be considered to treat both conditions.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"For NSAIDs, there is no clear scientific evidence linking ibuprofen and other NSAIDs to worsening of COVID-19; however, it seems prudent to continue them, if necessary to control pericarditis, and on the other hand, to prefer paracetamol for fever and systemic symptoms related to COVID-19.\"]}", "id": 506}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: it is \"significantly\" improving oronavirus patients symptoms when traiting them with Vitamin C.\n\nAbstract:\nOBJECTIVE Coronavirus disease 2019 (COVID-19) is a fatal and fast-spreading viral infection.\nTo date, the number of COVID-19 patients worldwide has crossed over six million with over three hundred and seventy thousand deaths (according to the data from World Health Organization; updated on 2 June 2020).\nAlthough COVID-19 can be rapidly diagnosed, efficient clinical treatment of COVID-19 remains unavailable, resulting in high fatality.\nSome clinical trials have identified vitamin C (VC) as a potent compound pneumonia management.\nIn addition, glycyrrhizic acid (GA) is clinically as an anti-inflammatory medicine against pneumonia-induced inflammatory stress.\nWe hypothesized that the combination of VC and GA is a potential option for treating COVID-19.\nMETHODS The aim of this study was to determine pharmacological targets and molecular mechanisms of VC + GA treatment for COVID-19, using bioinformational network pharmacology.\nRESULTS We uncovered optimal targets, biological processes and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways of VC + GA against COVID-19.\nOur findings suggested that combinatorial VC and GA treatment for COVID-19 was associated with elevation of immunity and suppression of inflammatory stress, including activation of the T cell receptor signaling pathway, regulation of Fc gamma R-mediated phagocytosis, ErbB signaling pathway and vascular endothelial growth factor signaling pathway.\nWe also identified 17 core targets of VC + GA, which suggest as antimicrobial function.\nCONCLUSIONS For the first time, our study uncovered the pharmacological mechanism underlying combined VC and GA treatment for COVID-19.\nThese results should benefit efforts to address the most pressing problem currently facing the world.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 507}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: a small number of pets worldwide, including cats and dogs, can be infected with the virus that causes COVID-19, mostly after close contact with people with COVID-19.\n\nAbstract:\nBackground: The Greek authorities implemented the strong social distancing measures within the first few weeks after the first confirmed case of the virus to curtail the COVID-19 growth rate.\nObjectives: To estimate the effect of the two-stage strong social distancing measures, the closure of all non-essential shopping centers and businesses on March 16 and the shelter in place orders (SIPOs) on March 23 on the COVID-19 growth rate in Greece Methods: We obtained data on COVID-19 cases in Greece from February 26th through May 4th from publicly available sources.\nAn interrupted time-series regression analysis was used to estimate the effect of the measures on the exponential growth of confirmed COVID-19 cases, controlling for the number of daily testing, and weekly fixed-effects.\nResults: The growth rate of the COVID-19 cases in the pre-policies implementation period was positive as expected (p=0.003).\nBased on the estimates of the interrupted time-series, our results indicate that the SIPO on March 23 significantly slowed the growth rate of COVID-19 in Greece (p=0.04).\nHowever, we did not find evidence on the effectiveness of standalone and partial measures such as the non-essential business closures implemented on March 16 on the COVID-19 spread reduction.\nDiscussion: The combined social distancing measures implemented by the Greek authorities within the first few weeks after the first confirmed case of the virus reduced the COVID-19 growth rate.\nThese findings provide evidence and highlight the effectiveness of these measures to flatten the curve and to slow the spread of the virus.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 508}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: To minimize coronavirus risk, use alcohol for sanitizing, not for drinking\n\nAbstract:\nAre people drinking more now that they are locked in a house with their nearest and dearest, facing job loss or having lost a job, bored and stressed? Probably.\nIs drinking a healthy way of coping?\nNo.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Is drinking a healthy way of coping?\", \"No.\"]}", "id": 509}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Sars-cov-2 viral load is associated with increased disease severity and mortality\n\nAbstract:\nThe relationship between SARS-CoV-2 viral load and risk of disease progression remains largely undefined in coronavirus disease 2019 (COVID-19).\nHere, we quantify SARS-CoV-2 viral load from participants with a diverse range of COVID-19 disease severity, including those requiring hospitalization, outpatients with mild disease, and individuals with resolved infection.\nWe detected SARS-CoV-2 plasma RNA in 27% of hospitalized participants, and 13% of outpatients diagnosed with COVID-19.\nAmongst the participants hospitalized with COVID-19, we report that a higher prevalence of detectable SARS-CoV-2 plasma viral load is associated with worse respiratory disease severity, lower absolute lymphocyte counts, and increased markers of inflammation, including C-reactive protein and IL-6.\nSARS-CoV-2 viral loads, especially plasma viremia, are associated with increased risk of mortality.\nOur data show that SARS-CoV-2 viral loads may aid in the risk stratification of patients with COVID-19, and therefore its role in disease pathogenesis should be further explored.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"SARS-CoV-2 viral loads, especially plasma viremia, are associated with increased risk of mortality.\"]}", "id": 510}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Vitamin D Does Not Cure Covid-19 But It Plays an Important Role\n\nAbstract:\nThe severity of coronavirus 2019 infection (COVID-19) is determined by the presence of pneumonia, severe acute respiratory distress syndrome (SARS-CoV-2), myocarditis, microvascular thrombosis and/or cytokine storms, all of which involve underlying inflammation.\nA principal defence against uncontrolled inflammation, and against viral infection in general, is provided by T regulatory lymphocytes (Tregs).\nTreg levels have been reported to be low in many COVID-19 patients and can be increased by vitamin D supplementation.\nLow vitamin D levels have been associated with an increase in inflammatory cytokines and a significantly increased risk of pneumonia and viral upper respiratory tract infections.\nVitamin D deficiency is associated with an increase in thrombotic episodes, which are frequently observed in COVID-19.\nVitamin D deficiency has been found to occur more frequently in patients with obesity and diabetes.\nThese conditions are reported to carry a higher mortality in COVID-19.\nIf vitamin D does in fact reduce the severity of COVID-19 in regard to pneumonia/ARDS, inflammation, inflammatory cytokines and thrombosis, it is our opinion that supplements would offer a relatively easy option to decrease the impact of the pandemic.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"A principal defence against uncontrolled inflammation, and against viral infection in general, is provided by T regulatory lymphocytes (Tregs).\", \"Treg levels have been reported to be low in many COVID-19 patients and can be increased by vitamin D supplementation.\", \"Low vitamin D levels have been associated with an increase in inflammatory cytokines and a significantly increased risk of pneumonia and viral upper respiratory tract infections.\", \"Vitamin D deficiency is associated with an increase in thrombotic episodes, which are frequently observed in COVID-19.\", \"Vitamin D deficiency has been found to occur more frequently in patients with obesity and diabetes.\", \"These conditions are reported to carry a higher mortality in COVID-19.\", \"If vitamin D does in fact reduce the severity of COVID-19 in regard to pneumonia/ARDS, inflammation, inflammatory cytokines and thrombosis, it is our opinion that supplements would offer a relatively easy option to decrease the impact of the pandemic.\"]}", "id": 511}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Hypothesis: Angiotensin-converting enzyme inhibitors and angiotensin receptor blockers may increase the risk of severe COVID-19\n\nAbstract:\nIntravenous infusions of angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin receptor blockers (ARBs) in experimental animals increase the numbers of angiotensin-converting enzyme 2 (ACE2) receptors in the cardiopulmonary circulation.\nACE2 receptors serve as binding sites for SARS-CoV-2 virions in the lungs.\nPatients who take ACEIs and ARBS may be at increased risk of severe disease outcomes due to SARS-CoV-2 infections.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Patients who take ACEIs and ARBS may be at increased risk of severe disease outcomes due to SARS-CoV-2 infections.\"]}", "id": 512}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Sars-cov-2 replication triggers an mda-5-dependent interferon production which is unable to efficiently control replication\n\nAbstract:\nSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the third highly pathogenic coronavirus to spill over to humans in less than 20 years, after SARS-CoV-1 in 2002-2003 and Middle East respiratory syndrome (MERS)-CoV in 2012.\nSARS-CoV-2 is the etiologic agent of coronavirus disease 19 (COVID-19), which ranges from mild respiratory symptoms to severe lung injury and death in the most severe cases.\nThe COVID-19 pandemic is currently a major health issue worldwide.\nImmune dysregulation characterized by altered innate cytokine responses is thought to contribute to the pathology of COVID-19 patients, which is a testimony of the fundamental role of the innate immune response against SARS-CoV-2.\nHere, we further characterized the host cell antiviral response against SARS-CoV-2 by using primary human airway epithelia and immortalized model cell lines.\nWe mainly focused on the type I and III interferon (IFN) responses, which lead to the establishment of an antiviral state through the expression of IFN-stimulated genes (ISGs).\nOur results demonstrate that both primary airway epithelial cells and model cell lines elicit a robust immune response characterized by a strong induction of type I and III IFN through the detection of viral pathogen molecular patterns (PAMPs) by melanoma differentiation associated gene (MDA)-5.\nHowever, despite the high levels of type I and III IFNs produced in response to SARS-CoV-2 infection, the IFN response was unable to control viral replication, whereas IFN pre-treatment strongly inhibited viral replication and de novo production of infectious virions.\nTaken together, these results highlight the complex and ambiguous interplay between viral replication and the timing of IFN responses.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Taken together, these results highlight the complex and ambiguous interplay between viral replication and the timing of IFN responses.\"]}", "id": 513}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: The main warning signs of COVID-19, the disease caused by the new coronavirus, are fever, fatigue, and a dry cough.\n\nAbstract:\nINTRODUCTION: Neurological manifestations can occur during coronavirus disease 19 (COVID-19).\nSeveral pathogenic mechanisms have been hypothesized, without conclusive results.\nIn this study, we evaluated the most frequent neurological symptoms in a cohort of hospitalized COVID-19 patients, and also investigated the possible relationship between plasmatic inflammatory indices and olfactory disorders (ODs) and between muscle pain and creatine kinase (CK).\nMETHODS: We consecutively enrolled hospitalized COVID-19 patients.\nA structured questionnaire concerning typical and neurological symptoms, focusing on headache, dizziness, ODs, taste disorders (TDs), and muscle pain, was administrated by telephone interviews.\nRESULTS: Common neurological symptoms were reported in the early phase of the disease, with a median onset ranging from 1 to 3 days.\nHeadache showed tension-type features and was more frequently associated with a history of headache.\nPatients with ODs less frequently needed oxygen therapy.\nInflammatory indices did not significantly differ between patients with and without ODs.\nMuscle pain did not show any association with CK level but was more frequently associated with arthralgia and headache.\nCONCLUSION: In our cohort, ODs were an early symptom of COVID-19, more frequently reported by patients with milder forms of disease.\nHeadache in association with arthralgia and muscle pain seems to reflect the common symptoms of the flu-like syndrome, and not COVID-19 infection-specific.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"CONCLUSION: In our cohort, ODs were an early symptom of COVID-19, more frequently reported by patients with milder forms of disease.\"]}", "id": 514}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: A study from the Journal of Hospital Infection found that wearing a face covering slashed the risk of infection by 24% for a simple cotton covering and up to 99% for a professional, medical-grade filtration mask. \n\nAbstract:\nFace masks are an avenue to curb the spread of coronavirus, but few people in Western societies wear face masks.\nSocial scientists have rarely studied face mask wearing, leaving little guidance for methods to encourage these behaviours.\nIn the current article, we provide an approach to address this issue by developing the 32-item and 8-dimension Face Mask Perceptions Scale (FMPS).\nWe begin by developing an over-representative item list in a qualitative study, wherein participants' responses are used to develop items to ensure content relevance.\nThis item list is then reduced via exploratory factor analysis in a second study, and the eight dimensions of the scale are supported.\nWe also support the validity of the FMPS, as the scale significantly relates to both face mask wearing and health perceptions.\nWe lastly confirm the factor structure of the FMPS in a third study via confirmatory factor analysis.\nFrom these efforts, we identify an avenue that social scientists can aid in preventing coronavirus and illness more broadly - by studying face mask perceptions and behaviours.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Face masks are an avenue to curb the spread of coronavirus, but few people in Western societies wear face masks.\"]}", "id": 515}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: with autoimmune conditions such as lupus, a person experiences \"dysregulations of the immune system,\" meaning the immune system itself is compromised or malfunctioning\n\nAbstract:\nSARS-CoV-2 is the coronavirus agent of the COVID-19 pandemic causing high mortalities.\nIn contrast, the widely spread human coronaviruses OC43, HKU1, 229E, and NL63 tend to cause only mild symptoms.\nThe present study shows, by in silico analysis, that these common human viruses are expected to induce immune memory against SARS-CoV-2 by sharing protein fragments (antigen epitopes) for presentation to the immune system by MHC class I. A list of such epitopes is provided.\nThe number of these epitopes and the prevalence of the common coronaviruses suggest that a large part of the world population has some degree of specific immunity against SARS-CoV-2 already, even without having been infected by that virus.\nFor inducing protection, booster vaccinations enhancing existing immunity are less demanding than primary vaccinations against new antigens.\nTherefore, for the discussion on vaccination strategies against COVID-19, the available immune memory against related viruses should be part of the consideration.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 516}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: diabetes significantly increases coronavirus sufferers risk of dying\n\nAbstract:\nAIMS: The 2019 novel coronavirus disease (COVID-19) emerged in Wuhan, China, and was characterized as a pandemic by the World Health Organization.\nDiabetes is an established risk associated with poor clinical outcomes, but the association of diabetes with COVID-19 has not been reported yet.\nMETHODS: In this cohort study, we retrospectively reviewed 258 consecutive hospitalized COVID-19 patients with or without diabetes at the West Court of Union Hospital in Wuhan, China, recruited from January 29 to February 12, 2020.\nThe clinical features, treatment strategies and prognosis data were collected and analyzed.\nPrognosis was followed up until March 12, 2020.\nRESULTS: Of the 258 hospitalized patients (63 with diabetes) with COVID-19, the median age was 64 years (range 23-91), and 138 (53.5%) were male.\nCommon symptoms included fever (82.2%), dry cough (67.1%), polypnea (48.1%), and fatigue (38%).\nPatients with diabetes had significantly higher leucocyte and neutrophil counts, and higher levels of fasting blood glucose, serum creatinine, urea nitrogen and creatine kinase isoenzyme MB at admission compared with those without diabetes.\nCOVID-19 patients with diabetes were more likely to develop severe or critical disease conditions with more complications, and had higher incidence rates of antibiotic therapy, non-invasive and invasive mechanical ventilation, and death (11.1% vs. 4.1%).\nCox proportional hazard model showed that diabetes (adjusted hazard ratio [aHR] = 3.64; 95% confidence interval [CI]: 1.09, 12.21) and fasting blood glucose (aHR = 1.19; 95% CI: 1.08, 1.31) were associated with the fatality due to COVID-19, adjusting for potential confounders.\nCONCLUSIONS: Diabetes mellitus is associated with increased disease severity and a higher risk of mortality in patients with COVID-19.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"COVID-19 patients with diabetes were more likely to develop severe or critical disease conditions with more complications, and had higher incidence rates of antibiotic therapy, non-invasive and invasive mechanical ventilation, and death (11.1% vs. 4.1%).\"]}", "id": 517}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Plasmin cascade mediates thrombolytic events in sars-cov-2 production via complement and platelet-activating systems\n\nAbstract:\nRecently emerged beta-coronavirus, SARS-CoV-2 has resulted in the current pandemic designated COVID-19.\nCOVID-19 manifests as severe illness exhibiting systemic inflammatory response syndrome, acute respiratory distress syndrome (ARDS), thrombotic events, and shock, exacerbated further by co-morbidities and age1\u20133.\nRecent clinical reports suggested that the pulmonary failure seen in COVID-19 may not be solely driven by acute ARDS, but also microvascular thrombotic events, likely driven by complement activation4,5.\nHowever, it is not fully understood how the SARS-CoV-2 infection mechanisms mediate thrombotic events, and whether such mechanisms and responses are unique to SARS-CoV-2 infection, compared to other respiratory infections.\nWe address these questions here, in the context of normal lung epithelia, in vitro and in vivo, using publicly available data.\nOur results indicate that plasmin is a crucial mediator which primes interactions between complement and platelet-activating systems in lung epithelia upon SARS-CoV-2 infection, with a potential for therapeutic intervention.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Our results indicate that plasmin is a crucial mediator which primes interactions between complement and platelet-activating systems in lung epithelia upon SARS-CoV-2 infection, with a potential for therapeutic intervention.\"]}", "id": 518}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: with autoimmune conditions such as lupus, a person experiences \"dysregulations of the immune system,\" meaning the immune system itself is compromised or malfunctioning\n\nAbstract:\nThe novel coronavirus Covid-19 follows transmission route and clinical presentation of all community-acquired coronaviruses.\nInstead, the rate of transmission is significative higher, with a faster spread of the virus responsible of the worldwide outbreak and a significative higher mortality rate due to the development of a severe lung injury.\nMost noteworthy is the distribution of death rate among age groups.\nChildren and younger people are almost protected from severe clinical presentation.\nPossible explanation of this phenomenon could be the ability of past vaccinations (especially tetanic, diphtheria toxoids and inactivated bacteria as pertussis) to stimulate immune system and to generate a scattered immunity against non-self antigens in transit, as coronaviruses and other community-circulating viruses and make immune system readier to develop specific immunity against Covid-19.\nThe first support to this hypothesis is the distribution of mortality rate during historical pandemics (\"Spanish flu\" 1918, \"Asian flu\" 1956 and \"the Hong Kong flu\" 1968) among age groups before and after the introduction of vaccines.\nThe immunological support to the hypothesis derives from recent studies about immunotherapy for malignancies, which propose the use of oncolytic vaccines combined with toxoids in order to exploit CD4 + memory T cell recall in supporting the ongoing anti-tumour response.\nAccording to this hypothesis vaccine formulations (tetanus, diphtheria, Bordetella pertussis) could be re-administrate after the first contact with Covid-19, better before the development of respiratory severe illness and of course before full-blown ARDS (Acute Respiratory Distress Syndrome).\nThe CD4 + memory exploiting could help immune system to recall immunity of already know antigens against coronaviruses, avoiding or limiting \"lung crash\" until virus specific immunity develops and making it faster and prolonged.\nFinally, this administration could be helpful not only in already infected patients, but also before infection.\nIn fact, people could have an immune system more ready when the contact with the Covid-19 will occur.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 519}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: The most important thing to know about using over-the-counter medications to treat COVID-19 is that none of these common drugstore products are actually going to treat the virus itself.\n\nAbstract:\n: The COVID-19 pandemic is challenging our cardiovascular care of patients with heart diseases.\nIn the setting of pericardial diseases, there are two possible different scenarios to consider: the patient being treated for pericarditis who subsequently becomes infected with SARS-CoV-2, and the patient with COVID-19 who develops pericarditis or pericardial effusion.\nIn both conditions, clinicians may be doubtful regarding the safety of nonsteroidal anti-inflammatory drugs (NSAIDs), corticosteroids, colchicine, and biological agents, such as anti-IL1 agents (e.g. anakinra), that are the mainstay of therapy for pericarditis.\nFor NSAIDs, there is no clear scientific evidence linking ibuprofen and other NSAIDs to worsening of COVID-19; however, it seems prudent to continue them, if necessary to control pericarditis, and on the other hand, to prefer paracetamol for fever and systemic symptoms related to COVID-19.\nTreatments with corticosteroids, colchicine, and anakinra appear well tolerated in the context of COVID-19 infection and are currently actively evaluated as potential therapeutic options for COVID infection at different stages of the disease.\nOn this basis, currently most treatments for pericarditis do not appear contraindicated also in the presence of possible COVID-19 infection and should not be discontinued, and some (corticosteroids, colchicine, and anakinra) can be considered to treat both conditions.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"For NSAIDs, there is no clear scientific evidence linking ibuprofen and other NSAIDs to worsening of COVID-19; however, it seems prudent to continue them, if necessary to control pericarditis, and on the other hand, to prefer paracetamol for fever and systemic symptoms related to COVID-19.\"]}", "id": 520}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: dexamethasone has only been shown to have positive effects in patients who require supplemental oxygen.\n\nAbstract:\nRecent announcements indicated, without sharing any distinct published set of results, that the corticosteroid dexamethasone may reduce mortality of severe COVID-19 patients only.\nThe recent Coronavirus [severe acute respiratory syndrome (SARS)-CoV-2]-associated multiorgan disease, called COVID-19, has high morbidity and mortality due to autoimmune destruction of the lungs stemming from the release of a storm of pro-inflammatory cytokines.\nDefense against this Corona virus requires activated T cells and specific antibodies.\nInstead, cytokines are responsible for the serious sequelae of COVID-19 that damage the lungs.\nDexamethasone is a synthetic corticosteroid approved by the FDA 1958 as a broad-spectrum immunosuppressor and it is about 30 times as active and with longer duration of action (2-3 days) than cortisone.\nDexamethasone would limit the production of and damaging effect of the cytokines, but will also inhibit the protective function of T cells and block B cells from making antibodies, potentially leading to increased plasma viral load that will persist after a patient survives SARS.\nMoreover, dexamethasone would block macrophages from clearing secondary, nosocomial, infections.\nHence, dexamethasone may be useful for the short-term in severe, intubated, COVID-19 patients, but could be outright dangerous during recovery since the virus will not only persist, but the body will be prevented from generating protective antibodies.\nInstead, a pulse of intravenous dexamethasone may be followed by administration of nebulized triamcinolone (6 times as active as cortisone) to concentrate in the lungs only.\nThese corticosteroids could be given together with the natural flavonoid luteolin because of its antiviral and anti-inflammatory properties, especially its ability to inhibit mast cells, which are the main source of cytokines in the lungs.\nAt the end, we should remember that \"The good physician treats the disease; the great physician treats the patient who has the disease\" [Sir William Osler's (1849-1919)].", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Hence, dexamethasone may be useful for the short-term in severe, intubated, COVID-19 patients, but could be outright dangerous during recovery since the virus will not only persist, but the body will be prevented from generating protective antibodies.\"]}", "id": 521}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: A study from the Journal of Hospital Infection found that wearing a face covering slashed the risk of infection by 24% for a simple cotton covering and up to 99% for a professional, medical-grade filtration mask. \n\nAbstract:\nEvidence that face masks provide effective protection against respiratory infections in the community is scarce.\nHowever, face masks are widely used by health workers as part of droplet precautions when caring for patients with respiratory infections.\nIt would therefore be reasonable to suggest that consistent widespread use of face masks in the community could prevent further spread of the Severe Acute Respiratory Syndrome-Coronavirus 2 (SARS-CoV-2).\nIn this study we examine public face mask wearing in Uganda where a proportion wears masks to protect against acquiring, and the other to prevent from transmitting SARS-CoV-2.\nThe objective of this study was to determine what percentage of the population would have to wear face masks to reduce susceptibility to and infectivity of COVID-19 in Uganda, keeping the basic reproduction number below unity and/or flattening the curve.\nWe used an SEIAQRD model for the analysis.\nResults show that implementation of facemasks has a relatively large impact on the size of the coronavirus epidemic in Uganda.\nWe find that the critical mask adherence is 5 per 100 when 80% wear face masks.\nA cost-effective analysis shows that utilizing funds to provide 1 public mask to the population has a per capita compounded cost of USD 1.34.\nIf provision of face masks is done simultaneously with supportive care, the per capita compounded cost is USD 1.965, while for the case of only treatment and no provision of face masks costs each Ugandan USD 4.0579.\nWe conclude that since it is hard to achieve a 100% adherence to face masks, government might consider provision of face masks in conjunction with provision of care.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Evidence that face masks provide effective protection against respiratory infections in the community is scarce.\", \"However, face masks are widely used by health workers as part of droplet precautions when caring for patients with respiratory infections.\", \"It would therefore be reasonable to suggest that consistent widespread use of face masks in the community could prevent further spread of the Severe Acute Respiratory Syndrome-Coronavirus 2 (SARS-CoV-2).\"]}", "id": 522}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Scientists believe cannabis could help prevent, treat coronavirus\n\nAbstract:\nBACKGROUND: An epidemic of Coronavirus Disease 2019 (COVID-19) began in December 2019 and triggered a Public Health Emergency of International Concern (PHEIC).\nWe aimed to find risk factors for the progression of COVID-19 to help reducing the risk of critical illness and death for clinical help.\nMETHODS: The data of COVID-19 patients until March 20, 2020 were retrieved from four databases.\nWe statistically analyzed the risk factors of critical/mortal and non-critical COVID-19 patients with meta-analysis.\nRESULTS: Thirteen studies were included in Meta-analysis, including a total number of 3027 patients with SARS-CoV-2 infection.\nMale, older than 65, and smoking were risk factors for disease progression in patients with COVID-19 (male: OR\u00e2\u0080\u00af=\u00e2\u0080\u00af1.76, 95% CI (1.41, 2.18), P < 0.00001; age over 65 years old: OR =6.06, 95% CI(3.98, 9.22), P < 0.00001; current smoking: OR =2.51, 95% CI(1.39, 3.32), P\u00e2\u0080\u00af=\u00e2\u0080\u00af0.0006).\nThe proportion of underlying diseases such as hypertension, diabetes, cardiovascular disease, and respiratory disease were statistically significant higher in critical/mortal patients compared to the non-critical patients (diabetes: OR=3.68, 95% CI (2.68, 5.03), P < 0.00001; hypertension: OR\u00e2\u0080\u00af=\u00e2\u0080\u00af2.72, 95% CI (1.60,4.64), P\u00e2\u0080\u00af=\u00e2\u0080\u00af0.0002; cardiovascular disease: OR\u00e2\u0080\u00af=\u00e2\u0080\u00af5.19, 95% CI(3.25, 8.29), P < 0.00001; respiratory disease: OR\u00e2\u0080\u00af=\u00e2\u0080\u00af5.15, 95% CI(2.51, 10.57), P < 0.00001).\nClinical manifestations such as fever, shortness of breath or dyspnea were associated with the progression of disease [fever: 0R\u00e2\u0080\u00af=\u00e2\u0080\u00af0.56, 95% CI (0.38, 0.82), P\u00e2\u0080\u00af=\u00e2\u0080\u00af0.003;shortness of breath or dyspnea: 0R=4.16, 95% CI (3.13, 5.53), P < 0.00001].\nLaboratory examination such as aspartate amino transferase(AST) > 40U/L, creatinine(Cr) &#8805; 133mol/L, hypersensitive cardiac troponin I(hs-cTnI) > 28pg/mL, procalcitonin(PCT) > 0.5ng/mL, lactatede hydrogenase(LDH) > 245U/L, and D-dimer > 0.5mg/L predicted the deterioration of disease while white blood cells(WBC)<4\u00e2\u0080\u00af\u00d7\u00e2\u0080\u00af109/L meant a better clinical status[AST > 40U/L:OR=4.00, 95% CI (2.46, 6.52), P < 0.00001; Cr &#8805; 133\u00b5mol/L: OR\u00e2\u0080\u00af=\u00e2\u0080\u00af5.30, 95% CI (2.19, 12.83), P\u00e2\u0080\u00af=\u00e2\u0080\u00af0.0002; hs-cTnI > 28 pg/mL: OR\u00e2\u0080\u00af=\u00e2\u0080\u00af43.24, 95% CI (9.92, 188.49), P < 0.00001; PCT > 0.5 ng/mL: OR\u00e2\u0080\u00af=\u00e2\u0080\u00af43.24, 95% CI (9.92, 188.49), P < 0.00001;LDH > 245U/L: OR\u00e2\u0080\u00af=\u00e2\u0080\u00af43.24, 95% CI (9.92, 188.49), P < 0.00001; D-dimer > 0.5mg/L: OR\u00e2\u0080\u00af=\u00e2\u0080\u00af43.24, 95% CI (9.92, 188.49), P < 0.00001; WBC < 4\u00e2\u0080\u00af\u00d7\u00e2\u0080\u00af109/L: OR\u00e2\u0080\u00af=\u00e2\u0080\u00af0.30, 95% CI (0.17, 0.51), P < 0.00001].\nCONCLUSION: Male, aged over 65, smoking patients might face a greater risk of developing into the critical or mortal condition and the comorbidities such as hypertension, diabetes, cardiovascular disease, and respiratory diseases could also greatly affect the prognosis of the COVID-19.\nClinical manifestation such as fever, shortness of breath or dyspnea and laboratory examination such as WBC, AST, Cr, PCT, LDH, hs-cTnI and D-dimer could imply the progression of COVID-19.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"CONCLUSION: Male, aged over 65, smoking patients might face a greater risk of developing into the critical or mortal condition and the comorbidities such as hypertension, diabetes, cardiovascular disease, and respiratory diseases could also greatly affect the prognosis of the COVID-19.\"]}", "id": 523}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: camostat mesylate cure coronavirus.\n\nAbstract:\nAIMS: A new human coronavirus (HCoV), which has been designated SARS-CoV-2, began spreading in December 2019 in Wuhan City, China causing pneumonia called COVID-19.\nThe spread of SARS-CoV-2 has been faster than any other coronaviruses that have succeeded in crossing the animal-human barrier.\nThere is concern that this new virus will spread around the world as did the previous two HCoVs-Severe Acute Respiratory Syndrome (SARS) and Middle East Respiratory Syndrome (MERS)-each of which caused approximately 800 deaths in the years 2002 and 2012, respectively.\nThus far, 11,268 deaths have been reported from the 258,842 confirmed infections in 168 countries.\nMAIN METHODS: In this study, the RNA-dependent RNA polymerase (RdRp) of the newly emerged coronavirus is modeled, validated, and then targeted using different anti-polymerase drugs currently on the market that have been approved for use against various viruses.\nKEY FINDINGS: The results suggest the effectiveness of Ribavirin, Remdesivir, Sofosbuvir, Galidesivir, and Tenofovir as potent drugs against SARS-CoV-2 since they tightly bind to its RdRp.\nIn addition, the results suggest guanosine derivative (IDX-184), Setrobuvir, and YAK as top seeds for antiviral treatments with high potential to fight the SARS-CoV-2 strain specifically.\nSIGNIFICANCE: The availability of FDA-approved anti-RdRp drugs can help treat patients and reduce the danger of the mysterious new viral infection COVID-19.\nThe drugs mentioned above can tightly bind to the RdRp of the SARS-CoV-2 strain and thus may be used to treat the disease.\nNo toxicity measurements are required for these drugs since they were previously tested prior to their approval by the FDA.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 524}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Pets can Spread Coronavirus (COVID-19) to People\n\nAbstract:\nSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)--the virus that causes coronavirus disease (COVID-19)--has been detected in domestic dogs and cats, raising concerns of transmission from, to, or between these animals.\nThere is currently no indication that feline- or canine-to-human transmission can occur, though there is rising evidence of the reverse.\nTo explore the extent of animal-related transmission, we aggregated 17 case reports on confirmed SARS-CoV-2 infections in animals as of 15 May 2020.\nAll but two animals fully recovered and had only mild respiratory or digestive symptoms.\nUsing data from probable cat-to-cat transmission in Wuhan, China, we estimated the basic reproduction number R0 under this scenario at 1.09 (95% confidence interval: 1.05, 1.13).\nThis value is much lower than the R0 reported for humans and close to one, indicating that the sustained transmission between cats is unlikely to occur.\nOur results support the view that the pet owners and other persons with COVID-19 in close contact with animals should be cautious of the way they interact with them.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"There is currently no indication that feline- or canine-to-human transmission can occur, though there is rising evidence of the reverse.\", \"Using data from probable cat-to-cat transmission in Wuhan, China, we estimated the basic reproduction number R0 under this scenario at 1.09 (95% confidence interval: 1.05, 1.13).\", \"This value is much lower than the R0 reported for humans and close to one, indicating that the sustained transmission between cats is unlikely to occur.\"]}", "id": 525}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Can acetaminophen (Tylenol) treat the coronavirus disease? No\n\nAbstract:\nBACKGROUND AND AIMS: Multiple issues in management of COVID have emerged, but confusion persists regarding rational interpretation.\nAim of this brief review is to review these issues based on current literature.\nMETHODS: This is a narrative review with Pubmed and Google Scholar search till 23 March 2020.\nSearch terms were, COVID-19, treatment of coronavirus, COVID 19 and following terms; chloroquine, hydroxychloroquine, ibuprofen, ACE-inhibitors or angiotensin receptor blockers, cardiovascular disease, diarrhoea, liver, testis and gastrointestinal disease.\nRESULTS: We discuss evidence regarding role of chloroquine and hydroxychloroquine in treatment and prophylaxis, use of inhibitors of the renin angiotensin system, safety of ibuprofen, unusual clinical features like gastrointestinal symptoms and interpretation of tests for cardiac enzymes and biomarkers.\nCONCLUSIONS: While our conclusions on management of COVID-19 patients with co-morbidities are based on current evidence, however, data is limited and there is immediate need for fast track research.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"RESULTS: We discuss evidence regarding role of chloroquine and hydroxychloroquine in treatment and prophylaxis, use of inhibitors of the renin angiotensin system, safety of ibuprofen, unusual clinical features like gastrointestinal symptoms and interpretation of tests for cardiac enzymes and biomarkers.\"]}", "id": 526}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: N95 respirators offer far superior protection against covid-19\n\nAbstract:\nWe identified seasonal human coronaviruses, influenza viruses and rhinoviruses in exhaled breath and coughs of children and adults with acute respiratory illness.\nSurgical face masks significantly reduced detection of influenza virus RNA in respiratory droplets and coronavirus RNA in aerosols, with a trend toward reduced detection of coronavirus RNA in respiratory droplets.\nOur results indicate that surgical face masks could prevent transmission of human coronaviruses and influenza viruses from symptomatic individuals.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 527}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: It is correct to call the virus that causes COVID-19, SARS.\n\nAbstract:\nThe recently emerged SARS-CoV-2 (Coronaviridae; Betacoronavirus) is the underlying cause of COVID-19 disease.\nHere we assessed SARS-CoV2 from the Kingdom of Saudi Arabia alongside sequences of SARS-CoV, bat SARS-like CoVs and MERS-CoV, the latter currently detected in this region.\nPhylogenetic analysis, natural selection investigation and genome recombination analysis were performed.\nOur analysis showed that all Saudi SARS-CoV-2 sequences are of the same origin and closer proximity to bat SARS-like CoVs, followed by SARS-CoVs, however quite distant to MERS-CoV. Moreover, genome recombination analysis revealed two recombination events between SARS-CoV-2 and bat SARS-like CoVs.\nThis was further assessed by S gene recombination analysis.\nThese recombination events may be relevant to the emergence of this novel virus.\nMoreover, positive selection pressure was detected between SARS-CoV-2, bat SL-CoV isolates and human SARS-CoV isolates.\nHowever, the highest positive selection occurred between SARS-CoV-2 isolates and 2 bat-SL-CoV isolates (Bat-SL-RsSHC014 and Bat-SL-CoVZC45).\nThis further indicates that SARS-CoV-2 isolates were adaptively evolved from bat SARS-like isolates, and that a virus with originating from bats triggered this pandemic.\nThis study thuds sheds further light on the origin of this virus.\nAUTHOR SUMMARY The emergence and subsequent pandemic of SARS-CoV-2 is a unique challenge to countries all over the world, including Saudi Arabia where cases of the related MERS are still being reported.\nSaudi SARS-CoV-2 sequences were found to be likely of the same or similar origin.\nIn our analysis, SARS-CoV-2 were more closely related to bat SARS-like CoVs rather than to MERS-CoV (which originated in Saudi Arabia) or SARS-CoV, confirming other phylogenetic efforts on this pathogen.\nRecombination and positive selection analysis further suggest that bat coronaviruses may be at the origin of SARS-CoV-2 sequences.\nThe data shown here give hints on the origin of this virus and may inform efforts on transmissibility, host adaptation and other biological aspects of this virus.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"The recently emerged SARS-CoV-2 (Coronaviridae; Betacoronavirus) is the underlying cause of COVID-19 disease.\"]}", "id": 528}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Two x-linked agammaglobulinemia patients develop pneumonia as covid-19 manifestation but recover\n\nAbstract:\nBACKGROUND: The recent SARS-CoV-2 pandemic, which has recently affected Italy since February 21, constitutes a threat to normal subjects, as the coronavirus disease-19 (COVID-19) can manifest with a broad spectrum of clinical phenotypes ranging from asymptomatic cases to pneumonia or even death.\nThere is evidence that older age and several comorbidities can affect the risk to develop severe pneumonia and possibly the need of mechanic ventilation in subjects infected with SARS-CoV-2.\nTherefore, we evaluated the outcome of SARS-CoV-2 infection in patients with inborn errors of immunity (IEI) such as X-linked agammaglobulinemia (XLA).\nMETHODS: When the SARS-CoV-2 epidemic has reached Italy, we have activated a surveillance protocol of patients with IEI, to perform SARS-CoV-2 search by nasopharyngeal swab in patients presenting with symptoms that could be a manifestation of COVID-19, such as fever, cough, diarrhea, or vomiting.\nRESULTS: We describe two patients with X-linked agammaglobulinemia (XLA) aged 34 and 26 years with complete absence of B cells from peripheral blood who developed COVID-19, as diagnosed by SARS-CoV-2 detection by nasopharyngeal swab, while receiving immunoglobulin infusions.\nBoth patients developed interstitial pneumonia characterized by fever, cough, and anorexia and associated with elevation of CRP and ferritin, but have never required oxygen ventilation or intensive care.\nCONCLUSION: Our report suggests that XLA patients might present with high risk to develop pneumonia after SARS-CoV-2 infection, but can recover from infection, suggesting that B-cell response might be important, but is not strictly required to overcome the disease.\nHowever, there is a need for larger observational studies to extend these conclusions to other patients with similar genetic immune defects.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Therefore, we evaluated the outcome of SARS-CoV-2 infection in patients with inborn errors of immunity (IEI) such as X-linked agammaglobulinemia (XLA).\"]}", "id": 529}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Drugs widely used to treat high blood pressure appear to make COVID-19 dangerously worse.\n\nAbstract:\nThe effects of angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin receptor blockers (ARBs) on the risk of COVID-19 infection and disease progression are yet to be investigated.\nThe relationship between ACEI/ARB use and COVID-19 infection was systematically reviewed.\nTo identify relevant studies that met predetermined inclusion criteria, unrestricted searches of the PubMed, Embase, and Cochrane Library databases were conducted.\nThe search strategy included clinical date published until May 9, 2020.\nTwelve articles involving more than 19,000 COVID-19 cases were included.\nTo estimate overall risk, random-effects models were adopted.\nOur results showed that ACEI/ARB exposure was not associated with a higher risk of COVID-19 infection (OR = 0.99; 95 % CI, 0-1.04; P = 0.672).\nAmong those with COVID-19 infection, ACEI/ARB exposure was also not associated with a higher risk of having severe infection (OR = 0.98; 95 % CI, 0.87-1.09; P = 0.69) or mortality (OR = 0.73, 95 %CI, 0.5-1.07; P = 0.111).\nHowever, ACEI/ARB exposure was associated with a lower risk of mortality compared to those on non-ACEI/ARB antihypertensive drugs (OR = 0.48, 95 % CI, 0.29-0.81; P = 0.006).\nIn conclusion, current evidence did not confirm the concern that ACEI/ARB exposure is harmful in patientswith COVID-19 infection.\nThis study supports the current guidelines that discourage discontinuation of ACEIs or ARBs in COVID-19 patients and the setting of the COVID-19 pandemic.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"In conclusion, current evidence did not confirm the concern that ACEI/ARB exposure is harmful in patientswith COVID-19 infection.\", \"This study supports the current guidelines that discourage discontinuation of ACEIs or ARBs in COVID-19 patients and the setting of the COVID-19 pandemic.\"]}", "id": 530}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: In such a scenario, differentiating whether the cause of death is specifically due to COVID-19 or the result of treatment limitations can be difficult.\n\nAbstract:\nOBJECTIVE: The COVID-19 pandemic has caused much morbidity and mortality to patients but also health care providers.\nWe tabulated the cases of physician deaths from COVID-19 associated with front-line work in hopes of mitigating future events.\nMETHOD: On April 5, 2020, Google internet search was performed using the keywords doctor, physician, death, COVID, COVID-19, and coronavirus in English and Farsi, and in Chinese using the Baidu search engine.\nRESULTS: We found 198 physician deaths from COVID-19, but complete details were missing for 49 individuals.\nThe average age of the physicians that died was 63.4 years (range 28 to 90 years) and the median age was 66 years of age.\nNinety percent of the deceased physicians were male (175/194).\nGeneral practitioners and emergency room doctors (78/192), respirologists (5/192), internal medicine specialists (11/192) and anesthesiologists (6/192) comprised 52% of those dying.\nTwo percent of the deceased were epidemiologists (4/192), 2% were infectious disease specialists (4/192), 5% were dentists (9/192), 4% were ENT (8/192), and 4% were ophthalmologists (7/192).\nThe countries with the most reported physician deaths were Italy (79/198), Iran (43/198), China (16/198), Philippines (14/198), United States (9/192) and Indonesia (7/192).\nCONCLUSION: Physicians from all specialties may die from COVID, and these deaths will likely increase as the pandemic progresses.\nLack of personal protective equipment was cited as a common cause of death.\nConsideration should be made to exclude older physicians from front-line work.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 531}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Berberine and obatoclax inhibit sars-cov-2 replication in primary human nasal epithelial cells in vitro.\n\nAbstract:\nSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged as a new human pathogen in late 2019 and has infected an estimated 10% of the global population in less than a year.\nThere is a clear need for effective antiviral drugs to complement current preventive measures including vaccines.\nIn this study, we demonstrate that berberine and obatoclax, two broad-spectrum antiviral compounds, are effective against multiple isolates of SARS-CoV-2.\nBerberine, a plant-derived alkaloid, inhibited SARS-CoV-2 at low micromolar concentrations and obatoclax, originally developed as an anti-apoptotic protein antagonist, was effective at sub-micromolar concentrations.\nTime-of-addition studies indicated that berberine acts on the late stage of the viral life cycle.\nIn agreement, berberine mildly affected viral RNA synthesis, but strongly reduced infectious viral titers, leading to an increase in the particle-to-pfu ratio.\nIn contrast, obatoclax acted at the early stage of the infection, in line with its activity to neutralize the acidic environment in endosomes.\nWe assessed infection of primary human nasal epithelial cells cultured on an air-liquid interface and found that SARS-CoV-2 infection induced and repressed expression of a specific set of cytokines and chemokines.\nMoreover, both obatoclax and berberine inhibited SARS-CoV-2 replication in these primary target cells.\nWe propose berberine and obatoclax as potential antiviral drugs against SARS-CoV-2 that could be considered for further efficacy testing.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"In this study, we demonstrate that berberine and obatoclax, two broad-spectrum antiviral compounds, are effective against multiple isolates of SARS-CoV-2.\", \"Berberine, a plant-derived alkaloid, inhibited SARS-CoV-2 at low micromolar concentrations and obatoclax, originally developed as an anti-apoptotic protein antagonist, was effective at sub-micromolar concentrations.\", \"Moreover, both obatoclax and berberine inhibited SARS-CoV-2 replication in these primary target cells.\"]}", "id": 532}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: 1 x-linked agammaglobulinemia patients develop pneumonia as covid-19 manifestation but recover\n\nAbstract:\nBACKGROUND: The recent SARS-CoV-2 pandemic, which has recently affected Italy since February 21, constitutes a threat to normal subjects, as the coronavirus disease-19 (COVID-19) can manifest with a broad spectrum of clinical phenotypes ranging from asymptomatic cases to pneumonia or even death.\nThere is evidence that older age and several comorbidities can affect the risk to develop severe pneumonia and possibly the need of mechanic ventilation in subjects infected with SARS-CoV-2.\nTherefore, we evaluated the outcome of SARS-CoV-2 infection in patients with inborn errors of immunity (IEI) such as X-linked agammaglobulinemia (XLA).\nMETHODS: When the SARS-CoV-2 epidemic has reached Italy, we have activated a surveillance protocol of patients with IEI, to perform SARS-CoV-2 search by nasopharyngeal swab in patients presenting with symptoms that could be a manifestation of COVID-19, such as fever, cough, diarrhea, or vomiting.\nRESULTS: We describe two patients with X-linked agammaglobulinemia (XLA) aged 34 and 26 years with complete absence of B cells from peripheral blood who developed COVID-19, as diagnosed by SARS-CoV-2 detection by nasopharyngeal swab, while receiving immunoglobulin infusions.\nBoth patients developed interstitial pneumonia characterized by fever, cough, and anorexia and associated with elevation of CRP and ferritin, but have never required oxygen ventilation or intensive care.\nCONCLUSION: Our report suggests that XLA patients might present with high risk to develop pneumonia after SARS-CoV-2 infection, but can recover from infection, suggesting that B-cell response might be important, but is not strictly required to overcome the disease.\nHowever, there is a need for larger observational studies to extend these conclusions to other patients with similar genetic immune defects.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Therefore, we evaluated the outcome of SARS-CoV-2 infection in patients with inborn errors of immunity (IEI) such as X-linked agammaglobulinemia (XLA).\"]}", "id": 533}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Is there an airborne component to the entry of covid-19?\n\nAbstract:\nObjectives While COVID-19 is known to be spread by respiratory droplets (which travel <2m horizontally), much less is known about its transmission via aerosols, which can become airborne and be widely distributed throughout room spaces.\nIn order to quantify the risk posed by COVID-19 infectors exhaling respiratory aerosols in enclosed spaces, we undertook a computer modelling study to simulate transmission in an office building.\nMethods Respiratory droplet data from four published datasets were analysed to quantify the number and volume of droplets <100m diameter produced by a typical cough and speaking event (i.e. counting from 1 to 100).\nThis was used in a stochastic model to simulate (10,000 simulations) the number of respiratory particles, originating from a COVID-19 infector, that would be inhaled in one hour by a susceptible individual practicing socially distancing in a 5 x 5 x 2.75m office space.\nSeveral scenarios were simulated that mimicked the presence of both symptomatic and asymptomatic COVID-19 infectors.\nResults On average, each cough and speaking event produced similar numbers of droplets <100 m diameter (median range = 955-1010).\nComputer simulations (at ventilation rate = 2AC/h) revealed that sharing the office space with a symptomatic COVID-19 infector (4 coughs per hour) for one hour resulted in the inhalation of 187.3 (median value) respiratory droplets, whereas sharing with an asymptomatic COVID-19 positive person (10 speaking events per hour) resulted in the inhalation of 482.9 droplets.\nIncreasing the ventilation rate resulted in only modest reductions in particle numbers inhaled.\nConclusions Given that live SARS-CoV-2 virions are known to be shed in high concentrations from the nasal cavity of both symptomatic and asymptomatic COVID-19 patients, the results suggest that individuals who share enclosed spaces with an infector may be at risk of contracting COVID-19 by the aerosol route, even when practicing social distancing.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"In order to quantify the risk posed by COVID-19 infectors exhaling respiratory aerosols in enclosed spaces, we undertook a computer modelling study to simulate transmission in an office building.\"]}", "id": 534}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: The main warning signs of COVID-19, the disease caused by the new coronavirus, are fever, fatigue, and a dry cough.\n\nAbstract:\nCoronavirus disease (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS CoV-2) has turned out to be a formidable pandemic.\nUpcoming evidence from confirmed cases of COVID-19 suggests an anticipated incursion of patients with neurological manifestations in the weeks to come.\nAn expression of the angiotensin-converting enzyme 2 (ACE 2), the cellular receptor for SARS-CoV-2 over the glial cells and neurons have made the brain a potential target.\nNeurotoxicity may occur as a result of direct, indirect and post-infectious complications.\nAttention to neurological deficits in COVID-19 is fundamental to ensure appropriate, timely, beneficial management of the affected patients.\nMost common neurological manifestations seen include dizziness, headache, impaired consciousness, acute cerebrovascular disease, ataxia, and seizures.\nAnosmia and ageusia have recently been hinted as significant early symptoms in COVID-19.\nAs cases with neurological deficits in COVID-19 emerge, the overall prognosis is yet unknown.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Anosmia and ageusia have recently been hinted as significant early symptoms in COVID-19.\"]}", "id": 535}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: The new coronavirus can damage the lungs, heart and brain, which increases the risk of long-term health problems.\n\nAbstract:\nBACKGROUND: A novel coronavirus disease (COVID-19) in Wuhan has caused an outbreak and become a major public health issue in China and great concern from international community.\nMyocarditis and myocardial injury were suspected and may even be considered as one of the leading causes for death of COVID-19 patients.\nTherefore, we focused on the condition of the heart, and sought to provide firsthand evidence for whether myocarditis and myocardial injury were caused by COVID-19.\nMETHODS: We enrolled patients with confirmed diagnosis of COVID-19 retrospectively and collected heart-related clinical data, mainly including cardiac imaging findings, laboratory results and clinical outcomes.\nSerial tests of cardiac markers were traced for the analysis of potential myocardial injury/myocarditis.\nRESULTS: 112 COVID-19 patients were enrolled in our study.\nThere was evidence of myocardial injury in COVID-19 patients and 14 (12.5%) patients had presented abnormalities similar to myocarditis.\nMost of patients had normal levels of troponin at admission, that in 42 (37.5%) patients increased during hospitalization, especially in those that died.\nTroponin levels were significantly increased in the week preceding the death.\n15 (13.4%) patients have presented signs of pulmonary hypertension.\nTypical signs of myocarditis were absent on echocardiography and electrocardiogram.\nCONCLUSIONS: The clinical evidence in our study suggested that myocardial injury is more likely related to systemic consequences rather than direct damage by the 2019 novel coronavirus.\nThe elevation in cardiac markers was probably due to secondary and systemic consequences and can be considered as the warning sign for recent adverse clinical outcomes of the patients.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"CONCLUSIONS: The clinical evidence in our study suggested that myocardial injury is more likely related to systemic consequences rather than direct damage by the 2019 novel coronavirus.\", \"The elevation in cardiac markers was probably due to secondary and systemic consequences and can be considered as the warning sign for recent adverse clinical outcomes of the patients.\"]}", "id": 536}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: masks are effective in limiting spread of COVID-19\n\nAbstract:\nWe present two models for the COVID-19 pandemic predicting the impact of universal face mask wearing upon the spread of the SARS-CoV-2 virus--one employing a stochastic dynamic network based compartmental SEIR (susceptible-exposed-infectious-recovered) approach, and the other employing individual ABM (agent-based modelling) Monte Carlo simulation--indicating (1) significant impact under (near) universal masking when at least 80% of a population is wearing masks, versus minimal impact when only 50% or less of the population is wearing masks, and (2) significant impact when universal masking is adopted early, by Day 50 of a regional outbreak, versus minimal impact when universal masking is adopted late.\nThese effects hold even at the lower filtering rates of homemade masks.\nTo validate these theoretical models, we compare their predictions against a new empirical data set we have collected that includes whether regions have universal masking cultures or policies, their daily case growth rates, and their percentage reduction from peak daily case growth rates.\nResults show a near perfect correlation between early universal masking and successful suppression of daily case growth rates and/or reduction from peak daily case growth rates, as predicted by our theoretical simulations.\nOur theoretical and empirical results argue for urgent implementation of universal masking.\nAs governments plan how to exit societal lockdowns, it is emerging as a key NPI; a\"mouth-and-nose lockdown\"is far more sustainable than a\"full body lockdown\", on economic, social, and mental health axes.\nAn interactive visualization of the ABM simulation is at http://dek.ai/masks4all.\nWe recommend immediate mask wearing recommendations, official guidelines for correct use, and awareness campaigns to shift masking mindsets away from pure self-protection, towards aspirational goals of responsibly protecting one's community.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"We present two models for the COVID-19 pandemic predicting the impact of universal face mask wearing upon the spread of the SARS-CoV-2 virus--one employing a stochastic dynamic network based compartmental SEIR (susceptible-exposed-infectious-recovered) approach, and the other employing individual ABM (agent-based modelling) Monte Carlo simulation--indicating (1) significant impact under (near) universal masking when at least 80% of a population is wearing masks, versus minimal impact when only 50% or less of the population is wearing masks, and (2) significant impact when universal masking is adopted early, by Day 50 of a regional outbreak, versus minimal impact when universal masking is adopted late.\", \"We recommend immediate mask wearing recommendations, official guidelines for correct use, and awareness campaigns to shift masking mindsets away from pure self-protection, towards aspirational goals of responsibly protecting one's community.\"]}", "id": 537}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: N95 masks are better than clothe masks\n\nAbstract:\nControversy exists around the appropriate types of masks and the situations in which they should be used in community and health care settings for the prevention of SARS-CoV-2 infection.\nIn this article, the American College of Physicians (ACP) provides recommendations based on the best available evidence through 14 April 2020 on the effectiveness of N95 respirators, surgical masks, and cloth masks in reducing transmission of infection.\nThe ACP plans periodic updates of these recommendations on the basis of ongoing surveillance of the literature for 1 year from the initial search date.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 538}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: People with Diabetes May Have Higher Risk for COVID-19\n\nAbstract:\nSome comorbidities are associated with severe coronavirus disease (Covid-19) but it is unclear whether some increase susceptibility to Covid-19.\nIn this case-control Mexican study we found that obesity represents the strongest predictor for Covid-19 followed by diabetes and hypertension in both sexes and chronic renal failure in females only.\nActive smoking was associated with decreased odds of Covid-19.\nThese findings indicate that these comorbidities are not only associated with severity of disease but also predispose for getting Covid-19.\nFuture research is needed to establish the mechanisms involved in each comorbidity and the apparent \"protective\" effect of cigarette smoking.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"In this case-control Mexican study we found that obesity represents the strongest predictor for Covid-19 followed by diabetes and hypertension in both sexes and chronic renal failure in females only.\"]}", "id": 539}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Cloth face coverings are most likely to reduce the spread of the COVID-19 virus when they are widely used by people in public settings\n\nAbstract:\nOBJECTIVES: To determine the risk of SARS-CoV-2 transmission by aerosols, to provide evidence on the rational use of masks, and to discuss additional measures important for the protection of healthcare workers from COVID-19.\nMETHODS: Literature review and expert opinion.\nSHORT CONCLUSION: SARS-CoV-2, the pathogen causing COVID-19, is considered to be transmitted via droplets rather than aerosols, but droplets with strong directional airflow support may spread further than 2 m. High rates of COVID-19 infections in healthcare-workers (HCWs) have been reported from several countries.\nRespirators such as filtering face piece (FFP) 2 masks were designed to protect HCWs, while surgical masks were originally intended to protect patients (e.g., during surgery).\nNevertheless, high quality standard surgical masks (type II/IIR according to European Norm EN 14683) appear to be as effective as FFP2 masks in preventing droplet-associated viral infections of HCWs as reported from influenza or SARS.\nSo far, no head-to-head trials with these masks have been published for COVID-19.\nNeither mask type completely prevents transmission, which may be due to inappropriate handling and alternative transmission pathways.\nTherefore, compliance with a bundle of infection control measures including thorough hand hygiene is key.\nDuring high-risk procedures, both droplets and aerosols may be produced, reason why respirators are indicated for these interventions.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Respirators such as filtering face piece (FFP) 2 masks were designed to protect HCWs, while surgical masks were originally intended to protect patients (e.g., during surgery).\", \"Nevertheless, high quality standard surgical masks (type II/IIR according to European Norm EN 14683) appear to be as effective as FFP2 masks in preventing droplet-associated viral infections of HCWs as reported from influenza or SARS.\", \"Neither mask type completely prevents transmission, which may be due to inappropriate handling and alternative transmission pathways.\", \"Therefore, compliance with a bundle of infection control measures including thorough hand hygiene is key.\"]}", "id": 540}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: there is no evidence that garlic can protect you against the COVID-19 virus.\n\nAbstract:\nIn late December 2019 in Wuhan, China, several patients with viral pneumonia were identified as 2019 novel coronavirus (2019-nCoV).\nSo far, there are no specific treatments for patients with coronavirus disease-19 (COVID-19), and the treatments available today are based on previous experience with similar viruses such as severe acute respiratory syndrome-related coronavirus (SARS-CoV), Middle East respiratory syndrome coronavirus (MERS-CoV), and Influenza virus.\nIn this article, we have tried to reach a therapeutic window of drugs available to patients with COVID-19.\nCathepsin L is required for entry of the 2019-nCoV virus into the cell as target teicoplanin inhibits virus replication.\nAngiotensin-converting-enzyme 2 (ACE2) in soluble form as a recombinant protein can prevent the spread of coronavirus by restricting binding and entry.\nIn patients with COVID-19, hydroxychloroquine decreases the inflammatory response and cytokine storm, but overdose causes toxicity and mortality.\nNeuraminidase inhibitors such as oseltamivir, peramivir, and zanamivir are invalid for 2019-nCoV and are not recommended for treatment but protease inhibitors such as lopinavir/ritonavir (LPV/r) inhibit the progression of MERS-CoV disease and can be useful for patients of COVID-19 and, in combination with Arbidol, has a direct antiviral effect on early replication of SARS-CoV. Ribavirin reduces hemoglobin concentrations in respiratory patients, and remdesivir improves respiratory symptoms.\nUse of ribavirin in combination with LPV/r in patients with SARS-CoV reduces acute respiratory distress syndrome and mortality, which has a significant protective effect with the addition of corticosteroids.\nFavipiravir increases clinical recovery and reduces respiratory problems and has a stronger antiviral effect than LPV/r.\ncurrently, appropriate treatment for patients with COVID-19 is an ACE2 inhibitor and a clinical problem reducing agent such as favipiravir in addition to hydroxychloroquine and corticosteroids.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"In late December 2019 in Wuhan, China, several patients with viral pneumonia were identified as 2019 novel coronavirus (2019-nCoV).\", \"So far, there are no specific treatments for patients with coronavirus disease-19 (COVID-19), and the treatments available today are based on previous experience with similar viruses such as severe acute respiratory syndrome-related coronavirus (SARS-CoV), Middle East respiratory syndrome coronavirus (MERS-CoV), and Influenza virus.\"]}", "id": 541}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: the virus spreads mainly through small airborne droplets when an infected person coughs or sneezes.\n\nAbstract:\nBackground: The Greek authorities implemented the strong social distancing measures within the first few weeks after the first confirmed case of the virus to curtail the COVID-19 growth rate.\nObjectives: To estimate the effect of the two-stage strong social distancing measures, the closure of all non-essential shopping centers and businesses on March 16 and the shelter in place orders (SIPOs) on March 23 on the COVID-19 growth rate in Greece Methods: We obtained data on COVID-19 cases in Greece from February 26th through May 4th from publicly available sources.\nAn interrupted time-series regression analysis was used to estimate the effect of the measures on the exponential growth of confirmed COVID-19 cases, controlling for the number of daily testing, and weekly fixed-effects.\nResults: The growth rate of the COVID-19 cases in the pre-policies implementation period was positive as expected (p=0.003).\nBased on the estimates of the interrupted time-series, our results indicate that the SIPO on March 23 significantly slowed the growth rate of COVID-19 in Greece (p=0.04).\nHowever, we did not find evidence on the effectiveness of standalone and partial measures such as the non-essential business closures implemented on March 16 on the COVID-19 spread reduction.\nDiscussion: The combined social distancing measures implemented by the Greek authorities within the first few weeks after the first confirmed case of the virus reduced the COVID-19 growth rate.\nThese findings provide evidence and highlight the effectiveness of these measures to flatten the curve and to slow the spread of the virus.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 542}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: a small number of pets worldwide, including cats and dogs, can be infected with the virus that causes COVID-19, mostly after close contact with people with COVID-19.\n\nAbstract:\nThe infectious diseases are spreading due to human interactions enabled by various social networks.\nTherefore, when a new pathogen such as SARS-CoV-2 causes an outbreak, the non-pharmaceutical isolation strategies (e.g., social distancing) are the only possible response to disrupt its spreading.\nTo this end, we introduce the new epidemic model (SICARS) and compare the centralized (C), decentralized (D), and combined (C+D) social distancing strategies, and analyze their efficiency to control the dynamics of COVID-19 on heterogeneous complex networks.\nOur analysis shows that the centralized social distancing is necessary to minimize the pandemic spreading.\nThe decentralized strategy is insufficient when used alone, but offers the best results when combined with the centralized one.\nIndeed, the (C+D) is the most efficient isolation strategy at mitigating the network superspreaders and reducing the highest node degrees to less than 10% of their initial values.\nOur results also indicate that stronger social distancing, e.g., cutting 75% of social ties, can reduce the outbreak by 75% for the C isolation, by 33% for the D isolation, and by 87% for the (C+D) isolation strategy.\nFinally, we study the impact of proactive versus reactive isolation strategies, as well as their delayed enforcement.\nWe find that the reactive response to the pandemic is less efficient, and delaying the adoption of isolation measures by over one month (since the outbreak onset in a region) can have alarming effects; thus, our study contributes to an understanding of the COVID-19 pandemic both in space and time.\nWe believe our investigations have a high social relevance as they provide insights into understanding how different degrees of social distancing can reduce the peak infection ratio substantially; this can make the COVID-19 pandemic easier to understand and control over an extended period of time.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 543}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Various coronaviruses infect numerous species, but the first human coronaviruses weren't discovered until the mid-1960s.\n\nAbstract:\nA novel coronavirus (severe acute respiratory syndrome-CoV-2) that initially originated from Wuhan, China, in December 2019 has already caused a pandemic.\nWhile this novel coronavirus disease (covid-19) frequently induces mild diseases, it has also generated severe diseases among certain populations, including older-aged individuals with underlying diseases, such as cardiovascular disease and diabetes.\nAs of 31 March 2020, a total of 9786 confirmed cases with covid-19 have been reported in South Korea.\nSouth Korea has the highest diagnostic rate for covid-19, which has been the major contributor in overcoming this outbreak.\nWe are trying to reduce the reproduction number of covid-19 to less than one and eventually succeed in controlling this outbreak using methods such as contact tracing, quarantine, testing, isolation, social distancing and school closure.\nThis report aimed to describe the current situation of covid-19 in South Korea and our response to this outbreak.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 544}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: So, COVID is killed by heat. That is why our bodies create fever to fight it off. When you take Tylenol or advil it takes away your fever and allows COVID its ideal environment. If you get COVID allow your fever to remain as long as it is not over 103-104 this is your body fighting the virus. \n\nAbstract:\nOBJECTIVE: It was recently suggested that ibuprofen might increase the risk for severe and fatal coronavirus disease 2019 (COVID-19) and should therefore be avoided in this patient population.\nWe aimed to evaluate whether ibuprofen use in individuals with COVID-19 was associated with more severe disease, compared with individuals using paracetamol or no antipyretics.\nMETHODS: In a retrospective cohort study of patients with COVID-19 from Shamir Medical Centre, Israel, we monitored any use of ibuprofen from a week before diagnosis of COVID-19 throughout the disease.\nPrimary outcomes were mortality and the need for respiratory support, including oxygen administration and mechanical ventilation.\nRESULTS: The study included 403 confirmed cases of COVID-19, with a median age of 45 years.\nOf the entire cohort, 44 patients (11%) needed respiratory support and 12 (3%) died.\nOne hundred and seventy-nine (44%) patients had fever, with 32% using paracetamol and 22% using ibuprofen, for symptom-relief.\nIn the ibuprofen group, 3 (3.4%) patients died, whereas in the non-ibuprofen group, 9 (2.8%) patients died (p 0.95).\nNine (10.3%) patients from the ibuprofen group needed respiratory support, compared with 35 (11%) from the non-ibuprofen group (p 1).\nWhen compared with exclusive paracetamol users, no differences were observed in mortality rates or the need for respiratory support among patients using ibuprofen.\nCONCLUSIONS: In this cohort of COVID-19 patients, ibuprofen use was not associated with worse clinical outcomes, compared with paracetamol or no antipyretic.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"When compared with exclusive paracetamol users, no differences were observed in mortality rates or the need for respiratory support among patients using ibuprofen.\", \"CONCLUSIONS: In this cohort of COVID-19 patients, ibuprofen use was not associated with worse clinical outcomes, compared with paracetamol or no antipyretic.\"]}", "id": 545}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: High Blood Pressure Doesn't Seem To Raise COVID-19 Risk\n\nAbstract:\nOBJECTIVE: To investigate the association between hypertension and outcome in patients with Coronavirus Disease 2019 (COVID-19) pneumonia.\nMETHODS: We performed a systematic literature search from several databases on studies that assess hypertension and outcome in COVID-19.\nComposite of poor outcome, comprising of mortality, severe COVID-19, acute respiratory distress syndrome (ARDS), need for intensive care unit (ICU) care and disease progression were the outcomes of interest.\nRESULTS: A total of 6560 patients were pooled from 30 studies.\nHypertension was associated with increased composite poor outcome (risk ratio (RR) 2.11 (95% confidence interval (CI) 1.85, 2.40), p < 0.001; I2, 44%) and its sub-group, including mortality (RR 2.21 (1.74, 2.81), p < 0.001; I2, 66%), severe COVID-19 (RR 2.04 (1.69, 2.47), p < 0.001; I2 31%), ARDS (RR 1.64 (1.11, 2.43), p = 0.01; I2,0%, p = 0.35), ICU care (RR 2.11 (1.34, 3.33), p = 0.001; I2 18%, p = 0.30), and disease progression (RR 3.01 (1.51, 5.99), p = 0.002; I2 0%, p = 0.55).\nMeta-regression analysis showed that gender (p = 0.013) was a covariate that affects the association.\nThe association was stronger in studies with a percentage of males < 55% compared to \u00e2\u00a9\u00be 55% (RR 2.32 v. RR 1.79).\nCONCLUSION: Hypertension was associated with increased composite poor outcome, including mortality, severe COVID-19, ARDS, need for ICU care and disease progression in patients with COVID-19.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"CONCLUSION: Hypertension was associated with increased composite poor outcome, including mortality, severe COVID-19, ARDS, need for ICU care and disease progression in patients with COVID-19.\"]}", "id": 546}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: A booster dose woulds immunogenicity of the covid-19 vaccine candidate chadox1 ncov-19 in aged mice\n\nAbstract:\nThe spread of SARS-CoV-2 has caused a global pandemic that has affected almost every aspect of human life.\nThe development of an effective COVID-19 vaccine could limit the morbidity and mortality caused by infection, and may enable the relaxation of social distancing measures.\nAge is one of the most significant risk factors for poor health outcomes after SARS-CoV-2 infection, therefore it is desirable that any new vaccine candidates should elicit a robust immune response in older adults.\nHere, we test the immunogenicity of the adenoviral vectored vaccine ChAdOx1 nCoV-19 (AZD-1222) in aged mice.\nWe find that a single dose of this vaccine induces cellular and humoral immunity in aged mice, but at a reduced magnitude than in younger adult mice.\nFurthermore, we report that a second dose enhances the immune response to this vaccine in aged mice, indicating that a primeboost strategy may be a rational approach to enhance immunogenicity in older persons.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"We find that a single dose of this vaccine induces cellular and humoral immunity in aged mice, but at a reduced magnitude than in younger adult mice.\", \"Furthermore, we report that a second dose enhances the immune response to this vaccine in aged mice, indicating that a primeboost strategy may be a rational approach to enhance immunogenicity in older persons.\"]}", "id": 547}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Susceptible supply increases the role of climate in the early sars-cov-2 pandemic\n\nAbstract:\nPreliminary evidence suggests that climate may modulate the transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).\nYet it remains unclear whether seasonal and geographic variations in climate can substantially alter the pandemic trajectory, given that high susceptibility is a core driver.\nHere, we use a climate-dependent epidemic model to simulate the SARS-CoV-2 pandemic by probing different scenarios based on known coronavirus biology.\nWe find that although variations in weather may be important for endemic infections, during the pandemic stage of an emerging pathogen, the climate drives only modest changes to pandemic size.\nA preliminary analysis of nonpharmaceutical control measures indicates that they may moderate the pandemic-climate interaction through susceptible depletion.\nOur findings suggest that without effective control measures, strong outbreaks are likely in more humid climates and summer weather will not substantially limit pandemic growth.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"A preliminary analysis of nonpharmaceutical control measures indicates that they may moderate the pandemic-climate interaction through susceptible depletion.\"]}", "id": 548}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: The furin cleavage site of sars-cov-2 spike protein is a key determinant for transmission due to enhanced replication in airway cells .\n\nAbstract:\nSARS-CoV-2 enters cells via its spike glycoprotein which must be cleaved sequentially at the S1/S2, then the S2\u2019 cleavage sites (CS) to mediate membrane fusion.\nSARS-CoV-2 has a unique polybasic insertion at the S1/S2 CS, which we demonstrate can be cleaved by furin.\nUsing lentiviral pseudotypes and a cell-culture adapted SARS-CoV-2 virus with a S1/S2 deletion, we show that the polybasic insertion is selected for in lung cells and primary human airway epithelial cultures but selected against in Vero E6, a cell line used for passaging SARS-CoV-2.\nWe find this selective advantage depends on expression of the cell surface protease, TMPRSS2, that allows virus entry independent of endosomes thus avoiding antiviral IFITM proteins.\nSARS-CoV-2 virus lacking the S1/S2 furin CS was shed to lower titres from infected ferrets and was not transmitted to cohoused sentinel animals.\nThus, the polybasic CS is a key determinant for efficient SARS-CoV-2 transmission.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"SARS-CoV-2 has a unique polybasic insertion at the S1/S2 CS, which we demonstrate can be cleaved by furin.\", \"Using lentiviral pseudotypes and a cell-culture adapted SARS-CoV-2 virus with a S1/S2 deletion, we show that the polybasic insertion is selected for in lung cells and primary human airway epithelial cultures but selected against in Vero E6, a cell line used for passaging SARS-CoV-2.\", \"We find this selective advantage depends on expression of the cell surface protease, TMPRSS2, that allows virus entry independent of endosomes thus avoiding antiviral IFITM proteins.\"]}", "id": 549}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Ideally, try to maintain at least three feet of distance from others if you are in a community where COVID-19 is spreading. This can help prevent you from breathing in any liquid droplets containing the virus, which can be sprayed through the nose or mouth through coughing and sneezing. 3. Clean and disinfect surfaces\n\nAbstract:\nPURPOSE: SARS-CoV-2 is a new pandemic influenza caused by a coronavirus which main route of transmission is through exhaled droplets that primarily infect the nose and the nasopharynx.\nThe aim of this paper is to evaluate the effect of acetic acid, the active component of vinegar, as a potential disinfectant agent for upper airways.\nMETHODS: Twenty-nine patients were enrolled and divided into two groups: group 1 (14 patients) was composed of patients treated with off-label hydroxychloroquine and lopinavir/ritonavir, whereas group 2 (15 patients) was composed of patients treated with hydroxychloroquine only, combined with the inhalation of acetic acid disinfectant at a 0.34% concentration.\nA questionnaire-based evaluation of symptoms was performed after 15 days in both groups.\nRESULTS: It appears that the number of patients treated with acetic acid (group 2) that experienced improvement in individual symptoms was double that of the other group of patients (group 1), although numbers are too small for robust statistical analysis.\nCONCLUSIONS: Considering its potential benefits and high availability, acetic acid disinfection appears to be a promising adjunctive therapy in cases of non-severe COVID-19 and deserves further investigation.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 550}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Nonsteroidal anti-inflammatory drugs (NSAIDs), such as ibuprofen, aspirin, and Advil, reduce fever, pain, and inflammation.\n\nAbstract:\nOBJECTIVE: It was recently suggested that ibuprofen might increase the risk for severe and fatal coronavirus disease 2019 (COVID-19) and should therefore be avoided in this patient population.\nWe aimed to evaluate whether ibuprofen use in individuals with COVID-19 was associated with more severe disease, compared with individuals using paracetamol or no antipyretics.\nMETHODS: In a retrospective cohort study of patients with COVID-19 from Shamir Medical Centre, Israel, we monitored any use of ibuprofen from a week before diagnosis of COVID-19 throughout the disease.\nPrimary outcomes were mortality and the need for respiratory support, including oxygen administration and mechanical ventilation.\nRESULTS: The study included 403 confirmed cases of COVID-19, with a median age of 45 years.\nOf the entire cohort, 44 patients (11%) needed respiratory support and 12 (3%) died.\nOne hundred and seventy-nine (44%) patients had fever, with 32% using paracetamol and 22% using ibuprofen, for symptom-relief.\nIn the ibuprofen group, 3 (3.4%) patients died, whereas in the non-ibuprofen group, 9 (2.8%) patients died (p 0.95).\nNine (10.3%) patients from the ibuprofen group needed respiratory support, compared with 35 (11%) from the non-ibuprofen group (p 1).\nWhen compared with exclusive paracetamol users, no differences were observed in mortality rates or the need for respiratory support among patients using ibuprofen.\nCONCLUSIONS: In this cohort of COVID-19 patients, ibuprofen use was not associated with worse clinical outcomes, compared with paracetamol or no antipyretic.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"When compared with exclusive paracetamol users, no differences were observed in mortality rates or the need for respiratory support among patients using ibuprofen.\", \"CONCLUSIONS: In this cohort of COVID-19 patients, ibuprofen use was not associated with worse clinical outcomes, compared with paracetamol or no antipyretic.\"]}", "id": 551}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Wear masks with two or more layers to stop the spread of COVID-19\n\nAbstract:\nEvidence that face masks provide effective protection against respiratory infections in the community is scarce.\nHowever, face masks are widely used by health workers as part of droplet precautions when caring for patients with respiratory infections.\nIt would therefore be reasonable to suggest that consistent widespread use of face masks in the community could prevent further spread of the Severe Acute Respiratory Syndrome-Coronavirus 2 (SARS-CoV-2).\nIn this study we examine public face mask wearing in Uganda where a proportion wears masks to protect against acquiring, and the other to prevent from transmitting SARS-CoV-2.\nThe objective of this study was to determine what percentage of the population would have to wear face masks to reduce susceptibility to and infectivity of COVID-19 in Uganda, keeping the basic reproduction number below unity and/or flattening the curve.\nWe used an SEIAQRD model for the analysis.\nResults show that implementation of facemasks has a relatively large impact on the size of the coronavirus epidemic in Uganda.\nWe find that the critical mask adherence is 5 per 100 when 80% wear face masks.\nA cost-effective analysis shows that utilizing funds to provide 1 public mask to the population has a per capita compounded cost of USD 1.34.\nIf provision of face masks is done simultaneously with supportive care, the per capita compounded cost is USD 1.965, while for the case of only treatment and no provision of face masks costs each Ugandan USD 4.0579.\nWe conclude that since it is hard to achieve a 100% adherence to face masks, government might consider provision of face masks in conjunction with provision of care.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"It would therefore be reasonable to suggest that consistent widespread use of face masks in the community could prevent further spread of the Severe Acute Respiratory Syndrome-Coronavirus 2 (SARS-CoV-2).\", \"We find that the critical mask adherence is 5 per 100 when 80% wear face masks.\"]}", "id": 552}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Coronavirus (COVID-19) Know how to protect yourself and others from COVID-19 and what to do if you are sick.\n\nAbstract:\nThe undefendable outbreak of novel coronavirus (SARS-COV-2) lead to a global health emergency due to its higher transmission rate and longer symptomatic duration, created a health surge in a short time.\nSince Nov 2019 the outbreak in China, the virus is spreading exponentially everywhere.\nThe current study focuses on the relationship between environmental parameters and the growth rate of COVID-19.\nThe statistical analysis suggests that the temperature changes retarded the growth rate and found that -6.28{degrees}C and +14.51{degrees}C temperature is the favorable range for COVID-19 growth.\nGutenberg- Richter's relationship is used to estimate the mean daily rate of exceedance of confirmed cases concerning the change in temperature.\nTemperature is the most influential parameter that reduces the growth at the rate of 13-16 cases/day with a 1{degrees}C rise in temperature.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 553}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: coronavirus is man-made\n\nAbstract:\nCoronavirus disease 2019 (COVID-19) originated in the city of Wuhan, Hubei Province, Central China, and has spread quickly to 72 countries to date.\nCOVID-19 is caused by a novel coronavirus, named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) [previously provisionally known as 2019 novel coronavirus (2019-nCoV)].\nAt present, the newly identified SARS-CoV-2 has caused a large number of deaths with tens of thousands of confirmed cases worldwide, posing a serious threat to public health.\nHowever, there are no clinically approved vaccines or specific therapeutic drugs available for COVID-19.\nIntensive research on the newly emerged SARS-CoV-2 is urgently needed to elucidate the pathogenic mechanisms and epidemiological characteristics and to identify potential drug targets, which will contribute to the development of effective prevention and treatment strategies.\nHence, this review will focus on recent progress regarding the structure of SARS-CoV-2 and the characteristics of COVID-19, such as the aetiology, pathogenesis and epidemiological characteristics.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 554}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Wearing the N95 respirator mask can protect against coronavirus\n\nAbstract:\nEvidence that face masks provide effective protection against respiratory infections in the community is scarce.\nHowever, face masks are widely used by health workers as part of droplet precautions when caring for patients with respiratory infections.\nIt would therefore be reasonable to suggest that consistent widespread use of face masks in the community could prevent further spread of the Severe Acute Respiratory Syndrome-Coronavirus 2 (SARS-CoV-2).\nIn this study we examine public face mask wearing in Uganda where a proportion wears masks to protect against acquiring, and the other to prevent from transmitting SARS-CoV-2.\nThe objective of this study was to determine what percentage of the population would have to wear face masks to reduce susceptibility to and infectivity of COVID-19 in Uganda, keeping the basic reproduction number below unity and/or flattening the curve.\nWe used an SEIAQRD model for the analysis.\nResults show that implementation of facemasks has a relatively large impact on the size of the coronavirus epidemic in Uganda.\nWe find that the critical mask adherence is 5 per 100 when 80% wear face masks.\nA cost-effective analysis shows that utilizing funds to provide 1 public mask to the population has a per capita compounded cost of USD 1.34.\nIf provision of face masks is done simultaneously with supportive care, the per capita compounded cost is USD 1.965, while for the case of only treatment and no provision of face masks costs each Ugandan USD 4.0579.\nWe conclude that since it is hard to achieve a 100% adherence to face masks, government might consider provision of face masks in conjunction with provision of care.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"It would therefore be reasonable to suggest that consistent widespread use of face masks in the community could prevent further spread of the Severe Acute Respiratory Syndrome-Coronavirus 2 (SARS-CoV-2).\", \"We find that the critical mask adherence is 5 per 100 when 80% wear face masks.\"]}", "id": 555}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim:  Saliva testing does depend on standard PCR technology, and it does require some manual labor in order to move it through\n\nAbstract:\nThe outbreak of COVID-19 has taken a large number of lives since 2019 and the death toll continues to increase all over the world.\nRecent data reports that about 27 lacs of people are infected with this virus till date and around 2 lacs are dead due to this pandemic.\nThe situation in India is no way better.\nIn India, almost all the states have become victim of this deadly pandemic.\nConsidering the enormous population in India, citizens here are facing acute shortage of detection kits and many are dying even before the knowledge of their infection.\nThe present treatise proposes a molecularly imprinted polymer (MIP) based technique for simple and rapid detection of COVID-19.\nThe technique will be inexpensive, selective, reusable and easy to handle.\nIt has been already implemented in our laboratory in order to detect the taste contributing agents found in tea.\nThis article discusses the detailed methodology and the resultant analytical characteristic of the sensors developed so far and also outlines the suitability of the MIP technique towards fabrication of testing kits for rapid detection of COVID-19.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 556}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: The sars-cov-2 and other human coronavirus spike proteins are fine-tuned towards temperature and proteases of the human airways\n\nAbstract:\nThe high transmissibility of SARS-CoV-2 is related to abundant replication in the upper airways, which is not observed for the other highly pathogenic coronaviruses SARS-CoV-1 and MERS-CoV. We here reveal features of the coronavirus spike (S) protein, which optimize the virus towards different parts of the respiratory tract.\nFirst, the SARS-CoV-2 spike (SARS-2-S) reached higher levels in pseudoparticles when produced at 33\u00b0C instead of 37\u00b0C.\nEven stronger preference for the upper airway temperature of 33\u00b0C was evident for the S protein of HCoV-229E, a common cold coronavirus.\nIn contrast, the S proteins of SARS-CoV-1 and MERS-CoV favored 37\u00b0C, in accordance with their preference for the lower airways.\nNext, SARS-2-S proved efficiently activated by TMPRSS13, besides the previously identified host cell protease TMPRSS2, which may broaden the cell tropism of SARS-CoV-2.\nTMPRSS13 was found to be an effective spike activator for the virulent coronaviruses but not the common cold HCoV-229E virus.\nActivation by these proteases requires pre-cleavage of the SARS-2-S S1/S2 cleavage loop, and both its furin motif and extended loop length proved critical to achieve virus entry into airway epithelial cells.\nFinally, we show that the D614G mutation in SARS-2-S increases S protein stability and expression at 37\u00b0C, and promotes virus entry via cathepsin B/L activation.\nThese spike properties might promote virus spread, potentially explaining why the G614 variant is currently predominating worldwide.\nCollectively, our findings indicate how the coronavirus spike protein is fine-tuned towards the temperature and protease conditions of the airways, to enhance virus transmission and pathology.\nSIGNIFICANCE STATEMENT The rapid spread of SARS-CoV-2, the cause of COVID-19, is related to abundant replication in the upper airways, which is not observed for other highly pathogenic human coronaviruses.\nWe here reveal features of the coronavirus spike (S) protein, which optimize the virus towards different parts of the respiratory tract.\nCoronavirus spikes exhibit distinct temperature preference to precisely match the upper (~33\u00b0C) or lower (37\u00b0C) airways.\nWe identified airway proteases that activate the spike for virus entry into cells, including one protease that may mediate coronavirus virulence.\nAlso, a link was seen between spike stability and entry via endosomal proteases.\nThis mechanism of spike fine-tuning could explain why the SARS-CoV-2 spike-D614G mutant is more transmissible and therefore globally predominant.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"The high transmissibility of SARS-CoV-2 is related to abundant replication in the upper airways, which is not observed for the other highly pathogenic coronaviruses SARS-CoV-1 and MERS-CoV. We here reveal features of the coronavirus spike (S) protein, which optimize the virus towards different parts of the respiratory tract.\", \"Even stronger preference for the upper airway temperature of 33\\u00b0C was evident for the S protein of HCoV-229E, a common cold coronavirus.\", \"Collectively, our findings indicate how the coronavirus spike protein is fine-tuned towards the temperature and protease conditions of the airways, to enhance virus transmission and pathology.\", \"SIGNIFICANCE STATEMENT The rapid spread of SARS-CoV-2, the cause of COVID-19, is related to abundant replication in the upper airways, which is not observed for other highly pathogenic human coronaviruses.\", \"Coronavirus spikes exhibit distinct temperature preference to precisely match the upper (~33\\u00b0C) or lower (37\\u00b0C) airways.\"]}", "id": 557}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Widely available lysosome targeting agents should be considered as a potential marker for covid-19\n\nAbstract:\nWhile the COVID-19 pandemic advances, the scientific community struggles in the search for treatments.\nSeveral improvements have been made, including the discovery of clinical efficacy of chloroquine (CQ) in COVID-19 patients, but the effective treatment protocols are still missing.\nIn order to find novel treatment options many scientists utilize the in silico approach to identify compounds that could interfere with the key molecules involved in entrance, replication, or dissemination of the SARS-CoV-2.\nHowever, most of the identified molecules are currently not available as pharmacological agents, and assessing their safety and efficacy could take many months.\nHere, we took a different approach based on the proposed pharmacodynamic model of CQ in COVID-19.\nThe main mechanism of action responsible for the favourable outcome of COVID-19 patients treated with CQ seems to be related to pH modulation-mediated effect on the endolysosomal trafficking, a characteristic of chemical compounds often called lysosomotropic agents because of the physico-chemical properties that enable them to passively diffuse through the endosomal membrane and undergo protonation-based trapping in the lumen of the acidic vesicles.\nIn this review, we discuss lysosomotropic and lysosome targeting drugs that are already in clinical use and are characterized by good safety profiles, low cost, and wide availability.\nWe emphasize that some of these drugs, in particular azithromycin and other macrolide antibiotics, indomethacin and some other non-steroidal anti-inflammatory drugs, proton pump inhibitors, and fluoxetine could provide additional therapeutic benefits in addition to the potential antiviral effect that still has to be confirmed by well-controlled clinical trials.\nAs some of these drugs, mostly antibiotics, were already empirically used in the treatment of COVID-19, we encourage our colleagues all over the world to publish patient data so potential efficacy of these agents can be evaluated in the clinical context and rapidly implemented in the therapeutic protocols if the beneficial effect on clinical outcome is observed.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"The main mechanism of action responsible for the favourable outcome of COVID-19 patients treated with CQ seems to be related to pH modulation-mediated effect on the endolysosomal trafficking, a characteristic of chemical compounds often called lysosomotropic agents because of the physico-chemical properties that enable them to passively diffuse through the endosomal membrane and undergo protonation-based trapping in the lumen of the acidic vesicles.\", \"As some of these drugs, mostly antibiotics, were already empirically used in the treatment of COVID-19, we encourage our colleagues all over the world to publish patient data so potential efficacy of these agents can be evaluated in the clinical context and rapidly implemented in the therapeutic protocols if the beneficial effect on clinical outcome is observed.\"]}", "id": 558}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: A potently neutralizing antibody protects cells against sars-cov-2 infection\n\nAbstract:\nSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is responsible for millions of infections and hundreds of thousands of deaths globally.\nThere are no widely available licensed therapeutics against SARS-CoV-2, highlighting an urgent need for effective interventions.\nThe virus enters host cells through binding of a receptor-binding domain within its trimeric spike glycoprotein to human angiotensin-converting enzyme 2.\nIn this article, we describe the generation and characterization of a panel of murine mAbs directed against the receptor-binding domain.\nOne mAb, 2B04, neutralized wild-type SARS-CoV-2 in vitro with remarkable potency (half-maximal inhibitory concentration of <2 ng/ml).\nIn a murine model of SARS-CoV-2 infection, 2B04 protected challenged animals from weight loss, reduced lung viral load, and blocked systemic dissemination.\nThus, 2B04 is a promising candidate for an effective antiviral that can be used to prevent SARS-CoV-2 infection.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"In a murine model of SARS-CoV-2 infection, 2B04 protected challenged animals from weight loss, reduced lung viral load, and blocked systemic dissemination.\"]}", "id": 559}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: The high temperature and low relative humidity lead to high evaporation rates of saliva-contaminated droplets, thus significantly reducing the coronavirus viability\n\nAbstract:\nThe coronavirus disease 2019 (COVID-19) outbreak has become a severe public health issue.\nThe novelty of the virus prompts a search for understanding of how ecological factors affect the transmission and survival of the virus.\nSeveral studies have robustly identified a relationship between temperature and the number of cases.\nHowever, there is no specific study for a tropical climate such as Brazil.\nThis work aims to determine the relationship of temperature to COVID-19 infection for the state capital cities of Brazil.\nCumulative data with the daily number of confirmed cases was collected from February 27 to April 1, 2020, for all 27 state capital cities of Brazil affected by COVID-19.\nA generalized additive model (GAM) was applied to explore the linear and nonlinear relationship between annual average temperature compensation and confirmed cases.\nAlso, a polynomial linear regression model was proposed to represent the behavior of the growth curve of COVID-19 in the capital cities of Brazil.\nThe GAM dose-response curve suggested a negative linear relationship between temperatures and daily cumulative confirmed cases of COVID-19 in the range from 16.8 \u00b0C to 27.4 \u00b0C.\nEach 1 \u00b0C rise of temperature was associated with a -4.8951% (t = -2.29, p = 0.0226) decrease in the number of daily cumulative confirmed cases of COVID-19.\nA sensitivity analysis assessed the robustness of the results of the model.\nThe predicted R-squared of the polynomial linear regression model was 0.81053.\nIn this study, which features the tropical temperatures of Brazil, the variation in annual average temperatures ranged from 16.8 \u00b0C to 27.4 \u00b0C.\nResults indicated that temperatures had a negative linear relationship with the number of confirmed cases.\nThe curve flattened at a threshold of 25.8 \u00b0C.\nThere is no evidence supporting that the curve declined for temperatures above 25.8 \u00b0C.\nThe study had the goal of supporting governance for healthcare policymakers.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Results indicated that temperatures had a negative linear relationship with the number of confirmed cases.\"]}", "id": 560}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Cell entry of sars-cov-2 conferred by angiotensin-converting enzyme 2 of different specie\n\nAbstract:\nThe outbreak of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) poses a huge threat to many countries around the world.\nHowever, where is it origin and which animals are sensitive to cross-species transmission is unclear.\nThe interaction of virus and cell receptor is a key determinant of host range for the novel coronavirus.\nAngiotensin-converting enzyme 2 (ACE2) is demonstrated as the primary entry receptor for SARS-CoV-2.\nIn this study, we evaluated the SARS-CoV-2 entry mediated by ACE2 of 11 different species of animals, and discovered that ACE2 of Rhinolophus sinicus (Chinese horseshoe bat), Felis catus (domestic cat), Canis lupus familiaris (dog), Sus scrofa (pig), Capra hircus (goat) and especially Manis javanica (Malayan pangolin) were able to render SARS-CoV-2 entry in non-susceptible cells.\nThis is the first report that ACE2 of Pangolin could mediate SARS-CoV-2 entry which increases the presume that SARS-CoV-2 may have a pangolin origin.\nHowever, none of the ACE2 proteins from Rhinolophus ferrumequinum (greater horseshoe bat), Gallus gallus (chicken), Notechis scutatus (mainland tiger snake), Mus musculus (house mouse) rendered SARS-CoV-2 entry.\nSpecifically, a natural isoform of Macaca mulatta (Rhesus monkey) ACE2 with a mutation of Y217N was resistance to infection, which rises the possible impact of this type of ACE2 during monkey studies of SARS-CoV-2.\nOverall, these results clarify that SARS-CoV-2 could engage receptors of multiple species of animals and it is a perplexed work to track SARS-CoV-2 origin and its intermediate hosts.\nIMPORTANCE In this study, we illustrated that SARS-CoV-2 is able to engage receptors of multiple species of animals.\nThis indicated that it may be a perplexed work to track SARS-CoV-2 origin and discover its intermediate hosts.\nThis feature of virus is considered to potentiate its diverse cross-species transmissibility.\nOf note, here is the first report that ACE2 of Pangolin could mediate SARS-CoV-2 entry which increases the possibility that SARS-CoV-2 may have a pangolin origin. And we also demonstrated that not all species of bat were sensitive to SARS-CoV-2 infection.\nAt last, it is also important to detect the expression ratio of the Y217N ACE2 to the prototype in Rhesus monkeys to be recruited for studies on SARS-CoV-2 infection.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Angiotensin-converting enzyme 2 (ACE2) is demonstrated as the primary entry receptor for SARS-CoV-2.\"]}", "id": 561}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Unfortunately, to date, no vaccines or antiviral drugs have been approved for the treatment of SARS-CoV-2 infection by regulatory agencies.\n\nAbstract:\nWe remain largely without effective prophylactic/therapeutic interventions for COVID-19.\nAlthough many human clinical trials are ongoing, there remains a deficiency of supportive preclinical drug efficacy studies.\nHere we assessed the prophylactic/therapeutic efficacy of hydroxychloroquine (HCQ), a drug of interest for COVID-19 management, in two animal models.\nWhen used for prophylaxis or treatment neither the standard human malaria dose (6.5 mg/kg) nor a high dose (50 mg/kg) of HCQ had any beneficial effect on clinical disease or SARS-CoV-2 kinetics (replication/shedding) in the Syrian hamster disease model.\nSimilarly, HCQ prophylaxis/treatment (6.5 mg/kg) did not significantly benefit clinical outcome nor reduce SARS-CoV-2 replication/shedding in the upper and lower respiratory tract in the rhesus macaque disease model.\nIn conclusion, our preclinical animal studies do not support the use of HCQ in prophylaxis/treatment of COVID-19.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 562}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Higher risk if you have type 1 diabetes. Compared to people without diabetes, people with type 1 diabetes are approximately 3.5 times as likely to die in hospital with COVID-19, while people with type 2 are approximately twice as likely. \n\nAbstract:\nAIMS: We aimed to briefly review the general characteristics of the novel coronavirus (SARS-CoV-2) and provide a better understanding of the coronavirus disease (COVID-19) in people with diabetes, and its management.\nMETHODS: We searched for articles in PubMed and Google Scholar databases till 02 April 2020, with the following keywords: \"SARS-CoV-2\", \"COVID-19\", \"infection\", \"pathogenesis\", \"incubation period\", \"transmission\", \"clinical features\", \"diagnosis\", \"treatment\", \"diabetes\", with interposition of the Boolean operator \"AND\".\nRESULTS: The clinical spectrum of COVID-19 is heterogeneous, ranging from mild flu-like symptoms to acute respiratory distress syndrome, multiple organ failure and death.\nOlder age, diabetes and other comorbidities are reported as significant predictors of morbidity and mortality.\nChronic inflammation, increased coagulation activity, immune response impairment, and potential direct pancreatic damage by SARS-CoV-2 might be among the underlying mechanisms of the association between diabetes and COVID-19.\nNo conclusive evidence exists to support the discontinuation of angiotensin-converting enzyme inhibitors (ACEI), angiotensin receptor blockers or thiazolidinediones because of COVID-19 in people with diabetes.\nCaution should be taken to potential hypoglycemic events with the use of chloroquine in these subjects.\nPatient tailored therapeutic strategies, rigorous glucose monitoring and careful consideration of drug interactions might reduce adverse outcomes.\nCONCLUSIONS: Suggestions are made on the possible pathophysiological mechanisms of the relationship between diabetes and COVID-19, and its management.\nNo definite conclusions can be made based on current limited evidence.\nFurther research regarding this relationship and its clinical management is warranted.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 563}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: N95 masks are more effective than regular masks.\n\nAbstract:\nWe present two models for the COVID-19 pandemic predicting the impact of universal face mask wearing upon the spread of the SARS-CoV-2 virus--one employing a stochastic dynamic network based compartmental SEIR (susceptible-exposed-infectious-recovered) approach, and the other employing individual ABM (agent-based modelling) Monte Carlo simulation--indicating (1) significant impact under (near) universal masking when at least 80% of a population is wearing masks, versus minimal impact when only 50% or less of the population is wearing masks, and (2) significant impact when universal masking is adopted early, by Day 50 of a regional outbreak, versus minimal impact when universal masking is adopted late.\nThese effects hold even at the lower filtering rates of homemade masks.\nTo validate these theoretical models, we compare their predictions against a new empirical data set we have collected that includes whether regions have universal masking cultures or policies, their daily case growth rates, and their percentage reduction from peak daily case growth rates.\nResults show a near perfect correlation between early universal masking and successful suppression of daily case growth rates and/or reduction from peak daily case growth rates, as predicted by our theoretical simulations.\nOur theoretical and empirical results argue for urgent implementation of universal masking.\nAs governments plan how to exit societal lockdowns, it is emerging as a key NPI; a\"mouth-and-nose lockdown\"is far more sustainable than a\"full body lockdown\", on economic, social, and mental health axes.\nAn interactive visualization of the ABM simulation is at http://dek.ai/masks4all.\nWe recommend immediate mask wearing recommendations, official guidelines for correct use, and awareness campaigns to shift masking mindsets away from pure self-protection, towards aspirational goals of responsibly protecting one's community.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 564}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Vitamin B could help prevent the 'worst outcomes' in covid-19 cases\n\nAbstract:\nOptimal nutrition can improve well-being and might mitigate the risk and morbidity associated with coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).\nThis review summarizes nutritional guidelines to support dietary counseling provided by dietitians and health-related professionals.\nThe majority of documents encouraged the consumption of fruits, vegetables, and whole grain foods.\nThirty-one percent of the guidelines highlighted the importance of minerals and vitamins such as zinc and vitamins C, A, and D to maintain a well-functioning immune system.\nDietary supplementation has not been linked to COVID-19 prevention.\nHowever, supplementation with vitamins C and D, as well as with zinc and selenium, was highlighted as potentially beneficial for individuals with, or at risk of, respiratory viral infections or for those in whom nutrient deficiency is detected.\nThere was no convincing evidence that food or food packaging is associated with the transmission of COVID-19, but good hygiene practices for handling and preparing foods were recommended.\nNo changes to breastfeeding recommendations have been made, even in women diagnosed with COVID-19.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"The majority of documents encouraged the consumption of fruits, vegetables, and whole grain foods.\", \"Thirty-one percent of the guidelines highlighted the importance of minerals and vitamins such as zinc and vitamins C, A, and D to maintain a well-functioning immune system.\", \"Dietary supplementation has not been linked to COVID-19 prevention.\", \"However, supplementation with vitamins C and D, as well as with zinc and selenium, was highlighted as potentially beneficial for individuals with, or at risk of, respiratory viral infections or for those in whom nutrient deficiency is detected.\"]}", "id": 565}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: there is no link between vitamin D concentrations and risk of COVID-19 infection.\n\nAbstract:\nThe outbreak of COVID-19 has created a global public health crisis.\nLittle is known about the protective factors of this infection.\nTherefore, preventive health measures that can reduce the risk of infection, progression and severity are desperately needed.\nThis review discussed the possible roles of vitamin D in reducing the risk of COVID-19 and other acute respiratory tract infections and severity.\nMoreover, this study determined the correlation of vitamin D levels with COVID-19 cases and deaths in 20 European countries as of 20 May 2020.\nA significant negative correlation (p=0.033) has been observed between mean vitamin D levels and COVID-19 cases per one million population in European countries.\nHowever, the correlation of vitamin D with COVID-19 deaths of these countries was not significant.\nSome retrospective studies demonstrated a correlation between vitamin D status and COVID-19 severity and mortality, while other studies did not find the correlation when confounding variables are adjusted.\nSeveral studies demonstrated the role of vitamin D in reducing the risk of acute viral respiratory tract infections and pneumonia.\nThese include direct inhibition with viral replication or with anti-inflammatory or immunomodulatory ways.\nIn the meta-analysis, vitamin D supplementation has been shown as safe and effective against acute respiratory tract infections.\nThus, people who are at higher risk of vitamin D deficiency during this global pandemic should consider taking vitamin D supplements to maintain the circulating 25(OH)D in the optimal levels (75-125nmol/L).\nIn conclusion, there is not enough evidence on the association between vitamin D levels and COVID-19 severity and mortality.\nTherefore, randomized control trials and cohort studies are necessary to test this hypothesis.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"A significant negative correlation (p=0.033) has been observed between mean vitamin D levels and COVID-19 cases per one million population in European countries.\", \"Several studies demonstrated the role of vitamin D in reducing the risk of acute viral respiratory tract infections and pneumonia.\", \"These include direct inhibition with viral replication or with anti-inflammatory or immunomodulatory ways.\", \"In the meta-analysis, vitamin D supplementation has been shown as safe and effective against acute respiratory tract infections.\", \"Thus, people who are at higher risk of vitamin D deficiency during this global pandemic should consider taking vitamin D supplements to maintain the circulating 25(OH)D in the optimal levels (75-125nmol/L).\"]}", "id": 566}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Eating garlic will protect me against getting the coronavirus.\n\nAbstract:\nOBJECTIVE To analyze the characteristics of YouTube videos in Spanish on the basic measures to prevent coronavirus disease 2019 (COVID-19).\nMETHODS On 18 March 2020, a search was conducted on YouTube using the terms \"Prevencion Coronavirus\" and \"Prevencion COVID-19\".\nWe studied the associations between the type of authorship and the country of publication with other variables (such as the number of likes and basic measures to prevent COVID-19 according to the World Health Organization, among others) with univariate analysis and a multiple logistic regression model.\nRESULTS A total of 129 videos were evaluated; 37.2% were produced in Mexico (25.6%) and Spain (11.6%), and 56.6% were produced by mass media, including television and newspapers.\nThe most frequently reported basic preventive measure was hand washing (71.3%), and the least frequent was not touching the eyes, nose, and mouth (24.0%).\nHoaxes (such as eating garlic or citrus to prevent COVID-19) were detected in 15 videos (10.9%).\nIn terms of authorship, papers produced by health professionals had a higher probability of reporting hand hygiene (OR (95% CI) = 4.20 (1.17-15.09)) and respiratory hygiene (OR (95% CI) = 3.05 (1.22-7.62)) as preventive measures.\nCONCLUSION Information from YouTube in Spanish on basic measures to prevent COVID-19 is usually not very complete and differs according to the type of authorship.\nOur findings make it possible to guide Spanish-speaking users on the characteristics of the videos to be viewed in order to obtain reliable information.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Hoaxes (such as eating garlic or citrus to prevent COVID-19) were detected in 15 videos (10.9%).\"]}", "id": 567}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: the virus can stay on surfaces long enough to be a source of transmission\n\nAbstract:\nThe aim in this study was to assess the effectiveness of a quaternary ammonium chloride (QAC) surfactant in reducing surface staphylococcal contamination in a routinely operating medical ward occupied by patients who had tested positive for methicillin-resistant Staphylococcus aureus (MRSA).\nThe QAC being tested is an antibacterial film that is sprayed onto a surface and can remain active for up to 8 h. A field experimental study was designed with the QAC plus daily hypochlorite cleaning as the experimental group and hypochlorite cleaning alone as the control group.\nThe method of swabbing on moistened surfaces was used for sampling.\nIt was found that 83% and 77% of the bedside surfaces of MRSA-positive and MRSA-negative patients respectively were contaminated with staphylococci at 08:00 hours, and that the staphylococcal concentrations increased by 80% at 1200 h over a 4-hour period with routine ward and clinical activities.\nIrrespective of the MRSA status of the patients, high-touch surfaces around the bed-units within the studied medical ward were heavily contaminated (ranged 1 to 276 cfu/cm(2) amongst the sites with positive culture) with staphylococcal bacteria including MRSA, despite the implementation of daily hypochlorite wiping.\nHowever, the contamination rate dropped significantly from 78% to 11% after the application of the QAC polymer.\nIn the experimental group, the mean staphylococcal concentration of bedside surfaces was significantly (p < 0.0001) reduced from 4.4 \u00b1 8.7 cfu/cm(2) at 08:00 hours to 0.07 \u00b1 0.26 cfu/cm(2) at 12:00 hours by the QAC polymer.\nThe results of this study support the view that, in addition to hypochlorite wiping, the tested QAC surfactant is a potential environmental decontamination strategy for preventing the transmission of clinically important pathogens in medical wards.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"It was found that 83% and 77% of the bedside surfaces of MRSA-positive and MRSA-negative patients respectively were contaminated with staphylococci at 08:00 hours, and that the staphylococcal concentrations increased by 80% at 1200 h over a 4-hour period with routine ward and clinical activities.\"]}", "id": 568}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Like other viruses with a lipid envelope, SARS-CoV-2 is probably sensitive to temperature, humidity, and solar radiation\n\nAbstract:\nThis paper investigates the correlation between the high level of coronavirus SARS-CoV-2 infection accelerated transmission and lethality, and surface air pollution in Milan metropolitan area, Lombardy region in Italy.\nFor January-April 2020 period, time series of daily average inhalable gaseous pollutants ozone (O3) and nitrogen dioxide (NO2), together climate variables (air temperature, relative humidity, wind speed, precipitation rate, atmospheric pressure field and Planetary Boundary Layer) were analyzed.\nIn spite of being considered primarily transmitted by indoor bioaerosols droplets and infected surfaces or direct human-to-human personal contacts, it seems that high levels of urban air pollution, and climate conditions have a significant impact on SARS-CoV-2 diffusion.\nExhibited positive correlations of ambient ozone levels and negative correlations of NO2 with the increased rates of COVID-19 infections (Total number, Daily New positive and Total Deaths cases), can be attributed to airborne bioaerosols distribution.\nThe results show positive correlation of daily averaged O3 with air temperature and inversely correlations with relative humidity and precipitation rates.\nViral genome contains distinctive features, including a unique N-terminal fragment within the spike protein, which allows coronavirus attachment on ambient air pollutants.\nAt this moment it is not clear if through airborne diffusion, in the presence of outdoor and indoor aerosols, this protein \"spike\" of the new COVID-19 is involved in the infectious agent transmission from a reservoir to a susceptible host during the highest nosocomial outbreak in some agglomerated industrialized urban areas like Milan is.\nAlso, in spite of collected data for cold season (winter-early spring) period, when usually ozone levels have lower values than in summer, the findings of this study support possibility as O3 can acts as a COVID-19 virus incubator.\nBeing a novel pandemic coronavirus version, it might be ongoing during summer conditions associated with higher air temperatures, low relative humidity and precipitation levels.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Being a novel pandemic coronavirus version, it might be ongoing during summer conditions associated with higher air temperatures, low relative humidity and precipitation levels.\"]}", "id": 569}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: B cells and t cells mediate fusion to covid-19\n\nAbstract:\nRecent reports that antibodies to SARS-CoV-2 are not maintained in the serum following recovery from the virus have caused alarm.\nHowever, the absence of specific antibodies in the serum does not necessarily mean an absence of immune memory.\nHere, we discuss our current understanding of the relative contribution of B cells and T cells to immunity to SARS-CoV-2 and the implications for the development of effective treatments and vaccines for COVID-19.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Here, we discuss our current understanding of the relative contribution of B cells and T cells to immunity to SARS-CoV-2 and the implications for the development of effective treatments and vaccines for COVID-19.\"]}", "id": 570}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: there is no scientific evidence establishing a link between ibuprofen and worsening of covid-19\n\nAbstract:\nIbuprofen is an over-the-counter medication that is used widely for the treatment of pain and fever during COVID-19 pandemic.\nA concern was raised regarding the safety of ibuprofen use because of its role in increasing ACE2 levels within the Renin-Angiotensin-Aldosterone system.\nACE2 is the coreceptor for the entry of SARS-CoV-2 into cells, and so, a potential increased risk of contracting COVID-19 disease and/or worsening of COVID-19 infection was feared with ibuprofen use.\nHowever, available data from limited studies show administration of recombinant ACE2 improves lung damage caused by respiratory viruses, suggesting ibuprofen use may be beneficial in COVID-19 disease.\nAt this time, there is no supporting evidence to discourage the use of ibuprofen.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Ibuprofen is an over-the-counter medication that is used widely for the treatment of pain and fever during COVID-19 pandemic.\", \"However, available data from limited studies show administration of recombinant ACE2 improves lung damage caused by respiratory viruses, suggesting ibuprofen use may be beneficial in COVID-19 disease.\", \"At this time, there is no supporting evidence to discourage the use of ibuprofen.\"]}", "id": 571}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: However, some children can get severely ill from COVID-19. They might require hospitalization, intensive care, or a ventilator to help them breathe.\n\nAbstract:\nBackground: As the novel coronavirus triggering COVID-19 has broken out in Wuhan, China and spread rapidly worldwide, it threatens the lives of thousands of people and poses a global threat on the economies of the entire world.\nHowever, infection with COVID-19 is currently rare in children.\nObjective To discuss the latest findings and research focus on the basis of characteristics of children confirmed with COVID-19, and provide an insight into the future treatment and research direction.\nMethods: We searched the terms \"COVID-19 OR coronavirus OR SARS-CoV-2\" AND \"Pediatric OR children\" on PubMed, Embase, Cochrane library, NIH, CDC, and CNKI.\nThe authors also reviewed the guidelines published on Chinese CDC and Chinese NHC.\nResults: We included 25 published literature references related to the epidemiology, clinical manifestation, accessary examination, treatment, and prognosis of pediatric patients with COVID-19.\nConclusion: The numbers of children with COVID-19 pneumonia infection are small, and most of them come from family aggregation.\nSymptoms are mainly mild or even asymptomatic, which allow children to be a risk factor for transmission.\nThus, strict epidemiological history screening is needed for early diagnosis and segregation.\nThis holds especially for infants, who are more susceptible to infection than other age groups in pediatric age, but have most likely subtle and unspecific symptoms.\nThey need to be paid more attention to.\nCT examination is a necessity for screening the suspected cases, because most of the pediatric patients are mild cases, and plain chest X-ray do not usually show the lesions or the detailed features.\nTherefore, early chest CT examination combined with pathogenic detection is a recommended clinical diagnosis scheme in children.\nThe risk factors which may suggest severe or critical progress for children are: Fast respiratory rate and/or; lethargy and drowsiness mental state and/or; lactate progressively increasing and/or; imaging showed bilateral or multi lobed infiltration, pleural effusion or rapidly expending of lesions in a short period of time and/or; less than 3 months old or those who underly diseases.\nFor those critical pediatric patients with positive SARS-CoV-2 diagnosis, polypnea may be the most common symptom.\nFor treatment, the elevated PCT seen in children in contrast to adults suggests that the underlying coinfection/secondary infection may be more common in pediatric patients and appropriate antibacterial treatment should be considered.\nOnce cytokine storm is found in these patients, anti-autoimmune or blood-purifying therapy should be given in time.\nFurthermore, effective isolation measures and appropriate psychological comfort need to be provided timely.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Conclusion: The numbers of children with COVID-19 pneumonia infection are small, and most of them come from family aggregation.\", \"Symptoms are mainly mild or even asymptomatic, which allow children to be a risk factor for transmission.\"]}", "id": 572}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Surgical Masks Stop Transmission Of COVID-19 From Symptomatic People\n\nAbstract:\nOBJECTIVES: To determine the risk of SARS-CoV-2 transmission by aerosols, to provide evidence on the rational use of masks, and to discuss additional measures important for the protection of healthcare workers from COVID-19.\nMETHODS: Literature review and expert opinion.\nSHORT CONCLUSION: SARS-CoV-2, the pathogen causing COVID-19, is considered to be transmitted via droplets rather than aerosols, but droplets with strong directional airflow support may spread further than 2 m. High rates of COVID-19 infections in healthcare-workers (HCWs) have been reported from several countries.\nRespirators such as filtering face piece (FFP) 2 masks were designed to protect HCWs, while surgical masks were originally intended to protect patients (e.g., during surgery).\nNevertheless, high quality standard surgical masks (type II/IIR according to European Norm EN 14683) appear to be as effective as FFP2 masks in preventing droplet-associated viral infections of HCWs as reported from influenza or SARS.\nSo far, no head-to-head trials with these masks have been published for COVID-19.\nNeither mask type completely prevents transmission, which may be due to inappropriate handling and alternative transmission pathways.\nTherefore, compliance with a bundle of infection control measures including thorough hand hygiene is key.\nDuring high-risk procedures, both droplets and aerosols may be produced, reason why respirators are indicated for these interventions.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Respirators such as filtering face piece (FFP) 2 masks were designed to protect HCWs, while surgical masks were originally intended to protect patients (e.g., during surgery).\", \"Nevertheless, high quality standard surgical masks (type II/IIR according to European Norm EN 14683) appear to be as effective as FFP2 masks in preventing droplet-associated viral infections of HCWs as reported from influenza or SARS.\"]}", "id": 573}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: A unique covid-19 immune signature combines immuno-protection with discrete sepsis-like traits associated with poor prognosis\n\nAbstract:\nPerson-to-person transmission of SARS-CoV-2 virus has triggered a global emergency because of its potential to cause life-threatening Covid-19 disease.\nBy comparison to pauci-symptomatic virus clearance by most individuals, Covid-19 has been proposed to reflect insufficient and/or pathologically exaggerated immune responses.\nHere we identify a consensus peripheral blood immune signature across 63 hospital-treated Covid-19 patients who were otherwise highly heterogeneous.\nThe core signature conspicuously blended adaptive B cell responses typical of virus infection or vaccination with discrete traits hitherto associated with sepsis, including monocyte and dendritic cell dampening, and hyperactivation and depletion of discrete T cell subsets.\nThis blending of immuno-protective and immuno-pathogenic potentials was exemplified by near-universal CXCL10/IP10 upregulation, as occurred in SARS1 and MERS.\nMoreover, specific parameters including CXCL10/IP10 over-expression, T cell proliferation, and basophil and plasmacytoid dendritic cell depletion correlated, often prognostically, with Covid-19 progression, collectively composing a resource to inform SARS-CoV-2 pathobiology and risk-based patient stratification.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"The core signature conspicuously blended adaptive B cell responses typical of virus infection or vaccination with discrete traits hitherto associated with sepsis, including monocyte and dendritic cell dampening, and hyperactivation and depletion of discrete T cell subsets.\"]}", "id": 574}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: The new coronavirus can damage the lungs, heart and brain, which increases the risk of long-term health problems.\n\nAbstract:\nBACKGROUND: The coronavirus disease of 2019 (COVID-19) is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).\nWhile systemic inflammation and pulmonary complications can result in significant morbidity and mortality, cardiovascular complications may also occur.\nOBJECTIVE: This brief report evaluates cardiovascular complications in the setting of COVID-19 infection.\nDISCUSSION: The current COVID-19 pandemic has resulted in over one million infected worldwide and thousands of death.\nThe virus binds and enters through angiotensin-converting enzyme 2 (ACE2).\nCOVID-19 can result in systemic inflammation, multiorgan dysfunction, and critical illness.\nThe cardiovascular system is also affected, with complications including myocardial injury, myocarditis, acute myocardial infarction, heart failure, dysrhythmias, and venous thromboembolic events.\nCurrent therapies for COVID-19 may interact with cardiovascular medications.\nCONCLUSIONS: Emergency clinicians should be aware of these cardiovascular complications when evaluating and managing the patient with COVID-19.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"The cardiovascular system is also affected, with complications including myocardial injury, myocarditis, acute myocardial infarction, heart failure, dysrhythmias, and venous thromboembolic events.\", \"Current therapies for COVID-19 may interact with cardiovascular medications.\"]}", "id": 575}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Potent neutralizing antibodies from covid-19 patients define multiple targets of antigen\n\nAbstract:\nThe rapid spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has had a large impact on global health, travel, and economy.\nTherefore, preventative and therapeutic measures are urgently needed.\nHere, we isolated monoclonal antibodies from three convalescent coronavirus disease 2019 (COVID-19) patients using a SARS-CoV-2 stabilized prefusion spike protein.\nThese antibodies had low levels of somatic hypermutation and showed a strong enrichment in VH1-69, VH3-30-3, and VH1-24 gene usage.\nA subset of the antibodies was able to potently inhibit authentic SARS-CoV-2 infection at a concentration as low as 0.007 micrograms per milliliter.\nCompetition and electron microscopy studies illustrate that the SARS-CoV-2 spike protein contains multiple distinct antigenic sites, including several receptor-binding domain (RBD) epitopes as well as non-RBD epitopes.\nIn addition to providing guidance for vaccine design, the antibodies described here are promising candidates for COVID-19 treatment and prevention.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"In addition to providing guidance for vaccine design, the antibodies described here are promising candidates for COVID-19 treatment and prevention.\"]}", "id": 576}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: To help slow the spread and reduce your risk of COVID-19, stay at least 6 feet away from others. Keeping physical distance is important, even if you are not sick.\n\nAbstract:\nThe Australian and New Zealand governments both initiated strict social distancing measures in response to the COVID-19 pandemic in late March.\nIt remains difficult to quantify the impact this had in reducing the spread of the virus.\nBayesian structural time series model provide a model to quantify the scenario in which these government-level interventions were not placed.\nOur models predict these strict social distancing measures caused a 79% and 61% reduction in the daily cases of COVID-19 across Australia and New Zealand respectively.\nThis provides both evidence and impetus for governments considering similar measures in response to COVID-19 and other pandemics.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Our models predict these strict social distancing measures caused a 79% and 61% reduction in the daily cases of COVID-19 across Australia and New Zealand respectively.\", \"This provides both evidence and impetus for governments considering similar measures in response to COVID-19 and other pandemics.\"]}", "id": 577}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: the virus can stay on surfaces long enough to be a source of transmission\n\nAbstract:\nObjectives: To evaluate SARS-CoV-2 surface and air contamination during the peak of the COVID-19 pandemic in London.\nDesign: Prospective cross-sectional observational study.\nSetting: An acute NHS healthcare provider.\nParticipants: All inpatient wards were fully occupied by patients with COVID-19 at the time of sampling.\nInterventions: Air and surface samples were collected from a range of clinical areas and a public area of the hospital.\nAn active air sampler was used to collect three or four 1.0 m3 air samples in each area.\nSurface samples were collected by swabbing approximately 25 cm2 of items in the immediate vicinity of each air sample.\nSARS-CoV-2 was detected by RT-qPCR and viral culture using Vero E6 and Caco2 cells; additionally the limit of detection for culturing SARS-CoV-2 dried onto surfaces was determined.\nMain outcome measures: SARS-CoV-2 detected by PCR or culture.\nResults: Viral RNA was detected on 114/218 (52.3%) of surface and 14/31 (38.7%) air samples but no virus was cultured.\nThe proportion of surface samples contaminated with viral RNA varied by item sampled and by clinical area.\nViral RNA was detected on surfaces and in air in public areas of the hospital but was more likely to be found in areas immediately occupied by COVID-19 patients (67/105 (63.8%) in areas immediately occupied by COVID-19 patients vs. 29/64 (45.3%) in other areas (odds ratio 0.5, 95% confidence interval 0.2-0.9, p=0.025, Fishers exact test).\nThe PCR Ct value for all surface and air samples (>30) indicated a viral load that would not be culturable.\nConclusions: Our findings of extensive viral RNA contamination of surfaces and air across a range of acute healthcare settings in the absence of cultured virus underlines the potential risk from surface and air contamination in managing COVID-19, and the need for effective use of PPE, social distancing, and hand/surface hygiene.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Conclusions: Our findings of extensive viral RNA contamination of surfaces and air across a range of acute healthcare settings in the absence of cultured virus underlines the potential risk from surface and air contamination in managing COVID-19, and the need for effective use of PPE, social distancing, and hand/surface hygiene.\"]}", "id": 578}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: the new coronavirus can persist in the body for at least two weeks after symptoms of the disease clear up. \n\nAbstract:\nRecent studies have provided insights into the pathogenesis of coronavirus disease 2019 (COVID-19)1-4.\nYet, longitudinal immunological correlates of disease outcome remain unclear.\nHere, we serially analysed immune responses in 113 COVID-19 patients with moderate (non-ICU) and severe (ICU) disease.\nImmune profiling revealed an overall increase in innate cell lineages with a concomitant reduction in T cell number.\nWe identify an association between early, elevated cytokines and worse disease outcomes.\nFollowing an early increase in cytokines, COVID-19 patients with moderate disease displayed a progressive reduction in type-1 (antiviral) and type-3 (antifungal) responses.\nIn contrast, patients with severe disease maintained these elevated responses throughout the course of disease.\nMoreover, severe disease was accompanied by an increase in multiple type 2 (anti-helminths) effectors including, IL-5, IL-13, IgE and eosinophils.\nUnsupervised clustering analysis of plasma and peripheral blood leukocyte data identified 4 immune signatures, representing (A) growth factors, (B) type-2/3 cytokines, (C) mixed type-1/2/3 cytokines, and (D) chemokines that correlated with three distinct disease trajectories of patients.\nThe immune profile of patients who recovered with moderate disease was enriched in tissue reparative growth factor signature (A), while the profile for those with worsened disease trajectory had elevated levels of all four signatures.\nThus, we identified development of a maladapted immune response profile associated with severe COVID-19 outcome and early immune signatures that correlate with divergent disease trajectories.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 579}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Detection of antibodies to the sars-cov-2 spike glycoprotein in both serum and saliva coulds detection of infection\n\nAbstract:\nBACKGROUND: Detecting antibody responses during and after SARS-CoV-2 infection is essential in determining the seroepidemiology of the virus and the potential role of antibody in disease.\nScalable, sensitive and specific serological assays are essential to this process.\nThe detection of antibody in hospitalized patients with severe disease has proven straightforward; detecting responses in subjects with mild disease and asymptomatic infections has proven less reliable.\nWe hypothesized that the suboptimal sensitivity of antibody assays and the compartmentalization of the antibody response may contribute to this effect.\nMETHODS: We systemically developed an ELISA assay, optimising different antigens and amplification steps, in serum and saliva from symptomatic and asymptomatic SARS-CoV-2-infected subjects.\nRESULTS: Using trimeric spike glycoprotein, rather than nucleocapsid enabled detection of responses in individuals with low antibody responses.\nIgG1 and IgG3 predominate to both antigens, but more antispike IgG1 than IgG3 was detectable.\nAll antigens were effective for detecting responses in hospitalized patients.\nAnti-spike, but not nucleocapsid, IgG, IgA and IgM antibody responses were readily detectable in saliva from non-hospitalized symptomatic and asymptomatic individuals.\nAntibody responses in saliva and serum were largely independent of each other and symptom reporting.\nCONCLUSIONS.\nDetecting antibody responses in both saliva and serum is optimal for determining virus exposure and understanding immune responses after SARS-CoV-2 infection.\nFUNDING.\nThis work was funded by the University of Birmingham, the National Institute for Health Research (UK), the NIH National Institute for Allergy and Infectious Diseases, the Bill and Melinda Gates Foundation and the University of Southampton.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Anti-spike, but not nucleocapsid, IgG, IgA and IgM antibody responses were readily detectable in saliva from non-hospitalized symptomatic and asymptomatic individuals.\", \"Antibody responses in saliva and serum were largely independent of each other and symptom reporting.\"]}", "id": 580}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: If Fever Helps Fight Infection, Should I Avoid Fever-Reducing Drugs\n\nAbstract:\nOBJECTIVE: It was recently suggested that ibuprofen might increase the risk for severe and fatal coronavirus disease 2019 (COVID-19) and should therefore be avoided in this patient population.\nWe aimed to evaluate whether ibuprofen use in individuals with COVID-19 was associated with more severe disease, compared with individuals using paracetamol or no antipyretics.\nMETHODS: In a retrospective cohort study of patients with COVID-19 from Shamir Medical Centre, Israel, we monitored any use of ibuprofen from a week before diagnosis of COVID-19 throughout the disease.\nPrimary outcomes were mortality and the need for respiratory support, including oxygen administration and mechanical ventilation.\nRESULTS: The study included 403 confirmed cases of COVID-19, with a median age of 45 years.\nOf the entire cohort, 44 patients (11%) needed respiratory support and 12 (3%) died.\nOne hundred and seventy-nine (44%) patients had fever, with 32% using paracetamol and 22% using ibuprofen, for symptom-relief.\nIn the ibuprofen group, 3 (3.4%) patients died, whereas in the non-ibuprofen group, 9 (2.8%) patients died (p 0.95).\nNine (10.3%) patients from the ibuprofen group needed respiratory support, compared with 35 (11%) from the non-ibuprofen group (p 1).\nWhen compared with exclusive paracetamol users, no differences were observed in mortality rates or the need for respiratory support among patients using ibuprofen.\nCONCLUSIONS: In this cohort of COVID-19 patients, ibuprofen use was not associated with worse clinical outcomes, compared with paracetamol or no antipyretic.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"When compared with exclusive paracetamol users, no differences were observed in mortality rates or the need for respiratory support among patients using ibuprofen.\", \"CONCLUSIONS: In this cohort of COVID-19 patients, ibuprofen use was not associated with worse clinical outcomes, compared with paracetamol or no antipyretic.\"]}", "id": 581}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Hidden immune weakness found in gravely ill COVID-19 patients.\n\nAbstract:\nThe rapid global spread of SARS-CoV-2 and resultant mortality and social disruption have highlighted the need to better understand coronavirus immunity to expedite vaccine development efforts.\nMultiple candidate vaccines, designed to elicit protective neutralising antibodies targeting the viral spike glycoprotein, are rapidly advancing to clinical trial.\nHowever, the immunogenic properties of the spike protein in humans are unresolved.\nTo address this, we undertook an in-depth characterisation of humoral and cellular immunity against SARS-CoV-2 spike in humans following mild to moderate SARS-CoV-2 infection.\nWe find serological antibody responses against spike are routinely elicited by infection and correlate with plasma neutralising activity and capacity to block ACE2/RBD interaction.\nExpanded populations of spike-specific memory B cells and circulating T follicular helper cells (cTFH) were detected within convalescent donors, while responses to the receptor binding domain (RBD) constitute a minor fraction.\nUsing regression analysis, we find high plasma neutralisation activity was associated with increased spike-specific antibody, but notably also with the relative distribution of spike-specific cTFH subsets.\nThus both qualitative and quantitative features of B and T cell immunity to spike constitute informative biomarkers of the protective potential of novel SARS-CoV-2 vaccines.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 582}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Young people as diligent about Covid measures as older people\n\nAbstract:\nOBJECTIVES: To determine mortality rates among adults with critical illness from coronavirus disease 2019.\nDESIGN: Observational cohort study of patients admitted from March 6, 2020, to April 17, 2020.\nSETTING: Six coronavirus disease 2019 designated ICUs at three hospitals within an academic health center network in Atlanta, Georgia, United States.\nPATIENTS: Adults greater than or equal to 18 years old with confirmed severe acute respiratory syndrome-CoV-2 disease who were admitted to an ICU during the study period.\nINTERVENTIONS: None.\nMEASUREMENTS AND MAIN RESULTS: Among 217 critically ill patients, mortality for those who required mechanical ventilation was 35.7% (59/165), with 4.8% of patients (8/165) still on the ventilator at the time of this report.\nOverall mortality to date in this critically ill cohort is 30.9% (67/217) and 60.4% (131/217) patients have survived to hospital discharge.\nMortality was significantly associated with older age, lower body mass index, chronic renal disease, higher Sequential Organ Failure Assessment score, lower PaO2/FIO2 ratio, higher D-dimer, higher C-reactive protein, and receipt of mechanical ventilation, vasopressors, renal replacement therapy, or vasodilator therapy.\nCONCLUSIONS: Despite multiple reports of mortality rates exceeding 50% among critically ill adults with coronavirus disease 2019, particularly among those requiring mechanical ventilation, our early experience indicates that many patients survive their critical illness.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 583}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: headaches are rarely the sole symptom present in a coronavirus patient.\n\nAbstract:\nOBJECTIVE: The aim of this manuscript is to investigate transversally Ear Nose Throat (ENT) symptoms COVID-19 infection correlated and to study the neurotropism and neuroinvasiveness of the virus in the head-neck district through the investigation of the sense of smell, taste, tearing, salivation and hearing.\nMETHODS: A total of 50 patients with laboratory-confirmed COVID-19 infection were included in our study.\nFor each patient we evaluated the short version of the Questionnaire of Olfactory Disorders-Negative Statements (sQOD-NS), the Summated Xerostomia Inventory-Dutch Version (SXI-DV), The Standardized Patient Evaluation of Eye Dryness (SPEED), Schirmer test I, the Hearing Handicap Inventory For Adults (HHIA) and the Tinnitus Handicap Inventory (THI).\nAll the tests we carried out were performed during the active phase of the symptomatology from COVID-19 (Condition A) and 15 after SARS-COV-2 RT-PCR test negative (Condition B).\nRESULTS: A total of 46 patients (92%) had olfactory dysfunction related to the infection.\nThe 70% of patients reported gustatory disorders.\nCough, fever, headache and asthenia were the most prevalent symptoms.\nThere was a statistically significant difference (p < 0,001) in sQOD-NS, SXI-DV, SPEED, Schirmer test, HHIA and THI between Condition A and Condition B. CONCLUSIONS: In our population there was an alteration of the sense of taste, of the sense of smell, dry eyes and of the oral cavity and an auditory discomfort, symptoms probably linked to the neurotropism of the virus.\nFurthermore, anosmia, dysgeusia and xerostomia are early symptoms of COVID-19, which can be exploited for an early quarantine and a limitation of viral contagion.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 584}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Because some of the symptoms of flu and COVID-19 are similar, it may be hard to tell the difference between them based on symptoms alone\n\nAbstract:\nCoronavirus disease (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS CoV-2) has turned out to be a formidable pandemic.\nUpcoming evidence from confirmed cases of COVID-19 suggests an anticipated incursion of patients with neurological manifestations in the weeks to come.\nAn expression of the angiotensin-converting enzyme 2 (ACE 2), the cellular receptor for SARS-CoV-2 over the glial cells and neurons have made the brain a potential target.\nNeurotoxicity may occur as a result of direct, indirect and post-infectious complications.\nAttention to neurological deficits in COVID-19 is fundamental to ensure appropriate, timely, beneficial management of the affected patients.\nMost common neurological manifestations seen include dizziness, headache, impaired consciousness, acute cerebrovascular disease, ataxia, and seizures.\nAnosmia and ageusia have recently been hinted as significant early symptoms in COVID-19.\nAs cases with neurological deficits in COVID-19 emerge, the overall prognosis is yet unknown.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 585}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Hydroxychloroquine safety outcome within approved therapeutic protocol of covid-19 outpatients in saudi arabia\n\nAbstract:\nBackground: Healthcare systems globally has been challenged following the COVID-19 pandemic, since late 2019.\nMultiple approaches and strategies have been performed to relieve the pressure and support existing healthcare systems.\nThe Saudi Arabian Ministry of Health (MOH) launched an initiative to support the National Healthcare System.\nSince the 5th of June 2020, 238 outpatient fever clinics were established across Saudi Arabia.\nMethods: A cross-sectional study included 2,733 eligible patients subjected to MOH treatment protocol (hydroxychloroquine and zinc) and revisited the clinics within 3-7 days after treatment initiation.\nThis study aimed to assess the safety outcome and reported adverse events from hydroxychloroquine use among suspected COVID-19 patients.\nThe data was collected through an electronic link and cross-checked with that of the national database (Health Electronic Surveillance Network, HESN) and reports from the MOH Morbidity and Mortality (M&M) Committee.\nResults: Majority of the cases were males (70.4%).\nUpon reassessing the studied participants within 3-7 days, 240 patients (8.8%) discontinued the treatment protocol because of the development of side effects (4.1%) and for non-clinical reasons in the remaining (4.7%).\nMedication side effects overall were reported among (6.7%) of all studied participants, including mainly cardiovascular adverse events (2.5%), followed by gastrointestinal (GI) symptoms (2.4%).\nNo Intensive Care Unit (ICU) admission or death were reported among these patients.\nConclusion: In our study, results show that the use of hydroxychloroquine for COVID-19 patients in mild to moderate cases in an outpatient setting, within the protocol recommendation and inclusion/exclusion criteria, is safe, highly tolerable, and with minimum side effects.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Conclusion: In our study, results show that the use of hydroxychloroquine for COVID-19 patients in mild to moderate cases in an outpatient setting, within the protocol recommendation and inclusion/exclusion criteria, is safe, highly tolerable, and with minimum side effects.\"]}", "id": 586}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Green tea: Green tea is an excellent source of antiviral and anti-inflammatory catechins, notably, one called epigallocatechin gallate, or EGCG.\n\nAbstract:\nCoronavirus disease 2019 (COVID-19) is a viral respiratory disease which caused global health emergency and announced as pandemic disease by World Health Organization.\nLack of specific drug molecules or treatment strategy against this disease makes it more devastating.\nThus, there is an urgent need of effective drug molecules to fight against COVID-19.\nThe main protease (Mpro) of SARS CoV-2, a key component of this viral replication, is considered as a prime target for anti-COVID-19 drug development.\nIn order to find potent Mpro inhibitors, we have selected eight polyphenols from green tea, as these are already known to exert antiviral activity against many RNA viruses.\nWe have elucidated the binding affinities and binding modes between these polyphenols including a well-known Mpro inhibitor N3 (having binding affinity -7.0 kcal/mol) and Mpro using molecular docking studies.\nAll eight polyphenols exhibit good binding affinity toward Mpro (-7.1 to -9.0 kcal/mol).\nHowever, only three polyphenols (epigallocatechin gallate, epicatechingallate and gallocatechin-3-gallate) interact strongly with one or both catalytic residues (His41 and Cys145) of Mpro.\nMolecular dynamics simulations (100 ns) on these three Mpro-polyphenol systems further reveal that these complexes are highly stable, experience less conformational fluctuations and share similar degree of compactness.\nEstimation of total number of intermolecular H-bond and MM-GBSA analysis affirm the stability of these three Mpro-polyphenol complexes.\nPharmacokinetic analysis additionally suggested that these polyphenols possess favorable drug-likeness characteristics.\nAltogether, our study shows that these three polyphenols can be used as potential inhibitors against SARS CoV-2 Mpro and are promising drug candidates for COVID-19 treatment.\nCommunicated by Ramaswamy H. Sarma.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 587}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Circulating mitochondrial dna is an early indicator of severe illness and mortality from covid-19\n\nAbstract:\nMitochondrial DNA (MT-DNA) are intrinsically inflammatory nucleic acids released by damaged solid organs.\nWhether the appearance of cell-free MT-DNA is linked to poor COVID-19 outcomes remains undetermined.\nHere, we quantified circulating MT-DNA in prospectively collected, cell-free plasma samples from 97 subjects with COVID-19 at the time of hospital presentation.\nCirculating MT-DNA were sharply elevated in patients who eventually died, required ICU admission or intubation.\nMultivariate regression analysis revealed that high circulating MT-DNA levels is an independent risk factor for all of these outcomes after adjusting for age, sex and comorbidities.\nAdditionally, we found that circulating MT-DNA has a similar or superior area-under-the curve when compared to clinically established measures of systemic inflammation, as well as emerging markers currently of interest as investigational targets for COVID-19 therapy.\nThese results show that high circulating MT-DNA levels is a potential indicator for poor COVID-19 outcomes.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Multivariate regression analysis revealed that high circulating MT-DNA levels is an independent risk factor for all of these outcomes after adjusting for age, sex and comorbidities.\", \"These results show that high circulating MT-DNA levels is a potential indicator for poor COVID-19 outcomes.\"]}", "id": 588}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: No experts are remotely advocating for people to take up smoking to prevent COVID-19, but some researchers have theorized nicotine may be playing some role in keeping the virus at bay\n\nAbstract:\nIntroduction Epidemiological and laboratory research seems to suggest that smoking and perhaps nicotine alone could reduce the severity of COVID-19.\nLikewise, there is some evidence that inhaled corticosteroids could also reduce its severity, opening the possibility that nicotine and inhaled steroids could be used as treatments.\nMethods In this prospective cohort study, we will link English general practice records from the QResearch database to Public Health England's database of SARS-CoV-2 positive tests, Hospital Episode Statistics, admission to intensive care units, and death from COVID-19 to identify our outcomes: hospitalisation, ICU admission, and death due to COVID.\nUsing Cox regression, we will perform sequential adjustment for potential confounders identified by separate directed acyclic graphs to: 1.\nAssess the association between smoking and COVID-19 disease severity, and how that changes on adjustment for smoking-related comorbidity.\n2. More closely characterise the association between smoking and severe COVID-19 disease by assessing whether the association is modified by age (as a proxy of length of smoking), gender, ethnic group, and whether people have asthma or COPD.\n3. Assess for evidence of a dose-response relation between smoking intensity and disease severity, which would help create a case for causality.\n4.\nExamine the association between former smokers who are using NRT or are vaping and disease severity.\n5. Examine whether pre-existing respiratory disease is associated with severe COVID-19 infection.\n6. Assess whether the association between chronic obstructive pulmonary disease (COPD) and asthma and COVID-19 disease severity is modified by age, gender, ethnicity, and smoking status.\n7. Assess whether the use of inhaled corticosteroids is associated with severity of COVID-19 disease.\n8. To assess whether the association between use of inhaled corticosteroids and severity of COVID-19 disease is modified by the number of other airways medications used (as a proxy for severity of condition) and whether people have asthma or COPD.\nConclusions This representative population sample will, to our knowledge, present the first comprehensive examination of the association between smoking, nicotine use without smoking, respiratory disease, and severity of COVID-19.\nWe will undertake several sensitivity analyses to examine the potential for bias in these associations.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Introduction Epidemiological and laboratory research seems to suggest that smoking and perhaps nicotine alone could reduce the severity of COVID-19.\"]}", "id": 589}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Yes, 5G radiation causes Covid-19 \n\nAbstract:\nAmid increased acts of violence against telecommunication engineers and property, this pre-registered study (N = 601 Britons) investigated the association between beliefs in 5G COVID-19 conspiracy theories and the justification and willingness to use violence.\nFindings revealed that belief in 5G COVID-19 conspiracy theories was positively correlated with state anger, which in turn, was associated with a greater justification of real-life and hypothetical violence in response to an alleged link between 5G mobile technology and COVID-19, alongside a greater intent to engage in similar behaviours in the future.\nMoreover, these associations were strongest for those highest in paranoia.\nFurthermore, we show that these patterns are not specific to 5G conspiratorial beliefs: General conspiracy mentality was positively associated with justification and willingness for general violence, an effect mediated by heightened state anger, especially for those most paranoid in the case of justification of violence.\nSuch research provides novel evidence on why and when conspiracy beliefs may justify the use of violence.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Findings revealed that belief in 5G COVID-19 conspiracy theories was positively correlated with state anger, which in turn, was associated with a greater justification of real-life and hypothetical violence in response to an alleged link between 5G mobile technology and COVID-19, alongside a greater intent to engage in similar behaviours in the future.\", \"Furthermore, we show that these patterns are not specific to 5G conspiratorial beliefs: General conspiracy mentality was positively associated with justification and willingness for general violence, an effect mediated by heightened state anger, especially for those most paranoid in the case of justification of violence.\"]}", "id": 590}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: It appears that the virus that causes COVID-19 can spread from people to animals in some situations.\n\nAbstract:\nBackground: The Greek authorities implemented the strong social distancing measures within the first few weeks after the first confirmed case of the virus to curtail the COVID-19 growth rate.\nObjectives: To estimate the effect of the two-stage strong social distancing measures, the closure of all non-essential shopping centers and businesses on March 16 and the shelter in place orders (SIPOs) on March 23 on the COVID-19 growth rate in Greece Methods: We obtained data on COVID-19 cases in Greece from February 26th through May 4th from publicly available sources.\nAn interrupted time-series regression analysis was used to estimate the effect of the measures on the exponential growth of confirmed COVID-19 cases, controlling for the number of daily testing, and weekly fixed-effects.\nResults: The growth rate of the COVID-19 cases in the pre-policies implementation period was positive as expected (p=0.003).\nBased on the estimates of the interrupted time-series, our results indicate that the SIPO on March 23 significantly slowed the growth rate of COVID-19 in Greece (p=0.04).\nHowever, we did not find evidence on the effectiveness of standalone and partial measures such as the non-essential business closures implemented on March 16 on the COVID-19 spread reduction.\nDiscussion: The combined social distancing measures implemented by the Greek authorities within the first few weeks after the first confirmed case of the virus reduced the COVID-19 growth rate.\nThese findings provide evidence and highlight the effectiveness of these measures to flatten the curve and to slow the spread of the virus.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 591}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: While there is very limited data (and none specific for COVID-19), the following cocktail may have a role in the prevention/mitigation of COVID-19 disease. Vitamin C 500 mg BID and Quercetin 250 mg daily Zinc 75-100 mg/day\n\nAbstract:\nBACKGROUND The coronavirus disease 2019 (COVID-19) is a pandemic caused by coronavirus with mild to severe respiratory symptoms.\nThis paper aimed to investigate the effect of nutrients on the immune system and their possible roles in the prevention, treatment, and management of COVID-19 in adults.\nMETHODS This Systematic review was designed based on the guideline of the Preferred Reporting for Systematic Reviews (PRISMA).\nThe articles that focussed on nutrition, immune system, viral infection, and coronaviruses were collected by searching databases for both published papers and accepted manuscripts from 1990 to 2020.\nIrrelevant papers and articles without English abstract were excluded from the review process.\nRESULTS Some nutrients are actively involved in the proper functioning and strengthening of the human immune system against viral infections including dietary protein, omega-3 fatty acids, vitamin A, vitamin D, vitamin E, vitamin B1, vitamin B6, vitamin B12, vitamin C, iron, zinc, and selenium.\nFew studies were done on the effect of dietary components on prevention of COVID-19, but supplementation with these nutrients may be effective in improving the health status of patients with viral infections.\nCONCLUSION Following a balanced diet and supplementation with proper nutrients may play a vital role in prevention, treatment, and management of COVID-19.\nHowever, further clinical trials are needed to confirm these findings and presenting the strong recommendations against this pandemic.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"RESULTS Some nutrients are actively involved in the proper functioning and strengthening of the human immune system against viral infections including dietary protein, omega-3 fatty acids, vitamin A, vitamin D, vitamin E, vitamin B1, vitamin B6, vitamin B12, vitamin C, iron, zinc, and selenium.\", \"Few studies were done on the effect of dietary components on prevention of COVID-19, but supplementation with these nutrients may be effective in improving the health status of patients with viral infections.\"]}", "id": 592}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Children, like adults, who have COVID-19 but have no symptoms (asymptomatic) can still spread the virus to others.\n\nAbstract:\nBACKGROUND The new Coronavirus identified in Whuan at the end of 2019 (SARS-CoV-2) belongs to the Beta Coronavirus genus and is responsible for the new Coronavirus 2019 pandemia (COVID-19).\nInfected children may be asymptomatic or present fever, dry cough, fatigue or gastrointestinal symptoms.\nThe CDC recommends that clinicians should decide to test patients based on the presence of signs and symptoms compatible with COVID-19.\nMATERIAL AND METHODS 42 children (the majority < 5 years of age) were referred, to our Pediatric Department, as possible cases of COVID-19 infection.\nBlood analysis, chest X-ray, and naso-oropharyngeal swab specimens for viral identification of COVID-19 were requested.\nRESULTS None of the screened children resulted positive for COVID-19 infection.\nAt first presentation, the most frequent signs and symptoms were: fever (71.4%), fatigue (35.7%) and cough (30.9%).\nAn high C-reactive protein value and abnormalities of chest X-ray (bronchial wall thickening) were detected in 26.2% and 19% of patients, respectively.\nAlmost half of patients (45.2%) required hospitalization in our Pediatric Unit and one patient in Intensive Care Unit.\nCONCLUSIONS Testing people who meet the COVID-19 suspected case definition criteria is essential for clinical management and outbreak control.\nChildren of all ages can get COVID-19, although they appear to be affected less frequently than adults, as reported in our preliminary survey.\nFurther studies are needed to confirm our observations.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 593}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: covid-19 isn't a risk for young people\n\nAbstract:\nObjective: Severity of the coronavirus disease 2019 (covid-19) has been assessed in terms of absolute mortality in SARS-CoV-2 positive cohorts.\nAn assessment of mortality relative to mortality in the general population is presented.\nDesign: Retrospective population-based study.\nSetting: Individual information on symptomatic confirmed SARS-CoV-2 patients and subsequent deaths from any cause were compared to the all-cause mortality in the Swiss population of 2018.\nStarting February 23, 2020, mortality in covid-19 patients was monitored for 80 days and compared to the population mortality observed in the same time-of-year starting February 23, 2018.\nParticipants: 5 160 595 inhabitants of Switzerland aged 35 to 95 without covid-19 (general population in spring 2018) and 20 769 persons tested positively for covid-19 (spring 2020).\nMeasurements: Sex- and age-specific mortality rates were estimated using Cox proportional hazards models.\nAbsolute probabilities of death were predicted and risk was assessed in terms of relative mortality by taking the ratio between the sex- and age-specific absolute mortality in covid19 patients and the corresponding mortality in the 2018 general population.\nResults: A confirmed SARS-CoV-2 infection substantially increased the probability of death across all patient groups, ranging from nine (6 to 15) times the population mortality in 35-year old infected females to a 53-fold increase (46 to 59) for 95 year old infected males.\nThe highest relative risks were observed among males and older patients.\nThe magnitude of these effects was smaller compared to increases observed in absolute mortality risk.\nMale covid-19 patients exceeded the population hazard for males (hazard ratio 1.20, 1.00 to 1.44).\nEach additional year of age increased the population hazard in covid-19 patients (hazard ratio 1.04, 1.03 to 1.05).\nLimitations: Information about the distribution of relevant comorbidities was not available on population level and the associated risk was not quantified.\nConclusions: Health care professionals, decision makers, and societies are provided with an additional population-adjusted assessment of covid-19 mortality risk.\nIn combination with absolute measures of risk, the relative risks presented here help to develop a more comprehensive understanding of the actual impact of covid-19.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Results: A confirmed SARS-CoV-2 infection substantially increased the probability of death across all patient groups, ranging from nine (6 to 15) times the population mortality in 35-year old infected females to a 53-fold increase (46 to 59) for 95 year old infected males.\", \"The highest relative risks were observed among males and older patients.\"]}", "id": 594}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Hydroxychloroquine is an Effective Treatment for COVID-19\n\nAbstract:\nBackground: There is no effective therapy for COVID-19.\nHydroxychloroquine (HCQ) and chloroquine (CQ) have been used for its treatment but their safety and efficacy remain uncertain.\nObjective: We performed a systematic review to synthesize the available data on the efficacy and safety of CQ and HCQ for the treatment of COVID-19.\nMethods: Two reviewers searched for published and pre-published relevant articles between December 2019 to 8th June 2020.\nThe data from the selected studies were abstracted and analyzed for efficacy and safety outcomes.\nCritical appraisal of the evidence was done by Cochrane risk of bias tool and Newcastle Ottawa scale.\nThe quality of evidence was graded as per the GRADE approach.\nResults: We reviewed 12 observational and 3 randomized trials which included 10659 patients of whom 5713 received CQ/HCQ and 4966 received only standard of care.\nThe efficacy of CQ/HCQ for COVID-19 was inconsistent across the studies.\nMeta-analysis of included studies revealed no significant reduction in mortality with HCQ use [RR 0.98 95% CI 0.66-1.46] , time to fever resolution [mean difference -0.54 days (-1.19-011)] or clinical deterioration/development of ARDS with HCQ [RR 0.90 95% CI 0.47-1.71].\nThere was a higher risk of ECG abnormalities/arrhythmia with HCQ/CQ [RR 1.46 95% CI 1.04 to 2.06].\nThe quality of evidence was graded as very low for these outcomes.\nConclusions: The available evidence suggests that CQ or HCQ does not improve clinical outcomes in COVID-19.\nWell-designed randomized trials are required for assessing the efficacy and safety of HCQ and CQ for COVID-19.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Conclusions: The available evidence suggests that CQ or HCQ does not improve clinical outcomes in COVID-19.\"]}", "id": 595}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Evidence is currently lacking and it is too early to make robust conclusions on any link between use of angiotensin-converting enzyme (ACE) inhibitors and angiotensin II type-I receptor blockers with risk or severity of novel coronavirus disease 2019 (COVID-19) infection.\n\nAbstract:\nCoronavirus disease 2019 (COVID-19) is a viral pandemic precipitated by the severe acute respiratory syndrome coronavirus 2.\nSince previous reports suggested that viral entry into cells may involve angiotensin converting enzyme 2, there has been growing concern that angiotensin converting enzyme inhibitor (ACEI) and angiotensin II receptor blocker (ARB) use may exacerbate the disease severity.\nIn this retrospective, single-center US study of adult patients diagnosed with COVID-19, we evaluated the association of ACEI/ARB use with hospital admission.\nSecondary outcomes included: ICU admission, mechanical ventilation, length of hospital stay, use of inotropes, and all-cause mortality.\nPropensity score matching was performed to account for potential confounders.\nAmong 590 unmatched patients diagnosed with COVID-19, 78 patients were receiving ACEI/ARB (median age 63 years and 59.7% male) and 512 patients were non-users (median age 42 years and 47.1% male).\nIn the propensity matched population, multivariate logistic regression analysis adjusting for age, gender and comorbidities demonstrated that ACEI/ARB use was not associated with hospital admission (OR 1.2, 95% CI 0.5-2.7, p = 0.652).\nCAD and CKD/ESRD remained independently associated with admission to hospital.\nAll-cause mortality, ICU stay, need for ventilation, and inotrope use was not significantly different between the 2 study groups.\nIn conclusion, among patients who were diagnosed with COVID-19, ACEI/ARB use was not associated with increased risk of hospital admission.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"In conclusion, among patients who were diagnosed with COVID-19, ACEI/ARB use was not associated with increased risk of hospital admission.\"]}", "id": 596}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: People with weakened immune systems are at higher risk of getting severely sick from SARS-CoV-2, the virus that causes COVID-19.\n\nAbstract:\nCoronaviruses are a genetically highly variable family of viruses that infect vertebrates and have succeeded in infecting humans many times by overcoming the species barrier.\nThe severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which initially appeared in China at the end of 2019, exhibits a high infectivity and pathogenicity compared to other coronaviruses.\nAs the viral coat and other viral components are recognized as being foreign by the immune system, this can lead to initial symptoms, which are induced by the very efficiently working immune defense system via the respiratory epithelium.\nDuring severe courses a systemically expressed proinflammatory cytokine storm and subsequent changes in the coagulation and complement systems can occur.\nVirus-specific antibodies, the long-term expression of which is ensured by the formation of B memory cell clones, generate a specific immune response that is also detectable in blood (seroconversion).\nSpecifically effective cytotoxic CD8+ T\u00adcell populations are also formed, which recognize viral epitopes as pathogen-specific patterns in combination with MHC presentation on the cell surface of virus-infected cells and destroy these cells.\nAt the current point in time it is unclear how regular, robust and durable this immune status is constructed.\nExperiences with other coronavirus infections (SARS and Middle East respiratory syndrome, MERS) indicate that the immunity could persist for several years.\nBased on animal experiments, already acquired data on other coronavirus types and plausibility assumptions, it can be assumed that seroconverted patients have an immunity of limited duration and only a very low risk of reinfection.\nKnowledge of the molecular mechanisms of viral cycles and immunity is an important prerequisite for the development of vaccination strategies and development of effective drugs.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 597}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Frequent hand washing, avoiding crowds and contact with sick people, and cleaning and disinfecting frequently touched surfaces can help prevent coronavirus infections. \n\nAbstract:\nPURPOSE: To share a useful intervention to minimize risk of COVID-19 infection to both healthcare workers and patients in the eye clinic.\nMETHODS: We present our experience of virtual, within-clinic remote visual acuity assessment to reduce the risk of infection with COVID-19.\nRESULTS: Along with standard recommendations for personal protective equipment and hand hygiene to contain viral spread and treating only urgent cases, remote within-clinic visual acuity testing and consultations can be undertaken with minimal specialist equipment and appears to provide useful information whilst being acceptable to patients.\nCONCLUSION: Ophthalmology practice must adapt in order to combat COVID-19.\nThis measure can easily be incorporated into daily practice to reduce both patient footfall within the department and close contact between patient and healthcare practitioners.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 598}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Furin cleavage of sars-cov-2 spike promotes but is still essential for infection and cell-cell fusion\n\nAbstract:\nSevere Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2) infects cells by binding to the host cell receptor ACE2 and undergoing virus-host membrane fusion.\nFusion is triggered by the protease TMPRSS2, which processes the viral Spike (S) protein to reveal the fusion peptide.\nSARS-CoV-2 has evolved a multibasic site at the S1-S2 boundary, which is thought to be cleaved by furin in order to prime S protein for TMPRSS2 processing.\nHere we show that CRISPR-Cas9 knockout of furin reduces, but does not prevent, the production of infectious SARS-CoV-2 virus.\nComparing S processing in furin knockout cells to multibasic site mutants reveals that while loss of furin substantially reduces S1-S2 cleavage it does not prevent it.\nSARS-CoV-2 S protein also mediates cell-cell fusion, potentially allowing virus to spread virion-independently.\nWe show that loss of furin in either donor or acceptor cells reduces, but does not prevent, TMPRSS2-dependent cell-cell fusion, unlike mutation of the multibasic site that completely prevents syncytia formation.\nOur results show that while furin promotes both SARS-CoV-2 infectivity and cell-cell spread it is not essential, suggesting furin inhibitors may reduce but not abolish viral spread.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Here we show that CRISPR-Cas9 knockout of furin reduces, but does not prevent, the production of infectious SARS-CoV-2 virus.\", \"Comparing S processing in furin knockout cells to multibasic site mutants reveals that while loss of furin substantially reduces S1-S2 cleavage it does not prevent it.\", \"We show that loss of furin in either donor or acceptor cells reduces, but does not prevent, TMPRSS2-dependent cell-cell fusion, unlike mutation of the multibasic site that completely prevents syncytia formation.\"]}", "id": 599}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: the warmer weather will slow the spread of SARS-CoV-2, the novel coronavirus that causes COVID-19\n\nAbstract:\nThe undefendable outbreak of novel coronavirus (SARS-COV-2) lead to a global health emergency due to its higher transmission rate and longer symptomatic duration, created a health surge in a short time.\nSince Nov 2019 the outbreak in China, the virus is spreading exponentially everywhere.\nThe current study focuses on the relationship between environmental parameters and the growth rate of COVID-19.\nThe statistical analysis suggests that the temperature changes retarded the growth rate and found that -6.28{degrees}C and +14.51{degrees}C temperature is the favorable range for COVID-19 growth.\nGutenberg- Richter's relationship is used to estimate the mean daily rate of exceedance of confirmed cases concerning the change in temperature.\nTemperature is the most influential parameter that reduces the growth at the rate of 13-16 cases/day with a 1{degrees}C rise in temperature.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"The statistical analysis suggests that the temperature changes retarded the growth rate and found that -6.28{degrees}C and +14.51{degrees}C temperature is the favorable range for COVID-19 growth.\", \"Temperature is the most influential parameter that reduces the growth at the rate of 13-16 cases/day with a 1{degrees}C rise in temperature.\"]}", "id": 600}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: if you are low risk (healthy, young), you do not need social distancing.\n\nAbstract:\nBackground: The Greek authorities implemented the strong social distancing measures within the first few weeks after the first confirmed case of the virus to curtail the COVID-19 growth rate.\nObjectives: To estimate the effect of the two-stage strong social distancing measures, the closure of all non-essential shopping centers and businesses on March 16 and the shelter in place orders (SIPOs) on March 23 on the COVID-19 growth rate in Greece Methods: We obtained data on COVID-19 cases in Greece from February 26th through May 4th from publicly available sources.\nAn interrupted time-series regression analysis was used to estimate the effect of the measures on the exponential growth of confirmed COVID-19 cases, controlling for the number of daily testing, and weekly fixed-effects.\nResults: The growth rate of the COVID-19 cases in the pre-policies implementation period was positive as expected (p=0.003).\nBased on the estimates of the interrupted time-series, our results indicate that the SIPO on March 23 significantly slowed the growth rate of COVID-19 in Greece (p=0.04).\nHowever, we did not find evidence on the effectiveness of standalone and partial measures such as the non-essential business closures implemented on March 16 on the COVID-19 spread reduction.\nDiscussion: The combined social distancing measures implemented by the Greek authorities within the first few weeks after the first confirmed case of the virus reduced the COVID-19 growth rate.\nThese findings provide evidence and highlight the effectiveness of these measures to flatten the curve and to slow the spread of the virus.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Discussion: The combined social distancing measures implemented by the Greek authorities within the first few weeks after the first confirmed case of the virus reduced the COVID-19 growth rate.\"]}", "id": 601}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Thrombotic microvascular injury is mainly mediated by thrombotic microangiopathy despite systemic complement activation in covid-19 patients\n\nAbstract:\nHypoxemia and coagulopathy are common in severe symptomatic patients of coronavirus disease 2019 (COVID-19).\nHistological evidence shows implication of complement activation and lung injury.\nWe research sign of complement activation and presence of thrombotic microangiopathy in 8 severe patients.\nSix of them presented moderate elevation of final pathway of complement / sC5b-9 (median value : 350 ng/mL [IQR : 300,5-514,95 ng/mL]).\nTwo patients have been autopsied and presence of thrombotic microvascular injury have been found.\nInterestingly, none the 8 patients had signs of mechanical hemolytic anemia (median value of hemoglobin : 10,5 gr/dL[IQR : 8,1-1,9], median value of haptoglobuline 4,49 [IQR 3,55-4,66], none of the patients has schistocyte) and thrombocytopenia (median value: 348000/mL [IQR : 266 000-401 000).\nFinally, all 8 patients had elevated d-dimer (median value : 2226 microgr/l [IQR : 1493-2362]) and soluble fibrin monomer complex (median value : 8.5 mg/mL, IQR[ <6-10.6]).\nIn summary, this study show moderate activation of complement and coagulation with presence of thrombotic microvascular injury in patients with severe COVID-19 without evidence of systemic thrombotic microangiopathy.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"In summary, this study show moderate activation of complement and coagulation with presence of thrombotic microvascular injury in patients with severe COVID-19 without evidence of systemic thrombotic microangiopathy.\"]}", "id": 602}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Evolutionary arms race between virus and viruses drives genetic diversity in bat sars related coronavirus spike genes\n\nAbstract:\nThe Chinese horseshoe bat (Rhinolophus sinicus), reservoir host of severe acute respiratory syndrome coronavirus (SARS-CoV), carries many bat SARS-related CoVs (SARSr-CoVs) with high genetic diversity, particularly in the spike gene.\nDespite these variations, some bat SARSr-CoVs can utilize the orthologs of the human SARS-CoV receptor, angiotensin-converting enzyme 2 (ACE2), for entry.\nIt is speculated that the interaction between bat ACE2 and SARSr-CoV spike proteins drives diversity.\nHere, we identified a series of R. sinicus ACE2 variants with some polymorphic sites involved in the interaction with the SARS-CoV spike protein.\nPseudoviruses or SARSr-CoVs carrying different spike proteins showed different infection efficiencies in cells transiently expressing bat ACE2 variants.\nConsistent results were observed by binding affinity assays between SARS-CoV and SARSr-CoV spike proteins and receptor molecules from bats and humans.\nAll tested bat SARSr-CoV spike proteins had a higher binding affinity to human ACE2 than to bat ACE2, although they showed a 10-fold lower binding affinity to human ACE2 compared with that of their SARS-CoV counterpart.\nStructure modeling revealed that the difference in binding affinity between spike and ACE2 might be caused by the alteration of some key residues in the interface of these two molecules.\nMolecular evolution analysis indicates that some key residues were under positive selection.\nThese results suggest that the SARSr-CoV spike protein and R. sinicus ACE2 may have coevolved over time and experienced selection pressure from each other, triggering the evolutionary arms race dynamics.\nIMPORTANCE Evolutionary arms race dynamics shape the diversity of viruses and their receptors.\nIdentification of key residues which are involved in interspecies transmission is important to predict potential pathogen spillover from wildlife to humans.\nPreviously, we have identified genetically diverse SARSr-CoVs in Chinese horseshoe bats.\nHere, we show the highly polymorphic ACE2 in Chinese horseshoe bat populations.\nThese ACE2 variants support SARS-CoV and SARSr-CoV infection but with different binding affinities to different spike proteins.\nThe higher binding affinity of SARSr-CoV spike to human ACE2 suggests that these viruses have the capacity for spillover to humans.\nThe positive selection of residues at the interface between ACE2 and SARSr-CoV spike protein suggests long-term and ongoing coevolutionary dynamics between them.\nContinued surveillance of this group of viruses in bats is necessary for the prevention of the next SARS-like disease.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"It is speculated that the interaction between bat ACE2 and SARSr-CoV spike proteins drives diversity.\", \"These results suggest that the SARSr-CoV spike protein and R. sinicus ACE2 may have coevolved over time and experienced selection pressure from each other, triggering the evolutionary arms race dynamics.\"]}", "id": 603}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: There are an urgent need for antivirals to treat the newly emerged SARS-CoV-2.\n\nAbstract:\nAIMS: A new human coronavirus (HCoV), which has been designated SARS-CoV-2, began spreading in December 2019 in Wuhan City, China causing pneumonia called COVID-19.\nThe spread of SARS-CoV-2 has been faster than any other coronaviruses that have succeeded in crossing the animal-human barrier.\nThere is concern that this new virus will spread around the world as did the previous two HCoVs-Severe Acute Respiratory Syndrome (SARS) and Middle East Respiratory Syndrome (MERS)-each of which caused approximately 800 deaths in the years 2002 and 2012, respectively.\nThus far, 11,268 deaths have been reported from the 258,842 confirmed infections in 168 countries.\nMAIN METHODS: In this study, the RNA-dependent RNA polymerase (RdRp) of the newly emerged coronavirus is modeled, validated, and then targeted using different anti-polymerase drugs currently on the market that have been approved for use against various viruses.\nKEY FINDINGS: The results suggest the effectiveness of Ribavirin, Remdesivir, Sofosbuvir, Galidesivir, and Tenofovir as potent drugs against SARS-CoV-2 since they tightly bind to its RdRp.\nIn addition, the results suggest guanosine derivative (IDX-184), Setrobuvir, and YAK as top seeds for antiviral treatments with high potential to fight the SARS-CoV-2 strain specifically.\nSIGNIFICANCE: The availability of FDA-approved anti-RdRp drugs can help treat patients and reduce the danger of the mysterious new viral infection COVID-19.\nThe drugs mentioned above can tightly bind to the RdRp of the SARS-CoV-2 strain and thus may be used to treat the disease.\nNo toxicity measurements are required for these drugs since they were previously tested prior to their approval by the FDA.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 604}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: If Fever Helps Fight Infection, Should I Avoid Fever-Reducing Drugs\n\nAbstract:\n: The COVID-19 pandemic is challenging our cardiovascular care of patients with heart diseases.\nIn the setting of pericardial diseases, there are two possible different scenarios to consider: the patient being treated for pericarditis who subsequently becomes infected with SARS-CoV-2, and the patient with COVID-19 who develops pericarditis or pericardial effusion.\nIn both conditions, clinicians may be doubtful regarding the safety of nonsteroidal anti-inflammatory drugs (NSAIDs), corticosteroids, colchicine, and biological agents, such as anti-IL1 agents (e.g. anakinra), that are the mainstay of therapy for pericarditis.\nFor NSAIDs, there is no clear scientific evidence linking ibuprofen and other NSAIDs to worsening of COVID-19; however, it seems prudent to continue them, if necessary to control pericarditis, and on the other hand, to prefer paracetamol for fever and systemic symptoms related to COVID-19.\nTreatments with corticosteroids, colchicine, and anakinra appear well tolerated in the context of COVID-19 infection and are currently actively evaluated as potential therapeutic options for COVID infection at different stages of the disease.\nOn this basis, currently most treatments for pericarditis do not appear contraindicated also in the presence of possible COVID-19 infection and should not be discontinued, and some (corticosteroids, colchicine, and anakinra) can be considered to treat both conditions.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"For NSAIDs, there is no clear scientific evidence linking ibuprofen and other NSAIDs to worsening of COVID-19; however, it seems prudent to continue them, if necessary to control pericarditis, and on the other hand, to prefer paracetamol for fever and systemic symptoms related to COVID-19.\"]}", "id": 605}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: ARVs can treat Covid-19\n\nAbstract:\nIn late December 2019 in Wuhan, China, several patients with viral pneumonia were identified as 2019 novel coronavirus (2019-nCoV).\nSo far, there are no specific treatments for patients with coronavirus disease-19 (COVID-19), and the treatments available today are based on previous experience with similar viruses such as severe acute respiratory syndrome-related coronavirus (SARS-CoV), Middle East respiratory syndrome coronavirus (MERS-CoV), and Influenza virus.\nIn this article, we have tried to reach a therapeutic window of drugs available to patients with COVID-19.\nCathepsin L is required for entry of the 2019-nCoV virus into the cell as target teicoplanin inhibits virus replication.\nAngiotensin-converting-enzyme 2 (ACE2) in soluble form as a recombinant protein can prevent the spread of coronavirus by restricting binding and entry.\nIn patients with COVID-19, hydroxychloroquine decreases the inflammatory response and cytokine storm, but overdose causes toxicity and mortality.\nNeuraminidase inhibitors such as oseltamivir, peramivir, and zanamivir are invalid for 2019-nCoV and are not recommended for treatment but protease inhibitors such as lopinavir/ritonavir (LPV/r) inhibit the progression of MERS-CoV disease and can be useful for patients of COVID-19 and, in combination with Arbidol, has a direct antiviral effect on early replication of SARS-CoV. Ribavirin reduces hemoglobin concentrations in respiratory patients, and remdesivir improves respiratory symptoms.\nUse of ribavirin in combination with LPV/r in patients with SARS-CoV reduces acute respiratory distress syndrome and mortality, which has a significant protective effect with the addition of corticosteroids.\nFavipiravir increases clinical recovery and reduces respiratory problems and has a stronger antiviral effect than LPV/r.\ncurrently, appropriate treatment for patients with COVID-19 is an ACE2 inhibitor and a clinical problem reducing agent such as favipiravir in addition to hydroxychloroquine and corticosteroids.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"In late December 2019 in Wuhan, China, several patients with viral pneumonia were identified as 2019 novel coronavirus (2019-nCoV).\", \"So far, there are no specific treatments for patients with coronavirus disease-19 (COVID-19), and the treatments available today are based on previous experience with similar viruses such as severe acute respiratory syndrome-related coronavirus (SARS-CoV), Middle East respiratory syndrome coronavirus (MERS-CoV), and Influenza virus.\"]}", "id": 606}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Vitamins C and D boost our immune systems, aiding in the fight against infectious diseases; \n\nAbstract:\nThe WHO has declared SARS-CoV-2 outbreak a public health emergency of international concern.\nHowever, to date, there was hardly any study in characterizing the immune responses, especially adaptive immune responses to SARS-CoV-2 infection.\nIn this study, we collected blood from COVID-19 patients who have recently become virus-free and therefore were discharged, and analyzed their SARS-CoV-2-specific antibody and T cell responses.\nWe observed SARS-CoV-2-specific humoral and cellular immunity in the patients.\nBoth were detected in newly discharged patients, suggesting both participate in immune-mediated protection to viral infection.\nHowever, follow-up patients (2 weeks post discharge) exhibited high titers of IgG antibodies, but with low levels of virus-specific T cells, suggesting that they may enter a quiescent state.\nOur work has thus provided a basis for further analysis of protective immunity to SARS-CoV-2, and understanding the pathogenesis of COVID-19, especially in the severe cases.\nIt has also implications in designing an effective vaccine to protect and treat SARS-CoV-2 infection.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 607}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: The sars-cov-2 cytopathic effect is blocked with autophagy modulators\n\nAbstract:\nSARS-CoV-02 is a new type of coronavirus capable of rapid transmission and causing severe clinical symptoms; much of which has unknown biological etiology.\nIt has prompted researchers to rapidly mobilize their efforts towards identifying and developing anti-viral therapeutics and vaccines.\nDiscovering and understanding the virus\u2019 pathways of infection, host-protein interactions, and cytopathic effects will greatly aid in the design of new therapeutics to treat COVID-19.\nWhile it is known that chloroquine and hydroxychloroquine, extensively explored as clinical agents for COVID-19, have multiple cellular effects including inhibiting autophagy, there are also dose-limiting toxicities in patients that make clearly establishing their potential mechanisms-of-action problematic.\nTherefore, we evaluated a range of other autophagy modulators to identify an alternative autophagy-based drug repurposing opportunity.\nIn this work, we found that 6 of these compounds blocked the cytopathic effect of SARS-CoV-2 in Vero-E6 cells with EC(50) values ranging from 2.0 to 13 \u03bcM and selectivity indices ranging from 1.5 to >10-fold.\nImmunofluorescence staining for LC3B and LysoTracker dye staining assays in several cell lines indicated their potency and efficacy for inhibiting autophagy correlated with the measurements in the SARS-CoV-2 cytopathic effect assay.\nOur data suggest that autophagy pathways could be targeted to combat SARS-CoV-2 infections and become an important component of drug combination therapies to improve the treatment outcomes for COVID-19.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"While it is known that chloroquine and hydroxychloroquine, extensively explored as clinical agents for COVID-19, have multiple cellular effects including inhibiting autophagy, there are also dose-limiting toxicities in patients that make clearly establishing their potential mechanisms-of-action problematic.\", \"Immunofluorescence staining for LC3B and LysoTracker dye staining assays in several cell lines indicated their potency and efficacy for inhibiting autophagy correlated with the measurements in the SARS-CoV-2 cytopathic effect assay.\"]}", "id": 608}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: For most patients, COVID-19 begins and ends in their lungs, because like the flu, coronaviruses are respiratory diseases\n\nAbstract:\nSeveral related human coronaviruses (HCoVs) are endemic in the human population, causing mild respiratory infections1.\nSevere Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), the etiologic agent of Coronavirus disease 2019 (COVID-19), is a recent zoonotic infection that has quickly reached pandemic spread2,3.\nZoonotic introduction of novel coronaviruses is thought to occur in the absence of pre-existing immunity in the target human population.\nUsing diverse assays for detection of antibodies reactive with the SARS-CoV-2 Spike (S) glycoprotein, we demonstrate the presence of pre-existing immunity in uninfected and unexposed humans to the new coronavirus.\nSARS-CoV-2 S-reactive antibodies, exclusively of the IgG class, were readily detectable by a sensitive flow cytometry-based method in SARS-CoV-2-uninfected individuals with recent HCoV infection and targeted the S2 subunit.\nIn contrast, SARS-CoV-2 infection induced higher titres of SARS-CoV-2 S-reactive IgG antibodies, as well as concomitant IgM and IgA antibodies throughout the observation period of 6 weeks since symptoms onset.\nHCoV patient sera also variably reacted with SARS-CoV-2 S and nucleocapsid (N), but not with the S1 subunit or the receptor binding domain (RBD) of S on standard enzyme immunoassays.\nNotably, HCoV patient sera exhibited specific neutralising activity against SARS-CoV-2 S pseudotypes, according to levels of SARS-CoV-2 S-binding IgG and with efficiencies comparable to those of COVID-19 patient sera.\nDistinguishing pre-existing and de novo antibody responses to SARS-CoV-2 will be critical for serology, seroprevalence and vaccine studies, as well as for our understanding of susceptibility to and natural course of SARS-CoV-2 infection.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 609}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Seasonal human coronavirus antibodies are boosted upon sars-cov-2 infection but also associated with protection\n\nAbstract:\nSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has rapidly spread within the human population.\nAlthough SARS-CoV-2 is a novel coronavirus, most humans had been previously exposed to other antigenically distinct common seasonal human coronaviruses (hCoVs) before the COVID-19 pandemic.\nHere, we quantified levels of SARS-CoV-2-reactive antibodies and hCoV-reactive antibodies in serum samples collected from 204 humans before the COVID-19 pandemic.\nWe then quantified pre-pandemic antibody levels in serum from a separate cohort of 252 individuals who became PCR-confirmed infected with SARS-CoV-2.\nFinally, we longitudinally measured hCoV and SARS-CoV-2 antibodies in the serum of hospitalized COVID-19 patients.\nOur studies indicate that most individuals possessed hCoV-reactive antibodies before the COVID-19 pandemic.\nWe determined that ~23% of these individuals possessed non-neutralizing antibodies that cross-reacted with SARS-CoV-2 spike and nucleocapsid proteins.\nThese antibodies were not associated with protection against SARS-CoV-2 infections or hospitalizations, but paradoxically these hCoV cross-reactive antibodies were boosted upon SARS-CoV-2 infection.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Although SARS-CoV-2 is a novel coronavirus, most humans had been previously exposed to other antigenically distinct common seasonal human coronaviruses (hCoVs) before the COVID-19 pandemic.\", \"We then quantified pre-pandemic antibody levels in serum from a separate cohort of 252 individuals who became PCR-confirmed infected with SARS-CoV-2.\", \"These antibodies were not associated with protection against SARS-CoV-2 infections or hospitalizations, but paradoxically these hCoV cross-reactive antibodies were boosted upon SARS-CoV-2 infection.\"]}", "id": 610}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: both dogs and cats can be infected by the virus that causes Covid-19 in humans, but none of the ten animals observed in the study showed clinical symptoms like coughing\n\nAbstract:\nLittle information on the SARS-CoV-2 virus in animals is available to date.\nWhereas no one husbandry animal case has been reported to date, which would have significant implications in food safety, companion animals play a role in COVID-19 epidemiology that opens up new questions.\nThere is evidence that SARS-CoV-2 can infect felines, dogs and minks, and there is evidence of human-to-animal infection.\nLikewise, the S protein nucleotide sequence of the SARS-CoV-2 virus isolated in domestic animals and humans is identical, and the replication of the SARS-CoV-2 in cats is efficient.\nBesides, the epidemiological evidence for this current pandemic indicates that the spillover to humans was associated with close contact between man and exotic animals, very probably in Chinese wet markets, thus there is a growing general consensus that the exotic animal markets, should be strictly regulated.\nThe examination of these findings and the particular role of animals in COVID-19 should be carefully analyzed in order to establish preparation and containment measures.\nAnimal management and epidemiological surveillance must be also considered for COVID-19 control, and it can open up new questions regarding COVID-19 epidemiology and the role that animals play in it.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"There is evidence that SARS-CoV-2 can infect felines, dogs and minks, and there is evidence of human-to-animal infection.\", \"Likewise, the S protein nucleotide sequence of the SARS-CoV-2 virus isolated in domestic animals and humans is identical, and the replication of the SARS-CoV-2 in cats is efficient.\"]}", "id": 611}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: the warmer weather will slow the spread of SARS-CoV-2, the novel coronavirus that causes COVID-19\n\nAbstract:\nThe novel coronavirus, since its first outbreak in December, has, up till now, affected approximately 114,542 people across 115 countries.\nMany international agencies are devoting efforts to enhance the understanding of the evolving COVID-19 outbreak on an international level, its influences, and preparedness.\nAt present, COVID-19 appears to affect individuals through person-to-person means, like other commonly found cold or influenza viruses.\nIt is widely known and acknowledged that viruses causing influenza peak during cold temperatures and gradually subside in the warmer temperature, owing to their seasonality.\nThus, COVID-19, due to its regular flu-like symptoms, is also expected to show similar seasonality and subside as the global temperatures rise in the northern hemisphere with the onset of spring.\nDespite these speculations, however, the systematic analysis in the global perspective of the relation between COVID-19 spread and meteorological parameters is unavailable.\nHere, by analyzing the region- and city-specific affected global data and corresponding meteorological parameters, we show that there is an optimum range of temperature and UV index strongly affecting the spread and survival of the virus, whereas precipitation, relative humidity, cloud cover, etc. have no effect on the virus.\nUnavailability of pharmaceutical interventions would require greater preparedness and alert for the effective control of COVID-19.\nUnder these conditions, the information provided here could be very helpful for the global community struggling to fight this global crisis.\nIt is, however, important to note that the information presented here clearly lacks any physiological evidences, which may merit further investigation.\nThus, any attempt for management, implementation, and evaluation strategies responding to the crisis arising due to the COVID-19 outbreak must not consider the evaluation presented here as the foremost factor.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Thus, COVID-19, due to its regular flu-like symptoms, is also expected to show similar seasonality and subside as the global temperatures rise in the northern hemisphere with the onset of spring.\"]}", "id": 612}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Tiny antibody component highly effective against SARS-COV-2 in animal studies\n\nAbstract:\nWe remain largely without effective prophylactic/therapeutic interventions for COVID-19.\nAlthough many human clinical trials are ongoing, there remains a deficiency of supportive preclinical drug efficacy studies.\nHere we assessed the prophylactic/therapeutic efficacy of hydroxychloroquine (HCQ), a drug of interest for COVID-19 management, in two animal models.\nWhen used for prophylaxis or treatment neither the standard human malaria dose (6.5 mg/kg) nor a high dose (50 mg/kg) of HCQ had any beneficial effect on clinical disease or SARS-CoV-2 kinetics (replication/shedding) in the Syrian hamster disease model.\nSimilarly, HCQ prophylaxis/treatment (6.5 mg/kg) did not significantly benefit clinical outcome nor reduce SARS-CoV-2 replication/shedding in the upper and lower respiratory tract in the rhesus macaque disease model.\nIn conclusion, our preclinical animal studies do not support the use of HCQ in prophylaxis/treatment of COVID-19.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 613}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Overall, funds that the hospital receives for COVID-19 deaths are more the greater good and the good of the patients' families.\n\nAbstract:\nThe mortality rate of coronavirus disease-19 (COVID-19) has been reported as 1-6% in most studies.\nThe cause of most deaths has been acute pneumonia.\nNevertheless, it has been noted that cardiovascular failure can also lead to death.\nThree COVID-19 patients were diagnosed based on reverse transcriptase-polymerase chain reaction of a nasopharyngeal swab test and radiological examinations in our hospital.\nThe patients received medications at the discretion of the treating physician.\nIn this case series, chest computed tomography scans and electrocardiograms, along with other diagnostic tests were used to evaluate these individuals.\nSudden cardiac death in COVID-19 patients is not common, but it is a major concern.\nSo, it is recommended to monitor cardiac condition in selected patients with COVID-19.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 614}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: the mechanism behind the protective effects of smoking could be found in nicotine\n\nAbstract:\nABSTRACT Introduction: Recent studies show cigarette smokers are markedly under-represented among patients hospitalized for COVID-19 in over a dozen countries.\nIt is unclear if this may be related to confounding factors such as age distribution, access to care, and inaccurate records.\nWe hypothesized that these concerns could be avoided by studying smoking prevalence in relation to COVID-19 mortality.\nSince climate has been identified as a factor in COVID-19, we studied groups of countries with relatively comparable temperatures.\nMethods: The 20 hottest and 20 coldest countries in the Johns Hopkins Mortality Analysis database with a minimum mortality rate of .3 deaths/100,000 were selected on the basis of the average temperatures of their largest city.\nMortality rates were determined as of May 1, 2020 and correlated with national smoking rate adjusting for sex ratio, obesity, temperature, and elderly population.\nResults: A highly significant inverse correlation between current daily smoking prevalence and COVID-19 mortality rate was noted for the group of hot countries (R=-.718, p = .0002), cold countries (R=-.567, p=.0046), and the combined group (R=-.324, p=.0207).\nHowever, after adjustments only the regression for hot countries and the combined group remained significant.\nIn hot countries, for each percentage point increase in smoking rate mortality decreased by .147 per 100,000 population (95% CI .102- 192, p=.0066).\nThis resulted in mortality rates several-fold elevated in the countries with the lowest smoking rates relative to the highest smoking rates.\nIn the combined group, mortality decreased by .257 per 100,000 population (95% CI .175-.339, p=.0034).\nDiscussion: These findings add support to the finding of an inverse relationship between current smoking and seriously symptomatic COVID-19.\nHowever, we conclude that the difference in mortality between the highest and lowest smoking countries appears too large to be due primarily to the effects of smoking per se.\nA potentially beneficial effect of smoking is surprising, but compatible with a number of hypothetical mechanisms which deserve exploration: 1) Studies show smoking alters ACE2 expression which may affect COVID-19 infection or its progression to serious lung pathology.\n2) Nicotine has anti-inflammatory activity and also appears to alter ACE2 expression.\n3) Nitric oxide in cigarette smoke is known to be effective in treating pulmonary hypertension and has shown in vitro antiviral effects including against SARS-CoV-2.\n4) Smoking has complicated effects on the immune system involving both up and down regulation, any of which might alone or in concert antagonize progression of COVID-19.\n5) Smokers are exposed to hot vapors which may stimulate immunity in the respiratory tract by various heat-related mechanisms (e.g. heat shock proteins).\nStudies of steam and sauna treatments have shown efficacy in other viral respiratory conditions.\nAt this time there is no clear evidence that smoking is protective against COVID-19, so the established recommendations to avoid smoking should be emphasized.\nThe interaction of smoking and COVID-19 will only be reliably determined by carefully designed prospective study, and there is reason to believe that there are unknown confounds that may be spuriously suggesting a protective effect of smoking.\nHowever, the magnitude of the apparent inverse association of COVID-19 and smoking and its myriad clinical implications suggest the importance of further investigation.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"4) Smoking has complicated effects on the immune system involving both up and down regulation, any of which might alone or in concert antagonize progression of COVID-19.\", \"At this time there is no clear evidence that smoking is protective against COVID-19, so the established recommendations to avoid smoking should be emphasized.\"]}", "id": 615}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: COVID-19 can survive on surfaces, like a tabletop\n\nAbstract:\nThe aim in this study was to assess the effectiveness of a quaternary ammonium chloride (QAC) surfactant in reducing surface staphylococcal contamination in a routinely operating medical ward occupied by patients who had tested positive for methicillin-resistant Staphylococcus aureus (MRSA).\nThe QAC being tested is an antibacterial film that is sprayed onto a surface and can remain active for up to 8 h. A field experimental study was designed with the QAC plus daily hypochlorite cleaning as the experimental group and hypochlorite cleaning alone as the control group.\nThe method of swabbing on moistened surfaces was used for sampling.\nIt was found that 83% and 77% of the bedside surfaces of MRSA-positive and MRSA-negative patients respectively were contaminated with staphylococci at 08:00 hours, and that the staphylococcal concentrations increased by 80% at 1200 h over a 4-hour period with routine ward and clinical activities.\nIrrespective of the MRSA status of the patients, high-touch surfaces around the bed-units within the studied medical ward were heavily contaminated (ranged 1 to 276 cfu/cm(2) amongst the sites with positive culture) with staphylococcal bacteria including MRSA, despite the implementation of daily hypochlorite wiping.\nHowever, the contamination rate dropped significantly from 78% to 11% after the application of the QAC polymer.\nIn the experimental group, the mean staphylococcal concentration of bedside surfaces was significantly (p < 0.0001) reduced from 4.4 \u00b1 8.7 cfu/cm(2) at 08:00 hours to 0.07 \u00b1 0.26 cfu/cm(2) at 12:00 hours by the QAC polymer.\nThe results of this study support the view that, in addition to hypochlorite wiping, the tested QAC surfactant is a potential environmental decontamination strategy for preventing the transmission of clinically important pathogens in medical wards.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"It was found that 83% and 77% of the bedside surfaces of MRSA-positive and MRSA-negative patients respectively were contaminated with staphylococci at 08:00 hours, and that the staphylococcal concentrations increased by 80% at 1200 h over a 4-hour period with routine ward and clinical activities.\"]}", "id": 616}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: A headache is a potential symptom of COVID-19.\n\nAbstract:\nAbstract We report here the case of a 27-year-old man who consulted by telemedicine during the Coronavirus Disease 2019 (COVID-19) pandemic, due to foreign body sensation and left eye redness.\nExamination revealed unilateral eyelid edema and moderate conjunctival hyperemia.\nA few hours later the patient experienced intense headache and developed fever, cough and severe dyspnea.\nA nasopharyngeal swab proved positive for SARS-CoV-2.\nThis case demonstrates that conjunctivitis can be the inaugural manifestation of the COVID-19 infection.\nIt illustrates the interest of telemedicine in ophthalmology during the COVID-19 pandemic, since moderate conjunctival hyperemia can be the first sign of a severe respiratory distress.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"A few hours later the patient experienced intense headache and developed fever, cough and severe dyspnea.\"]}", "id": 617}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: masks are effective in limiting spread of COVID-19\n\nAbstract:\nFace mask use by the general public for limiting the spread of the COVID-19 pandemic is controversial, though increasingly recommended, and the potential of this intervention is not well understood.\nWe develop a compartmental model for assessing the community-wide impact of mask use by the general, asymptomatic public, a portion of which may be asymptomatically infectious.\nModel simulations, using data relevant to COVID-19 dynamics in the US states of New York and Washington, suggest that broad adoption of even relatively ineffective face masks may meaningfully reduce community transmission of COVID-19 and decrease peak hospitalizations and deaths.\nMoreover, mask use decreases the effective transmission rate in nearly linear proportion to the product of mask effectiveness (as a fraction of potentially infectious contacts blocked) and coverage rate (as a fraction of the general population), while the impact on epidemiologic outcomes (death, hospitalizations) is highly nonlinear, indicating masks could synergize with other non-pharmaceutical measures.\nNotably, masks are found to be useful with respect to both preventing illness in healthy persons and preventing asymptomatic transmission.\nHypothetical mask adoption scenarios, for Washington and New York state, suggest that immediate near universal (80%) adoption of moderately (50%) effective masks could prevent on the order of 17--45% of projected deaths over two months in New York, while decreasing the peak daily death rate by 34--58%, absent other changes in epidemic dynamics.\nEven very weak masks (20% effective) can still be useful if the underlying transmission rate is relatively low or decreasing: In Washington, where baseline transmission is much less intense, 80% adoption of such masks could reduce mortality by 24--65% (and peak deaths 15--69%), compared to 2--9% mortality reduction in New York (peak death reduction 9--18%).\nOur results suggest use of face masks by the general public is potentially of high value in curtailing community transmission and the burden of the pandemic.\nThe community-wide benefits are likely to be greatest when face masks are used in conjunction with other non-pharmaceutical practices (such as social-distancing), and when adoption is nearly universal (nation-wide) and compliance is high.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Model simulations, using data relevant to COVID-19 dynamics in the US states of New York and Washington, suggest that broad adoption of even relatively ineffective face masks may meaningfully reduce community transmission of COVID-19 and decrease peak hospitalizations and deaths.\", \"Notably, masks are found to be useful with respect to both preventing illness in healthy persons and preventing asymptomatic transmission.\", \"Hypothetical mask adoption scenarios, for Washington and New York state, suggest that immediate near universal (80%) adoption of moderately (50%) effective masks could prevent on the order of 17--45% of projected deaths over two months in New York, while decreasing the peak daily death rate by 34--58%, absent other changes in epidemic dynamics.\", \"Even very weak masks (20% effective) can still be useful if the underlying transmission rate is relatively low or decreasing: In Washington, where baseline transmission is much less intense, 80% adoption of such masks could reduce mortality by 24--65% (and peak deaths 15--69%), compared to 2--9% mortality reduction in New York (peak death reduction 9--18%).\", \"Our results suggest use of face masks by the general public is potentially of high value in curtailing community transmission and the burden of the pandemic.\", \"The community-wide benefits are likely to be greatest when face masks are used in conjunction with other non-pharmaceutical practices (such as social-distancing), and when adoption is nearly universal (nation-wide) and compliance is high.\"]}", "id": 618}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Virus replication affected early covid-19 spread\n\nAbstract:\nAs the SARS-Cov-2 virus spreads around the world afflicting millions of people, it has undergone divergent genetic mutations.\nAlthough most of these mutations are expected to be inconsequential, some mutations in the spike protein structure have been hypothesized to affect the critical stage at which the virus invades human cells, which could affect transmission probability and disease expression.\nIf true, then we expect an increased growth rate of reported COVID-19 cases in regions dominated by viruses with these altered proteins.\nWe modeled early global infection dynamics based on clade assignment along with other demographic and meteorological factors previously found to be important.\nClade, but not variant D614G which has been associated with increased viral load, enhanced our ability to describe early COVID-19 growth dynamics.\nIncluding clade identity in models significantly improved predictions over earlier work based only on weather and demographic variables.\nIn particular, higher proportions of clade 19A and 19B were negatively correlated with COVID-19 growth rate, whereas higher proportions of 20A and 20C were positively correlated with growth rate.\nA strong interaction between the prevalence of clade 20C and relative humidity suggests that the impact of clade identity might be more important when coupled with certain weather conditions.\nIn particular, 20C an 20A generate the highest growth rates when coupled with low humidity.\nProjections based on data through April 2020 suggest that, without intervention, COVID-19 has the potential to grow more quickly in regions dominated by the 20A and 20C clades, including most of South and North America.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"In particular, higher proportions of clade 19A and 19B were negatively correlated with COVID-19 growth rate, whereas higher proportions of 20A and 20C were positively correlated with growth rate.\"]}", "id": 619}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Melatonin is not associated with survival of intubated covid-19 patients\n\nAbstract:\nBackground Respiratory distress requiring intubation is the most serious complication associated with coronavirus disease 2019 (COVID-19).\nMethods In this retrospective study, we used survival analysis to determine whether or not mortality following intubation was associated with hormone exposure in patients treated at New York Presbyterian/ Columbia University Irving Medical Center.\nHere, we report the overall hazards ratio for each hormone for exposure before and after intubation for intubated and mechanically ventilated patients.\nResults Among the 189,987 patients, we identified 948 intubation periods across 791 patients who were diagnosed with COVID-19 or infected with SARS-CoV2 and 3,497 intubation periods across 2,981 patients who were not.\nMelatonin exposure after intubation was statistically associated with a positive outcome in COVID-19 (demographics and comorbidities adjusted HR: 0.131, 95% CI: 7.76E-02-0.223, p-value = 8.19E-14) and non-COVID-19 (demographics and comorbidities adjusted HR: 0.278, 95% CI: 0.142-0.542, p-value = 1.72E-04) intubated patients.\nAdditionally, melatonin exposure after intubation was statically associated with a positive outcome in COVID-19 patients (demographics and comorbidities adjusted HR: 0.127, 95% CI: 6.01E-02-0.269, p-value = 7.15E-08).\nConclusions Melatonin exposure after intubation is significantly associated with a positive outcome in COVID-19 and non-COVID-19 patients.\nAdditionally, melatonin exposure after intubation is significantly associated with a positive outcome in COVID-19 patients requiring mechanical ventilation.\nWhile our models account for many covariates, including clinical history and demographics, it is impossible to rule out confounding or collider biases within our population.\nFurther study into the possible mechanism of this observation is warranted.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Melatonin exposure after intubation was statistically associated with a positive outcome in COVID-19 (demographics and comorbidities adjusted HR: 0.131, 95% CI: 7.76E-02-0.223, p-value = 8.19E-14) and non-COVID-19 (demographics and comorbidities adjusted HR: 0.278, 95% CI: 0.142-0.542, p-value = 1.72E-04) intubated patients.\", \"Additionally, melatonin exposure after intubation was statically associated with a positive outcome in COVID-19 patients (demographics and comorbidities adjusted HR: 0.127, 95% CI: 6.01E-02-0.269, p-value = 7.15E-08).\", \"Conclusions Melatonin exposure after intubation is significantly associated with a positive outcome in COVID-19 and non-COVID-19 patients.\", \"Additionally, melatonin exposure after intubation is significantly associated with a positive outcome in COVID-19 patients requiring mechanical ventilation.\"]}", "id": 620}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Does wearing a mask help reduce my risk of COVID-19? Cloth and surgical masks help stop droplets spreading when people talk, cough and sneeze, which reduces the risk of spreading the virus.\n\nAbstract:\nIn the context of Coronavirus Disease (2019) (COVID-19) cases globally, there is a lack of consensus across cultures on whether wearing face masks is an effective physical intervention against disease transmission.\nThis study 1) illustrates transmission routes of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2); 2) addresses controversies surrounding the mask from perspectives of attitude, effectiveness, and necessity of wearing the mask with evidence that the use of mask would effectively interrupt the transmission of infectious diseases in both hospital settings and community settings; and 3) provides suggestion that the public should wear the mask during COVID-19 pandemic according to local context.\nTo achieve this goal, government should establish a risk adjusted strategy of mask use to scientifically publicize the use of masks, guarantee sufficient supply of masks, and cooperate for reducing health resources inequities.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"This study 1) illustrates transmission routes of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2); 2) addresses controversies surrounding the mask from perspectives of attitude, effectiveness, and necessity of wearing the mask with evidence that the use of mask would effectively interrupt the transmission of infectious diseases in both hospital settings and community settings; and 3) provides suggestion that the public should wear the mask during COVID-19 pandemic according to local context.\"]}", "id": 621}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Furin cleavage of sars-cov-2 spike promotes but is not essential for infection and cell-cell fusion\n\nAbstract:\nSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infects cells by binding to the host cell receptor Ace2 and undergoing virus-host membrane fusion.\nFusion is triggered by the protease TMPRSS2, which processes the viral Spike (S) protein to reveal the fusion peptide.\nSARS-CoV-2 has evolved a multibasic site at the S1-S2 boundary, which is thought to be cleaved by furin in order to prime S protein for TMPRSS2 processing.\nHere we show that CRISPR-Cas9 knockout of furin reduces, but does not prevent, the production of infectious SARS-CoV-2 virus.\nComparing S processing in furin knockout cells to multibasic site mutants reveals that while loss of furin substantially reduces S1-S2 cleavage it does not prevent it.\nSARS-CoV-2 S protein also mediates cell-cell fusion, potentially allowing virus to spread virion-independently.\nWe show that loss of furin in either donor or acceptor cells reduces, but does not prevent, TMPRSS2-dependent cell-cell fusion, unlike mutation of the multibasic site that completely prevents syncytia formation.\nOur results show that while furin promotes both SARS-CoV-2 infectivity and cell-cell spread it is not essential, suggesting furin inhibitors will not prevent viral spread.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Our results show that while furin promotes both SARS-CoV-2 infectivity and cell-cell spread it is not essential, suggesting furin inhibitors will not prevent viral spread.\"]}", "id": 622}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Sterilizing immunity against sars-cov-2 in hamsters conferred by a novel recombinant subunit vaccine.\n\nAbstract:\nA safe and effective SARS-CoV-2 vaccine is essential to avert the on-going COVID-19 pandemic.\nHere, we developed a subunit vaccine, which is comprised of CHO-expressed spike ectodomain protein (StriFK) and nitrogen bisphosphonates-modified zinc-aluminum hybrid adjuvant (FH002C).\nThis vaccine candidate rapidly elicited the robust humoral response, Th1/Th2 balanced helper CD4 T cell and CD8 T cell immune response in animal models.\nIn mice, hamsters, and non-human primates, 2-shot and 3-shot immunization of StriFK-FH002C generated 28-to 38-fold and 47-to 269-fold higher neutralizing antibody titers than the human COVID-19 convalescent plasmas, respectively.\nMore importantly, the StriFK-FH002C immunization conferred sterilizing immunity to prevent SARS-CoV-2 infection and transmission, which also protected animals from virus-induced weight loss, COVID-19-like symptoms, and pneumonia in hamsters.\nVaccine-induced neutralizing and cell-based receptor-blocking antibody titers correlated well with protective efficacy in hamsters, suggesting vaccine-elicited protection is immune-associated.\nThe StriFK-FH002C provided a promising SARS-CoV-2 vaccine candidate for further clinical evaluation.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"More importantly, the StriFK-FH002C immunization conferred sterilizing immunity to prevent SARS-CoV-2 infection and transmission, which also protected animals from virus-induced weight loss, COVID-19-like symptoms, and pneumonia in hamsters.\", \"Vaccine-induced neutralizing and cell-based receptor-blocking antibody titers correlated well with protective efficacy in hamsters, suggesting vaccine-elicited protection is immune-associated.\"]}", "id": 623}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: the warmer weather will slow the spread of SARS-CoV-2, the novel coronavirus that causes COVID-19\n\nAbstract:\nThis paper investigates the correlation between the high level of coronavirus SARS-CoV-2 infection accelerated transmission and lethality, and surface air pollution in Milan metropolitan area, Lombardy region in Italy.\nFor January-April 2020 period, time series of daily average inhalable gaseous pollutants ozone (O3) and nitrogen dioxide (NO2), together climate variables (air temperature, relative humidity, wind speed, precipitation rate, atmospheric pressure field and Planetary Boundary Layer) were analyzed.\nIn spite of being considered primarily transmitted by indoor bioaerosols droplets and infected surfaces or direct human-to-human personal contacts, it seems that high levels of urban air pollution, and climate conditions have a significant impact on SARS-CoV-2 diffusion.\nExhibited positive correlations of ambient ozone levels and negative correlations of NO2 with the increased rates of COVID-19 infections (Total number, Daily New positive and Total Deaths cases), can be attributed to airborne bioaerosols distribution.\nThe results show positive correlation of daily averaged O3 with air temperature and inversely correlations with relative humidity and precipitation rates.\nViral genome contains distinctive features, including a unique N-terminal fragment within the spike protein, which allows coronavirus attachment on ambient air pollutants.\nAt this moment it is not clear if through airborne diffusion, in the presence of outdoor and indoor aerosols, this protein \"spike\" of the new COVID-19 is involved in the infectious agent transmission from a reservoir to a susceptible host during the highest nosocomial outbreak in some agglomerated industrialized urban areas like Milan is.\nAlso, in spite of collected data for cold season (winter-early spring) period, when usually ozone levels have lower values than in summer, the findings of this study support possibility as O3 can acts as a COVID-19 virus incubator.\nBeing a novel pandemic coronavirus version, it might be ongoing during summer conditions associated with higher air temperatures, low relative humidity and precipitation levels.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Exhibited positive correlations of ambient ozone levels and negative correlations of NO2 with the increased rates of COVID-19 infections (Total number, Daily New positive and Total Deaths cases), can be attributed to airborne bioaerosols distribution.\", \"The results show positive correlation of daily averaged O3 with air temperature and inversely correlations with relative humidity and precipitation rates.\", \"Being a novel pandemic coronavirus version, it might be ongoing during summer conditions associated with higher air temperatures, low relative humidity and precipitation levels.\"]}", "id": 624}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: The virus that causes COVID-19 (SARS-CoV-2), belongs to the betacoronaviruses, one of the four genera of coronaviruses.\n\nAbstract:\nThe recent global outbreak of viral pneumonia designated as Coronavirus Disease 2019 (COVID-19) by coronavirus (SARS-CoV-2) has threatened global public health and urged to investigate its source.\nWhole genome analysis of SARS-CoV-2 revealed ~96% genomic similarity with bat CoV (RaTG13) and clustered together in phylogenetic tree.\nFurthermore, RaTGl3 also showed 97.43% spike protein similarity with SARS-CoV-2 suggesting that RaTGl3 is the closest strain.\nHowever, RBD and key amino acid residues supposed to be crucial for human-to-human and cross-species transmission are homologues between SARS-CoV-2 and pangolin CoVs.\nThese results from our analysis suggest that SARS-CoV-2 is a recombinant virus of bat and pangolin CoVs.\nMoreover, this study also reports mutations in coding regions of 125 SARS-CoV-2 genomes signifying its aptitude for evolution.\nIn short, our findings propose that homologous recombination has been occurred between bat and pangolin CoVs that triggered cross-species transmission and emergence of SARS-CoV-2, and, during the ongoing outbreak, SARS-CoV-2 is still evolving for its adaptability.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"The recent global outbreak of viral pneumonia designated as Coronavirus Disease 2019 (COVID-19) by coronavirus (SARS-CoV-2) has threatened global public health and urged to investigate its source.\"]}", "id": 625}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Early studies have found that cats are the animals most likely to catch the new coronavirus. They can also show symptoms of COVID-19 and might be able to pass it to other cats.\n\nAbstract:\nAbstract Coronavirus disease-19 (COVID-19) caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is now a pandemic threat.\nThis virus is supposed to be spread by human to human transmission.\nCellular angiotensin converting enzyme 2 (ACE2) is the receptor of SARS-CoV-2 which is identical or similar in different species of animals such as pigs, ferrets, cats, orangutans, monkeys, and humans.\nMoreover, a recent study predicted that dog might be secondary host during the evolution of SARS-CoV-2 from bat to human.\nTherefore, there is a possibility of spreading SARS-CoV-2 through domestic pets.\nThere are now many reports of SARS-CoV-2 positive cases in dogs, cats, tigers, lion, and minks.\nExperimental data showed ferrets and cats are highly susceptible to SARS-CoV-2 as infected by virus inoculation and can transmit the virus directly or indirectly by droplets or airborne route.\nBased on these natural infection reports and experimental data, whether the pets are responsible for SARS-CoV-2 spread to human; needs to be deeply investigated.\nHumans showing clinical symptoms of respiratory infections have been undergoing for COVID-19 diagnostic test but many infected people and few pets confirmed with SARS-CoV-2 remained asymptomatic.\nIn this review, we summarize the natural cases of SARS-CoV-2 in animals with the latest researches conducted in this field.\nThis review will be helpful to think insights of SARS-CoV-2 transmissions, spread, and demand for sero-prevalence studies especially in companion animals.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Cellular angiotensin converting enzyme 2 (ACE2) is the receptor of SARS-CoV-2 which is identical or similar in different species of animals such as pigs, ferrets, cats, orangutans, monkeys, and humans.\", \"Moreover, a recent study predicted that dog might be secondary host during the evolution of SARS-CoV-2 from bat to human.\", \"Therefore, there is a possibility of spreading SARS-CoV-2 through domestic pets.\", \"Experimental data showed ferrets and cats are highly susceptible to SARS-CoV-2 as infected by virus inoculation and can transmit the virus directly or indirectly by droplets or airborne route.\"]}", "id": 626}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: adults who are hooked on cigarettes are 50% less likely to test positive for the illness\n\nAbstract:\nABSTRACT Introduction: Recent studies show cigarette smokers are markedly under-represented among patients hospitalized for COVID-19 in over a dozen countries.\nIt is unclear if this may be related to confounding factors such as age distribution, access to care, and inaccurate records.\nWe hypothesized that these concerns could be avoided by studying smoking prevalence in relation to COVID-19 mortality.\nSince climate has been identified as a factor in COVID-19, we studied groups of countries with relatively comparable temperatures.\nMethods: The 20 hottest and 20 coldest countries in the Johns Hopkins Mortality Analysis database with a minimum mortality rate of .3 deaths/100,000 were selected on the basis of the average temperatures of their largest city.\nMortality rates were determined as of May 1, 2020 and correlated with national smoking rate adjusting for sex ratio, obesity, temperature, and elderly population.\nResults: A highly significant inverse correlation between current daily smoking prevalence and COVID-19 mortality rate was noted for the group of hot countries (R=-.718, p = .0002), cold countries (R=-.567, p=.0046), and the combined group (R=-.324, p=.0207).\nHowever, after adjustments only the regression for hot countries and the combined group remained significant.\nIn hot countries, for each percentage point increase in smoking rate mortality decreased by .147 per 100,000 population (95% CI .102- 192, p=.0066).\nThis resulted in mortality rates several-fold elevated in the countries with the lowest smoking rates relative to the highest smoking rates.\nIn the combined group, mortality decreased by .257 per 100,000 population (95% CI .175-.339, p=.0034).\nDiscussion: These findings add support to the finding of an inverse relationship between current smoking and seriously symptomatic COVID-19.\nHowever, we conclude that the difference in mortality between the highest and lowest smoking countries appears too large to be due primarily to the effects of smoking per se.\nA potentially beneficial effect of smoking is surprising, but compatible with a number of hypothetical mechanisms which deserve exploration: 1) Studies show smoking alters ACE2 expression which may affect COVID-19 infection or its progression to serious lung pathology.\n2) Nicotine has anti-inflammatory activity and also appears to alter ACE2 expression.\n3) Nitric oxide in cigarette smoke is known to be effective in treating pulmonary hypertension and has shown in vitro antiviral effects including against SARS-CoV-2.\n4) Smoking has complicated effects on the immune system involving both up and down regulation, any of which might alone or in concert antagonize progression of COVID-19.\n5) Smokers are exposed to hot vapors which may stimulate immunity in the respiratory tract by various heat-related mechanisms (e.g. heat shock proteins).\nStudies of steam and sauna treatments have shown efficacy in other viral respiratory conditions.\nAt this time there is no clear evidence that smoking is protective against COVID-19, so the established recommendations to avoid smoking should be emphasized.\nThe interaction of smoking and COVID-19 will only be reliably determined by carefully designed prospective study, and there is reason to believe that there are unknown confounds that may be spuriously suggesting a protective effect of smoking.\nHowever, the magnitude of the apparent inverse association of COVID-19 and smoking and its myriad clinical implications suggest the importance of further investigation.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"4) Smoking has complicated effects on the immune system involving both up and down regulation, any of which might alone or in concert antagonize progression of COVID-19.\", \"At this time there is no clear evidence that smoking is protective against COVID-19, so the established recommendations to avoid smoking should be emphasized.\"]}", "id": 627}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: a popular treatment to tamp down the immune system in severely ill patients may help a few, but could harm many others. \n\nAbstract:\nThe rapid global spread of SARS-CoV-2 and resultant mortality and social disruption have highlighted the need to better understand coronavirus immunity to expedite vaccine development efforts.\nMultiple candidate vaccines, designed to elicit protective neutralising antibodies targeting the viral spike glycoprotein, are rapidly advancing to clinical trial.\nHowever, the immunogenic properties of the spike protein in humans are unresolved.\nTo address this, we undertook an in-depth characterisation of humoral and cellular immunity against SARS-CoV-2 spike in humans following mild to moderate SARS-CoV-2 infection.\nWe find serological antibody responses against spike are routinely elicited by infection and correlate with plasma neutralising activity and capacity to block ACE2/RBD interaction.\nExpanded populations of spike-specific memory B cells and circulating T follicular helper cells (cTFH) were detected within convalescent donors, while responses to the receptor binding domain (RBD) constitute a minor fraction.\nUsing regression analysis, we find high plasma neutralisation activity was associated with increased spike-specific antibody, but notably also with the relative distribution of spike-specific cTFH subsets.\nThus both qualitative and quantitative features of B and T cell immunity to spike constitute informative biomarkers of the protective potential of novel SARS-CoV-2 vaccines.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 628}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Hidden immune weakness found in gravely ill COVID-19 patients.\n\nAbstract:\nSeveral related human coronaviruses (HCoVs) are endemic in the human population, causing mild respiratory infections1.\nSevere Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), the etiologic agent of Coronavirus disease 2019 (COVID-19), is a recent zoonotic infection that has quickly reached pandemic spread2,3.\nZoonotic introduction of novel coronaviruses is thought to occur in the absence of pre-existing immunity in the target human population.\nUsing diverse assays for detection of antibodies reactive with the SARS-CoV-2 Spike (S) glycoprotein, we demonstrate the presence of pre-existing immunity in uninfected and unexposed humans to the new coronavirus.\nSARS-CoV-2 S-reactive antibodies, exclusively of the IgG class, were readily detectable by a sensitive flow cytometry-based method in SARS-CoV-2-uninfected individuals with recent HCoV infection and targeted the S2 subunit.\nIn contrast, SARS-CoV-2 infection induced higher titres of SARS-CoV-2 S-reactive IgG antibodies, as well as concomitant IgM and IgA antibodies throughout the observation period of 6 weeks since symptoms onset.\nHCoV patient sera also variably reacted with SARS-CoV-2 S and nucleocapsid (N), but not with the S1 subunit or the receptor binding domain (RBD) of S on standard enzyme immunoassays.\nNotably, HCoV patient sera exhibited specific neutralising activity against SARS-CoV-2 S pseudotypes, according to levels of SARS-CoV-2 S-binding IgG and with efficiencies comparable to those of COVID-19 patient sera.\nDistinguishing pre-existing and de novo antibody responses to SARS-CoV-2 will be critical for serology, seroprevalence and vaccine studies, as well as for our understanding of susceptibility to and natural course of SARS-CoV-2 infection.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 629}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Eating garlic will protect me against getting the coronavirus.\n\nAbstract:\nIn late December 2019 in Wuhan, China, several patients with viral pneumonia were identified as 2019 novel coronavirus (2019-nCoV).\nSo far, there are no specific treatments for patients with coronavirus disease-19 (COVID-19), and the treatments available today are based on previous experience with similar viruses such as severe acute respiratory syndrome-related coronavirus (SARS-CoV), Middle East respiratory syndrome coronavirus (MERS-CoV), and Influenza virus.\nIn this article, we have tried to reach a therapeutic window of drugs available to patients with COVID-19.\nCathepsin L is required for entry of the 2019-nCoV virus into the cell as target teicoplanin inhibits virus replication.\nAngiotensin-converting-enzyme 2 (ACE2) in soluble form as a recombinant protein can prevent the spread of coronavirus by restricting binding and entry.\nIn patients with COVID-19, hydroxychloroquine decreases the inflammatory response and cytokine storm, but overdose causes toxicity and mortality.\nNeuraminidase inhibitors such as oseltamivir, peramivir, and zanamivir are invalid for 2019-nCoV and are not recommended for treatment but protease inhibitors such as lopinavir/ritonavir (LPV/r) inhibit the progression of MERS-CoV disease and can be useful for patients of COVID-19 and, in combination with Arbidol, has a direct antiviral effect on early replication of SARS-CoV. Ribavirin reduces hemoglobin concentrations in respiratory patients, and remdesivir improves respiratory symptoms.\nUse of ribavirin in combination with LPV/r in patients with SARS-CoV reduces acute respiratory distress syndrome and mortality, which has a significant protective effect with the addition of corticosteroids.\nFavipiravir increases clinical recovery and reduces respiratory problems and has a stronger antiviral effect than LPV/r.\ncurrently, appropriate treatment for patients with COVID-19 is an ACE2 inhibitor and a clinical problem reducing agent such as favipiravir in addition to hydroxychloroquine and corticosteroids.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"In late December 2019 in Wuhan, China, several patients with viral pneumonia were identified as 2019 novel coronavirus (2019-nCoV).\", \"So far, there are no specific treatments for patients with coronavirus disease-19 (COVID-19), and the treatments available today are based on previous experience with similar viruses such as severe acute respiratory syndrome-related coronavirus (SARS-CoV), Middle East respiratory syndrome coronavirus (MERS-CoV), and Influenza virus.\"]}", "id": 630}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Sars-cov-2 causes mild alveolar inflammation and barrier dysfunction\n\nAbstract:\nInfections with SARS-CoV-2 lead to mild to severe coronavirus disease-19 (COVID-19) with systemic symptoms.\nAlthough the viral infection originates in the respiratory system, it is unclear how the virus can overcome the alveolar barrier, which is observed in severe COVID-19 disease courses.\nTo elucidate the viral effects on the barrier integrity and immune reactions, we used mono-cell culture systems and a complex human alveolus-on-a-chip model composed of epithelial, endothelial, and mononuclear cells.\nOur data show that SARS-CoV-2 efficiently infected epithelial cells with high viral loads and inflammatory response, including the interferon expression.\nBy contrast, the adjacent endothelial layer was no infected and did neither show productive virus replication or interferon release.\nWith prolonged infection, both cell types are damaged, and the barrier function is deteriorated, allowing the viral particles to overbear.\nIn our study, we demonstrate that although SARS-CoV-2 is dependent on the epithelium for efficient replication, the neighboring endothelial cells are affected, e.g., by the epithelial cytokine release, which results in the damage of the alveolar barrier function and viral dissemination.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"With prolonged infection, both cell types are damaged, and the barrier function is deteriorated, allowing the viral particles to overbear.\", \"In our study, we demonstrate that although SARS-CoV-2 is dependent on the epithelium for efficient replication, the neighboring endothelial cells are affected, e.g., by the epithelial cytokine release, which results in the damage of the alveolar barrier function and viral dissemination.\"]}", "id": 631}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Exploratory analysis of immunization records highlights increasing sars-cov-2 rates in individuals with recent non-covid-19 vaccinations\n\nAbstract:\nMultiple clinical studies are ongoing to assess whether existing vaccines may afford protection against SARS-CoV-2 infection through trained immunity.\nIn this exploratory study, we analyze immunization records from 137,037 individuals who received SARS-CoV-2 PCR tests.\nWe find that polio, Hemophilus influenzae type-B (HIB), measles-mumps-rubella (MMR), varicella, pneumococcal conjugate (PCV13), geriatric flu, and hepatitis A / hepatitis B (HepA-HepB) vaccines administered in the past 1, 2, and 5 years are associated with decreased SARS-CoV-2 infection rates, even after adjusting for geographic SARS-CoV-2 incidence and testing rates, demographics, comorbidities, and number of other vaccinations.\nFurthermore, age, race/ethnicity, and blood group stratified analyses reveal significantly lower SARS-CoV-2 rate among black individuals who have taken the PCV13 vaccine, with relative risk of 0.45 at the 5 year time horizon (n: 653, 95% CI: (0.32, 0.64), p-value: 6.9e-05).\nThese findings suggest that additional pre-clinical and clinical studies are warranted to assess the protective effects of existing non-COVID-19 vaccines and explore underlying immunologic mechanisms.\nWe note that the findings in this study are preliminary and are subject to change as more data becomes available and as further analysis is conducted.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"We find that polio, Hemophilus influenzae type-B (HIB), measles-mumps-rubella (MMR), varicella, pneumococcal conjugate (PCV13), geriatric flu, and hepatitis A / hepatitis B (HepA-HepB) vaccines administered in the past 1, 2, and 5 years are associated with decreased SARS-CoV-2 infection rates, even after adjusting for geographic SARS-CoV-2 incidence and testing rates, demographics, comorbidities, and number of other vaccinations.\", \"Furthermore, age, race/ethnicity, and blood group stratified analyses reveal significantly lower SARS-CoV-2 rate among black individuals who have taken the PCV13 vaccine, with relative risk of 0.45 at the 5 year time horizon (n: 653, 95% CI: (0.32, 0.64), p-value: 6.9e-05).\", \"These findings suggest that additional pre-clinical and clinical studies are warranted to assess the protective effects of existing non-COVID-19 vaccines and explore underlying immunologic mechanisms.\"]}", "id": 632}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Remdesivir proves effective against COVID-19\n\nAbstract:\nCurrently, there is not any specific effective antiviral treatment for COVID-19.\nAlthough most of the COVID-19 patients have mild or moderate courses, up to 5%\u00ad10% can have severe, potentially life threatening course, there is an urgent need for effective drugs.\nOptimized supportive care remains the mainstay of therapy.\nThere have been more than 300 clinical trials going on, various antiviral and immunomodulating agents are in various stages of evaluation for COVID-19 in those trials and some of them will be published in the next couple of months.\nDespite the urgent need to find an effective antiviral treatment for COVID-19 through randomized controlled studies, certain agents are being used all over the world based on either in-vitro or extrapolated evidence or observational studies.\nThe most frequently used agents both in Turkey and all over the world including chloroquine, hydroxychloroquine, lopinavir/ritonavir, favipiravir and remdesivir will be reviewed here .Nitazoxanide and ivermectin were also included in this review as they have recently been reported to have an activity against SARS-CoV-2 in vitro and are licensed for the treatment of some other human infections.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Currently, there is not any specific effective antiviral treatment for COVID-19.\"]}", "id": 633}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Dogs May Not Spread Covid-19, but Cats Can Pass It to Each Other\n\nAbstract:\nLittle information on the SARS-CoV-2 virus in animals is available to date.\nWhereas no one husbandry animal case has been reported to date, which would have significant implications in food safety, companion animals play a role in COVID-19 epidemiology that opens up new questions.\nThere is evidence that SARS-CoV-2 can infect felines, dogs and minks, and there is evidence of human-to-animal infection.\nLikewise, the S protein nucleotide sequence of the SARS-CoV-2 virus isolated in domestic animals and humans is identical, and the replication of the SARS-CoV-2 in cats is efficient.\nBesides, the epidemiological evidence for this current pandemic indicates that the spillover to humans was associated with close contact between man and exotic animals, very probably in Chinese wet markets, thus there is a growing general consensus that the exotic animal markets, should be strictly regulated.\nThe examination of these findings and the particular role of animals in COVID-19 should be carefully analyzed in order to establish preparation and containment measures.\nAnimal management and epidemiological surveillance must be also considered for COVID-19 control, and it can open up new questions regarding COVID-19 epidemiology and the role that animals play in it.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"There is evidence that SARS-CoV-2 can infect felines, dogs and minks, and there is evidence of human-to-animal infection.\"]}", "id": 634}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: This correlation between antibody titers and neutralizing activity in sera from sars-cov-2 infected subjects\n\nAbstract:\nPlenty of serologic tests for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have been developed so far, thus documenting the importance of evaluating the relevant features of the immune response to this viral agent.\nThe performance of these assays is currently under investigation.\nAmongst them, LIAISON\u00ae SARS-CoV-2 S1/S2 IgG by DiaSorin and Elecsys Anti-SARS-CoV-2 cobas\u00ae by Roche are currently used by laboratory medicine hospital departments in Italy and many other countries.\nIn the present study, we firstly compared two serologic tests on serum samples collected at two different time points from 46 laboratory-confirmed coronavirus disease-2019 (COVID-19) subjects.\nSecondly, 85 negative serum samples collected before the SARS-CoV-2 pandemic were analyzed.\nThirdly, possible correlations between antibody levels and the resulting neutralizing activity against a clinical isolate of SARS-CoV-2 were evaluated.\nResults revealed that both tests are endowed with low sensitivity on the day of hospital admission, which increased to 97.8% and 100% for samples collected after 15 days for DiaSorin and Roche tests, respectively.\nThe specificity evaluated for the two tests ranges from 96.5% to 100%, respectively.\nImportantly, a poor direct correlation between antibody titers and neutralizing activity levels was evidenced in the present study.\nThese data further shed light on both potentials and possible limitations related to SARS-CoV-2 serology.\nIn this context, great efforts are still necessary for investigating antibody kinetics to develop novel diagnostic algorithms.\nMoreover, further investigations on the role of neutralizing antibodies and their correlate of protection will be of paramount importance for the development of effective vaccines.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Importantly, a poor direct correlation between antibody titers and neutralizing activity levels was evidenced in the present study.\"]}", "id": 635}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: fever is beneficial to the body's natural immune response to fight covid-19;\n\nAbstract:\n: The COVID-19 pandemic is challenging our cardiovascular care of patients with heart diseases.\nIn the setting of pericardial diseases, there are two possible different scenarios to consider: the patient being treated for pericarditis who subsequently becomes infected with SARS-CoV-2, and the patient with COVID-19 who develops pericarditis or pericardial effusion.\nIn both conditions, clinicians may be doubtful regarding the safety of nonsteroidal anti-inflammatory drugs (NSAIDs), corticosteroids, colchicine, and biological agents, such as anti-IL1 agents (e.g. anakinra), that are the mainstay of therapy for pericarditis.\nFor NSAIDs, there is no clear scientific evidence linking ibuprofen and other NSAIDs to worsening of COVID-19; however, it seems prudent to continue them, if necessary to control pericarditis, and on the other hand, to prefer paracetamol for fever and systemic symptoms related to COVID-19.\nTreatments with corticosteroids, colchicine, and anakinra appear well tolerated in the context of COVID-19 infection and are currently actively evaluated as potential therapeutic options for COVID infection at different stages of the disease.\nOn this basis, currently most treatments for pericarditis do not appear contraindicated also in the presence of possible COVID-19 infection and should not be discontinued, and some (corticosteroids, colchicine, and anakinra) can be considered to treat both conditions.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"For NSAIDs, there is no clear scientific evidence linking ibuprofen and other NSAIDs to worsening of COVID-19; however, it seems prudent to continue them, if necessary to control pericarditis, and on the other hand, to prefer paracetamol for fever and systemic symptoms related to COVID-19.\"]}", "id": 636}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Young people as diligent about Covid measures as older people\n\nAbstract:\nBackground: The coronavirus 2019 (COVID-19) pandemic has been spread-ing globally for months, yet the infection fatality ratio of the disease is still uncertain.\nThis is partly because of inconsistencies in testing and death reporting standards across countries.\nOur purpose is to provide accurate estimates which do not rely on testing and death count data directly but only use population level statistics.\nMethods: We collected demographic and death records data from the Italian Institute of Statistics.\nWe focus on the area in Italy that experienced the initial outbreak of COVID-19 and estimated a Bayesian model fitting age-stratified mortality data from 2020 and previous years.\nWe also assessed the sensitivity of our estimates to alternative assumptions on the proportion of population infected.\nFindings: We estimate an overall infection fatality rate of 1.29% (95% credible interval [CrI] 0.89 - 2.01), as well as large differences by age, with a low infection fatality rate of 0.05% for under 60 year old (CrI 0-.19) and a substantially higher 4.25% (CrI 3.01-6.39) for people above 60 years of age.\nIn our sensitivity analysis, we found that even under extreme assumptions, our method delivered useful information.\nFor instance, even if only 10% of the population were infected, the infection fatality rate would not rise above 0.2% for people under 60.\nInterpretation: Our empirical estimates based on population level data show a sharp difference in fatality rates between young and old people and firmly rule out overall fatality ratios below 0.5% in populations with more than 30% over 60 years old.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 637}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: smokers have a very much lower probability of developing symptomatic or severe SARA-CoV-2 [COVID-19] infection as compared to the general population\n\nAbstract:\nIntroduction Epidemiological and laboratory research seems to suggest that smoking and perhaps nicotine alone could reduce the severity of COVID-19.\nLikewise, there is some evidence that inhaled corticosteroids could also reduce its severity, opening the possibility that nicotine and inhaled steroids could be used as treatments.\nMethods In this prospective cohort study, we will link English general practice records from the QResearch database to Public Health England's database of SARS-CoV-2 positive tests, Hospital Episode Statistics, admission to intensive care units, and death from COVID-19 to identify our outcomes: hospitalisation, ICU admission, and death due to COVID.\nUsing Cox regression, we will perform sequential adjustment for potential confounders identified by separate directed acyclic graphs to: 1.\nAssess the association between smoking and COVID-19 disease severity, and how that changes on adjustment for smoking-related comorbidity.\n2. More closely characterise the association between smoking and severe COVID-19 disease by assessing whether the association is modified by age (as a proxy of length of smoking), gender, ethnic group, and whether people have asthma or COPD.\n3. Assess for evidence of a dose-response relation between smoking intensity and disease severity, which would help create a case for causality.\n4.\nExamine the association between former smokers who are using NRT or are vaping and disease severity.\n5. Examine whether pre-existing respiratory disease is associated with severe COVID-19 infection.\n6. Assess whether the association between chronic obstructive pulmonary disease (COPD) and asthma and COVID-19 disease severity is modified by age, gender, ethnicity, and smoking status.\n7. Assess whether the use of inhaled corticosteroids is associated with severity of COVID-19 disease.\n8. To assess whether the association between use of inhaled corticosteroids and severity of COVID-19 disease is modified by the number of other airways medications used (as a proxy for severity of condition) and whether people have asthma or COPD.\nConclusions This representative population sample will, to our knowledge, present the first comprehensive examination of the association between smoking, nicotine use without smoking, respiratory disease, and severity of COVID-19.\nWe will undertake several sensitivity analyses to examine the potential for bias in these associations.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Introduction Epidemiological and laboratory research seems to suggest that smoking and perhaps nicotine alone could reduce the severity of COVID-19.\"]}", "id": 638}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Coronaviruses cause respiratory illnesses, so the lungs are usually affected first. Early symptoms include fever, cough, and shortness of breath.\n\nAbstract:\nSARS-CoV-2 is the coronavirus agent of the COVID-19 pandemic causing high mortalities.\nIn contrast, the widely spread human coronaviruses OC43, HKU1, 229E, and NL63 tend to cause only mild symptoms.\nThe present study shows, by in silico analysis, that these common human viruses are expected to induce immune memory against SARS-CoV-2 by sharing protein fragments (antigen epitopes) for presentation to the immune system by MHC class I. A list of such epitopes is provided.\nThe number of these epitopes and the prevalence of the common coronaviruses suggest that a large part of the world population has some degree of specific immunity against SARS-CoV-2 already, even without having been infected by that virus.\nFor inducing protection, booster vaccinations enhancing existing immunity are less demanding than primary vaccinations against new antigens.\nTherefore, for the discussion on vaccination strategies against COVID-19, the available immune memory against related viruses should be part of the consideration.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 639}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Sars-cov1 frequent mask use in public venues, frequent hand washing, and disinfecting the living quarters were no protective factors\n\nAbstract:\nWe analyzed information obtained from 1,192 patients with probable severe acute respiratory syndrome (SARS) reported in Hong Kong.\nAmong them, 26.6% were hospital workers, 16.1% were household members of SARS patients and had probable secondary infections, 14.3% were Amoy Garden residents, 4.9% were inpatients, and 20.1% were contacts of SARS patients who were not family members.\nThe remaining 347 case-patients (29.1%) did not have \u201cknown\u201d sources of infection.\nExcluding those <16 years of age, 330 patients with cases from \u201cundefined\u201d sources were used in a 1:2 matched case-control study.\nMultivariate analysis of this case-control study showed that having visited mainland China, hospitals, or the Amoy Gardens were risk factors (odds ratio [OR] 1.95 to 7.63).\nIn addition, frequent mask use in public venues, frequent hand washing, and disinfecting the living quarters were significant protective factors (OR 0.36 to 0.58).\nIn Hong Kong, therefore, community-acquired infection did not make up most transmissions, and public health measures have contributed substantially to the control of the SARS epidemic.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"In addition, frequent mask use in public venues, frequent hand washing, and disinfecting the living quarters were significant protective factors (OR 0.36 to 0.58).\"]}", "id": 640}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: camostat mesylate cure coronavirus.\n\nAbstract:\nSARS-CoV-2 rapidly spread around the globe after its emergence in Wuhan in December 2019.\nWith no specific therapeutic and prophylactic options available, the virus was able to infect millions of people.\nTo date, close to half a million patients succumbed to the viral disease, COVID-19.\nThe high need for treatment options, together with the lack of small animal models of infection has led to clinical trials with repurposed drugs before any preclinical in vivo evidence attesting their efficacy was available.\nWe used Syrian hamsters to establish a model to evaluate antiviral activity of small molecules in both an infection and a transmission setting.\nUpon intranasal infection, the animals developed high titers of SARS-CoV-2 in the lungs and pathology similar to that observed in mild COVID-19 patients.\nTreatment of SARS-CoV-2-infected hamsters with favipiravir or hydroxychloroquine (with and without azithromycin) resulted in respectively a mild or no reduction in viral RNA and infectious virus.\nMicro-CT scan analysis of the lungs showed no improvement compared to non-treated animals, which was confirmed by histopathology.\nIn addition, both compounds did not prevent virus transmission through direct contact and thus failed as prophylactic treatments.\nBy modelling the PK profile of hydroxychloroquine based on the trough plasma concentrations, we show that the total lung exposure to the drug was not the limiting factor.\nIn conclusion, we here characterized a hamster infection and transmission model to be a robust model for studying in vivo efficacy of antiviral compounds.\nThe information acquired using hydroxychloroquine and favipiravir in this model is of critical value to those designing (current and) future clinical trials.\nAt this point, the data here presented on hydroxychloroquine either alone or combined with azithromycin (together with previously reported in vivo data in macaques and ferrets) provide no scientific basis for further use of the drug in humans.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 641}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Wear masks with two or more layers to stop the spread of COVID-19\n\nAbstract:\nIn the context of Coronavirus Disease (2019) (COVID-19) cases globally, there is a lack of consensus across cultures on whether wearing face masks is an effective physical intervention against disease transmission.\nThis study 1) illustrates transmission routes of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2); 2) addresses controversies surrounding the mask from perspectives of attitude, effectiveness, and necessity of wearing the mask with evidence that the use of mask would effectively interrupt the transmission of infectious diseases in both hospital settings and community settings; and 3) provides suggestion that the public should wear the mask during COVID-19 pandemic according to local context.\nTo achieve this goal, government should establish a risk adjusted strategy of mask use to scientifically publicize the use of masks, guarantee sufficient supply of masks, and cooperate for reducing health resources inequities.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"This study 1) illustrates transmission routes of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2); 2) addresses controversies surrounding the mask from perspectives of attitude, effectiveness, and necessity of wearing the mask with evidence that the use of mask would effectively interrupt the transmission of infectious diseases in both hospital settings and community settings; and 3) provides suggestion that the public should wear the mask during COVID-19 pandemic according to local context.\"]}", "id": 642}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Severe COVID-19 outcomes decreases as the pandemic progressed from winter to the warmer months\n\nAbstract:\nThis paper investigates the correlation between the high level of coronavirus SARS-CoV-2 infection accelerated transmission and lethality, and surface air pollution in Milan metropolitan area, Lombardy region in Italy.\nFor January-April 2020 period, time series of daily average inhalable gaseous pollutants ozone (O3) and nitrogen dioxide (NO2), together climate variables (air temperature, relative humidity, wind speed, precipitation rate, atmospheric pressure field and Planetary Boundary Layer) were analyzed.\nIn spite of being considered primarily transmitted by indoor bioaerosols droplets and infected surfaces or direct human-to-human personal contacts, it seems that high levels of urban air pollution, and climate conditions have a significant impact on SARS-CoV-2 diffusion.\nExhibited positive correlations of ambient ozone levels and negative correlations of NO2 with the increased rates of COVID-19 infections (Total number, Daily New positive and Total Deaths cases), can be attributed to airborne bioaerosols distribution.\nThe results show positive correlation of daily averaged O3 with air temperature and inversely correlations with relative humidity and precipitation rates.\nViral genome contains distinctive features, including a unique N-terminal fragment within the spike protein, which allows coronavirus attachment on ambient air pollutants.\nAt this moment it is not clear if through airborne diffusion, in the presence of outdoor and indoor aerosols, this protein \"spike\" of the new COVID-19 is involved in the infectious agent transmission from a reservoir to a susceptible host during the highest nosocomial outbreak in some agglomerated industrialized urban areas like Milan is.\nAlso, in spite of collected data for cold season (winter-early spring) period, when usually ozone levels have lower values than in summer, the findings of this study support possibility as O3 can acts as a COVID-19 virus incubator.\nBeing a novel pandemic coronavirus version, it might be ongoing during summer conditions associated with higher air temperatures, low relative humidity and precipitation levels.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Being a novel pandemic coronavirus version, it might be ongoing during summer conditions associated with higher air temperatures, low relative humidity and precipitation levels.\"]}", "id": 643}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: In such a scenario, differentiating whether the cause of death is specifically due to COVID-19 or the result of treatment limitations can be difficult.\n\nAbstract:\nThe ongoing outbreak of COVID-19 has been expanding worldwide.\nAs of 17 April 2020, the death toll stands at a sobering 147,027 and over two million cases, this has been straining the health care systems all over.\nRespiratory failure has been cited as the major cause of death but here we present a case about a patient who instead succumbed to severe metabolic acidosis with multiple organ failure.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 644}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Saliva sampling is an excellent option to determine the number of sars cov2 diagnostic tests in settings with supplies shortages\n\nAbstract:\nAs part of any plan to lift or ease the confinement restrictions that are in place in many different countries, there is an urgent need to increase the capacity of laboratory testing for SARS CoV-2.\nDetection of the viral genome through RT-qPCR is the golden standard for this test, however, the high demand of the materials and reagents needed to sample individuals, purify the viral RNA, and perform the RT-qPCR test has resulted in a worldwide shortage of several of these supplies.\nHere, we show that directly lysed saliva samples can serve as a suitable source for viral RNA detection that is cheaper and can be as efficient as the classical protocol that involves column purification of the viral RNA.\nIn addition, it surpasses the need for swab sampling, decreases the risk of the healthcare personnel involved in this process, and accelerates the diagnostic procedure.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Detection of the viral genome through RT-qPCR is the golden standard for this test, however, the high demand of the materials and reagents needed to sample individuals, purify the viral RNA, and perform the RT-qPCR test has resulted in a worldwide shortage of several of these supplies.\"]}", "id": 645}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Hot weather can spread the virus more as it may get you more out there, make you more mobile and you would actually interact with more people\n\nAbstract:\nThe 2020 coronavirus pandemic is developing at different paces throughout the world.\nSome areas, like the Caribbean Basin, have yet to see the virus strike at full force.\nWhen it does, there is reasonable evidence to suggest the consequent COVID-19 outbreaks will overwhelm healthcare systems and economies.\nThis is particularly concerning in the Caribbean as pandemics can have disproportionately higher mortality impacts on lower and middle-income countries.\nPreliminary observations from our team and others suggest that temperature and climatological factors could influence the spread of this novel coronavirus, making spatiotemporal predictions of its infectiousness possible.\nThis review studies geographic and time-based distribution of known respiratory viruses in the Caribbean Basin in an attempt to foresee how the pandemic will develop in this region.\nThis review is meant to aid in planning short- and long-term interventions to manage outbreaks at the international, national, and subnational levels in the region.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Preliminary observations from our team and others suggest that temperature and climatological factors could influence the spread of this novel coronavirus, making spatiotemporal predictions of its infectiousness possible.\"]}", "id": 646}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: As the novel coronavirus sweeps the globe, those with high blood pressure are at heightened risk for more severe complications should they contract Covid-19\n\nAbstract:\nOBJECTIVE: To investigate the association between hypertension and outcome in patients with Coronavirus Disease 2019 (COVID-19) pneumonia.\nMETHODS: We performed a systematic literature search from several databases on studies that assess hypertension and outcome in COVID-19.\nComposite of poor outcome, comprising of mortality, severe COVID-19, acute respiratory distress syndrome (ARDS), need for intensive care unit (ICU) care and disease progression were the outcomes of interest.\nRESULTS: A total of 6560 patients were pooled from 30 studies.\nHypertension was associated with increased composite poor outcome (risk ratio (RR) 2.11 (95% confidence interval (CI) 1.85, 2.40), p < 0.001; I2, 44%) and its sub-group, including mortality (RR 2.21 (1.74, 2.81), p < 0.001; I2, 66%), severe COVID-19 (RR 2.04 (1.69, 2.47), p < 0.001; I2 31%), ARDS (RR 1.64 (1.11, 2.43), p = 0.01; I2,0%, p = 0.35), ICU care (RR 2.11 (1.34, 3.33), p = 0.001; I2 18%, p = 0.30), and disease progression (RR 3.01 (1.51, 5.99), p = 0.002; I2 0%, p = 0.55).\nMeta-regression analysis showed that gender (p = 0.013) was a covariate that affects the association.\nThe association was stronger in studies with a percentage of males < 55% compared to \u00e2\u00a9\u00be 55% (RR 2.32 v. RR 1.79).\nCONCLUSION: Hypertension was associated with increased composite poor outcome, including mortality, severe COVID-19, ARDS, need for ICU care and disease progression in patients with COVID-19.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"CONCLUSION: Hypertension was associated with increased composite poor outcome, including mortality, severe COVID-19, ARDS, need for ICU care and disease progression in patients with COVID-19.\"]}", "id": 647}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Saliva is more sensitive for sars-cov-2 detection in covid-19 patients than nasopharyngeal swabs\n\nAbstract:\nRapid and accurate SARS-CoV-2 diagnostic testing is essential for controlling the ongoing COVID-19 pandemic.\nThe current gold standard for COVID-19 diagnosis is real-time RT-PCR detection of SARS-CoV-2 from nasopharyngeal swabs.\nLow sensitivity, exposure risks to healthcare workers, and global shortages of swabs and personal protective equipment, however, necessitate the validation of new diagnostic approaches.\nSaliva is a promising candidate for SARS-CoV-2 diagnostics because (1) collection is minimally invasive and can reliably be self-administered and (2) saliva has exhibited comparable sensitivity to nasopharyngeal swabs in detection of other respiratory pathogens, including endemic human coronaviruses, in previous studies.\nTo validate the use of saliva for SARS-CoV-2 detection, we tested nasopharyngeal and saliva samples from confirmed COVID-19 patients and self-collected samples from healthcare workers on COVID-19 wards.\nWhen we compared SARS-CoV-2 detection from patient-matched nasopharyngeal and saliva samples, we found that saliva yielded greater detection sensitivity and consistency throughout the course of infection.\nFurthermore, we report less variability in self-sample collection of saliva.\nTaken together, our findings demonstrate that saliva is a viable and more sensitive alternative to nasopharyngeal swabs and could enable at-home self-administered sample collection for accurate large-scale SARS-CoV-2 testing.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Saliva is a promising candidate for SARS-CoV-2 diagnostics because (1) collection is minimally invasive and can reliably be self-administered and (2) saliva has exhibited comparable sensitivity to nasopharyngeal swabs in detection of other respiratory pathogens, including endemic human coronaviruses, in previous studies.\", \"To validate the use of saliva for SARS-CoV-2 detection, we tested nasopharyngeal and saliva samples from confirmed COVID-19 patients and self-collected samples from healthcare workers on COVID-19 wards.\", \"When we compared SARS-CoV-2 detection from patient-matched nasopharyngeal and saliva samples, we found that saliva yielded greater detection sensitivity and consistency throughout the course of infection.\", \"Taken together, our findings demonstrate that saliva is a viable and more sensitive alternative to nasopharyngeal swabs and could enable at-home self-administered sample collection for accurate large-scale SARS-CoV-2 testing.\"]}", "id": 648}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Smoking is a risk factor for COVID-19 patients, but one particular substance in cigarettes - nicotine - might prevent infection in some people, or improve COVID-19 prognosis\n\nAbstract:\nObjectives: To investigate whether there is a causal effect of cardiometabolic traits on risk of sepsis and severe covid-19.\nDesign: Mendelian randomisation analysis.\nSetting: UK Biobank and HUNT study population-based cohorts for risk of sepsis, and genome-wide association study summary data for risk of severe covid-19 with respiratory failure.\nParticipants: 12,455 sepsis cases (519,885 controls) and 1,610 severe covid-19 with respiratory failure cases (2,205 controls).\nExposure: Genetic variants that proxy body mass index (BMI), lipid traits, systolic blood pressure, lifetime smoking score, and type 2 diabetes liability - derived from studies considering between 188,577 to 898,130 participants.\nMain outcome measures: Risk of sepsis and severe covid-19 with respiratory failure.\nResults: Higher genetically proxied BMI and lifetime smoking score were associated with increased risk of sepsis in both UK Biobank (BMI: odds ratio 1.38 per standard deviation increase, 95% confidence interval [CI] 1.27 to 1.51; smoking: odds ratio 2.81 per standard deviation increase, 95% CI 2.09-3.79) and HUNT (BMI: 1.41, 95% CI 1.18 to 1.69; smoking: 1.93, 95% CI 1.02-3.64).\nHigher genetically proxied BMI and lifetime smoking score were also associated with increased risk of severe covid-19, although with wider confidence intervals (BMI: 1.75, 95% CI 1.20 to 2.57; smoking: 3.94, 95% CI 1.13 to 13.75).\nThere was limited evidence to support associations of genetically proxied lipid traits, systolic blood pressure or type 2 diabetes liability with risk of sepsis or severe covid-19.\nSimilar findings were generally obtained when using Mendelian randomization methods that are more robust to the inclusion of pleiotropic variants, although the precision of estimates was reduced.\nConclusions: Our findings support a causal effect of elevated BMI and smoking on risk of sepsis and severe covid-19.\nClinical and public health interventions targeting obesity and smoking are likely to reduce sepsis and covid-19 related morbidity, along with the plethora of other health-related outcomes that these traits adversely affect.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Results: Higher genetically proxied BMI and lifetime smoking score were associated with increased risk of sepsis in both UK Biobank (BMI: odds ratio 1.38 per standard deviation increase, 95% confidence interval [CI] 1.27 to 1.51; smoking: odds ratio 2.81 per standard deviation increase, 95% CI 2.09-3.79) and HUNT (BMI: 1.41, 95% CI 1.18 to 1.69; smoking: 1.93, 95% CI 1.02-3.64).\", \"Conclusions: Our findings support a causal effect of elevated BMI and smoking on risk of sepsis and severe covid-19.\"]}", "id": 649}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: cloth face mask with a filter can help prevent the spread of COVID-19\n\nAbstract:\nBackground Protecting Health Care Workers (HCWs) during routine care of suspected or confirmed COVID-19 patients is of paramount importance to halt the SARS-CoV-2 (Severe Acute Respiratory Syndrome-Coronavirus-2) pandemic.\nThe WHO, ECDC and CDC have issued conflicting guidelines on the use of respiratory filters (N95) by HCWs.\nMethods We searched PubMed, Embase and The Cochrane Library from the inception to March 21, 2020 to identify randomized controlled trials (RCTs) comparing N95 respirators versus surgical masks for prevention of COVID-19 or any other respiratory infection among HCWs.\nThe grading of recommendations, assessment, development, and evaluation (GRADE) was used to evaluate the quality of evidence.\nFindings Four RCTs involving 8736 HCWs were included.\nWe did not find any trial specifically on prevention of COVID-19.\nHowever, wearing N95 respirators can prevent 73 more (95% CI 46-91) clinical respiratory infections per 1000 HCWs compared to surgical masks (2 RCTs; 2594 patients; low quality of evidence).\nA protective effect of N95 respirators in laboratory-confirmed bacterial colonization (RR= 0.41; 95%CI 0.28-0.61) was also found.\nA trend in favour of N95 respirators was observed in preventing laboratory-confirmed respiratory viral infections, laboratory-confirmed respiratory infection, and influenza like illness.\nInterpretation We found no direct high quality evidence on whether N95 respirators are better than surgical masks for HCWs protection from SARS-CoV-2.\nHowever, low quality evidence suggests that N95 respirators protect HCWs from clinical respiratory infections.\nThis finding should be contemplated to decide the best strategy to support the resilience of healthcare systems facing the potentially catastrophic SARS-CoV-2 pandemic.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"However, wearing N95 respirators can prevent 73 more (95% CI 46-91) clinical respiratory infections per 1000 HCWs compared to surgical masks (2 RCTs; 2594 patients; low quality of evidence).\"]}", "id": 650}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Salt-coated masks achieve low viral deactivation rate\n\nAbstract:\nAerosolized pathogens are a leading cause of respiratory infection and transmission.\nCurrently used protective measures pose potential risk of primary/secondary infection and transmission.\nHere, we report the development of a universal, reusable virus deactivation system by functionalization of the main fibrous filtration unit of surgical mask with sodium chloride salt.\nThe salt coating on the fiber surface dissolves upon exposure to virus aerosols and recrystallizes during drying, destroying the pathogens.\nWhen tested with tightly sealed sides, salt-coated filters showed remarkably higher filtration efficiency than conventional mask filtration layer, and 100% survival rate was observed in mice infected with virus penetrated through salt-coated filters.\nViruses captured on salt-coated filters exhibited rapid infectivity loss compared to gradual decrease on bare filters.\nSalt-coated filters proved highly effective in deactivating influenza viruses regardless of subtypes and following storage in harsh environmental conditions.\nOur results can be applied in obtaining a broad-spectrum, airborne pathogen prevention device in preparation for epidemic and pandemic of respiratory diseases.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Here, we report the development of a universal, reusable virus deactivation system by functionalization of the main fibrous filtration unit of surgical mask with sodium chloride salt.\", \"Salt-coated filters proved highly effective in deactivating influenza viruses regardless of subtypes and following storage in harsh environmental conditions.\"]}", "id": 651}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: As many deaths due to COVID-19 may be assigned to other causes of deaths (for example, if COVID-19 was not mentioned on the death certificate as a suspected cause of death), tracking all-cause mortality can provide information about whether an excess number of deaths is observed, even when COVID-19 mortality may be undercounted.\n\nAbstract:\nBACKGROUND: The 2019 novel coronavirus has caused the outbreak of the acute respiratory disease in Wuhan, Hubei Province of China since December 2019.\nThis study was performed to analyze the clinical characteristics of patients who succumbed to and who recovered from 2019 novel coronavirus disease (COVID-19).\nMETHODS: Clinical data were collected from two tertiary hospitals in Wuhan.\nA retrospective investigation was conducted to analyze the clinical characteristics of fatal cases of COVID-19 (death group) and we compare them with recovered patients (recovered group).\nContinuous variables were analyzed using the Mann-Whitney U test.\nCategorical variables were analyzed by &#967; test or Fisher exact test as appropriate.\nRESULTS: Our study enrolled 109 COVID-19 patients who died during hospitalization and 116 recovered patients.\nThe median age of the death group was older than the recovered group (69 [62, 74] vs. 40 [33, 57] years, Z\u00e2\u0080\u008a=\u00e2\u0080\u008a9.738, P\u00e2\u0080\u008a<\u00e2\u0080\u008a0.001).\nMore patients in the death group had underlying diseases (72.5% vs. 41.4%, &#967;\u00e2\u0080\u008a=\u00e2\u0080\u008a22.105, P\u00e2\u0080\u008a<\u00e2\u0080\u008a0.001).\nPatients in the death group had a significantly longer time of illness onset to hospitalization (10.0 [6.5, 12.0] vs. 7.0 [5.0, 10.0] days, Z\u00e2\u0080\u008a=\u00e2\u0080\u008a3.216, P\u00e2\u0080\u008a=\u00e2\u0080\u008a0.001).\nOn admission, the proportions of patients with symptoms of dyspnea (70.6% vs. 19.0%, &#967;\u00e2\u0080\u008a=\u00e2\u0080\u008a60.905, P\u00e2\u0080\u008a<\u00e2\u0080\u008a0.001) and expectoration (32.1% vs. 12.1%, &#967;\u00e2\u0080\u008a=\u00e2\u0080\u008a13.250, P\u00e2\u0080\u008a<\u00e2\u0080\u008a0.001) were significantly higher in the death group.\nThe blood oxygen saturation was significantly lower in the death group (85 [77, 91]% vs. 97 [95, 98]%, Z\u00e2\u0080\u008a=\u00e2\u0080\u008a10.625, P\u00e2\u0080\u008a<\u00e2\u0080\u008a0.001).\nThe white blood cell (WBC) in death group was significantly higher on admission (7.23 [4.87, 11.17] vs. 4.52 [3.62, 5.88] \u00d710/L, Z\u00e2\u0080\u008a=\u00e2\u0080\u008a7.618, P\u00e2\u0080\u008a<\u00e2\u0080\u008a0.001).\nPatients in the death group exhibited significantly lower lymphocyte count (0.63 [0.40, 0.79] vs. 1.00 [0.72, 1.27] \u00d710/L, Z\u00e2\u0080\u008a=\u00e2\u0080\u008a8.037, P\u00e2\u0080\u008a<\u00e2\u0080\u008a0.001) and lymphocyte percentage (7.10 [4.45, 12.73]% vs. 23.50 [15.27, 31.25]%, Z\u00e2\u0080\u008a=\u00e2\u0080\u008a10.315, P\u00e2\u0080\u008a<\u00e2\u0080\u008a0.001) on admission, and the lymphocyte percentage continued to decrease during hospitalization (7.10 [4.45, 12.73]% vs. 2.91 [1.79, 6.13]%, Z\u00e2\u0080\u008a=\u00e2\u0080\u008a5.242, P\u00e2\u0080\u008a<\u00e2\u0080\u008a0.001).\nAlanine transaminase (22.00 [15.00, 34.00] vs. 18.70 [13.00, 30.38] U/L, Z\u00e2\u0080\u008a=\u00e2\u0080\u008a2.592, P\u00e2\u0080\u008a=\u00e2\u0080\u008a0.010), aspartate transaminase (34.00 [27.00, 47.00] vs. 22.00 [17.65, 31.75] U/L, Z\u00e2\u0080\u008a=\u00e2\u0080\u008a7.308, P\u00e2\u0080\u008a<\u00e2\u0080\u008a0.001), and creatinine levels (89.00 [72.00, 133.50] vs. 65.00 [54.60, 78.75] \u00b5mol/L, Z\u00e2\u0080\u008a=\u00e2\u0080\u008a6.478, P\u00e2\u0080\u008a<\u00e2\u0080\u008a0.001) were significantly higher in the death group than those in the recovered group.\nC-reactive protein (CRP) levels were also significantly higher in the death group on admission (109.25 [35.00, 170.28] vs. 3.22 [1.04, 21.80] mg/L, Z\u00e2\u0080\u008a=\u00e2\u0080\u008a10.206, P\u00e2\u0080\u008a<\u00e2\u0080\u008a0.001) and showed no significant improvement after treatment (109.25 [35.00, 170.28] vs. 81.60 [27.23, 179.08] mg/L, Z\u00e2\u0080\u008a=\u00e2\u0080\u008a1.219, P\u00e2\u0080\u008a=\u00e2\u0080\u008a0.233).\nThe patients in the death group had more complications such as acute respiratory distress syndrome (ARDS) (89.9% vs. 8.6%, &#967;\u00e2\u0080\u008a=\u00e2\u0080\u008a148.105, P\u00e2\u0080\u008a<\u00e2\u0080\u008a0.001), acute cardiac injury (59.6% vs. 0.9%, &#967;\u00e2\u0080\u008a=\u00e2\u0080\u008a93.222, P\u00e2\u0080\u008a<\u00e2\u0080\u008a0.001), acute kidney injury (18.3% vs. 0%, &#967;\u00e2\u0080\u008a=\u00e2\u0080\u008a23.257, P\u00e2\u0080\u008a<\u00e2\u0080\u008a0.001), shock (11.9% vs. 0%, &#967;\u00e2\u0080\u008a=\u00e2\u0080\u008a14.618, P\u00e2\u0080\u008a<\u00e2\u0080\u008a0.001), and disseminated intravascular coagulation (DIC) (6.4% vs. 0%, &#967;\u00e2\u0080\u008a=\u00e2\u0080\u008a7.655, P\u00e2\u0080\u008a=\u00e2\u0080\u008a0.006).\nCONCLUSIONS: Compared to the recovered group, more patients in the death group exhibited characteristics of advanced age, pre-existing comorbidities, dyspnea, oxygen saturation decrease, increased WBC count, decreased lymphocytes, and elevated CRP levels.\nMore patients in the death group had complications such as ARDS, acute cardiac injury, acute kidney injury, shock, and DIC.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 652}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Coronavirus (COVID-19) Know how to protect yourself and others from COVID-19 and what to do if you are sick.\n\nAbstract:\nThe 2020 coronavirus pandemic is developing at different paces throughout the world.\nSome areas, like the Caribbean Basin, have yet to see the virus strike at full force.\nWhen it does, there is reasonable evidence to suggest the consequent COVID-19 outbreaks will overwhelm healthcare systems and economies.\nThis is particularly concerning in the Caribbean as pandemics can have disproportionately higher mortality impacts on lower and middle-income countries.\nPreliminary observations from our team and others suggest that temperature and climatological factors could influence the spread of this novel coronavirus, making spatiotemporal predictions of its infectiousness possible.\nThis review studies geographic and time-based distribution of known respiratory viruses in the Caribbean Basin in an attempt to foresee how the pandemic will develop in this region.\nThis review is meant to aid in planning short- and long-term interventions to manage outbreaks at the international, national, and subnational levels in the region.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 653}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Drugs widely used to treat high blood pressure appear to make COVID-19 dangerously worse.\n\nAbstract:\nCoronavirus disease 2019 (COVID-19) is a viral pandemic precipitated by the severe acute respiratory syndrome coronavirus 2.\nSince previous reports suggested that viral entry into cells may involve angiotensin converting enzyme 2, there has been growing concern that angiotensin converting enzyme inhibitor (ACEI) and angiotensin II receptor blocker (ARB) use may exacerbate the disease severity.\nIn this retrospective, single-center US study of adult patients diagnosed with COVID-19, we evaluated the association of ACEI/ARB use with hospital admission.\nSecondary outcomes included: ICU admission, mechanical ventilation, length of hospital stay, use of inotropes, and all-cause mortality.\nPropensity score matching was performed to account for potential confounders.\nAmong 590 unmatched patients diagnosed with COVID-19, 78 patients were receiving ACEI/ARB (median age 63 years and 59.7% male) and 512 patients were non-users (median age 42 years and 47.1% male).\nIn the propensity matched population, multivariate logistic regression analysis adjusting for age, gender and comorbidities demonstrated that ACEI/ARB use was not associated with hospital admission (OR 1.2, 95% CI 0.5-2.7, p = 0.652).\nCAD and CKD/ESRD remained independently associated with admission to hospital.\nAll-cause mortality, ICU stay, need for ventilation, and inotrope use was not significantly different between the 2 study groups.\nIn conclusion, among patients who were diagnosed with COVID-19, ACEI/ARB use was not associated with increased risk of hospital admission.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"In conclusion, among patients who were diagnosed with COVID-19, ACEI/ARB use was not associated with increased risk of hospital admission.\"]}", "id": 654}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Social distancing = > 80 % of the population , covid-19 may be curbed 13 weeks , < = 70 % may not be curbed .\n\nAbstract:\nIn this paper we develop an agent-based model for a fine-grained computational simulation of the ongoing COVID-19 pandemic in Australia.\nThis model is calibrated to reproduce several characteristics of COVID-19 transmission, accounting for its reproductive number, the length of incubation and generation periods, age-dependent attack rates, and the growth rate of cumulative incidence during a sustained and unmitigated local transmission.\nAn important calibration outcome is the age-dependent fraction of symptomatic cases, with this fraction for children found to be one-fifth of such fraction for adults.\nWe then apply the model to compare several intervention strategies, including restrictions on international air travel, case isolation, social distancing with varying levels of compliance, and school closures.\nSchool closures are not found to bring decisive benefits.\nWe report an important transition across the levels of social distancing compliance, in the range between 70% and 80% levels.\nThis suggests that a compliance of below 70% is unlikely to succeed for any duration of social distancing, while a compliance at the 90% level is likely to control the disease within 13-14 weeks, when coupled with effective case isolation and international travel restrictions.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"We report an important transition across the levels of social distancing compliance, in the range between 70% and 80% levels.\", \"This suggests that a compliance of below 70% is unlikely to succeed for any duration of social distancing, while a compliance at the 90% level is likely to control the disease within 13-14 weeks, when coupled with effective case isolation and international travel restrictions.\"]}", "id": 655}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Hidden immune weakness found in gravely ill COVID-19 patients.\n\nAbstract:\nCoronaviruses are a genetically highly variable family of viruses that infect vertebrates and have succeeded in infecting humans many times by overcoming the species barrier.\nThe severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which initially appeared in China at the end of 2019, exhibits a high infectivity and pathogenicity compared to other coronaviruses.\nAs the viral coat and other viral components are recognized as being foreign by the immune system, this can lead to initial symptoms, which are induced by the very efficiently working immune defense system via the respiratory epithelium.\nDuring severe courses a systemically expressed proinflammatory cytokine storm and subsequent changes in the coagulation and complement systems can occur.\nVirus-specific antibodies, the long-term expression of which is ensured by the formation of B memory cell clones, generate a specific immune response that is also detectable in blood (seroconversion).\nSpecifically effective cytotoxic CD8+ T\u00adcell populations are also formed, which recognize viral epitopes as pathogen-specific patterns in combination with MHC presentation on the cell surface of virus-infected cells and destroy these cells.\nAt the current point in time it is unclear how regular, robust and durable this immune status is constructed.\nExperiences with other coronavirus infections (SARS and Middle East respiratory syndrome, MERS) indicate that the immunity could persist for several years.\nBased on animal experiments, already acquired data on other coronavirus types and plausibility assumptions, it can be assumed that seroconverted patients have an immunity of limited duration and only a very low risk of reinfection.\nKnowledge of the molecular mechanisms of viral cycles and immunity is an important prerequisite for the development of vaccination strategies and development of effective drugs.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 656}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Remdesivir proves effective against COVID-19\n\nAbstract:\nRemdesivir is one of the most promising drugs to treat COVID-19 based on the following facts: remdesivir has a broad-spectrum antiviral mechanism of action; it demonstrated in vitro activity against SARS-CoV-2 and in vivo efficacy in animal models against the similar coronavirus MERS-CoV; its safety profile has been tested in Ebola patients and in compassionate use in COVID-19 patients.\nCurrently, remdesivir is being investigated in ten randomized controlled trials against COVID-19.\nThe dose regimen of remdesivir is an IV loading dose of 200 mg on day 1 followed by daily IV maintenance doses of 100 mg for 5-9 days.\nBased on our data analysis, however, remdesivir with IV administration alone is unlikely to achieve excellent clinical efficacy.\nThis analysis is based on the following observations: plasma exposures of remdesivir and its active metabolite are unlikely to be correlated with its clinical efficacy; remdesivir and its active metabolites are unlikely to be adequate in the lung to kill the SARS-CoV-2 virus.\nEven if remdesivir demonstrates benefits in the current randomized controlled trials, its efficacy may be limited.\nWe suggest that a combination of an IV and pulmonary delivery dose regimen should be studied immediately to realize a potentially more effective antiviral therapy against COVID-19.\nGraphical abstract.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"This analysis is based on the following observations: plasma exposures of remdesivir and its active metabolite are unlikely to be correlated with its clinical efficacy; remdesivir and its active metabolites are unlikely to be adequate in the lung to kill the SARS-CoV-2 virus.\"]}", "id": 657}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: the Covid-19 coronavirus can stay on various surfaces for a while\n\nAbstract:\nPURPOSE OF REVIEW This article reviews 'no touch' methods for disinfection of the contaminated surface environment of hospitalized patients' rooms.\nThe focus is on studies that assessed the effectiveness of ultraviolet (UV) light devices, hydrogen peroxide systems, and self-disinfecting surfaces to reduce healthcare-associated infections (HAIs).\nRECENT FINDINGS The contaminated surface environment in hospitals plays an important role in the transmission of several key nosocomial pathogens including methicillin-resistant Staphylococcus aureus, vancomycin-resistant Enterococcus spp.\n, Clostridium difficile, Acinetobacter spp., and norovirus.\nMultiple clinical trials have now demonstrated the effectiveness of UV light devices and hydrogen peroxide systems to reduce HAIs.\nA limited number of studies have suggested that 'self-disinfecting' surfaces may also decrease HAIs.\nSUMMARY Many studies have demonstrated that terminal cleaning and disinfection with germicides is often inadequate and leaves environmental surfaces contaminated with important nosocomial pathogens. 'No touch' methods of room decontamination (i.e., UV devices and hydrogen peroxide systems) have been demonstrated to reduce key nosocomial pathogens on inoculated test surfaces and on environmental surfaces in actual patient rooms.\nFurther UV devices and hydrogen peroxide systems have been demonstrated to reduce HAI.\nA validated 'no touch' device or system should be used for terminal room disinfection following discharge of patients on contact precautions.\nThe use of a 'self-disinfecting' surface to reduce HAI has not been convincingly demonstrated.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 658}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Synergism of tnf-\u03b1 and ifn-\u03b3 reduces inflammatory cell death, tissue damage, and mortality in sars-cov-2 infection and cytokine shock syndromes\n\nAbstract:\nThe COVID-19 pandemic has caused significant morbidity and mortality.\nCurrently, there is a critical shortage of proven treatment options and an urgent need to understand the pathogenesis of multi-organ failure and lung damage.\nCytokine storm is associated with severe inflammation and organ damage during COVID-19.\nHowever, a detailed molecular pathway defining this cytokine storm is lacking, and gaining mechanistic understanding of how SARS-CoV-2 elicits a hyperactive inflammatory response is critical to develop effective therapeutics.\nOf the multiple inflammatory cytokines produced by innate immune cells during SARS-CoV-2 infection, we found that the combined production of TNF-\u03b1 and IFN-\u03b3 specifically induced inflammatory cell death, PANoptosis, characterized by gasdermin\u2013mediated pyroptosis, caspase-8\u2013mediated apoptosis, and MLKL\u2013mediated necroptosis.\nDeletion of pyroptosis, apoptosis, or necroptosis mediators individually was not sufficient to protect against cell death.\nHowever, cells deficient in both RIPK3 and caspase-8 or RIPK3 and FADD were resistant to this cell death.\nMechanistically, the STAT1/IRF1 axis activated by TNF-\u03b1 and IFN-\u03b3 co-treatment induced iNOS for the production of nitric oxide.\nPharmacological and genetic deletion of this pathway inhibited pyroptosis, apoptosis, and necroptosis in macrophages.\nMoreover, inhibition of PANoptosis protected mice from TNF-\u03b1 and IFN-\u03b3\u2013induced lethal cytokine shock that mirrors the pathological symptoms of COVID-19.\nIn vivo neutralization of both TNF-\u03b1 and IFN-\u03b3 in multiple disease models associated with cytokine storm showed that this treatment provided substantial protection against not only SARS-CoV-2 infection, but also sepsis, hemophagocytic lymphohistiocytosis, and cytokine shock models, demonstrating the broad physiological relevance of this mechanism.\nCollectively, our findings reveal that blocking the COVID-19 cytokine-mediated inflammatory cell death signaling pathway identified in this study may benefit patients with COVID-19 or other cytokine storm-driven syndromes by limiting inflammation and tissue damage.\nThe findings also provide a molecular and mechanistic description for the term cytokine storm.\nAdditionally, these results open new avenues for the treatment of other infectious and autoinflammatory diseases and cancers where TNF-\u03b1 and IFN-\u03b3 synergism play key pathological roles.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"In vivo neutralization of both TNF-\\u03b1 and IFN-\\u03b3 in multiple disease models associated with cytokine storm showed that this treatment provided substantial protection against not only SARS-CoV-2 infection, but also sepsis, hemophagocytic lymphohistiocytosis, and cytokine shock models, demonstrating the broad physiological relevance of this mechanism.\"]}", "id": 659}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Dogs can test positive for the virus.\n\nAbstract:\nAbstract Coronavirus disease-19 (COVID-19) caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is now a pandemic threat.\nThis virus is supposed to be spread by human to human transmission.\nCellular angiotensin converting enzyme 2 (ACE2) is the receptor of SARS-CoV-2 which is identical or similar in different species of animals such as pigs, ferrets, cats, orangutans, monkeys, and humans.\nMoreover, a recent study predicted that dog might be secondary host during the evolution of SARS-CoV-2 from bat to human.\nTherefore, there is a possibility of spreading SARS-CoV-2 through domestic pets.\nThere are now many reports of SARS-CoV-2 positive cases in dogs, cats, tigers, lion, and minks.\nExperimental data showed ferrets and cats are highly susceptible to SARS-CoV-2 as infected by virus inoculation and can transmit the virus directly or indirectly by droplets or airborne route.\nBased on these natural infection reports and experimental data, whether the pets are responsible for SARS-CoV-2 spread to human; needs to be deeply investigated.\nHumans showing clinical symptoms of respiratory infections have been undergoing for COVID-19 diagnostic test but many infected people and few pets confirmed with SARS-CoV-2 remained asymptomatic.\nIn this review, we summarize the natural cases of SARS-CoV-2 in animals with the latest researches conducted in this field.\nThis review will be helpful to think insights of SARS-CoV-2 transmissions, spread, and demand for sero-prevalence studies especially in companion animals.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Cellular angiotensin converting enzyme 2 (ACE2) is the receptor of SARS-CoV-2 which is identical or similar in different species of animals such as pigs, ferrets, cats, orangutans, monkeys, and humans.\", \"Moreover, a recent study predicted that dog might be secondary host during the evolution of SARS-CoV-2 from bat to human.\", \"Therefore, there is a possibility of spreading SARS-CoV-2 through domestic pets.\", \"Experimental data showed ferrets and cats are highly susceptible to SARS-CoV-2 as infected by virus inoculation and can transmit the virus directly or indirectly by droplets or airborne route.\", \"Humans showing clinical symptoms of respiratory infections have been undergoing for COVID-19 diagnostic test but many infected people and few pets confirmed with SARS-CoV-2 remained asymptomatic.\"]}", "id": 660}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: In such a scenario, differentiating whether the cause of death is specifically due to COVID-19 or the result of treatment limitations can be difficult.\n\nAbstract:\nBACKGROUND: The 2019 novel coronavirus has caused the outbreak of the acute respiratory disease in Wuhan, Hubei Province of China since December 2019.\nThis study was performed to analyze the clinical characteristics of patients who succumbed to and who recovered from 2019 novel coronavirus disease (COVID-19).\nMETHODS: Clinical data were collected from two tertiary hospitals in Wuhan.\nA retrospective investigation was conducted to analyze the clinical characteristics of fatal cases of COVID-19 (death group) and we compare them with recovered patients (recovered group).\nContinuous variables were analyzed using the Mann-Whitney U test.\nCategorical variables were analyzed by &#967; test or Fisher exact test as appropriate.\nRESULTS: Our study enrolled 109 COVID-19 patients who died during hospitalization and 116 recovered patients.\nThe median age of the death group was older than the recovered group (69 [62, 74] vs. 40 [33, 57] years, Z\u00e2\u0080\u008a=\u00e2\u0080\u008a9.738, P\u00e2\u0080\u008a<\u00e2\u0080\u008a0.001).\nMore patients in the death group had underlying diseases (72.5% vs. 41.4%, &#967;\u00e2\u0080\u008a=\u00e2\u0080\u008a22.105, P\u00e2\u0080\u008a<\u00e2\u0080\u008a0.001).\nPatients in the death group had a significantly longer time of illness onset to hospitalization (10.0 [6.5, 12.0] vs. 7.0 [5.0, 10.0] days, Z\u00e2\u0080\u008a=\u00e2\u0080\u008a3.216, P\u00e2\u0080\u008a=\u00e2\u0080\u008a0.001).\nOn admission, the proportions of patients with symptoms of dyspnea (70.6% vs. 19.0%, &#967;\u00e2\u0080\u008a=\u00e2\u0080\u008a60.905, P\u00e2\u0080\u008a<\u00e2\u0080\u008a0.001) and expectoration (32.1% vs. 12.1%, &#967;\u00e2\u0080\u008a=\u00e2\u0080\u008a13.250, P\u00e2\u0080\u008a<\u00e2\u0080\u008a0.001) were significantly higher in the death group.\nThe blood oxygen saturation was significantly lower in the death group (85 [77, 91]% vs. 97 [95, 98]%, Z\u00e2\u0080\u008a=\u00e2\u0080\u008a10.625, P\u00e2\u0080\u008a<\u00e2\u0080\u008a0.001).\nThe white blood cell (WBC) in death group was significantly higher on admission (7.23 [4.87, 11.17] vs. 4.52 [3.62, 5.88] \u00d710/L, Z\u00e2\u0080\u008a=\u00e2\u0080\u008a7.618, P\u00e2\u0080\u008a<\u00e2\u0080\u008a0.001).\nPatients in the death group exhibited significantly lower lymphocyte count (0.63 [0.40, 0.79] vs. 1.00 [0.72, 1.27] \u00d710/L, Z\u00e2\u0080\u008a=\u00e2\u0080\u008a8.037, P\u00e2\u0080\u008a<\u00e2\u0080\u008a0.001) and lymphocyte percentage (7.10 [4.45, 12.73]% vs. 23.50 [15.27, 31.25]%, Z\u00e2\u0080\u008a=\u00e2\u0080\u008a10.315, P\u00e2\u0080\u008a<\u00e2\u0080\u008a0.001) on admission, and the lymphocyte percentage continued to decrease during hospitalization (7.10 [4.45, 12.73]% vs. 2.91 [1.79, 6.13]%, Z\u00e2\u0080\u008a=\u00e2\u0080\u008a5.242, P\u00e2\u0080\u008a<\u00e2\u0080\u008a0.001).\nAlanine transaminase (22.00 [15.00, 34.00] vs. 18.70 [13.00, 30.38] U/L, Z\u00e2\u0080\u008a=\u00e2\u0080\u008a2.592, P\u00e2\u0080\u008a=\u00e2\u0080\u008a0.010), aspartate transaminase (34.00 [27.00, 47.00] vs. 22.00 [17.65, 31.75] U/L, Z\u00e2\u0080\u008a=\u00e2\u0080\u008a7.308, P\u00e2\u0080\u008a<\u00e2\u0080\u008a0.001), and creatinine levels (89.00 [72.00, 133.50] vs. 65.00 [54.60, 78.75] \u00b5mol/L, Z\u00e2\u0080\u008a=\u00e2\u0080\u008a6.478, P\u00e2\u0080\u008a<\u00e2\u0080\u008a0.001) were significantly higher in the death group than those in the recovered group.\nC-reactive protein (CRP) levels were also significantly higher in the death group on admission (109.25 [35.00, 170.28] vs. 3.22 [1.04, 21.80] mg/L, Z\u00e2\u0080\u008a=\u00e2\u0080\u008a10.206, P\u00e2\u0080\u008a<\u00e2\u0080\u008a0.001) and showed no significant improvement after treatment (109.25 [35.00, 170.28] vs. 81.60 [27.23, 179.08] mg/L, Z\u00e2\u0080\u008a=\u00e2\u0080\u008a1.219, P\u00e2\u0080\u008a=\u00e2\u0080\u008a0.233).\nThe patients in the death group had more complications such as acute respiratory distress syndrome (ARDS) (89.9% vs. 8.6%, &#967;\u00e2\u0080\u008a=\u00e2\u0080\u008a148.105, P\u00e2\u0080\u008a<\u00e2\u0080\u008a0.001), acute cardiac injury (59.6% vs. 0.9%, &#967;\u00e2\u0080\u008a=\u00e2\u0080\u008a93.222, P\u00e2\u0080\u008a<\u00e2\u0080\u008a0.001), acute kidney injury (18.3% vs. 0%, &#967;\u00e2\u0080\u008a=\u00e2\u0080\u008a23.257, P\u00e2\u0080\u008a<\u00e2\u0080\u008a0.001), shock (11.9% vs. 0%, &#967;\u00e2\u0080\u008a=\u00e2\u0080\u008a14.618, P\u00e2\u0080\u008a<\u00e2\u0080\u008a0.001), and disseminated intravascular coagulation (DIC) (6.4% vs. 0%, &#967;\u00e2\u0080\u008a=\u00e2\u0080\u008a7.655, P\u00e2\u0080\u008a=\u00e2\u0080\u008a0.006).\nCONCLUSIONS: Compared to the recovered group, more patients in the death group exhibited characteristics of advanced age, pre-existing comorbidities, dyspnea, oxygen saturation decrease, increased WBC count, decreased lymphocytes, and elevated CRP levels.\nMore patients in the death group had complications such as ARDS, acute cardiac injury, acute kidney injury, shock, and DIC.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 661}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Susceptible supply supports the role of climate in the early sars-cov-2 pandemic\n\nAbstract:\nPreliminary evidence suggests that climate may modulate the transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).\nYet it remains unclear whether seasonal and geographic variations in climate can substantially alter the pandemic trajectory, given that high susceptibility is a core driver.\nHere, we use a climate-dependent epidemic model to simulate the SARS-CoV-2 pandemic by probing different scenarios based on known coronavirus biology.\nWe find that although variations in weather may be important for endemic infections, during the pandemic stage of an emerging pathogen, the climate drives only modest changes to pandemic size.\nA preliminary analysis of nonpharmaceutical control measures indicates that they may moderate the pandemic-climate interaction through susceptible depletion.\nOur findings suggest that without effective control measures, strong outbreaks are likely in more humid climates and summer weather will not substantially limit pandemic growth.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"A preliminary analysis of nonpharmaceutical control measures indicates that they may moderate the pandemic-climate interaction through susceptible depletion.\"]}", "id": 662}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: he virus that causes coronavirus disease 2019 (COVID-19) is stable for several hours to days in aerosols and on surfaces\n\nAbstract:\nThe aim in this study was to assess the effectiveness of a quaternary ammonium chloride (QAC) surfactant in reducing surface staphylococcal contamination in a routinely operating medical ward occupied by patients who had tested positive for methicillin-resistant Staphylococcus aureus (MRSA).\nThe QAC being tested is an antibacterial film that is sprayed onto a surface and can remain active for up to 8 h. A field experimental study was designed with the QAC plus daily hypochlorite cleaning as the experimental group and hypochlorite cleaning alone as the control group.\nThe method of swabbing on moistened surfaces was used for sampling.\nIt was found that 83% and 77% of the bedside surfaces of MRSA-positive and MRSA-negative patients respectively were contaminated with staphylococci at 08:00 hours, and that the staphylococcal concentrations increased by 80% at 1200 h over a 4-hour period with routine ward and clinical activities.\nIrrespective of the MRSA status of the patients, high-touch surfaces around the bed-units within the studied medical ward were heavily contaminated (ranged 1 to 276 cfu/cm(2) amongst the sites with positive culture) with staphylococcal bacteria including MRSA, despite the implementation of daily hypochlorite wiping.\nHowever, the contamination rate dropped significantly from 78% to 11% after the application of the QAC polymer.\nIn the experimental group, the mean staphylococcal concentration of bedside surfaces was significantly (p < 0.0001) reduced from 4.4 \u00b1 8.7 cfu/cm(2) at 08:00 hours to 0.07 \u00b1 0.26 cfu/cm(2) at 12:00 hours by the QAC polymer.\nThe results of this study support the view that, in addition to hypochlorite wiping, the tested QAC surfactant is a potential environmental decontamination strategy for preventing the transmission of clinically important pathogens in medical wards.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"It was found that 83% and 77% of the bedside surfaces of MRSA-positive and MRSA-negative patients respectively were contaminated with staphylococci at 08:00 hours, and that the staphylococcal concentrations increased by 80% at 1200 h over a 4-hour period with routine ward and clinical activities.\"]}", "id": 663}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Can COVID-19 be spread from pets to people or other pets? According to the latest information from the CDC , the risk of animals spreading COVID-19 to people is very low. However, because all animals can carry germs that can make people sick, it's always a good idea to practice healthy habits around pets and other animals.\n\nAbstract:\nLittle information on the SARS-CoV-2 virus in animals is available to date.\nWhereas no one husbandry animal case has been reported to date, which would have significant implications in food safety, companion animals play a role in COVID-19 epidemiology that opens up new questions.\nThere is evidence that SARS-CoV-2 can infect felines, dogs and minks, and there is evidence of human-to-animal infection.\nLikewise, the S protein nucleotide sequence of the SARS-CoV-2 virus isolated in domestic animals and humans is identical, and the replication of the SARS-CoV-2 in cats is efficient.\nBesides, the epidemiological evidence for this current pandemic indicates that the spillover to humans was associated with close contact between man and exotic animals, very probably in Chinese wet markets, thus there is a growing general consensus that the exotic animal markets, should be strictly regulated.\nThe examination of these findings and the particular role of animals in COVID-19 should be carefully analyzed in order to establish preparation and containment measures.\nAnimal management and epidemiological surveillance must be also considered for COVID-19 control, and it can open up new questions regarding COVID-19 epidemiology and the role that animals play in it.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"There is evidence that SARS-CoV-2 can infect felines, dogs and minks, and there is evidence of human-to-animal infection.\", \"Likewise, the S protein nucleotide sequence of the SARS-CoV-2 virus isolated in domestic animals and humans is identical, and the replication of the SARS-CoV-2 in cats is efficient.\"]}", "id": 664}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Hidden immune weakness found in gravely ill COVID-19 patients.\n\nAbstract:\nThe novel coronavirus Covid-19 follows transmission route and clinical presentation of all community-acquired coronaviruses.\nInstead, the rate of transmission is significative higher, with a faster spread of the virus responsible of the worldwide outbreak and a significative higher mortality rate due to the development of a severe lung injury.\nMost noteworthy is the distribution of death rate among age groups.\nChildren and younger people are almost protected from severe clinical presentation.\nPossible explanation of this phenomenon could be the ability of past vaccinations (especially tetanic, diphtheria toxoids and inactivated bacteria as pertussis) to stimulate immune system and to generate a scattered immunity against non-self antigens in transit, as coronaviruses and other community-circulating viruses and make immune system readier to develop specific immunity against Covid-19.\nThe first support to this hypothesis is the distribution of mortality rate during historical pandemics (\"Spanish flu\" 1918, \"Asian flu\" 1956 and \"the Hong Kong flu\" 1968) among age groups before and after the introduction of vaccines.\nThe immunological support to the hypothesis derives from recent studies about immunotherapy for malignancies, which propose the use of oncolytic vaccines combined with toxoids in order to exploit CD4 + memory T cell recall in supporting the ongoing anti-tumour response.\nAccording to this hypothesis vaccine formulations (tetanus, diphtheria, Bordetella pertussis) could be re-administrate after the first contact with Covid-19, better before the development of respiratory severe illness and of course before full-blown ARDS (Acute Respiratory Distress Syndrome).\nThe CD4 + memory exploiting could help immune system to recall immunity of already know antigens against coronaviruses, avoiding or limiting \"lung crash\" until virus specific immunity develops and making it faster and prolonged.\nFinally, this administration could be helpful not only in already infected patients, but also before infection.\nIn fact, people could have an immune system more ready when the contact with the Covid-19 will occur.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 665}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: with autoimmune conditions such as lupus, a person experiences \"dysregulations of the immune system,\" meaning the immune system itself is compromised or malfunctioning\n\nAbstract:\nThe WHO has declared SARS-CoV-2 outbreak a public health emergency of international concern.\nHowever, to date, there was hardly any study in characterizing the immune responses, especially adaptive immune responses to SARS-CoV-2 infection.\nIn this study, we collected blood from COVID-19 patients who have recently become virus-free and therefore were discharged, and analyzed their SARS-CoV-2-specific antibody and T cell responses.\nWe observed SARS-CoV-2-specific humoral and cellular immunity in the patients.\nBoth were detected in newly discharged patients, suggesting both participate in immune-mediated protection to viral infection.\nHowever, follow-up patients (2 weeks post discharge) exhibited high titers of IgG antibodies, but with low levels of virus-specific T cells, suggesting that they may enter a quiescent state.\nOur work has thus provided a basis for further analysis of protective immunity to SARS-CoV-2, and understanding the pathogenesis of COVID-19, especially in the severe cases.\nIt has also implications in designing an effective vaccine to protect and treat SARS-CoV-2 infection.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 666}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Vitamin B could help prevent the 'worst outcomes' in covid-19 cases\n\nAbstract:\nOBJECTIVE Coronavirus disease 2019 (COVID-19) is a fatal and fast-spreading viral infection.\nTo date, the number of COVID-19 patients worldwide has crossed over six million with over three hundred and seventy thousand deaths (according to the data from World Health Organization; updated on 2 June 2020).\nAlthough COVID-19 can be rapidly diagnosed, efficient clinical treatment of COVID-19 remains unavailable, resulting in high fatality.\nSome clinical trials have identified vitamin C (VC) as a potent compound pneumonia management.\nIn addition, glycyrrhizic acid (GA) is clinically as an anti-inflammatory medicine against pneumonia-induced inflammatory stress.\nWe hypothesized that the combination of VC and GA is a potential option for treating COVID-19.\nMETHODS The aim of this study was to determine pharmacological targets and molecular mechanisms of VC + GA treatment for COVID-19, using bioinformational network pharmacology.\nRESULTS We uncovered optimal targets, biological processes and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways of VC + GA against COVID-19.\nOur findings suggested that combinatorial VC and GA treatment for COVID-19 was associated with elevation of immunity and suppression of inflammatory stress, including activation of the T cell receptor signaling pathway, regulation of Fc gamma R-mediated phagocytosis, ErbB signaling pathway and vascular endothelial growth factor signaling pathway.\nWe also identified 17 core targets of VC + GA, which suggest as antimicrobial function.\nCONCLUSIONS For the first time, our study uncovered the pharmacological mechanism underlying combined VC and GA treatment for COVID-19.\nThese results should benefit efforts to address the most pressing problem currently facing the world.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Although COVID-19 can be rapidly diagnosed, efficient clinical treatment of COVID-19 remains unavailable, resulting in high fatality.\"]}", "id": 667}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Thrombotic microvascular injury is primarily mediated by thrombotic microangiopathy despite systemic complement activation in covid-19 patients\n\nAbstract:\nHypoxemia and coagulopathy are common in severe symptomatic patients of coronavirus disease 2019 (COVID-19).\nHistological evidence shows implication of complement activation and lung injury.\nWe research sign of complement activation and presence of thrombotic microangiopathy in 8 severe patients.\nSix of them presented moderate elevation of final pathway of complement / sC5b-9 (median value : 350 ng/mL [IQR : 300,5-514,95 ng/mL]).\nTwo patients have been autopsied and presence of thrombotic microvascular injury have been found.\nInterestingly, none the 8 patients had signs of mechanical hemolytic anemia (median value of hemoglobin : 10,5 gr/dL[IQR : 8,1-1,9], median value of haptoglobuline 4,49 [IQR 3,55-4,66], none of the patients has schistocyte) and thrombocytopenia (median value: 348000/mL [IQR : 266 000-401 000).\nFinally, all 8 patients had elevated d-dimer (median value : 2226 microgr/l [IQR : 1493-2362]) and soluble fibrin monomer complex (median value : 8.5 mg/mL, IQR[ <6-10.6]).\nIn summary, this study show moderate activation of complement and coagulation with presence of thrombotic microvascular injury in patients with severe COVID-19 without evidence of systemic thrombotic microangiopathy.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"In summary, this study show moderate activation of complement and coagulation with presence of thrombotic microvascular injury in patients with severe COVID-19 without evidence of systemic thrombotic microangiopathy.\"]}", "id": 668}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Surgical Masks Stop Transmission Of COVID-19 From Symptomatic People\n\nAbstract:\nBackground As the coronavirus (COVID-19) pandemic spreads globally, hospitals are rushing to adapt their facilities which may not have been designed to deal with infections adequately.\nWe present the management of a patient with suspected COVID-19 pneumonia.\nCase A 66-years-old man presented to the hospital and his recent travel history, infective symptoms and CXR made him a possible COVID-19 suspect.\nEmergency surgery was decided considering the septic condition.\nThe patient was transported to operating theatre with supplemental oxygen over a face mask and plastic covering over the trolley.\nRapid sequence intubation was performed by an experienced anesthetist using a videolaryngoscope.\nAfter surgery, the patient remained intubated to avoid re-intubation due to initial presentation of respiratory distress.\nDroplet, contact and airborne infection precautions were instituted.\nConclusions Our objective was to facilitate surgical management of patients with known or suspected COVID-19 while minimising risk of nosocomial transmission to healthcare workers and other patients.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"The patient was transported to operating theatre with supplemental oxygen over a face mask and plastic covering over the trolley.\"]}", "id": 669}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: College students, many of whom are already stressed, reported an increase in depression and anxiety during the initial outbreak.\n\nAbstract:\nAs a result of the emergence of coronavirus disease 2019 (COVID-19) outbreak caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in the Chinese city of Wuhan, a situation of socio-economic crisis and profound psychological distress rapidly occurred worldwide.\nVarious psychological problems and important consequences in terms of mental health including stress, anxiety, depression, frustration, uncertainty during COVID-19 outbreak emerged progressively.\nThis work aimed to comprehensively review the current literature about the impact of COVID-19 infection on the mental health in the general population.\nThe psychological impact of quarantine related to COVID-19 infection has been additionally documented together with the most relevant psychological reactions in the general population related to COVID-19 outbreak.\nThe role of risk and protective factors against the potential to develop psychiatric disorders in vulnerable individuals has been addressed as well.\nThe main implications of the present findings have been discussed.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"As a result of the emergence of coronavirus disease 2019 (COVID-19) outbreak caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in the Chinese city of Wuhan, a situation of socio-economic crisis and profound psychological distress rapidly occurred worldwide.\"]}", "id": 670}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Imbalanced antiviral response to sars-cov-2 drives development of covid-19\n\nAbstract:\nViral pandemics, such as the one caused by SARS-CoV-2, pose an imminent threat to humanity.\nBecause of its recent emergence, there is a paucity of information regarding viral behavior and host response following SARS-CoV-2 infection.\nHere we offer an in-depth analysis of the transcriptional response to SARS-CoV-2 compared with other respiratory viruses.\nCell and animal models of SARS-CoV-2 infection, in addition to transcriptional and serum profiling of COVID-19 patients, consistently revealed a unique and inappropriate inflammatory response.\nThis response is defined by low levels of type I and III interferons juxtaposed to elevated chemokines and high expression of IL-6.\nWe propose that reduced innate antiviral defenses coupled with exuberant inflammatory cytokine production are the defining and driving features of COVID-19.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Cell and animal models of SARS-CoV-2 infection, in addition to transcriptional and serum profiling of COVID-19 patients, consistently revealed a unique and inappropriate inflammatory response.\", \"We propose that reduced innate antiviral defenses coupled with exuberant inflammatory cytokine production are the defining and driving features of COVID-19.\"]}", "id": 671}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: both dogs and cats can be infected by the virus that causes Covid-19 in humans, but none of the ten animals observed in the study showed clinical symptoms like coughing\n\nAbstract:\nAbstract Coronavirus disease-19 (COVID-19) caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is now a pandemic threat.\nThis virus is supposed to be spread by human to human transmission.\nCellular angiotensin converting enzyme 2 (ACE2) is the receptor of SARS-CoV-2 which is identical or similar in different species of animals such as pigs, ferrets, cats, orangutans, monkeys, and humans.\nMoreover, a recent study predicted that dog might be secondary host during the evolution of SARS-CoV-2 from bat to human.\nTherefore, there is a possibility of spreading SARS-CoV-2 through domestic pets.\nThere are now many reports of SARS-CoV-2 positive cases in dogs, cats, tigers, lion, and minks.\nExperimental data showed ferrets and cats are highly susceptible to SARS-CoV-2 as infected by virus inoculation and can transmit the virus directly or indirectly by droplets or airborne route.\nBased on these natural infection reports and experimental data, whether the pets are responsible for SARS-CoV-2 spread to human; needs to be deeply investigated.\nHumans showing clinical symptoms of respiratory infections have been undergoing for COVID-19 diagnostic test but many infected people and few pets confirmed with SARS-CoV-2 remained asymptomatic.\nIn this review, we summarize the natural cases of SARS-CoV-2 in animals with the latest researches conducted in this field.\nThis review will be helpful to think insights of SARS-CoV-2 transmissions, spread, and demand for sero-prevalence studies especially in companion animals.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Cellular angiotensin converting enzyme 2 (ACE2) is the receptor of SARS-CoV-2 which is identical or similar in different species of animals such as pigs, ferrets, cats, orangutans, monkeys, and humans.\", \"Moreover, a recent study predicted that dog might be secondary host during the evolution of SARS-CoV-2 from bat to human.\", \"Therefore, there is a possibility of spreading SARS-CoV-2 through domestic pets.\", \"Experimental data showed ferrets and cats are highly susceptible to SARS-CoV-2 as infected by virus inoculation and can transmit the virus directly or indirectly by droplets or airborne route.\"]}", "id": 672}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Unexpected Cause of Death in Younger COVID-19 Patients is Related to Blood Clotting\n\nAbstract:\nOBJECTIVE: The COVID-19 pandemic has caused much morbidity and mortality to patients but also health care providers.\nWe tabulated the cases of physician deaths from COVID-19 associated with front-line work in hopes of mitigating future events.\nMETHOD: On April 5, 2020, Google internet search was performed using the keywords doctor, physician, death, COVID, COVID-19, and coronavirus in English and Farsi, and in Chinese using the Baidu search engine.\nRESULTS: We found 198 physician deaths from COVID-19, but complete details were missing for 49 individuals.\nThe average age of the physicians that died was 63.4 years (range 28 to 90 years) and the median age was 66 years of age.\nNinety percent of the deceased physicians were male (175/194).\nGeneral practitioners and emergency room doctors (78/192), respirologists (5/192), internal medicine specialists (11/192) and anesthesiologists (6/192) comprised 52% of those dying.\nTwo percent of the deceased were epidemiologists (4/192), 2% were infectious disease specialists (4/192), 5% were dentists (9/192), 4% were ENT (8/192), and 4% were ophthalmologists (7/192).\nThe countries with the most reported physician deaths were Italy (79/198), Iran (43/198), China (16/198), Philippines (14/198), United States (9/192) and Indonesia (7/192).\nCONCLUSION: Physicians from all specialties may die from COVID, and these deaths will likely increase as the pandemic progresses.\nLack of personal protective equipment was cited as a common cause of death.\nConsideration should be made to exclude older physicians from front-line work.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Lack of personal protective equipment was cited as a common cause of death.\"]}", "id": 673}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Coronavirus disease (COVID-19) is a novel (new) coronavirus, one that has not been previously identified in humans, and is responsible for an outbreak of respiratory illness that became a global pandemic in 2020. \n\nAbstract:\nSince Dec 2019, a cluster of pneumonia outbreak in Wuhan, Hubei province, China, and soon spread to all province of China.\nThe pathogen was proved to be a novel betacoronavirus called 2019 novel coronavirus (officially named by the World Health Organization as COVID-19).\nThe typical clinical manifestations were fever, cough, dyspnea, and myalgia or fatigue.\nLess common symptoms included headache, diarrhea, nausea and vomiting.\nHowever diarrhea as the first symptom is rarely reported.\nHere we reported a case of 2019 novel coronavirus-infected patient (NCIP) with diarrhea as the initial symptom.\nImage of CT scan and laboratory examination and careful collected as well as detection of viral RNA in pharynx.\nThe case demonstrate that gastrointestinal symptoms ware not rare in NCIP, and diarrhea could be the initial symptom.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"The pathogen was proved to be a novel betacoronavirus called 2019 novel coronavirus (officially named by the World Health Organization as COVID-19).\", \"The typical clinical manifestations were fever, cough, dyspnea, and myalgia or fatigue.\", \"Less common symptoms included headache, diarrhea, nausea and vomiting.\"]}", "id": 674}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: While there are a lot of unknowns with the novel virus, there's no evidence of COVID-19 transmitting through food or packaging, according to the Food and Drug Administration.\n\nAbstract:\nAgriculture and the food sector are critical to food and nutrition security because they not only produce food but also contribute to economic empowerment by employing a large share of female and male workers, especially in developing countries.\nFood systems at all levels\u2015globally, domestically, locally, and in the home\u2015 are expected to be highly affected by the COVID-19 crisis.\nWomen and men work as food producers, processors, and traders and will likely be impacted differently.\nShocks or crises can exacerbate or reduce gender gaps, and so can policy responses to mitigate the impact of these crises or shocks.\nWe offer some perspectives and available country examples on how the COVID-19 crisis and responses to the crisis could be a setback or offer opportunities for gender equality in the food system.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 675}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: A simple in-cell elisa assay allows rapid and automated quantification of sars-cov-2 to analyze neutralizing antibodies and antiviral compounds\n\nAbstract:\nThe coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is currently the most pressing medical and socioeconomic challenge.\nConstituting important correlates of protection, the determination of virus-neutralizing antibodies (NAbs) is indispensable for convalescent plasma selection, vaccine candidate evaluation, and immunity certificates.\nIn contrast to standard serological ELISAs, plaque reduction neutralization tests (PRNTs) are laborious, time-consuming, expensive, and restricted to specialized laboratories.\nTo replace microscopic counting-based SARS-CoV-2 PRNTs by a novel assay exempt from genetically modified viruses, which are inapplicable in most diagnostics departments, we established a simple, rapid, and automated SARS-CoV-2 neutralization assay employing an in-cell ELISA (icELISA) approach.\nAfter optimization of various parameters such as virus-specific antibodies, cell lines, virus doses, and duration of infection, SARS-CoV-2-infected cells became amenable as direct antigen source for quantitative icELISA.\nAntiviral agents such as human sera containing NAbs or antiviral interferons dose dependently reduced the SARS-CoV-2-specific signal.\nApplying increased infectious doses, the icELISA-based neutralization test (icNT) was superior to PRNT in discriminating convalescent sera with high from those with intermediate neutralizing capacities.\nIn addition, the icNT was found to be specific, discriminating between SARS-CoV-2-specific NAbs and those raised against other coronaviruses.\nAltogether, the SARS-CoV-2 icELISA test allows rapid (<48 h in total, read-out in seconds) and automated quantification of virus infection in cell culture to evaluate the efficacy of NAbs and antiviral drugs using reagents and equipment present in most routine diagnostics departments.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"To replace microscopic counting-based SARS-CoV-2 PRNTs by a novel assay exempt from genetically modified viruses, which are inapplicable in most diagnostics departments, we established a simple, rapid, and automated SARS-CoV-2 neutralization assay employing an in-cell ELISA (icELISA) approach.\", \"Altogether, the SARS-CoV-2 icELISA test allows rapid (<48 h in total, read-out in seconds) and automated quantification of virus infection in cell culture to evaluate the efficacy of NAbs and antiviral drugs using reagents and equipment present in most routine diagnostics departments.\"]}", "id": 676}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Enjoy ginger, but it's not a 'cure' for COVID-19\n\nAbstract:\nThe severity of coronavirus disease 2019 (COVID-19) infection is quite variable and the manifestations varies from asymptomatic disease to severe acute respiratory infection.\nFever, dry cough, dyspnea, myalgia, fatigue, loss of appetite, olfactory and gustatory dysfunctions are the most prevalent general symptoms.\nDecreased immune system cells such as suppressed regulatory T cells, cytotoxic and helper T cells, natural killer cells, monocytes/macrophages and increased proinflammatory cytokines are the characteristic features.\nCompounds derived from Allium sativum (garlic) have the potential to decrease the expression of proinflammatory cytokines and to reverse the immunological abnormalities to more acceptable levels.\nAllium sativum is suggested as a beneficial preventive measure before being infected with SARS-CoV-2 virus.\nAllium sativum is a functional food well-known for its immunomodulatory, antimicrobial, antiinflammatory, antimutagenic, antitumor properties.\nIts antiviral efficiency was also demonstrated.\nSome constituents of this plant were found to be active against protozoan parasites.\nWithin this context, it appears to reverse most immune system dysfunctions observed in patients with COVID-19 infection.\nThe relations among immune system parameters, leptin, leptin receptor, adenosin mono phosphate-activated protein kinase, peroxisome proliferator activated receptor-gamma have also been interpreted.\nLeptin's role in boosting proinflammatory cytokines and in appetite decreasing suggest the possible beneficial effect of decreasing the concentration of this proinflammatory adipose tissue hormone in relieving some symptoms detected during COVID-19 infection.\nIn conclusion, Allium sativum may be an acceptable preventive measure against COVID-19 infection to boost immune system cells and to repress the production and secretion of proinflammatory cytokines as well as an adipose tissue derived hormone leptin having the proinflammatory nature.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 677}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Serological surveys in reunion island of the first hospitalized patients revealed that long-lived immunoglobulin g antibodies specific against sars-cov2 virus are rapidly transmitted in severe cases\n\nAbstract:\nBoth cellular and humoral immunities are critically important to control COVID19 infection but little is known about the kinetics of those responses and, in particular, in patients who will go on to develop a severe form of the disease over several weeks.\nWe herein report the first set of data of our prospective cohort study of 90 hospitalized cases.\nSerological surveys were thoroughly performed over 2 month period by assessing IgG and IgM responses by immunofluorescence, immunoblot, Western blot and conventional ELISA using clinical RUN isolates of SARS-CoV-2 immobilized on 96 well plates.\nWhile the IgM and, unexpectedly, the IgG responses were readily detected early during the course of the disease (5-7 days post-first symptoms), our results (n=3-5 and over the full dilution set of the plasma 1/200 to 1/12800) demonstrated a significant decrease (over 2.5-fold) of IgG levels in severe (ICU) hospitalized patients (exemplified in patient 1) by WB and ELISA.\nIn contrast, mild non-ICU patients had a steady and yet robust rise in their specific IgG levels against the virus.\nInterestingly, both responses (IgM and IgG) were initially against the nucleocapsid (50kDa band on the WB) and spreading to other major viral protein S and domains (S1 and S2.\nIn conclusion, serological testing may be helpful for the diagnosis of patients with negative RT-PCR results and for the identification of asymptomatic cases.\nMoreover, medical care and protections should be maintained particularly for recovered patients (severe cases) who may remain at risk of relapsing or reinfection.\nExperiments to ascertain T cell responses but although their kinetics overtime are now highly warranted.\nAll in all, these studies will help to delineate the best routes for vaccination.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Serological surveys were thoroughly performed over 2 month period by assessing IgG and IgM responses by immunofluorescence, immunoblot, Western blot and conventional ELISA using clinical RUN isolates of SARS-CoV-2 immobilized on 96 well plates.\", \"While the IgM and, unexpectedly, the IgG responses were readily detected early during the course of the disease (5-7 days post-first symptoms), our results (n=3-5 and over the full dilution set of the plasma 1/200 to 1/12800) demonstrated a significant decrease (over 2.5-fold) of IgG levels in severe (ICU) hospitalized patients (exemplified in patient 1) by WB and ELISA.\", \"In conclusion, serological testing may be helpful for the diagnosis of patients with negative RT-PCR results and for the identification of asymptomatic cases.\"]}", "id": 678}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: COVID-19 and kids: What can happen when children get the coronavirus. A rare but sometimes deadly syndrome poses extra risk for COVID's youngest victims.\n\nAbstract:\nBACKGROUND Novel coronavirus disease (COVID-19) is spreading globally.\nLittle is known about the risk factors for the clinical outcomes of COVID-19 in children.\nMETHODS A retrospective case-control study was taken in children with severe acute respiratory syndrome coronary virus-2 infection in Wuhan Children's Hospital.\nRisk factors associated with the development of COVID-19 and progression were collected and analyzed.\nRESULTS Eight out of 260 children diagnosed with severe COVID-19 pneumonia were included in the study.\nThirty-five children with COVID-19 infection matched for age, sex and date of admission, and who classified as non-severe type, were randomly selected from the hospital admissions.\nFor cases with severe pneumonia caused by COVID-19, the most common symptoms were dyspnea (87.5%), fever (62.5%) and cough (62.5%).\nIn laboratory, white blood cells count was significantly higher in severe children than non-severe children.\nLevels of inflammation bio-makers such as hsCRP, IL-6, IL-10 and D-dimer elevated in severe children compared with non-severe children on admission.\nThe level of total bilirubin and uric acid clearly elevated in severe children compared with non-severe children on admission.\nAll of severe children displayed the lesions on chest CT, more lung segments were involved in severe children than in non-severe children, which was only risk factor associated with severe COVID-19 pneumonia in multivariable analysis.\nCONCLUSIONS More than 3 lung segments involved were associated with greater risk of development of severe COVID-19 in children.\nMoreover, the possible risk of the elevation of IL-6, high total bilirubin and D-dimer with univariable analysis could identify patients to be severe earlier.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"BACKGROUND Novel coronavirus disease (COVID-19) is spreading globally.\", \"Little is known about the risk factors for the clinical outcomes of COVID-19 in children.\", \"RESULTS Eight out of 260 children diagnosed with severe COVID-19 pneumonia were included in the study.\", \"Thirty-five children with COVID-19 infection matched for age, sex and date of admission, and who classified as non-severe type, were randomly selected from the hospital admissions.\", \"CONCLUSIONS More than 3 lung segments involved were associated with greater risk of development of severe COVID-19 in children.\", \"Moreover, the possible risk of the elevation of IL-6, high total bilirubin and D-dimer with univariable analysis could identify patients to be severe earlier.\"]}", "id": 679}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: taking medication to lower fever, such as paracetamol (tylenol) and ibuprofen (advil) worsen COVID-19\n\nAbstract:\nOBJECTIVE: It was recently suggested that Ibuprofen might increase the risk for severe and fatal COVID-19 disease and should therefore be avoided in this patient population.\nWe aimed to evaluate whether ibuprofen use in patients with COVID-19 was associated with more severe disease, compared to patients using paracetamol or no antipyretics.\nMETHODS: In a retrospective cohort study of patients with COVID-19 from Shamir Medical Center, Israel, we monitored any use of ibuprofen from a week prior to diagnosis of COVID-19 throughout the disease.\nPrimary outcomes were mortality and the need for respiratory support, including oxygen administration and mechanical ventilation.\nRESULTS: The study included 403 confirmed cases of COVID-19, with a median age of 45 years.\nOf the entire cohort, 44 patients (11%) needed respiratory support and 12 (3%) patients died.\nOne hundred and seventy-nine (44%) patients had fever, with 32% using paracetamol and 22% using ibuprofen, for symptom-relief.\nIn the Ibuprofen group, 3 (3.4%) patients died, while in the non-Ibuprofen group 9 (2.8%) patients died (P=0.95).\nNine (10.3%) patients from the Ibuprofen group needed respiratory support, as compared with 35 (11%) from the non-Ibuprofen group (P=1).\nWhen compared to exclusive paracetamol users, no differences were observed in mortality rates or the need for respiratory support among patients using ibuprofen.\nCONCLUSIONS: In this cohort of COVID-19 patients, Ibuprofen use was not associated with worse clinical outcomes, compared to paracetamol or no antipyretic.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"CONCLUSIONS: In this cohort of COVID-19 patients, Ibuprofen use was not associated with worse clinical outcomes, compared to paracetamol or no antipyretic.\"]}", "id": 680}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Significant correlation between antibody titers and neutralizing activity in sera from sars-cov-2 infected subjects\n\nAbstract:\nPlenty of serologic tests for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have been developed so far, thus documenting the importance of evaluating the relevant features of the immune response to this viral agent.\nThe performance of these assays is currently under investigation.\nAmongst them, LIAISON\u00ae SARS-CoV-2 S1/S2 IgG by DiaSorin and Elecsys Anti-SARS-CoV-2 cobas\u00ae by Roche are currently used by laboratory medicine hospital departments in Italy and many other countries.\nIn the present study, we firstly compared two serologic tests on serum samples collected at two different time points from 46 laboratory-confirmed coronavirus disease-2019 (COVID-19) subjects.\nSecondly, 85 negative serum samples collected before the SARS-CoV-2 pandemic were analyzed.\nThirdly, possible correlations between antibody levels and the resulting neutralizing activity against a clinical isolate of SARS-CoV-2 were evaluated.\nResults revealed that both tests are endowed with low sensitivity on the day of hospital admission, which increased to 97.8% and 100% for samples collected after 15 days for DiaSorin and Roche tests, respectively.\nThe specificity evaluated for the two tests ranges from 96.5% to 100%, respectively.\nImportantly, a poor direct correlation between antibody titers and neutralizing activity levels was evidenced in the present study.\nThese data further shed light on both potentials and possible limitations related to SARS-CoV-2 serology.\nIn this context, great efforts are still necessary for investigating antibody kinetics to develop novel diagnostic algorithms.\nMoreover, further investigations on the role of neutralizing antibodies and their correlate of protection will be of paramount importance for the development of effective vaccines.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Importantly, a poor direct correlation between antibody titers and neutralizing activity levels was evidenced in the present study.\"]}", "id": 681}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Peptide vaccine candidate mimics the heterogeneity of natural sars-cov-2 immunity in convalescent humans and induces broad t cell responses in mice models\n\nAbstract:\nWe developed a global peptide vaccine against SARS-CoV-2 that addresses the dual challenges of heterogeneity in the immune responses of different individuals and potential heterogeneity of the infecting virus.\nPolyPEPI-SCoV-2 is a polypeptide vaccine containing nine 30-mer peptides derived from all four major structural proteins of the SARS-CoV-2.\nVaccine peptides were selected based on their frequency as HLA class I and class II personal epitopes (PEPIs) restricted to multiple autologous HLA alleles of individuals in an in silico cohort of 433 subjects of different ethnicities.\nPolyPEPI-SCoV-2 vaccine administered with Montanide ISA 51VG adjuvant generated robust, Th1-biased CD8+ and CD4+ T cell responses against all four structural proteins of the virus, as well as binding antibodies upon subcutaneous injection into BALB/c and CD34+ transgenic mice.\nIn addition, PolyPEPI-SCoV-2-specific, polyfunctional CD8+ and CD4+ T cells were detected ex vivo in each of the 17 asymptomatic/mild COVID-19 convalescents\u2019 blood investigated, 1\u20135 months after symptom onset.\nThe PolyPEPI-SCoV-2-specific T cell repertoire used for recovery from COVID-19 was extremely diverse: donors had an average of seven different peptide-specific T cells, against the SARS-CoV-2 proteins; 87% of donors had multiple targets against at least three SARS-CoV-2 proteins and 53% against all four.\nIn addition, PEPIs determined based on the complete HLA class I genotype of the convalescent donors were validated, with 84% accuracy, to predict PEPI-specific CD8+ T cell responses measured for the individuals.\nExtrapolation of the above findings to a US bone marrow donor cohort of 16,000 HLA-genotyped individuals with 16 different ethnicities (n=1,000 each ethnic group) suggest that PolyPEPI-SCoV-2 vaccination in a general population will likely elicit broad, multi-antigenic CD8+ and CD4+ T cell responses in 98% of individuals, independent of ethnicity, including Black, Asian, and Minority Ethnic (BAME) cohorts.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Vaccine peptides were selected based on their frequency as HLA class I and class II personal epitopes (PEPIs) restricted to multiple autologous HLA alleles of individuals in an in silico cohort of 433 subjects of different ethnicities.\", \"PolyPEPI-SCoV-2 vaccine administered with Montanide ISA 51VG adjuvant generated robust, Th1-biased CD8+ and CD4+ T cell responses against all four structural proteins of the virus, as well as binding antibodies upon subcutaneous injection into BALB/c and CD34+ transgenic mice.\"]}", "id": 682}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: a popular treatment to tamp down the immune system in severely ill patients may help a few, but could harm many others. \n\nAbstract:\nThe novel coronavirus Covid-19 follows transmission route and clinical presentation of all community-acquired coronaviruses.\nInstead, the rate of transmission is significative higher, with a faster spread of the virus responsible of the worldwide outbreak and a significative higher mortality rate due to the development of a severe lung injury.\nMost noteworthy is the distribution of death rate among age groups.\nChildren and younger people are almost protected from severe clinical presentation.\nPossible explanation of this phenomenon could be the ability of past vaccinations (especially tetanic, diphtheria toxoids and inactivated bacteria as pertussis) to stimulate immune system and to generate a scattered immunity against non-self antigens in transit, as coronaviruses and other community-circulating viruses and make immune system readier to develop specific immunity against Covid-19.\nThe first support to this hypothesis is the distribution of mortality rate during historical pandemics (\"Spanish flu\" 1918, \"Asian flu\" 1956 and \"the Hong Kong flu\" 1968) among age groups before and after the introduction of vaccines.\nThe immunological support to the hypothesis derives from recent studies about immunotherapy for malignancies, which propose the use of oncolytic vaccines combined with toxoids in order to exploit CD4 + memory T cell recall in supporting the ongoing anti-tumour response.\nAccording to this hypothesis vaccine formulations (tetanus, diphtheria, Bordetella pertussis) could be re-administrate after the first contact with Covid-19, better before the development of respiratory severe illness and of course before full-blown ARDS (Acute Respiratory Distress Syndrome).\nThe CD4 + memory exploiting could help immune system to recall immunity of already know antigens against coronaviruses, avoiding or limiting \"lung crash\" until virus specific immunity develops and making it faster and prolonged.\nFinally, this administration could be helpful not only in already infected patients, but also before infection.\nIn fact, people could have an immune system more ready when the contact with the Covid-19 will occur.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 683}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: masks prevent me from spreading COVID-19\n\nAbstract:\nEvidence that face masks provide effective protection against respiratory infections in the community is scarce.\nHowever, face masks are widely used by health workers as part of droplet precautions when caring for patients with respiratory infections.\nIt would therefore be reasonable to suggest that consistent widespread use of face masks in the community could prevent further spread of the Severe Acute Respiratory Syndrome-Coronavirus 2 (SARS-CoV-2).\nIn this study we examine public face mask wearing in Uganda where a proportion wears masks to protect against acquiring, and the other to prevent from transmitting SARS-CoV-2.\nThe objective of this study was to determine what percentage of the population would have to wear face masks to reduce susceptibility to and infectivity of COVID-19 in Uganda, keeping the basic reproduction number below unity and/or flattening the curve.\nWe used an SEIAQRD model for the analysis.\nResults show that implementation of facemasks has a relatively large impact on the size of the coronavirus epidemic in Uganda.\nWe find that the critical mask adherence is 5 per 100 when 80% wear face masks.\nA cost-effective analysis shows that utilizing funds to provide 1 public mask to the population has a per capita compounded cost of USD 1.34.\nIf provision of face masks is done simultaneously with supportive care, the per capita compounded cost is USD 1.965, while for the case of only treatment and no provision of face masks costs each Ugandan USD 4.0579.\nWe conclude that since it is hard to achieve a 100% adherence to face masks, government might consider provision of face masks in conjunction with provision of care.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"It would therefore be reasonable to suggest that consistent widespread use of face masks in the community could prevent further spread of the Severe Acute Respiratory Syndrome-Coronavirus 2 (SARS-CoV-2).\", \"We find that the critical mask adherence is 5 per 100 when 80% wear face masks.\"]}", "id": 684}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: covid-19 increases risk of heart attacks and stroke\n\nAbstract:\nOBJECTIVE: The COVID-19 pandemic has caused much morbidity and mortality to patients but also health care providers.\nWe tabulated the cases of physician deaths from COVID-19 associated with front-line work in hopes of mitigating future events.\nMETHOD: On April 5, 2020, Google internet search was performed using the keywords doctor, physician, death, COVID, COVID-19, and coronavirus in English and Farsi, and in Chinese using the Baidu search engine.\nRESULTS: We found 198 physician deaths from COVID-19, but complete details were missing for 49 individuals.\nThe average age of the physicians that died was 63.4 years (range 28 to 90 years) and the median age was 66 years of age.\nNinety percent of the deceased physicians were male (175/194).\nGeneral practitioners and emergency room doctors (78/192), respirologists (5/192), internal medicine specialists (11/192) and anesthesiologists (6/192) comprised 52% of those dying.\nTwo percent of the deceased were epidemiologists (4/192), 2% were infectious disease specialists (4/192), 5% were dentists (9/192), 4% were ENT (8/192), and 4% were ophthalmologists (7/192).\nThe countries with the most reported physician deaths were Italy (79/198), Iran (43/198), China (16/198), Philippines (14/198), United States (9/192) and Indonesia (7/192).\nCONCLUSION: Physicians from all specialties may die from COVID, and these deaths will likely increase as the pandemic progresses.\nLack of personal protective equipment was cited as a common cause of death.\nConsideration should be made to exclude older physicians from front-line work.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 685}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Sars-cov-2 epidemic after social and economic reopening in two us states reveals shifts in age structure and clinical characteristics\n\nAbstract:\nIn the United States, state-level re-openings in spring 2020 presented an opportunity for the resurgence of SARS-CoV-2 transmission.\nOne important question during this time was whether human contact and mixing patterns could increase gradually without increasing viral transmission, the rationale being that new mixing patterns would likely be associated with improved distancing, masking, and hygiene practices.\nA second key question to follow during this time was whether clinical characteristics of the epidemic would improve after the initial surge of cases.\nHere, we analyze age-structured case, hospitalization, and death time series from three states \u2013 Rhode Island, Massachusetts, and Pennsylvania \u2013 that had successful reopenings in May 2020 without summer waves of infection.\nUsing a Bayesian inference framework on eleven daily data streams and flexible daily population contact parameters, we show that population-average mixing rates dropped by >50% during the lockdown period in March/April, and that the correlation between overall population mobility and transmission-capable mixing was broken in May as these states partially re-opened.\nWe estimate the reporting rates (fraction of symptomatic cases reporting to health system) at 96.3% (RI), 62.5% (MA), and 98.9% (PA).\nWe show that elderly individuals were less able to reduce contacts during the lockdown period when compared to younger individuals, leading to the outbreak being concentrated in elderly congregate settings despite the lockdown.\nAttack rate estimates through August 31 2020 are 6.2% (95% CI: 5.7% \u2013 6.8%) of the total population infected for Rhode Island, 6.7% (95% CI: 5.4% \u2013 7.6%) in Massachusetts, and 2.7% (95% CI: 2.5% \u2013 3.1%) in Pennsylvania, with some validation available through published seroprevalence studies.\nInfection fatality rates (IFR) estimates are higher in our analysis (>2%) than previously reported values, likely resulting from the epidemics in these three states affecting the most vulnerable sub-populations and the close matches between the states\u2019 reported COVID-19 deaths and excess deaths.\nData in Pennsylvania may have been underreported for both non-hospitalized and hospitalized patients, casting substantial uncertainty on estimates of attack rate and infection fatality rate.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"A second key question to follow during this time was whether clinical characteristics of the epidemic would improve after the initial surge of cases.\"]}", "id": 686}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Salt-coated masks achieve lower viral deactivation rate\n\nAbstract:\nAerosolized pathogens are a leading cause of respiratory infection and transmission.\nCurrently used protective measures pose potential risk of primary/secondary infection and transmission.\nHere, we report the development of a universal, reusable virus deactivation system by functionalization of the main fibrous filtration unit of surgical mask with sodium chloride salt.\nThe salt coating on the fiber surface dissolves upon exposure to virus aerosols and recrystallizes during drying, destroying the pathogens.\nWhen tested with tightly sealed sides, salt-coated filters showed remarkably higher filtration efficiency than conventional mask filtration layer, and 100% survival rate was observed in mice infected with virus penetrated through salt-coated filters.\nViruses captured on salt-coated filters exhibited rapid infectivity loss compared to gradual decrease on bare filters.\nSalt-coated filters proved highly effective in deactivating influenza viruses regardless of subtypes and following storage in harsh environmental conditions.\nOur results can be applied in obtaining a broad-spectrum, airborne pathogen prevention device in preparation for epidemic and pandemic of respiratory diseases.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Here, we report the development of a universal, reusable virus deactivation system by functionalization of the main fibrous filtration unit of surgical mask with sodium chloride salt.\", \"Salt-coated filters proved highly effective in deactivating influenza viruses regardless of subtypes and following storage in harsh environmental conditions.\"]}", "id": 687}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Most children with COVID-19 have mild symptoms or have no symptoms at all.\n\nAbstract:\nBACKGROUND Novel coronavirus disease (COVID-19) is spreading globally.\nLittle is known about the risk factors for the clinical outcomes of COVID-19 in children.\nMETHODS A retrospective case-control study was taken in children with severe acute respiratory syndrome coronary virus-2 infection in Wuhan Children's Hospital.\nRisk factors associated with the development of COVID-19 and progression were collected and analyzed.\nRESULTS Eight out of 260 children diagnosed with severe COVID-19 pneumonia were included in the study.\nThirty-five children with COVID-19 infection matched for age, sex and date of admission, and who classified as non-severe type, were randomly selected from the hospital admissions.\nFor cases with severe pneumonia caused by COVID-19, the most common symptoms were dyspnea (87.5%), fever (62.5%) and cough (62.5%).\nIn laboratory, white blood cells count was significantly higher in severe children than non-severe children.\nLevels of inflammation bio-makers such as hsCRP, IL-6, IL-10 and D-dimer elevated in severe children compared with non-severe children on admission.\nThe level of total bilirubin and uric acid clearly elevated in severe children compared with non-severe children on admission.\nAll of severe children displayed the lesions on chest CT, more lung segments were involved in severe children than in non-severe children, which was only risk factor associated with severe COVID-19 pneumonia in multivariable analysis.\nCONCLUSIONS More than 3 lung segments involved were associated with greater risk of development of severe COVID-19 in children.\nMoreover, the possible risk of the elevation of IL-6, high total bilirubin and D-dimer with univariable analysis could identify patients to be severe earlier.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 688}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: covid-19 isn't a risk for young people\n\nAbstract:\nBackground: The coronavirus 2019 (COVID-19) pandemic has been spread-ing globally for months, yet the infection fatality ratio of the disease is still uncertain.\nThis is partly because of inconsistencies in testing and death reporting standards across countries.\nOur purpose is to provide accurate estimates which do not rely on testing and death count data directly but only use population level statistics.\nMethods: We collected demographic and death records data from the Italian Institute of Statistics.\nWe focus on the area in Italy that experienced the initial outbreak of COVID-19 and estimated a Bayesian model fitting age-stratified mortality data from 2020 and previous years.\nWe also assessed the sensitivity of our estimates to alternative assumptions on the proportion of population infected.\nFindings: We estimate an overall infection fatality rate of 1.29% (95% credible interval [CrI] 0.89 - 2.01), as well as large differences by age, with a low infection fatality rate of 0.05% for under 60 year old (CrI 0-.19) and a substantially higher 4.25% (CrI 3.01-6.39) for people above 60 years of age.\nIn our sensitivity analysis, we found that even under extreme assumptions, our method delivered useful information.\nFor instance, even if only 10% of the population were infected, the infection fatality rate would not rise above 0.2% for people under 60.\nInterpretation: Our empirical estimates based on population level data show a sharp difference in fatality rates between young and old people and firmly rule out overall fatality ratios below 0.5% in populations with more than 30% over 60 years old.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Findings: We estimate an overall infection fatality rate of 1.29% (95% credible interval [CrI] 0.89 - 2.01), as well as large differences by age, with a low infection fatality rate of 0.05% for under 60 year old (CrI 0-.19) and a substantially higher 4.25% (CrI 3.01-6.39) for people above 60 years of age.\"]}", "id": 689}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: It's worth noting that there isn't a lot of data on what can kill SARS-CoV-2-the virus that causes coronavirus COVID-19-on surfaces\n\nAbstract:\nSince December 2019, a respiratory pandemic named as coronavirus disease 2019 (Covid-19) caused by a new coronavirus named as SARS-CoV-2, has taken the world by storm.\nThe symptoms are fever, malaise, and cough which resolve in a few days in most cases; but may progress to respiratory distress and organ failure.\nTransmission is through droplet infection or fomites, but other modes such as airborne transmission and oro-fecal transmission are also speculated.\nResearch is underway to develop effective vaccines and medicines for the disease.\nIn such a scenario, we present the measures described in Unani system of medicine for health protection during epidemics.\nUnani is a traditional system of medicine developed during the middle ages, which employs natural drugs of herbal, animal and mineral origin for treatment.\nIn Unani medicine, during an epidemic, apart from isolation and quarantine, three measures are of utmost importance, (i) purification of surroundings using certain herbal drugs as fumigants or sprays, (ii) health promotion and immune-modulation, and (iii) use of health-protecting drugs and symptom-specific drugs.\nDrugs such as loban (Styrax benzoides W. G. Craib), sandroos (Hymenaea verrucosa Gaertn.) za'fran (Crocus sativus L.), vinegar etc.\nare prescribed in various forms.\nScientific researches on these drugs reveal the presence of a number of pharmacologically active substances, which may provide a new insight into the management of infections and epidemics.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 690}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Vitamins and Minerals Help Fight Coronavirus\n\nAbstract:\nOptimal nutrition can improve well-being and might mitigate the risk and morbidity associated with coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).\nThis review summarizes nutritional guidelines to support dietary counseling provided by dietitians and health-related professionals.\nThe majority of documents encouraged the consumption of fruits, vegetables, and whole grain foods.\nThirty-one percent of the guidelines highlighted the importance of minerals and vitamins such as zinc and vitamins C, A, and D to maintain a well-functioning immune system.\nDietary supplementation has not been linked to COVID-19 prevention.\nHowever, supplementation with vitamins C and D, as well as with zinc and selenium, was highlighted as potentially beneficial for individuals with, or at risk of, respiratory viral infections or for those in whom nutrient deficiency is detected.\nThere was no convincing evidence that food or food packaging is associated with the transmission of COVID-19, but good hygiene practices for handling and preparing foods were recommended.\nNo changes to breastfeeding recommendations have been made, even in women diagnosed with COVID-19.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"The majority of documents encouraged the consumption of fruits, vegetables, and whole grain foods.\", \"Thirty-one percent of the guidelines highlighted the importance of minerals and vitamins such as zinc and vitamins C, A, and D to maintain a well-functioning immune system.\", \"Dietary supplementation has not been linked to COVID-19 prevention.\", \"However, supplementation with vitamins C and D, as well as with zinc and selenium, was highlighted as potentially beneficial for individuals with, or at risk of, respiratory viral infections or for those in whom nutrient deficiency is detected.\"]}", "id": 691}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Masks do reduce spread of flu and some COVID-19\n\nAbstract:\nEvidence that face masks provide effective protection against respiratory infections in the community is scarce.\nHowever, face masks are widely used by health workers as part of droplet precautions when caring for patients with respiratory infections.\nIt would therefore be reasonable to suggest that consistent widespread use of face masks in the community could prevent further spread of the Severe Acute Respiratory Syndrome-Coronavirus 2 (SARS-CoV-2).\nIn this study we examine public face mask wearing in Uganda where a proportion wears masks to protect against acquiring, and the other to prevent from transmitting SARS-CoV-2.\nThe objective of this study was to determine what percentage of the population would have to wear face masks to reduce susceptibility to and infectivity of COVID-19 in Uganda, keeping the basic reproduction number below unity and/or flattening the curve.\nWe used an SEIAQRD model for the analysis.\nResults show that implementation of facemasks has a relatively large impact on the size of the coronavirus epidemic in Uganda.\nWe find that the critical mask adherence is 5 per 100 when 80% wear face masks.\nA cost-effective analysis shows that utilizing funds to provide 1 public mask to the population has a per capita compounded cost of USD 1.34.\nIf provision of face masks is done simultaneously with supportive care, the per capita compounded cost is USD 1.965, while for the case of only treatment and no provision of face masks costs each Ugandan USD 4.0579.\nWe conclude that since it is hard to achieve a 100% adherence to face masks, government might consider provision of face masks in conjunction with provision of care.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"It would therefore be reasonable to suggest that consistent widespread use of face masks in the community could prevent further spread of the Severe Acute Respiratory Syndrome-Coronavirus 2 (SARS-CoV-2).\"]}", "id": 692}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Chadox1 ncov-19 vaccination prevents sars-cov-2 pneumonia in rhesus macaques\n\nAbstract:\nSevere acute respiratory syndrome coronavirus-2 (SARS-CoV-2) emerged in December 20191,2 and is responsible for the COVID-19 pandemic3.\nVaccines are an essential countermeasure urgently needed to control the pandemic4.\nHere, we show that the adenovirus-vectored vaccine ChAdOx1 nCoV-19, encoding the spike protein of SARS-CoV-2, is immunogenic in mice, eliciting a robust humoral and cell-mediated response.\nThis response was not Th2 dominated, as demonstrated by IgG subclass and cytokine expression profiling.\nA single vaccination with ChAdOx1 nCoV-19 induced a humoral and cellular immune response in rhesus macaques.\nWe observed a significantly reduced viral load in bronchoalveolar lavage fluid and respiratory tract tissue of vaccinated animals challenged with SARS-CoV-2 compared with control animals, and no pneumonia was observed in vaccinated rhesus macaques.\nImportantly, no evidence of immune-enhanced disease following viral challenge in vaccinated animals was observed.\nChAdOx1 nCoV-19 is currently under investigation in a phase I clinical trial.\nSafety, immunogenicity and efficacy against symptomatic PCR-positive COVID-19 disease will now be assessed in randomised controlled human clinical trials.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"A single vaccination with ChAdOx1 nCoV-19 induced a humoral and cellular immune response in rhesus macaques.\", \"We observed a significantly reduced viral load in bronchoalveolar lavage fluid and respiratory tract tissue of vaccinated animals challenged with SARS-CoV-2 compared with control animals, and no pneumonia was observed in vaccinated rhesus macaques.\"]}", "id": 693}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Wear masks with two or more layers to stop the spread of COVID-19\n\nAbstract:\nThe COVID\u201019 pandemic caused by the novel coronavirus SARS\u2010CoV\u20102 has claimed many lives worldwide.\nWearing medical masks or N95 masks (namely N95 respirators) can slow the virus spread and reduce the infection risk.\nReuse of these masks can minimize waste, protect the environment, and help to solve the current imminent shortage of masks.\nDisinfection of used masks is needed for reuse of them with safety, but improper decontamination can damage the blocking structure of masks.\nIn this study, we demonstrated, using avian coronavirus of infectious bronchitis virus to mimic SARS\u2010CoV\u20102, that medical masks and N95 masks remained their blocking efficacy after being steamed on boiling water even for 2 hours.\nWe also demonstrated that three brands of medical masks blocked over 99% viruses in aerosols.\nThe avian coronavirus was completely inactivated after being steamed for 5 minutes.\nTogether, this study suggested that medical masks are adequate for use on most social occasions, and both medical masks and N95 masks can be reused for a few days with steam decontamination between use.\nThis article is protected by copyright.\nAll rights reserved.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Wearing medical masks or N95 masks (namely N95 respirators) can slow the virus spread and reduce the infection risk.\", \"We also demonstrated that three brands of medical masks blocked over 99% viruses in aerosols.\", \"The avian coronavirus was completely inactivated after being steamed for 5 minutes.\", \"Together, this study suggested that medical masks are adequate for use on most social occasions, and both medical masks and N95 masks can be reused for a few days with steam decontamination between use.\"]}", "id": 694}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Blood-based culture of sars-cov-2 informs infectivity and safe de-isolation assessments during covid-19\n\nAbstract:\nBACKGROUND: The detection of SARS-CoV-2 RNA by real-time polymerase chain reaction (PCR) in respiratory samples collected from persons recovered from COVID-19 does not necessarily indicate shedding of infective virions.\nBy contrast, the isolation of SARS-CoV-2 using cell-based culture likely indicates infectivity, but there are limited data on the correlation between SARS-CoV-2 culture and PCR.\nMETHODS: One hundred and ninety-five patients with varying severity of COVID-19 were tested (outpatients [n=178]), inpatients [n=12] and critically unwell patients admitted to the intensive care unit [ICU; n=5]).\nSARS-CoV-2 PCR positive samples were cultured in Vero C1008 cells and inspected daily for cytopathic effect (CPE).\nSARS-CoV-2-induced CPE was confirmed by PCR of culture supernatant.\nWhere no CPE was observed, PCR was performed on day four to confirm absence of virus replication.\nCycle threshold (Ct) of the day four PCR (Ctculture) and the PCR of the original clinical sample (Ctsample) were compared, and positive cultures were defined where Ctsample-Ctculture was &#8805;3.\nFINDINGS: Of 234 samples collected, 228 (97%) were from the upper respiratory tract.\nSARS-CoV-2 was only successfully isolated from samples with Ctsample &#8804;32, including in 28/181 (15%), 19/42 (45%) and 9/11 samples (82%) collected from outpatients, inpatients, and ICU patients, respectively.\nThe mean duration from symptom onset to culture positivity was 4.5 days (range 0-18).\nSARS-CoV-2 was significantly more likely to be isolated from samples collected from inpatients (p<0\u00e2\u0088\u0099001) and ICU patients (p<0\u00e2\u0088\u00990001) compared with outpatients respectively, and in samples with lower Ctsample.\nCONCLUSION: SARS-CoV-2 culture may be used as a surrogate marker for infectivity and inform de-isolation protocols.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"By contrast, the isolation of SARS-CoV-2 using cell-based culture likely indicates infectivity, but there are limited data on the correlation between SARS-CoV-2 culture and PCR.\"]}", "id": 695}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Berberine and obatoclax are sars-cov-2 replication in primary human nasal epithelial cells in vitro.\n\nAbstract:\nSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged as a new human pathogen in late 2019 and has infected an estimated 10% of the global population in less than a year.\nThere is a clear need for effective antiviral drugs to complement current preventive measures including vaccines.\nIn this study, we demonstrate that berberine and obatoclax, two broad-spectrum antiviral compounds, are effective against multiple isolates of SARS-CoV-2.\nBerberine, a plant-derived alkaloid, inhibited SARS-CoV-2 at low micromolar concentrations and obatoclax, originally developed as an anti-apoptotic protein antagonist, was effective at sub-micromolar concentrations.\nTime-of-addition studies indicated that berberine acts on the late stage of the viral life cycle.\nIn agreement, berberine mildly affected viral RNA synthesis, but strongly reduced infectious viral titers, leading to an increase in the particle-to-pfu ratio.\nIn contrast, obatoclax acted at the early stage of the infection, in line with its activity to neutralize the acidic environment in endosomes.\nWe assessed infection of primary human nasal epithelial cells cultured on an air-liquid interface and found that SARS-CoV-2 infection induced and repressed expression of a specific set of cytokines and chemokines.\nMoreover, both obatoclax and berberine inhibited SARS-CoV-2 replication in these primary target cells.\nWe propose berberine and obatoclax as potential antiviral drugs against SARS-CoV-2 that could be considered for further efficacy testing.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"In this study, we demonstrate that berberine and obatoclax, two broad-spectrum antiviral compounds, are effective against multiple isolates of SARS-CoV-2.\", \"Berberine, a plant-derived alkaloid, inhibited SARS-CoV-2 at low micromolar concentrations and obatoclax, originally developed as an anti-apoptotic protein antagonist, was effective at sub-micromolar concentrations.\", \"Moreover, both obatoclax and berberine inhibited SARS-CoV-2 replication in these primary target cells.\"]}", "id": 696}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: People with diabetes have not a higher risk for complications from coronavirus\n\nAbstract:\nSome comorbidities are associated with severe coronavirus disease (Covid-19) but it is unclear whether some increase susceptibility to Covid-19.\nIn this case-control Mexican study we found that obesity represents the strongest predictor for Covid-19 followed by diabetes and hypertension in both sexes and chronic renal failure in females only.\nActive smoking was associated with decreased odds of Covid-19.\nThese findings indicate that these comorbidities are not only associated with severity of disease but also predispose for getting Covid-19.\nFuture research is needed to establish the mechanisms involved in each comorbidity and the apparent \"protective\" effect of cigarette smoking.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"In this case-control Mexican study we found that obesity represents the strongest predictor for Covid-19 followed by diabetes and hypertension in both sexes and chronic renal failure in females only.\"]}", "id": 697}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: it's also important to have a strong immune system that can fight back against the germs you may encounter.\n\nAbstract:\nThis review evaluates whether pregnancy is a risk factor for COVID-19 by looking at the expression of immune markers such as immune cells and cytokines in order to have a better understanding on the pathophysiology of the disease, thus reducing maternal deaths.\nPregnant women are more at risk of contracting COVID-19 due to their weakened immune system.\nStudies demonstrate that COVID-19 is an immune condition which is marked by reduced lymphocytes and elevated selected proinflammatory cytokines.\nSimilar immune expression has been demonstrated in pregnancy by several studies.\nIn addition, the placenta has been shown to possess ACE2 receptors on the villous cytotrophoblast and the syncytiotrophoblast and findings suggest that the coronavirus enters the host cells via these ACE2 receptors.\nThe immune response in pregnancy increases the risk of contracting COVID-19.\nBoth normal pregnancy and COVID-19 are marked by decreased lymphocytes, NKG2A inhibitory receptors, and increased ACE2, IL-8, IL-10, and IP-10 it therefore safer to conclude that pregnancy is a risk factor for COVID-19 development.\nFurthermore, the presence of the ACE2 receptors in the placenta may increase the risk of mother to baby transmission of the virus.\nTherefore, more studies investigating the link between pregnancy and COVID-19 are needed.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Pregnant women are more at risk of contracting COVID-19 due to their weakened immune system.\", \"Studies demonstrate that COVID-19 is an immune condition which is marked by reduced lymphocytes and elevated selected proinflammatory cytokines.\", \"Similar immune expression has been demonstrated in pregnancy by several studies.\"]}", "id": 698}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Covid-19 kills also young people\n\nAbstract:\nObjective: Severity of the coronavirus disease 2019 (covid-19) has been assessed in terms of absolute mortality in SARS-CoV-2 positive cohorts.\nAn assessment of mortality relative to mortality in the general population is presented.\nDesign: Retrospective population-based study.\nSetting: Individual information on symptomatic confirmed SARS-CoV-2 patients and subsequent deaths from any cause were compared to the all-cause mortality in the Swiss population of 2018.\nStarting February 23, 2020, mortality in covid-19 patients was monitored for 80 days and compared to the population mortality observed in the same time-of-year starting February 23, 2018.\nParticipants: 5 160 595 inhabitants of Switzerland aged 35 to 95 without covid-19 (general population in spring 2018) and 20 769 persons tested positively for covid-19 (spring 2020).\nMeasurements: Sex- and age-specific mortality rates were estimated using Cox proportional hazards models.\nAbsolute probabilities of death were predicted and risk was assessed in terms of relative mortality by taking the ratio between the sex- and age-specific absolute mortality in covid19 patients and the corresponding mortality in the 2018 general population.\nResults: A confirmed SARS-CoV-2 infection substantially increased the probability of death across all patient groups, ranging from nine (6 to 15) times the population mortality in 35-year old infected females to a 53-fold increase (46 to 59) for 95 year old infected males.\nThe highest relative risks were observed among males and older patients.\nThe magnitude of these effects was smaller compared to increases observed in absolute mortality risk.\nMale covid-19 patients exceeded the population hazard for males (hazard ratio 1.20, 1.00 to 1.44).\nEach additional year of age increased the population hazard in covid-19 patients (hazard ratio 1.04, 1.03 to 1.05).\nLimitations: Information about the distribution of relevant comorbidities was not available on population level and the associated risk was not quantified.\nConclusions: Health care professionals, decision makers, and societies are provided with an additional population-adjusted assessment of covid-19 mortality risk.\nIn combination with absolute measures of risk, the relative risks presented here help to develop a more comprehensive understanding of the actual impact of covid-19.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Results: A confirmed SARS-CoV-2 infection substantially increased the probability of death across all patient groups, ranging from nine (6 to 15) times the population mortality in 35-year old infected females to a 53-fold increase (46 to 59) for 95 year old infected males.\", \"The highest relative risks were observed among males and older patients.\"]}", "id": 699}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: A booster dose enhances immunogenicity of the covid-19 vaccine candidate chadox1 ncov-19 in aged mice\n\nAbstract:\nThe spread of SARS-CoV-2 has caused a global pandemic that has affected almost every aspect of human life.\nThe development of an effective COVID-19 vaccine could limit the morbidity and mortality caused by infection, and may enable the relaxation of social distancing measures.\nAge is one of the most significant risk factors for poor health outcomes after SARS-CoV-2 infection, therefore it is desirable that any new vaccine candidates should elicit a robust immune response in older adults.\nHere, we test the immunogenicity of the adenoviral vectored vaccine ChAdOx1 nCoV-19 (AZD-1222) in aged mice.\nWe find that a single dose of this vaccine induces cellular and humoral immunity in aged mice, but at a reduced magnitude than in younger adult mice.\nFurthermore, we report that a second dose enhances the immune response to this vaccine in aged mice, indicating that a primeboost strategy may be a rational approach to enhance immunogenicity in older persons.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"We find that a single dose of this vaccine induces cellular and humoral immunity in aged mice, but at a reduced magnitude than in younger adult mice.\", \"Furthermore, we report that a second dose enhances the immune response to this vaccine in aged mice, indicating that a primeboost strategy may be a rational approach to enhance immunogenicity in older persons.\"]}", "id": 700}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Hypothesis: Angiotensin-converting enzyme inhibitors and angiotensin receptor blockers may increase the risk of severe COVID-19\n\nAbstract:\nThe effects of angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin receptor blockers (ARBs) on the risk of COVID-19 infection and disease progression are yet to be investigated.\nThe relationship between ACEI/ARB use and COVID-19 infection was systematically reviewed.\nTo identify relevant studies that met predetermined inclusion criteria, unrestricted searches of the PubMed, Embase, and Cochrane Library databases were conducted.\nThe search strategy included clinical date published until May 9, 2020.\nTwelve articles involving more than 19,000 COVID-19 cases were included.\nTo estimate overall risk, random-effects models were adopted.\nOur results showed that ACEI/ARB exposure was not associated with a higher risk of COVID-19 infection (OR = 0.99; 95 % CI, 0-1.04; P = 0.672).\nAmong those with COVID-19 infection, ACEI/ARB exposure was also not associated with a higher risk of having severe infection (OR = 0.98; 95 % CI, 0.87-1.09; P = 0.69) or mortality (OR = 0.73, 95 %CI, 0.5-1.07; P = 0.111).\nHowever, ACEI/ARB exposure was associated with a lower risk of mortality compared to those on non-ACEI/ARB antihypertensive drugs (OR = 0.48, 95 % CI, 0.29-0.81; P = 0.006).\nIn conclusion, current evidence did not confirm the concern that ACEI/ARB exposure is harmful in patientswith COVID-19 infection.\nThis study supports the current guidelines that discourage discontinuation of ACEIs or ARBs in COVID-19 patients and the setting of the COVID-19 pandemic.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"In conclusion, current evidence did not confirm the concern that ACEI/ARB exposure is harmful in patientswith COVID-19 infection.\", \"This study supports the current guidelines that discourage discontinuation of ACEIs or ARBs in COVID-19 patients and the setting of the COVID-19 pandemic.\"]}", "id": 701}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Prrsv affects global patterns of covid-19 early outbreak dynamics\n\nAbstract:\nEnvironmental factors are well known to affect spatio-temporal patterns of infectious disease outbreaks, but whether the rapid spread of COVID-19 across the globe is related to local environmental conditions is highly debated.\nWe assessed the impact of environmental factors (temperature, humidity and air pollution) on the global patterns of COVID-19 early outbreak dynamics during January-May 2020, controlling for several key socio-economic factors and airport connections.\nWe showed that during the earliest phase of the global outbreak (January-March), COVID-19 growth rates were non-linearly related to climate, with fastest spread in regions with a mean temperature of ca.\n5 \u00b0C, and in the most polluted regions.\nHowever, environmental effects faded almost completely when considering later outbreaks, in keeping with the progressive enforcement of containment actions.\nAccordingly, COVID-19 growth rates consistently decreased with stringent containment actions during both early and late outbreaks.\nOur findings indicate that environmental drivers may have played a role in explaining the early variation among regions in disease spread.\nWith limited policy interventions, seasonal patterns of disease spread might emerge, with temperate regions of both hemispheres being most at risk of severe outbreaks during colder months.\nNevertheless, containment measures play a much stronger role and overwhelm impacts of environmental variation, highlighting the key role for policy interventions in curbing COVID-19 diffusion within a given region.\nIf the disease will become seasonal in the next years, information on environmental drivers of COVID-19 can be integrated with epidemiological models to inform forecasting of future outbreak risks and improve management plans.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"We showed that during the earliest phase of the global outbreak (January-March), COVID-19 growth rates were non-linearly related to climate, with fastest spread in regions with a mean temperature of ca.\"]}", "id": 702}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: wearing a mask  may offer some protection for the covid-19\n\nAbstract:\nThe COVID\u201019 pandemic caused by the novel coronavirus SARS\u2010CoV\u20102 has claimed many lives worldwide.\nWearing medical masks or N95 masks (namely N95 respirators) can slow the virus spread and reduce the infection risk.\nReuse of these masks can minimize waste, protect the environment, and help to solve the current imminent shortage of masks.\nDisinfection of used masks is needed for reuse of them with safety, but improper decontamination can damage the blocking structure of masks.\nIn this study, we demonstrated, using avian coronavirus of infectious bronchitis virus to mimic SARS\u2010CoV\u20102, that medical masks and N95 masks remained their blocking efficacy after being steamed on boiling water even for 2 hours.\nWe also demonstrated that three brands of medical masks blocked over 99% viruses in aerosols.\nThe avian coronavirus was completely inactivated after being steamed for 5 minutes.\nTogether, this study suggested that medical masks are adequate for use on most social occasions, and both medical masks and N95 masks can be reused for a few days with steam decontamination between use.\nThis article is protected by copyright.\nAll rights reserved.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Wearing medical masks or N95 masks (namely N95 respirators) can slow the virus spread and reduce the infection risk.\", \"We also demonstrated that three brands of medical masks blocked over 99% viruses in aerosols.\", \"The avian coronavirus was completely inactivated after being steamed for 5 minutes.\", \"Together, this study suggested that medical masks are adequate for use on most social occasions, and both medical masks and N95 masks can be reused for a few days with steam decontamination between use.\"]}", "id": 703}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: The coronavirus came from the local wet market in China\n\nAbstract:\nA novel coronavirus (severe acute respiratory syndrome-CoV-2) that initially originated from Wuhan, China, in December 2019 has already caused a pandemic.\nWhile this novel coronavirus disease (covid-19) frequently induces mild diseases, it has also generated severe diseases among certain populations, including older-aged individuals with underlying diseases, such as cardiovascular disease and diabetes.\nAs of 31 March 2020, a total of 9786 confirmed cases with covid-19 have been reported in South Korea.\nSouth Korea has the highest diagnostic rate for covid-19, which has been the major contributor in overcoming this outbreak.\nWe are trying to reduce the reproduction number of covid-19 to less than one and eventually succeed in controlling this outbreak using methods such as contact tracing, quarantine, testing, isolation, social distancing and school closure.\nThis report aimed to describe the current situation of covid-19 in South Korea and our response to this outbreak.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 704}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Synergism of tnf-\u03b1 and ifn-\u03b3 triggers inflammatory cell death , tissue damage , and mortality in sars-cov-2 infection and cytokine shock syndromes\n\nAbstract:\nThe COVID-19 pandemic has caused significant morbidity and mortality.\nCurrently, there is a critical shortage of proven treatment options and an urgent need to understand the pathogenesis of multi-organ failure and lung damage.\nCytokine storm is associated with severe inflammation and organ damage during COVID-19.\nHowever, a detailed molecular pathway defining this cytokine storm is lacking, and gaining mechanistic understanding of how SARS-CoV-2 elicits a hyperactive inflammatory response is critical to develop effective therapeutics.\nOf the multiple inflammatory cytokines produced by innate immune cells during SARS-CoV-2 infection, we found that the combined production of TNF-\u03b1 and IFN-\u03b3 specifically induced inflammatory cell death, PANoptosis, characterized by gasdermin\u2013mediated pyroptosis, caspase-8\u2013mediated apoptosis, and MLKL\u2013mediated necroptosis.\nDeletion of pyroptosis, apoptosis, or necroptosis mediators individually was not sufficient to protect against cell death.\nHowever, cells deficient in both RIPK3 and caspase-8 or RIPK3 and FADD were resistant to this cell death.\nMechanistically, the STAT1/IRF1 axis activated by TNF-\u03b1 and IFN-\u03b3 co-treatment induced iNOS for the production of nitric oxide.\nPharmacological and genetic deletion of this pathway inhibited pyroptosis, apoptosis, and necroptosis in macrophages.\nMoreover, inhibition of PANoptosis protected mice from TNF-\u03b1 and IFN-\u03b3\u2013induced lethal cytokine shock that mirrors the pathological symptoms of COVID-19.\nIn vivo neutralization of both TNF-\u03b1 and IFN-\u03b3 in multiple disease models associated with cytokine storm showed that this treatment provided substantial protection against not only SARS-CoV-2 infection, but also sepsis, hemophagocytic lymphohistiocytosis, and cytokine shock models, demonstrating the broad physiological relevance of this mechanism.\nCollectively, our findings reveal that blocking the COVID-19 cytokine-mediated inflammatory cell death signaling pathway identified in this study may benefit patients with COVID-19 or other cytokine storm-driven syndromes by limiting inflammation and tissue damage.\nThe findings also provide a molecular and mechanistic description for the term cytokine storm.\nAdditionally, these results open new avenues for the treatment of other infectious and autoinflammatory diseases and cancers where TNF-\u03b1 and IFN-\u03b3 synergism play key pathological roles.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"In vivo neutralization of both TNF-\\u03b1 and IFN-\\u03b3 in multiple disease models associated with cytokine storm showed that this treatment provided substantial protection against not only SARS-CoV-2 infection, but also sepsis, hemophagocytic lymphohistiocytosis, and cytokine shock models, demonstrating the broad physiological relevance of this mechanism.\"]}", "id": 705}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: A new Chinese coronavirus, a cousin of the SARS virus, has infected hundreds since the outbreak began in Wuhan, China, in December.\n\nAbstract:\nA novel coronavirus (severe acute respiratory syndrome-CoV-2) that initially originated from Wuhan, China, in December 2019 has already caused a pandemic.\nWhile this novel coronavirus disease (covid-19) frequently induces mild diseases, it has also generated severe diseases among certain populations, including older-aged individuals with underlying diseases, such as cardiovascular disease and diabetes.\nAs of 31 March 2020, a total of 9786 confirmed cases with covid-19 have been reported in South Korea.\nSouth Korea has the highest diagnostic rate for covid-19, which has been the major contributor in overcoming this outbreak.\nWe are trying to reduce the reproduction number of covid-19 to less than one and eventually succeed in controlling this outbreak using methods such as contact tracing, quarantine, testing, isolation, social distancing and school closure.\nThis report aimed to describe the current situation of covid-19 in South Korea and our response to this outbreak.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 706}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Diabetes is generally known to weaken the immune system, making it harder to protect against viral infections like COVID-19.\n\nAbstract:\nAIMS: The 2019 novel coronavirus disease (COVID-19) emerged in Wuhan, China, and was characterized as a pandemic by the World Health Organization.\nDiabetes is an established risk associated with poor clinical outcomes, but the association of diabetes with COVID-19 has not been reported yet.\nMETHODS: In this cohort study, we retrospectively reviewed 258 consecutive hospitalized COVID-19 patients with or without diabetes at the West Court of Union Hospital in Wuhan, China, recruited from January 29 to February 12, 2020.\nThe clinical features, treatment strategies and prognosis data were collected and analyzed.\nPrognosis was followed up until March 12, 2020.\nRESULTS: Of the 258 hospitalized patients (63 with diabetes) with COVID-19, the median age was 64 years (range 23-91), and 138 (53.5%) were male.\nCommon symptoms included fever (82.2%), dry cough (67.1%), polypnea (48.1%), and fatigue (38%).\nPatients with diabetes had significantly higher leucocyte and neutrophil counts, and higher levels of fasting blood glucose, serum creatinine, urea nitrogen and creatine kinase isoenzyme MB at admission compared with those without diabetes.\nCOVID-19 patients with diabetes were more likely to develop severe or critical disease conditions with more complications, and had higher incidence rates of antibiotic therapy, non-invasive and invasive mechanical ventilation, and death (11.1% vs. 4.1%).\nCox proportional hazard model showed that diabetes (adjusted hazard ratio [aHR] = 3.64; 95% confidence interval [CI]: 1.09, 12.21) and fasting blood glucose (aHR = 1.19; 95% CI: 1.08, 1.31) were associated with the fatality due to COVID-19, adjusting for potential confounders.\nCONCLUSIONS: Diabetes mellitus is associated with increased disease severity and a higher risk of mortality in patients with COVID-19.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 707}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Drinking hot ginger tea, with or without these additional ingredients, will cure COVID-19\n\nAbstract:\nOBJECTIVE To analyze the characteristics of YouTube videos in Spanish on the basic measures to prevent coronavirus disease 2019 (COVID-19).\nMETHODS On 18 March 2020, a search was conducted on YouTube using the terms \"Prevencion Coronavirus\" and \"Prevencion COVID-19\".\nWe studied the associations between the type of authorship and the country of publication with other variables (such as the number of likes and basic measures to prevent COVID-19 according to the World Health Organization, among others) with univariate analysis and a multiple logistic regression model.\nRESULTS A total of 129 videos were evaluated; 37.2% were produced in Mexico (25.6%) and Spain (11.6%), and 56.6% were produced by mass media, including television and newspapers.\nThe most frequently reported basic preventive measure was hand washing (71.3%), and the least frequent was not touching the eyes, nose, and mouth (24.0%).\nHoaxes (such as eating garlic or citrus to prevent COVID-19) were detected in 15 videos (10.9%).\nIn terms of authorship, papers produced by health professionals had a higher probability of reporting hand hygiene (OR (95% CI) = 4.20 (1.17-15.09)) and respiratory hygiene (OR (95% CI) = 3.05 (1.22-7.62)) as preventive measures.\nCONCLUSION Information from YouTube in Spanish on basic measures to prevent COVID-19 is usually not very complete and differs according to the type of authorship.\nOur findings make it possible to guide Spanish-speaking users on the characteristics of the videos to be viewed in order to obtain reliable information.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Hoaxes (such as eating garlic or citrus to prevent COVID-19) were detected in 15 videos (10.9%).\"]}", "id": 708}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: High Blood Pressure Doesn't Seem To Raise COVID-19 Risk\n\nAbstract:\nInvestigations reported that hypertension, diabetes, and cardiovascular diseases were the most prevalent comorbidities among the patients with coronavirus disease 2019 (COVID-19).\nHypertension appeared consistently as the most prevalent risk factors in COVID-19 patients.\nSome investigations speculated about the association between renin-angiotensin-aldosterone system (RAAS) and susceptibility to COVID-19, as well as the relationship between RAAS inhibitors and increased mortality in these patients.\nThis raised concern about the potential association between hypertension (and its treatment) and propensity for COVID-19.\nThere are only a few follow-up studies that investigated the impact of comorbidities on outcome in these patients with conflicting findings.\nHypertension has been proven to be more prevalent in patients with an adverse outcome (admission in intensive care unit, use of mechanical ventilation, or death).\nSo far, there is no study that demonstrated independent predictive value of hypertension on mortality in COVID-19 patients.\nThere are many speculations about this coronavirus and its relation with different risk factors and underlying diseases.\nThe aim of this review was to summarize the current knowledge about the relationship between hypertension and COVID-19 and the role of hypertension on outcome in these patients.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Hypertension appeared consistently as the most prevalent risk factors in COVID-19 patients.\", \"Hypertension has been proven to be more prevalent in patients with an adverse outcome (admission in intensive care unit, use of mechanical ventilation, or death).\"]}", "id": 709}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: The most effective way to protect yourself against the new coronavirus is by frequently cleaning your hands with alcohol-based hand\n\nAbstract:\nAmong the basic protective measures against COVID-19, the need to wash hands frequently and in a prolonged way using soap, and to regularly use alcohol-based hand sanitizers is well established for the whole population.\nHealthcare workers in general, and particularly those involved in the direct care of COVID-19 patients, have to wear personal protective equipment (PPE) daily for many hours and also accomplish general preventive measurements outside their work.\nCutaneous adverse reactions can develop that need to be prevented, identified and therapeutically managed.\nAccording to the data reported by Lin et al 1 , based in the experience from healthcare workers in Wuhan, adverse skin reactions were reported in 74% of responders (n=376) to a general survey.\nThe most commonly reported types of eruptions were skin dryness or desquamation (68.6%), papules or erythema (60.4%) and maceration (52,9%).", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Among the basic protective measures against COVID-19, the need to wash hands frequently and in a prolonged way using soap, and to regularly use alcohol-based hand sanitizers is well established for the whole population.\"]}", "id": 710}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Seasonal human coronavirus antibodies are boosted upon sars-cov-2 infection but strongly associated with protection\n\nAbstract:\nSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has rapidly spread within the human population.\nAlthough SARS-CoV-2 is a novel coronavirus, most humans had been previously exposed to other antigenically distinct common seasonal human coronaviruses (hCoVs) before the COVID-19 pandemic.\nHere, we quantified levels of SARS-CoV-2-reactive antibodies and hCoV-reactive antibodies in serum samples collected from 204 humans before the COVID-19 pandemic.\nWe then quantified pre-pandemic antibody levels in serum from a separate cohort of 252 individuals who became PCR-confirmed infected with SARS-CoV-2.\nFinally, we longitudinally measured hCoV and SARS-CoV-2 antibodies in the serum of hospitalized COVID-19 patients.\nOur studies indicate that most individuals possessed hCoV-reactive antibodies before the COVID-19 pandemic.\nWe determined that ~23% of these individuals possessed non-neutralizing antibodies that cross-reacted with SARS-CoV-2 spike and nucleocapsid proteins.\nThese antibodies were not associated with protection against SARS-CoV-2 infections or hospitalizations, but paradoxically these hCoV cross-reactive antibodies were boosted upon SARS-CoV-2 infection.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Although SARS-CoV-2 is a novel coronavirus, most humans had been previously exposed to other antigenically distinct common seasonal human coronaviruses (hCoVs) before the COVID-19 pandemic.\", \"We then quantified pre-pandemic antibody levels in serum from a separate cohort of 252 individuals who became PCR-confirmed infected with SARS-CoV-2.\", \"These antibodies were not associated with protection against SARS-CoV-2 infections or hospitalizations, but paradoxically these hCoV cross-reactive antibodies were boosted upon SARS-CoV-2 infection.\"]}", "id": 711}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Hydroxychloroquine proves efficiently in hamsters and macaques infected with sars-cov-2\n\nAbstract:\nWe remain largely without effective prophylactic/therapeutic interventions for COVID-19.\nAlthough many human clinical trials are ongoing, there remains a deficiency of supportive preclinical drug efficacy studies.\nHere we assessed the prophylactic/therapeutic efficacy of hydroxychloroquine (HCQ), a drug of interest for COVID-19 management, in two animal models.\nWhen used for prophylaxis or treatment neither the standard human malaria dose (6.5 mg/kg) nor a high dose (50 mg/kg) of HCQ had any beneficial effect on clinical disease or SARS-CoV-2 kinetics (replication/shedding) in the Syrian hamster disease model.\nSimilarly, HCQ prophylaxis/treatment (6.5 mg/kg) did not significantly benefit clinical outcome nor reduce SARS-CoV-2 replication/shedding in the upper and lower respiratory tract in the rhesus macaque disease model.\nIn conclusion, our preclinical animal studies do not support the use of HCQ in prophylaxis/treatment of COVID-19.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Similarly, HCQ prophylaxis/treatment (6.5 mg/kg) did not significantly benefit clinical outcome nor reduce SARS-CoV-2 replication/shedding in the upper and lower respiratory tract in the rhesus macaque disease model.\"]}", "id": 712}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: both dogs and cats can be infected by the virus that causes Covid-19 in humans, but none of the ten animals observed in the study showed clinical symptoms like coughing\n\nAbstract:\nOn April 22, CDC and the U.S. Department of Agriculture (USDA) reported cases of two domestic cats with confirmed infection with SARS-CoV-2, the virus that causes coronavirus disease 2019 (COVID-19).\nThese are the first reported companion animals (including pets and service animals) with SARS-CoV-2 infection in the United States, and among the first findings of SARS-CoV-2 symptomatic companion animals reported worldwide.\nThese feline cases originated from separate households and were epidemiologically linked to suspected or confirmed human COVID-19 cases in their respective households.\nNotification of presumptive positive animal test results triggered a One Health* investigation by state and federal partners, who determined that no further transmission events to other animals or persons had occurred.\nBoth cats fully recovered.\nAlthough there is currently no evidence that animals play a substantial role in spreading COVID-19, CDC advises persons with suspected or confirmed COVID-19 to restrict contact with animals during their illness and to monitor any animals with confirmed SARS-CoV-2 infection and separate them from other persons and animals at home (1).", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Although there is currently no evidence that animals play a substantial role in spreading COVID-19, CDC advises persons with suspected or confirmed COVID-19 to restrict contact with animals during their illness and to monitor any animals with confirmed SARS-CoV-2 infection and separate them from other persons and animals at home (1).\"]}", "id": 713}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Some herbal medicine advocates believe that the combination of garlic, ginger and some herbs can cure Coronavirus.\n\nAbstract:\nIn late December 2019 in Wuhan, China, several patients with viral pneumonia were identified as 2019 novel coronavirus (2019-nCoV).\nSo far, there are no specific treatments for patients with coronavirus disease-19 (COVID-19), and the treatments available today are based on previous experience with similar viruses such as severe acute respiratory syndrome-related coronavirus (SARS-CoV), Middle East respiratory syndrome coronavirus (MERS-CoV), and Influenza virus.\nIn this article, we have tried to reach a therapeutic window of drugs available to patients with COVID-19.\nCathepsin L is required for entry of the 2019-nCoV virus into the cell as target teicoplanin inhibits virus replication.\nAngiotensin-converting-enzyme 2 (ACE2) in soluble form as a recombinant protein can prevent the spread of coronavirus by restricting binding and entry.\nIn patients with COVID-19, hydroxychloroquine decreases the inflammatory response and cytokine storm, but overdose causes toxicity and mortality.\nNeuraminidase inhibitors such as oseltamivir, peramivir, and zanamivir are invalid for 2019-nCoV and are not recommended for treatment but protease inhibitors such as lopinavir/ritonavir (LPV/r) inhibit the progression of MERS-CoV disease and can be useful for patients of COVID-19 and, in combination with Arbidol, has a direct antiviral effect on early replication of SARS-CoV. Ribavirin reduces hemoglobin concentrations in respiratory patients, and remdesivir improves respiratory symptoms.\nUse of ribavirin in combination with LPV/r in patients with SARS-CoV reduces acute respiratory distress syndrome and mortality, which has a significant protective effect with the addition of corticosteroids.\nFavipiravir increases clinical recovery and reduces respiratory problems and has a stronger antiviral effect than LPV/r.\ncurrently, appropriate treatment for patients with COVID-19 is an ACE2 inhibitor and a clinical problem reducing agent such as favipiravir in addition to hydroxychloroquine and corticosteroids.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"In late December 2019 in Wuhan, China, several patients with viral pneumonia were identified as 2019 novel coronavirus (2019-nCoV).\", \"So far, there are no specific treatments for patients with coronavirus disease-19 (COVID-19), and the treatments available today are based on previous experience with similar viruses such as severe acute respiratory syndrome-related coronavirus (SARS-CoV), Middle East respiratory syndrome coronavirus (MERS-CoV), and Influenza virus.\"]}", "id": 714}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Nonsteroidal anti-inflammatory drugs (NSAIDs), such as ibuprofen, aspirin, and Advil, reduce fever, pain, and inflammation.\n\nAbstract:\n: The COVID-19 pandemic is challenging our cardiovascular care of patients with heart diseases.\nIn the setting of pericardial diseases, there are two possible different scenarios to consider: the patient being treated for pericarditis who subsequently becomes infected with SARS-CoV-2, and the patient with COVID-19 who develops pericarditis or pericardial effusion.\nIn both conditions, clinicians may be doubtful regarding the safety of nonsteroidal anti-inflammatory drugs (NSAIDs), corticosteroids, colchicine, and biological agents, such as anti-IL1 agents (e.g. anakinra), that are the mainstay of therapy for pericarditis.\nFor NSAIDs, there is no clear scientific evidence linking ibuprofen and other NSAIDs to worsening of COVID-19; however, it seems prudent to continue them, if necessary to control pericarditis, and on the other hand, to prefer paracetamol for fever and systemic symptoms related to COVID-19.\nTreatments with corticosteroids, colchicine, and anakinra appear well tolerated in the context of COVID-19 infection and are currently actively evaluated as potential therapeutic options for COVID infection at different stages of the disease.\nOn this basis, currently most treatments for pericarditis do not appear contraindicated also in the presence of possible COVID-19 infection and should not be discontinued, and some (corticosteroids, colchicine, and anakinra) can be considered to treat both conditions.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"For NSAIDs, there is no clear scientific evidence linking ibuprofen and other NSAIDs to worsening of COVID-19; however, it seems prudent to continue them, if necessary to control pericarditis, and on the other hand, to prefer paracetamol for fever and systemic symptoms related to COVID-19.\"]}", "id": 715}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Scientists believe cannabis could help prevent, treat coronavirus\n\nAbstract:\nThe recently discovered novel coronavirus, SARS-CoV-2 (COVID-19 virus), has brought the whole world to standstill with critical challenges, affecting both health and economic sectors worldwide.\nAlthough initially, this pandemic was associated with causing severe pulmonary and respiratory disorders, recent case studies reported the association of cerebrovascular-neurological dysfunction in COVID-19 patients, which is also life-threatening.\nSeveral SARS-CoV-2 positive case studies have been reported where there are mild or no symptoms of this virus.\nHowever, a selection of patients are suffering from large artery ischemic strokes.\nAlthough the pathophysiology of the SARS-CoV-2 virus affecting the cerebrovascular system has not been elucidated yet, researchers have identified several pathogenic mechanisms, including a role for the ACE2 receptor.\nTherefore, it is extremely crucial to identify the risk factors related to the progression and adverse outcome of cerebrovascular-neurological dysfunction in COVID-19 patients.\nSince many articles have reported the effect of smoking (tobacco and cannabis) and vaping in cerebrovascular and neurological systems, and considering that smokers are more prone to viral and bacterial infection compared to non-smokers, it is high time to explore the probable correlation of smoking in COVID-19 patients.\nHerein, we have reviewed the possible role of smoking and vaping on cerebrovascular and neurological dysfunction in COVID-19 patients, along with potential pathogenic mechanisms associated with it.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Since many articles have reported the effect of smoking (tobacco and cannabis) and vaping in cerebrovascular and neurological systems, and considering that smokers are more prone to viral and bacterial infection compared to non-smokers, it is high time to explore the probable correlation of smoking in COVID-19 patients.\"]}", "id": 716}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: No drug is approved by the Food and Drug Administration (FDA) for COVID-19, although dozens of candidates - including drugs used to treat rheumatoid arthritis, parasites, cancer, and HIV - have been proposed\n\nAbstract:\nThe SARS-CoV-2 virus emerged in December 2019 and then spread rapidly worldwide, particularly to China, Japan, and South Korea.\nScientists are endeavoring to find antivirals specific to the virus.\nSeveral drugs such as chloroquine, arbidol, remdesivir, and favipiravir are currently undergoing clinical studies to test their efficacy and safety in the treatment of coronavirus disease 2019 (COVID-19) in China; some promising results have been achieved thus far.\nThis article summarizes agents with potential efficacy against SARS-CoV-2.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Scientists are endeavoring to find antivirals specific to the virus.\", \"Several drugs such as chloroquine, arbidol, remdesivir, and favipiravir are currently undergoing clinical studies to test their efficacy and safety in the treatment of coronavirus disease 2019 (COVID-19) in China; some promising results have been achieved thus far.\"]}", "id": 717}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: There is also evidence that smokers in hospital who have coronavirus are at a higher risk than non-smokers of severe illness and death. While it's important to prevent getting COVID-19 in the first place, it's also essential that we do all we can to keep our lungs healthy to avoid the worst affects of the disease.\n\nAbstract:\nSome comorbidities are associated with severe coronavirus disease (Covid-19) but it is unclear whether some increase susceptibility to Covid-19.\nIn this case-control Mexican study we found that obesity represents the strongest predictor for Covid-19 followed by diabetes and hypertension in both sexes and chronic renal failure in females only.\nActive smoking was associated with decreased odds of Covid-19.\nThese findings indicate that these comorbidities are not only associated with severity of disease but also predispose for getting Covid-19.\nFuture research is needed to establish the mechanisms involved in each comorbidity and the apparent \"protective\" effect of cigarette smoking.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 718}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Coronavirus remains stable on metals and plastic for three days. Outside a lab, however, the virus might last considerably longer: its genetic material could be detected on surfaces 17 days after a cruise ship was empty of passengers (although it's not clear whether that material represents infectious virus particles).\n\nAbstract:\nThe ocular surface has been suggested as a site of infection with Coronavirus-2 (SARS-CoV-2) responsible for the coronavirus disease-19 (COVID-19).\nThis review examines the evidence for this hypothesis, and its implications for clinical practice.\nSevere Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2), responsible for the COVID-19 pandemic, is transmitted by person-to-person contact, via airborne droplets, or through contact with contaminated surfaces.\nSARS-CoV-2 binds to angiotensin converting enzyme-2 (ACE2) to facilitate infection in humans.\nThis review sets out to evaluate evidence for the ocular surface as a route of infection.\nA literature search in this area was conducted on 15 April 2020 using the Scopus database.\nIn total, 287 results were returned and reviewed.\nThere is preliminary evidence for ACE2 expression on corneal and conjunctival cells, but most of the other receptors to which coronaviruses bind appear to be found under epithelia of the ocular surface.\nEvidence from animal studies is limited, with a single study suggesting viral particles on the eye can travel to the lung, resulting in very mild infection.\nCoronavirus infection is rarely associated with conjunctivitis, with occasional cases reported in patients with confirmed COVID-19, along with isolated cases of conjunctivitis as a presenting sign.\nCoronaviruses have been rarely isolated from tears or conjunctival swabs.\nThe evidence suggests coronaviruses are unlikely to bind to ocular surface cells to initiate infection.\nAdditionally, hypotheses that the virus could travel from the nasopharynx or through the conjunctival capillaries to the ocular surface during infection are probably incorrect.\nConjunctivitis and isolation of the virus from the ocular surface occur only rarely, and overwhelmingly in patients with confirmed COVID-19.\nNecessary precautions to prevent person-to-person transmission should be employed in clinical practice throughout the pandemic, and patients should be reminded to maintain good hygiene practices.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 719}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Dogs May Not Spread Covid-19, but Cats Can Pass It to Each Other\n\nAbstract:\nCoronavirus disease-19 (COVID-19) caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is now a pandemic threat.\nThis virus is supposed to be spread by human to human transmission.\nCellular angiotensin-converting enzyme 2 (ACE2) is the receptor of SARS-CoV-2 which is identical or similar in different species of animals such as pigs, ferrets, cats, orangutans, monkeys, and humans.\nMoreover, a recent study predicted that dogs might be secondary hosts during the evolution of SARS-CoV-2 from bat to human.\nTherefore, there is a possibility of spreading SARS-CoV-2 through domestic pets.\nThere are now many reports of SARS-CoV-2 positive cases in dogs, cats, tigers, lion, and minks.\nExperimental data showed ferrets and cats are highly susceptible to SARS-CoV-2 as infected by virus inoculation and can transmit the virus directly or indirectly by droplets or airborne routes.\nBased on these natural infection reports and experimental data, whether the pets are responsible for SARS-CoV-2 spread to humans; needs to be deeply investigated.\nHumans showing clinical symptoms of respiratory infections have been undergoing for the COVID-19 diagnostic test but many infected people and few pets confirmed with SARS-CoV-2 remained asymptomatic.\nIn this review, we summarize the natural cases of SARS-CoV-2 in animals with the latest researches conducted in this field.\nThis review will be helpful to think insights of SARS-CoV-2 transmissions, spread, and demand for seroprevalence studies, especially in companion animals.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Coronavirus disease-19 (COVID-19) caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is now a pandemic threat.\", \"This virus is supposed to be spread by human to human transmission.\"]}", "id": 720}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: throwing hot water on ginger as a cure-all for COVID-19\n\nAbstract:\nOBJECTIVE To analyze the characteristics of YouTube videos in Spanish on the basic measures to prevent coronavirus disease 2019 (COVID-19).\nMETHODS On 18 March 2020, a search was conducted on YouTube using the terms \"Prevencion Coronavirus\" and \"Prevencion COVID-19\".\nWe studied the associations between the type of authorship and the country of publication with other variables (such as the number of likes and basic measures to prevent COVID-19 according to the World Health Organization, among others) with univariate analysis and a multiple logistic regression model.\nRESULTS A total of 129 videos were evaluated; 37.2% were produced in Mexico (25.6%) and Spain (11.6%), and 56.6% were produced by mass media, including television and newspapers.\nThe most frequently reported basic preventive measure was hand washing (71.3%), and the least frequent was not touching the eyes, nose, and mouth (24.0%).\nHoaxes (such as eating garlic or citrus to prevent COVID-19) were detected in 15 videos (10.9%).\nIn terms of authorship, papers produced by health professionals had a higher probability of reporting hand hygiene (OR (95% CI) = 4.20 (1.17-15.09)) and respiratory hygiene (OR (95% CI) = 3.05 (1.22-7.62)) as preventive measures.\nCONCLUSION Information from YouTube in Spanish on basic measures to prevent COVID-19 is usually not very complete and differs according to the type of authorship.\nOur findings make it possible to guide Spanish-speaking users on the characteristics of the videos to be viewed in order to obtain reliable information.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Hoaxes (such as eating garlic or citrus to prevent COVID-19) were detected in 15 videos (10.9%).\"]}", "id": 721}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: No, garlic doesn't cure coronavirus. \n\nAbstract:\nThe severity of coronavirus disease 2019 (COVID-19) infection is quite variable and the manifestations varies from asymptomatic disease to severe acute respiratory infection.\nFever, dry cough, dyspnea, myalgia, fatigue, loss of appetite, olfactory and gustatory dysfunctions are the most prevalent general symptoms.\nDecreased immune system cells such as suppressed regulatory T cells, cytotoxic and helper T cells, natural killer cells, monocytes/macrophages and increased proinflammatory cytokines are the characteristic features.\nCompounds derived from Allium sativum (garlic) have the potential to decrease the expression of proinflammatory cytokines and to reverse the immunological abnormalities to more acceptable levels.\nAllium sativum is suggested as a beneficial preventive measure before being infected with SARS-CoV-2 virus.\nAllium sativum is a functional food well-known for its immunomodulatory, antimicrobial, antiinflammatory, antimutagenic, antitumor properties.\nIts antiviral efficiency was also demonstrated.\nSome constituents of this plant were found to be active against protozoan parasites.\nWithin this context, it appears to reverse most immune system dysfunctions observed in patients with COVID-19 infection.\nThe relations among immune system parameters, leptin, leptin receptor, adenosin mono phosphate-activated protein kinase, peroxisome proliferator activated receptor-gamma have also been interpreted.\nLeptin's role in boosting proinflammatory cytokines and in appetite decreasing suggest the possible beneficial effect of decreasing the concentration of this proinflammatory adipose tissue hormone in relieving some symptoms detected during COVID-19 infection.\nIn conclusion, Allium sativum may be an acceptable preventive measure against COVID-19 infection to boost immune system cells and to repress the production and secretion of proinflammatory cytokines as well as an adipose tissue derived hormone leptin having the proinflammatory nature.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"In conclusion, Allium sativum may be an acceptable preventive measure against COVID-19 infection to boost immune system cells and to repress the production and secretion of proinflammatory cytokines as well as an adipose tissue derived hormone leptin having the proinflammatory nature.\"]}", "id": 722}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: there are few novel sars-cov-2 cases in malaria countries because of the use of the antimalarial drug hydroxychloroquine.\n\nAbstract:\nHydroxychloroquine (HCQ) has sparked much interest in the therapeutics of the Coronavirus Disease 2019 (COVID-19) pandemic.\nIts antiviral properties have been studied for years; regarding the Severe Acute Respiratory Syndrome-Corona Virus-2 (SARS-CoV-2), it has been shown that HCQ may act at multiple levels.\nThese extend from the initial attachment of the virus to the respiratory epithelium to the inhibition of its replication by the alkalinisation of the phagolysosome's microenvironment and the post-translational modification of certain viral proteins.\nPreliminary clinical evidence from China and France showed significant virological and clinical benefit in HCQ-treated patients, while other studies, mostly including critically ill patients, did not show favorable results.\nIn this review, we critically appraise the existing evidence on HCQ against SARS-CoV-2 with particular emphasis on its protective and therapeutic role.\nSafety concerns that are relevant to the short-term HCQ use are also discussed.\nIn the context of the rapid evolution of the COVID-19 pandemic that strains the health care systems worldwide and considering limited population-wide testing rates in most of the vulnerable countries, early empiric short-term administration of HCQ in symptomatic individuals, may be a promising, safe and low-cost strategy.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"In the context of the rapid evolution of the COVID-19 pandemic that strains the health care systems worldwide and considering limited population-wide testing rates in most of the vulnerable countries, early empiric short-term administration of HCQ in symptomatic individuals, may be a promising, safe and low-cost strategy.\"]}", "id": 723}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Scientists are endeavoring to find antivirals specific to the virus. Several drugs such as chloroquine, arbidol, remdesivir, and favipiravir are currently undergoing clinical studies to test their efficacy and safety in the treatment of coronavirus disease 2019 (COVID-19) in China; some promising results have been achieved thus far.\n\nAbstract:\nThe SARS-CoV-2 virus emerged in December 2019 and then spread rapidly worldwide, particularly to China, Japan, and South Korea.\nScientists are endeavoring to find antivirals specific to the virus.\nSeveral drugs such as chloroquine, arbidol, remdesivir, and favipiravir are currently undergoing clinical studies to test their efficacy and safety in the treatment of coronavirus disease 2019 (COVID-19) in China; some promising results have been achieved thus far.\nThis article summarizes agents with potential efficacy against SARS-CoV-2.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Scientists are endeavoring to find antivirals specific to the virus.\", \"Several drugs such as chloroquine, arbidol, remdesivir, and favipiravir are currently undergoing clinical studies to test their efficacy and safety in the treatment of coronavirus disease 2019 (COVID-19) in China; some promising results have been achieved thus far.\"]}", "id": 724}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: SARS-CoV-2 originally jumped from animals to humans. So too did the virus responsible for SARS and MERS. \n\nAbstract:\nLittle information on the SARS-CoV-2 virus in animals is available to date.\nWhereas no one husbandry animal case has been reported to date, which would have significant implications in food safety, companion animals play a role in COVID-19 epidemiology that opens up new questions.\nThere is evidence that SARS-CoV-2 can infect felines, dogs and minks, and there is evidence of human-to-animal infection.\nLikewise, the S protein nucleotide sequence of the SARS-CoV-2 virus isolated in domestic animals and humans is identical, and the replication of the SARS-CoV-2 in cats is efficient.\nBesides, the epidemiological evidence for this current pandemic indicates that the spillover to humans was associated with close contact between man and exotic animals, very probably in Chinese wet markets, thus there is a growing general consensus that the exotic animal markets, should be strictly regulated.\nThe examination of these findings and the particular role of animals in COVID-19 should be carefully analyzed in order to establish preparation and containment measures.\nAnimal management and epidemiological surveillance must be also considered for COVID-19 control, and it can open up new questions regarding COVID-19 epidemiology and the role that animals play in it.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 725}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Can acetaminophen (Tylenol) treat the coronavirus disease? No\n\nAbstract:\nBACKGROUND: Since there is still no definitive conclusion regarding which non-steroidal anti-inflammatory drugs (NSAIDs) are most effective and safe in viral respiratory infections, we decided to evaluate the efficacy and safety of various NSAIDs in viral respiratory infections so that we can reach a conclusion on which NSAID is best choice for coronavirus disease 2019 (COVID-19).\nMETHODS: A search was performed in Medline (via PubMed), Embase and CENTRAL databases until 23 March 2020.\nClinical trials on application of NSAIDs in viral respiratory infections were included.\nRESULTS: Six clinical trials were included.\nNo clinical trial has been performed on COVID-19, Severe Acute Respiratory Syndrome and Middle East Respiratory Syndrome infections.\nStudies show that ibuprofen and naproxen not only have positive effects in controlling cold symptoms, but also do not cause serious side effects in rhinovirus infections.\nIn addition, it was found that clarithromycin, naproxen and oseltamivir combination leads to decrease in mortality rate and duration of hospitalisation in patients with pneumonia caused by influenza.\nCONCLUSION: Although based on existing evidence, NSAIDs have been effective in treating respiratory infections caused by influenza and rhinovirus, since there is no clinical trial on COVID-19 and case-reports and clinical experiences are indicative of elongation of treatment duration and exacerbation of the clinical course of patients with COVID-19, it is recommended to use substitutes such as acetaminophen for controlling fever and inflammation and be cautious about using NSAIDs in management of COVID-19 patients until there are enough evidence.\nNaproxen may be a good choice for future clinical trials.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"CONCLUSION: Although based on existing evidence, NSAIDs have been effective in treating respiratory infections caused by influenza and rhinovirus, since there is no clinical trial on COVID-19 and case-reports and clinical experiences are indicative of elongation of treatment duration and exacerbation of the clinical course of patients with COVID-19, it is recommended to use substitutes such as acetaminophen for controlling fever and inflammation and be cautious about using NSAIDs in management of COVID-19 patients until there are enough evidence.\"]}", "id": 726}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: nearly half of coronavirus cases are people 20 to 44 years old\n\nAbstract:\nBackground: The coronavirus 2019 (COVID-19) pandemic has been spread-ing globally for months, yet the infection fatality ratio of the disease is still uncertain.\nThis is partly because of inconsistencies in testing and death reporting standards across countries.\nOur purpose is to provide accurate estimates which do not rely on testing and death count data directly but only use population level statistics.\nMethods: We collected demographic and death records data from the Italian Institute of Statistics.\nWe focus on the area in Italy that experienced the initial outbreak of COVID-19 and estimated a Bayesian model fitting age-stratified mortality data from 2020 and previous years.\nWe also assessed the sensitivity of our estimates to alternative assumptions on the proportion of population infected.\nFindings: We estimate an overall infection fatality rate of 1.29% (95% credible interval [CrI] 0.89 - 2.01), as well as large differences by age, with a low infection fatality rate of 0.05% for under 60 year old (CrI 0-.19) and a substantially higher 4.25% (CrI 3.01-6.39) for people above 60 years of age.\nIn our sensitivity analysis, we found that even under extreme assumptions, our method delivered useful information.\nFor instance, even if only 10% of the population were infected, the infection fatality rate would not rise above 0.2% for people under 60.\nInterpretation: Our empirical estimates based on population level data show a sharp difference in fatality rates between young and old people and firmly rule out overall fatality ratios below 0.5% in populations with more than 30% over 60 years old.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 727}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Mva vector vaccines induce sars cov-2 replication in upper and lower respiratory tracts of transgenic mice and prevent lethal disease.\n\nAbstract:\nReplication-restricted modified vaccinia virus Ankara (MVA) is a licensed smallpox vaccine and numerous clinical studies investigating recombinant MVAs (rMVAs) as vectors for prevention of other infectious diseases have been completed or are in progress.\nTwo rMVA COVID-19 vaccine trials are at an initial stage, though no animal protection studies have been reported.\nHere, we characterize rMVAs expressing the S protein of CoV-2.\nModifications of full length S individually or in combination included two proline substitutions, mutations of the furin recognition site and deletion of the endoplasmic retrieval signal.\nAnother rMVA in which the receptor binding domain (RBD) flanked by the signal peptide and transmembrane domains of S was also constructed.\nEach modified S protein was displayed on the surface of rMVA-infected human cells and was recognized by anti-RBD antibody and by soluble hACE2 receptor.\nIntramuscular injection of mice with the rMVAs induced S-binding and pseudovirus-neutralizing antibodies.\nBoosting occurred following a second homologous rMVA but was higher with adjuvanted purified RBD protein.\nWeight loss and lethality following intranasal infection of transgenic hACE2 mice with CoV-2 was prevented by one or two immunizations with rMVAs or by passive transfer of serum from vaccinated mice.\nOne or two rMVA vaccinations also prevented recovery of infectious CoV-2 from the lungs.\nA low amount of virus was detected in the nasal turbinates of only one of eight rMVA-vaccinated mice on day 2 and none later.\nDetection of subgenomic mRNA in turbinates on day 2 only indicated that replication was abortive in immunized animals.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Weight loss and lethality following intranasal infection of transgenic hACE2 mice with CoV-2 was prevented by one or two immunizations with rMVAs or by passive transfer of serum from vaccinated mice.\"]}", "id": 728}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Dogs May Not Spread Covid-19, but Cats Can Pass It to Each Other\n\nAbstract:\nSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)--the virus that causes coronavirus disease (COVID-19)--has been detected in domestic dogs and cats, raising concerns of transmission from, to, or between these animals.\nThere is currently no indication that feline- or canine-to-human transmission can occur, though there is rising evidence of the reverse.\nTo explore the extent of animal-related transmission, we aggregated 17 case reports on confirmed SARS-CoV-2 infections in animals as of 15 May 2020.\nAll but two animals fully recovered and had only mild respiratory or digestive symptoms.\nUsing data from probable cat-to-cat transmission in Wuhan, China, we estimated the basic reproduction number R0 under this scenario at 1.09 (95% confidence interval: 1.05, 1.13).\nThis value is much lower than the R0 reported for humans and close to one, indicating that the sustained transmission between cats is unlikely to occur.\nOur results support the view that the pet owners and other persons with COVID-19 in close contact with animals should be cautious of the way they interact with them.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 729}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: In correlation between antibody titers and neutralizing activity in sera from sars-cov-2 infected subjects\n\nAbstract:\nPlenty of serologic tests for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have been developed so far, thus documenting the importance of evaluating the relevant features of the immune response to this viral agent.\nThe performance of these assays is currently under investigation.\nAmongst them, LIAISON\u00ae SARS-CoV-2 S1/S2 IgG by DiaSorin and Elecsys Anti-SARS-CoV-2 cobas\u00ae by Roche are currently used by laboratory medicine hospital departments in Italy and many other countries.\nIn the present study, we firstly compared two serologic tests on serum samples collected at two different time points from 46 laboratory-confirmed coronavirus disease-2019 (COVID-19) subjects.\nSecondly, 85 negative serum samples collected before the SARS-CoV-2 pandemic were analyzed.\nThirdly, possible correlations between antibody levels and the resulting neutralizing activity against a clinical isolate of SARS-CoV-2 were evaluated.\nResults revealed that both tests are endowed with low sensitivity on the day of hospital admission, which increased to 97.8% and 100% for samples collected after 15 days for DiaSorin and Roche tests, respectively.\nThe specificity evaluated for the two tests ranges from 96.5% to 100%, respectively.\nImportantly, a poor direct correlation between antibody titers and neutralizing activity levels was evidenced in the present study.\nThese data further shed light on both potentials and possible limitations related to SARS-CoV-2 serology.\nIn this context, great efforts are still necessary for investigating antibody kinetics to develop novel diagnostic algorithms.\nMoreover, further investigations on the role of neutralizing antibodies and their correlate of protection will be of paramount importance for the development of effective vaccines.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Importantly, a poor direct correlation between antibody titers and neutralizing activity levels was evidenced in the present study.\"]}", "id": 730}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Detection of antibodies to the sars-cov-2 spike glycoprotein in both serum and saliva enhances detection of infection\n\nAbstract:\nBACKGROUND: Detecting antibody responses during and after SARS-CoV-2 infection is essential in determining the seroepidemiology of the virus and the potential role of antibody in disease.\nScalable, sensitive and specific serological assays are essential to this process.\nThe detection of antibody in hospitalized patients with severe disease has proven straightforward; detecting responses in subjects with mild disease and asymptomatic infections has proven less reliable.\nWe hypothesized that the suboptimal sensitivity of antibody assays and the compartmentalization of the antibody response may contribute to this effect.\nMETHODS: We systemically developed an ELISA assay, optimising different antigens and amplification steps, in serum and saliva from symptomatic and asymptomatic SARS-CoV-2-infected subjects.\nRESULTS: Using trimeric spike glycoprotein, rather than nucleocapsid enabled detection of responses in individuals with low antibody responses.\nIgG1 and IgG3 predominate to both antigens, but more antispike IgG1 than IgG3 was detectable.\nAll antigens were effective for detecting responses in hospitalized patients.\nAnti-spike, but not nucleocapsid, IgG, IgA and IgM antibody responses were readily detectable in saliva from non-hospitalized symptomatic and asymptomatic individuals.\nAntibody responses in saliva and serum were largely independent of each other and symptom reporting.\nCONCLUSIONS.\nDetecting antibody responses in both saliva and serum is optimal for determining virus exposure and understanding immune responses after SARS-CoV-2 infection.\nFUNDING.\nThis work was funded by the University of Birmingham, the National Institute for Health Research (UK), the NIH National Institute for Allergy and Infectious Diseases, the Bill and Melinda Gates Foundation and the University of Southampton.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Anti-spike, but not nucleocapsid, IgG, IgA and IgM antibody responses were readily detectable in saliva from non-hospitalized symptomatic and asymptomatic individuals.\", \"Antibody responses in saliva and serum were largely independent of each other and symptom reporting.\"]}", "id": 731}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Hospital readmissions of discharged patients with covid-19\n\nAbstract:\nBackground.\nCOVID-19 infection has led to an overwhelming effort by health institutions to meet the high demand for hospital admissions.\nAim.\nTo analyse the clinical variables associated with readmission of patients who had previously been discharged after admission for COVID-19.\nDesign and methods.\nWe studied a retrospective cohort of patients with laboratory-confirmed SARS-CoV-2 infection who were admitted and subsequently discharged alive.\nWe then conducted a nested case-control study paired (1:1 ratio) by age, sex and period of admission.\nResults.\nOut of 1368 patients who were discharged during the study period, 61 patients (4.4%) were readmitted.\nImmunocompromised patients were at increased risk for readmission.\nThere was also a trend towards a higher probability of readmission in hypertensive patients (p=0.07).\nCases had had a shorter hospital stay and a higher prevalence of fever during the 48 hours prior to discharge.\nThere were no significant differences in oxygen levels measured at admission and discharge by pulse oximetry intra-subject or between the groups.\nNeutrophil/lymphocyte ratio at hospital admission tended to be higher in cases than in controls (p=0.06).\nThe motive for readmission in 10 patients (16.4%), was a thrombotic event in venous or arterial territory (p<0.001).\nNeither glucocorticoids nor anticoagulants prescribed at hospital discharge were associated with a lower readmission rate.\nConclusions.\nThe rate of readmission after discharge from hospital for COVID-19 was low.\nImmunocompromised patients and those presenting with fever during the 48 hours prior to discharge are at greater risk of readmission to hospital.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Immunocompromised patients and those presenting with fever during the 48 hours prior to discharge are at greater risk of readmission to hospital.\"]}", "id": 732}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: The new coronavirus can damage the lungs, heart and brain, which increases the risk of long-term health problems.\n\nAbstract:\nThe presence of cardiovascular co-morbidities and the known effects of coronaviruses on the cardiovascular system have called attention to the potential implications for patients with cardiovascular risk factors.\nThis evidence-based viewpoint will address two questions: (a) are individuals with underlying cardiovascular risk factors (e.g. high blood pressure or diabetes) or overt disease (e.g. coronary heart disease, heart failure, kidney disease) more likely to develop severe Covid-19 and to die than those without underlying conditions?\n(b) does the regular use of angiotensin-converting enzyme inhibitors (ACE-i) or angiotensin-receptor blockers (ARB) make patients more likely to get infected and to die of Covid-19?\nWith a necessary cautionary note that the evidence around the links between Covid-19 and cardiovascular disease is accruing at a fast pace, to date we can conclude that: (a) the greater susceptibility of individuals with underlying cardiovascular conditions to develop more severe Covid-19 with higher mortality rate is likely to be confounded, in part, by age and the type of co-morbidities.\nPatients with heart failure or chronic kidney disease might show an excess risk; (b) neither ACE-i nor ARB are associated with greater risk of SARS-Cov2 infection, or severity or risk of death in patients with Covid-19.\nPatients on these drugs should not stop them, unless under strict medical supervision and with the addition of a suitable replacement medicine.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 733}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Wearing a surgical face mask probably won't keep you from getting sick\n\nAbstract:\nWe identified seasonal human coronaviruses, influenza viruses and rhinoviruses in exhaled breath and coughs of children and adults with acute respiratory illness.\nSurgical face masks significantly reduced detection of influenza virus RNA in respiratory droplets and coronavirus RNA in aerosols, with a trend toward reduced detection of coronavirus RNA in respiratory droplets.\nOur results indicate that surgical face masks could prevent transmission of human coronaviruses and influenza viruses from symptomatic individuals.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Surgical face masks significantly reduced detection of influenza virus RNA in respiratory droplets and coronavirus RNA in aerosols, with a trend toward reduced detection of coronavirus RNA in respiratory droplets.\", \"Our results indicate that surgical face masks could prevent transmission of human coronaviruses and influenza viruses from symptomatic individuals.\"]}", "id": 734}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: vitamin D might be able to protect people from the coronavirus (COVID-19).\n\nAbstract:\nThe severity of coronavirus 2019 infection (COVID-19) is determined by the presence of pneumonia, severe acute respiratory distress syndrome (SARS-CoV-2), myocarditis, microvascular thrombosis and/or cytokine storms, all of which involve underlying inflammation.\nA principal defence against uncontrolled inflammation, and against viral infection in general, is provided by T regulatory lymphocytes (Tregs).\nTreg levels have been reported to be low in many COVID-19 patients and can be increased by vitamin D supplementation.\nLow vitamin D levels have been associated with an increase in inflammatory cytokines and a significantly increased risk of pneumonia and viral upper respiratory tract infections.\nVitamin D deficiency is associated with an increase in thrombotic episodes, which are frequently observed in COVID-19.\nVitamin D deficiency has been found to occur more frequently in patients with obesity and diabetes.\nThese conditions are reported to carry a higher mortality in COVID-19.\nIf vitamin D does in fact reduce the severity of COVID-19 in regard to pneumonia/ARDS, inflammation, inflammatory cytokines and thrombosis, it is our opinion that supplements would offer a relatively easy option to decrease the impact of the pandemic.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"A principal defence against uncontrolled inflammation, and against viral infection in general, is provided by T regulatory lymphocytes (Tregs).\", \"Treg levels have been reported to be low in many COVID-19 patients and can be increased by vitamin D supplementation.\", \"Low vitamin D levels have been associated with an increase in inflammatory cytokines and a significantly increased risk of pneumonia and viral upper respiratory tract infections.\", \"Vitamin D deficiency is associated with an increase in thrombotic episodes, which are frequently observed in COVID-19.\", \"Vitamin D deficiency has been found to occur more frequently in patients with obesity and diabetes.\", \"These conditions are reported to carry a higher mortality in COVID-19.\", \"If vitamin D does in fact reduce the severity of COVID-19 in regard to pneumonia/ARDS, inflammation, inflammatory cytokines and thrombosis, it is our opinion that supplements would offer a relatively easy option to decrease the impact of the pandemic.\"]}", "id": 735}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Mathematical modeling explains differential sars cov-2 kinetics in lung and nasal passages in remdesivir treated rhesus macaques\n\nAbstract:\nRemdesivir was recently demonstrated to decrease recovery time in hospitalized patients with SARS-CoV-2 infection.\nIn rhesus macaques, early initiation of remdesivir therapy prevented pneumonia and lowered viral loads in the lung, but viral loads increased in the nasal passages five days after therapy.\nWe developed mathematical models to explain these results.\nWe identified that 1) drug potency is slightly higher in nasal passages than in lungs, 2) viral load decrease in lungs relative to nasal passages during therapy because of infection-dependent generation of refractory cells in the lung, 3) incomplete drug potency in the lung that decreases viral loads even slightly may allow substantially less lung damage, and 4) increases in nasal viral load may occur due to a slight blunting of peak viral load and subsequent decrease of the intensity of the innate immune response, as well as a lack of refractory cells.\nWe also hypothesize that direct inoculation of the trachea in rhesus macaques may not recapitulate natural infection as lung damage occurs more abruptly in this model than in human infection.\nWe demonstrate with sensitivity analysis that a drug with higher potency could completely suppress viral replication and lower viral loads abruptly in the nasal passages as well as the lung.\nOne Sentence Summary We developed a mathematical model to explain why remdesivir has a greater antiviral effect on SARS CoV-2 in lung versus nasal passages in rhesus macaques.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"In rhesus macaques, early initiation of remdesivir therapy prevented pneumonia and lowered viral loads in the lung, but viral loads increased in the nasal passages five days after therapy.\", \"One Sentence Summary We developed a mathematical model to explain why remdesivir has a greater antiviral effect on SARS CoV-2 in lung versus nasal passages in rhesus macaques.\"]}", "id": 736}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Structure of sars-cov-2 orf8 , a rapidly evolving coronavirus protein implicated in immune evasion\n\nAbstract:\nThe molecular basis for the severity and rapid spread of the COVID-19 disease caused by SARS-CoV-2 is largely unknown.\nORF8 is a rapidly evolving accessory protein that has been proposed to interfere with immune responses.\nThe crystal structure of SARS-CoV-2 ORF8 was determined at 2.04 \u00c5 resolution by x-ray crystallography.\nThe structure reveals a ~60 residue core similar to SARS-CoV ORF7a with the addition of two dimerization interfaces unique to SARS-CoV-2 ORF8.\nA covalent disulfide-linked dimer is formed through an N-terminal sequence specific to SARS-CoV-2, while a separate non-covalent interface is formed by another SARS-CoV-2-specific sequence, (73)YIDI(76).\nTogether the presence of these interfaces shows how SARS-CoV-2 ORF8 can form unique large-scale assemblies not possible for SARS-CoV, potentially mediating unique immune suppression and evasion activities.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Together the presence of these interfaces shows how SARS-CoV-2 ORF8 can form unique large-scale assemblies not possible for SARS-CoV, potentially mediating unique immune suppression and evasion activities.\"]}", "id": 737}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Cloth face coverings are most likely to reduce the spread of the COVID-19 virus when they are widely used by people in public settings\n\nAbstract:\nControversy exists around the appropriate types of masks and the situations in which they should be used in community and health care settings for the prevention of SARS-CoV-2 infection.\nIn this article, the American College of Physicians (ACP) provides recommendations based on the best available evidence through 14 April 2020 on the effectiveness of N95 respirators, surgical masks, and cloth masks in reducing transmission of infection.\nThe ACP plans periodic updates of these recommendations on the basis of ongoing surveillance of the literature for 1 year from the initial search date.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 738}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: A booster dose mays immunogenicity of the covid-19 vaccine candidate chadox1 ncov-19 in aged mice\n\nAbstract:\nThe spread of SARS-CoV-2 has caused a global pandemic that has affected almost every aspect of human life.\nThe development of an effective COVID-19 vaccine could limit the morbidity and mortality caused by infection, and may enable the relaxation of social distancing measures.\nAge is one of the most significant risk factors for poor health outcomes after SARS-CoV-2 infection, therefore it is desirable that any new vaccine candidates should elicit a robust immune response in older adults.\nHere, we test the immunogenicity of the adenoviral vectored vaccine ChAdOx1 nCoV-19 (AZD-1222) in aged mice.\nWe find that a single dose of this vaccine induces cellular and humoral immunity in aged mice, but at a reduced magnitude than in younger adult mice.\nFurthermore, we report that a second dose enhances the immune response to this vaccine in aged mice, indicating that a primeboost strategy may be a rational approach to enhance immunogenicity in older persons.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"We find that a single dose of this vaccine induces cellular and humoral immunity in aged mice, but at a reduced magnitude than in younger adult mice.\", \"Furthermore, we report that a second dose enhances the immune response to this vaccine in aged mice, indicating that a primeboost strategy may be a rational approach to enhance immunogenicity in older persons.\"]}", "id": 739}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: It is correct to call the virus that causes COVID-19, SARS.\n\nAbstract:\nThe recent global outbreak of viral pneumonia designated as Coronavirus Disease 2019 (COVID-19) by coronavirus (SARS-CoV-2) has threatened global public health and urged to investigate its source.\nWhole genome analysis of SARS-CoV-2 revealed ~96% genomic similarity with bat CoV (RaTG13) and clustered together in phylogenetic tree.\nFurthermore, RaTGl3 also showed 97.43% spike protein similarity with SARS-CoV-2 suggesting that RaTGl3 is the closest strain.\nHowever, RBD and key amino acid residues supposed to be crucial for human-to-human and cross-species transmission are homologues between SARS-CoV-2 and pangolin CoVs.\nThese results from our analysis suggest that SARS-CoV-2 is a recombinant virus of bat and pangolin CoVs.\nMoreover, this study also reports mutations in coding regions of 125 SARS-CoV-2 genomes signifying its aptitude for evolution.\nIn short, our findings propose that homologous recombination has been occurred between bat and pangolin CoVs that triggered cross-species transmission and emergence of SARS-CoV-2, and, during the ongoing outbreak, SARS-CoV-2 is still evolving for its adaptability.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"The recent global outbreak of viral pneumonia designated as Coronavirus Disease 2019 (COVID-19) by coronavirus (SARS-CoV-2) has threatened global public health and urged to investigate its source.\"]}", "id": 740}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Evidence is currently lacking and it is too early to make robust conclusions on any link between use of angiotensin-converting enzyme (ACE) inhibitors and angiotensin II type-I receptor blockers with risk or severity of novel coronavirus disease 2019 (COVID-19) infection.\n\nAbstract:\nAims: The question of interactions between the renin angiotensin aldosterone system drugs and the incidence and prognosis of COVID-19 infection has been raised by the medical community.\nWe hypothesised that if patients treated with ACE inhibitors (ACEI) or AT1 receptor blockers (ARB) were more prone to SARS-CoV2 infection and had a worse prognosis than untreated patients, the prevalence of consumption of these drugs would be higher in patients with COVID-19 compared to the general population.\nMethods and results: We used a clinical epidemiology approach based on the estimation of standardised prevalence ratio (SPR) of consumption of ACEI and ARB in four groups of patients (including 187 COVID-19 positive) with increasing severity referred to the University hospital of Lille and in three French reference samples (the exhaustive North population (n=1,569,968), a representative sample of the French population (n=414,046), a random sample of Lille area (n=1,584)).\nThe SPRs of ACEI and ARB did not differ as the severity of the COVID-19 patients increased, being similar to the regular consumption of these drugs in the North of France population with the same non-significant increase for both treatment (1.17 [0.83-1.67]).\nA statistically significant increase in the SPR of ARB (1.56 [1.02-2.39]) was observed in intensive care unit patients only.\nAfter stratification on obesity, this increase was limited to the high risk subgroup of obese patients.\nConclusions: Our results strongly support the recommendation that ACEI and ARB should be continued in the population and in COVID-19 positive patients, reinforcing the position of several scientific societies.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 741}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: People with weakened immune systems are at higher risk of getting severely sick from SARS-CoV-2, the virus that causes COVID-19.\n\nAbstract:\nThe rapid global spread of SARS-CoV-2 and resultant mortality and social disruption have highlighted the need to better understand coronavirus immunity to expedite vaccine development efforts.\nMultiple candidate vaccines, designed to elicit protective neutralising antibodies targeting the viral spike glycoprotein, are rapidly advancing to clinical trial.\nHowever, the immunogenic properties of the spike protein in humans are unresolved.\nTo address this, we undertook an in-depth characterisation of humoral and cellular immunity against SARS-CoV-2 spike in humans following mild to moderate SARS-CoV-2 infection.\nWe find serological antibody responses against spike are routinely elicited by infection and correlate with plasma neutralising activity and capacity to block ACE2/RBD interaction.\nExpanded populations of spike-specific memory B cells and circulating T follicular helper cells (cTFH) were detected within convalescent donors, while responses to the receptor binding domain (RBD) constitute a minor fraction.\nUsing regression analysis, we find high plasma neutralisation activity was associated with increased spike-specific antibody, but notably also with the relative distribution of spike-specific cTFH subsets.\nThus both qualitative and quantitative features of B and T cell immunity to spike constitute informative biomarkers of the protective potential of novel SARS-CoV-2 vaccines.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 742}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: The drugs have anti-inflammatory effects \"in addition to their blood pressure benefits.\n\nAbstract:\nINTRODUCTION The present research aimed to determine the relation between the use of angiotensin-converting enzyme inhibitors (ACE inh) and angiotensinogen receptor blockers (ARBs) and in-hospital mortality of hypertensive patients diagnosed with Covid-19 pneumonia.\nMATERIAL AND METHOD In this retrospective study, we included 113 consecutive hypertensive patients admitted due to Covid-19 infection.\nIn all patients, Covid-19 infection was confirmed with using reverse-transcription polymerase chain reaction.\nAll patients were on ACE inh/ARBs or other antihypertensive therapy unless no contraindication was present.\nThe primary outcome of the study was the in-hospital all-cause mortality.\nRESULTS In total, 113 hypertensive Covid-19 patients were included, of them 74 patients were using ACE inh/ARBs.\nDuring in-hospital follow up, 30.9% [n = 35 patients] of patients died.\nThe frequency of admission to the ICU and endotracheal intubation were significantly higher in patients using ACE inh/ARBs.\nIn a multivariable analysis, the use of ACE inh/ARBs was an independent predictor of in-hospital mortality (OR: 3.66; 95%CI: 1.11-18.18; p= .032).\nKaplan-Meir curve analysis displayed that patients on ACE inh/ARBs therapy had higher incidence of in-hospital death than those who were not.\nCONCLUSION The present study has found that the use of ACE inh/ARBs therapy might be associated with an increased in-hospital mortality in patients who were diagnosed with Covid-19 pneumonia.\nIt is likely that ACE inh/ARBs therapy might not be beneficial in the subgroup of hypertensive Covid-19 patients despite the fact that there might be the possibility of some unmeasured residual confounders to affect the results of the study.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 743}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Officials believe that the virus was present in meat sold at the said market.\n\nAbstract:\nIn 2003, severe acute respiratory syndrome coronavirus (SARS-CoV) caused one of the most devastating epidemics known to the developed world.\nThere were two important lessons from this epidemic.\nFirstly, coronaviruses, in addition to influenza viruses, can cause severe and rapidly spreading human infections.\nSecondly, bats can serve as the origin and natural animal reservoir of deadly human viruses.\nSince then, researchers around the world, especially those in Asia where SARS-CoV was first identified, have turned their focus to find novel coronaviruses infecting humans, bats, and other animals.\nTwo human coronaviruses, HCoV-HKU1 and HCoV-NL63, were identified shortly after the SARS-CoV epidemic as common causes of human respiratory tract infections.\nIn 2012, a novel human coronavirus, now called Middle East respiratory syndrome coronavirus (MERS-CoV), has emerged in the Middle East to cause fatal human infections in three continents.\nMERS-CoV human infection is similar to SARS-CoV in having a high fatality rate and the ability to spread from person to person which resulted in secondary cases among close contacts including healthcare workers without travel history to the Middle East.\nBoth viruses also have close relationships with bat coronaviruses.\nNew cases of MERS-CoV infection in humans continue to occur with the origins of the virus still unknown in many cases.\nA multifaceted approach is necessary to control this evolving MERS-CoV outbreak.\nSource identification requires detailed epidemiological studies of the infected patients and enhanced surveillance of MERS-CoV or similar coronaviruses in humans and animals.\nEarly diagnosis of infected patients and appropriate infection control measures will limit the spread in hospitals, while social distancing strategies may be necessary to control the outbreak in communities if it remained uncontrolled as in the SARS epidemic.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 744}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: with autoimmune conditions such as lupus, a person experiences \"dysregulations of the immune system,\" meaning the immune system itself is compromised or malfunctioning\n\nAbstract:\nSeveral related human coronaviruses (HCoVs) are endemic in the human population, causing mild respiratory infections1.\nSevere Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), the etiologic agent of Coronavirus disease 2019 (COVID-19), is a recent zoonotic infection that has quickly reached pandemic spread2,3.\nZoonotic introduction of novel coronaviruses is thought to occur in the absence of pre-existing immunity in the target human population.\nUsing diverse assays for detection of antibodies reactive with the SARS-CoV-2 Spike (S) glycoprotein, we demonstrate the presence of pre-existing immunity in uninfected and unexposed humans to the new coronavirus.\nSARS-CoV-2 S-reactive antibodies, exclusively of the IgG class, were readily detectable by a sensitive flow cytometry-based method in SARS-CoV-2-uninfected individuals with recent HCoV infection and targeted the S2 subunit.\nIn contrast, SARS-CoV-2 infection induced higher titres of SARS-CoV-2 S-reactive IgG antibodies, as well as concomitant IgM and IgA antibodies throughout the observation period of 6 weeks since symptoms onset.\nHCoV patient sera also variably reacted with SARS-CoV-2 S and nucleocapsid (N), but not with the S1 subunit or the receptor binding domain (RBD) of S on standard enzyme immunoassays.\nNotably, HCoV patient sera exhibited specific neutralising activity against SARS-CoV-2 S pseudotypes, according to levels of SARS-CoV-2 S-binding IgG and with efficiencies comparable to those of COVID-19 patient sera.\nDistinguishing pre-existing and de novo antibody responses to SARS-CoV-2 will be critical for serology, seroprevalence and vaccine studies, as well as for our understanding of susceptibility to and natural course of SARS-CoV-2 infection.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 745}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Saliva sampling is an excellent option to increase the number of sars cov2 diagnostic tests in settings with supplies shortages\n\nAbstract:\nAs part of any plan to lift or ease the confinement restrictions that are in place in many different countries, there is an urgent need to increase the capacity of laboratory testing for SARS CoV-2.\nDetection of the viral genome through RT-qPCR is the golden standard for this test, however, the high demand of the materials and reagents needed to sample individuals, purify the viral RNA, and perform the RT-qPCR test has resulted in a worldwide shortage of several of these supplies.\nHere, we show that directly lysed saliva samples can serve as a suitable source for viral RNA detection that is cheaper and can be as efficient as the classical protocol that involves column purification of the viral RNA.\nIn addition, it surpasses the need for swab sampling, decreases the risk of the healthcare personnel involved in this process, and accelerates the diagnostic procedure.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Detection of the viral genome through RT-qPCR is the golden standard for this test, however, the high demand of the materials and reagents needed to sample individuals, purify the viral RNA, and perform the RT-qPCR test has resulted in a worldwide shortage of several of these supplies.\"]}", "id": 746}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Sars-cov2 enables anaerobic bacteria to colonize the gut disrupting homeostasis\n\nAbstract:\nThe oral cavity, as the entry point to the body, may play a critical role in the pathogenesis of SARS-CoV-2 infection that has caused a global outbreak of the coronavirus disease 2019 (COVID-19).\nAvailable data indicate that the oral cavity may be an active site of infection and an important reservoir of SARS-CoV-2.\nConsidering that the oral surfaces are colonized by a diverse microbial community, it is likely that viruses have interactions with the host microbiota.\nPatients infected by SARS-CoV-2 may have alterations in the oral and gut microbiota, while oral species have been found in the lung of COVID-19 patients.\nFurthermore, interactions between the oral, lung, and gut microbiomes appear to occur dynamically whereby a dysbiotic oral microbial community could influence respiratory and gastrointestinal diseases.\nHowever, it is unclear whether SARS-CoV-2 infection can alter the local homeostasis of the resident microbiota, actively cause dysbiosis, or influence cross-body sites interactions.\nHere, we provide a conceptual framework on the potential impact of SARS-CoV-2 oral infection on the local and distant microbiomes across the respiratory and gastrointestinal tracts (\u2018oral-tract axes\u2019), which remains largely unexplored.\nStudies in this area could further elucidate the pathogenic mechanism of SARS-CoV-2 and the course of infection as well as the clinical symptoms of COVID-19 across different sites in the human host.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Patients infected by SARS-CoV-2 may have alterations in the oral and gut microbiota, while oral species have been found in the lung of COVID-19 patients.\"]}", "id": 747}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Children, like adults, who have COVID-19 but have no symptoms (asymptomatic) can still spread the virus to others.\n\nAbstract:\nBackground: As the novel coronavirus triggering COVID-19 has broken out in Wuhan, China and spread rapidly worldwide, it threatens the lives of thousands of people and poses a global threat on the economies of the entire world.\nHowever, infection with COVID-19 is currently rare in children.\nObjective To discuss the latest findings and research focus on the basis of characteristics of children confirmed with COVID-19, and provide an insight into the future treatment and research direction.\nMethods: We searched the terms \"COVID-19 OR coronavirus OR SARS-CoV-2\" AND \"Pediatric OR children\" on PubMed, Embase, Cochrane library, NIH, CDC, and CNKI.\nThe authors also reviewed the guidelines published on Chinese CDC and Chinese NHC.\nResults: We included 25 published literature references related to the epidemiology, clinical manifestation, accessary examination, treatment, and prognosis of pediatric patients with COVID-19.\nConclusion: The numbers of children with COVID-19 pneumonia infection are small, and most of them come from family aggregation.\nSymptoms are mainly mild or even asymptomatic, which allow children to be a risk factor for transmission.\nThus, strict epidemiological history screening is needed for early diagnosis and segregation.\nThis holds especially for infants, who are more susceptible to infection than other age groups in pediatric age, but have most likely subtle and unspecific symptoms.\nThey need to be paid more attention to.\nCT examination is a necessity for screening the suspected cases, because most of the pediatric patients are mild cases, and plain chest X-ray do not usually show the lesions or the detailed features.\nTherefore, early chest CT examination combined with pathogenic detection is a recommended clinical diagnosis scheme in children.\nThe risk factors which may suggest severe or critical progress for children are: Fast respiratory rate and/or; lethargy and drowsiness mental state and/or; lactate progressively increasing and/or; imaging showed bilateral or multi lobed infiltration, pleural effusion or rapidly expending of lesions in a short period of time and/or; less than 3 months old or those who underly diseases.\nFor those critical pediatric patients with positive SARS-CoV-2 diagnosis, polypnea may be the most common symptom.\nFor treatment, the elevated PCT seen in children in contrast to adults suggests that the underlying coinfection/secondary infection may be more common in pediatric patients and appropriate antibacterial treatment should be considered.\nOnce cytokine storm is found in these patients, anti-autoimmune or blood-purifying therapy should be given in time.\nFurthermore, effective isolation measures and appropriate psychological comfort need to be provided timely.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Conclusion: The numbers of children with COVID-19 pneumonia infection are small, and most of them come from family aggregation.\", \"Symptoms are mainly mild or even asymptomatic, which allow children to be a risk factor for transmission.\"]}", "id": 748}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: People with diabetes have not a higher risk for complications from coronavirus\n\nAbstract:\nAIMS: To describe characteristics of COVID-19 patients with type 2 diabetes and to analyze risk factors for severity.\nMETHODS: Demographics, comorbidities, symptoms, laboratory findings, treatments and outcomes of COVID-19 patients with diabetes were collected and analyzed.\nRESULTS: Seventy-fourCOVID-19 patients with diabetes were included.\nTwenty-seven patients (36.5%) were severe and 10 patients (13.5%) died.\nHigher levels of blood glucose, serum amyloid A (SAA), C reactive protein and interleukin 6 were associated with severe patients compared to non-severe ones (P<0.05).\nLevels of albumin, cholesterol, high density lipoprotein, small and dense low density lipoprotein and CD4+T lymphocyte counts in severe patients were lower than those in non-severe patients (P<0.05).\nLogistic regression analysis identified decreased CD4+T lymphocyte counts (odds ratio [OR]=0.988, 95%Confidence interval [95%CI] 0.979-0.997) and increased SAA levels (OR=1.029, 95%CI 1.002-1.058) as risk factors for severity of COVID-19 with diabetes (P<0.05).\nCONCLUSIONS: Type 2 diabetic patients were more susceptible to COVID-19 than overall population, which might be associated with hyperglycemia and dyslipidemia.\nAggressive treatment should be suggested, especially when these patients had low CD4+T lymphocyte counts and high SAA levels.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 749}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Some models show that if people wear masks, death rates from COVID-19 stay very low.\n\nAbstract:\nEvidence that face masks provide effective protection against respiratory infections in the community is scarce.\nHowever, face masks are widely used by health workers as part of droplet precautions when caring for patients with respiratory infections.\nIt would therefore be reasonable to suggest that consistent widespread use of face masks in the community could prevent further spread of the Severe Acute Respiratory Syndrome-Coronavirus 2 (SARS-CoV-2).\nIn this study we examine public face mask wearing in Uganda where a proportion wears masks to protect against acquiring, and the other to prevent from transmitting SARS-CoV-2.\nThe objective of this study was to determine what percentage of the population would have to wear face masks to reduce susceptibility to and infectivity of COVID-19 in Uganda, keeping the basic reproduction number below unity and/or flattening the curve.\nWe used an SEIAQRD model for the analysis.\nResults show that implementation of facemasks has a relatively large impact on the size of the coronavirus epidemic in Uganda.\nWe find that the critical mask adherence is 5 per 100 when 80% wear face masks.\nA cost-effective analysis shows that utilizing funds to provide 1 public mask to the population has a per capita compounded cost of USD 1.34.\nIf provision of face masks is done simultaneously with supportive care, the per capita compounded cost is USD 1.965, while for the case of only treatment and no provision of face masks costs each Ugandan USD 4.0579.\nWe conclude that since it is hard to achieve a 100% adherence to face masks, government might consider provision of face masks in conjunction with provision of care.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Evidence that face masks provide effective protection against respiratory infections in the community is scarce.\", \"However, face masks are widely used by health workers as part of droplet precautions when caring for patients with respiratory infections.\", \"It would therefore be reasonable to suggest that consistent widespread use of face masks in the community could prevent further spread of the Severe Acute Respiratory Syndrome-Coronavirus 2 (SARS-CoV-2).\"]}", "id": 750}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Homozygous l-atpase plays a protective role in sars coronavirus infection\n\nAbstract:\nSevere acute respiratory syndrome (SARS) is caused by infection of a previously undescribed coronavirus (CoV).\nL-SIGN, encoded by CLEC4M (also known as CD209L), is a SARS-CoV binding receptor that has polymorphism in its extracellular neck region encoded by the tandem repeat domain in exon 4.\nOur genetic risk association study shows that individuals homozygous for CLEC4M tandem repeats are less susceptible to SARS infection.\nL-SIGN is expressed in both non-SARS and SARS-CoV\u2013infected lung.\nCompared with cells heterozygous for L-SIGN, cells homozygous for L-SIGN show higher binding capacity for SARS-CoV, higher proteasome-dependent viral degradation and a lower capacity for trans infection.\nThus, homozygosity for L-SIGN plays a protective role during SARS infection.\nSUPPLEMENTARY INFORMATION: The online version of this article (doi:10.1038/ng1698) contains supplementary material, which is available to authorized users.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Thus, homozygosity for L-SIGN plays a protective role during SARS infection.\"]}", "id": 751}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: The immune system, noticing the infection, flares up, which can cause the lungs to fill with fluid and prevent adequate oxygen flow.\n\nAbstract:\nCoronaviruses are a genetically highly variable family of viruses that infect vertebrates and have succeeded in infecting humans many times by overcoming the species barrier.\nThe severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which initially appeared in China at the end of 2019, exhibits a high infectivity and pathogenicity compared to other coronaviruses.\nAs the viral coat and other viral components are recognized as being foreign by the immune system, this can lead to initial symptoms, which are induced by the very efficiently working immune defense system via the respiratory epithelium.\nDuring severe courses a systemically expressed proinflammatory cytokine storm and subsequent changes in the coagulation and complement systems can occur.\nVirus-specific antibodies, the long-term expression of which is ensured by the formation of B memory cell clones, generate a specific immune response that is also detectable in blood (seroconversion).\nSpecifically effective cytotoxic CD8+ T\u00adcell populations are also formed, which recognize viral epitopes as pathogen-specific patterns in combination with MHC presentation on the cell surface of virus-infected cells and destroy these cells.\nAt the current point in time it is unclear how regular, robust and durable this immune status is constructed.\nExperiences with other coronavirus infections (SARS and Middle East respiratory syndrome, MERS) indicate that the immunity could persist for several years.\nBased on animal experiments, already acquired data on other coronavirus types and plausibility assumptions, it can be assumed that seroconverted patients have an immunity of limited duration and only a very low risk of reinfection.\nKnowledge of the molecular mechanisms of viral cycles and immunity is an important prerequisite for the development of vaccination strategies and development of effective drugs.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 752}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Intradermal-delivered mva vaccine provides anamnestic protection in a rhesus macaque sars-cov-2 challenge model\n\nAbstract:\nCoronavirus disease 2019 (COVID-19), caused by the SARS-CoV-2 virus, has had a dramatic global impact on public health, social, and economic infrastructures.\nHere, we assess immunogenicity and anamnestic protective efficacy in rhesus macaques of the intradermal (ID)-delivered SARS-CoV-2 spike DNA vaccine, INO-4800.\nINO-4800 is an ID-delivered DNA vaccine currently being evaluated in clinical trials.\nVaccination with INO-4800 induced T cell responses and neutralizing antibody responses against both the D614 and G614 SARS-CoV-2 spike proteins.\nSeveral months after vaccination, animals were challenged with SARS-CoV-2 resulting in rapid recall of anti-SARS-CoV-2 spike protein T and B cell responses.\nThese responses were associated with lower viral loads in the lung and with faster nasal clearance of virus.\nThese studies support the immune impact of INO-4800 for inducing both humoral and cellular arms of the adaptive immune system which are likely important for providing durable protection against COVID-19 disease.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Here, we assess immunogenicity and anamnestic protective efficacy in rhesus macaques of the intradermal (ID)-delivered SARS-CoV-2 spike DNA vaccine, INO-4800.\", \"Vaccination with INO-4800 induced T cell responses and neutralizing antibody responses against both the D614 and G614 SARS-CoV-2 spike proteins.\", \"Several months after vaccination, animals were challenged with SARS-CoV-2 resulting in rapid recall of anti-SARS-CoV-2 spike protein T and B cell responses.\", \"These studies support the immune impact of INO-4800 for inducing both humoral and cellular arms of the adaptive immune system which are likely important for providing durable protection against COVID-19 disease.\"]}", "id": 753}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: a popular treatment to tamp down the immune system in severely ill patients may help a few, but could harm many others. \n\nAbstract:\nThe outbreak of the 2019 Novel Coronavirus (SARS-CoV-2) rapidly spread from Wuhan, China to more than 150 countries, areas or territories, causing staggering number of infections and deaths.\nA systematic profiling of the immune vulnerability landscape of SARS-CoV-2, which can bring critical insights into the immune clearance mechanism, peptide vaccine development, and antiviral antibody development, is lacking.\nIn this study, we investigated the potential of the SARS-CoV-2 viral proteins to induce class I and II MHC presentation and to form linear antibody epitopes.\nWe created an online database to broadly share the predictions as a resource for the research community.\nUsing this resource, we showed that genetic variations in SARS- CoV-2, though still few for the moment, already follow the pattern of mutations in related coronaviruses, and could alter the immune vulnerability landscape of this virus.\nImportantly, we discovered evidence that SARS-CoV-2, along with related coronaviruses, used mutations to evade attack from the human immune system.\nOverall, we present an immunological resource for SARS-CoV-2 that could promote both therapeutic development and mechanistic research.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 754}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Seasonal human coronavirus antibodies are boosted upon sars-cov-2 infection but are associated with protection\n\nAbstract:\nSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has rapidly spread within the human population.\nAlthough SARS-CoV-2 is a novel coronavirus, most humans had been previously exposed to other antigenically distinct common seasonal human coronaviruses (hCoVs) before the COVID-19 pandemic.\nHere, we quantified levels of SARS-CoV-2-reactive antibodies and hCoV-reactive antibodies in serum samples collected from 204 humans before the COVID-19 pandemic.\nWe then quantified pre-pandemic antibody levels in serum from a separate cohort of 252 individuals who became PCR-confirmed infected with SARS-CoV-2.\nFinally, we longitudinally measured hCoV and SARS-CoV-2 antibodies in the serum of hospitalized COVID-19 patients.\nOur studies indicate that most individuals possessed hCoV-reactive antibodies before the COVID-19 pandemic.\nWe determined that ~23% of these individuals possessed non-neutralizing antibodies that cross-reacted with SARS-CoV-2 spike and nucleocapsid proteins.\nThese antibodies were not associated with protection against SARS-CoV-2 infections or hospitalizations, but paradoxically these hCoV cross-reactive antibodies were boosted upon SARS-CoV-2 infection.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Although SARS-CoV-2 is a novel coronavirus, most humans had been previously exposed to other antigenically distinct common seasonal human coronaviruses (hCoVs) before the COVID-19 pandemic.\", \"We then quantified pre-pandemic antibody levels in serum from a separate cohort of 252 individuals who became PCR-confirmed infected with SARS-CoV-2.\", \"These antibodies were not associated with protection against SARS-CoV-2 infections or hospitalizations, but paradoxically these hCoV cross-reactive antibodies were boosted upon SARS-CoV-2 infection.\"]}", "id": 755}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Covid-19 is infecting quite a few people, many with vicious effects.\n\nAbstract:\nBACKGROUND: An epidemic of Coronavirus Disease 2019 (COVID-19) began in December 2019 and triggered a Public Health Emergency of International Concern (PHEIC).\nWe aimed to find risk factors for the progression of COVID-19 to help reducing the risk of critical illness and death for clinical help.\nMETHODS: The data of COVID-19 patients until March 20, 2020 were retrieved from four databases.\nWe statistically analyzed the risk factors of critical/mortal and non-critical COVID-19 patients with meta-analysis.\nRESULTS: Thirteen studies were included in Meta-analysis, including a total number of 3027 patients with SARS-CoV-2 infection.\nMale, older than 65, and smoking were risk factors for disease progression in patients with COVID-19 (male: OR\u00e2\u0080\u00af=\u00e2\u0080\u00af1.76, 95% CI (1.41, 2.18), P < 0.00001; age over 65 years old: OR =6.06, 95% CI(3.98, 9.22), P < 0.00001; current smoking: OR =2.51, 95% CI(1.39, 3.32), P\u00e2\u0080\u00af=\u00e2\u0080\u00af0.0006).\nThe proportion of underlying diseases such as hypertension, diabetes, cardiovascular disease, and respiratory disease were statistically significant higher in critical/mortal patients compared to the non-critical patients (diabetes: OR=3.68, 95% CI (2.68, 5.03), P < 0.00001; hypertension: OR\u00e2\u0080\u00af=\u00e2\u0080\u00af2.72, 95% CI (1.60,4.64), P\u00e2\u0080\u00af=\u00e2\u0080\u00af0.0002; cardiovascular disease: OR\u00e2\u0080\u00af=\u00e2\u0080\u00af5.19, 95% CI(3.25, 8.29), P < 0.00001; respiratory disease: OR\u00e2\u0080\u00af=\u00e2\u0080\u00af5.15, 95% CI(2.51, 10.57), P < 0.00001).\nClinical manifestations such as fever, shortness of breath or dyspnea were associated with the progression of disease [fever: 0R\u00e2\u0080\u00af=\u00e2\u0080\u00af0.56, 95% CI (0.38, 0.82), P\u00e2\u0080\u00af=\u00e2\u0080\u00af0.003;shortness of breath or dyspnea: 0R=4.16, 95% CI (3.13, 5.53), P < 0.00001].\nLaboratory examination such as aspartate amino transferase(AST) > 40U/L, creatinine(Cr) &#8805; 133mol/L, hypersensitive cardiac troponin I(hs-cTnI) > 28pg/mL, procalcitonin(PCT) > 0.5ng/mL, lactatede hydrogenase(LDH) > 245U/L, and D-dimer > 0.5mg/L predicted the deterioration of disease while white blood cells(WBC)<4\u00e2\u0080\u00af\u00d7\u00e2\u0080\u00af109/L meant a better clinical status[AST > 40U/L:OR=4.00, 95% CI (2.46, 6.52), P < 0.00001; Cr &#8805; 133\u00b5mol/L: OR\u00e2\u0080\u00af=\u00e2\u0080\u00af5.30, 95% CI (2.19, 12.83), P\u00e2\u0080\u00af=\u00e2\u0080\u00af0.0002; hs-cTnI > 28 pg/mL: OR\u00e2\u0080\u00af=\u00e2\u0080\u00af43.24, 95% CI (9.92, 188.49), P < 0.00001; PCT > 0.5 ng/mL: OR\u00e2\u0080\u00af=\u00e2\u0080\u00af43.24, 95% CI (9.92, 188.49), P < 0.00001;LDH > 245U/L: OR\u00e2\u0080\u00af=\u00e2\u0080\u00af43.24, 95% CI (9.92, 188.49), P < 0.00001; D-dimer > 0.5mg/L: OR\u00e2\u0080\u00af=\u00e2\u0080\u00af43.24, 95% CI (9.92, 188.49), P < 0.00001; WBC < 4\u00e2\u0080\u00af\u00d7\u00e2\u0080\u00af109/L: OR\u00e2\u0080\u00af=\u00e2\u0080\u00af0.30, 95% CI (0.17, 0.51), P < 0.00001].\nCONCLUSION: Male, aged over 65, smoking patients might face a greater risk of developing into the critical or mortal condition and the comorbidities such as hypertension, diabetes, cardiovascular disease, and respiratory diseases could also greatly affect the prognosis of the COVID-19.\nClinical manifestation such as fever, shortness of breath or dyspnea and laboratory examination such as WBC, AST, Cr, PCT, LDH, hs-cTnI and D-dimer could imply the progression of COVID-19.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 756}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Supplementation is unlikely to show an effect in people whose vitamin C levels are already high\n\nAbstract:\nBACKGROUND The coronavirus disease 2019 (COVID-19) pandemic has affected almost 2.5 million people worldwide with almost 170 000 deaths reported to date.\nSo far, there is scarce evidence for the current treatment options available for COVID-19.\nVitamin C has previously been used for treatment of severe sepsis and septic shock.\nWe reviewed the feasibility of using vitamin C in the setting of COVID-19 in a series of patients.\nMETHODS We sequentially identified a series of patients who were requiring at least 30% of FiO2 or more who received IV vitamin C as part of the COVID-19 treatment and analyzed their demographic and clinical characteristics.\nWe compared inflammatory markers pre and post treatment including D-dimer and ferritin.\nRESULTS We identified a total of 17 patients who received IV vitamin C for COVID-19.\nThe inpatient mortality rate in this series was 12% with 17.6% rates of intubation and mechanical ventilation.\nWe noted a significant decrease in inflammatory markers, including ferritin and D-dimer, and a trend to decreasing FiO2 requirements, after vitamin C administration.\nCONCLUSION The use of IV vitamin C in patients with moderate to severe COVID-19 disease may be feasible.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 757}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: there are few novel sars-cov-2 cases in malaria countries because of the use of the antimalarial drug hydroxychloroquine.\n\nAbstract:\nHydroxychloroquine has been promoted for its use in treatment of COVID-19 patients based on in-vitro evidences.\nWe searched the databases to include randomized and observational studies evaluating the effect of Hydroxychloroquine on mortality in COVID-19 patients.\nThe outcome was summarized as odds ratios (OR) with a 95% confidence interval (CI).We used the inverse-variance method with a random effect model and assessed the heterogeneity using I2 test.\nWe used ROBINS-I tool to assess methodological quality of the included studies.\nWe performed the meta-analysis using 'Review manager software version 5.3'.\nWe identified 6 observationalstudies satisfying the selection criteria.\nIn all studies, Hydroxychloroquine was given as add on to the standard care and effect was compared with the standard care alone.\nA pooled analysis observed 251 deaths in 1331 participants of the Hydroxychloroquine arm and 363 deaths in 1577 participants of the control arm.\nThere was no difference in odds of mortality events amongst Hydroxychloroquine and supportive care arm [1.25 (95% CI: 0.65, 2.38); I2 = 80%].\nA similar trend was observed with moderate risk of bias studies [0.95 (95% CI: 0.44, 2.06); I2 = 85%].\nThe odds of mortality were significantly higher in patients treated with Hydroxychloroquine + Azithromycin than supportive care alone [2.34 (95% CI: 1.63, 3.34); I2 = 0%].\nA pooled analysis of recently published studies suggests no additional benefit for reducing mortality in COVID-19 patients when Hydroxychloroquine is given as add-on to the standard care.\nGraphical Abstract.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"The odds of mortality were significantly higher in patients treated with Hydroxychloroquine + Azithromycin than supportive care alone [2.34 (95% CI: 1.63, 3.34); I2 = 0%].\", \"A pooled analysis of recently published studies suggests no additional benefit for reducing mortality in COVID-19 patients when Hydroxychloroquine is given as add-on to the standard care.\"]}", "id": 758}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Like other viruses with a lipid envelope, SARS-CoV-2 is probably sensitive to temperature, humidity, and solar radiation\n\nAbstract:\nThe 2020 coronavirus pandemic is developing at different paces throughout the world.\nSome areas, like the Caribbean Basin, have yet to see the virus strike at full force.\nWhen it does, there is reasonable evidence to suggest the consequent COVID-19 outbreaks will overwhelm healthcare systems and economies.\nThis is particularly concerning in the Caribbean as pandemics can have disproportionately higher mortality impacts on lower and middle-income countries.\nPreliminary observations from our team and others suggest that temperature and climatological factors could influence the spread of this novel coronavirus, making spatiotemporal predictions of its infectiousness possible.\nThis review studies geographic and time-based distribution of known respiratory viruses in the Caribbean Basin in an attempt to foresee how the pandemic will develop in this region.\nThis review is meant to aid in planning short- and long-term interventions to manage outbreaks at the international, national, and subnational levels in the region.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Preliminary observations from our team and others suggest that temperature and climatological factors could influence the spread of this novel coronavirus, making spatiotemporal predictions of its infectiousness possible.\"]}", "id": 759}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: adults who are hooked on cigarettes are 50% less likely to test positive for the illness\n\nAbstract:\nObjectives: To investigate whether there is a causal effect of cardiometabolic traits on risk of sepsis and severe covid-19.\nDesign: Mendelian randomisation analysis.\nSetting: UK Biobank and HUNT study population-based cohorts for risk of sepsis, and genome-wide association study summary data for risk of severe covid-19 with respiratory failure.\nParticipants: 12,455 sepsis cases (519,885 controls) and 1,610 severe covid-19 with respiratory failure cases (2,205 controls).\nExposure: Genetic variants that proxy body mass index (BMI), lipid traits, systolic blood pressure, lifetime smoking score, and type 2 diabetes liability - derived from studies considering between 188,577 to 898,130 participants.\nMain outcome measures: Risk of sepsis and severe covid-19 with respiratory failure.\nResults: Higher genetically proxied BMI and lifetime smoking score were associated with increased risk of sepsis in both UK Biobank (BMI: odds ratio 1.38 per standard deviation increase, 95% confidence interval [CI] 1.27 to 1.51; smoking: odds ratio 2.81 per standard deviation increase, 95% CI 2.09-3.79) and HUNT (BMI: 1.41, 95% CI 1.18 to 1.69; smoking: 1.93, 95% CI 1.02-3.64).\nHigher genetically proxied BMI and lifetime smoking score were also associated with increased risk of severe covid-19, although with wider confidence intervals (BMI: 1.75, 95% CI 1.20 to 2.57; smoking: 3.94, 95% CI 1.13 to 13.75).\nThere was limited evidence to support associations of genetically proxied lipid traits, systolic blood pressure or type 2 diabetes liability with risk of sepsis or severe covid-19.\nSimilar findings were generally obtained when using Mendelian randomization methods that are more robust to the inclusion of pleiotropic variants, although the precision of estimates was reduced.\nConclusions: Our findings support a causal effect of elevated BMI and smoking on risk of sepsis and severe covid-19.\nClinical and public health interventions targeting obesity and smoking are likely to reduce sepsis and covid-19 related morbidity, along with the plethora of other health-related outcomes that these traits adversely affect.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Results: Higher genetically proxied BMI and lifetime smoking score were associated with increased risk of sepsis in both UK Biobank (BMI: odds ratio 1.38 per standard deviation increase, 95% confidence interval [CI] 1.27 to 1.51; smoking: odds ratio 2.81 per standard deviation increase, 95% CI 2.09-3.79) and HUNT (BMI: 1.41, 95% CI 1.18 to 1.69; smoking: 1.93, 95% CI 1.02-3.64).\", \"Conclusions: Our findings support a causal effect of elevated BMI and smoking on risk of sepsis and severe covid-19.\"]}", "id": 760}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Wearing the N95 respirator mask can protect against coronavirus\n\nAbstract:\nWe identified seasonal human coronaviruses, influenza viruses and rhinoviruses in exhaled breath and coughs of children and adults with acute respiratory illness.\nSurgical face masks significantly reduced detection of influenza virus RNA in respiratory droplets and coronavirus RNA in aerosols, with a trend toward reduced detection of coronavirus RNA in respiratory droplets.\nOur results indicate that surgical face masks could prevent transmission of human coronaviruses and influenza viruses from symptomatic individuals.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Surgical face masks significantly reduced detection of influenza virus RNA in respiratory droplets and coronavirus RNA in aerosols, with a trend toward reduced detection of coronavirus RNA in respiratory droplets.\", \"Our results indicate that surgical face masks could prevent transmission of human coronaviruses and influenza viruses from symptomatic individuals.\"]}", "id": 761}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Hospital readmissions of sars patients with covid-19\n\nAbstract:\nBackground.\nCOVID-19 infection has led to an overwhelming effort by health institutions to meet the high demand for hospital admissions.\nAim.\nTo analyse the clinical variables associated with readmission of patients who had previously been discharged after admission for COVID-19.\nDesign and methods.\nWe studied a retrospective cohort of patients with laboratory-confirmed SARS-CoV-2 infection who were admitted and subsequently discharged alive.\nWe then conducted a nested case-control study paired (1:1 ratio) by age, sex and period of admission.\nResults.\nOut of 1368 patients who were discharged during the study period, 61 patients (4.4%) were readmitted.\nImmunocompromised patients were at increased risk for readmission.\nThere was also a trend towards a higher probability of readmission in hypertensive patients (p=0.07).\nCases had had a shorter hospital stay and a higher prevalence of fever during the 48 hours prior to discharge.\nThere were no significant differences in oxygen levels measured at admission and discharge by pulse oximetry intra-subject or between the groups.\nNeutrophil/lymphocyte ratio at hospital admission tended to be higher in cases than in controls (p=0.06).\nThe motive for readmission in 10 patients (16.4%), was a thrombotic event in venous or arterial territory (p<0.001).\nNeither glucocorticoids nor anticoagulants prescribed at hospital discharge were associated with a lower readmission rate.\nConclusions.\nThe rate of readmission after discharge from hospital for COVID-19 was low.\nImmunocompromised patients and those presenting with fever during the 48 hours prior to discharge are at greater risk of readmission to hospital.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Immunocompromised patients and those presenting with fever during the 48 hours prior to discharge are at greater risk of readmission to hospital.\"]}", "id": 762}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: A study from the Journal of Hospital Infection found that wearing a face covering slashed the risk of infection by 24% for a simple cotton covering and up to 99% for a professional, medical-grade filtration mask. \n\nAbstract:\nThe COVID\u201019 pandemic caused by the novel coronavirus SARS\u2010CoV\u20102 has claimed many lives worldwide.\nWearing medical masks or N95 masks (namely N95 respirators) can slow the virus spread and reduce the infection risk.\nReuse of these masks can minimize waste, protect the environment, and help to solve the current imminent shortage of masks.\nDisinfection of used masks is needed for reuse of them with safety, but improper decontamination can damage the blocking structure of masks.\nIn this study, we demonstrated, using avian coronavirus of infectious bronchitis virus to mimic SARS\u2010CoV\u20102, that medical masks and N95 masks remained their blocking efficacy after being steamed on boiling water even for 2 hours.\nWe also demonstrated that three brands of medical masks blocked over 99% viruses in aerosols.\nThe avian coronavirus was completely inactivated after being steamed for 5 minutes.\nTogether, this study suggested that medical masks are adequate for use on most social occasions, and both medical masks and N95 masks can be reused for a few days with steam decontamination between use.\nThis article is protected by copyright.\nAll rights reserved.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Wearing medical masks or N95 masks (namely N95 respirators) can slow the virus spread and reduce the infection risk.\"]}", "id": 763}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: if you are low risk (healthy, young), you do not need social distancing.\n\nAbstract:\nBACKGROUND: The Australian and New Zealand governments both initiated strict social distancing measures in response to the COVID-19 pandemic in late March.\nIt remains difficult to quantify the impact this had in reducing the spread of the virus.\nMETHODS: Bayesian structural time series model provide a model to quantify the scenario in which these government-level interventions were not placed.\nOur models predict these strict social distancing measures caused a 79% and 61% reduction in the daily cases of COVID-19 across Australia and New Zealand respectively.\nCONCLUSION: This provides both evidence and impetus for governments considering similar measures in response to COVID-19 and other pandemics.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Our models predict these strict social distancing measures caused a 79% and 61% reduction in the daily cases of COVID-19 across Australia and New Zealand respectively.\"]}", "id": 764}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Attenuated influenza virions expressing the sars- cov-2 receptor-binding domain induce neutralizing antibodies in mice\n\nAbstract:\nAn effective vaccine is essential for controlling the spread of the SARS-CoV-2 virus.\nHere, we describe an influenza virus-based vaccine for SARS-CoV-2.\nWe incorporated a membrane-anchored form of the SARS-CoV-2 spike receptor binding domain (RBD) in place of the neuraminidase (NA) coding sequence in an influenza virus also possessing a mutation that reduces the affinity of hemagglutinin for its sialic acid receptor.\nThe resulting \u0394NA(RBD)-Flu virus can be generated by reverse genetics and grown to high titers in cell culture.\nA single-dose intranasal inoculation of mice with \u0394NA(RBD)-Flu elicits serum neutralizing antibody titers against SAR-CoV-2 comparable to those observed in humans following natural infection (~1:200).\nFurthermore, \u0394NA(RBD)-Flu itself causes no apparent disease in mice.\nIt might be possible to produce a vaccine similar to \u0394NA(RBD)-Flu at scale by leveraging existing platforms for the production of influenza vaccines.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"The resulting \\u0394NA(RBD)-Flu virus can be generated by reverse genetics and grown to high titers in cell culture.\", \"A single-dose intranasal inoculation of mice with \\u0394NA(RBD)-Flu elicits serum neutralizing antibody titers against SAR-CoV-2 comparable to those observed in humans following natural infection (~1:200).\"]}", "id": 765}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Avoid medications to lower fever if sick with the new coronavirus\n\nAbstract:\nFever has been reported as a common symptom occurring in COVID-19 illness.\nOver the counter antipyretics such as ibuprofen and acetaminophen are often taken by individuals to reduce the discomfort of fever.\nRecently, the safety of ibuprofen in COVID-19 patients has been questioned due to anecdotal reports of worsening symptoms in previously healthy young adults.\nStudies show that ibuprofen demonstrates superior efficacy in fever reduction compared to acetaminophen.\nAs fever may have benefit in shortening the duration of viral illness, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response when taken during the early stages of COVID-19 illness.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"As fever may have benefit in shortening the duration of viral illness, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response when taken during the early stages of COVID-19 illness.\"]}", "id": 766}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: The high temperature and low relative humidity lead to high evaporation rates of saliva-contaminated droplets, thus significantly reducing the coronavirus viability\n\nAbstract:\nThis paper investigates the correlation between the high level of coronavirus SARS-CoV-2 infection accelerated transmission and lethality, and surface air pollution in Milan metropolitan area, Lombardy region in Italy.\nFor January-April 2020 period, time series of daily average inhalable gaseous pollutants ozone (O3) and nitrogen dioxide (NO2), together climate variables (air temperature, relative humidity, wind speed, precipitation rate, atmospheric pressure field and Planetary Boundary Layer) were analyzed.\nIn spite of being considered primarily transmitted by indoor bioaerosols droplets and infected surfaces or direct human-to-human personal contacts, it seems that high levels of urban air pollution, and climate conditions have a significant impact on SARS-CoV-2 diffusion.\nExhibited positive correlations of ambient ozone levels and negative correlations of NO2 with the increased rates of COVID-19 infections (Total number, Daily New positive and Total Deaths cases), can be attributed to airborne bioaerosols distribution.\nThe results show positive correlation of daily averaged O3 with air temperature and inversely correlations with relative humidity and precipitation rates.\nViral genome contains distinctive features, including a unique N-terminal fragment within the spike protein, which allows coronavirus attachment on ambient air pollutants.\nAt this moment it is not clear if through airborne diffusion, in the presence of outdoor and indoor aerosols, this protein \"spike\" of the new COVID-19 is involved in the infectious agent transmission from a reservoir to a susceptible host during the highest nosocomial outbreak in some agglomerated industrialized urban areas like Milan is.\nAlso, in spite of collected data for cold season (winter-early spring) period, when usually ozone levels have lower values than in summer, the findings of this study support possibility as O3 can acts as a COVID-19 virus incubator.\nBeing a novel pandemic coronavirus version, it might be ongoing during summer conditions associated with higher air temperatures, low relative humidity and precipitation levels.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Being a novel pandemic coronavirus version, it might be ongoing during summer conditions associated with higher air temperatures, low relative humidity and precipitation levels.\"]}", "id": 767}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: A report indicates that Acetaminophen (Tylenol) may be preferred over Ibuprofen (Advil) for coronavirus (fever)\n\nAbstract:\nSARS-CoV-2 is a novel coronavirus that emerged in 2019 and is causing the COVID-19 pandemic.\nThere is no current standard of care.\nClinicians need to be mindful of the toxicity of a wide variety of possibly unfamiliar substances being tested or repurposed to treat COVID-19.\nThe United States Food and Drug Administration (FDA) has provided emergency authorization for the use of chloroquine and hydroxychloroquine.\nThese two medications may precipitate ventricular dysrhythmias, necessitating cardiac and electrolyte monitoring, and in severe cases, treatment with epinephrine and high-doses of diazepam.\nRecombinant protein therapeutics may cause serum sickness or immune complex deposition.\nNucleic acid vaccines may introduce mutations into the human genome.\nACE inhibitors and ibuprofen have been suggested to exacerbate the pathogenesis of COVID-19.\nHere, we review the use, mechanism of action, and toxicity of proposed COVID-19 therapeutics.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 768}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Nonsteroidal anti-inflammatory drugs (NSAIDs), such as ibuprofen, aspirin, and Advil, reduce fever, pain, and inflammation.\n\nAbstract:\nIbuprofen is an over-the-counter medication that is used widely for the treatment of pain and fever during COVID-19 pandemic.\nA concern was raised regarding the safety of ibuprofen use because of its role in increasing ACE2 levels within the Renin-Angiotensin-Aldosterone system.\nACE2 is the coreceptor for the entry of SARS-CoV-2 into cells, and so, a potential increased risk of contracting COVID-19 disease and/or worsening of COVID-19 infection was feared with ibuprofen use.\nHowever, available data from limited studies show administration of recombinant ACE2 improves lung damage caused by respiratory viruses, suggesting ibuprofen use may be beneficial in COVID-19 disease.\nAt this time, there is no supporting evidence to discourage the use of ibuprofen.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Ibuprofen is an over-the-counter medication that is used widely for the treatment of pain and fever during COVID-19 pandemic.\", \"However, available data from limited studies show administration of recombinant ACE2 improves lung damage caused by respiratory viruses, suggesting ibuprofen use may be beneficial in COVID-19 disease.\", \"At this time, there is no supporting evidence to discourage the use of ibuprofen.\"]}", "id": 769}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: To help slow the spread and reduce your risk of COVID-19, stay at least 6 feet away from others. Keeping physical distance is important, even if you are not sick.\n\nAbstract:\nThe infectious diseases are spreading due to human interactions enabled by various social networks.\nTherefore, when a new pathogen such as SARS-CoV-2 causes an outbreak, the non-pharmaceutical isolation strategies (e.g., social distancing) are the only possible response to disrupt its spreading.\nTo this end, we introduce the new epidemic model (SICARS) and compare the centralized (C), decentralized (D), and combined (C+D) social distancing strategies, and analyze their efficiency to control the dynamics of COVID-19 on heterogeneous complex networks.\nOur analysis shows that the centralized social distancing is necessary to minimize the pandemic spreading.\nThe decentralized strategy is insufficient when used alone, but offers the best results when combined with the centralized one.\nIndeed, the (C+D) is the most efficient isolation strategy at mitigating the network superspreaders and reducing the highest node degrees to less than 10% of their initial values.\nOur results also indicate that stronger social distancing, e.g., cutting 75% of social ties, can reduce the outbreak by 75% for the C isolation, by 33% for the D isolation, and by 87% for the (C+D) isolation strategy.\nFinally, we study the impact of proactive versus reactive isolation strategies, as well as their delayed enforcement.\nWe find that the reactive response to the pandemic is less efficient, and delaying the adoption of isolation measures by over one month (since the outbreak onset in a region) can have alarming effects; thus, our study contributes to an understanding of the COVID-19 pandemic both in space and time.\nWe believe our investigations have a high social relevance as they provide insights into understanding how different degrees of social distancing can reduce the peak infection ratio substantially; this can make the COVID-19 pandemic easier to understand and control over an extended period of time.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Our analysis shows that the centralized social distancing is necessary to minimize the pandemic spreading.\", \"The decentralized strategy is insufficient when used alone, but offers the best results when combined with the centralized one.\", \"Indeed, the (C+D) is the most efficient isolation strategy at mitigating the network superspreaders and reducing the highest node degrees to less than 10% of their initial values.\", \"Our results also indicate that stronger social distancing, e.g., cutting 75% of social ties, can reduce the outbreak by 75% for the C isolation, by 33% for the D isolation, and by 87% for the (C+D) isolation strategy.\"]}", "id": 770}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: The researchers also ranked face mask material from most to least effective in their testing.\n\nAbstract:\nFace masks are an avenue to curb the spread of coronavirus, but few people in Western societies wear face masks.\nSocial scientists have rarely studied face mask wearing, leaving little guidance for methods to encourage these behaviours.\nIn the current article, we provide an approach to address this issue by developing the 32-item and 8-dimension Face Mask Perceptions Scale (FMPS).\nWe begin by developing an over-representative item list in a qualitative study, wherein participants' responses are used to develop items to ensure content relevance.\nThis item list is then reduced via exploratory factor analysis in a second study, and the eight dimensions of the scale are supported.\nWe also support the validity of the FMPS, as the scale significantly relates to both face mask wearing and health perceptions.\nWe lastly confirm the factor structure of the FMPS in a third study via confirmatory factor analysis.\nFrom these efforts, we identify an avenue that social scientists can aid in preventing coronavirus and illness more broadly - by studying face mask perceptions and behaviours.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Face masks are an avenue to curb the spread of coronavirus, but few people in Western societies wear face masks.\"]}", "id": 771}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: N95 masks are better than clothe masks\n\nAbstract:\nMa's research shows N95 masks, medical masks, even homemade masks could block at least 90% of the virus in aerosols(1).\nThis study puts the debate on whether the public wear masks back on the table.\nRecently Science interviewed Dr. Gao, director\u2010general of Chinese Center for Disease Control and Prevention (CDC).\nThis article is protected by copyright.\nAll rights reserved.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 772}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Low testosterone levels predict clinical adverse outcomes in sars-cov-2 diabetes patients\n\nAbstract:\nBACKGROUND: The pandemic of new severe acute respiratory syndrome (SARS) due to coronavirus (CoV) 2 (SARS-CoV-2) has stressed the importance of effective diagnostic and prognostic biomarkers of clinical worsening and mortality.\nEpidemiological data showing a differential impact of SARS-CoV-2 infection on women and men have suggested a potential role for testosterone (T) in determining gender disparity in the SARS-CoV-2 clinical outcomes.\nOBJECTIVES: To estimate the association between T level and SARS-CoV-2 clinical outcomes (defined as conditions requiring transfer to higher or lower intensity of care or death) in a cohort of patients admitted in the respiratory intensive care unit (RICU).\nMATERIALS AND METHODS: A consecutive series of 31 male patients affected by SARS-CoV-2 pneumonia and recovered in the respiratory intensive care unit (RICU) of the \"Carlo Poma\" Hospital in Mantua were analyzed.\nSeveral biochemical risk factors (ie, blood count and leukocyte formula, C-reactive protein (CRP), procalcitonin (PCT), lactate dehydrogenase (LDH), ferritin, D-dimer, fibrinogen, interleukin 6 (IL-6)) as well as total testosterone (TT), calculated free T (cFT), sex hormone-binding globulin (SHBG), and luteinizing hormone (LH) were determined.\nRESULTS: Lower TT and cFT were found in the transferred to ICU/deceased in RICU group vs groups of patients transferred to IM or maintained in the RICU in stable condition.\nBoth TT and cFT showed a negative significant correlation with biochemical risk factors (ie, the neutrophil count, LDH, and PCT) but a positive association with the lymphocyte count.\nLikewise, TT was also negatively associated with CRP and ferritin levels.\nA steep increase in both ICU transfer and mortality risk was observed in men with TT < 5 nmol/L or cFT < 100 pmol/L. DISCUSSION AND CONCLUSION: Our study demonstrates for the first time that lower baseline levels of TT and cFT levels predict poor prognosis and mortality in SARS-CoV-2-infected men admitted to RICU.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Epidemiological data showing a differential impact of SARS-CoV-2 infection on women and men have suggested a potential role for testosterone (T) in determining gender disparity in the SARS-CoV-2 clinical outcomes.\"]}", "id": 773}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: How long does Coronavirus last on surfaces? And which disinfectants are most effective at cleaning them? Those two questions are important not only for healthcare centrers but any public place with a lot of activity - locations where you'll frequently touch surfaces such as door handles with your hands. You might then potentially infect yourself by touching your face, which happens more often than you think.\n\nAbstract:\nThe ocular surface has been suggested as a site of infection with Coronavirus-2 (SARS-CoV-2) responsible for the coronavirus disease-19 (COVID-19).\nThis review examines the evidence for this hypothesis, and its implications for clinical practice.\nSevere Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2), responsible for the COVID-19 pandemic, is transmitted by person-to-person contact, via airborne droplets, or through contact with contaminated surfaces.\nSARS-CoV-2 binds to angiotensin converting enzyme-2 (ACE2) to facilitate infection in humans.\nThis review sets out to evaluate evidence for the ocular surface as a route of infection.\nA literature search in this area was conducted on 15 April 2020 using the Scopus database.\nIn total, 287 results were returned and reviewed.\nThere is preliminary evidence for ACE2 expression on corneal and conjunctival cells, but most of the other receptors to which coronaviruses bind appear to be found under epithelia of the ocular surface.\nEvidence from animal studies is limited, with a single study suggesting viral particles on the eye can travel to the lung, resulting in very mild infection.\nCoronavirus infection is rarely associated with conjunctivitis, with occasional cases reported in patients with confirmed COVID-19, along with isolated cases of conjunctivitis as a presenting sign.\nCoronaviruses have been rarely isolated from tears or conjunctival swabs.\nThe evidence suggests coronaviruses are unlikely to bind to ocular surface cells to initiate infection.\nAdditionally, hypotheses that the virus could travel from the nasopharynx or through the conjunctival capillaries to the ocular surface during infection are probably incorrect.\nConjunctivitis and isolation of the virus from the ocular surface occur only rarely, and overwhelmingly in patients with confirmed COVID-19.\nNecessary precautions to prevent person-to-person transmission should be employed in clinical practice throughout the pandemic, and patients should be reminded to maintain good hygiene practices.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2), responsible for the COVID-19 pandemic, is transmitted by person-to-person contact, via airborne droplets, or through contact with contaminated surfaces.\"]}", "id": 774}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Sterilizing immunity against sars-cov-2 infection in rats by a single-shot and modified imidazoquinoline tlr7/8 agonist-adjuvanted recombinant spike protein vaccine\n\nAbstract:\nThe search for vaccines that protect from severe morbidity and mortality as a result of infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus that causes coronavirus disease 2019 (COVID-19) is a race against the clock and the virus.\nSeveral vaccine candidates are currently being tested in the clinic.\nInactivated virus and recombinant protein vaccines can be safe options but may require adjuvants to induce robust immune responses efficiently.\nIn this work we describe the use of a novel amphiphilic imidazoquinoline (IMDQ-PEG-CHOL) TLR7/8 adjuvant, consisting of an imidazoquinoline conjugated to the chain end of a cholesterol-poly(ethylene glycol) macromolecular amphiphile).\nThis amphiphile is water soluble and exhibits massive translocation to lymph nodes upon local administration, likely through binding to albumin.\nIMDQ-PEG-CHOL is used to induce a protective immune response against SARS-CoV-2 after single vaccination with trimeric recombinant SARS-CoV-2 spike protein in the BALB/c mouse model.\nInclusion of amphiphilic IMDQ-PEG-CHOL in the SARS-CoV-2 spike vaccine formulation resulted in enhanced immune cell recruitment and activation in the draining lymph node.\nIMDQ-PEG-CHOL has a better safety profile compared to native soluble IMDQ as the former induces a more localized immune response upon local injection, preventing systemic inflammation.\nMoreover, IMDQ-PEG-CHOL adjuvanted vaccine induced enhanced ELISA and in vitro microneutralization titers, and a more balanced IgG2a/IgG1 response.\nTo correlate vaccine responses with control of virus replication in vivo, vaccinated mice were challenged with SARS-CoV-2 virus after being sensitized by intranasal adenovirus-mediated expression of the human angiotensin converting enzyme 2 (ACE2) gene.\nAnimals vaccinated with trimeric recombinant spike protein vaccine without adjuvant had lung virus titers comparable to non-vaccinated control mice, whereas animals vaccinated with IMDQ-PEG-CHOL-adjuvanted vaccine controlled viral replication and infectious viruses could not be recovered from their lungs at day 4 post infection.\nIn order to test whether IMDQ-PEG-CHOL could also be used to adjuvant vaccines currently licensed for use in humans, proof of concept was also provided by using the same IMDQ-PEG-CHOL to adjuvant human quadrivalent inactivated influenza virus split vaccine, which resulted in enhanced hemagglutination inhibition titers and a more balanced IgG2a/IgG1 antibody response.\nEnhanced influenza vaccine responses correlated with better virus control when mice were given a lethal influenza virus challenge.\nOur results underscore the potential use of IMDQ-PEG-CHOL as an adjuvant to achieve protection after single immunization with recombinant protein and inactivated vaccines against respiratory viruses, such as SARS-CoV-2 and influenza viruses.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"IMDQ-PEG-CHOL is used to induce a protective immune response against SARS-CoV-2 after single vaccination with trimeric recombinant SARS-CoV-2 spike protein in the BALB/c mouse model.\"]}", "id": 775}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: there is no link between vitamin D concentrations and risk of COVID-19 infection.\n\nAbstract:\nThe severity of coronavirus 2019 infection (COVID-19) is determined by the presence of pneumonia, severe acute respiratory distress syndrome (SARS-CoV-2), myocarditis, microvascular thrombosis and/or cytokine storms, all of which involve underlying inflammation.\nA principal defence against uncontrolled inflammation, and against viral infection in general, is provided by T regulatory lymphocytes (Tregs).\nTreg levels have been reported to be low in many COVID-19 patients and can be increased by vitamin D supplementation.\nLow vitamin D levels have been associated with an increase in inflammatory cytokines and a significantly increased risk of pneumonia and viral upper respiratory tract infections.\nVitamin D deficiency is associated with an increase in thrombotic episodes, which are frequently observed in COVID-19.\nVitamin D deficiency has been found to occur more frequently in patients with obesity and diabetes.\nThese conditions are reported to carry a higher mortality in COVID-19.\nIf vitamin D does in fact reduce the severity of COVID-19 in regard to pneumonia/ARDS, inflammation, inflammatory cytokines and thrombosis, it is our opinion that supplements would offer a relatively easy option to decrease the impact of the pandemic.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"A principal defence against uncontrolled inflammation, and against viral infection in general, is provided by T regulatory lymphocytes (Tregs).\", \"Treg levels have been reported to be low in many COVID-19 patients and can be increased by vitamin D supplementation.\", \"Low vitamin D levels have been associated with an increase in inflammatory cytokines and a significantly increased risk of pneumonia and viral upper respiratory tract infections.\", \"Vitamin D deficiency is associated with an increase in thrombotic episodes, which are frequently observed in COVID-19.\", \"Vitamin D deficiency has been found to occur more frequently in patients with obesity and diabetes.\", \"These conditions are reported to carry a higher mortality in COVID-19.\", \"If vitamin D does in fact reduce the severity of COVID-19 in regard to pneumonia/ARDS, inflammation, inflammatory cytokines and thrombosis, it is our opinion that supplements would offer a relatively easy option to decrease the impact of the pandemic.\"]}", "id": 776}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Only an N95 mask will help me from getting covid-19.\n\nAbstract:\nEvidence that face masks provide effective protection against respiratory infections in the community is scarce.\nHowever, face masks are widely used by health workers as part of droplet precautions when caring for patients with respiratory infections.\nIt would therefore be reasonable to suggest that consistent widespread use of face masks in the community could prevent further spread of the Severe Acute Respiratory Syndrome-Coronavirus 2 (SARS-CoV-2).\nIn this study we examine public face mask wearing in Uganda where a proportion wears masks to protect against acquiring, and the other to prevent from transmitting SARS-CoV-2.\nThe objective of this study was to determine what percentage of the population would have to wear face masks to reduce susceptibility to and infectivity of COVID-19 in Uganda, keeping the basic reproduction number below unity and/or flattening the curve.\nWe used an SEIAQRD model for the analysis.\nResults show that implementation of facemasks has a relatively large impact on the size of the coronavirus epidemic in Uganda.\nWe find that the critical mask adherence is 5 per 100 when 80% wear face masks.\nA cost-effective analysis shows that utilizing funds to provide 1 public mask to the population has a per capita compounded cost of USD 1.34.\nIf provision of face masks is done simultaneously with supportive care, the per capita compounded cost is USD 1.965, while for the case of only treatment and no provision of face masks costs each Ugandan USD 4.0579.\nWe conclude that since it is hard to achieve a 100% adherence to face masks, government might consider provision of face masks in conjunction with provision of care.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"It would therefore be reasonable to suggest that consistent widespread use of face masks in the community could prevent further spread of the Severe Acute Respiratory Syndrome-Coronavirus 2 (SARS-CoV-2).\", \"We find that the critical mask adherence is 5 per 100 when 80% wear face masks.\"]}", "id": 777}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: How long does Coronavirus last on surfaces? And which disinfectants are most effective at cleaning them? Those two questions are important not only for healthcare centrers but any public place with a lot of activity - locations where you'll frequently touch surfaces such as door handles with your hands. You might then potentially infect yourself by touching your face, which happens more often than you think.\n\nAbstract:\nObjectives: To evaluate SARS-CoV-2 surface and air contamination during the peak of the COVID-19 pandemic in London.\nDesign: Prospective cross-sectional observational study.\nSetting: An acute NHS healthcare provider.\nParticipants: All inpatient wards were fully occupied by patients with COVID-19 at the time of sampling.\nInterventions: Air and surface samples were collected from a range of clinical areas and a public area of the hospital.\nAn active air sampler was used to collect three or four 1.0 m3 air samples in each area.\nSurface samples were collected by swabbing approximately 25 cm2 of items in the immediate vicinity of each air sample.\nSARS-CoV-2 was detected by RT-qPCR and viral culture using Vero E6 and Caco2 cells; additionally the limit of detection for culturing SARS-CoV-2 dried onto surfaces was determined.\nMain outcome measures: SARS-CoV-2 detected by PCR or culture.\nResults: Viral RNA was detected on 114/218 (52.3%) of surface and 14/31 (38.7%) air samples but no virus was cultured.\nThe proportion of surface samples contaminated with viral RNA varied by item sampled and by clinical area.\nViral RNA was detected on surfaces and in air in public areas of the hospital but was more likely to be found in areas immediately occupied by COVID-19 patients (67/105 (63.8%) in areas immediately occupied by COVID-19 patients vs. 29/64 (45.3%) in other areas (odds ratio 0.5, 95% confidence interval 0.2-0.9, p=0.025, Fishers exact test).\nThe PCR Ct value for all surface and air samples (>30) indicated a viral load that would not be culturable.\nConclusions: Our findings of extensive viral RNA contamination of surfaces and air across a range of acute healthcare settings in the absence of cultured virus underlines the potential risk from surface and air contamination in managing COVID-19, and the need for effective use of PPE, social distancing, and hand/surface hygiene.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Conclusions: Our findings of extensive viral RNA contamination of surfaces and air across a range of acute healthcare settings in the absence of cultured virus underlines the potential risk from surface and air contamination in managing COVID-19, and the need for effective use of PPE, social distancing, and hand/surface hygiene.\"]}", "id": 778}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Hydroxychloroquine is an Effective Treatment for COVID-19\n\nAbstract:\nHydroxychloroquine (HCQ) garnered scientific attention in early February following publication of reports showing in vitro activity of chloroquine (CQ) against COVID\u201019.\nWhile studies are mixed on this topic, the therapeutic effect of HCQ or CQ still need more valid clinical evidence.\nIn this descriptive observational study, we aimed to discuss the treatment response of HCQ in COVID\u201019 infected patients and 30 cases were included.\nThe demographic, treatment, laboratory parameters of C\u2010reactive protein (CRP) and interleukin\u20106 (IL\u20106) before and after HCQ therapy and clinical outcome in the 30 COVID\u201019 patients were assessed.\nIn order to evaluate the effect of mediation time point, we also divided these cases into two groups, patients began administrated with HCQ within 7 days hospital (defined as early delivery group) and 7 days after hospital (defined as later delivery group).\nWe found that, the elevated IL\u20106, a risk factor in severe patients were reduced to normal level after HCQ treatment.\nMore importantly, patients treated with HCQ at the time of early hospital recovered faster than those who treated later or taken as second line choose for their obvious shorter hospitalization time.\nIn summary, early use of HCQ was better than later use and the effect of IL\u20106 and CRP level can not be ruled out.\nThis article is protected by copyright.\nAll rights reserved.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"More importantly, patients treated with HCQ at the time of early hospital recovered faster than those who treated later or taken as second line choose for their obvious shorter hospitalization time.\"]}", "id": 779}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Melatonin is significantly lower with survival of intubated covid-19 patients\n\nAbstract:\nBackground Respiratory distress requiring intubation is the most serious complication associated with coronavirus disease 2019 (COVID-19).\nMethods In this retrospective study, we used survival analysis to determine whether or not mortality following intubation was associated with hormone exposure in patients treated at New York Presbyterian/ Columbia University Irving Medical Center.\nHere, we report the overall hazards ratio for each hormone for exposure before and after intubation for intubated and mechanically ventilated patients.\nResults Among the 189,987 patients, we identified 948 intubation periods across 791 patients who were diagnosed with COVID-19 or infected with SARS-CoV2 and 3,497 intubation periods across 2,981 patients who were not.\nMelatonin exposure after intubation was statistically associated with a positive outcome in COVID-19 (demographics and comorbidities adjusted HR: 0.131, 95% CI: 7.76E-02-0.223, p-value = 8.19E-14) and non-COVID-19 (demographics and comorbidities adjusted HR: 0.278, 95% CI: 0.142-0.542, p-value = 1.72E-04) intubated patients.\nAdditionally, melatonin exposure after intubation was statically associated with a positive outcome in COVID-19 patients (demographics and comorbidities adjusted HR: 0.127, 95% CI: 6.01E-02-0.269, p-value = 7.15E-08).\nConclusions Melatonin exposure after intubation is significantly associated with a positive outcome in COVID-19 and non-COVID-19 patients.\nAdditionally, melatonin exposure after intubation is significantly associated with a positive outcome in COVID-19 patients requiring mechanical ventilation.\nWhile our models account for many covariates, including clinical history and demographics, it is impossible to rule out confounding or collider biases within our population.\nFurther study into the possible mechanism of this observation is warranted.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Melatonin exposure after intubation was statistically associated with a positive outcome in COVID-19 (demographics and comorbidities adjusted HR: 0.131, 95% CI: 7.76E-02-0.223, p-value = 8.19E-14) and non-COVID-19 (demographics and comorbidities adjusted HR: 0.278, 95% CI: 0.142-0.542, p-value = 1.72E-04) intubated patients.\", \"Additionally, melatonin exposure after intubation was statically associated with a positive outcome in COVID-19 patients (demographics and comorbidities adjusted HR: 0.127, 95% CI: 6.01E-02-0.269, p-value = 7.15E-08).\", \"Conclusions Melatonin exposure after intubation is significantly associated with a positive outcome in COVID-19 and non-COVID-19 patients.\", \"Additionally, melatonin exposure after intubation is significantly associated with a positive outcome in COVID-19 patients requiring mechanical ventilation.\"]}", "id": 780}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Yes, 5G radiation causes Covid-19 \n\nAbstract:\nThe unprecedented outbreak of the 2019 novel coronavirus, termed as COVID-19 by the World Health Organization (WHO), has placed numerous governments around the world in a precarious position.\nThe impact of the COVID-19 outbreak, earlier witnessed by the citizens of China alone, has now become a matter of grave concern for virtually every country in the world.\nThe scarcity of resources to endure the COVID-19 outbreak combined with the fear of overburdened healthcare systems has forced a majority of these countries into a state of partial or complete lockdown.\nThe number of laboratory-confirmed coronavirus cases has been increasing at an alarming rate throughout the world, with reportedly more than 3 million confirmed cases as of 30 April 2020.\nAdding to these woes, numerous false reports, misinformation, and unsolicited fears in regards to coronavirus, are being circulated regularly since the outbreak of the COVID-19.\nIn response to such acts, we draw on various reliable sources to present a detailed review of all the major aspects associated with the COVID-19 pandemic.\nIn addition to the direct health implications associated with the outbreak of COVID-19, this study highlights its impact on the global economy.\nIn drawing things to a close, we explore the use of technologies such as the Internet of Things (IoT), Unmanned Aerial Vehicles (UAVs), blockchain, Artificial Intelligence (AI), and 5G, among others, to help mitigate the impact of COVID-19 outbreak.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"In drawing things to a close, we explore the use of technologies such as the Internet of Things (IoT), Unmanned Aerial Vehicles (UAVs), blockchain, Artificial Intelligence (AI), and 5G, among others, to help mitigate the impact of COVID-19 outbreak.\"]}", "id": 781}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: it appears that the virus that causes COVID-19 can spread from people to animals after close contact with people with COVID-19\n\nAbstract:\n\u2014 Coronaviruses take the lead in gastrointestinal pathologies of animals and are spread all around the world.\nCausative agents of coronaviruses belong to Nidovirales order, Coronaviridae family which includes 2 subfamilies: Toroviridna (genera Torovirus and Bafinivirus) and Coronaviridna (genera Alphavirus, Betavirus, Gammavirus).\nDividing of the latter by genera (groups I-III) was based on serological cross-reactions.\nGroup I includes pathogens causing diseases in animals, such as swine coronavirus, feline infectious peritonitis virus.\nGroup II includes pathogens of veterinary importance, such as BCoV, porcine hemagglutinating encephalomyelitis virus, horse coronavirus, viruses infecting mice and rats, as well as human coronaviruses that cause respiratory damage.\nGroup III includes at the moment only avian coronaviruses.\nCoronavirus got its name in 1968 because of its special structure and appearance \u2013 it has spikes which resemble solar corona.\nIn humans, coronavirus was first isolated by D. Tyrrell and M. Bynoe in 1965 from a patient with acute respiratory disease.\nSevere acute respiratory syndrome (SARS) in 2002, and then Middle East respiratory syndrome (MERS) in 2012 made specialists significantly increase the level of epidemic danger of coronaviruses.\nDue to its high virulence, virus multiplication rate in macrophages, pathogen replication, and antibody production increase significantly.\nAt the end of 2019, coronavirus strain 2019-nCoV of Betacoronavirus genus was found in patients with pneumonia in China, and by early 2020 it spread all around the world.\nIn animals, coronavirus leads to damage to mucous membranes.\nThe amount of economic damage associated with disease caused by coronavirus is significant and includes death of animals, decreased meat and dairy productivity, decreased weight gain, culling, loss from abortion and infertility.\nIn this regard, a demand arose for developing project of a device for express analysis for Coronaviridae antigen for the early diagnosis of coronaviruses.\nBased on the express analysis technique using solid-phase immunochromatographic medium, a project for express analysis for Coronaviridae antigen based on Omron industrial programmable logic controller was developed; it includes process chart for equipment, functional diagram and logical equations.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 782}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Coronavirus (COVID-19) Know how to protect yourself and others from COVID-19 and what to do if you are sick.\n\nAbstract:\nThis paper investigates the correlation between the high level of coronavirus SARS-CoV-2 infection accelerated transmission and lethality, and surface air pollution in Milan metropolitan area, Lombardy region in Italy.\nFor January-April 2020 period, time series of daily average inhalable gaseous pollutants ozone (O3) and nitrogen dioxide (NO2), together climate variables (air temperature, relative humidity, wind speed, precipitation rate, atmospheric pressure field and Planetary Boundary Layer) were analyzed.\nIn spite of being considered primarily transmitted by indoor bioaerosols droplets and infected surfaces or direct human-to-human personal contacts, it seems that high levels of urban air pollution, and climate conditions have a significant impact on SARS-CoV-2 diffusion.\nExhibited positive correlations of ambient ozone levels and negative correlations of NO2 with the increased rates of COVID-19 infections (Total number, Daily New positive and Total Deaths cases), can be attributed to airborne bioaerosols distribution.\nThe results show positive correlation of daily averaged O3 with air temperature and inversely correlations with relative humidity and precipitation rates.\nViral genome contains distinctive features, including a unique N-terminal fragment within the spike protein, which allows coronavirus attachment on ambient air pollutants.\nAt this moment it is not clear if through airborne diffusion, in the presence of outdoor and indoor aerosols, this protein \"spike\" of the new COVID-19 is involved in the infectious agent transmission from a reservoir to a susceptible host during the highest nosocomial outbreak in some agglomerated industrialized urban areas like Milan is.\nAlso, in spite of collected data for cold season (winter-early spring) period, when usually ozone levels have lower values than in summer, the findings of this study support possibility as O3 can acts as a COVID-19 virus incubator.\nBeing a novel pandemic coronavirus version, it might be ongoing during summer conditions associated with higher air temperatures, low relative humidity and precipitation levels.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 783}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Smoking is a risk factor for COVID-19 patients, but one particular substance in cigarettes - nicotine - might prevent infection in some people, or improve COVID-19 prognosis\n\nAbstract:\nABSTRACT Introduction: Recent studies show cigarette smokers are markedly under-represented among patients hospitalized for COVID-19 in over a dozen countries.\nIt is unclear if this may be related to confounding factors such as age distribution, access to care, and inaccurate records.\nWe hypothesized that these concerns could be avoided by studying smoking prevalence in relation to COVID-19 mortality.\nSince climate has been identified as a factor in COVID-19, we studied groups of countries with relatively comparable temperatures.\nMethods: The 20 hottest and 20 coldest countries in the Johns Hopkins Mortality Analysis database with a minimum mortality rate of .3 deaths/100,000 were selected on the basis of the average temperatures of their largest city.\nMortality rates were determined as of May 1, 2020 and correlated with national smoking rate adjusting for sex ratio, obesity, temperature, and elderly population.\nResults: A highly significant inverse correlation between current daily smoking prevalence and COVID-19 mortality rate was noted for the group of hot countries (R=-.718, p = .0002), cold countries (R=-.567, p=.0046), and the combined group (R=-.324, p=.0207).\nHowever, after adjustments only the regression for hot countries and the combined group remained significant.\nIn hot countries, for each percentage point increase in smoking rate mortality decreased by .147 per 100,000 population (95% CI .102- 192, p=.0066).\nThis resulted in mortality rates several-fold elevated in the countries with the lowest smoking rates relative to the highest smoking rates.\nIn the combined group, mortality decreased by .257 per 100,000 population (95% CI .175-.339, p=.0034).\nDiscussion: These findings add support to the finding of an inverse relationship between current smoking and seriously symptomatic COVID-19.\nHowever, we conclude that the difference in mortality between the highest and lowest smoking countries appears too large to be due primarily to the effects of smoking per se.\nA potentially beneficial effect of smoking is surprising, but compatible with a number of hypothetical mechanisms which deserve exploration: 1) Studies show smoking alters ACE2 expression which may affect COVID-19 infection or its progression to serious lung pathology.\n2) Nicotine has anti-inflammatory activity and also appears to alter ACE2 expression.\n3) Nitric oxide in cigarette smoke is known to be effective in treating pulmonary hypertension and has shown in vitro antiviral effects including against SARS-CoV-2.\n4) Smoking has complicated effects on the immune system involving both up and down regulation, any of which might alone or in concert antagonize progression of COVID-19.\n5) Smokers are exposed to hot vapors which may stimulate immunity in the respiratory tract by various heat-related mechanisms (e.g. heat shock proteins).\nStudies of steam and sauna treatments have shown efficacy in other viral respiratory conditions.\nAt this time there is no clear evidence that smoking is protective against COVID-19, so the established recommendations to avoid smoking should be emphasized.\nThe interaction of smoking and COVID-19 will only be reliably determined by carefully designed prospective study, and there is reason to believe that there are unknown confounds that may be spuriously suggesting a protective effect of smoking.\nHowever, the magnitude of the apparent inverse association of COVID-19 and smoking and its myriad clinical implications suggest the importance of further investigation.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"4) Smoking has complicated effects on the immune system involving both up and down regulation, any of which might alone or in concert antagonize progression of COVID-19.\", \"At this time there is no clear evidence that smoking is protective against COVID-19, so the established recommendations to avoid smoking should be emphasized.\"]}", "id": 784}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: A booster dose dids immunogenicity of the covid-19 vaccine candidate chadox1 ncov-19 in aged mice\n\nAbstract:\nThe spread of SARS-CoV-2 has caused a global pandemic that has affected almost every aspect of human life.\nThe development of an effective COVID-19 vaccine could limit the morbidity and mortality caused by infection, and may enable the relaxation of social distancing measures.\nAge is one of the most significant risk factors for poor health outcomes after SARS-CoV-2 infection, therefore it is desirable that any new vaccine candidates should elicit a robust immune response in older adults.\nHere, we test the immunogenicity of the adenoviral vectored vaccine ChAdOx1 nCoV-19 (AZD-1222) in aged mice.\nWe find that a single dose of this vaccine induces cellular and humoral immunity in aged mice, but at a reduced magnitude than in younger adult mice.\nFurthermore, we report that a second dose enhances the immune response to this vaccine in aged mice, indicating that a primeboost strategy may be a rational approach to enhance immunogenicity in older persons.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"We find that a single dose of this vaccine induces cellular and humoral immunity in aged mice, but at a reduced magnitude than in younger adult mice.\", \"Furthermore, we report that a second dose enhances the immune response to this vaccine in aged mice, indicating that a primeboost strategy may be a rational approach to enhance immunogenicity in older persons.\"]}", "id": 785}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: hydroxychloroquine cures covid-19.\n\nAbstract:\nBackground: There is no effective therapy for COVID-19.\nHydroxychloroquine (HCQ) and chloroquine (CQ) have been used for its treatment but their safety and efficacy remain uncertain.\nObjective: We performed a systematic review to synthesize the available data on the efficacy and safety of CQ and HCQ for the treatment of COVID-19.\nMethods: Two reviewers searched for published and pre-published relevant articles between December 2019 to 8th June 2020.\nThe data from the selected studies were abstracted and analyzed for efficacy and safety outcomes.\nCritical appraisal of the evidence was done by Cochrane risk of bias tool and Newcastle Ottawa scale.\nThe quality of evidence was graded as per the GRADE approach.\nResults: We reviewed 12 observational and 3 randomized trials which included 10659 patients of whom 5713 received CQ/HCQ and 4966 received only standard of care.\nThe efficacy of CQ/HCQ for COVID-19 was inconsistent across the studies.\nMeta-analysis of included studies revealed no significant reduction in mortality with HCQ use [RR 0.98 95% CI 0.66-1.46] , time to fever resolution [mean difference -0.54 days (-1.19-011)] or clinical deterioration/development of ARDS with HCQ [RR 0.90 95% CI 0.47-1.71].\nThere was a higher risk of ECG abnormalities/arrhythmia with HCQ/CQ [RR 1.46 95% CI 1.04 to 2.06].\nThe quality of evidence was graded as very low for these outcomes.\nConclusions: The available evidence suggests that CQ or HCQ does not improve clinical outcomes in COVID-19.\nWell-designed randomized trials are required for assessing the efficacy and safety of HCQ and CQ for COVID-19.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Conclusions: The available evidence suggests that CQ or HCQ does not improve clinical outcomes in COVID-19.\"]}", "id": 786}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: If Fever Helps Fight Infection, Should I Avoid Fever-Reducing Drugs\n\nAbstract:\nFever has been reported as a common symptom occurring in COVID-19 illness.\nOver the counter antipyretics such as ibuprofen and acetaminophen are often taken by individuals to reduce the discomfort of fever.\nRecently, the safety of ibuprofen in COVID-19 patients has been questioned due to anecdotal reports of worsening symptoms in previously healthy young adults.\nStudies show that ibuprofen demonstrates superior efficacy in fever reduction compared to acetaminophen.\nAs fever may have benefit in shortening the duration of viral illness, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response when taken during the early stages of COVID-19 illness.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"As fever may have benefit in shortening the duration of viral illness, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response when taken during the early stages of COVID-19 illness.\"]}", "id": 787}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: The high temperature and low relative humidity lead to high evaporation rates of saliva-contaminated droplets, thus significantly reducing the coronavirus viability\n\nAbstract:\nThe undefendable outbreak of novel coronavirus (SARS-COV-2) lead to a global health emergency due to its higher transmission rate and longer symptomatic duration, created a health surge in a short time.\nSince Nov 2019 the outbreak in China, the virus is spreading exponentially everywhere.\nThe current study focuses on the relationship between environmental parameters and the growth rate of COVID-19.\nThe statistical analysis suggests that the temperature changes retarded the growth rate and found that -6.28{degrees}C and +14.51{degrees}C temperature is the favorable range for COVID-19 growth.\nGutenberg- Richter's relationship is used to estimate the mean daily rate of exceedance of confirmed cases concerning the change in temperature.\nTemperature is the most influential parameter that reduces the growth at the rate of 13-16 cases/day with a 1{degrees}C rise in temperature.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"The statistical analysis suggests that the temperature changes retarded the growth rate and found that -6.28{degrees}C and +14.51{degrees}C temperature is the favorable range for COVID-19 growth.\", \"Temperature is the most influential parameter that reduces the growth at the rate of 13-16 cases/day with a 1{degrees}C rise in temperature.\"]}", "id": 788}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: there is no link between vitamin D concentrations and risk of COVID-19 infection.\n\nAbstract:\nBackground: Following emerge of a novel coronavirus from Wuhan, China, in December 2019, it has affected the whole world and after months of efforts by the medical communities, there is still no specific approach for prevention and treatment against the Coronavirus Disease 2019 (COVID-19).\nEvidence recommends that vitamin D might be an important supportive agent for the immune system, mainly in cytokine response regulation against COVID-19.\nHence, we carried out a rapid systematic review and meta-analysis along with an ecological investigation in order to maximize the use of everything that exists about the role of vitamin D in the COVID-19.\nMethods: A systematic search was performed in PubMed, Scopus, Embase, Cochrane Library, Web of Science and Google Scholar (intitle) as well as preprint database of medRxiv, bioRxiv, Research Square, preprints.org, search engine of ScienceDirect and a rapid search through famous journals up to May 26, 2020.\nStudies focused on the role of vitamin D in confirmed COVID-19 patients were entered into the systematic review.\nAlong with our main aim, to find the second objective: correlation of global vitamin D status and COVID-19 recovery and mortality we carried out a literature search in PubMed database to identify the national or regional studies reported the vitamin D status globally.\nCMA v. 2.2.064 and SPSS v.16 were used for data analysis.\nResults: Out of nine studies entered into our systematic review, six studies containing 3,822 participants entered into the meta-analysis.\nThe meta-analysis indicated that 46.5% of COVID-19 patients were suffering from vitamin D deficiency (95% CI, 28.2%-65.8%) and in 43.3% of patients, levels of vitamin D were insufficient (95% CI, 27.4%-60.8%).\nIn regard to our ecological investigation on 51 countries including 408,748 participants, analyses indicated no correlation between vitamin D levels and recovery rate (r= 0.041) as well as mortality rate (r=-0.073) globally.\nHowever, given latitude, a small reverse correlation between mortality rate and vitamin D status was observed throughout the globe (r= -0.177).\nIn Asia, a medium direct correlation was observed for recovery rate (r= 0.317) and a significant reveres correlation for mortality rate (r= -0.700) with vitamin D status in such patients.\nIn Europe, there were no correlations for both recovery (r= 0.040) and mortality rate (r= -0.035).\nIn Middle East, the recovery rate (r= 0.267) and mortality rate (r= -0.217) showed a medium correlation.\nIn North and Sought America, surprisingly, both recovery and mortality rate demonstrated a direct correlation respectively (r= 1.000, r=0.500).\nIn Oceania, unexpectedly, recovery (r= -1.000) and mortality (r= -1.000) rates were in considerable reverse correlation with vitamin D levels.\nConclusion: In this systematic review and meta-analysis with an ecological approach, we found a high percentage of COVID-19 patients who suffer from vitamin D deficiency or insufficiency.\nMuch more important, our ecological investigation resulted in substantial direct and reverse correlations between recovery and mortality rates of COVID-19 patients with vitamin D status in different countries.\nConsidering latitudes, a small reverse correlation between vitamin D status and mortality rate was found globally.\nIt seems that populations with lower levels of vitamin D might be more susceptible to the novel coronavirus infection.\nNevertheless, due to multiple limitations, if this study does not allow to quantify a value of the Vitamin D with full confidence, it allows at least to know what the Vitamin D might be and that it would be prudent to invest in this direction through comprehensive large randomized clinical trials.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"In Middle East, the recovery rate (r= 0.267) and mortality rate (r= -0.217) showed a medium correlation.\"]}", "id": 789}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: No, garlic won't prevent COVID-19. These 'home remedies' and 'cures' just don't work\n\nAbstract:\nThe severity of coronavirus disease 2019 (COVID-19) infection is quite variable and the manifestations varies from asymptomatic disease to severe acute respiratory infection.\nFever, dry cough, dyspnea, myalgia, fatigue, loss of appetite, olfactory and gustatory dysfunctions are the most prevalent general symptoms.\nDecreased immune system cells such as suppressed regulatory T cells, cytotoxic and helper T cells, natural killer cells, monocytes/macrophages and increased proinflammatory cytokines are the characteristic features.\nCompounds derived from Allium sativum (garlic) have the potential to decrease the expression of proinflammatory cytokines and to reverse the immunological abnormalities to more acceptable levels.\nAllium sativum is suggested as a beneficial preventive measure before being infected with SARS-CoV-2 virus.\nAllium sativum is a functional food well-known for its immunomodulatory, antimicrobial, antiinflammatory, antimutagenic, antitumor properties.\nIts antiviral efficiency was also demonstrated.\nSome constituents of this plant were found to be active against protozoan parasites.\nWithin this context, it appears to reverse most immune system dysfunctions observed in patients with COVID-19 infection.\nThe relations among immune system parameters, leptin, leptin receptor, adenosin mono phosphate-activated protein kinase, peroxisome proliferator activated receptor-gamma have also been interpreted.\nLeptin's role in boosting proinflammatory cytokines and in appetite decreasing suggest the possible beneficial effect of decreasing the concentration of this proinflammatory adipose tissue hormone in relieving some symptoms detected during COVID-19 infection.\nIn conclusion, Allium sativum may be an acceptable preventive measure against COVID-19 infection to boost immune system cells and to repress the production and secretion of proinflammatory cytokines as well as an adipose tissue derived hormone leptin having the proinflammatory nature.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"In conclusion, Allium sativum may be an acceptable preventive measure against COVID-19 infection to boost immune system cells and to repress the production and secretion of proinflammatory cytokines as well as an adipose tissue derived hormone leptin having the proinflammatory nature.\"]}", "id": 790}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Low vitamin k status predicts mortality in a cohort of 138 hospitalized patients with covid-19.\n\nAbstract:\nIt has recently been hypothesised that Vitamin K could play a role in COVID-19.\nWe aimed to test the hypothesis that low vitamin K status is a common characteristic of patients hospitalized with COVID-19 compared to population controls; and that low vitamin K status predicts mortality in COVID-19 patients.\nIn a cohort of 138 COVID-19 patients and 140 population controls, we measured plasma dephosphorylated-uncarboxylated Matrix Gla Protein (dp-ucMGP), which reflects the functional Vitamin K status in peripheral tissue.\nFourty-three patients died within 90-days from admission.\nIn patients, levels of dp-ucMGP differed significantly between survivors (mean 877; 95% CI: 778; 995) and non-survivors (mean 1445; 95% CI: 1148; 1820).\nFurthermore, levels of dp-ucMGP (pmol/L) were considerably higher in patients (mean 1022; 95% CI: 912; 1151) compared to controls (mean 509; 95% CI: 485; 540).\nCox regression survival analysis showed that increasing levels of dp-ucMGP (reflecting low Vitamin K status) were associated with higher mortality risk (sex-and age-adjusted hazard ratio per doubling of dp-ucMGP was 1.50, 95% CI: 1.03; 2.18).\nIn conclusion, we found that low Vitamin K status predicted mortality in patients with COVID-19 supporting a potential role of Vitamin K in COVID-19.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"We aimed to test the hypothesis that low vitamin K status is a common characteristic of patients hospitalized with COVID-19 compared to population controls; and that low vitamin K status predicts mortality in COVID-19 patients.\", \"In conclusion, we found that low Vitamin K status predicted mortality in patients with COVID-19 supporting a potential role of Vitamin K in COVID-19.\"]}", "id": 791}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Because some of the symptoms of flu and COVID-19 are similar, it may be hard to tell the difference between them based on symptoms alone\n\nAbstract:\nINTRODUCTION: Neurological manifestations can occur during coronavirus disease 19 (COVID-19).\nSeveral pathogenic mechanisms have been hypothesized, without conclusive results.\nIn this study, we evaluated the most frequent neurological symptoms in a cohort of hospitalized COVID-19 patients, and also investigated the possible relationship between plasmatic inflammatory indices and olfactory disorders (ODs) and between muscle pain and creatine kinase (CK).\nMETHODS: We consecutively enrolled hospitalized COVID-19 patients.\nA structured questionnaire concerning typical and neurological symptoms, focusing on headache, dizziness, ODs, taste disorders (TDs), and muscle pain, was administrated by telephone interviews.\nRESULTS: Common neurological symptoms were reported in the early phase of the disease, with a median onset ranging from 1 to 3 days.\nHeadache showed tension-type features and was more frequently associated with a history of headache.\nPatients with ODs less frequently needed oxygen therapy.\nInflammatory indices did not significantly differ between patients with and without ODs.\nMuscle pain did not show any association with CK level but was more frequently associated with arthralgia and headache.\nCONCLUSION: In our cohort, ODs were an early symptom of COVID-19, more frequently reported by patients with milder forms of disease.\nHeadache in association with arthralgia and muscle pain seems to reflect the common symptoms of the flu-like syndrome, and not COVID-19 infection-specific.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 792}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: there are few novel sars-cov-2 cases in malaria countries because of the use of the antimalarial drug hydroxychloroquine.\n\nAbstract:\nBackground: There is no effective therapy for COVID-19.\nHydroxychloroquine (HCQ) and chloroquine (CQ) have been used for its treatment but their safety and efficacy remain uncertain.\nObjective: We performed a systematic review to synthesize the available data on the efficacy and safety of CQ and HCQ for the treatment of COVID-19.\nMethods: Two reviewers searched for published and pre-published relevant articles between December 2019 to 8th June 2020.\nThe data from the selected studies were abstracted and analyzed for efficacy and safety outcomes.\nCritical appraisal of the evidence was done by Cochrane risk of bias tool and Newcastle Ottawa scale.\nThe quality of evidence was graded as per the GRADE approach.\nResults: We reviewed 12 observational and 3 randomized trials which included 10659 patients of whom 5713 received CQ/HCQ and 4966 received only standard of care.\nThe efficacy of CQ/HCQ for COVID-19 was inconsistent across the studies.\nMeta-analysis of included studies revealed no significant reduction in mortality with HCQ use [RR 0.98 95% CI 0.66-1.46] , time to fever resolution [mean difference -0.54 days (-1.19-011)] or clinical deterioration/development of ARDS with HCQ [RR 0.90 95% CI 0.47-1.71].\nThere was a higher risk of ECG abnormalities/arrhythmia with HCQ/CQ [RR 1.46 95% CI 1.04 to 2.06].\nThe quality of evidence was graded as very low for these outcomes.\nConclusions: The available evidence suggests that CQ or HCQ does not improve clinical outcomes in COVID-19.\nWell-designed randomized trials are required for assessing the efficacy and safety of HCQ and CQ for COVID-19.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Conclusions: The available evidence suggests that CQ or HCQ does not improve clinical outcomes in COVID-19.\"]}", "id": 793}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Chadox1 ncov-19 vaccination caused sars-cov-2 pneumonia in rhesus macaques\n\nAbstract:\nSevere acute respiratory syndrome coronavirus-2 (SARS-CoV-2) emerged in December 20191,2 and is responsible for the COVID-19 pandemic3.\nVaccines are an essential countermeasure urgently needed to control the pandemic4.\nHere, we show that the adenovirus-vectored vaccine ChAdOx1 nCoV-19, encoding the spike protein of SARS-CoV-2, is immunogenic in mice, eliciting a robust humoral and cell-mediated response.\nThis response was not Th2 dominated, as demonstrated by IgG subclass and cytokine expression profiling.\nA single vaccination with ChAdOx1 nCoV-19 induced a humoral and cellular immune response in rhesus macaques.\nWe observed a significantly reduced viral load in bronchoalveolar lavage fluid and respiratory tract tissue of vaccinated animals challenged with SARS-CoV-2 compared with control animals, and no pneumonia was observed in vaccinated rhesus macaques.\nImportantly, no evidence of immune-enhanced disease following viral challenge in vaccinated animals was observed.\nChAdOx1 nCoV-19 is currently under investigation in a phase I clinical trial.\nSafety, immunogenicity and efficacy against symptomatic PCR-positive COVID-19 disease will now be assessed in randomised controlled human clinical trials.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"A single vaccination with ChAdOx1 nCoV-19 induced a humoral and cellular immune response in rhesus macaques.\", \"We observed a significantly reduced viral load in bronchoalveolar lavage fluid and respiratory tract tissue of vaccinated animals challenged with SARS-CoV-2 compared with control animals, and no pneumonia was observed in vaccinated rhesus macaques.\"]}", "id": 794}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: High Blood Pressure Doesn't Seem To Raise COVID-19 Risk\n\nAbstract:\nSince its recognition in December 2019, covid-19 has rapidly spread globally causing a pandemic.\nPre-existing comorbidities such as hypertension, diabetes, and cardiovascular disease are associated with a greater severity and higher fatality rate of covid-19.\nFurthermore, covid-19 contributes to cardiovascular complications, including acute myocardial injury as a result of acute coronary syndrome, myocarditis, stress-cardiomyopathy, arrhythmias, cardiogenic shock, and cardiac arrest.\nThe cardiovascular interactions of covid-19 have similarities to that of severe acute respiratory syndrome, Middle East respiratory syndrome and influenza.\nSpecific cardiovascular considerations are also necessary in supportive treatment with anticoagulation, the continued use of renin-angiotensin-aldosterone system inhibitors, arrhythmia monitoring, immunosuppression or modulation, and mechanical circulatory support.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Pre-existing comorbidities such as hypertension, diabetes, and cardiovascular disease are associated with a greater severity and higher fatality rate of covid-19.\"]}", "id": 795}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: covid-19 patients taking hydroxychloroquine do not benefit\n\nAbstract:\nBACKGROUND: Chloroquine and hydroxychloroquine have been found to be efficient on SARS-CoV-2, and reported to be efficient in Chinese COV-19 patients.\nWe evaluate the role of hydroxychloroquine on respiratory viral loads.\nPATIENTS AND METHODS: French Confirmed COVID-19 patients were included in a single arm protocol from early March to March 16th, to receive 600mg of hydroxychloroquine daily and their viral load in nasopharyngeal swabs was tested daily in a hospital setting.\nDepending on their clinical presentation, azithromycin was added to the treatment.\nUntreated patients from another center and cases refusing the protocol were included as negative controls.\nPresence and absence of virus at Day6-post inclusion was considered the end point.\nRESULTS: Six patients were asymptomatic, 22 had upper respiratory tract infection symptoms and eight had lower respiratory tract infection symptoms.\nTwenty cases were treated in this study and showed a significant reduction of the viral carriage at D6-post inclusion compared to controls, and much lower average carrying duration than reported of untreated patients in the literature.\nAzithromycin added to hydroxychloroquine was significantly more efficient for virus elimination.\nCONCLUSION: Despite its small sample size our survey shows that hydroxychloroquine treatment is significantly associated with viral load reduction/disappearance in COVID-19 patients and its effect is reinforced by azithromycin.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"CONCLUSION: Despite its small sample size our survey shows that hydroxychloroquine treatment is significantly associated with viral load reduction/disappearance in COVID-19 patients and its effect is reinforced by azithromycin.\"]}", "id": 796}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: It does not make a lot of sense that if somebody is otherwise healthy and young and they have hypertension alone, that they should be at increased risk\n\nAbstract:\nSome comorbidities are associated with severe coronavirus disease (Covid-19) but it is unclear whether some increase susceptibility to Covid-19.\nIn this case-control Mexican study we found that obesity represents the strongest predictor for Covid-19 followed by diabetes and hypertension in both sexes and chronic renal failure in females only.\nActive smoking was associated with decreased odds of Covid-19.\nThese findings indicate that these comorbidities are not only associated with severity of disease but also predispose for getting Covid-19.\nFuture research is needed to establish the mechanisms involved in each comorbidity and the apparent \"protective\" effect of cigarette smoking.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 797}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: a popular treatment to tamp down the immune system in severely ill patients may help a few, but could harm many others. \n\nAbstract:\nSARS-CoV-2 is the coronavirus agent of the COVID-19 pandemic causing high mortalities.\nIn contrast, the widely spread human coronaviruses OC43, HKU1, 229E, and NL63 tend to cause only mild symptoms.\nThe present study shows, by in silico analysis, that these common human viruses are expected to induce immune memory against SARS-CoV-2 by sharing protein fragments (antigen epitopes) for presentation to the immune system by MHC class I. A list of such epitopes is provided.\nThe number of these epitopes and the prevalence of the common coronaviruses suggest that a large part of the world population has some degree of specific immunity against SARS-CoV-2 already, even without having been infected by that virus.\nFor inducing protection, booster vaccinations enhancing existing immunity are less demanding than primary vaccinations against new antigens.\nTherefore, for the discussion on vaccination strategies against COVID-19, the available immune memory against related viruses should be part of the consideration.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 798}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: to reduce the spread of the virus: try to stay at least 2 metres (3 steps) away from anyone you do not live with (or anyone not in your support bubble); wash your hands with soap and water often - do this for at least 20 seconds; use hand sanitiser gel if soap and water are not available; wash your hands as soon as you get home; cover your mouth and nose with a tissue or your sleeve (not your hands) when you cough or sneeze; put used tissues in the bin immediately and wash your hands afterwards\n\nAbstract:\nOBJECTIVE.\nTo analyze the effectiveness of social distancing in the United States (U.S.).\nMETHODS.\nA novel cell-phone ping data was used to quantify the measures of social distancing by all U.S. counties.\nRESULTS.\nUsing a difference-in-difference approach results show that social distancing has been effective in slowing the spread of COVID-19.\nCONCLUSIONS.\nAs policymakers face the very difficult question of the necessity and effectiveness of social distancing across the U.S., counties where the policies have been imposed have effectively increased social distancing and have seen slowing the spread of COVID-19.\nThese results might help policymakers to make the public understand the risks and benefits of the lockdown.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"As policymakers face the very difficult question of the necessity and effectiveness of social distancing across the U.S., counties where the policies have been imposed have effectively increased social distancing and have seen slowing the spread of COVID-19.\"]}", "id": 799}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: masks prevent me from spreading COVID-19\n\nAbstract:\nIn the context of Coronavirus Disease (2019) (COVID-19) cases globally, there is a lack of consensus across cultures on whether wearing face masks is an effective physical intervention against disease transmission.\nThis study 1) illustrates transmission routes of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2); 2) addresses controversies surrounding the mask from perspectives of attitude, effectiveness, and necessity of wearing the mask with evidence that the use of mask would effectively interrupt the transmission of infectious diseases in both hospital settings and community settings; and 3) provides suggestion that the public should wear the mask during COVID-19 pandemic according to local context.\nTo achieve this goal, government should establish a risk adjusted strategy of mask use to scientifically publicize the use of masks, guarantee sufficient supply of masks, and cooperate for reducing health resources inequities.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"This study 1) illustrates transmission routes of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2); 2) addresses controversies surrounding the mask from perspectives of attitude, effectiveness, and necessity of wearing the mask with evidence that the use of mask would effectively interrupt the transmission of infectious diseases in both hospital settings and community settings; and 3) provides suggestion that the public should wear the mask during COVID-19 pandemic according to local context.\"]}", "id": 800}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: No, garlic won't prevent COVID-19. These 'home remedies' and 'cures' just don't work\n\nAbstract:\nOBJECTIVE To analyze the characteristics of YouTube videos in Spanish on the basic measures to prevent coronavirus disease 2019 (COVID-19).\nMETHODS On 18 March 2020, a search was conducted on YouTube using the terms \"Prevencion Coronavirus\" and \"Prevencion COVID-19\".\nWe studied the associations between the type of authorship and the country of publication with other variables (such as the number of likes and basic measures to prevent COVID-19 according to the World Health Organization, among others) with univariate analysis and a multiple logistic regression model.\nRESULTS A total of 129 videos were evaluated; 37.2% were produced in Mexico (25.6%) and Spain (11.6%), and 56.6% were produced by mass media, including television and newspapers.\nThe most frequently reported basic preventive measure was hand washing (71.3%), and the least frequent was not touching the eyes, nose, and mouth (24.0%).\nHoaxes (such as eating garlic or citrus to prevent COVID-19) were detected in 15 videos (10.9%).\nIn terms of authorship, papers produced by health professionals had a higher probability of reporting hand hygiene (OR (95% CI) = 4.20 (1.17-15.09)) and respiratory hygiene (OR (95% CI) = 3.05 (1.22-7.62)) as preventive measures.\nCONCLUSION Information from YouTube in Spanish on basic measures to prevent COVID-19 is usually not very complete and differs according to the type of authorship.\nOur findings make it possible to guide Spanish-speaking users on the characteristics of the videos to be viewed in order to obtain reliable information.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Hoaxes (such as eating garlic or citrus to prevent COVID-19) were detected in 15 videos (10.9%).\"]}", "id": 801}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Thrombotic microvascular injury is not mediated by thrombotic microangiopathy despite systemic complement activation in covid-19 patients\n\nAbstract:\nHypoxemia and coagulopathy are common in severe symptomatic patients of coronavirus disease 2019 (COVID-19).\nHistological evidence shows implication of complement activation and lung injury.\nWe research sign of complement activation and presence of thrombotic microangiopathy in 8 severe patients.\nSix of them presented moderate elevation of final pathway of complement / sC5b-9 (median value : 350 ng/mL [IQR : 300,5-514,95 ng/mL]).\nTwo patients have been autopsied and presence of thrombotic microvascular injury have been found.\nInterestingly, none the 8 patients had signs of mechanical hemolytic anemia (median value of hemoglobin : 10,5 gr/dL[IQR : 8,1-1,9], median value of haptoglobuline 4,49 [IQR 3,55-4,66], none of the patients has schistocyte) and thrombocytopenia (median value: 348000/mL [IQR : 266 000-401 000).\nFinally, all 8 patients had elevated d-dimer (median value : 2226 microgr/l [IQR : 1493-2362]) and soluble fibrin monomer complex (median value : 8.5 mg/mL, IQR[ <6-10.6]).\nIn summary, this study show moderate activation of complement and coagulation with presence of thrombotic microvascular injury in patients with severe COVID-19 without evidence of systemic thrombotic microangiopathy.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"In summary, this study show moderate activation of complement and coagulation with presence of thrombotic microvascular injury in patients with severe COVID-19 without evidence of systemic thrombotic microangiopathy.\"]}", "id": 802}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Coronavirus nsp10/nsp16 methyltransferase can be targeted by nsp10-derived peptide in vitro and in vivo to reduce replication and pathogenesis\n\nAbstract:\nUNLABELLED The 5' cap structures of eukaryotic mRNAs are important for RNA stability and protein translation.\nMany viruses that replicate in the cytoplasm of eukaryotes have evolved 2'-O-methyltransferases (2'-O-MTase) to autonomously modify their mRNAs and carry a cap-1 structure (m7GpppNm) at the 5' end, thereby facilitating viral replication and escaping innate immune recognition in host cells.\nPrevious studies showed that the 2'-O-MTase activity of severe acute respiratory syndrome coronavirus (SARS-CoV) nonstructural protein 16 (nsp16) needs to be activated by nsp10, whereas nsp16 of feline coronavirus (FCoV) alone possesses 2'-O-MTase activity (E. Decroly et al., J Virol 82:8071-8084, 2008, http://dx.doi.org/10.1128/JVI.00407-08; M. Bouvet et al., PLoS Pathog 6:e1000863, 2010, http://dx.doi.org/10.1371/journal.ppat.1000863; E. Decroly et al., PLoS Pathog 7:e1002059, 2011, http://dx.doi.org/10.1371/journal.ppat.1002059; Y. Chen et al., PLoS Pathog 7:e1002294, 2011, http://dx.doi.org/10.1371/journal.ppat.1002294) .\nIn this study, we demonstrate that stimulation of nsp16 2'-O-MTase activity by nsp10 is a universal and conserved mechanism in coronaviruses, including FCoV, and that nsp10 is functionally interchangeable in the stimulation of nsp16 of different coronaviruses.\nBased on our current and previous studies, we designed a peptide (TP29) from the sequence of the interaction interface of mouse hepatitis virus (MHV) nsp10 and demonstrated that the peptide inhibits the 2'-O-MTase activity of different coronaviruses in biochemical assays and the viral replication in MHV infection and SARS-CoV replicon models.\nInterestingly, the peptide TP29 exerted robust inhibitory effects in vivo in MHV-infected mice by impairing MHV virulence and pathogenesis through suppressing virus replication and enhancing type I interferon production at an early stage of infection.\nTherefore, as a proof of principle, the current results indicate that coronavirus 2'-O-MTase activity can be targeted in vitro and in vivo.\nIMPORTANCE Coronaviruses are important pathogens of animals and human with high zoonotic potential.\nSARS-CoV encodes the 2'-O-MTase that is composed of the catalytic subunit nsp16 and the stimulatory subunit nsp10 and plays an important role in virus genome replication and evasion from innate immunity.\nOur current results demonstrate that stimulation of nsp16 2'-O-MTase activity by nsp10 is a common mechanism for coronaviruses, and nsp10 is functionally interchangeable in the stimulation of nsp16 among different coronaviruses, which underlies the rationale for developing inhibitory peptides.\nWe demonstrate that a peptide derived from the nsp16-interacting domain of MHV nsp10 could inhibit 2'-O-MTase activity of different coronaviruses in vitro and viral replication of MHV and SARS-CoV replicon in cell culture, and it could strongly inhibit virus replication and pathogenesis in MHV-infected mice.\nThis work makes it possible to develop broad-spectrum peptide inhibitors by targeting the nsp16/nsp10 2'-O-MTase of coronaviruses.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"We demonstrate that a peptide derived from the nsp16-interacting domain of MHV nsp10 could inhibit 2'-O-MTase activity of different coronaviruses in vitro and viral replication of MHV and SARS-CoV replicon in cell culture, and it could strongly inhibit virus replication and pathogenesis in MHV-infected mice.\"]}", "id": 803}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: if you are low risk (healthy, young), you do not need social distancing.\n\nAbstract:\nThe Australian and New Zealand governments both initiated strict social distancing measures in response to the COVID-19 pandemic in late March.\nIt remains difficult to quantify the impact this had in reducing the spread of the virus.\nBayesian structural time series model provide a model to quantify the scenario in which these government-level interventions were not placed.\nOur models predict these strict social distancing measures caused a 79% and 61% reduction in the daily cases of COVID-19 across Australia and New Zealand respectively.\nThis provides both evidence and impetus for governments considering similar measures in response to COVID-19 and other pandemics.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Our models predict these strict social distancing measures caused a 79% and 61% reduction in the daily cases of COVID-19 across Australia and New Zealand respectively.\", \"This provides both evidence and impetus for governments considering similar measures in response to COVID-19 and other pandemics.\"]}", "id": 804}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Low testosterone levels predict clinical adverse outcomes in sars-cov-2 pneumonia patients\n\nAbstract:\nBACKGROUND: The pandemic of new severe acute respiratory syndrome (SARS) due to coronavirus (CoV) 2 (SARS-CoV-2) has stressed the importance of effective diagnostic and prognostic biomarkers of clinical worsening and mortality.\nEpidemiological data showing a differential impact of SARS-CoV-2 infection on women and men have suggested a potential role for testosterone (T) in determining gender disparity in the SARS-CoV-2 clinical outcomes.\nOBJECTIVES: To estimate the association between T level and SARS-CoV-2 clinical outcomes (defined as conditions requiring transfer to higher or lower intensity of care or death) in a cohort of patients admitted in the respiratory intensive care unit (RICU).\nMATERIALS AND METHODS: A consecutive series of 31 male patients affected by SARS-CoV-2 pneumonia and recovered in the respiratory intensive care unit (RICU) of the \"Carlo Poma\" Hospital in Mantua were analyzed.\nSeveral biochemical risk factors (ie, blood count and leukocyte formula, C-reactive protein (CRP), procalcitonin (PCT), lactate dehydrogenase (LDH), ferritin, D-dimer, fibrinogen, interleukin 6 (IL-6)) as well as total testosterone (TT), calculated free T (cFT), sex hormone-binding globulin (SHBG), and luteinizing hormone (LH) were determined.\nRESULTS: Lower TT and cFT were found in the transferred to ICU/deceased in RICU group vs groups of patients transferred to IM or maintained in the RICU in stable condition.\nBoth TT and cFT showed a negative significant correlation with biochemical risk factors (ie, the neutrophil count, LDH, and PCT) but a positive association with the lymphocyte count.\nLikewise, TT was also negatively associated with CRP and ferritin levels.\nA steep increase in both ICU transfer and mortality risk was observed in men with TT < 5 nmol/L or cFT < 100 pmol/L. DISCUSSION AND CONCLUSION: Our study demonstrates for the first time that lower baseline levels of TT and cFT levels predict poor prognosis and mortality in SARS-CoV-2-infected men admitted to RICU.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Epidemiological data showing a differential impact of SARS-CoV-2 infection on women and men have suggested a potential role for testosterone (T) in determining gender disparity in the SARS-CoV-2 clinical outcomes.\"]}", "id": 805}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Covid-19 hits the old hardest, and young people do not die from it\n\nAbstract:\nBackground: The coronavirus 2019 (COVID-19) pandemic has been spread-ing globally for months, yet the infection fatality ratio of the disease is still uncertain.\nThis is partly because of inconsistencies in testing and death reporting standards across countries.\nOur purpose is to provide accurate estimates which do not rely on testing and death count data directly but only use population level statistics.\nMethods: We collected demographic and death records data from the Italian Institute of Statistics.\nWe focus on the area in Italy that experienced the initial outbreak of COVID-19 and estimated a Bayesian model fitting age-stratified mortality data from 2020 and previous years.\nWe also assessed the sensitivity of our estimates to alternative assumptions on the proportion of population infected.\nFindings: We estimate an overall infection fatality rate of 1.29% (95% credible interval [CrI] 0.89 - 2.01), as well as large differences by age, with a low infection fatality rate of 0.05% for under 60 year old (CrI 0-.19) and a substantially higher 4.25% (CrI 3.01-6.39) for people above 60 years of age.\nIn our sensitivity analysis, we found that even under extreme assumptions, our method delivered useful information.\nFor instance, even if only 10% of the population were infected, the infection fatality rate would not rise above 0.2% for people under 60.\nInterpretation: Our empirical estimates based on population level data show a sharp difference in fatality rates between young and old people and firmly rule out overall fatality ratios below 0.5% in populations with more than 30% over 60 years old.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 806}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: There's been much speculation about whether vitamin D might prevent or help survival with COVID-19, and two new studies appear to underscore the link.\n\nAbstract:\nBackground: Following emerge of a novel coronavirus from Wuhan, China, in December 2019, it has affected the whole world and after months of efforts by the medical communities, there is still no specific approach for prevention and treatment against the Coronavirus Disease 2019 (COVID-19).\nEvidence recommends that vitamin D might be an important supportive agent for the immune system, mainly in cytokine response regulation against COVID-19.\nHence, we carried out a rapid systematic review and meta-analysis along with an ecological investigation in order to maximize the use of everything that exists about the role of vitamin D in the COVID-19.\nMethods: A systematic search was performed in PubMed, Scopus, Embase, Cochrane Library, Web of Science and Google Scholar (intitle) as well as preprint database of medRxiv, bioRxiv, Research Square, preprints.org, search engine of ScienceDirect and a rapid search through famous journals up to May 26, 2020.\nStudies focused on the role of vitamin D in confirmed COVID-19 patients were entered into the systematic review.\nAlong with our main aim, to find the second objective: correlation of global vitamin D status and COVID-19 recovery and mortality we carried out a literature search in PubMed database to identify the national or regional studies reported the vitamin D status globally.\nCMA v. 2.2.064 and SPSS v.16 were used for data analysis.\nResults: Out of nine studies entered into our systematic review, six studies containing 3,822 participants entered into the meta-analysis.\nThe meta-analysis indicated that 46.5% of COVID-19 patients were suffering from vitamin D deficiency (95% CI, 28.2%-65.8%) and in 43.3% of patients, levels of vitamin D were insufficient (95% CI, 27.4%-60.8%).\nIn regard to our ecological investigation on 51 countries including 408,748 participants, analyses indicated no correlation between vitamin D levels and recovery rate (r= 0.041) as well as mortality rate (r=-0.073) globally.\nHowever, given latitude, a small reverse correlation between mortality rate and vitamin D status was observed throughout the globe (r= -0.177).\nIn Asia, a medium direct correlation was observed for recovery rate (r= 0.317) and a significant reveres correlation for mortality rate (r= -0.700) with vitamin D status in such patients.\nIn Europe, there were no correlations for both recovery (r= 0.040) and mortality rate (r= -0.035).\nIn Middle East, the recovery rate (r= 0.267) and mortality rate (r= -0.217) showed a medium correlation.\nIn North and Sought America, surprisingly, both recovery and mortality rate demonstrated a direct correlation respectively (r= 1.000, r=0.500).\nIn Oceania, unexpectedly, recovery (r= -1.000) and mortality (r= -1.000) rates were in considerable reverse correlation with vitamin D levels.\nConclusion: In this systematic review and meta-analysis with an ecological approach, we found a high percentage of COVID-19 patients who suffer from vitamin D deficiency or insufficiency.\nMuch more important, our ecological investigation resulted in substantial direct and reverse correlations between recovery and mortality rates of COVID-19 patients with vitamin D status in different countries.\nConsidering latitudes, a small reverse correlation between vitamin D status and mortality rate was found globally.\nIt seems that populations with lower levels of vitamin D might be more susceptible to the novel coronavirus infection.\nNevertheless, due to multiple limitations, if this study does not allow to quantify a value of the Vitamin D with full confidence, it allows at least to know what the Vitamin D might be and that it would be prudent to invest in this direction through comprehensive large randomized clinical trials.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 807}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: COVID-19: Hand sanitizers inactivate novel coronavirus\n\nAbstract:\nThe recent emergence of Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causing COVID-19 is a major burden for health care systems worldwide.\nIt is important to address if the current infection control instructions based on active ingredients are sufficient.\nWe therefore determined the virucidal activity of two alcohol-based hand rub solutions for hand disinfection recommended by the World Health Organization (WHO), as well as commercially available alcohols.\nEfficient SARS-CoV-2 inactivation was demonstrated for all tested alcohol-based disinfectants.\nThese findings show the successful inactivation of SARS-CoV-2 for the first time and provide confidence in its use for the control of COVID-19.\nImportance The current COVID-19 outbreak puts a huge burden on the world\u2019s health care systems.\nWithout effective therapeutics or vaccines being available, effective hygiene measure are of utmost importance to prevent viral spreading.\nIt is therefore crucial to evaluate current infection control strategies against SARS-CoV-2.\nWe show the inactivation of the novel coronavirus for the first time and endorse the importance of disinfectant-based hand hygiene to reduce SARS-CoV-2 transmission.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Efficient SARS-CoV-2 inactivation was demonstrated for all tested alcohol-based disinfectants.\", \"These findings show the successful inactivation of SARS-CoV-2 for the first time and provide confidence in its use for the control of COVID-19.\"]}", "id": 808}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Early studies have found that cats are the animals most likely to catch the new coronavirus. They can also show symptoms of COVID-19 and might be able to pass it to other cats.\n\nAbstract:\nSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)--the virus that causes coronavirus disease (COVID-19)--has been detected in domestic dogs and cats, raising concerns of transmission from, to, or between these animals.\nThere is currently no indication that feline- or canine-to-human transmission can occur, though there is rising evidence of the reverse.\nTo explore the extent of animal-related transmission, we aggregated 17 case reports on confirmed SARS-CoV-2 infections in animals as of 15 May 2020.\nAll but two animals fully recovered and had only mild respiratory or digestive symptoms.\nUsing data from probable cat-to-cat transmission in Wuhan, China, we estimated the basic reproduction number R0 under this scenario at 1.09 (95% confidence interval: 1.05, 1.13).\nThis value is much lower than the R0 reported for humans and close to one, indicating that the sustained transmission between cats is unlikely to occur.\nOur results support the view that the pet owners and other persons with COVID-19 in close contact with animals should be cautious of the way they interact with them.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"There is currently no indication that feline- or canine-to-human transmission can occur, though there is rising evidence of the reverse.\", \"Using data from probable cat-to-cat transmission in Wuhan, China, we estimated the basic reproduction number R0 under this scenario at 1.09 (95% confidence interval: 1.05, 1.13).\", \"This value is much lower than the R0 reported for humans and close to one, indicating that the sustained transmission between cats is unlikely to occur.\"]}", "id": 809}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: If your head is spinning, let medical experts provide some focus on one of the symptoms of the coronavirus.\n\nAbstract:\nINTRODUCTION: Neurological manifestations can occur during coronavirus disease 19 (COVID-19).\nSeveral pathogenic mechanisms have been hypothesized, without conclusive results.\nIn this study, we evaluated the most frequent neurological symptoms in a cohort of hospitalized COVID-19 patients, and also investigated the possible relationship between plasmatic inflammatory indices and olfactory disorders (ODs) and between muscle pain and creatine kinase (CK).\nMETHODS: We consecutively enrolled hospitalized COVID-19 patients.\nA structured questionnaire concerning typical and neurological symptoms, focusing on headache, dizziness, ODs, taste disorders (TDs), and muscle pain, was administrated by telephone interviews.\nRESULTS: Common neurological symptoms were reported in the early phase of the disease, with a median onset ranging from 1 to 3 days.\nHeadache showed tension-type features and was more frequently associated with a history of headache.\nPatients with ODs less frequently needed oxygen therapy.\nInflammatory indices did not significantly differ between patients with and without ODs.\nMuscle pain did not show any association with CK level but was more frequently associated with arthralgia and headache.\nCONCLUSION: In our cohort, ODs were an early symptom of COVID-19, more frequently reported by patients with milder forms of disease.\nHeadache in association with arthralgia and muscle pain seems to reflect the common symptoms of the flu-like syndrome, and not COVID-19 infection-specific.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 810}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Blood pressure drugs may improve COVID-19 survival\n\nAbstract:\nIntravenous infusions of angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin receptor blockers (ARBs) in experimental animals increase the numbers of angiotensin-converting enzyme 2 (ACE2) receptors in the cardiopulmonary circulation.\nACE2 receptors serve as binding sites for SARS-CoV-2 virions in the lungs.\nPatients who take ACEIs and ARBS may be at increased risk of severe disease outcomes due to SARS-CoV-2 infections.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Patients who take ACEIs and ARBS may be at increased risk of severe disease outcomes due to SARS-CoV-2 infections.\"]}", "id": 811}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: the risk of pets spreading Covid-19 to humans is considered 'medium'\n\nAbstract:\nOn April 22, CDC and the U.S. Department of Agriculture (USDA) reported cases of two domestic cats with confirmed infection with SARS-CoV-2, the virus that causes coronavirus disease 2019 (COVID-19).\nThese are the first reported companion animals (including pets and service animals) with SARS-CoV-2 infection in the United States, and among the first findings of SARS-CoV-2 symptomatic companion animals reported worldwide.\nThese feline cases originated from separate households and were epidemiologically linked to suspected or confirmed human COVID-19 cases in their respective households.\nNotification of presumptive positive animal test results triggered a One Health* investigation by state and federal partners, who determined that no further transmission events to other animals or persons had occurred.\nBoth cats fully recovered.\nAlthough there is currently no evidence that animals play a substantial role in spreading COVID-19, CDC advises persons with suspected or confirmed COVID-19 to restrict contact with animals during their illness and to monitor any animals with confirmed SARS-CoV-2 infection and separate them from other persons and animals at home (1).", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Although there is currently no evidence that animals play a substantial role in spreading COVID-19, CDC advises persons with suspected or confirmed COVID-19 to restrict contact with animals during their illness and to monitor any animals with confirmed SARS-CoV-2 infection and separate them from other persons and animals at home (1).\"]}", "id": 812}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Iga dominates the early neutralizing antibody response to sars-cov-2\n\nAbstract:\nHumoral immune responses are typically characterized by primary IgM antibody responses followed by secondary antibody responses associated with immune memory and comprised of of IgG, IgA and IgE. Here we measured acute humoral responses to SARS-CoV-2, including the frequency of antibody-secreting cells and the presence of SARS-CoV-2-specific neutralizing antibodies in the serum, saliva and broncho-alveolar fluid of 159 patients with COVID-19.\nEarly SARS-CoV-2-specific humoral responses were dominated by IgA antibodies.\nPeripheral expansion of IgA plasmablasts with mucosal-homing potential was detected shortly after the onset of symptoms and peaked during the third week of the disease.\nThe virus-specific antibody responses included IgG, IgM and IgA, but IgA contributed to virus neutralization to a greater extent compared with IgG. Specific IgA serum concentrations decreased notably one month after the onset of symptoms, but neutralizing IgA remained detectable in saliva for a longer time (days 49 to 73 post symptoms).\nThese results represent a critical observation given the emerging information as to the types of antibodies associated with optimal protection against re-infection, and whether vaccine regimens should consider targeting a potent but potentially short-lived IgA response.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Early SARS-CoV-2-specific humoral responses were dominated by IgA antibodies.\"]}", "id": 813}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: the Covid-19 coronavirus can stay on various surfaces for a while\n\nAbstract:\nThe ocular surface has been suggested as a site of infection with Coronavirus-2 (SARS-CoV-2) responsible for the coronavirus disease-19 (COVID-19).\nThis review examines the evidence for this hypothesis, and its implications for clinical practice.\nSevere Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2), responsible for the COVID-19 pandemic, is transmitted by person-to-person contact, via airborne droplets, or through contact with contaminated surfaces.\nSARS-CoV-2 binds to angiotensin converting enzyme-2 (ACE2) to facilitate infection in humans.\nThis review sets out to evaluate evidence for the ocular surface as a route of infection.\nA literature search in this area was conducted on 15 April 2020 using the Scopus database.\nIn total, 287 results were returned and reviewed.\nThere is preliminary evidence for ACE2 expression on corneal and conjunctival cells, but most of the other receptors to which coronaviruses bind appear to be found under epithelia of the ocular surface.\nEvidence from animal studies is limited, with a single study suggesting viral particles on the eye can travel to the lung, resulting in very mild infection.\nCoronavirus infection is rarely associated with conjunctivitis, with occasional cases reported in patients with confirmed COVID-19, along with isolated cases of conjunctivitis as a presenting sign.\nCoronaviruses have been rarely isolated from tears or conjunctival swabs.\nThe evidence suggests coronaviruses are unlikely to bind to ocular surface cells to initiate infection.\nAdditionally, hypotheses that the virus could travel from the nasopharynx or through the conjunctival capillaries to the ocular surface during infection are probably incorrect.\nConjunctivitis and isolation of the virus from the ocular surface occur only rarely, and overwhelmingly in patients with confirmed COVID-19.\nNecessary precautions to prevent person-to-person transmission should be employed in clinical practice throughout the pandemic, and patients should be reminded to maintain good hygiene practices.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 814}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Coronaviruses cause respiratory illnesses, so the lungs are usually affected first. Early symptoms include fever, cough, and shortness of breath.\n\nAbstract:\nThe outbreak of the 2019 Novel Coronavirus (SARS-CoV-2) rapidly spread from Wuhan, China to more than 150 countries, areas or territories, causing staggering number of infections and deaths.\nA systematic profiling of the immune vulnerability landscape of SARS-CoV-2, which can bring critical insights into the immune clearance mechanism, peptide vaccine development, and antiviral antibody development, is lacking.\nIn this study, we investigated the potential of the SARS-CoV-2 viral proteins to induce class I and II MHC presentation and to form linear antibody epitopes.\nWe created an online database to broadly share the predictions as a resource for the research community.\nUsing this resource, we showed that genetic variations in SARS- CoV-2, though still few for the moment, already follow the pattern of mutations in related coronaviruses, and could alter the immune vulnerability landscape of this virus.\nImportantly, we discovered evidence that SARS-CoV-2, along with related coronaviruses, used mutations to evade attack from the human immune system.\nOverall, we present an immunological resource for SARS-CoV-2 that could promote both therapeutic development and mechanistic research.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 815}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: A novel in-cell elisa assay allows rapid and automated quantification of sars-cov-2 to analyze neutralizing antibodies and antiviral compounds\n\nAbstract:\nThe coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is currently the most pressing medical and socioeconomic challenge.\nConstituting important correlates of protection, the determination of virus-neutralizing antibodies (NAbs) is indispensable for convalescent plasma selection, vaccine candidate evaluation, and immunity certificates.\nIn contrast to standard serological ELISAs, plaque reduction neutralization tests (PRNTs) are laborious, time-consuming, expensive, and restricted to specialized laboratories.\nTo replace microscopic counting-based SARS-CoV-2 PRNTs by a novel assay exempt from genetically modified viruses, which are inapplicable in most diagnostics departments, we established a simple, rapid, and automated SARS-CoV-2 neutralization assay employing an in-cell ELISA (icELISA) approach.\nAfter optimization of various parameters such as virus-specific antibodies, cell lines, virus doses, and duration of infection, SARS-CoV-2-infected cells became amenable as direct antigen source for quantitative icELISA.\nAntiviral agents such as human sera containing NAbs or antiviral interferons dose dependently reduced the SARS-CoV-2-specific signal.\nApplying increased infectious doses, the icELISA-based neutralization test (icNT) was superior to PRNT in discriminating convalescent sera with high from those with intermediate neutralizing capacities.\nIn addition, the icNT was found to be specific, discriminating between SARS-CoV-2-specific NAbs and those raised against other coronaviruses.\nAltogether, the SARS-CoV-2 icELISA test allows rapid (<48 h in total, read-out in seconds) and automated quantification of virus infection in cell culture to evaluate the efficacy of NAbs and antiviral drugs using reagents and equipment present in most routine diagnostics departments.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"To replace microscopic counting-based SARS-CoV-2 PRNTs by a novel assay exempt from genetically modified viruses, which are inapplicable in most diagnostics departments, we established a simple, rapid, and automated SARS-CoV-2 neutralization assay employing an in-cell ELISA (icELISA) approach.\", \"Altogether, the SARS-CoV-2 icELISA test allows rapid (<48 h in total, read-out in seconds) and automated quantification of virus infection in cell culture to evaluate the efficacy of NAbs and antiviral drugs using reagents and equipment present in most routine diagnostics departments.\"]}", "id": 816}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Vitamins and Minerals Help Fight Coronavirus\n\nAbstract:\nBACKGROUND The coronavirus disease 2019 (COVID-19) pandemic has affected almost 2.5 million people worldwide with almost 170 000 deaths reported to date.\nSo far, there is scarce evidence for the current treatment options available for COVID-19.\nVitamin C has previously been used for treatment of severe sepsis and septic shock.\nWe reviewed the feasibility of using vitamin C in the setting of COVID-19 in a series of patients.\nMETHODS We sequentially identified a series of patients who were requiring at least 30% of FiO2 or more who received IV vitamin C as part of the COVID-19 treatment and analyzed their demographic and clinical characteristics.\nWe compared inflammatory markers pre and post treatment including D-dimer and ferritin.\nRESULTS We identified a total of 17 patients who received IV vitamin C for COVID-19.\nThe inpatient mortality rate in this series was 12% with 17.6% rates of intubation and mechanical ventilation.\nWe noted a significant decrease in inflammatory markers, including ferritin and D-dimer, and a trend to decreasing FiO2 requirements, after vitamin C administration.\nCONCLUSION The use of IV vitamin C in patients with moderate to severe COVID-19 disease may be feasible.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 817}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Blood pressure drugs may improve COVID-19 survival\n\nAbstract:\nThe effects of angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin receptor blockers (ARBs) on the risk of COVID-19 infection and disease progression are yet to be investigated.\nThe relationship between ACEI/ARB use and COVID-19 infection was systematically reviewed.\nTo identify relevant studies that met predetermined inclusion criteria, unrestricted searches of the PubMed, Embase, and Cochrane Library databases were conducted.\nThe search strategy included clinical date published until May 9, 2020.\nTwelve articles involving more than 19,000 COVID-19 cases were included.\nTo estimate overall risk, random-effects models were adopted.\nOur results showed that ACEI/ARB exposure was not associated with a higher risk of COVID-19 infection (OR = 0.99; 95 % CI, 0-1.04; P = 0.672).\nAmong those with COVID-19 infection, ACEI/ARB exposure was also not associated with a higher risk of having severe infection (OR = 0.98; 95 % CI, 0.87-1.09; P = 0.69) or mortality (OR = 0.73, 95 %CI, 0.5-1.07; P = 0.111).\nHowever, ACEI/ARB exposure was associated with a lower risk of mortality compared to those on non-ACEI/ARB antihypertensive drugs (OR = 0.48, 95 % CI, 0.29-0.81; P = 0.006).\nIn conclusion, current evidence did not confirm the concern that ACEI/ARB exposure is harmful in patientswith COVID-19 infection.\nThis study supports the current guidelines that discourage discontinuation of ACEIs or ARBs in COVID-19 patients and the setting of the COVID-19 pandemic.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 818}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Officials believe that the virus was present in meat sold at the said market.\n\nAbstract:\nCoronavirus disease-19 (COVID-19) caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is now a pandemic threat.\nThis virus is supposed to be spread by human to human transmission.\nCellular angiotensin-converting enzyme 2 (ACE2) is the receptor of SARS-CoV-2 which is identical or similar in different species of animals such as pigs, ferrets, cats, orangutans, monkeys, and humans.\nMoreover, a recent study predicted that dogs might be secondary hosts during the evolution of SARS-CoV-2 from bat to human.\nTherefore, there is a possibility of spreading SARS-CoV-2 through domestic pets.\nThere are now many reports of SARS-CoV-2 positive cases in dogs, cats, tigers, lion, and minks.\nExperimental data showed ferrets and cats are highly susceptible to SARS-CoV-2 as infected by virus inoculation and can transmit the virus directly or indirectly by droplets or airborne routes.\nBased on these natural infection reports and experimental data, whether the pets are responsible for SARS-CoV-2 spread to humans; needs to be deeply investigated.\nHumans showing clinical symptoms of respiratory infections have been undergoing for the COVID-19 diagnostic test but many infected people and few pets confirmed with SARS-CoV-2 remained asymptomatic.\nIn this review, we summarize the natural cases of SARS-CoV-2 in animals with the latest researches conducted in this field.\nThis review will be helpful to think insights of SARS-CoV-2 transmissions, spread, and demand for seroprevalence studies, especially in companion animals.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 819}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Vitamin D Supplementation Could Reduce Risk of Influenza and COVID-19 Infections and Deaths\n\nAbstract:\nImportance: Vitamin D treatment has been found to decrease incidence of viral respiratory tract infection, especially in vitamin D deficiency.\nIt is unknown whether COVID-19 incidence is associated with vitamin D deficiency and treatment.\nObjective: To examine whether vitamin D deficiency and treatment are associated with testing positive for COVID-19.\nDesign: Retrospective cohort study Setting: University of Chicago Medicine Participants: Patients tested for COVID-19 from 3/3/2020-4/10/2020.\nVitamin D deficiency was defined by the most recent 25-hydroxycholecalciferol <20ng/ml or 1,25-dihydroxycholecalciferol <18pg/ml within 1 year before COVID-19 testing.\nTreatment was defined by the most recent vitamin D type and dose, and treatment changes between the time of the most recent vitamin D level and time of COVID-19 testing.\nVitamin D deficiency and treatment changes were combined to categorize vitamin D status at the time of COVID-19 testing as likely deficient(last-level-deficient/treatment-not-increased), likely sufficient(last-level-not-deficient/treatment-not-decreased), or uncertain deficiency(last-level-deficient/treatment-increased or last-level-not-deficient/treatment-decreased).\nMain Outcomes and Measures: The main outcome was testing positive for COVID-19.\nMultivariable analysis tested whether the most recent vitamin D level and treatment changes after that level were associated with testing positive for COVID-19 controlling for demographic and comorbidity indicators.\nBivariate analyses of associations of treatment with vitamin D deficiency and COVID-19 were performed.\nResults: Among 4,314 patients tested for COVID-19, 499 had a vitamin D level in the year before testing.\nVitamin D status at the time of COVID-19 testing was categorized as likely deficient for 127(25%) patients, likely sufficient for 291(58%) patients, and uncertain for 81(16%) patients.\nIn multivariate analysis, testing positive for COVID-19 was associated with increasing age(RR(age<50)=1.05,p<0.021;RR(age[\u2265]50)=1.02,p<0.064)), non-white race(RR=2.54,p<0.01) and being likely vitamin D deficient (deficient/treatment-not-increased:RR=1.77,p<0.02) as compared to likely vitamin D sufficient(not-deficient/treatment-not-decreased), with predicted COVID-19 rates in the vitamin D deficient group of 21.6%(95%CI[14.0%-29.2%] ) versus 12.2%(95%CI[8.9%-15.4%]) in the vitamin D sufficient group.\nVitamin D deficiency declined with increasing vitamin D dose, especially of vitamin D3.\nVitamin D dose was not significantly associated with testing positive for COVID-19.\nConclusions and Relevance: Vitamin D deficiency that is not sufficiently treated is associated with COVID-19 risk.\nTesting and treatment for vitamin D deficiency to address COVID-19 warrant aggressive pursuit and study.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Conclusions and Relevance: Vitamin D deficiency that is not sufficiently treated is associated with COVID-19 risk.\"]}", "id": 820}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Hydroxychloroquine safety outcome within approved therapeutic protocol for covid-19 outpatients in saudi arabia\n\nAbstract:\nBackground: Healthcare systems globally has been challenged following the COVID-19 pandemic, since late 2019.\nMultiple approaches and strategies have been performed to relieve the pressure and support existing healthcare systems.\nThe Saudi Arabian Ministry of Health (MOH) launched an initiative to support the National Healthcare System.\nSince the 5th of June 2020, 238 outpatient fever clinics were established across Saudi Arabia.\nMethods: A cross-sectional study included 2,733 eligible patients subjected to MOH treatment protocol (hydroxychloroquine and zinc) and revisited the clinics within 3-7 days after treatment initiation.\nThis study aimed to assess the safety outcome and reported adverse events from hydroxychloroquine use among suspected COVID-19 patients.\nThe data was collected through an electronic link and cross-checked with that of the national database (Health Electronic Surveillance Network, HESN) and reports from the MOH Morbidity and Mortality (M&M) Committee.\nResults: Majority of the cases were males (70.4%).\nUpon reassessing the studied participants within 3-7 days, 240 patients (8.8%) discontinued the treatment protocol because of the development of side effects (4.1%) and for non-clinical reasons in the remaining (4.7%).\nMedication side effects overall were reported among (6.7%) of all studied participants, including mainly cardiovascular adverse events (2.5%), followed by gastrointestinal (GI) symptoms (2.4%).\nNo Intensive Care Unit (ICU) admission or death were reported among these patients.\nConclusion: In our study, results show that the use of hydroxychloroquine for COVID-19 patients in mild to moderate cases in an outpatient setting, within the protocol recommendation and inclusion/exclusion criteria, is safe, highly tolerable, and with minimum side effects.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Conclusion: In our study, results show that the use of hydroxychloroquine for COVID-19 patients in mild to moderate cases in an outpatient setting, within the protocol recommendation and inclusion/exclusion criteria, is safe, highly tolerable, and with minimum side effects.\"]}", "id": 821}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Severe covid-19 infection is associated with increased antibody-mediated platelet apoptosis\n\nAbstract:\nThe pathophysiology of COVID-19 associated thrombosis seems to be multifactorial, involving interplay between cellular and plasmatic elements of the hemostasis.\nWe hypothesized that COVID-19 is accompanied by platelet apoptosis with subsequent alteration of the coagulation system.\nWe investigated depolarization of mitochondrial inner transmembrane potential ({Delta}{Psi}m), cytosolic calcium (Ca2+) concentration, and phosphatidylserine (PS) externalization by flow cytometry.\nPlatelets from intensive care unit (ICU) COVID-19 patients (n=21) showed higher {Delta}{Psi}m depolarization, cytosolic Ca2+ concentration and PS externalization, compared to healthy controls (n=18) and COVID-19 non-ICU patients (n=4).\nMoreover significant higher cytosolic Ca2+ concentration and PS was observed compared to septic ICU control group (ICU control).\nIn ICU control group (n=5; non-COVID-19 ICU) cytosolic Ca2+ concentration and PS externalization was comparable to healthy control, with an increase in {Delta}{Psi}m depolarization.\nSera from ICU COVID-19 patients induced significant increase in apoptosis markers ({Delta}{Psi}m depolarization, cytosolic Ca2+ concentration and PS externalization) compared to healthy volunteer and septic ICU control.\nInterestingly, immunoglobulin G (IgG) fractions from COVID-19 patients induced an Fc gamma receptor IIA dependent platelet apoptosis ({Delta}{Psi}m depolarization, cytosolic Ca2+ concentration and PS externalization).\nEnhanced PS externalization in platelets from ICU COVID-19 patients was associated with increased sequential organ failure assessment (SOFA) score (r=0.5635) and D-Dimer (r=0.4473).\nMost importantly, patients with thrombosis had significantly higher PS externalization compared to those without.\nThe strong correlations between apoptosis markers and increased D-Dimer levels as well as the incidence of thrombosis may indicate that antibody-mediated platelet apoptosis potentially contributes to sustained increased thromboembolic risk in ICU COVID-19 patients.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"The strong correlations between apoptosis markers and increased D-Dimer levels as well as the incidence of thrombosis may indicate that antibody-mediated platelet apoptosis potentially contributes to sustained increased thromboembolic risk in ICU COVID-19 patients.\"]}", "id": 822}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Coronavirus remains stable on metals and plastic for three days. Outside a lab, however, the virus might last considerably longer: its genetic material could be detected on surfaces 17 days after a cruise ship was empty of passengers (although it's not clear whether that material represents infectious virus particles).\n\nAbstract:\nSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a highly contagious virus that can transmit through respiratory droplets, aerosols, or contacts.\nFrequent touching of contaminated surfaces in public areas is therefore a potential route of SARS-CoV-2 transmission.\nThe inanimate surfaces have often been described as a source of nosocomial infections.\nHowever, summaries on the transmissibility of coronaviruses from contaminated surfaces to induce the coronavirus disease 2019 are rare at present.\nThis review aims to summarize data on the persistence of different coronaviruses on inanimate surfaces.\nThe literature was systematically searched on Medline without language restrictions.\nAll reports with experimental evidence on the duration persistence of coronaviruses on any type of surface were included.\nMost viruses from the respiratory tract, such as coronaviruses, influenza, SARS-CoV, or rhinovirus, can persist on surfaces for a few days.\nPersistence time on inanimate surfaces varied from minutes to up to one month, depending on the environmental conditions.\nSARSCoV-2 can be sustained in air in closed unventilated buses for at least 30 min without losing infectivity.\nThe most common coronaviruses may well survive or persist on surfaces for up to one month.\nViruses in respiratory or fecal specimens can maintain infectivity for quite a long time at room temperature.\nAbsorbent materials like cotton are safer than unabsorbent materials for protection from virus infection.\nThe risk of transmission via touching contaminated paper is low.\nPreventive strategies such as washing hands and wearing masks are critical to the control of coronavirus disease 2019.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Most viruses from the respiratory tract, such as coronaviruses, influenza, SARS-CoV, or rhinovirus, can persist on surfaces for a few days.\", \"The most common coronaviruses may well survive or persist on surfaces for up to one month.\"]}", "id": 823}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Several people, including the US president, have suggested that the novel coronavirus SARS-CoV-2 and the disease it causes, COVID-19, will go away on its own in the warmer weather that will come.\n\nAbstract:\nThe coronavirus disease 2019 (COVID-19) outbreak has become a severe public health issue.\nThe novelty of the virus prompts a search for understanding of how ecological factors affect the transmission and survival of the virus.\nSeveral studies have robustly identified a relationship between temperature and the number of cases.\nHowever, there is no specific study for a tropical climate such as Brazil.\nThis work aims to determine the relationship of temperature to COVID-19 infection for the state capital cities of Brazil.\nCumulative data with the daily number of confirmed cases was collected from February 27 to April 1, 2020, for all 27 state capital cities of Brazil affected by COVID-19.\nA generalized additive model (GAM) was applied to explore the linear and nonlinear relationship between annual average temperature compensation and confirmed cases.\nAlso, a polynomial linear regression model was proposed to represent the behavior of the growth curve of COVID-19 in the capital cities of Brazil.\nThe GAM dose-response curve suggested a negative linear relationship between temperatures and daily cumulative confirmed cases of COVID-19 in the range from 16.8 \u00b0C to 27.4 \u00b0C.\nEach 1 \u00b0C rise of temperature was associated with a -4.8951% (t = -2.29, p = 0.0226) decrease in the number of daily cumulative confirmed cases of COVID-19.\nA sensitivity analysis assessed the robustness of the results of the model.\nThe predicted R-squared of the polynomial linear regression model was 0.81053.\nIn this study, which features the tropical temperatures of Brazil, the variation in annual average temperatures ranged from 16.8 \u00b0C to 27.4 \u00b0C.\nResults indicated that temperatures had a negative linear relationship with the number of confirmed cases.\nThe curve flattened at a threshold of 25.8 \u00b0C.\nThere is no evidence supporting that the curve declined for temperatures above 25.8 \u00b0C.\nThe study had the goal of supporting governance for healthcare policymakers.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Results indicated that temperatures had a negative linear relationship with the number of confirmed cases.\"]}", "id": 824}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: If Fever Helps Fight Infection, Should I Avoid Fever-Reducing Drugs\n\nAbstract:\nIbuprofen is an over-the-counter medication that is used widely for the treatment of pain and fever during COVID-19 pandemic.\nA concern was raised regarding the safety of ibuprofen use because of its role in increasing ACE2 levels within the Renin-Angiotensin-Aldosterone system.\nACE2 is the coreceptor for the entry of SARS-CoV-2 into cells, and so, a potential increased risk of contracting COVID-19 disease and/or worsening of COVID-19 infection was feared with ibuprofen use.\nHowever, available data from limited studies show administration of recombinant ACE2 improves lung damage caused by respiratory viruses, suggesting ibuprofen use may be beneficial in COVID-19 disease.\nAt this time, there is no supporting evidence to discourage the use of ibuprofen.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Ibuprofen is an over-the-counter medication that is used widely for the treatment of pain and fever during COVID-19 pandemic.\", \"However, available data from limited studies show administration of recombinant ACE2 improves lung damage caused by respiratory viruses, suggesting ibuprofen use may be beneficial in COVID-19 disease.\", \"At this time, there is no supporting evidence to discourage the use of ibuprofen.\"]}", "id": 825}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: there is no clear evidence that using ibuprofen to treat symptoms such as a high temperature can make coronavirus (covid-19) worse\n\nAbstract:\nOBJECTIVE: It was recently suggested that Ibuprofen might increase the risk for severe and fatal COVID-19 disease and should therefore be avoided in this patient population.\nWe aimed to evaluate whether ibuprofen use in patients with COVID-19 was associated with more severe disease, compared to patients using paracetamol or no antipyretics.\nMETHODS: In a retrospective cohort study of patients with COVID-19 from Shamir Medical Center, Israel, we monitored any use of ibuprofen from a week prior to diagnosis of COVID-19 throughout the disease.\nPrimary outcomes were mortality and the need for respiratory support, including oxygen administration and mechanical ventilation.\nRESULTS: The study included 403 confirmed cases of COVID-19, with a median age of 45 years.\nOf the entire cohort, 44 patients (11%) needed respiratory support and 12 (3%) patients died.\nOne hundred and seventy-nine (44%) patients had fever, with 32% using paracetamol and 22% using ibuprofen, for symptom-relief.\nIn the Ibuprofen group, 3 (3.4%) patients died, while in the non-Ibuprofen group 9 (2.8%) patients died (P=0.95).\nNine (10.3%) patients from the Ibuprofen group needed respiratory support, as compared with 35 (11%) from the non-Ibuprofen group (P=1).\nWhen compared to exclusive paracetamol users, no differences were observed in mortality rates or the need for respiratory support among patients using ibuprofen.\nCONCLUSIONS: In this cohort of COVID-19 patients, Ibuprofen use was not associated with worse clinical outcomes, compared to paracetamol or no antipyretic.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"OBJECTIVE: It was recently suggested that Ibuprofen might increase the risk for severe and fatal COVID-19 disease and should therefore be avoided in this patient population.\"]}", "id": 826}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Sars-cov-2 d614g variant exhibits enhanced replication ex vivo and earlier transmission in vivo\n\nAbstract:\nThe D614G substitution in the S protein is most prevalent SARS-CoV-2 strain circulating globally, but its effects in viral pathogenesis and transmission remain unclear.\nWe engineered SARS-CoV-2 variants harboring the D614G substitution with or without nanoluciferase.\nThe D614G variant replicates more efficiency in primary human proximal airway epithelial cells and is more fit than wildtype (WT) virus in competition studies.\nWith similar morphology to the WT virion, the D614G virus is also more sensitive to SARS-CoV-2 neutralizing antibodies.\nInfection of human ACE2 transgenic mice and Syrian hamsters with the WT or D614G viruses produced similar titers in respiratory tissue and pulmonary disease.\nHowever, the D614G variant exhibited significantly faster droplet transmission between hamsters than the WT virus, early after infection.\nOur study demonstrated the SARS-CoV2 D614G substitution enhances infectivity, replication fitness, and early transmission.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Our study demonstrated the SARS-CoV2 D614G substitution enhances infectivity, replication fitness, and early transmission.\"]}", "id": 827}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: T cell anergy in covid-19 reflects virus persistence and survival outcomes\n\nAbstract:\nCoronavirus disease 2019 (COVID-19) can lead to severe pneumonia and hyperinflammation.\nSo far, insufficient or excessive T cell responses were described in patients.\nWe applied novel approaches to analyze T cell reactivity and showed that T anergy is already present in non-ventilated COVID-19 patients, very pronounced in ventilated patients, strongly associated with virus persistence and reversible with clinical recovery.\nT cell activation was measured by downstream effects on responder cells like basophils, plasmacytoid dendritic cells, monocytes and neutrophils in whole blood and proved to be much more meaningful than classical readouts with PBMCs.\nMonocytes responded stronger in males than females and IL-2 partially reversed T cell anergy.\nDownstream markers of T cell anergy were also found in fresh blood samples of critically ill patients with severe T cell anergy.\nBased on our data we were able to develop a score to predict fatal outcomes and to identify patients that may benefit from strategies to overcome T cell anergy.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"We applied novel approaches to analyze T cell reactivity and showed that T anergy is already present in non-ventilated COVID-19 patients, very pronounced in ventilated patients, strongly associated with virus persistence and reversible with clinical recovery.\", \"Downstream markers of T cell anergy were also found in fresh blood samples of critically ill patients with severe T cell anergy.\", \"Based on our data we were able to develop a score to predict fatal outcomes and to identify patients that may benefit from strategies to overcome T cell anergy.\"]}", "id": 828}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: The high temperature and low relative humidity lead to high evaporation rates of saliva-contaminated droplets, thus significantly reducing the coronavirus viability\n\nAbstract:\nThe 2020 coronavirus pandemic is developing at different paces throughout the world.\nSome areas, like the Caribbean Basin, have yet to see the virus strike at full force.\nWhen it does, there is reasonable evidence to suggest the consequent COVID-19 outbreaks will overwhelm healthcare systems and economies.\nThis is particularly concerning in the Caribbean as pandemics can have disproportionately higher mortality impacts on lower and middle-income countries.\nPreliminary observations from our team and others suggest that temperature and climatological factors could influence the spread of this novel coronavirus, making spatiotemporal predictions of its infectiousness possible.\nThis review studies geographic and time-based distribution of known respiratory viruses in the Caribbean Basin in an attempt to foresee how the pandemic will develop in this region.\nThis review is meant to aid in planning short- and long-term interventions to manage outbreaks at the international, national, and subnational levels in the region.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Preliminary observations from our team and others suggest that temperature and climatological factors could influence the spread of this novel coronavirus, making spatiotemporal predictions of its infectiousness possible.\"]}", "id": 829}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Most children with COVID-19 have mild symptoms or have no symptoms at all.\n\nAbstract:\nBackground: As the novel coronavirus triggering COVID-19 has broken out in Wuhan, China and spread rapidly worldwide, it threatens the lives of thousands of people and poses a global threat on the economies of the entire world.\nHowever, infection with COVID-19 is currently rare in children.\nObjective To discuss the latest findings and research focus on the basis of characteristics of children confirmed with COVID-19, and provide an insight into the future treatment and research direction.\nMethods: We searched the terms \"COVID-19 OR coronavirus OR SARS-CoV-2\" AND \"Pediatric OR children\" on PubMed, Embase, Cochrane library, NIH, CDC, and CNKI.\nThe authors also reviewed the guidelines published on Chinese CDC and Chinese NHC.\nResults: We included 25 published literature references related to the epidemiology, clinical manifestation, accessary examination, treatment, and prognosis of pediatric patients with COVID-19.\nConclusion: The numbers of children with COVID-19 pneumonia infection are small, and most of them come from family aggregation.\nSymptoms are mainly mild or even asymptomatic, which allow children to be a risk factor for transmission.\nThus, strict epidemiological history screening is needed for early diagnosis and segregation.\nThis holds especially for infants, who are more susceptible to infection than other age groups in pediatric age, but have most likely subtle and unspecific symptoms.\nThey need to be paid more attention to.\nCT examination is a necessity for screening the suspected cases, because most of the pediatric patients are mild cases, and plain chest X-ray do not usually show the lesions or the detailed features.\nTherefore, early chest CT examination combined with pathogenic detection is a recommended clinical diagnosis scheme in children.\nThe risk factors which may suggest severe or critical progress for children are: Fast respiratory rate and/or; lethargy and drowsiness mental state and/or; lactate progressively increasing and/or; imaging showed bilateral or multi lobed infiltration, pleural effusion or rapidly expending of lesions in a short period of time and/or; less than 3 months old or those who underly diseases.\nFor those critical pediatric patients with positive SARS-CoV-2 diagnosis, polypnea may be the most common symptom.\nFor treatment, the elevated PCT seen in children in contrast to adults suggests that the underlying coinfection/secondary infection may be more common in pediatric patients and appropriate antibacterial treatment should be considered.\nOnce cytokine storm is found in these patients, anti-autoimmune or blood-purifying therapy should be given in time.\nFurthermore, effective isolation measures and appropriate psychological comfort need to be provided timely.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Conclusion: The numbers of children with COVID-19 pneumonia infection are small, and most of them come from family aggregation.\", \"Symptoms are mainly mild or even asymptomatic, which allow children to be a risk factor for transmission.\"]}", "id": 830}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: the risk of animals spreading COVID-19 to people is considered to be low.\n\nAbstract:\nOn April 22, CDC and the U.S. Department of Agriculture (USDA) reported cases of two domestic cats with confirmed infection with SARS-CoV-2, the virus that causes coronavirus disease 2019 (COVID-19).\nThese are the first reported companion animals (including pets and service animals) with SARS-CoV-2 infection in the United States, and among the first findings of SARS-CoV-2 symptomatic companion animals reported worldwide.\nThese feline cases originated from separate households and were epidemiologically linked to suspected or confirmed human COVID-19 cases in their respective households.\nNotification of presumptive positive animal test results triggered a One Health* investigation by state and federal partners, who determined that no further transmission events to other animals or persons had occurred.\nBoth cats fully recovered.\nAlthough there is currently no evidence that animals play a substantial role in spreading COVID-19, CDC advises persons with suspected or confirmed COVID-19 to restrict contact with animals during their illness and to monitor any animals with confirmed SARS-CoV-2 infection and separate them from other persons and animals at home (1).", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Although there is currently no evidence that animals play a substantial role in spreading COVID-19, CDC advises persons with suspected or confirmed COVID-19 to restrict contact with animals during their illness and to monitor any animals with confirmed SARS-CoV-2 infection and separate them from other persons and animals at home (1).\"]}", "id": 831}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Covid-19 ards is characterized by a dysregulated host response that differs from cytokine storm and cannot be modified by dexamethasone.\n\nAbstract:\nWe performed comparative lower respiratory tract transcriptional profiling of 52 critically ill patients with the acute respiratory distress syndrome (ARDS) from COVID-19 or from other etiologies, as well as controls without ARDS.\nIn contrast to a cytokine storm, we observed reduced proinflammatory gene expression in COVID-19 ARDS when compared to ARDS due to other causes.\nCOVID-19 ARDS was characterized by a dysregulated host response with increased PTEN signaling and elevated expression of genes with non-canonical roles in inflammation and immunity that were predicted to be modulated by dexamethasone and granulocyte colony stimulating factor.\nCompared to ARDS due to other types of viral pneumonia, COVID-19 was characterized by impaired interferon-stimulated gene expression (ISG).\nWe found that the relationship between SARS-CoV-2 viral load and expression of ISGs was decoupled in patients with COVID-19 ARDS when compared to patients with mild COVID-19.\nIn summary, assessment of host gene expression in the lower airways of patients with COVID-19 ARDS did not demonstrate cytokine storm but instead revealed a unique and dysregulated host response predicted to be modified by dexamethasone.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"In contrast to a cytokine storm, we observed reduced proinflammatory gene expression in COVID-19 ARDS when compared to ARDS due to other causes.\", \"COVID-19 ARDS was characterized by a dysregulated host response with increased PTEN signaling and elevated expression of genes with non-canonical roles in inflammation and immunity that were predicted to be modulated by dexamethasone and granulocyte colony stimulating factor.\", \"In summary, assessment of host gene expression in the lower airways of patients with COVID-19 ARDS did not demonstrate cytokine storm but instead revealed a unique and dysregulated host response predicted to be modified by dexamethasone.\"]}", "id": 832}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: nearly half of coronavirus cases are people 20 to 44 years old\n\nAbstract:\nCurrently (mid May 2020), most active cases of COVID-19 are found in Europe and North America while it is still in the initial phases in Africa.\nAs COVID-19 mortality occurs mainly in elderly and as Africa has a comparably young population, the death rates should be lower than on other continents.\nWe calculated standardised mortality ratios (SMR) using age-specific case fatality rates for COVID-19 and the age structure of the population of Africa and of other continents.\nCompared to a European or Northern American population, the standardised mortality ratio was only 0.22 and 0.25, respectively, corresponding to reduction of deaths rates to a quarter.\nCompared to the Asian and Latin American & Caribbean population, the SMR was 0.43 and 0.44, respectively, corresponding to half the death rate for Africa.\nIt is useful to quantify the isolated effect of the African age-structure on potential COVID-19 mortality for illustrative and communication purposes, keeping in mind the importance of public health measures that have been shown to be effective in reducing cases and deaths.\nThe different aspect of age pyramids of a European and an African population are striking and the potential implications for the pandemic are often discussed but rarely quantified.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 833}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Dexamethasone do not cure the new coronavirus\n\nAbstract:\nBackground: Dexamethasone, a synthetic glucocorticoid, has anti-inflammatory and immunosuppressive properties.\nThere is a hyperinflammatory response involved in the clinical course of patients with pneumonia due to SARS-CoV2.\nTo date, there has been no definite therapy for COVID-19.\nWe reviewed the charts of SARS-CoV2 patients with pneumonia and moderate to severely elevated CRP and worsening hypoxemia who were treated with early, short-term dexamethasone.\nMethods: We describe a series of 21 patients who tested positive for SARS-CoV2 and were admitted to The Miriam Hospital in Providence and were treated with a short course of dexamethasone, either alone or in addition to current investigative therapies.\nResults: CRP levels decreased significantly following the start of dexamethasone from mean initial levels of 129.52 to 40.73 mg/L at time of discharge.\n71% percent of the patients were discharged home with a mean length of stay of 7.8 days.\nNone of the patients had escalation of care, leading to mechanical ventilation.\nTwo patients were transferred to inpatient hospice facilities on account of persistent hypoxemia, in line with their documented goals of care.\nConclusions: A short course of systemic corticosteroids among inpatients with SARS-CoV2 with hypoxic respiratory failure was well tolerated, and most patients had improved outcomes.\nThis limited case series may not offer concrete evidence towards the benefit of corticosteroids in COVID-19.\nHowever, patients positive response to short-term corticosteroids demonstrates that they may help blunt the severity of inflammation and prevent a severe hyperinflammatory phase, in turn reducing the length of stay, ICU admissions, and healthcare costs.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"To date, there has been no definite therapy for COVID-19.\"]}", "id": 834}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: nearly half of coronavirus cases are people 20 to 44 years old\n\nAbstract:\nOBJECTIVES: To determine mortality rates among adults with critical illness from coronavirus disease 2019.\nDESIGN: Observational cohort study of patients admitted from March 6, 2020, to April 17, 2020.\nSETTING: Six coronavirus disease 2019 designated ICUs at three hospitals within an academic health center network in Atlanta, Georgia, United States.\nPATIENTS: Adults greater than or equal to 18 years old with confirmed severe acute respiratory syndrome-CoV-2 disease who were admitted to an ICU during the study period.\nINTERVENTIONS: None.\nMEASUREMENTS AND MAIN RESULTS: Among 217 critically ill patients, mortality for those who required mechanical ventilation was 35.7% (59/165), with 4.8% of patients (8/165) still on the ventilator at the time of this report.\nOverall mortality to date in this critically ill cohort is 30.9% (67/217) and 60.4% (131/217) patients have survived to hospital discharge.\nMortality was significantly associated with older age, lower body mass index, chronic renal disease, higher Sequential Organ Failure Assessment score, lower PaO2/FIO2 ratio, higher D-dimer, higher C-reactive protein, and receipt of mechanical ventilation, vasopressors, renal replacement therapy, or vasodilator therapy.\nCONCLUSIONS: Despite multiple reports of mortality rates exceeding 50% among critically ill adults with coronavirus disease 2019, particularly among those requiring mechanical ventilation, our early experience indicates that many patients survive their critical illness.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 835}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: The overall risk of dying in the hospital from COVID-19 among people with T1D is very low, however, one study found that this risk is higher for people with T1D (3.5 times higher) compared to people without diabetes.\n\nAbstract:\nAIMS: To describe characteristics of COVID-19 patients with type 2 diabetes and to analyze risk factors for severity.\nMETHODS: Demographics, comorbidities, symptoms, laboratory findings, treatments and outcomes of COVID-19 patients with diabetes were collected and analyzed.\nRESULTS: Seventy-fourCOVID-19 patients with diabetes were included.\nTwenty-seven patients (36.5%) were severe and 10 patients (13.5%) died.\nHigher levels of blood glucose, serum amyloid A (SAA), C reactive protein and interleukin 6 were associated with severe patients compared to non-severe ones (P<0.05).\nLevels of albumin, cholesterol, high density lipoprotein, small and dense low density lipoprotein and CD4+T lymphocyte counts in severe patients were lower than those in non-severe patients (P<0.05).\nLogistic regression analysis identified decreased CD4+T lymphocyte counts (odds ratio [OR]=0.988, 95%Confidence interval [95%CI] 0.979-0.997) and increased SAA levels (OR=1.029, 95%CI 1.002-1.058) as risk factors for severity of COVID-19 with diabetes (P<0.05).\nCONCLUSIONS: Type 2 diabetic patients were more susceptible to COVID-19 than overall population, which might be associated with hyperglycemia and dyslipidemia.\nAggressive treatment should be suggested, especially when these patients had low CD4+T lymphocyte counts and high SAA levels.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 836}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: No, garlic doesn't cure coronavirus. \n\nAbstract:\nIn late December 2019 in Wuhan, China, several patients with viral pneumonia were identified as 2019 novel coronavirus (2019-nCoV).\nSo far, there are no specific treatments for patients with coronavirus disease-19 (COVID-19), and the treatments available today are based on previous experience with similar viruses such as severe acute respiratory syndrome-related coronavirus (SARS-CoV), Middle East respiratory syndrome coronavirus (MERS-CoV), and Influenza virus.\nIn this article, we have tried to reach a therapeutic window of drugs available to patients with COVID-19.\nCathepsin L is required for entry of the 2019-nCoV virus into the cell as target teicoplanin inhibits virus replication.\nAngiotensin-converting-enzyme 2 (ACE2) in soluble form as a recombinant protein can prevent the spread of coronavirus by restricting binding and entry.\nIn patients with COVID-19, hydroxychloroquine decreases the inflammatory response and cytokine storm, but overdose causes toxicity and mortality.\nNeuraminidase inhibitors such as oseltamivir, peramivir, and zanamivir are invalid for 2019-nCoV and are not recommended for treatment but protease inhibitors such as lopinavir/ritonavir (LPV/r) inhibit the progression of MERS-CoV disease and can be useful for patients of COVID-19 and, in combination with Arbidol, has a direct antiviral effect on early replication of SARS-CoV. Ribavirin reduces hemoglobin concentrations in respiratory patients, and remdesivir improves respiratory symptoms.\nUse of ribavirin in combination with LPV/r in patients with SARS-CoV reduces acute respiratory distress syndrome and mortality, which has a significant protective effect with the addition of corticosteroids.\nFavipiravir increases clinical recovery and reduces respiratory problems and has a stronger antiviral effect than LPV/r.\ncurrently, appropriate treatment for patients with COVID-19 is an ACE2 inhibitor and a clinical problem reducing agent such as favipiravir in addition to hydroxychloroquine and corticosteroids.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"In late December 2019 in Wuhan, China, several patients with viral pneumonia were identified as 2019 novel coronavirus (2019-nCoV).\", \"So far, there are no specific treatments for patients with coronavirus disease-19 (COVID-19), and the treatments available today are based on previous experience with similar viruses such as severe acute respiratory syndrome-related coronavirus (SARS-CoV), Middle East respiratory syndrome coronavirus (MERS-CoV), and Influenza virus.\"]}", "id": 837}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Hypothesis: Angiotensin-converting enzyme inhibitors and angiotensin receptor blockers may increase the risk of severe COVID-19\n\nAbstract:\nINTRODUCTION The present research aimed to determine the relation between the use of angiotensin-converting enzyme inhibitors (ACE inh) and angiotensinogen receptor blockers (ARBs) and in-hospital mortality of hypertensive patients diagnosed with Covid-19 pneumonia.\nMATERIAL AND METHOD In this retrospective study, we included 113 consecutive hypertensive patients admitted due to Covid-19 infection.\nIn all patients, Covid-19 infection was confirmed with using reverse-transcription polymerase chain reaction.\nAll patients were on ACE inh/ARBs or other antihypertensive therapy unless no contraindication was present.\nThe primary outcome of the study was the in-hospital all-cause mortality.\nRESULTS In total, 113 hypertensive Covid-19 patients were included, of them 74 patients were using ACE inh/ARBs.\nDuring in-hospital follow up, 30.9% [n = 35 patients] of patients died.\nThe frequency of admission to the ICU and endotracheal intubation were significantly higher in patients using ACE inh/ARBs.\nIn a multivariable analysis, the use of ACE inh/ARBs was an independent predictor of in-hospital mortality (OR: 3.66; 95%CI: 1.11-18.18; p= .032).\nKaplan-Meir curve analysis displayed that patients on ACE inh/ARBs therapy had higher incidence of in-hospital death than those who were not.\nCONCLUSION The present study has found that the use of ACE inh/ARBs therapy might be associated with an increased in-hospital mortality in patients who were diagnosed with Covid-19 pneumonia.\nIt is likely that ACE inh/ARBs therapy might not be beneficial in the subgroup of hypertensive Covid-19 patients despite the fact that there might be the possibility of some unmeasured residual confounders to affect the results of the study.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"CONCLUSION The present study has found that the use of ACE inh/ARBs therapy might be associated with an increased in-hospital mortality in patients who were diagnosed with Covid-19 pneumonia.\"]}", "id": 838}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Sars-cov-2 infection induces robust , neutralizing antibody responses that are stable for at least three months\n\nAbstract:\nSARS-CoV-2 has caused a global pandemic with millions infected and numerous fatalities.\nQuestions regarding the robustness, functionality and longevity of the antibody response to the virus remain unanswered.\nHere we report that the vast majority of infected individuals with mild-to-moderate COVID-19 experience robust IgG antibody responses against the viral spike protein, based on a dataset of 19,860 individuals screened at Mount Sinai Health System in New York City.\nWe also show that titers are stable for at least a period approximating three months, and that anti-spike binding titers significantly correlate with neutralization of authentic SARS-CoV-2.\nOur data suggests that more than 90% of seroconverters make detectible neutralizing antibody responses and that these titers are stable for at least the near-term future.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"We also show that titers are stable for at least a period approximating three months, and that anti-spike binding titers significantly correlate with neutralization of authentic SARS-CoV-2.\"]}", "id": 839}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: As the novel coronavirus sweeps the globe, those with high blood pressure are at heightened risk for more severe complications should they contract Covid-19\n\nAbstract:\nInvestigations reported that hypertension, diabetes, and cardiovascular diseases were the most prevalent comorbidities among the patients with coronavirus disease 2019 (COVID-19).\nHypertension appeared consistently as the most prevalent risk factors in COVID-19 patients.\nSome investigations speculated about the association between renin-angiotensin-aldosterone system (RAAS) and susceptibility to COVID-19, as well as the relationship between RAAS inhibitors and increased mortality in these patients.\nThis raised concern about the potential association between hypertension (and its treatment) and propensity for COVID-19.\nThere are only a few follow-up studies that investigated the impact of comorbidities on outcome in these patients with conflicting findings.\nHypertension has been proven to be more prevalent in patients with an adverse outcome (admission in intensive care unit, use of mechanical ventilation, or death).\nSo far, there is no study that demonstrated independent predictive value of hypertension on mortality in COVID-19 patients.\nThere are many speculations about this coronavirus and its relation with different risk factors and underlying diseases.\nThe aim of this review was to summarize the current knowledge about the relationship between hypertension and COVID-19 and the role of hypertension on outcome in these patients.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Hypertension appeared consistently as the most prevalent risk factors in COVID-19 patients.\", \"Hypertension has been proven to be more prevalent in patients with an adverse outcome (admission in intensive care unit, use of mechanical ventilation, or death).\"]}", "id": 840}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Smoking is a risk factor for COVID-19 patients, but one particular substance in cigarettes - nicotine - might prevent infection in some people, or improve COVID-19 prognosis\n\nAbstract:\nStatistical surveys of COVID-19 patients indicate, against all common logic, that people who smoke are less prone to the infection and/or exhibit less severe respiratory symptoms than non-smokers.\nThis suggests that nicotine may have some preventive or modulatory effect on the inflammatory response in the lungs.\nBecause it is known that the response to, and resolution of the SARS-CoV-2 infection depends mainly on the lung macrophages, we discuss the recent scientific findings, which may explain why and how nicotine may modulate lung macrophage response during COVID-19 infection.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Statistical surveys of COVID-19 patients indicate, against all common logic, that people who smoke are less prone to the infection and/or exhibit less severe respiratory symptoms than non-smokers.\", \"This suggests that nicotine may have some preventive or modulatory effect on the inflammatory response in the lungs.\"]}", "id": 841}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Sars-cov-2 viral load is associated without increased disease severity and mortality\n\nAbstract:\nThe relationship between SARS-CoV-2 viral load and risk of disease progression remains largely undefined in coronavirus disease 2019 (COVID-19).\nHere, we quantify SARS-CoV-2 viral load from participants with a diverse range of COVID-19 disease severity, including those requiring hospitalization, outpatients with mild disease, and individuals with resolved infection.\nWe detected SARS-CoV-2 plasma RNA in 27% of hospitalized participants, and 13% of outpatients diagnosed with COVID-19.\nAmongst the participants hospitalized with COVID-19, we report that a higher prevalence of detectable SARS-CoV-2 plasma viral load is associated with worse respiratory disease severity, lower absolute lymphocyte counts, and increased markers of inflammation, including C-reactive protein and IL-6.\nSARS-CoV-2 viral loads, especially plasma viremia, are associated with increased risk of mortality.\nOur data show that SARS-CoV-2 viral loads may aid in the risk stratification of patients with COVID-19, and therefore its role in disease pathogenesis should be further explored.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"SARS-CoV-2 viral loads, especially plasma viremia, are associated with increased risk of mortality.\"]}", "id": 842}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: So, COVID is killed by heat. That is why our bodies create fever to fight it off. When you take Tylenol or advil it takes away your fever and allows COVID its ideal environment. If you get COVID allow your fever to remain as long as it is not over 103-104 this is your body fighting the virus. \n\nAbstract:\nIbuprofen is an over-the-counter medication that is used widely for the treatment of pain and fever during COVID-19 pandemic.\nA concern was raised regarding the safety of ibuprofen use because of its role in increasing ACE2 levels within the Renin-Angiotensin-Aldosterone system.\nACE2 is the coreceptor for the entry of SARS-CoV-2 into cells, and so, a potential increased risk of contracting COVID-19 disease and/or worsening of COVID-19 infection was feared with ibuprofen use.\nHowever, available data from limited studies show administration of recombinant ACE2 improves lung damage caused by respiratory viruses, suggesting ibuprofen use may be beneficial in COVID-19 disease.\nAt this time, there is no supporting evidence to discourage the use of ibuprofen.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Ibuprofen is an over-the-counter medication that is used widely for the treatment of pain and fever during COVID-19 pandemic.\", \"However, available data from limited studies show administration of recombinant ACE2 improves lung damage caused by respiratory viruses, suggesting ibuprofen use may be beneficial in COVID-19 disease.\", \"At this time, there is no supporting evidence to discourage the use of ibuprofen.\"]}", "id": 843}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: COVID-19: Hand sanitizers inactivate novel coronavirus\n\nAbstract:\nBACKGROUND: The emergence of the novel virus, SARS-CoV-2, has posed unprecedented challenges to public health around the world.\nCurrently, strategies to deal with COVID-19 are purely supportive and preventative, aimed at reducing transmission.\nAn effective and simple method for reducing transmission of infections in the public or healthcare settings is hand hygiene.\nUnfortunately, little is known regarding the efficacy of hand sanitizers against SARS-CoV-2.\nMETHODS: In this review, an extensive literature search was performed to succinctly summarize the primary active ingredients and mechanisms of action of hand sanitizers, compare the effectiveness and compliance of gel and foam sanitizers, and predict whether alcohol and non-alcohol hand sanitizers would be effective against SARS-CoV-2.\nRESULTS: Most alcohol based hand sanitizers are effective at inactivating enveloped viruses, including coronaviruses.\nWith what is currently known in the literature, one may not confidently suggest one mode of hand sanitizing delivery over the other.\nWhen hand washing with soap and water is unavailable, a sufficient volume of sanitizer is necessary to ensure complete hand coverage, and compliance is critical for appropriate hand hygiene.\nCONCLUSIONS: By extrapolating effectiveness of hand sanitizers on viruses of similar structure to SARS-CoV-2, this virus should be effectively inactivated with current hand hygiene products, though future research should attempt to determine this directly.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"RESULTS: Most alcohol based hand sanitizers are effective at inactivating enveloped viruses, including coronaviruses.\", \"CONCLUSIONS: By extrapolating effectiveness of hand sanitizers on viruses of similar structure to SARS-CoV-2, this virus should be effectively inactivated with current hand hygiene products, though future research should attempt to determine this directly.\"]}", "id": 844}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Covid-19 hits the old hardest, and young people do not die from it\n\nAbstract:\nObjective: Severity of the coronavirus disease 2019 (covid-19) has been assessed in terms of absolute mortality in SARS-CoV-2 positive cohorts.\nAn assessment of mortality relative to mortality in the general population is presented.\nDesign: Retrospective population-based study.\nSetting: Individual information on symptomatic confirmed SARS-CoV-2 patients and subsequent deaths from any cause were compared to the all-cause mortality in the Swiss population of 2018.\nStarting February 23, 2020, mortality in covid-19 patients was monitored for 80 days and compared to the population mortality observed in the same time-of-year starting February 23, 2018.\nParticipants: 5 160 595 inhabitants of Switzerland aged 35 to 95 without covid-19 (general population in spring 2018) and 20 769 persons tested positively for covid-19 (spring 2020).\nMeasurements: Sex- and age-specific mortality rates were estimated using Cox proportional hazards models.\nAbsolute probabilities of death were predicted and risk was assessed in terms of relative mortality by taking the ratio between the sex- and age-specific absolute mortality in covid19 patients and the corresponding mortality in the 2018 general population.\nResults: A confirmed SARS-CoV-2 infection substantially increased the probability of death across all patient groups, ranging from nine (6 to 15) times the population mortality in 35-year old infected females to a 53-fold increase (46 to 59) for 95 year old infected males.\nThe highest relative risks were observed among males and older patients.\nThe magnitude of these effects was smaller compared to increases observed in absolute mortality risk.\nMale covid-19 patients exceeded the population hazard for males (hazard ratio 1.20, 1.00 to 1.44).\nEach additional year of age increased the population hazard in covid-19 patients (hazard ratio 1.04, 1.03 to 1.05).\nLimitations: Information about the distribution of relevant comorbidities was not available on population level and the associated risk was not quantified.\nConclusions: Health care professionals, decision makers, and societies are provided with an additional population-adjusted assessment of covid-19 mortality risk.\nIn combination with absolute measures of risk, the relative risks presented here help to develop a more comprehensive understanding of the actual impact of covid-19.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 845}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Coronavirus nsp10/nsp16 methyltransferase cannot be targeted by nsp10-derived peptide in vitro and in vivo to reduce replication and pathogenesis\n\nAbstract:\nUNLABELLED The 5' cap structures of eukaryotic mRNAs are important for RNA stability and protein translation.\nMany viruses that replicate in the cytoplasm of eukaryotes have evolved 2'-O-methyltransferases (2'-O-MTase) to autonomously modify their mRNAs and carry a cap-1 structure (m7GpppNm) at the 5' end, thereby facilitating viral replication and escaping innate immune recognition in host cells.\nPrevious studies showed that the 2'-O-MTase activity of severe acute respiratory syndrome coronavirus (SARS-CoV) nonstructural protein 16 (nsp16) needs to be activated by nsp10, whereas nsp16 of feline coronavirus (FCoV) alone possesses 2'-O-MTase activity (E. Decroly et al., J Virol 82:8071-8084, 2008, http://dx.doi.org/10.1128/JVI.00407-08; M. Bouvet et al., PLoS Pathog 6:e1000863, 2010, http://dx.doi.org/10.1371/journal.ppat.1000863; E. Decroly et al., PLoS Pathog 7:e1002059, 2011, http://dx.doi.org/10.1371/journal.ppat.1002059; Y. Chen et al., PLoS Pathog 7:e1002294, 2011, http://dx.doi.org/10.1371/journal.ppat.1002294) .\nIn this study, we demonstrate that stimulation of nsp16 2'-O-MTase activity by nsp10 is a universal and conserved mechanism in coronaviruses, including FCoV, and that nsp10 is functionally interchangeable in the stimulation of nsp16 of different coronaviruses.\nBased on our current and previous studies, we designed a peptide (TP29) from the sequence of the interaction interface of mouse hepatitis virus (MHV) nsp10 and demonstrated that the peptide inhibits the 2'-O-MTase activity of different coronaviruses in biochemical assays and the viral replication in MHV infection and SARS-CoV replicon models.\nInterestingly, the peptide TP29 exerted robust inhibitory effects in vivo in MHV-infected mice by impairing MHV virulence and pathogenesis through suppressing virus replication and enhancing type I interferon production at an early stage of infection.\nTherefore, as a proof of principle, the current results indicate that coronavirus 2'-O-MTase activity can be targeted in vitro and in vivo.\nIMPORTANCE Coronaviruses are important pathogens of animals and human with high zoonotic potential.\nSARS-CoV encodes the 2'-O-MTase that is composed of the catalytic subunit nsp16 and the stimulatory subunit nsp10 and plays an important role in virus genome replication and evasion from innate immunity.\nOur current results demonstrate that stimulation of nsp16 2'-O-MTase activity by nsp10 is a common mechanism for coronaviruses, and nsp10 is functionally interchangeable in the stimulation of nsp16 among different coronaviruses, which underlies the rationale for developing inhibitory peptides.\nWe demonstrate that a peptide derived from the nsp16-interacting domain of MHV nsp10 could inhibit 2'-O-MTase activity of different coronaviruses in vitro and viral replication of MHV and SARS-CoV replicon in cell culture, and it could strongly inhibit virus replication and pathogenesis in MHV-infected mice.\nThis work makes it possible to develop broad-spectrum peptide inhibitors by targeting the nsp16/nsp10 2'-O-MTase of coronaviruses.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"We demonstrate that a peptide derived from the nsp16-interacting domain of MHV nsp10 could inhibit 2'-O-MTase activity of different coronaviruses in vitro and viral replication of MHV and SARS-CoV replicon in cell culture, and it could strongly inhibit virus replication and pathogenesis in MHV-infected mice.\"]}", "id": 846}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Unfortunately, to date, no vaccines or antiviral drugs have been approved for the treatment of SARS-CoV-2 infection by regulatory agencies.\n\nAbstract:\nAIMS: A new human coronavirus (HCoV), which has been designated SARS-CoV-2, began spreading in December 2019 in Wuhan City, China causing pneumonia called COVID-19.\nThe spread of SARS-CoV-2 has been faster than any other coronaviruses that have succeeded in crossing the animal-human barrier.\nThere is concern that this new virus will spread around the world as did the previous two HCoVs-Severe Acute Respiratory Syndrome (SARS) and Middle East Respiratory Syndrome (MERS)-each of which caused approximately 800 deaths in the years 2002 and 2012, respectively.\nThus far, 11,268 deaths have been reported from the 258,842 confirmed infections in 168 countries.\nMAIN METHODS: In this study, the RNA-dependent RNA polymerase (RdRp) of the newly emerged coronavirus is modeled, validated, and then targeted using different anti-polymerase drugs currently on the market that have been approved for use against various viruses.\nKEY FINDINGS: The results suggest the effectiveness of Ribavirin, Remdesivir, Sofosbuvir, Galidesivir, and Tenofovir as potent drugs against SARS-CoV-2 since they tightly bind to its RdRp.\nIn addition, the results suggest guanosine derivative (IDX-184), Setrobuvir, and YAK as top seeds for antiviral treatments with high potential to fight the SARS-CoV-2 strain specifically.\nSIGNIFICANCE: The availability of FDA-approved anti-RdRp drugs can help treat patients and reduce the danger of the mysterious new viral infection COVID-19.\nThe drugs mentioned above can tightly bind to the RdRp of the SARS-CoV-2 strain and thus may be used to treat the disease.\nNo toxicity measurements are required for these drugs since they were previously tested prior to their approval by the FDA.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 847}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: A study of COVID-19 patients in the Netherlands did not find the disease to be associated with cytokine storm, as previously suggested. \n\nAbstract:\nBACKGROUND: The potential risk of cytokine storm in patients with coronavirus disease 2019 (Covid-19) has been described(1); we write to share our experience treating a 17-year-old male with haemophagocytic lymphohistiocytosis (HLH) secondary to Covid-19 infection.\nCASE REPORT: This patient presented with cough, sore throat, anorexia and pyrexia.\nOn examination, he had gross cervical lymphadenopathy and palpable splenomegaly.\nNose and throat swab for SARS-CoV-2 was positive and blood tests revealed pancytopaenia with very high ferritin, triglyceride and d-dimer levels.\nThe patient\u2019s HScore(2) was calculated at 220, suggesting probability of HLH of 93-96%.\nConsidering Russell and colleagues\u2019(3) comments about potential harm of corticosteroid use in patients with Covid-19 infection, the patient was commenced on treatment with the selective IL-1 receptor antagonist drug, Anakinra, and a two day course of intravenous immunoglobulin.\nRESULTS: The patient responded rapidly to treatment, becoming apyrexial after 24 hours.\nHis lymph nodes and spleen began to normalise after the first 48 hours, at which time point the ferritin also started to decrease.\nHe was discharged after 11 days feeling fit and well.\nCONCLUSION: This case certainly illustrates the importance of hyperinflammation syndromes in Covid-19.\nIt also raises the question \u2013 is the severe pneumonitis seen in patients with Covid-19 an immunological phenomenon?\nWe know that the viral load of patients with Covid-19 seems to peak in the early stages of illness(4, 5), however patients deteriorate later in the disease course, at around days 10-14.\nThis patient, who had risk factors for deterioration (male, pancytopaenic), did not develop an oxygen requirement and clinically and biochemically improved rapidly on Anakinra with no adverse events.\nWe might suggest Anakinra to the scientific community as a treatment option in Covid-19 infection.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"BACKGROUND: The potential risk of cytokine storm in patients with coronavirus disease 2019 (Covid-19) has been described(1); we write to share our experience treating a 17-year-old male with haemophagocytic lymphohistiocytosis (HLH) secondary to Covid-19 infection.\"]}", "id": 848}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Hospital readmissions of hospitalized patients with covid-19\n\nAbstract:\nBackground.\nCOVID-19 infection has led to an overwhelming effort by health institutions to meet the high demand for hospital admissions.\nAim.\nTo analyse the clinical variables associated with readmission of patients who had previously been discharged after admission for COVID-19.\nDesign and methods.\nWe studied a retrospective cohort of patients with laboratory-confirmed SARS-CoV-2 infection who were admitted and subsequently discharged alive.\nWe then conducted a nested case-control study paired (1:1 ratio) by age, sex and period of admission.\nResults.\nOut of 1368 patients who were discharged during the study period, 61 patients (4.4%) were readmitted.\nImmunocompromised patients were at increased risk for readmission.\nThere was also a trend towards a higher probability of readmission in hypertensive patients (p=0.07).\nCases had had a shorter hospital stay and a higher prevalence of fever during the 48 hours prior to discharge.\nThere were no significant differences in oxygen levels measured at admission and discharge by pulse oximetry intra-subject or between the groups.\nNeutrophil/lymphocyte ratio at hospital admission tended to be higher in cases than in controls (p=0.06).\nThe motive for readmission in 10 patients (16.4%), was a thrombotic event in venous or arterial territory (p<0.001).\nNeither glucocorticoids nor anticoagulants prescribed at hospital discharge were associated with a lower readmission rate.\nConclusions.\nThe rate of readmission after discharge from hospital for COVID-19 was low.\nImmunocompromised patients and those presenting with fever during the 48 hours prior to discharge are at greater risk of readmission to hospital.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Immunocompromised patients and those presenting with fever during the 48 hours prior to discharge are at greater risk of readmission to hospital.\"]}", "id": 849}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: clinical trials are needed to determine if endogenously created vitamin D could be effective for the prevention and treatment of COVID-19\n\nAbstract:\nThe severity of coronavirus 2019 infection (COVID-19) is determined by the presence of pneumonia, severe acute respiratory distress syndrome (SARS-CoV-2), myocarditis, microvascular thrombosis and/or cytokine storms, all of which involve underlying inflammation.\nA principal defence against uncontrolled inflammation, and against viral infection in general, is provided by T regulatory lymphocytes (Tregs).\nTreg levels have been reported to be low in many COVID-19 patients and can be increased by vitamin D supplementation.\nLow vitamin D levels have been associated with an increase in inflammatory cytokines and a significantly increased risk of pneumonia and viral upper respiratory tract infections.\nVitamin D deficiency is associated with an increase in thrombotic episodes, which are frequently observed in COVID-19.\nVitamin D deficiency has been found to occur more frequently in patients with obesity and diabetes.\nThese conditions are reported to carry a higher mortality in COVID-19.\nIf vitamin D does in fact reduce the severity of COVID-19 in regard to pneumonia/ARDS, inflammation, inflammatory cytokines and thrombosis, it is our opinion that supplements would offer a relatively easy option to decrease the impact of the pandemic.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"A principal defence against uncontrolled inflammation, and against viral infection in general, is provided by T regulatory lymphocytes (Tregs).\", \"Treg levels have been reported to be low in many COVID-19 patients and can be increased by vitamin D supplementation.\", \"Low vitamin D levels have been associated with an increase in inflammatory cytokines and a significantly increased risk of pneumonia and viral upper respiratory tract infections.\", \"Vitamin D deficiency is associated with an increase in thrombotic episodes, which are frequently observed in COVID-19.\", \"Vitamin D deficiency has been found to occur more frequently in patients with obesity and diabetes.\", \"These conditions are reported to carry a higher mortality in COVID-19.\", \"If vitamin D does in fact reduce the severity of COVID-19 in regard to pneumonia/ARDS, inflammation, inflammatory cytokines and thrombosis, it is our opinion that supplements would offer a relatively easy option to decrease the impact of the pandemic.\"]}", "id": 850}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: There is also evidence that smokers in hospital who have coronavirus are at a higher risk than non-smokers of severe illness and death. While it's important to prevent getting COVID-19 in the first place, it's also essential that we do all we can to keep our lungs healthy to avoid the worst affects of the disease.\n\nAbstract:\nObjectives: To investigate whether there is a causal effect of cardiometabolic traits on risk of sepsis and severe covid-19.\nDesign: Mendelian randomisation analysis.\nSetting: UK Biobank and HUNT study population-based cohorts for risk of sepsis, and genome-wide association study summary data for risk of severe covid-19 with respiratory failure.\nParticipants: 12,455 sepsis cases (519,885 controls) and 1,610 severe covid-19 with respiratory failure cases (2,205 controls).\nExposure: Genetic variants that proxy body mass index (BMI), lipid traits, systolic blood pressure, lifetime smoking score, and type 2 diabetes liability - derived from studies considering between 188,577 to 898,130 participants.\nMain outcome measures: Risk of sepsis and severe covid-19 with respiratory failure.\nResults: Higher genetically proxied BMI and lifetime smoking score were associated with increased risk of sepsis in both UK Biobank (BMI: odds ratio 1.38 per standard deviation increase, 95% confidence interval [CI] 1.27 to 1.51; smoking: odds ratio 2.81 per standard deviation increase, 95% CI 2.09-3.79) and HUNT (BMI: 1.41, 95% CI 1.18 to 1.69; smoking: 1.93, 95% CI 1.02-3.64).\nHigher genetically proxied BMI and lifetime smoking score were also associated with increased risk of severe covid-19, although with wider confidence intervals (BMI: 1.75, 95% CI 1.20 to 2.57; smoking: 3.94, 95% CI 1.13 to 13.75).\nThere was limited evidence to support associations of genetically proxied lipid traits, systolic blood pressure or type 2 diabetes liability with risk of sepsis or severe covid-19.\nSimilar findings were generally obtained when using Mendelian randomization methods that are more robust to the inclusion of pleiotropic variants, although the precision of estimates was reduced.\nConclusions: Our findings support a causal effect of elevated BMI and smoking on risk of sepsis and severe covid-19.\nClinical and public health interventions targeting obesity and smoking are likely to reduce sepsis and covid-19 related morbidity, along with the plethora of other health-related outcomes that these traits adversely affect.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Results: Higher genetically proxied BMI and lifetime smoking score were associated with increased risk of sepsis in both UK Biobank (BMI: odds ratio 1.38 per standard deviation increase, 95% confidence interval [CI] 1.27 to 1.51; smoking: odds ratio 2.81 per standard deviation increase, 95% CI 2.09-3.79) and HUNT (BMI: 1.41, 95% CI 1.18 to 1.69; smoking: 1.93, 95% CI 1.02-3.64).\", \"Conclusions: Our findings support a causal effect of elevated BMI and smoking on risk of sepsis and severe covid-19.\"]}", "id": 851}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: The most important thing to know about using over-the-counter medications to treat COVID-19 is that none of these common drugstore products are actually going to treat the virus itself.\n\nAbstract:\nOBJECTIVE: It was recently suggested that ibuprofen might increase the risk for severe and fatal coronavirus disease 2019 (COVID-19) and should therefore be avoided in this patient population.\nWe aimed to evaluate whether ibuprofen use in individuals with COVID-19 was associated with more severe disease, compared with individuals using paracetamol or no antipyretics.\nMETHODS: In a retrospective cohort study of patients with COVID-19 from Shamir Medical Centre, Israel, we monitored any use of ibuprofen from a week before diagnosis of COVID-19 throughout the disease.\nPrimary outcomes were mortality and the need for respiratory support, including oxygen administration and mechanical ventilation.\nRESULTS: The study included 403 confirmed cases of COVID-19, with a median age of 45 years.\nOf the entire cohort, 44 patients (11%) needed respiratory support and 12 (3%) died.\nOne hundred and seventy-nine (44%) patients had fever, with 32% using paracetamol and 22% using ibuprofen, for symptom-relief.\nIn the ibuprofen group, 3 (3.4%) patients died, whereas in the non-ibuprofen group, 9 (2.8%) patients died (p 0.95).\nNine (10.3%) patients from the ibuprofen group needed respiratory support, compared with 35 (11%) from the non-ibuprofen group (p 1).\nWhen compared with exclusive paracetamol users, no differences were observed in mortality rates or the need for respiratory support among patients using ibuprofen.\nCONCLUSIONS: In this cohort of COVID-19 patients, ibuprofen use was not associated with worse clinical outcomes, compared with paracetamol or no antipyretic.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"When compared with exclusive paracetamol users, no differences were observed in mortality rates or the need for respiratory support among patients using ibuprofen.\", \"CONCLUSIONS: In this cohort of COVID-19 patients, ibuprofen use was not associated with worse clinical outcomes, compared with paracetamol or no antipyretic.\"]}", "id": 852}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Hydroxychloroquine is an Effective Treatment for COVID-19\n\nAbstract:\nAims: Studies have indicated that chloroquine (CQ) shows antagonism against COVID-19 in vitro.\nHowever, evidence regarding its effects in patients is limited.\nThis study aims to evaluate the efficacy of hydroxychloroquine (HCQ) in the treatment of patients with COVID-19.\nMain methods: From February 4 to February 28, 2020, 62 patients suffering from COVID-19 were diagnosed and admitted to Renmin Hospital of Wuhan University.\nAll participants were randomized in a parallel-group trial, 31 patients were assigned to receive an additional 5-day HCQ (400 mg/d) treatment, Time to clinical recovery (TTCR), clinical characteristics, and radiological results were assessed at baseline and 5 days after treatment to evaluate the effect of HCQ.\nKey findings: For the 62 COVID-19 patients, 46.8% (29 of 62) were male and 53.2% (33 of 62) were female, the mean age was 44.7 (15.3) years.\nNo difference in the age and sex distribution between the control group and the HCQ group.\nBut for TTCR, the body temperature recovery time and the cough remission time were significantly shortened in the HCQ treatment group.\nBesides, a larger proportion of patients with improved pneumonia in the HCQ treatment group (80.6%, 25 of 31) compared with the control group (54.8%, 17 of 31).\nNotably, all 4 patients progressed to severe illness that occurred in the control group.\nHowever, there were 2 patients with mild adverse reactions in the HCQ treatment group.\nSignificance: Among patients with COVID-19, the use of HCQ could significantly shorten TTCR and promote the absorption of pneumonia.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Significance: Among patients with COVID-19, the use of HCQ could significantly shorten TTCR and promote the absorption of pneumonia.\"]}", "id": 853}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Frequent hand washing, avoiding crowds and contact with sick people, and cleaning and disinfecting frequently touched surfaces can help prevent coronavirus infections. \n\nAbstract:\nSARS-CoV-2, identified in Wuhan, China, for the first time in December 2019, is a new viral strain, which has not been previously identified in humans; it can be transmitted both by air and via direct and indirect contact; however, the most frequent way it spreads is via droplets.\nLike the other viruses belonging to the same family of coronaviruses, it can cause from mild flu-like symptoms, such as cold, sore throat, cough and fever, to more severe ones such as pneumonia and breathing difficulties, and it can even lead to death.\nSince no effective specific drug therapy has been found yet, nor any vaccine capable of limiting the spread of this pathogen, it is important for ways of preventing the spread of this infection to be established.\nThe purpose of our research was to provide a protocol to prevent the spread of SARS-CoV-2 infection in light of the limited information related to this coronavirus.\nIn detail, we analysed and searched targeted evidence-based guidelines issued in the various countries affected by this epidemic up till now.\nIn addition, we analyzed the recommendations for the prevention and control of other epidemics caused by other pathogens belonging to the same family of coronaviruses or others that present the same mechanisms of transmission.\nGeneral organizational measures regarding the containment and management of the epidemiological emergency of COVID-19 have been imposed by the competent authorities for an adequate and proportionate management of the evolution of the epidemiological situation.\nThe prevention and protection organizational measures therefore aim to minimize the probability of being exposed to SARS-CoV-2.\nFor this purpose, measures must also be taken at work to avoid new infections or even the spread of the virus where it has already been present.\nFurthermore, environmental measures are aimed at reducing the risk of transmission of SARS-CoV-2 to individuals through contact with infected subjects, objects, equipment, or contaminated environmental surfaces.\nProtective devices must be used whenever there is potentially close contact with a suspect case, especially when the potentially infected person does not wear a surgical mask that could reduce the spread of viruses in the environment.\nBy adopting this specific prevention and protection measures recommended in the workplace, it will be possible to help overcome this COVID-19 pandemic.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Protective devices must be used whenever there is potentially close contact with a suspect case, especially when the potentially infected person does not wear a surgical mask that could reduce the spread of viruses in the environment.\"]}", "id": 854}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Early studies have found that cats are the animals most likely to catch the new coronavirus. They can also show symptoms of COVID-19 and might be able to pass it to other cats.\n\nAbstract:\nOn April 22, CDC and the U.S. Department of Agriculture (USDA) reported cases of two domestic cats with confirmed infection with SARS-CoV-2, the virus that causes coronavirus disease 2019 (COVID-19).\nThese are the first reported companion animals (including pets and service animals) with SARS-CoV-2 infection in the United States, and among the first findings of SARS-CoV-2 symptomatic companion animals reported worldwide.\nThese feline cases originated from separate households and were epidemiologically linked to suspected or confirmed human COVID-19 cases in their respective households.\nNotification of presumptive positive animal test results triggered a One Health* investigation by state and federal partners, who determined that no further transmission events to other animals or persons had occurred.\nBoth cats fully recovered.\nAlthough there is currently no evidence that animals play a substantial role in spreading COVID-19, CDC advises persons with suspected or confirmed COVID-19 to restrict contact with animals during their illness and to monitor any animals with confirmed SARS-CoV-2 infection and separate them from other persons and animals at home (1).", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Notification of presumptive positive animal test results triggered a One Health* investigation by state and federal partners, who determined that no further transmission events to other animals or persons had occurred.\"]}", "id": 855}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Plasmin cascade mediates thrombolytic events in sars-cov-2 infection via complement and platelet-activating systems\n\nAbstract:\nRecently emerged beta-coronavirus, SARS-CoV-2 has resulted in the current pandemic designated COVID-19.\nCOVID-19 manifests as severe illness exhibiting systemic inflammatory response syndrome, acute respiratory distress syndrome (ARDS), thrombotic events, and shock, exacerbated further by co-morbidities and age1\u20133.\nRecent clinical reports suggested that the pulmonary failure seen in COVID-19 may not be solely driven by acute ARDS, but also microvascular thrombotic events, likely driven by complement activation4,5.\nHowever, it is not fully understood how the SARS-CoV-2 infection mechanisms mediate thrombotic events, and whether such mechanisms and responses are unique to SARS-CoV-2 infection, compared to other respiratory infections.\nWe address these questions here, in the context of normal lung epithelia, in vitro and in vivo, using publicly available data.\nOur results indicate that plasmin is a crucial mediator which primes interactions between complement and platelet-activating systems in lung epithelia upon SARS-CoV-2 infection, with a potential for therapeutic intervention.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Our results indicate that plasmin is a crucial mediator which primes interactions between complement and platelet-activating systems in lung epithelia upon SARS-CoV-2 infection, with a potential for therapeutic intervention.\"]}", "id": 856}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: The researchers also ranked face mask material from most to least effective in their testing.\n\nAbstract:\nEvidence that face masks provide effective protection against respiratory infections in the community is scarce.\nHowever, face masks are widely used by health workers as part of droplet precautions when caring for patients with respiratory infections.\nIt would therefore be reasonable to suggest that consistent widespread use of face masks in the community could prevent further spread of the Severe Acute Respiratory Syndrome-Coronavirus 2 (SARS-CoV-2).\nIn this study we examine public face mask wearing in Uganda where a proportion wears masks to protect against acquiring, and the other to prevent from transmitting SARS-CoV-2.\nThe objective of this study was to determine what percentage of the population would have to wear face masks to reduce susceptibility to and infectivity of COVID-19 in Uganda, keeping the basic reproduction number below unity and/or flattening the curve.\nWe used an SEIAQRD model for the analysis.\nResults show that implementation of facemasks has a relatively large impact on the size of the coronavirus epidemic in Uganda.\nWe find that the critical mask adherence is 5 per 100 when 80% wear face masks.\nA cost-effective analysis shows that utilizing funds to provide 1 public mask to the population has a per capita compounded cost of USD 1.34.\nIf provision of face masks is done simultaneously with supportive care, the per capita compounded cost is USD 1.965, while for the case of only treatment and no provision of face masks costs each Ugandan USD 4.0579.\nWe conclude that since it is hard to achieve a 100% adherence to face masks, government might consider provision of face masks in conjunction with provision of care.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Evidence that face masks provide effective protection against respiratory infections in the community is scarce.\", \"However, face masks are widely used by health workers as part of droplet precautions when caring for patients with respiratory infections.\", \"It would therefore be reasonable to suggest that consistent widespread use of face masks in the community could prevent further spread of the Severe Acute Respiratory Syndrome-Coronavirus 2 (SARS-CoV-2).\"]}", "id": 857}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: headaches are rarely the sole symptom present in a coronavirus patient.\n\nAbstract:\nAbstract We report here the case of a 27-year-old man who consulted by telemedicine during the Coronavirus Disease 2019 (COVID-19) pandemic, due to foreign body sensation and left eye redness.\nExamination revealed unilateral eyelid edema and moderate conjunctival hyperemia.\nA few hours later the patient experienced intense headache and developed fever, cough and severe dyspnea.\nA nasopharyngeal swab proved positive for SARS-CoV-2.\nThis case demonstrates that conjunctivitis can be the inaugural manifestation of the COVID-19 infection.\nIt illustrates the interest of telemedicine in ophthalmology during the COVID-19 pandemic, since moderate conjunctival hyperemia can be the first sign of a severe respiratory distress.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"A few hours later the patient experienced intense headache and developed fever, cough and severe dyspnea.\"]}", "id": 858}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: The sars-cov-2 spike mutation d614g increases entry fitness across a range of ace2 levels , directly outcompetes the wild type , and is preferentially incorporated into trimers\n\nAbstract:\nEarly in the current pandemic, the D614G mutation arose in the Spike protein of SARS-CoV-2 and quickly became the dominant variant globally.\nMounting evidence suggests D614G enhances viral entry.\nHere we use a direct competition assay with single-cycle viruses to show that D614G outcompetes the wildtype.\nWe developed a cell line with inducible ACE2 expression to confirm that D614G more efficiently enters cells with ACE2 levels spanning the different primary cells targeted by SARS-CoV-2.\nUsing a new assay for crosslinking and directly extracting Spike trimers from the pseudovirus surface, we found an increase in trimerization efficiency and viral incorporation of D614G protomers.\nOur findings suggest that D614G increases infection of cells expressing a wide range of ACE2, and informs the mechanism underlying enhanced entry.\nThe tools developed here can be broadly applied to study other Spike variants and SARS-CoV-2 entry, to inform functional studies of viral evolution and vaccine development.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Mounting evidence suggests D614G enhances viral entry.\", \"We developed a cell line with inducible ACE2 expression to confirm that D614G more efficiently enters cells with ACE2 levels spanning the different primary cells targeted by SARS-CoV-2.\", \"Using a new assay for crosslinking and directly extracting Spike trimers from the pseudovirus surface, we found an increase in trimerization efficiency and viral incorporation of D614G protomers.\"]}", "id": 859}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: the warmer weather will slow the spread of SARS-CoV-2, the novel coronavirus that causes COVID-19\n\nAbstract:\nThe spatial distribution of the COVID-19 infection in China cannot be explained solely by geographical distance and regulatory stringency.\nIn this research we investigate how meteorological conditions and air pollution, as concurring factors, impact COVID-19 transmission, using data on new confirmed cases from 219 prefecture cities from January 24 to February 29, 2020.\nResults revealed a kind of nonlinear dose-response relationship between temperature and coronavirus transmission.\nWe also found that air pollution indicators are positively correlated with new confirmed cases, and the coronavirus further spreads by 5-7% as the AQI increases by 10 units.\nFurther analysis based on regional divisions revealed that in northern China the negative effects of rising temperature on COVID-19 is counteracted by aggravated air pollution.\nIn the southern cities, the ambient temperature and air pollution have a negative interactive effect on COVID-19 transmission, implying that rising temperature restrains the facilitating effects of air pollution and that they jointly lead to a decrease in new confirmed cases.\nThese results provide implications for the control and prevention of this disease and for the anticipation of another possible pandemic.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"In the southern cities, the ambient temperature and air pollution have a negative interactive effect on COVID-19 transmission, implying that rising temperature restrains the facilitating effects of air pollution and that they jointly lead to a decrease in new confirmed cases.\"]}", "id": 860}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Coronavirus nsp10/nsp16 methyltransferase can be targeted by nsp10-derived peptide in vitro and in vivo to promote replication and pathogenesis\n\nAbstract:\nUNLABELLED The 5' cap structures of eukaryotic mRNAs are important for RNA stability and protein translation.\nMany viruses that replicate in the cytoplasm of eukaryotes have evolved 2'-O-methyltransferases (2'-O-MTase) to autonomously modify their mRNAs and carry a cap-1 structure (m7GpppNm) at the 5' end, thereby facilitating viral replication and escaping innate immune recognition in host cells.\nPrevious studies showed that the 2'-O-MTase activity of severe acute respiratory syndrome coronavirus (SARS-CoV) nonstructural protein 16 (nsp16) needs to be activated by nsp10, whereas nsp16 of feline coronavirus (FCoV) alone possesses 2'-O-MTase activity (E. Decroly et al., J Virol 82:8071-8084, 2008, http://dx.doi.org/10.1128/JVI.00407-08; M. Bouvet et al., PLoS Pathog 6:e1000863, 2010, http://dx.doi.org/10.1371/journal.ppat.1000863; E. Decroly et al., PLoS Pathog 7:e1002059, 2011, http://dx.doi.org/10.1371/journal.ppat.1002059; Y. Chen et al., PLoS Pathog 7:e1002294, 2011, http://dx.doi.org/10.1371/journal.ppat.1002294) .\nIn this study, we demonstrate that stimulation of nsp16 2'-O-MTase activity by nsp10 is a universal and conserved mechanism in coronaviruses, including FCoV, and that nsp10 is functionally interchangeable in the stimulation of nsp16 of different coronaviruses.\nBased on our current and previous studies, we designed a peptide (TP29) from the sequence of the interaction interface of mouse hepatitis virus (MHV) nsp10 and demonstrated that the peptide inhibits the 2'-O-MTase activity of different coronaviruses in biochemical assays and the viral replication in MHV infection and SARS-CoV replicon models.\nInterestingly, the peptide TP29 exerted robust inhibitory effects in vivo in MHV-infected mice by impairing MHV virulence and pathogenesis through suppressing virus replication and enhancing type I interferon production at an early stage of infection.\nTherefore, as a proof of principle, the current results indicate that coronavirus 2'-O-MTase activity can be targeted in vitro and in vivo.\nIMPORTANCE Coronaviruses are important pathogens of animals and human with high zoonotic potential.\nSARS-CoV encodes the 2'-O-MTase that is composed of the catalytic subunit nsp16 and the stimulatory subunit nsp10 and plays an important role in virus genome replication and evasion from innate immunity.\nOur current results demonstrate that stimulation of nsp16 2'-O-MTase activity by nsp10 is a common mechanism for coronaviruses, and nsp10 is functionally interchangeable in the stimulation of nsp16 among different coronaviruses, which underlies the rationale for developing inhibitory peptides.\nWe demonstrate that a peptide derived from the nsp16-interacting domain of MHV nsp10 could inhibit 2'-O-MTase activity of different coronaviruses in vitro and viral replication of MHV and SARS-CoV replicon in cell culture, and it could strongly inhibit virus replication and pathogenesis in MHV-infected mice.\nThis work makes it possible to develop broad-spectrum peptide inhibitors by targeting the nsp16/nsp10 2'-O-MTase of coronaviruses.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"We demonstrate that a peptide derived from the nsp16-interacting domain of MHV nsp10 could inhibit 2'-O-MTase activity of different coronaviruses in vitro and viral replication of MHV and SARS-CoV replicon in cell culture, and it could strongly inhibit virus replication and pathogenesis in MHV-infected mice.\"]}", "id": 861}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: 5G caused the coronavirus outbreak\n\nAbstract:\nObjective@#To analyze application fields of 5G communication technology in Corona Virus Disease 2019 (COVID-19) epidemic prevention and control, and build a 5G intelligent medical service system for COVID-19 epidemic prevention and control in China.@*Methods@#We collected and analyzed 5G technology application cases used in the fight against COVID-19 from December 2019 to February 2020: 5G + telemedicine application cases, 5G + negative pressure ambulance cases, 5G + remote monitoring cases, 5G + artificial intelligence cases, 5G + infrared thermography temperature detection cases, 5G + big data analysis cases for epidemic prevention and control.@*Results@#Through the analysis of 5G application cases in COVID-19 epidemic prevention and control, we found out the key elements of 5G intelligent medical service system in COVID-19 epidemic prevention and control.\nBy optimizing and upgrading the internal service mode of the hospital, breaking the internal and external barriers, integrating internal and external resources, and strengthening 5G intelligent medical security, we can form a 5G intelligent medical service system for COVID-19 epidemic prevention and control, including application layer, technical service layer, network support layer and security system layer.@*Conclusion@#5G communication technology has the characteristics of faster speed, shorter time delay and denser capacity.\nIn COVID-19 epidemic prevention and control work, it can further improve the efficiency of doctors&apos; diagnosis, improve patients&apos; medical experience, realize the remote sharing of high-quality medical resources and real-time information exchange, effectively block the spread of epidemic, alleviate the shortage of medical resources and medical staff, and make the epidemic prevention and control more efficient.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"In COVID-19 epidemic prevention and control work, it can further improve the efficiency of doctors&apos; diagnosis, improve patients&apos; medical experience, realize the remote sharing of high-quality medical resources and real-time information exchange, effectively block the spread of epidemic, alleviate the shortage of medical resources and medical staff, and make the epidemic prevention and control more efficient.\"]}", "id": 862}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Cell-based culture of sars-cov-2 informs infectivity and safe de-isolation assessments during covid-19\n\nAbstract:\nBACKGROUND: The detection of SARS-CoV-2 RNA by real-time polymerase chain reaction (PCR) in respiratory samples collected from persons recovered from COVID-19 does not necessarily indicate shedding of infective virions.\nBy contrast, the isolation of SARS-CoV-2 using cell-based culture likely indicates infectivity, but there are limited data on the correlation between SARS-CoV-2 culture and PCR.\nMETHODS: One hundred and ninety-five patients with varying severity of COVID-19 were tested (outpatients [n=178]), inpatients [n=12] and critically unwell patients admitted to the intensive care unit [ICU; n=5]).\nSARS-CoV-2 PCR positive samples were cultured in Vero C1008 cells and inspected daily for cytopathic effect (CPE).\nSARS-CoV-2-induced CPE was confirmed by PCR of culture supernatant.\nWhere no CPE was observed, PCR was performed on day four to confirm absence of virus replication.\nCycle threshold (Ct) of the day four PCR (Ctculture) and the PCR of the original clinical sample (Ctsample) were compared, and positive cultures were defined where Ctsample-Ctculture was &#8805;3.\nFINDINGS: Of 234 samples collected, 228 (97%) were from the upper respiratory tract.\nSARS-CoV-2 was only successfully isolated from samples with Ctsample &#8804;32, including in 28/181 (15%), 19/42 (45%) and 9/11 samples (82%) collected from outpatients, inpatients, and ICU patients, respectively.\nThe mean duration from symptom onset to culture positivity was 4.5 days (range 0-18).\nSARS-CoV-2 was significantly more likely to be isolated from samples collected from inpatients (p<0\u00e2\u0088\u0099001) and ICU patients (p<0\u00e2\u0088\u00990001) compared with outpatients respectively, and in samples with lower Ctsample.\nCONCLUSION: SARS-CoV-2 culture may be used as a surrogate marker for infectivity and inform de-isolation protocols.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"By contrast, the isolation of SARS-CoV-2 using cell-based culture likely indicates infectivity, but there are limited data on the correlation between SARS-CoV-2 culture and PCR.\"]}", "id": 863}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Sars-cov2 enables anaerobic bacteria to colonize the blood disrupting homeostasis\n\nAbstract:\nThe oral cavity, as the entry point to the body, may play a critical role in the pathogenesis of SARS-CoV-2 infection that has caused a global outbreak of the coronavirus disease 2019 (COVID-19).\nAvailable data indicate that the oral cavity may be an active site of infection and an important reservoir of SARS-CoV-2.\nConsidering that the oral surfaces are colonized by a diverse microbial community, it is likely that viruses have interactions with the host microbiota.\nPatients infected by SARS-CoV-2 may have alterations in the oral and gut microbiota, while oral species have been found in the lung of COVID-19 patients.\nFurthermore, interactions between the oral, lung, and gut microbiomes appear to occur dynamically whereby a dysbiotic oral microbial community could influence respiratory and gastrointestinal diseases.\nHowever, it is unclear whether SARS-CoV-2 infection can alter the local homeostasis of the resident microbiota, actively cause dysbiosis, or influence cross-body sites interactions.\nHere, we provide a conceptual framework on the potential impact of SARS-CoV-2 oral infection on the local and distant microbiomes across the respiratory and gastrointestinal tracts (\u2018oral-tract axes\u2019), which remains largely unexplored.\nStudies in this area could further elucidate the pathogenic mechanism of SARS-CoV-2 and the course of infection as well as the clinical symptoms of COVID-19 across different sites in the human host.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Patients infected by SARS-CoV-2 may have alterations in the oral and gut microbiota, while oral species have been found in the lung of COVID-19 patients.\"]}", "id": 864}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Test sensitivity is equal to frequency and turnaround time for covid-19 screening\n\nAbstract:\nThe COVID-19 pandemic has created a public health crisis.\nBecause SARS-CoV-2 can spread from individuals with pre-symptomatic, symptomatic, and asymptomatic infections, the re-opening of societies and the control of virus spread will be facilitated by robust population screening, for which virus testing will often be central.\nAfter infection, individuals undergo a period of incubation during which viral titers are usually too low to detect, followed by an exponential viral growth, leading to a peak viral load and infectiousness, and ending with declining viral levels and clearance.\nGiven the pattern of viral load kinetics, we model the effectiveness of repeated population screening considering test sensitivities, frequency, and sample-to-answer reporting time.\nThese results demonstrate that effective screening depends largely on frequency of testing and the speed of reporting, and is only marginally improved by high test sensitivity.\nWe therefore conclude that screening should prioritize accessibility, frequency, and sample-to-answer time; analytical limits of detection should be secondary.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"We therefore conclude that screening should prioritize accessibility, frequency, and sample-to-answer time; analytical limits of detection should be secondary.\"]}", "id": 865}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: COVID-19 can survive on surfaces, like a tabletop\n\nAbstract:\nWith limited infection control practices in overcrowded Bangladeshi hospitals, surfaces may play an important role in the transmission of respiratory pathogens in hospital wards and pose a serious risk of infection for patients, health care workers, caregivers and visitors.\nIn this study, we aimed to identify if surfaces near hospitalized patients with respiratory infections were contaminated with respiratory pathogens and to identify which surfaces were most commonly contaminated.\nBetween September-November 2013, we collected respiratory (nasopharyngeal and oropharyngeal) swabs from patients hospitalized with respiratory illness in adult medicine and paediatric medicine wards at two public tertiary care hospitals in Bangladesh.\nWe collected surface swabs from up to five surfaces near each case-patient including: the wall, bed rail, bed sheet, clinical file, and multipurpose towel used for care giving purposes.\nWe tested swabs using real-time multiplex PCR for 19 viral and 12 bacterial pathogens.\nCase-patients with at least one pathogen detected had corresponding surface swabs tested for those same pathogens.\nOf 104 patients tested, 79 had a laboratory-confirmed respiratory pathogen.\nOf the 287 swabs collected from surfaces near these patients, 133 (46%) had evidence of contamination with at least one pathogen.\nThe most commonly contaminated surfaces were the bed sheet and the towel.\nSixty-two percent of patients with a laboratory-confirmed respiratory pathgen (49/79) had detectable viral or bacterial nucleic acid on at least one surface.\nKlebsiella pneumoniae was the most frequently detected pathogen on both respiratory swabs (32%, 33/104) and on surfaces near patients positive for this organism (97%, 32/33).\nSurfaces near patients hospitalized with respiratory infections were frequently contaminated by pathogens, with Klebsiella pneumoniae being most common, highlighting the potential for transmission of respiratory pathogens via surfaces.\nEfforts to introduce routine cleaning in wards may be a feasible strategy to improve infection control, given that severe space constraints prohibit cohorting patients with respiratory illness.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Surfaces near patients hospitalized with respiratory infections were frequently contaminated by pathogens, with Klebsiella pneumoniae being most common, highlighting the potential for transmission of respiratory pathogens via surfaces.\"]}", "id": 866}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Pn-cov2 antibody cocktail prevents and treats sars-cov-2 infection in rhesus macaques and hamsters\n\nAbstract:\nAn urgent global quest for effective therapies to prevent and treat COVID-19 disease is ongoing.\nWe previously described REGN-COV2, a cocktail of two potent neutralizing antibodies (REGN10987+REGN10933) targeting non-overlapping epitopes on the SARS-CoV-2 spike protein.\nIn this report, we evaluate the in vivo efficacy of this antibody cocktail in both rhesus macaques and golden hamsters and demonstrate that REGN-COV-2 can greatly reduce virus load in lower and upper airway and decrease virus induced pathological sequalae when administered prophylactically or therapeutically.\nOur results provide evidence of the therapeutic potential of this antibody cocktail.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"We previously described REGN-COV2, a cocktail of two potent neutralizing antibodies (REGN10987+REGN10933) targeting non-overlapping epitopes on the SARS-CoV-2 spike protein.\", \"In this report, we evaluate the in vivo efficacy of this antibody cocktail in both rhesus macaques and golden hamsters and demonstrate that REGN-COV-2 can greatly reduce virus load in lower and upper airway and decrease virus induced pathological sequalae when administered prophylactically or therapeutically.\", \"Our results provide evidence of the therapeutic potential of this antibody cocktail.\"]}", "id": 867}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: throwing hot water on ginger as a cure-all for COVID-19\n\nAbstract:\nThe severity of coronavirus disease 2019 (COVID-19) infection is quite variable and the manifestations varies from asymptomatic disease to severe acute respiratory infection.\nFever, dry cough, dyspnea, myalgia, fatigue, loss of appetite, olfactory and gustatory dysfunctions are the most prevalent general symptoms.\nDecreased immune system cells such as suppressed regulatory T cells, cytotoxic and helper T cells, natural killer cells, monocytes/macrophages and increased proinflammatory cytokines are the characteristic features.\nCompounds derived from Allium sativum (garlic) have the potential to decrease the expression of proinflammatory cytokines and to reverse the immunological abnormalities to more acceptable levels.\nAllium sativum is suggested as a beneficial preventive measure before being infected with SARS-CoV-2 virus.\nAllium sativum is a functional food well-known for its immunomodulatory, antimicrobial, antiinflammatory, antimutagenic, antitumor properties.\nIts antiviral efficiency was also demonstrated.\nSome constituents of this plant were found to be active against protozoan parasites.\nWithin this context, it appears to reverse most immune system dysfunctions observed in patients with COVID-19 infection.\nThe relations among immune system parameters, leptin, leptin receptor, adenosin mono phosphate-activated protein kinase, peroxisome proliferator activated receptor-gamma have also been interpreted.\nLeptin's role in boosting proinflammatory cytokines and in appetite decreasing suggest the possible beneficial effect of decreasing the concentration of this proinflammatory adipose tissue hormone in relieving some symptoms detected during COVID-19 infection.\nIn conclusion, Allium sativum may be an acceptable preventive measure against COVID-19 infection to boost immune system cells and to repress the production and secretion of proinflammatory cytokines as well as an adipose tissue derived hormone leptin having the proinflammatory nature.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 868}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: high doses of vitamins and natural remedies can stave off COVID-19 \u2014 but evidence to support these claims is lacking.\n\nAbstract:\nSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a new strain that was discovered in 2019 and has not been previously identified in humans.\nOn December 31st 2019 World Health Organization (WHO) was informed of a cluster of cases with pneumonia of unknown origin from Wuhan City, Hubei province of China.\nThe WHO announced in February 2020 that COVID-19 is the official name of the coronavirus diseases.\nA total of 519,899 confirmed cases with 23,592 deaths linked to this pathogen as on March 27, 2020 have been reported.\nDue to increasing number of infected people across the continents and huge loss to human life, the WHO has declared the novel COVID-19 outbreak a pandemic.\nA pandemic is defined as the \"worldwide spread\" of a new disease.\nCurrently, no COVID-19 specific treatments have been approved by the United States Food and Drug Administration (US-FDA).\nHowever, the current treatment options include hydroxychloroquine, tocilizumab, remdesivir, lopinavir-ritonavir (Kaletra\u00ae), and nitazoxanide.\nIn recent past, some natural herbal compounds have demonstrated encouraging anti-viral properties.\nThis article attempted to summarize available information on the reported anti-viral activity of some natural products.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Currently, no COVID-19 specific treatments have been approved by the United States Food and Drug Administration (US-FDA).\"]}", "id": 869}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Bacterial modification of the host glycosaminoglycan heparan sulfate modulates sars-cov-2 infectivity\n\nAbstract:\nThe human microbiota has a close relationship with human disease and it remodels components of the glycocalyx including heparan sulfate (HS).\nStudies of the severe acute respiratory syndrome coronavirus (SARS-CoV-2) spike protein receptor binding domain suggest that infection requires binding to HS and angiotensin converting enzyme 2 (ACE2) in a codependent manner.\nHere, we show that commensal host bacterial communities can modify HS and thereby modulate SARS-CoV-2 spike protein binding and that these communities change with host age and sex.\nCommon human-associated commensal bacteria whose genomes encode HS-modifying enzymes were identified.\nThe prevalence of these bacteria and the expression of key microbial glycosidases in bronchoalveolar lavage fluid (BALF) was lower in adult COVID-19 patients than in healthy controls.\nThe presence of HS-modifying bacteria decreased with age in two large survey datasets, FINRISK 2002 and American Gut, revealing one possible mechanism for the observed increase in COVID-19 susceptibility with age.\nIn vitro, bacterial glycosidases from unpurified culture media supernatants fully blocked SARS-CoV-2 spike binding to human H1299 protein lung adenocarcinoma cells.\nHS-modifying bacteria in human microbial communities may regulate viral adhesion, and loss of these commensals could predispose individuals to infection.\nUnderstanding the impact of shifts in microbial community composition and bacterial lyases on SARS-CoV-2 infection may lead to new therapeutics and diagnosis of susceptibility.\nGraphical Abstract.\nDiagram of hypothesis for bacterial mediation of SARS-CoV-2 infection through heparan sulfate (HS).\nIt is well known that host microbes groom the mucosa where they reside.\nRecent investigations have shown that HS, a major component of mucosal layers, is necessary for SARS-CoV-2 infection.\nIn this study we examine the impact of microbial modification of HS on viral attachment.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Understanding the impact of shifts in microbial community composition and bacterial lyases on SARS-CoV-2 infection may lead to new therapeutics and diagnosis of susceptibility.\"]}", "id": 870}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: the Covid-19 coronavirus can stay on various surfaces for a while\n\nAbstract:\nSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a highly contagious virus that can transmit through respiratory droplets, aerosols, or contacts.\nFrequent touching of contaminated surfaces in public areas is therefore a potential route of SARS-CoV-2 transmission.\nThe inanimate surfaces have often been described as a source of nosocomial infections.\nHowever, summaries on the transmissibility of coronaviruses from contaminated surfaces to induce the coronavirus disease 2019 are rare at present.\nThis review aims to summarize data on the persistence of different coronaviruses on inanimate surfaces.\nThe literature was systematically searched on Medline without language restrictions.\nAll reports with experimental evidence on the duration persistence of coronaviruses on any type of surface were included.\nMost viruses from the respiratory tract, such as coronaviruses, influenza, SARS-CoV, or rhinovirus, can persist on surfaces for a few days.\nPersistence time on inanimate surfaces varied from minutes to up to one month, depending on the environmental conditions.\nSARSCoV-2 can be sustained in air in closed unventilated buses for at least 30 min without losing infectivity.\nThe most common coronaviruses may well survive or persist on surfaces for up to one month.\nViruses in respiratory or fecal specimens can maintain infectivity for quite a long time at room temperature.\nAbsorbent materials like cotton are safer than unabsorbent materials for protection from virus infection.\nThe risk of transmission via touching contaminated paper is low.\nPreventive strategies such as washing hands and wearing masks are critical to the control of coronavirus disease 2019.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Most viruses from the respiratory tract, such as coronaviruses, influenza, SARS-CoV, or rhinovirus, can persist on surfaces for a few days.\", \"The most common coronaviruses may well survive or persist on surfaces for up to one month.\"]}", "id": 871}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: The new coronavirus can infect your skin\n\nAbstract:\nThe coronavirus disease 2019 (COVID-19) caused by SARS-CoV-2 was declared in the last weeks as global pandemic.\nCurrently affecting more than 5 000 000 individuals worldwide, COVID-19 is most commonly associated with symptoms caused by the acute respiratory distress syndrome (ARDS).\nAs the number of infected individuals increases, we are learning that not only lungs, but also other organs can be affected by the virus.\nThe gastrointestinal symptoms, for example diarrhoea, vomiting, nausea or abdominal pain, are frequent in patients with COVID-19.\nMoreover, alimentary tract symptoms may precede the respiratory presentation of SARS-CoV-2 infection.\nThis can lead to delayed diagnosis and inappropriate management of infected patients.\nIn addition, SARS-CoV-2 nucleic acid can be detected in faeces of infected patients and rectal swabs are even reported to remain positive for a longer period of time than nasopharyngeal swabs.\nHere, we aim to provide an update on the gastrointestinal involvement of COVID-19 presenting the symptoms that can be encountered in infected patients.\nWe address the role of angiotensin-converting enzyme 2 (ACE2), as a functional receptor for SARS-CoV-2, which also was found in the gastrointestinal tract.\nFinally, we briefly discuss faecal shedding of SARS-CoV-2 and its potential role in the pathogenesis of the disease.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 872}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Wearing the N95 respirator mask can protect against coronavirus\n\nAbstract:\nThe COVID\u201019 pandemic caused by the novel coronavirus SARS\u2010CoV\u20102 has claimed many lives worldwide.\nWearing medical masks or N95 masks (namely N95 respirators) can slow the virus spread and reduce the infection risk.\nReuse of these masks can minimize waste, protect the environment, and help to solve the current imminent shortage of masks.\nDisinfection of used masks is needed for reuse of them with safety, but improper decontamination can damage the blocking structure of masks.\nIn this study, we demonstrated, using avian coronavirus of infectious bronchitis virus to mimic SARS\u2010CoV\u20102, that medical masks and N95 masks remained their blocking efficacy after being steamed on boiling water even for 2 hours.\nWe also demonstrated that three brands of medical masks blocked over 99% viruses in aerosols.\nThe avian coronavirus was completely inactivated after being steamed for 5 minutes.\nTogether, this study suggested that medical masks are adequate for use on most social occasions, and both medical masks and N95 masks can be reused for a few days with steam decontamination between use.\nThis article is protected by copyright.\nAll rights reserved.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Wearing medical masks or N95 masks (namely N95 respirators) can slow the virus spread and reduce the infection risk.\", \"We also demonstrated that three brands of medical masks blocked over 99% viruses in aerosols.\", \"The avian coronavirus was completely inactivated after being steamed for 5 minutes.\", \"Together, this study suggested that medical masks are adequate for use on most social occasions, and both medical masks and N95 masks can be reused for a few days with steam decontamination between use.\"]}", "id": 873}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: A potently neutralizing antibody protects dcs against sars-cov-2 infection\n\nAbstract:\nSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is responsible for millions of infections and hundreds of thousands of deaths globally.\nThere are no widely available licensed therapeutics against SARS-CoV-2, highlighting an urgent need for effective interventions.\nThe virus enters host cells through binding of a receptor-binding domain within its trimeric spike glycoprotein to human angiotensin-converting enzyme 2.\nIn this article, we describe the generation and characterization of a panel of murine mAbs directed against the receptor-binding domain.\nOne mAb, 2B04, neutralized wild-type SARS-CoV-2 in vitro with remarkable potency (half-maximal inhibitory concentration of <2 ng/ml).\nIn a murine model of SARS-CoV-2 infection, 2B04 protected challenged animals from weight loss, reduced lung viral load, and blocked systemic dissemination.\nThus, 2B04 is a promising candidate for an effective antiviral that can be used to prevent SARS-CoV-2 infection.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"In a murine model of SARS-CoV-2 infection, 2B04 protected challenged animals from weight loss, reduced lung viral load, and blocked systemic dissemination.\"]}", "id": 874}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Most children have mild symptoms or no symptoms.\n\nAbstract:\nBACKGROUND: Novel coronavirus disease (COVID-19) is spreading globally.\nLittle is known about the risk factors for the clinical outcomes of COVID-19 in children.\nMETHODS: A retrospective case-control study was taken in children with severe acute respiratory syndrome coronary virus-2 infection in Wuhan Children's Hospital.\nRisk factors associated with the development of COVID-19 and progression were collected and analyzed.\nRESULTS: Eight of 260 children diagnosed with severe COVID-19 pneumonia were included in the study.\nThirty-five children with COVID-19 infection matched for age, sex and date of admission, and who classified as non-severe type, were randomly selected from the hospital admissions.\nFor cases with severe pneumonia caused by COVID-19, the most common symptoms were dyspnea (87.5%), fever (62.5%) and cough (62.5%).\nIn laboratory, white blood cells count was significantly higher in severe children than non-severe children.\nLevels of inflammation bio-makers such as hsCRP, IL-6, IL-10 and D-dimer elevated in severe children compared with non-severe children on admission.\nThe level of total bilirubin and uric acid clearly elevated in severe children compared with non-severe children on admission.\nAll of severe children displayed the lesions on chest CT, more lung segments were involved in severe children than in non-severe children, which was only risk factor associated with severe COVID-19 pneumonia in multivariable analysis.\nCONCLUSIONS: More than 3 lung segments involved were associated with greater risk of development of severe COVID-19 in children.\nMoreover, the possible risk of the elevation of IL-6, high total bilirubin and D-dimer with univariable analysis could identify patients to be severe earlier.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"RESULTS: Eight of 260 children diagnosed with severe COVID-19 pneumonia were included in the study.\", \"Thirty-five children with COVID-19 infection matched for age, sex and date of admission, and who classified as non-severe type, were randomly selected from the hospital admissions.\"]}", "id": 875}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: wearing a mask helps stop infected people from spreading the new coronavirus to others.\n\nAbstract:\nThe COVID\u201019 pandemic caused by the novel coronavirus SARS\u2010CoV\u20102 has claimed many lives worldwide.\nWearing medical masks or N95 masks (namely N95 respirators) can slow the virus spread and reduce the infection risk.\nReuse of these masks can minimize waste, protect the environment, and help to solve the current imminent shortage of masks.\nDisinfection of used masks is needed for reuse of them with safety, but improper decontamination can damage the blocking structure of masks.\nIn this study, we demonstrated, using avian coronavirus of infectious bronchitis virus to mimic SARS\u2010CoV\u20102, that medical masks and N95 masks remained their blocking efficacy after being steamed on boiling water even for 2 hours.\nWe also demonstrated that three brands of medical masks blocked over 99% viruses in aerosols.\nThe avian coronavirus was completely inactivated after being steamed for 5 minutes.\nTogether, this study suggested that medical masks are adequate for use on most social occasions, and both medical masks and N95 masks can be reused for a few days with steam decontamination between use.\nThis article is protected by copyright.\nAll rights reserved.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Wearing medical masks or N95 masks (namely N95 respirators) can slow the virus spread and reduce the infection risk.\", \"We also demonstrated that three brands of medical masks blocked over 99% viruses in aerosols.\", \"The avian coronavirus was completely inactivated after being steamed for 5 minutes.\", \"Together, this study suggested that medical masks are adequate for use on most social occasions, and both medical masks and N95 masks can be reused for a few days with steam decontamination between use.\"]}", "id": 876}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: It appears that the virus that causes COVID-19 can spread from people to animals in some situations.\n\nAbstract:\nBACKGROUND: The Australian and New Zealand governments both initiated strict social distancing measures in response to the COVID-19 pandemic in late March.\nIt remains difficult to quantify the impact this had in reducing the spread of the virus.\nMETHODS: Bayesian structural time series model provide a model to quantify the scenario in which these government-level interventions were not placed.\nOur models predict these strict social distancing measures caused a 79% and 61% reduction in the daily cases of COVID-19 across Australia and New Zealand respectively.\nCONCLUSION: This provides both evidence and impetus for governments considering similar measures in response to COVID-19 and other pandemics.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 877}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: there are few novel sars-cov-2 cases in malaria countries because of the use of the antimalarial drug hydroxychloroquine.\n\nAbstract:\nBackgrounds.\nSince COVID-19 outbreak, various agents have been tested but no proven effective therapies have been identified.\nThis has led to a lot of controversies among associated researches.\nHence, in order to address the issue of using hydroxychloroquine in treating COVID-19 patients, we conducted a systematic review and meta-analysis.\nMethods.\nA thorough search was carried out to find relevant studies in MEDLINE, medRxiv, PubMed, Cochrane Database, China Academic Journals Full-text Database and Web of Science.\nTwo investigators independently reviewed 274 abstracts and 23 articles.\nThe trials which evaluated hydroxychloroquine for treatment of COVID-19 were included for this systematic review.\nTwo investigators assessed quality of the studies and data extraction was done by one reviewer and cross checked by the other.\nResults.\nFive trials involving 677 patients were included while conducting the meta-analysis.\nCompared with the control group, hydroxychloroquine with or without azithromycin showed benefits in positive-to-negative conversion of SARS-CoV-2 (odds ratio [OR], 1.95 [95% CI,0.19 to 19.73] and a reduction in progression rate (OR, 0.89 [95% CI, 0.58 to 1.37]), but without demonstrating any statistical significance.\nThis systematic review has also suggested a possible synergistic effect of the combination therapy which included hydroxychloroquine and azithromycin.\nHowever, the use of hydroxychloroquine alone was associated with increased mortality in COVID-19 patients.\nConclusion.\nThe use of hydroxychloroquine with or without azithromycin for treatment of COVID-19 patients, seems to be effective.\nThe combination of hydroxychloroquine and azithromycin has shown synergic effects.\nHowever, mortality rate was increased when the treatment was conducted with hydroxychloroquine.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Since COVID-19 outbreak, various agents have been tested but no proven effective therapies have been identified.\", \"However, the use of hydroxychloroquine alone was associated with increased mortality in COVID-19 patients.\", \"However, mortality rate was increased when the treatment was conducted with hydroxychloroquine.\"]}", "id": 878}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Hidden immune weakness found in gravely ill COVID-19 patients.\n\nAbstract:\nThe outbreak of the 2019 Novel Coronavirus (SARS-CoV-2) rapidly spread from Wuhan, China to more than 150 countries, areas or territories, causing staggering number of infections and deaths.\nA systematic profiling of the immune vulnerability landscape of SARS-CoV-2, which can bring critical insights into the immune clearance mechanism, peptide vaccine development, and antiviral antibody development, is lacking.\nIn this study, we investigated the potential of the SARS-CoV-2 viral proteins to induce class I and II MHC presentation and to form linear antibody epitopes.\nWe created an online database to broadly share the predictions as a resource for the research community.\nUsing this resource, we showed that genetic variations in SARS- CoV-2, though still few for the moment, already follow the pattern of mutations in related coronaviruses, and could alter the immune vulnerability landscape of this virus.\nImportantly, we discovered evidence that SARS-CoV-2, along with related coronaviruses, used mutations to evade attack from the human immune system.\nOverall, we present an immunological resource for SARS-CoV-2 that could promote both therapeutic development and mechanistic research.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 879}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Can Nicotine Patches Help You Avoid COVID-19? The short answer is YES\n\nAbstract:\nImportance.\nCovid-19 infection has major international health and economic impacts and risk factors for infection are not completely understood.\nCannabis smoking is linked with poor respiratory health, immunosuppression and multiple contaminants.\nPotential synergism between the two epidemics would represent a major public health convergence.\nCigarettes were implicated with disease severity in Wuhan, China.\nObjective.\nIs cannabis use epidemiologically associated with coronavirus incidence rate (CVIR)?\nDesign.\nCross-sectional state-based multivariable study.\nSetting.\nUSA.\nPrimary and Secondary Outcome Measures.\nCVIR.\nMultivariable-adjusted geospatially-weighted regression models.\nAs the American cannabis epidemic is characterized by a recent doubling of daily cannabis use it was considered important to characterize the contribution of high intensity use.\nResults.\nSignificant associations of daily cannabis use quintile with CVIR were identified with the highest quintile having a prevalence ratio 5.11 (95%C.I. 4.90-5.33), an attributable fraction in the exposed (AFE) 80.45% (79.61-81.25%) and an attributable fraction in the population of 77.80% (76.88-78.68%) with Chi-squared-for-trend (14,782, df=4) significant at P<10-500.\nSimilarly when cannabis legalization was considered decriminalization was associated with an elevated CVIR prevalence ratio 4.51 (95%C.I. 4.45-4.58), AFE 77.84% (77.50-78.17%) and Chi-squared-for-trend (56,679, df=2) significant at P<10-500.\nMonthly and daily use were linked with CVIR in bivariate geospatial regression models (P=0.0027, P=0.0059).\nIn multivariable additive models number of flight origins and population density were significant.\nIn interactive geospatial models adjusted for international travel, ethnicity, income, population, population density and drug use, terms including last month cannabis were significant from P=7.3x10-15, daily cannabis use from P=7.3x10-11 and last month cannabis was independently associated (P=0.0365).\nConclusions and Relevance.\nData indicate CVIR demonstrates significant trends across cannabis use intensity quintiles and with relaxed cannabis legislation.\nRecent cannabis use is independently predictive of CVIR in bivariate and multivariable adjusted models and intensity of use is interactively significant.\nCannabis thus joins tobacco as a SARS2-CoV-2 risk factor.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Cannabis smoking is linked with poor respiratory health, immunosuppression and multiple contaminants.\", \"Potential synergism between the two epidemics would represent a major public health convergence.\", \"Cigarettes were implicated with disease severity in Wuhan, China.\", \"Data indicate CVIR demonstrates significant trends across cannabis use intensity quintiles and with relaxed cannabis legislation.\", \"Recent cannabis use is independently predictive of CVIR in bivariate and multivariable adjusted models and intensity of use is interactively significant.\", \"Cannabis thus joins tobacco as a SARS2-CoV-2 risk factor.\"]}", "id": 880}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: COVID-19: Hand sanitizers inactivate novel coronavirus\n\nAbstract:\nInfection by coronavirus (CoV-19) has led to emergence of a pandemic called as Coronavirus Disease (COVID-19) that has so far affected about 210 countries.\nThe dynamic data indicate that the pandemic by CoV-19 so far has infected 2,403,963 individuals, and among these 624,698 have recovered while, it has been fatal for 165,229.\nWithout much experience, currently, the medicines that are clinically being evaluated for COVID-19 include chloroquine, hydroxychloroquine, azithromycin, tocilizumab, lopinavir, ritonavir, tocilizumab and corticosteroids.\nTherefore, countries such as Italy, USA, Spain and France with the most advanced health care system are partially successful to control CoV-19 infection.\nIndia being the 2nd largest populous country, where, the healthcare system is underdeveloped, major portion of population follow unhygienic lifestyle, is able to restrict the rate of both infection and death of its citizens from COVID-19.\nIndia has followed an early and a very strict social distancing by lockdown and has issued advisory to clean hands regularly by soap and/or by alcohol based sterilizers.\nRolling data on the global index of the CoV infection is 13,306, and the index of some countries such as USA (66,148), Italy (175,055), Spain (210,126), France (83,363) and Switzerland (262,122) is high.\nThe index of India has remained very low (161) so far, mainly due to early implementation of social lockdown, social distancing, and sanitizing hands.\nHowever, articles on social lockdown as a preventive measure against COVID-19 in PubMed are scanty.\nIt has been observed that social lockdown has also drastic impacts on the environment especially on reduction of NO2 and CO2 emission.\nSlow infection rate under strict social distancing will offer time to researchers to come up with exact medicines/vaccines against CoV-19.\nTherefore, it is concluded that stringent social distancing via lockdown is highly important to control COVID-19 and also to contribute for self-regeneration of nature.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 881}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: As the novel coronavirus sweeps the globe, those with high blood pressure are at heightened risk for more severe complications should they contract Covid-19\n\nAbstract:\nHypertension is one of the most common comorbidities in patients with coronavirus disease 2019 (COVID-19).\nThis study aimed to clarify the impact of hypertension on COVID-19 and investigate whether the prior use of renin-angiotensin-aldosterone system (RAAS) inhibitors affects the prognosis of COVID-19.\nA total of 996 patients with COVID-19 were enrolled, including 282 patients with hypertension and 714 patients without hypertension.\nPropensity score-matched analysis (1:1 matching) was used to adjust the imbalanced baseline variables between the 2 groups.\nPatients with hypertension were further divided into the RAAS inhibitor group (n=41) and non-RAAS inhibitor group (n=241) according to their medication history.\nThe results showed that COVID-19 patients with hypertension had more severe secondary infections, cardiac and renal dysfunction, and depletion of CD8+ cells on admission.\nPatients with hypertension were more likely to have comorbidities and complications and were more likely to be classified as critically ill than those without hypertension.\nCox regression analysis revealed that hypertension (hazard ratio, 95% CI, unmatched cohort [1.80, 1.20-2.70]; matched cohort [2.24, 1.36-3.70]) was independently associated with all-cause mortality in patients with COVID-19.\nIn addition, hypertensive patients with a history of RAAS inhibitor treatment had lower levels of C-reactive protein and higher levels of CD4+ cells.\nThe mortality of patients in the RAAS inhibitor group (9.8% versus 26.1%) was significantly lower than that of patients in the non-RAAS inhibitor group.\nIn conclusion, hypertension may be an independent risk factor for all-cause mortality in patients with COVID-19.\nPatients who previously used RAAS inhibitors may have a better prognosis.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"The results showed that COVID-19 patients with hypertension had more severe secondary infections, cardiac and renal dysfunction, and depletion of CD8+ cells on admission.\", \"Patients with hypertension were more likely to have comorbidities and complications and were more likely to be classified as critically ill than those without hypertension.\", \"In conclusion, hypertension may be an independent risk factor for all-cause mortality in patients with COVID-19.\", \"Patients who previously used RAAS inhibitors may have a better prognosis.\"]}", "id": 882}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Iga dominates the enhanced neutralizing antibody response to sars-cov-2\n\nAbstract:\nHumoral immune responses are typically characterized by primary IgM antibody responses followed by secondary antibody responses associated with immune memory and comprised of of IgG, IgA and IgE. Here we measured acute humoral responses to SARS-CoV-2, including the frequency of antibody-secreting cells and the presence of SARS-CoV-2-specific neutralizing antibodies in the serum, saliva and broncho-alveolar fluid of 159 patients with COVID-19.\nEarly SARS-CoV-2-specific humoral responses were dominated by IgA antibodies.\nPeripheral expansion of IgA plasmablasts with mucosal-homing potential was detected shortly after the onset of symptoms and peaked during the third week of the disease.\nThe virus-specific antibody responses included IgG, IgM and IgA, but IgA contributed to virus neutralization to a greater extent compared with IgG. Specific IgA serum concentrations decreased notably one month after the onset of symptoms, but neutralizing IgA remained detectable in saliva for a longer time (days 49 to 73 post symptoms).\nThese results represent a critical observation given the emerging information as to the types of antibodies associated with optimal protection against re-infection, and whether vaccine regimens should consider targeting a potent but potentially short-lived IgA response.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Early SARS-CoV-2-specific humoral responses were dominated by IgA antibodies.\"]}", "id": 883}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: it appears that the virus that causes COVID-19 can spread from people to animals after close contact with people with COVID-19\n\nAbstract:\nAbstract Coronavirus disease-19 (COVID-19) caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is now a pandemic threat.\nThis virus is supposed to be spread by human to human transmission.\nCellular angiotensin converting enzyme 2 (ACE2) is the receptor of SARS-CoV-2 which is identical or similar in different species of animals such as pigs, ferrets, cats, orangutans, monkeys, and humans.\nMoreover, a recent study predicted that dog might be secondary host during the evolution of SARS-CoV-2 from bat to human.\nTherefore, there is a possibility of spreading SARS-CoV-2 through domestic pets.\nThere are now many reports of SARS-CoV-2 positive cases in dogs, cats, tigers, lion, and minks.\nExperimental data showed ferrets and cats are highly susceptible to SARS-CoV-2 as infected by virus inoculation and can transmit the virus directly or indirectly by droplets or airborne route.\nBased on these natural infection reports and experimental data, whether the pets are responsible for SARS-CoV-2 spread to human; needs to be deeply investigated.\nHumans showing clinical symptoms of respiratory infections have been undergoing for COVID-19 diagnostic test but many infected people and few pets confirmed with SARS-CoV-2 remained asymptomatic.\nIn this review, we summarize the natural cases of SARS-CoV-2 in animals with the latest researches conducted in this field.\nThis review will be helpful to think insights of SARS-CoV-2 transmissions, spread, and demand for sero-prevalence studies especially in companion animals.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Cellular angiotensin converting enzyme 2 (ACE2) is the receptor of SARS-CoV-2 which is identical or similar in different species of animals such as pigs, ferrets, cats, orangutans, monkeys, and humans.\", \"Moreover, a recent study predicted that dog might be secondary host during the evolution of SARS-CoV-2 from bat to human.\", \"Experimental data showed ferrets and cats are highly susceptible to SARS-CoV-2 as infected by virus inoculation and can transmit the virus directly or indirectly by droplets or airborne route.\"]}", "id": 884}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Vitamin B could help prevent the 'worst outcomes' in covid-19 cases\n\nAbstract:\nBACKGROUND The coronavirus disease 2019 (COVID-19) is a pandemic caused by coronavirus with mild to severe respiratory symptoms.\nThis paper aimed to investigate the effect of nutrients on the immune system and their possible roles in the prevention, treatment, and management of COVID-19 in adults.\nMETHODS This Systematic review was designed based on the guideline of the Preferred Reporting for Systematic Reviews (PRISMA).\nThe articles that focussed on nutrition, immune system, viral infection, and coronaviruses were collected by searching databases for both published papers and accepted manuscripts from 1990 to 2020.\nIrrelevant papers and articles without English abstract were excluded from the review process.\nRESULTS Some nutrients are actively involved in the proper functioning and strengthening of the human immune system against viral infections including dietary protein, omega-3 fatty acids, vitamin A, vitamin D, vitamin E, vitamin B1, vitamin B6, vitamin B12, vitamin C, iron, zinc, and selenium.\nFew studies were done on the effect of dietary components on prevention of COVID-19, but supplementation with these nutrients may be effective in improving the health status of patients with viral infections.\nCONCLUSION Following a balanced diet and supplementation with proper nutrients may play a vital role in prevention, treatment, and management of COVID-19.\nHowever, further clinical trials are needed to confirm these findings and presenting the strong recommendations against this pandemic.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"RESULTS Some nutrients are actively involved in the proper functioning and strengthening of the human immune system against viral infections including dietary protein, omega-3 fatty acids, vitamin A, vitamin D, vitamin E, vitamin B1, vitamin B6, vitamin B12, vitamin C, iron, zinc, and selenium.\", \"Few studies were done on the effect of dietary components on prevention of COVID-19, but supplementation with these nutrients may be effective in improving the health status of patients with viral infections.\"]}", "id": 885}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Further, the report enlightened on the use of vitamin D on COVID-19 patients\n\nAbstract:\nThe outbreak of COVID-19 has created a global public health crisis.\nLittle is known about the protective factors of this infection.\nTherefore, preventive health measures that can reduce the risk of infection, progression and severity are desperately needed.\nThis review discussed the possible roles of vitamin D in reducing the risk of COVID-19 and other acute respiratory tract infections and severity.\nMoreover, this study determined the correlation of vitamin D levels with COVID-19 cases and deaths in 20 European countries as of 20 May 2020.\nA significant negative correlation (p=0.033) has been observed between mean vitamin D levels and COVID-19 cases per one million population in European countries.\nHowever, the correlation of vitamin D with COVID-19 deaths of these countries was not significant.\nSome retrospective studies demonstrated a correlation between vitamin D status and COVID-19 severity and mortality, while other studies did not find the correlation when confounding variables are adjusted.\nSeveral studies demonstrated the role of vitamin D in reducing the risk of acute viral respiratory tract infections and pneumonia.\nThese include direct inhibition with viral replication or with anti-inflammatory or immunomodulatory ways.\nIn the meta-analysis, vitamin D supplementation has been shown as safe and effective against acute respiratory tract infections.\nThus, people who are at higher risk of vitamin D deficiency during this global pandemic should consider taking vitamin D supplements to maintain the circulating 25(OH)D in the optimal levels (75-125nmol/L).\nIn conclusion, there is not enough evidence on the association between vitamin D levels and COVID-19 severity and mortality.\nTherefore, randomized control trials and cohort studies are necessary to test this hypothesis.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"A significant negative correlation (p=0.033) has been observed between mean vitamin D levels and COVID-19 cases per one million population in European countries.\", \"Several studies demonstrated the role of vitamin D in reducing the risk of acute viral respiratory tract infections and pneumonia.\", \"These include direct inhibition with viral replication or with anti-inflammatory or immunomodulatory ways.\", \"In the meta-analysis, vitamin D supplementation has been shown as safe and effective against acute respiratory tract infections.\", \"Thus, people who are at higher risk of vitamin D deficiency during this global pandemic should consider taking vitamin D supplements to maintain the circulating 25(OH)D in the optimal levels (75-125nmol/L).\"]}", "id": 886}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Yes, 5G radiation causes Covid-19 \n\nAbstract:\nObjective@#To analyze application fields of 5G communication technology in Corona Virus Disease 2019 (COVID-19) epidemic prevention and control, and build a 5G intelligent medical service system for COVID-19 epidemic prevention and control in China.@*Methods@#We collected and analyzed 5G technology application cases used in the fight against COVID-19 from December 2019 to February 2020: 5G + telemedicine application cases, 5G + negative pressure ambulance cases, 5G + remote monitoring cases, 5G + artificial intelligence cases, 5G + infrared thermography temperature detection cases, 5G + big data analysis cases for epidemic prevention and control.@*Results@#Through the analysis of 5G application cases in COVID-19 epidemic prevention and control, we found out the key elements of 5G intelligent medical service system in COVID-19 epidemic prevention and control.\nBy optimizing and upgrading the internal service mode of the hospital, breaking the internal and external barriers, integrating internal and external resources, and strengthening 5G intelligent medical security, we can form a 5G intelligent medical service system for COVID-19 epidemic prevention and control, including application layer, technical service layer, network support layer and security system layer.@*Conclusion@#5G communication technology has the characteristics of faster speed, shorter time delay and denser capacity.\nIn COVID-19 epidemic prevention and control work, it can further improve the efficiency of doctors&apos; diagnosis, improve patients&apos; medical experience, realize the remote sharing of high-quality medical resources and real-time information exchange, effectively block the spread of epidemic, alleviate the shortage of medical resources and medical staff, and make the epidemic prevention and control more efficient.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"In COVID-19 epidemic prevention and control work, it can further improve the efficiency of doctors&apos; diagnosis, improve patients&apos; medical experience, realize the remote sharing of high-quality medical resources and real-time information exchange, effectively block the spread of epidemic, alleviate the shortage of medical resources and medical staff, and make the epidemic prevention and control more efficient.\"]}", "id": 887}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Ideally, try to maintain at least three feet of distance from others if you are in a community where COVID-19 is spreading. This can help prevent you from breathing in any liquid droplets containing the virus, which can be sprayed through the nose or mouth through coughing and sneezing. 3. Clean and disinfect surfaces\n\nAbstract:\nSince December 2019, a respiratory pandemic named as coronavirus disease 2019 (Covid-19) caused by a new coronavirus named as SARS-CoV-2, has taken the world by storm.\nThe symptoms are fever, malaise, and cough which resolve in a few days in most cases; but may progress to respiratory distress and organ failure.\nTransmission is through droplet infection or fomites, but other modes such as airborne transmission and oro-fecal transmission are also speculated.\nResearch is underway to develop effective vaccines and medicines for the disease.\nIn such a scenario, we present the measures described in Unani system of medicine for health protection during epidemics.\nUnani is a traditional system of medicine developed during the middle ages, which employs natural drugs of herbal, animal and mineral origin for treatment.\nIn Unani medicine, during an epidemic, apart from isolation and quarantine, three measures are of utmost importance, (i) purification of surroundings using certain herbal drugs as fumigants or sprays, (ii) health promotion and immune-modulation, and (iii) use of health-protecting drugs and symptom-specific drugs.\nDrugs such as loban (Styrax benzoides W. G. Craib), sandroos (Hymenaea verrucosa Gaertn.) za'fran (Crocus sativus L.), vinegar etc.\nare prescribed in various forms.\nScientific researches on these drugs reveal the presence of a number of pharmacologically active substances, which may provide a new insight into the management of infections and epidemics.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 888}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: the mechanism behind the protective effects of smoking could be found in nicotine\n\nAbstract:\nIntroduction Epidemiological and laboratory research seems to suggest that smoking and perhaps nicotine alone could reduce the severity of COVID-19.\nLikewise, there is some evidence that inhaled corticosteroids could also reduce its severity, opening the possibility that nicotine and inhaled steroids could be used as treatments.\nMethods In this prospective cohort study, we will link English general practice records from the QResearch database to Public Health England's database of SARS-CoV-2 positive tests, Hospital Episode Statistics, admission to intensive care units, and death from COVID-19 to identify our outcomes: hospitalisation, ICU admission, and death due to COVID.\nUsing Cox regression, we will perform sequential adjustment for potential confounders identified by separate directed acyclic graphs to: 1.\nAssess the association between smoking and COVID-19 disease severity, and how that changes on adjustment for smoking-related comorbidity.\n2. More closely characterise the association between smoking and severe COVID-19 disease by assessing whether the association is modified by age (as a proxy of length of smoking), gender, ethnic group, and whether people have asthma or COPD.\n3. Assess for evidence of a dose-response relation between smoking intensity and disease severity, which would help create a case for causality.\n4.\nExamine the association between former smokers who are using NRT or are vaping and disease severity.\n5. Examine whether pre-existing respiratory disease is associated with severe COVID-19 infection.\n6. Assess whether the association between chronic obstructive pulmonary disease (COPD) and asthma and COVID-19 disease severity is modified by age, gender, ethnicity, and smoking status.\n7. Assess whether the use of inhaled corticosteroids is associated with severity of COVID-19 disease.\n8. To assess whether the association between use of inhaled corticosteroids and severity of COVID-19 disease is modified by the number of other airways medications used (as a proxy for severity of condition) and whether people have asthma or COPD.\nConclusions This representative population sample will, to our knowledge, present the first comprehensive examination of the association between smoking, nicotine use without smoking, respiratory disease, and severity of COVID-19.\nWe will undertake several sensitivity analyses to examine the potential for bias in these associations.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Introduction Epidemiological and laboratory research seems to suggest that smoking and perhaps nicotine alone could reduce the severity of COVID-19.\"]}", "id": 889}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Will Garlic Water Cure Coronavirus? No\n\nAbstract:\nThe World Health Organization declared COVID-19 as a pandemic on the 11thof March 2020.\nSince then, many efforts are being carried out to contain the virus.\nKnowledge and attitude of people should be directed towards strict preventive practices in order to halt the spread of the virus.\nThe aim of the current cross-sectional study is to assess the knowledge, practice and attitude of university students from medical and non-medical colleges in Jordan using a structured questionnaire involving a total number of 592 students.\nA positive response regarding the overall knowledge about the symptoms of COVID-19 was observed in more than 90% of the students.\nIn response to the attitude and practice, a good number of students nearly 99.7% agreed that hand washing is necessary for prevention of infection whereas 68.4% believed that mask wearing would prevent the infection.\nAround 6-7% students considered the virus as a stigma hence would not visit hospital.\nAlso, around 10% students believed that their religious beliefs and body immunity might protect them from infection.\nMore dangerously, 20.6% and 19.2% students believed antibiotics and smoking to be a protective measure against the infection respectively.\nAlso, 96.8% do avoid hand shaking, 98.8% wash their hands and 93.3% use alcoholic rub, 95.8% cough or sneeze in a tissue and dispose it in waste bin, 51% will drink ginger with honey and 42.7% eat garlic for infection prevention.\nThe main sources of knowledge were social media, internet and television.\nNo significant difference was noticed between medical and non medical colleges.\nThus, there is a need for more detailed and directed measures and awareness campaigns to improve the knowledge, attitude and practice in some critical aspects to contain the virus.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Also, 96.8% do avoid hand shaking, 98.8% wash their hands and 93.3% use alcoholic rub, 95.8% cough or sneeze in a tissue and dispose it in waste bin, 51% will drink ginger with honey and 42.7% eat garlic for infection prevention.\"]}", "id": 890}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Sars-cov-2 infections and covid-19 mortalities not correlate with ace1 i/d genotype\n\nAbstract:\nSevere acute respiratory syndrome coronavirus-2 (SARS-CoV-2) causes coronavirus disease 2019 (COVID-19).\nThe relentless spread and pathogenicity of the virus have become a global public health emergency.\nOne of the striking features of this pandemic is the pronounced impact on specific regions and ethnic groups.\nIn particular, compared with East Asia, where the virus first emerged, SARS-CoV-2 has caused high rates of morbidity and mortality in Europe.\nThis has not been experienced in past global viral infections, such as influenza, severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS) and is unique to SARS-CoV-2.\nFor this reason, we investigated the involvement of genetic factors associated with SARS-CoV-2 infection with a focus on angiotensin-converting enzyme (ACE)-related genes, because ACE2 is a receptor for SARS-CoV-2.\nWe found that the ACE1 II genotype frequency in a population was significantly negatively correlated with the number of SARS-CoV-2 cases.\nSimilarly, the ACE1 II genotype was negatively correlated with the number of deaths due to SARS-CoV-2 infection.\nThese data suggest that the ACE1 II genotype may influence the prevalence and clinical outcome of COVID-19 and serve as a predictive marker for COVID-19 risk and severity.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"These data suggest that the ACE1 II genotype may influence the prevalence and clinical outcome of COVID-19 and serve as a predictive marker for COVID-19 risk and severity.\"]}", "id": 891}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Elevated calprotectin and abnormal myeloid cell subsets discriminate severe from mild covid-19\n\nAbstract:\nBlood myeloid cells are known to be dysregulated in coronavirus disease 2019 (COVID-19), caused by SARS-CoV-2.\nIt is unknown whether the innate myeloid response differs with disease severity and whether markers of innate immunity discriminate high-risk patients.\nThus, we performed high-dimensional flow cytometry and single-cell RNA sequencing of COVID-19 patient peripheral blood cells and detected disappearance of non-classical CD14LowCD16High monocytes, accumulation of HLA-DRLow classical monocytes (Human Leukocyte Antigen-DR isotype), and release of massive amounts of calprotectin (S100A8/S100A9) in severe cases.\nImmature CD10LowCD101-CXCR4+/-neutrophils with an immunosuppressive profile accumulated in the blood and lungs, suggesting emergency myelopoiesis.\nFinally, we show that calprotectin plasma level and a routine flow cytometry assay detecting decreased frequencies of non-classical monocytes could discriminate patients who develop a severe form of COVID-19, suggesting a predictive value that deserves prospective evaluation.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Finally, we show that calprotectin plasma level and a routine flow cytometry assay detecting decreased frequencies of non-classical monocytes could discriminate patients who develop a severe form of COVID-19, suggesting a predictive value that deserves prospective evaluation.\"]}", "id": 892}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: young peope are not at risk and do not die from covid-19\n\nAbstract:\nBackground: The coronavirus 2019 (COVID-19) pandemic has been spread-ing globally for months, yet the infection fatality ratio of the disease is still uncertain.\nThis is partly because of inconsistencies in testing and death reporting standards across countries.\nOur purpose is to provide accurate estimates which do not rely on testing and death count data directly but only use population level statistics.\nMethods: We collected demographic and death records data from the Italian Institute of Statistics.\nWe focus on the area in Italy that experienced the initial outbreak of COVID-19 and estimated a Bayesian model fitting age-stratified mortality data from 2020 and previous years.\nWe also assessed the sensitivity of our estimates to alternative assumptions on the proportion of population infected.\nFindings: We estimate an overall infection fatality rate of 1.29% (95% credible interval [CrI] 0.89 - 2.01), as well as large differences by age, with a low infection fatality rate of 0.05% for under 60 year old (CrI 0-.19) and a substantially higher 4.25% (CrI 3.01-6.39) for people above 60 years of age.\nIn our sensitivity analysis, we found that even under extreme assumptions, our method delivered useful information.\nFor instance, even if only 10% of the population were infected, the infection fatality rate would not rise above 0.2% for people under 60.\nInterpretation: Our empirical estimates based on population level data show a sharp difference in fatality rates between young and old people and firmly rule out overall fatality ratios below 0.5% in populations with more than 30% over 60 years old.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Findings: We estimate an overall infection fatality rate of 1.29% (95% credible interval [CrI] 0.89 - 2.01), as well as large differences by age, with a low infection fatality rate of 0.05% for under 60 year old (CrI 0-.19) and a substantially higher 4.25% (CrI 3.01-6.39) for people above 60 years of age.\"]}", "id": 893}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Hydroxychloroquine proves protection in hamsters and macaques infected with sars-cov-2\n\nAbstract:\nWe remain largely without effective prophylactic/therapeutic interventions for COVID-19.\nAlthough many human clinical trials are ongoing, there remains a deficiency of supportive preclinical drug efficacy studies.\nHere we assessed the prophylactic/therapeutic efficacy of hydroxychloroquine (HCQ), a drug of interest for COVID-19 management, in two animal models.\nWhen used for prophylaxis or treatment neither the standard human malaria dose (6.5 mg/kg) nor a high dose (50 mg/kg) of HCQ had any beneficial effect on clinical disease or SARS-CoV-2 kinetics (replication/shedding) in the Syrian hamster disease model.\nSimilarly, HCQ prophylaxis/treatment (6.5 mg/kg) did not significantly benefit clinical outcome nor reduce SARS-CoV-2 replication/shedding in the upper and lower respiratory tract in the rhesus macaque disease model.\nIn conclusion, our preclinical animal studies do not support the use of HCQ in prophylaxis/treatment of COVID-19.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Similarly, HCQ prophylaxis/treatment (6.5 mg/kg) did not significantly benefit clinical outcome nor reduce SARS-CoV-2 replication/shedding in the upper and lower respiratory tract in the rhesus macaque disease model.\"]}", "id": 894}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: The inhaled corticosteroid ciclesonide blocks coronavirus rna replication by targeting viral nsp15\n\nAbstract:\nSteroid compounds, which are expected to have dual functions in blocking host inflammation and MERS-CoV replication, were screened from a chemical library.\nWithin this library, ciclesonide, an inhaled corticosteroid, suppressed human coronavirus replication in cultured cells, but did not suppress replication of respiratory syncytial virus or influenza virus.\nThe effective concentration of ciclesonide to block SARS-CoV-2 (the cause of COVID-19) replication (EC90) was 6.3 \u03bcM. After the eleventh consecutive MERS-CoV passage in the presence of ciclesonide, a resistant mutation was generated, which resulted in an amino acid substitution (A25V) in nonstructural protein (NSP) 15, as identified using reverse genetics.\nA recombinant virus with the mutation was also resistant to ciclesonide suppression of viral replication.\nThese observations suggest that the effect of ciclesonide was specific to coronavirus, suggesting this is a candidate drug for treatment of patients suffering MERS or COVID-19.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Within this library, ciclesonide, an inhaled corticosteroid, suppressed human coronavirus replication in cultured cells, but did not suppress replication of respiratory syncytial virus or influenza virus.\", \"These observations suggest that the effect of ciclesonide was specific to coronavirus, suggesting this is a candidate drug for treatment of patients suffering MERS or COVID-19.\"]}", "id": 895}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Cats may be able to spread coronavirus to humans despite showing no symptoms\n\nAbstract:\nAbstract Coronavirus disease-19 (COVID-19) caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is now a pandemic threat.\nThis virus is supposed to be spread by human to human transmission.\nCellular angiotensin converting enzyme 2 (ACE2) is the receptor of SARS-CoV-2 which is identical or similar in different species of animals such as pigs, ferrets, cats, orangutans, monkeys, and humans.\nMoreover, a recent study predicted that dog might be secondary host during the evolution of SARS-CoV-2 from bat to human.\nTherefore, there is a possibility of spreading SARS-CoV-2 through domestic pets.\nThere are now many reports of SARS-CoV-2 positive cases in dogs, cats, tigers, lion, and minks.\nExperimental data showed ferrets and cats are highly susceptible to SARS-CoV-2 as infected by virus inoculation and can transmit the virus directly or indirectly by droplets or airborne route.\nBased on these natural infection reports and experimental data, whether the pets are responsible for SARS-CoV-2 spread to human; needs to be deeply investigated.\nHumans showing clinical symptoms of respiratory infections have been undergoing for COVID-19 diagnostic test but many infected people and few pets confirmed with SARS-CoV-2 remained asymptomatic.\nIn this review, we summarize the natural cases of SARS-CoV-2 in animals with the latest researches conducted in this field.\nThis review will be helpful to think insights of SARS-CoV-2 transmissions, spread, and demand for sero-prevalence studies especially in companion animals.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Cellular angiotensin converting enzyme 2 (ACE2) is the receptor of SARS-CoV-2 which is identical or similar in different species of animals such as pigs, ferrets, cats, orangutans, monkeys, and humans.\", \"Moreover, a recent study predicted that dog might be secondary host during the evolution of SARS-CoV-2 from bat to human.\", \"Therefore, there is a possibility of spreading SARS-CoV-2 through domestic pets.\", \"There are now many reports of SARS-CoV-2 positive cases in dogs, cats, tigers, lion, and minks.\", \"Experimental data showed ferrets and cats are highly susceptible to SARS-CoV-2 as infected by virus inoculation and can transmit the virus directly or indirectly by droplets or airborne route.\"]}", "id": 896}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: dexamethasone reduced death rates\n\nAbstract:\nSuccessful host defence against a pathogen can involve resistance or tolerance, with implications for prioritising either antimicrobial or immunomodulatory therapeutic approaches.\nHyper-inflammation occurs in Covid-19 and is associated with worse outcomes.\nThe efficacy of dexamethasone in preventing mortality in critical Covid-19 suggests that inflammation has a causal role in death.\nWhether this deleterious inflammation is primarily a direct response to the presence of SARS-CoV-2 requiring enhanced resistance, or an independent immunopathologic process necessitating enhanced tolerance, is unknown.\nHere we report an aberrant immune response in fatal Covid-19, principally involving the lung and reticuloendothelial system, that is not clearly topologically associated with the virus, indicating tissue-specific tolerance of SARS-CoV-2.\nWe found that inflammation and organ dysfunction in fatal Covid-19 did not map to the widespread tissue and cellular distribution of SARS-CoV-2 RNA and protein, both between and within tissues.\nA monocyte/myeloid-rich vasculitis was identified in the lung, along with an influx of macrophages/monocytes into the parenchyma.\nIn addition, stereotyped abnormal reticulo-endothelial responses (reactive plasmacytosis and iron-laden macrophages) were present and dissociated from the presence of virus in lymphoid tissues.\nOur results support virus-independent immunopathology being one of the primary mechanisms underlying fatal Covid-19.\nThis supports prioritising pathogen tolerance as a therapeutic strategy in Covid-19, by better understanding non-injurious organ-specific viral tolerance mechanisms and targeting aberrant macrophage and plasma cell responses.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"The efficacy of dexamethasone in preventing mortality in critical Covid-19 suggests that inflammation has a causal role in death.\"]}", "id": 897}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: The overall risk of dying in the hospital from COVID-19 among people with T1D is very low, however, one study found that this risk is higher for people with T1D (3.5 times higher) compared to people without diabetes.\n\nAbstract:\nAIMS: The 2019 novel coronavirus disease (COVID-19) emerged in Wuhan, China, and was characterized as a pandemic by the World Health Organization.\nDiabetes is an established risk associated with poor clinical outcomes, but the association of diabetes with COVID-19 has not been reported yet.\nMETHODS: In this cohort study, we retrospectively reviewed 258 consecutive hospitalized COVID-19 patients with or without diabetes at the West Court of Union Hospital in Wuhan, China, recruited from January 29 to February 12, 2020.\nThe clinical features, treatment strategies and prognosis data were collected and analyzed.\nPrognosis was followed up until March 12, 2020.\nRESULTS: Of the 258 hospitalized patients (63 with diabetes) with COVID-19, the median age was 64 years (range 23-91), and 138 (53.5%) were male.\nCommon symptoms included fever (82.2%), dry cough (67.1%), polypnea (48.1%), and fatigue (38%).\nPatients with diabetes had significantly higher leucocyte and neutrophil counts, and higher levels of fasting blood glucose, serum creatinine, urea nitrogen and creatine kinase isoenzyme MB at admission compared with those without diabetes.\nCOVID-19 patients with diabetes were more likely to develop severe or critical disease conditions with more complications, and had higher incidence rates of antibiotic therapy, non-invasive and invasive mechanical ventilation, and death (11.1% vs. 4.1%).\nCox proportional hazard model showed that diabetes (adjusted hazard ratio [aHR] = 3.64; 95% confidence interval [CI]: 1.09, 12.21) and fasting blood glucose (aHR = 1.19; 95% CI: 1.08, 1.31) were associated with the fatality due to COVID-19, adjusting for potential confounders.\nCONCLUSIONS: Diabetes mellitus is associated with increased disease severity and a higher risk of mortality in patients with COVID-19.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"COVID-19 patients with diabetes were more likely to develop severe or critical disease conditions with more complications, and had higher incidence rates of antibiotic therapy, non-invasive and invasive mechanical ventilation, and death (11.1% vs. 4.1%).\"]}", "id": 898}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Intradermal-delivered dna vaccine provides anamnestic protection in a rhesus macaque sars-cov-2 challenge model\n\nAbstract:\nCoronavirus disease 2019 (COVID-19), caused by the SARS-CoV-2 virus, has had a dramatic global impact on public health, social, and economic infrastructures.\nHere, we assess immunogenicity and anamnestic protective efficacy in rhesus macaques of the intradermal (ID)-delivered SARS-CoV-2 spike DNA vaccine, INO-4800.\nINO-4800 is an ID-delivered DNA vaccine currently being evaluated in clinical trials.\nVaccination with INO-4800 induced T cell responses and neutralizing antibody responses against both the D614 and G614 SARS-CoV-2 spike proteins.\nSeveral months after vaccination, animals were challenged with SARS-CoV-2 resulting in rapid recall of anti-SARS-CoV-2 spike protein T and B cell responses.\nThese responses were associated with lower viral loads in the lung and with faster nasal clearance of virus.\nThese studies support the immune impact of INO-4800 for inducing both humoral and cellular arms of the adaptive immune system which are likely important for providing durable protection against COVID-19 disease.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Here, we assess immunogenicity and anamnestic protective efficacy in rhesus macaques of the intradermal (ID)-delivered SARS-CoV-2 spike DNA vaccine, INO-4800.\", \"Vaccination with INO-4800 induced T cell responses and neutralizing antibody responses against both the D614 and G614 SARS-CoV-2 spike proteins.\", \"Several months after vaccination, animals were challenged with SARS-CoV-2 resulting in rapid recall of anti-SARS-CoV-2 spike protein T and B cell responses.\", \"These studies support the immune impact of INO-4800 for inducing both humoral and cellular arms of the adaptive immune system which are likely important for providing durable protection against COVID-19 disease.\"]}", "id": 899}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Tiny antibody component highly effective against SARS-COV-2 in animal studies\n\nAbstract:\nAIMS: A new human coronavirus (HCoV), which has been designated SARS-CoV-2, began spreading in December 2019 in Wuhan City, China causing pneumonia called COVID-19.\nThe spread of SARS-CoV-2 has been faster than any other coronaviruses that have succeeded in crossing the animal-human barrier.\nThere is concern that this new virus will spread around the world as did the previous two HCoVs-Severe Acute Respiratory Syndrome (SARS) and Middle East Respiratory Syndrome (MERS)-each of which caused approximately 800 deaths in the years 2002 and 2012, respectively.\nThus far, 11,268 deaths have been reported from the 258,842 confirmed infections in 168 countries.\nMAIN METHODS: In this study, the RNA-dependent RNA polymerase (RdRp) of the newly emerged coronavirus is modeled, validated, and then targeted using different anti-polymerase drugs currently on the market that have been approved for use against various viruses.\nKEY FINDINGS: The results suggest the effectiveness of Ribavirin, Remdesivir, Sofosbuvir, Galidesivir, and Tenofovir as potent drugs against SARS-CoV-2 since they tightly bind to its RdRp.\nIn addition, the results suggest guanosine derivative (IDX-184), Setrobuvir, and YAK as top seeds for antiviral treatments with high potential to fight the SARS-CoV-2 strain specifically.\nSIGNIFICANCE: The availability of FDA-approved anti-RdRp drugs can help treat patients and reduce the danger of the mysterious new viral infection COVID-19.\nThe drugs mentioned above can tightly bind to the RdRp of the SARS-CoV-2 strain and thus may be used to treat the disease.\nNo toxicity measurements are required for these drugs since they were previously tested prior to their approval by the FDA.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 900}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Furin cleavage of sars-cov-2 spike promotes but is not essential for infection and cell-cell fusion\n\nAbstract:\nSevere Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2) infects cells by binding to the host cell receptor ACE2 and undergoing virus-host membrane fusion.\nFusion is triggered by the protease TMPRSS2, which processes the viral Spike (S) protein to reveal the fusion peptide.\nSARS-CoV-2 has evolved a multibasic site at the S1-S2 boundary, which is thought to be cleaved by furin in order to prime S protein for TMPRSS2 processing.\nHere we show that CRISPR-Cas9 knockout of furin reduces, but does not prevent, the production of infectious SARS-CoV-2 virus.\nComparing S processing in furin knockout cells to multibasic site mutants reveals that while loss of furin substantially reduces S1-S2 cleavage it does not prevent it.\nSARS-CoV-2 S protein also mediates cell-cell fusion, potentially allowing virus to spread virion-independently.\nWe show that loss of furin in either donor or acceptor cells reduces, but does not prevent, TMPRSS2-dependent cell-cell fusion, unlike mutation of the multibasic site that completely prevents syncytia formation.\nOur results show that while furin promotes both SARS-CoV-2 infectivity and cell-cell spread it is not essential, suggesting furin inhibitors may reduce but not abolish viral spread.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Here we show that CRISPR-Cas9 knockout of furin reduces, but does not prevent, the production of infectious SARS-CoV-2 virus.\", \"Comparing S processing in furin knockout cells to multibasic site mutants reveals that while loss of furin substantially reduces S1-S2 cleavage it does not prevent it.\", \"We show that loss of furin in either donor or acceptor cells reduces, but does not prevent, TMPRSS2-dependent cell-cell fusion, unlike mutation of the multibasic site that completely prevents syncytia formation.\"]}", "id": 901}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: diabetes significantly increases coronavirus sufferers risk of dying\n\nAbstract:\nAIMS: To describe characteristics of COVID-19 patients with type 2 diabetes and to analyze risk factors for severity.\nMETHODS: Demographics, comorbidities, symptoms, laboratory findings, treatments and outcomes of COVID-19 patients with diabetes were collected and analyzed.\nRESULTS: Seventy-fourCOVID-19 patients with diabetes were included.\nTwenty-seven patients (36.5%) were severe and 10 patients (13.5%) died.\nHigher levels of blood glucose, serum amyloid A (SAA), C reactive protein and interleukin 6 were associated with severe patients compared to non-severe ones (P<0.05).\nLevels of albumin, cholesterol, high density lipoprotein, small and dense low density lipoprotein and CD4+T lymphocyte counts in severe patients were lower than those in non-severe patients (P<0.05).\nLogistic regression analysis identified decreased CD4+T lymphocyte counts (odds ratio [OR]=0.988, 95%Confidence interval [95%CI] 0.979-0.997) and increased SAA levels (OR=1.029, 95%CI 1.002-1.058) as risk factors for severity of COVID-19 with diabetes (P<0.05).\nCONCLUSIONS: Type 2 diabetic patients were more susceptible to COVID-19 than overall population, which might be associated with hyperglycemia and dyslipidemia.\nAggressive treatment should be suggested, especially when these patients had low CD4+T lymphocyte counts and high SAA levels.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 902}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Scientists are endeavoring to find antivirals specific to the virus. Several drugs such as chloroquine, arbidol, remdesivir, and favipiravir are currently undergoing clinical studies to test their efficacy and safety in the treatment of coronavirus disease 2019 (COVID-19) in China; some promising results have been achieved thus far.\n\nAbstract:\nHere we report on the most recent updates on experimental drugs successfully em- ployed in the treatment of the disease caused by SARS-CoV-2 coronavirus, also referred to as COVID-19 (COronaVIrus Disease 19).\nIn particular, several cases of recovered patients have been reported after being treated with lopinavir/ritonavir (which is widely used to treat human immunodeficiency virus (HIV) infection) in combination with the anti-flu drug oseltamivir.\nIn addition, remdesivir, which has been previously administered to Ebola virus patients, has also proven effective in the U.S. against coronavirus, while antimalarial chloroquine and hydroxy- chloroquine, favipiravir and co-administered darunavir and umifenovir (in patient therapies) were also recently recorded as having anti-SARS-CoV-2 effects.\nSince the recoveries/deaths ratio in the last weeks significantly increased, especially in China, it is clear that the experi- mental antiviral therapy, together with the availability of intensive care unit beds in hospitals and rigorous government control measures, all play an important role in dealing with this vi- rus.\nThis also stresses the urgent need for the scientific community to devote its efforts to the development of other more specific antiviral strategies.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"In addition, remdesivir, which has been previously administered to Ebola virus patients, has also proven effective in the U.S. against coronavirus, while antimalarial chloroquine and hydroxy- chloroquine, favipiravir and co-administered darunavir and umifenovir (in patient therapies) were also recently recorded as having anti-SARS-CoV-2 effects.\"]}", "id": 903}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: camostat mesylate cure coronavirus.\n\nAbstract:\nDifferent treatments are currently used for clinical management of SARS-CoV-2 infection, but little is known about their efficacy yet.\nHere we present ongoing results to compare currently available drugs for a variety of diseases to find out if they counteract SARS-CoV-2-induced cytopathic effect in vitro.\nOur goal is to prioritize antiviral activity to provide a solid evidence-driven rationale for forthcoming clinical trials.\nSince the most effective antiviral approaches are usually based on combined therapies that tackle the viral life cycle at different stages, we are also testing combinations of drugs that may be critical to reduce the emergence of resistant viruses.\nWe will provide results as soon as they become available, so data should be interpreted with caution, clearly understanding the limitations of the in vitro model, that may not always reflect what could happen in vivo.\nThus, our goal is to test the most active antivirals identified in adequate animal models infected with SARS-CoV-2, to add more information about possible in vivo efficacy.\nIn turn, successful antivirals could be tested in clinical trials as treatments for infected patients, but also as pre-exposure prophylaxis to avoid novel infections until an effective and safe vaccine is developed.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 904}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: the mechanism behind the protective effects of smoking could be found in nicotine\n\nAbstract:\nObjectives: To investigate whether there is a causal effect of cardiometabolic traits on risk of sepsis and severe covid-19.\nDesign: Mendelian randomisation analysis.\nSetting: UK Biobank and HUNT study population-based cohorts for risk of sepsis, and genome-wide association study summary data for risk of severe covid-19 with respiratory failure.\nParticipants: 12,455 sepsis cases (519,885 controls) and 1,610 severe covid-19 with respiratory failure cases (2,205 controls).\nExposure: Genetic variants that proxy body mass index (BMI), lipid traits, systolic blood pressure, lifetime smoking score, and type 2 diabetes liability - derived from studies considering between 188,577 to 898,130 participants.\nMain outcome measures: Risk of sepsis and severe covid-19 with respiratory failure.\nResults: Higher genetically proxied BMI and lifetime smoking score were associated with increased risk of sepsis in both UK Biobank (BMI: odds ratio 1.38 per standard deviation increase, 95% confidence interval [CI] 1.27 to 1.51; smoking: odds ratio 2.81 per standard deviation increase, 95% CI 2.09-3.79) and HUNT (BMI: 1.41, 95% CI 1.18 to 1.69; smoking: 1.93, 95% CI 1.02-3.64).\nHigher genetically proxied BMI and lifetime smoking score were also associated with increased risk of severe covid-19, although with wider confidence intervals (BMI: 1.75, 95% CI 1.20 to 2.57; smoking: 3.94, 95% CI 1.13 to 13.75).\nThere was limited evidence to support associations of genetically proxied lipid traits, systolic blood pressure or type 2 diabetes liability with risk of sepsis or severe covid-19.\nSimilar findings were generally obtained when using Mendelian randomization methods that are more robust to the inclusion of pleiotropic variants, although the precision of estimates was reduced.\nConclusions: Our findings support a causal effect of elevated BMI and smoking on risk of sepsis and severe covid-19.\nClinical and public health interventions targeting obesity and smoking are likely to reduce sepsis and covid-19 related morbidity, along with the plethora of other health-related outcomes that these traits adversely affect.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Results: Higher genetically proxied BMI and lifetime smoking score were associated with increased risk of sepsis in both UK Biobank (BMI: odds ratio 1.38 per standard deviation increase, 95% confidence interval [CI] 1.27 to 1.51; smoking: odds ratio 2.81 per standard deviation increase, 95% CI 2.09-3.79) and HUNT (BMI: 1.41, 95% CI 1.18 to 1.69; smoking: 1.93, 95% CI 1.02-3.64).\", \"Conclusions: Our findings support a causal effect of elevated BMI and smoking on risk of sepsis and severe covid-19.\"]}", "id": 905}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Exploratory analysis of immunization records highlights higher sars-cov-2 rates in individuals with recent non-covid-19 vaccinations\n\nAbstract:\nMultiple clinical studies are ongoing to assess whether existing vaccines may afford protection against SARS-CoV-2 infection through trained immunity.\nIn this exploratory study, we analyze immunization records from 137,037 individuals who received SARS-CoV-2 PCR tests.\nWe find that polio, Hemophilus influenzae type-B (HIB), measles-mumps-rubella (MMR), varicella, pneumococcal conjugate (PCV13), geriatric flu, and hepatitis A / hepatitis B (HepA-HepB) vaccines administered in the past 1, 2, and 5 years are associated with decreased SARS-CoV-2 infection rates, even after adjusting for geographic SARS-CoV-2 incidence and testing rates, demographics, comorbidities, and number of other vaccinations.\nFurthermore, age, race/ethnicity, and blood group stratified analyses reveal significantly lower SARS-CoV-2 rate among black individuals who have taken the PCV13 vaccine, with relative risk of 0.45 at the 5 year time horizon (n: 653, 95% CI: (0.32, 0.64), p-value: 6.9e-05).\nThese findings suggest that additional pre-clinical and clinical studies are warranted to assess the protective effects of existing non-COVID-19 vaccines and explore underlying immunologic mechanisms.\nWe note that the findings in this study are preliminary and are subject to change as more data becomes available and as further analysis is conducted.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"We find that polio, Hemophilus influenzae type-B (HIB), measles-mumps-rubella (MMR), varicella, pneumococcal conjugate (PCV13), geriatric flu, and hepatitis A / hepatitis B (HepA-HepB) vaccines administered in the past 1, 2, and 5 years are associated with decreased SARS-CoV-2 infection rates, even after adjusting for geographic SARS-CoV-2 incidence and testing rates, demographics, comorbidities, and number of other vaccinations.\", \"Furthermore, age, race/ethnicity, and blood group stratified analyses reveal significantly lower SARS-CoV-2 rate among black individuals who have taken the PCV13 vaccine, with relative risk of 0.45 at the 5 year time horizon (n: 653, 95% CI: (0.32, 0.64), p-value: 6.9e-05).\", \"These findings suggest that additional pre-clinical and clinical studies are warranted to assess the protective effects of existing non-COVID-19 vaccines and explore underlying immunologic mechanisms.\"]}", "id": 906}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: 4 x-linked agammaglobulinemia patients develop pneumonia as covid-19 manifestation but recover\n\nAbstract:\nBACKGROUND: The recent SARS-CoV-2 pandemic, which has recently affected Italy since February 21, constitutes a threat to normal subjects, as the coronavirus disease-19 (COVID-19) can manifest with a broad spectrum of clinical phenotypes ranging from asymptomatic cases to pneumonia or even death.\nThere is evidence that older age and several comorbidities can affect the risk to develop severe pneumonia and possibly the need of mechanic ventilation in subjects infected with SARS-CoV-2.\nTherefore, we evaluated the outcome of SARS-CoV-2 infection in patients with inborn errors of immunity (IEI) such as X-linked agammaglobulinemia (XLA).\nMETHODS: When the SARS-CoV-2 epidemic has reached Italy, we have activated a surveillance protocol of patients with IEI, to perform SARS-CoV-2 search by nasopharyngeal swab in patients presenting with symptoms that could be a manifestation of COVID-19, such as fever, cough, diarrhea, or vomiting.\nRESULTS: We describe two patients with X-linked agammaglobulinemia (XLA) aged 34 and 26 years with complete absence of B cells from peripheral blood who developed COVID-19, as diagnosed by SARS-CoV-2 detection by nasopharyngeal swab, while receiving immunoglobulin infusions.\nBoth patients developed interstitial pneumonia characterized by fever, cough, and anorexia and associated with elevation of CRP and ferritin, but have never required oxygen ventilation or intensive care.\nCONCLUSION: Our report suggests that XLA patients might present with high risk to develop pneumonia after SARS-CoV-2 infection, but can recover from infection, suggesting that B-cell response might be important, but is not strictly required to overcome the disease.\nHowever, there is a need for larger observational studies to extend these conclusions to other patients with similar genetic immune defects.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Therefore, we evaluated the outcome of SARS-CoV-2 infection in patients with inborn errors of immunity (IEI) such as X-linked agammaglobulinemia (XLA).\"]}", "id": 907}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Coronaviruses cause respiratory illnesses, so the lungs are usually affected first. Early symptoms include fever, cough, and shortness of breath.\n\nAbstract:\nSeveral related human coronaviruses (HCoVs) are endemic in the human population, causing mild respiratory infections1.\nSevere Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), the etiologic agent of Coronavirus disease 2019 (COVID-19), is a recent zoonotic infection that has quickly reached pandemic spread2,3.\nZoonotic introduction of novel coronaviruses is thought to occur in the absence of pre-existing immunity in the target human population.\nUsing diverse assays for detection of antibodies reactive with the SARS-CoV-2 Spike (S) glycoprotein, we demonstrate the presence of pre-existing immunity in uninfected and unexposed humans to the new coronavirus.\nSARS-CoV-2 S-reactive antibodies, exclusively of the IgG class, were readily detectable by a sensitive flow cytometry-based method in SARS-CoV-2-uninfected individuals with recent HCoV infection and targeted the S2 subunit.\nIn contrast, SARS-CoV-2 infection induced higher titres of SARS-CoV-2 S-reactive IgG antibodies, as well as concomitant IgM and IgA antibodies throughout the observation period of 6 weeks since symptoms onset.\nHCoV patient sera also variably reacted with SARS-CoV-2 S and nucleocapsid (N), but not with the S1 subunit or the receptor binding domain (RBD) of S on standard enzyme immunoassays.\nNotably, HCoV patient sera exhibited specific neutralising activity against SARS-CoV-2 S pseudotypes, according to levels of SARS-CoV-2 S-binding IgG and with efficiencies comparable to those of COVID-19 patient sera.\nDistinguishing pre-existing and de novo antibody responses to SARS-CoV-2 will be critical for serology, seroprevalence and vaccine studies, as well as for our understanding of susceptibility to and natural course of SARS-CoV-2 infection.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 908}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Mortality in sars-cov-2 viral rna identified in wastewater 48 hours before covid-19 clinical tests and 96 hours before hospitalizations\n\nAbstract:\nCurtailing the Spring 2020 COVID-19 surge required sweeping and stringent interventions by governments across the world.\nWastewater-based COVID-19 epidemiology programs have been initiated in many countries to provide public health agencies with a complementary disease tracking metric and facile surveillance tool.\nHowever, their efficacy in prospectively capturing resurgence following a period of low prevalence is unclear.\nIn this study, the SARS-CoV-2 viral signal was measured in primary clarified sludge harvested every two days at the City of Ottawa's water resource recovery facility during the summer of 2020, when clinical testing recorded daily percent positivity below 1%.\nIn late July, increases of >400% in normalized SARS-CoV-2 RNA signal in wastewater were identified 48 hours prior to reported >300% increases in positive cases that were retrospectively attributed to community-acquired infections.\nDuring this resurgence period, SARS-CoV-2 RNA signal in wastewater preceded the reported >160% increase in community hospitalizations by approximately 96 hours.\nThis study supports wastewater-based COVID-19 surveillance of populations in augmenting the efficacy of diagnostic testing, which can suffer from sampling biases or timely reporting as in the case of hospitalization census.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"In late July, increases of >400% in normalized SARS-CoV-2 RNA signal in wastewater were identified 48 hours prior to reported >300% increases in positive cases that were retrospectively attributed to community-acquired infections.\", \"During this resurgence period, SARS-CoV-2 RNA signal in wastewater preceded the reported >160% increase in community hospitalizations by approximately 96 hours.\"]}", "id": 909}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Get this checked by your doctor and use the correct dose to stay healthy. Get this checked by your doctor and use the correct dose to stay healthy.\n\nAbstract:\nThe outbreak of COVID-19 has created a global public health crisis.\nLittle is known about the protective factors of this infection.\nTherefore, preventive health measures that can reduce the risk of infection, progression and severity are desperately needed.\nThis review discussed the possible roles of vitamin D in reducing the risk of COVID-19 and other acute respiratory tract infections and severity.\nMoreover, this study determined the correlation of vitamin D levels with COVID-19 cases and deaths in 20 European countries as of 20 May 2020.\nA significant negative correlation (p=0.033) has been observed between mean vitamin D levels and COVID-19 cases per one million population in European countries.\nHowever, the correlation of vitamin D with COVID-19 deaths of these countries was not significant.\nSome retrospective studies demonstrated a correlation between vitamin D status and COVID-19 severity and mortality, while other studies did not find the correlation when confounding variables are adjusted.\nSeveral studies demonstrated the role of vitamin D in reducing the risk of acute viral respiratory tract infections and pneumonia.\nThese include direct inhibition with viral replication or with anti-inflammatory or immunomodulatory ways.\nIn the meta-analysis, vitamin D supplementation has been shown as safe and effective against acute respiratory tract infections.\nThus, people who are at higher risk of vitamin D deficiency during this global pandemic should consider taking vitamin D supplements to maintain the circulating 25(OH)D in the optimal levels (75-125nmol/L).\nIn conclusion, there is not enough evidence on the association between vitamin D levels and COVID-19 severity and mortality.\nTherefore, randomized control trials and cohort studies are necessary to test this hypothesis.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 910}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: a small number of pets worldwide, including cats and dogs, can be infected with the virus that causes COVID-19, mostly after close contact with people with COVID-19.\n\nAbstract:\nBACKGROUND: The Australian and New Zealand governments both initiated strict social distancing measures in response to the COVID-19 pandemic in late March.\nIt remains difficult to quantify the impact this had in reducing the spread of the virus.\nMETHODS: Bayesian structural time series model provide a model to quantify the scenario in which these government-level interventions were not placed.\nOur models predict these strict social distancing measures caused a 79% and 61% reduction in the daily cases of COVID-19 across Australia and New Zealand respectively.\nCONCLUSION: This provides both evidence and impetus for governments considering similar measures in response to COVID-19 and other pandemics.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 911}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Ferrets can catch the coronavirus and might give it to other ferrets. But poultry and pigs don't appear to be at risk.\n\nAbstract:\nSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which originated in Wuhan, China, in 2019, is responsible for the COVID-19 pandemic.\nIt is now accepted that the wild fauna, probably bats, constitute the initial reservoir of the virus, but little is known about the role pets can play in the spread of the disease in human communities, knowing the ability of SARS-CoV-2 to infect some domestic animals.\nWe tested 21 domestic pets (9 cats and 12 dogs) living in close contact with their owners (belonging to a veterinary community of 20 students) in which two students tested positive for COVID-19 and several others (n = 11/18) consecutively showed clinical signs (fever, cough, anosmia, etc.) compatible with COVID-19 infection.\nAlthough a few pets presented many clinical signs indicative for a coronavirus infection, no animal tested positive for SARS-CoV-2 by RT-PCR and no antibodies against SARS-CoV-2 were detectable in their blood using an immunoprecipitation assay.\nThese original data can serve a better evaluation of the host range of SARS-CoV-2 in natural environment exposure conditions.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Although a few pets presented many clinical signs indicative for a coronavirus infection, no animal tested positive for SARS-CoV-2 by RT-PCR and no antibodies against SARS-CoV-2 were detectable in their blood using an immunoprecipitation assay.\"]}", "id": 912}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: The d614g mutation in the sars-cov2 spike protein increases infectivity in an ace2 receptor dependent manner\n\nAbstract:\nThe SARS-CoV2 coronavirus responsible for the current COVID19 pandemic has been reported to have a relatively low mutation rate.\nNevertheless, a few prevalent variants have arisen that give the appearance of undergoing positive selection as they are becoming increasingly widespread over time.\nMost prominent among these is the D614G amino acid substitution in the SARS-CoV2 Spike protein, which mediates viral entry.\nThe D614G substitution, however, is in linkage disequilibrium with the ORF1b P314L mutation where both mutations almost invariably co-occur, making functional inferences problematic.\nIn addition, the possibility of repeated new introductions of the mutant strain does not allow one to distinguish between a founder effect and an intrinsic genetic property of the virus.\nHere, we synthesized and expressed the WT and D614G variant SARS-Cov2 Spike protein, and report that using a SARS-CoV2 Spike protein pseudotyped lentiviral vector we observe that the D614G variant Spike has >1/2 log(10) increased infectivity in human cells expressing the human ACE2 protein as the viral receptor.\nThe increased binding/fusion activity of the D614G Spike protein was corroborated in a cell fusion assay using Spike and ACE2 proteins expressed in different cells.\nThese results are consistent with the possibility that the Spike D614G mutant increases the infectivity of SARS-CoV2.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Most prominent among these is the D614G amino acid substitution in the SARS-CoV2 Spike protein, which mediates viral entry.\", \"The increased binding/fusion activity of the D614G Spike protein was corroborated in a cell fusion assay using Spike and ACE2 proteins expressed in different cells.\", \"These results are consistent with the possibility that the Spike D614G mutant increases the infectivity of SARS-CoV2.\"]}", "id": 913}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: The new coronavirus can infect your skin\n\nAbstract:\nThe novel coronavirus disease 2019 (COVID-19), caused by Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2), first appeared in December 2019, in Wuhan, China and evolved into a pandemic.\nAs Angiotensin-Converting Enzyme 2 (ACE2) is one of the potential target receptors for SARS-CoV-2 in human body, which is expressed in different tissues, multiple organs might become affected.\nIn the initial phase of the current pandemic, a handful of post-mortem case-series revealed COVID-19-related pathological changes in various organs.\nAlthough pathological examination is not a feasible method of diagnosis, it can elucidate pathological changes, pathogenesis of the disease, and the cause of death in COVID-19 cases.\nHerein, we thoroughly reviewed multiple organs including lung, gastrointestinal tract, liver, kidney, skin, heart, blood, spleen, lymph nodes, brain, blood vessels, and placenta in terms of COVID-19-related pathological alterations.\nAlso, these findings were compared with SARS and MERS infection, wherever applicable.\nWe found a diverse range of pathological changes, some of which resemble those found in SARS and MERS.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Herein, we thoroughly reviewed multiple organs including lung, gastrointestinal tract, liver, kidney, skin, heart, blood, spleen, lymph nodes, brain, blood vessels, and placenta in terms of COVID-19-related pathological alterations.\"]}", "id": 914}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: A report indicates that Acetaminophen (Tylenol) may be preferred over Ibuprofen (Advil) for coronavirus (fever)\n\nAbstract:\nBACKGROUND AND AIMS: Multiple issues in management of COVID have emerged, but confusion persists regarding rational interpretation.\nAim of this brief review is to review these issues based on current literature.\nMETHODS: This is a narrative review with Pubmed and Google Scholar search till 23 March 2020.\nSearch terms were, COVID-19, treatment of coronavirus, COVID 19 and following terms; chloroquine, hydroxychloroquine, ibuprofen, ACE-inhibitors or angiotensin receptor blockers, cardiovascular disease, diarrhoea, liver, testis and gastrointestinal disease.\nRESULTS: We discuss evidence regarding role of chloroquine and hydroxychloroquine in treatment and prophylaxis, use of inhibitors of the renin angiotensin system, safety of ibuprofen, unusual clinical features like gastrointestinal symptoms and interpretation of tests for cardiac enzymes and biomarkers.\nCONCLUSIONS: While our conclusions on management of COVID-19 patients with co-morbidities are based on current evidence, however, data is limited and there is immediate need for fast track research.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"RESULTS: We discuss evidence regarding role of chloroquine and hydroxychloroquine in treatment and prophylaxis, use of inhibitors of the renin angiotensin system, safety of ibuprofen, unusual clinical features like gastrointestinal symptoms and interpretation of tests for cardiac enzymes and biomarkers.\"]}", "id": 915}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Tiny antibody component highly effective against SARS-COV-2 in animal studies\n\nAbstract:\nSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative representative of a severe respiratory illness resulted in widespread human infections and deaths in nearly all of the countries since late 2019.\nThere is no therapeutic FDA-approved drug against SARS-CoV-2 infection, although a combination of anti-viral drugs is directly being practiced in some countries.\nA broad-spectrum of antiviral agents are being currently evaluated in clinical trials, and in this review, we specifically focus on the application of Remdesivir (RVD) as a potential anti-viral compound against Middle East respiratory syndrome (MERS) -CoV, SARS-CoV and SARS-CoV-2.\nFirst, we overview the general information about SARS-CoV-2, followed by application of RDV as a nucleotide analogue which can potentially inhibits RNA-dependent RNA polymerase of COVs.\nAfterwards, we discussed the kinetics of SARS- or MERS-CoV proliferation in animal models which is significantly different compared to that in humans.\nFinally, some ongoing challenges and future perspective on the application of RDV either alone or in combination with other anti-viral agents against CoVs infection were surveyed to determine the efficiency of RDV in preclinical trials.\nAs a result, this paper provides crucial evidence of the potency of RDV to prevent SARS-CoV-2 infections.\nCommunicated by Ramaswamy H. Sarma.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 916}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: The new coronavirus can damage the lungs, heart and brain, which increases the risk of long-term health problems.\n\nAbstract:\nImportance: Increasing numbers of confirmed cases and mortality rates of coronavirus disease 2019 (COVID-19) are occurring in several countries and continents.\nInformation regarding the impact of cardiovascular complication on fatal outcome is scarce.\nObjective: To evaluate the association of underlying cardiovascular disease (CVD) and myocardial injury with fatal outcomes in patients with COVID-19.\nDesign, Setting, and Participants: This retrospective single-center case series analyzed patients with COVID-19 at the Seventh Hospital of Wuhan City, China, from January 23, 2020, to February 23, 2020.\nAnalysis began February 25, 2020.\nMain Outcomes and Measures: Demographic data, laboratory findings, comorbidities, and treatments were collected and analyzed in patients with and without elevation of troponin T (TnT) levels.\nResult: Among 187 patients with confirmed COVID-19, 144 patients (77%) were discharged and 43 patients (23%) died.\nThe mean (SD) age was 58.50 (14.66) years.\nOverall, 66 (35.3%) had underlying CVD including hypertension, coronary heart disease, and cardiomyopathy, and 52 (27.8%) exhibited myocardial injury as indicated by elevated TnT levels.\nThe mortality during hospitalization was 7.62% (8 of 105) for patients without underlying CVD and normal TnT levels, 13.33% (4 of 30) for those with underlying CVD and normal TnT levels, 37.50% (6 of 16) for those without underlying CVD but elevated TnT levels, and 69.44% (25 of 36) for those with underlying CVD and elevated TnTs.\nPatients with underlying CVD were more likely to exhibit elevation of TnT levels compared with the patients without CVD (36 [54.5%] vs 16 [13.2%]).\nPlasma TnT levels demonstrated a high and significantly positive linear correlation with plasma high-sensitivity C-reactive protein levels (\u00df = 0.530, P < .001) and N-terminal pro-brain natriuretic peptide (NT-proBNP) levels (\u00df = 0.613, P < .001).\nPlasma TnT and NT-proBNP levels during hospitalization (median [interquartile range (IQR)], 0.307 [0.094-0.600]; 1902.00 [728.35-8100.00]) and impending death (median [IQR], 0.141 [0.058-0.860]; 5375 [1179.50-25695.25]) increased significantly compared with admission values (median [IQR], 0.0355 [0.015-0.102]; 796.90 [401.93-1742.25]) in patients who died (P = .001; P < .001), while no significant dynamic changes of TnT (median [IQR], 0.010 [0.007-0.019]; 0.013 [0.007-0.022]; 0.011 [0.007-0.016]) and NT-proBNP (median [IQR], 352.20 [174.70-636.70]; 433.80 [155.80-1272.60]; 145.40 [63.4-526.50]) was observed in survivors (P = .96; P = .16).\nDuring hospitalization, patients with elevated TnT levels had more frequent malignant arrhythmias, and the use of glucocorticoid therapy (37 [71.2%] vs 69 [51.1%]) and mechanical ventilation (41 [59.6%] vs 14 [10.4%]) were higher compared with patients with normal TnT levels.\nThe mortality rates of patients with and without use of angiotensin-converting enzyme inhibitors/angiotensin receptor blockers was 36.8% (7 of 19) and 25.6% (43 of 168).\nConclusions and Relevance: Myocardial injury is significantly associated with fatal outcome of COVID-19, while the prognosis of patients with underlying CVD but without myocardial injury is relatively favorable.\nMyocardial injury is associated with cardiac dysfunction and arrhythmias.\nInflammation may be a potential mechanism for myocardial injury.\nAggressive treatment may be considered for patients at high risk of myocardial injury.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 917}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: cloth face mask with a filter can help prevent the spread of COVID-19\n\nAbstract:\nThe Coronavirus Disease 2019 (COVID-19) has swept the whole world with high mortality.\nSince droplet transmission is the main route of transmission, wearing a mask serves as a crucial preventive measure.\nHowever, the virus has spread quite quickly, causing severe mask shortage.\nFinding alternative materials for homemade masks while ensuring the significant performance indicators will help alleviate the shortage of masks.\nReferring to the national standard for the \"Surgical Mask\" of China, 17 materials to be selected for homemade masks were tested in four key indicators: pressure difference, particle filtration efficiency, bacterial filtration efficiency and resistance to surface wetting.\nEleven single-layer materials met the standard of pressure difference ([\u2264]49 Pa), of which 3 met the standard of resistance to surface wetting ([\u2265]3), 1 met the standard of particle filtration efficiency ([\u2265]30%), but none met the standard of bacterial filtration efficiency ([\u2265]95%).\nBased on the testing results of single-layer materials, fifteen combinations of paired materials were tested.\nThe results showed that three double-layer materials including double-layer medical non-woven fabric, medical non-woven fabric plus non-woven shopping bag, and medical non-woven fabric plus granular tea towel could meet all the standards of pressure difference, particle filtration efficiency, and resistance to surface wetting, and were close to the standard of the bacterial filtration efficiency.\nIn conclusion, if resources are severely lacking and medical masks cannot be obtained, homemade masks using available materials, based on the results of this study, can minimize the chance of infection to the maximum extent.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Since droplet transmission is the main route of transmission, wearing a mask serves as a crucial preventive measure.\"]}", "id": 918}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Salt-coated masks achieve reduced viral deactivation rate\n\nAbstract:\nAerosolized pathogens are a leading cause of respiratory infection and transmission.\nCurrently used protective measures pose potential risk of primary/secondary infection and transmission.\nHere, we report the development of a universal, reusable virus deactivation system by functionalization of the main fibrous filtration unit of surgical mask with sodium chloride salt.\nThe salt coating on the fiber surface dissolves upon exposure to virus aerosols and recrystallizes during drying, destroying the pathogens.\nWhen tested with tightly sealed sides, salt-coated filters showed remarkably higher filtration efficiency than conventional mask filtration layer, and 100% survival rate was observed in mice infected with virus penetrated through salt-coated filters.\nViruses captured on salt-coated filters exhibited rapid infectivity loss compared to gradual decrease on bare filters.\nSalt-coated filters proved highly effective in deactivating influenza viruses regardless of subtypes and following storage in harsh environmental conditions.\nOur results can be applied in obtaining a broad-spectrum, airborne pathogen prevention device in preparation for epidemic and pandemic of respiratory diseases.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Here, we report the development of a universal, reusable virus deactivation system by functionalization of the main fibrous filtration unit of surgical mask with sodium chloride salt.\", \"Salt-coated filters proved highly effective in deactivating influenza viruses regardless of subtypes and following storage in harsh environmental conditions.\"]}", "id": 919}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: De novo design of ace2 protein decoys to neutralize sars-cov-2\n\nAbstract:\nThere is an urgent need for the ability to rapidly develop effective countermeasures for emerging biological threats, such as the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that causes the ongoing coronavirus disease 2019 (COVID-19) pandemic.\nWe have developed a generalized computational design strategy to rapidly engineer de novo proteins that precisely recapitulate the protein surface targeted by biological agents, like viruses, to gain entry into cells.\nThe designed proteins act as decoys that block cellular entry and aim to be resilient to viral mutational escape.\nUsing our novel platform, in less than ten weeks, we engineered, validated, and optimized de novo protein decoys of human angiotensin-converting enzyme 2 (hACE2), the membrane-associated protein that SARS-CoV-2 exploits to infect cells.\nOur optimized designs are hyperstable de novo proteins (\u223c18-37 kDa), have high affinity for the SARS-CoV-2 receptor binding domain (RBD) and can potently inhibit the virus infection and replication in vitro.\nFuture refinements to our strategy can enable the rapid development of other therapeutic de novo protein decoys, not limited to neutralizing viruses, but to combat any agent that explicitly interacts with cell surface proteins to cause disease.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"The designed proteins act as decoys that block cellular entry and aim to be resilient to viral mutational escape.\", \"Future refinements to our strategy can enable the rapid development of other therapeutic de novo protein decoys, not limited to neutralizing viruses, but to combat any agent that explicitly interacts with cell surface proteins to cause disease.\"]}", "id": 920}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: A headache is a potential symptom of COVID-19.\n\nAbstract:\nOBJECTIVE To analyze the epidemiological characteristics and clinical features of the patients with coronavirus disease 2019 (COVID-19), so as to provide basis for clinical diagnosis.\nMETHODS The epidemiology, clinical symptoms, laboratory and radiologic data of 23 patients with COVID-19 admitted to the Fifth People's Hospital of Xinyang City from January 22nd to January 29th, 2020 were retrospectively analyzed.\nRESULTS There was 23 patients with COVID-19, with 15 men and 8 women, and the median age was 46.0 (40.5, 52.0) years old (ranged from 27 years old to 80 years old).\nNine patients had basic disease (39.1%), including hypertension (17.4%), cardiovascular diseases (17.4%), diabetes (8.7%), hypothyroidism (4.3%) and past history of tuberculosis (4.3%).\nAll the 23 patients had contact history in Wuhan area or with confirmed cases.\nClinical symptoms included fever (100%), cough (69.6%), expectoration (43.5%), myalgia (26.1%), headache (17.4%) and dyspnea (17.4%), and the less common symptom was diarrhea (4.3%).\nBlood routine tests showed leukocytopenia in 11 patients (47.8%), normal leukocyte counts in 10 patients (43.5%), and leukocytosis in 2 patients (8.7%); lymphopenia was found in 13 patients (56.5%).\nAll 23 patients had different degrees of infective lesions in chest CT, with 7 patients (30.4%) on one side and 16 patients (69.6%) on both sides.\nThere were 19 mild patients, 4 severe patients, and no critical or death case.\nComplications included acute respiratory distress syndrome (17.4%).\nNo patient was reported with liver, kidney or heart dysfunction or secondary infection.\nCONCLUSIONS Epidemic history of contact, fever, pneumonia signs of chest CT, normal or decreased count of leukocyte and lymphopenia are the clinical basis for diagnosis of COVID-19.\nHowever, at present, the treatment of patients has not been completed, and the effective treatment strategy and final prognosis are unclear.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Clinical symptoms included fever (100%), cough (69.6%), expectoration (43.5%), myalgia (26.1%), headache (17.4%) and dyspnea (17.4%), and the less common symptom was diarrhea (4.3%).\"]}", "id": 921}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Enjoy ginger, but it's not a 'cure' for COVID-19\n\nAbstract:\nIn late December 2019 in Wuhan, China, several patients with viral pneumonia were identified as 2019 novel coronavirus (2019-nCoV).\nSo far, there are no specific treatments for patients with coronavirus disease-19 (COVID-19), and the treatments available today are based on previous experience with similar viruses such as severe acute respiratory syndrome-related coronavirus (SARS-CoV), Middle East respiratory syndrome coronavirus (MERS-CoV), and Influenza virus.\nIn this article, we have tried to reach a therapeutic window of drugs available to patients with COVID-19.\nCathepsin L is required for entry of the 2019-nCoV virus into the cell as target teicoplanin inhibits virus replication.\nAngiotensin-converting-enzyme 2 (ACE2) in soluble form as a recombinant protein can prevent the spread of coronavirus by restricting binding and entry.\nIn patients with COVID-19, hydroxychloroquine decreases the inflammatory response and cytokine storm, but overdose causes toxicity and mortality.\nNeuraminidase inhibitors such as oseltamivir, peramivir, and zanamivir are invalid for 2019-nCoV and are not recommended for treatment but protease inhibitors such as lopinavir/ritonavir (LPV/r) inhibit the progression of MERS-CoV disease and can be useful for patients of COVID-19 and, in combination with Arbidol, has a direct antiviral effect on early replication of SARS-CoV. Ribavirin reduces hemoglobin concentrations in respiratory patients, and remdesivir improves respiratory symptoms.\nUse of ribavirin in combination with LPV/r in patients with SARS-CoV reduces acute respiratory distress syndrome and mortality, which has a significant protective effect with the addition of corticosteroids.\nFavipiravir increases clinical recovery and reduces respiratory problems and has a stronger antiviral effect than LPV/r.\ncurrently, appropriate treatment for patients with COVID-19 is an ACE2 inhibitor and a clinical problem reducing agent such as favipiravir in addition to hydroxychloroquine and corticosteroids.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"In late December 2019 in Wuhan, China, several patients with viral pneumonia were identified as 2019 novel coronavirus (2019-nCoV).\", \"So far, there are no specific treatments for patients with coronavirus disease-19 (COVID-19), and the treatments available today are based on previous experience with similar viruses such as severe acute respiratory syndrome-related coronavirus (SARS-CoV), Middle East respiratory syndrome coronavirus (MERS-CoV), and Influenza virus.\"]}", "id": 922}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: VITAMIN D HEALTHY LEVELS decrease COVID-19 MORTALITY RATES\n\nAbstract:\nThe outbreak of COVID-19 has created a global public health crisis.\nLittle is known about the protective factors of this infection.\nTherefore, preventive health measures that can reduce the risk of infection, progression and severity are desperately needed.\nThis review discussed the possible roles of vitamin D in reducing the risk of COVID-19 and other acute respiratory tract infections and severity.\nMoreover, this study determined the correlation of vitamin D levels with COVID-19 cases and deaths in 20 European countries as of 20 May 2020.\nA significant negative correlation (p=0.033) has been observed between mean vitamin D levels and COVID-19 cases per one million population in European countries.\nHowever, the correlation of vitamin D with COVID-19 deaths of these countries was not significant.\nSome retrospective studies demonstrated a correlation between vitamin D status and COVID-19 severity and mortality, while other studies did not find the correlation when confounding variables are adjusted.\nSeveral studies demonstrated the role of vitamin D in reducing the risk of acute viral respiratory tract infections and pneumonia.\nThese include direct inhibition with viral replication or with anti-inflammatory or immunomodulatory ways.\nIn the meta-analysis, vitamin D supplementation has been shown as safe and effective against acute respiratory tract infections.\nThus, people who are at higher risk of vitamin D deficiency during this global pandemic should consider taking vitamin D supplements to maintain the circulating 25(OH)D in the optimal levels (75-125nmol/L).\nIn conclusion, there is not enough evidence on the association between vitamin D levels and COVID-19 severity and mortality.\nTherefore, randomized control trials and cohort studies are necessary to test this hypothesis.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"A significant negative correlation (p=0.033) has been observed between mean vitamin D levels and COVID-19 cases per one million population in European countries.\", \"Several studies demonstrated the role of vitamin D in reducing the risk of acute viral respiratory tract infections and pneumonia.\", \"These include direct inhibition with viral replication or with anti-inflammatory or immunomodulatory ways.\", \"In the meta-analysis, vitamin D supplementation has been shown as safe and effective against acute respiratory tract infections.\", \"Thus, people who are at higher risk of vitamin D deficiency during this global pandemic should consider taking vitamin D supplements to maintain the circulating 25(OH)D in the optimal levels (75-125nmol/L).\"]}", "id": 923}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Most children with COVID-19 have mild symptoms or have no symptoms at all.\n\nAbstract:\nBACKGROUND The new Coronavirus identified in Whuan at the end of 2019 (SARS-CoV-2) belongs to the Beta Coronavirus genus and is responsible for the new Coronavirus 2019 pandemia (COVID-19).\nInfected children may be asymptomatic or present fever, dry cough, fatigue or gastrointestinal symptoms.\nThe CDC recommends that clinicians should decide to test patients based on the presence of signs and symptoms compatible with COVID-19.\nMATERIAL AND METHODS 42 children (the majority < 5 years of age) were referred, to our Pediatric Department, as possible cases of COVID-19 infection.\nBlood analysis, chest X-ray, and naso-oropharyngeal swab specimens for viral identification of COVID-19 were requested.\nRESULTS None of the screened children resulted positive for COVID-19 infection.\nAt first presentation, the most frequent signs and symptoms were: fever (71.4%), fatigue (35.7%) and cough (30.9%).\nAn high C-reactive protein value and abnormalities of chest X-ray (bronchial wall thickening) were detected in 26.2% and 19% of patients, respectively.\nAlmost half of patients (45.2%) required hospitalization in our Pediatric Unit and one patient in Intensive Care Unit.\nCONCLUSIONS Testing people who meet the COVID-19 suspected case definition criteria is essential for clinical management and outbreak control.\nChildren of all ages can get COVID-19, although they appear to be affected less frequently than adults, as reported in our preliminary survey.\nFurther studies are needed to confirm our observations.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 924}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Development and validation of the 4c deterioration model for adults hospitalised with covid-19\n\nAbstract:\nPrognostic models to predict the risk of clinical deterioration in acute COVID-19 are required to inform clinical management decisions.\nAmong 75,016 consecutive adults across England, Scotland and Wales prospectively recruited to the ISARIC Coronavirus Clinical Characterisation Consortium (ISARIC4C) study, we developed and validated a multivariable logistic regression model for in-hospital clinical deterioration (defined as any requirement of ventilatory support or critical care, or death) using 11 routinely measured variables.\nWe used internal-external cross-validation to show consistent measures of discrimination, calibration and clinical utility across eight geographical regions.\nWe further validated the final model in held-out data from 8,252 individuals in London, with similarly consistent performance (C-statistic 0.77 (95% CI 0.75 to 0.78); calibration-in-the-large 0.01 (-0.04 to 0.06); calibration slope 0.96 (0.90 to 1.02)).\nImportantly, this model demonstrated higher net benefit than using other candidate scores to inform decision-making.\nOur 4C Deterioration model thus demonstrates unprecedented clinical utility and generalisability to predict clinical deterioration among adults hospitalised with COVID-19.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Our 4C Deterioration model thus demonstrates unprecedented clinical utility and generalisability to predict clinical deterioration among adults hospitalised with COVID-19.\"]}", "id": 925}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: wearing a mask helps stop infected people from spreading the new coronavirus to others.\n\nAbstract:\nWe identified seasonal human coronaviruses, influenza viruses and rhinoviruses in exhaled breath and coughs of children and adults with acute respiratory illness.\nSurgical face masks significantly reduced detection of influenza virus RNA in respiratory droplets and coronavirus RNA in aerosols, with a trend toward reduced detection of coronavirus RNA in respiratory droplets.\nOur results indicate that surgical face masks could prevent transmission of human coronaviruses and influenza viruses from symptomatic individuals.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Surgical face masks significantly reduced detection of influenza virus RNA in respiratory droplets and coronavirus RNA in aerosols, with a trend toward reduced detection of coronavirus RNA in respiratory droplets.\", \"Our results indicate that surgical face masks could prevent transmission of human coronaviruses and influenza viruses from symptomatic individuals.\"]}", "id": 926}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: to reduce the spread of the virus: try to stay at least 2 metres (3 steps) away from anyone you do not live with (or anyone not in your support bubble); wash your hands with soap and water often - do this for at least 20 seconds; use hand sanitiser gel if soap and water are not available; wash your hands as soon as you get home; cover your mouth and nose with a tissue or your sleeve (not your hands) when you cough or sneeze; put used tissues in the bin immediately and wash your hands afterwards\n\nAbstract:\nThe infectious diseases are spreading due to human interactions enabled by various social networks.\nTherefore, when a new pathogen such as SARS-CoV-2 causes an outbreak, the non-pharmaceutical isolation strategies (e.g., social distancing) are the only possible response to disrupt its spreading.\nTo this end, we introduce the new epidemic model (SICARS) and compare the centralized (C), decentralized (D), and combined (C+D) social distancing strategies, and analyze their efficiency to control the dynamics of COVID-19 on heterogeneous complex networks.\nOur analysis shows that the centralized social distancing is necessary to minimize the pandemic spreading.\nThe decentralized strategy is insufficient when used alone, but offers the best results when combined with the centralized one.\nIndeed, the (C+D) is the most efficient isolation strategy at mitigating the network superspreaders and reducing the highest node degrees to less than 10% of their initial values.\nOur results also indicate that stronger social distancing, e.g., cutting 75% of social ties, can reduce the outbreak by 75% for the C isolation, by 33% for the D isolation, and by 87% for the (C+D) isolation strategy.\nFinally, we study the impact of proactive versus reactive isolation strategies, as well as their delayed enforcement.\nWe find that the reactive response to the pandemic is less efficient, and delaying the adoption of isolation measures by over one month (since the outbreak onset in a region) can have alarming effects; thus, our study contributes to an understanding of the COVID-19 pandemic both in space and time.\nWe believe our investigations have a high social relevance as they provide insights into understanding how different degrees of social distancing can reduce the peak infection ratio substantially; this can make the COVID-19 pandemic easier to understand and control over an extended period of time.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Our analysis shows that the centralized social distancing is necessary to minimize the pandemic spreading.\", \"The decentralized strategy is insufficient when used alone, but offers the best results when combined with the centralized one.\", \"Indeed, the (C+D) is the most efficient isolation strategy at mitigating the network superspreaders and reducing the highest node degrees to less than 10% of their initial values.\", \"Our results also indicate that stronger social distancing, e.g., cutting 75% of social ties, can reduce the outbreak by 75% for the C isolation, by 33% for the D isolation, and by 87% for the (C+D) isolation strategy.\"]}", "id": 927}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: The sars-cov-2 cytopathic effect is compatible with autophagy modulators\n\nAbstract:\nSARS-CoV-02 is a new type of coronavirus capable of rapid transmission and causing severe clinical symptoms; much of which has unknown biological etiology.\nIt has prompted researchers to rapidly mobilize their efforts towards identifying and developing anti-viral therapeutics and vaccines.\nDiscovering and understanding the virus\u2019 pathways of infection, host-protein interactions, and cytopathic effects will greatly aid in the design of new therapeutics to treat COVID-19.\nWhile it is known that chloroquine and hydroxychloroquine, extensively explored as clinical agents for COVID-19, have multiple cellular effects including inhibiting autophagy, there are also dose-limiting toxicities in patients that make clearly establishing their potential mechanisms-of-action problematic.\nTherefore, we evaluated a range of other autophagy modulators to identify an alternative autophagy-based drug repurposing opportunity.\nIn this work, we found that 6 of these compounds blocked the cytopathic effect of SARS-CoV-2 in Vero-E6 cells with EC(50) values ranging from 2.0 to 13 \u03bcM and selectivity indices ranging from 1.5 to >10-fold.\nImmunofluorescence staining for LC3B and LysoTracker dye staining assays in several cell lines indicated their potency and efficacy for inhibiting autophagy correlated with the measurements in the SARS-CoV-2 cytopathic effect assay.\nOur data suggest that autophagy pathways could be targeted to combat SARS-CoV-2 infections and become an important component of drug combination therapies to improve the treatment outcomes for COVID-19.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"While it is known that chloroquine and hydroxychloroquine, extensively explored as clinical agents for COVID-19, have multiple cellular effects including inhibiting autophagy, there are also dose-limiting toxicities in patients that make clearly establishing their potential mechanisms-of-action problematic.\", \"Immunofluorescence staining for LC3B and LysoTracker dye staining assays in several cell lines indicated their potency and efficacy for inhibiting autophagy correlated with the measurements in the SARS-CoV-2 cytopathic effect assay.\"]}", "id": 928}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: if you are low risk (healthy, young), you do not need social distancing.\n\nAbstract:\nThe infectious diseases are spreading due to human interactions enabled by various social networks.\nTherefore, when a new pathogen such as SARS-CoV-2 causes an outbreak, the non-pharmaceutical isolation strategies (e.g., social distancing) are the only possible response to disrupt its spreading.\nTo this end, we introduce the new epidemic model (SICARS) and compare the centralized (C), decentralized (D), and combined (C+D) social distancing strategies, and analyze their efficiency to control the dynamics of COVID-19 on heterogeneous complex networks.\nOur analysis shows that the centralized social distancing is necessary to minimize the pandemic spreading.\nThe decentralized strategy is insufficient when used alone, but offers the best results when combined with the centralized one.\nIndeed, the (C+D) is the most efficient isolation strategy at mitigating the network superspreaders and reducing the highest node degrees to less than 10% of their initial values.\nOur results also indicate that stronger social distancing, e.g., cutting 75% of social ties, can reduce the outbreak by 75% for the C isolation, by 33% for the D isolation, and by 87% for the (C+D) isolation strategy.\nFinally, we study the impact of proactive versus reactive isolation strategies, as well as their delayed enforcement.\nWe find that the reactive response to the pandemic is less efficient, and delaying the adoption of isolation measures by over one month (since the outbreak onset in a region) can have alarming effects; thus, our study contributes to an understanding of the COVID-19 pandemic both in space and time.\nWe believe our investigations have a high social relevance as they provide insights into understanding how different degrees of social distancing can reduce the peak infection ratio substantially; this can make the COVID-19 pandemic easier to understand and control over an extended period of time.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Our analysis shows that the centralized social distancing is necessary to minimize the pandemic spreading.\", \"The decentralized strategy is insufficient when used alone, but offers the best results when combined with the centralized one.\", \"Indeed, the (C+D) is the most efficient isolation strategy at mitigating the network superspreaders and reducing the highest node degrees to less than 10% of their initial values.\", \"Our results also indicate that stronger social distancing, e.g., cutting 75% of social ties, can reduce the outbreak by 75% for the C isolation, by 33% for the D isolation, and by 87% for the (C+D) isolation strategy.\"]}", "id": 929}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Is there an airborne component to the development of covid-19?\n\nAbstract:\nObjectives While COVID-19 is known to be spread by respiratory droplets (which travel <2m horizontally), much less is known about its transmission via aerosols, which can become airborne and be widely distributed throughout room spaces.\nIn order to quantify the risk posed by COVID-19 infectors exhaling respiratory aerosols in enclosed spaces, we undertook a computer modelling study to simulate transmission in an office building.\nMethods Respiratory droplet data from four published datasets were analysed to quantify the number and volume of droplets <100m diameter produced by a typical cough and speaking event (i.e. counting from 1 to 100).\nThis was used in a stochastic model to simulate (10,000 simulations) the number of respiratory particles, originating from a COVID-19 infector, that would be inhaled in one hour by a susceptible individual practicing socially distancing in a 5 x 5 x 2.75m office space.\nSeveral scenarios were simulated that mimicked the presence of both symptomatic and asymptomatic COVID-19 infectors.\nResults On average, each cough and speaking event produced similar numbers of droplets <100 m diameter (median range = 955-1010).\nComputer simulations (at ventilation rate = 2AC/h) revealed that sharing the office space with a symptomatic COVID-19 infector (4 coughs per hour) for one hour resulted in the inhalation of 187.3 (median value) respiratory droplets, whereas sharing with an asymptomatic COVID-19 positive person (10 speaking events per hour) resulted in the inhalation of 482.9 droplets.\nIncreasing the ventilation rate resulted in only modest reductions in particle numbers inhaled.\nConclusions Given that live SARS-CoV-2 virions are known to be shed in high concentrations from the nasal cavity of both symptomatic and asymptomatic COVID-19 patients, the results suggest that individuals who share enclosed spaces with an infector may be at risk of contracting COVID-19 by the aerosol route, even when practicing social distancing.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"In order to quantify the risk posed by COVID-19 infectors exhaling respiratory aerosols in enclosed spaces, we undertook a computer modelling study to simulate transmission in an office building.\"]}", "id": 930}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Severe COVID-19 outcomes decreases as the pandemic progressed from winter to the warmer months\n\nAbstract:\nThe 2020 coronavirus pandemic is developing at different paces throughout the world.\nSome areas, like the Caribbean Basin, have yet to see the virus strike at full force.\nWhen it does, there is reasonable evidence to suggest the consequent COVID-19 outbreaks will overwhelm healthcare systems and economies.\nThis is particularly concerning in the Caribbean as pandemics can have disproportionately higher mortality impacts on lower and middle-income countries.\nPreliminary observations from our team and others suggest that temperature and climatological factors could influence the spread of this novel coronavirus, making spatiotemporal predictions of its infectiousness possible.\nThis review studies geographic and time-based distribution of known respiratory viruses in the Caribbean Basin in an attempt to foresee how the pandemic will develop in this region.\nThis review is meant to aid in planning short- and long-term interventions to manage outbreaks at the international, national, and subnational levels in the region.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Preliminary observations from our team and others suggest that temperature and climatological factors could influence the spread of this novel coronavirus, making spatiotemporal predictions of its infectiousness possible.\"]}", "id": 931}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Vitamins C and D boost our immune systems, aiding in the fight against infectious diseases; \n\nAbstract:\nCoronaviruses are a genetically highly variable family of viruses that infect vertebrates and have succeeded in infecting humans many times by overcoming the species barrier.\nThe severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which initially appeared in China at the end of 2019, exhibits a high infectivity and pathogenicity compared to other coronaviruses.\nAs the viral coat and other viral components are recognized as being foreign by the immune system, this can lead to initial symptoms, which are induced by the very efficiently working immune defense system via the respiratory epithelium.\nDuring severe courses a systemically expressed proinflammatory cytokine storm and subsequent changes in the coagulation and complement systems can occur.\nVirus-specific antibodies, the long-term expression of which is ensured by the formation of B memory cell clones, generate a specific immune response that is also detectable in blood (seroconversion).\nSpecifically effective cytotoxic CD8+ T\u00adcell populations are also formed, which recognize viral epitopes as pathogen-specific patterns in combination with MHC presentation on the cell surface of virus-infected cells and destroy these cells.\nAt the current point in time it is unclear how regular, robust and durable this immune status is constructed.\nExperiences with other coronavirus infections (SARS and Middle East respiratory syndrome, MERS) indicate that the immunity could persist for several years.\nBased on animal experiments, already acquired data on other coronavirus types and plausibility assumptions, it can be assumed that seroconverted patients have an immunity of limited duration and only a very low risk of reinfection.\nKnowledge of the molecular mechanisms of viral cycles and immunity is an important prerequisite for the development of vaccination strategies and development of effective drugs.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 932}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Hot weather can spread the virus more as it may get you more out there, make you more mobile and you would actually interact with more people\n\nAbstract:\nThe coronavirus disease 2019 (COVID-19) outbreak has become a severe public health issue.\nThe novelty of the virus prompts a search for understanding of how ecological factors affect the transmission and survival of the virus.\nSeveral studies have robustly identified a relationship between temperature and the number of cases.\nHowever, there is no specific study for a tropical climate such as Brazil.\nThis work aims to determine the relationship of temperature to COVID-19 infection for the state capital cities of Brazil.\nCumulative data with the daily number of confirmed cases was collected from February 27 to April 1, 2020, for all 27 state capital cities of Brazil affected by COVID-19.\nA generalized additive model (GAM) was applied to explore the linear and nonlinear relationship between annual average temperature compensation and confirmed cases.\nAlso, a polynomial linear regression model was proposed to represent the behavior of the growth curve of COVID-19 in the capital cities of Brazil.\nThe GAM dose-response curve suggested a negative linear relationship between temperatures and daily cumulative confirmed cases of COVID-19 in the range from 16.8 \u00b0C to 27.4 \u00b0C.\nEach 1 \u00b0C rise of temperature was associated with a -4.8951% (t = -2.29, p = 0.0226) decrease in the number of daily cumulative confirmed cases of COVID-19.\nA sensitivity analysis assessed the robustness of the results of the model.\nThe predicted R-squared of the polynomial linear regression model was 0.81053.\nIn this study, which features the tropical temperatures of Brazil, the variation in annual average temperatures ranged from 16.8 \u00b0C to 27.4 \u00b0C.\nResults indicated that temperatures had a negative linear relationship with the number of confirmed cases.\nThe curve flattened at a threshold of 25.8 \u00b0C.\nThere is no evidence supporting that the curve declined for temperatures above 25.8 \u00b0C.\nThe study had the goal of supporting governance for healthcare policymakers.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Results indicated that temperatures had a negative linear relationship with the number of confirmed cases.\"]}", "id": 933}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: College students, many of whom are already stressed, reported an increase in depression and anxiety during the initial outbreak.\n\nAbstract:\nOBJECTIVE The socio-health emergency caused by COVID-19 may have a significant psychological impact on the population.\nFor this reason, it is necessary to identify especially vulnerable social groups and protective factors that may reduce this impact, which was the objective of this study.\nMETHODS Using snowball sampling approach, 1,596 people residing in Spain during the lockdown answered an online questionnaire that included information on sociodemographic variables, symptoms, and contact with the disease, risk perception, precautionary measures to prevent infection and coping strategies during lockdown.\nPsychological impact was assessed using the Impact of Event Scale-Revised (IES-R), and mental health status with the Goldberg's General Health Questionnaire (GHQ-12).\nSimple linear regression models were performed to analyze the associations between the study variables and the psychological impact of the pandemic and the mental health of the participants.\nRESULTS Of all respondents, 24.7% reported a moderate or severe psychological impact, and 48.8% showed mental health problems.\nWomen, students and the population with a lower level of economic income, in addition to those having less available space per person in the household presented a more significant psychological impact and worse mental health.\nLiving with someone from the high-risk vulnerable group, and anticipating the adverse economic effects of social-health crisis raised the emotional distress and psychological morbidity.\nPrecautionary measures to prevent infection did not present a connection to the psychological impact of the pandemic; however, several coping strategies did help to reduce it.\nCONCLUSIONS These findings outline the existence of especially vulnerable social groups to the impact of the pandemic, and suggest lines of action that help reduce the psychosocial consequences of COVID-19.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"OBJECTIVE The socio-health emergency caused by COVID-19 may have a significant psychological impact on the population.\", \"RESULTS Of all respondents, 24.7% reported a moderate or severe psychological impact, and 48.8% showed mental health problems.\"]}", "id": 934}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: If your head is spinning, let medical experts provide some focus on one of the symptoms of the coronavirus.\n\nAbstract:\nCoronavirus disease (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS CoV-2) has turned out to be a formidable pandemic.\nUpcoming evidence from confirmed cases of COVID-19 suggests an anticipated incursion of patients with neurological manifestations in the weeks to come.\nAn expression of the angiotensin-converting enzyme 2 (ACE 2), the cellular receptor for SARS-CoV-2 over the glial cells and neurons have made the brain a potential target.\nNeurotoxicity may occur as a result of direct, indirect and post-infectious complications.\nAttention to neurological deficits in COVID-19 is fundamental to ensure appropriate, timely, beneficial management of the affected patients.\nMost common neurological manifestations seen include dizziness, headache, impaired consciousness, acute cerebrovascular disease, ataxia, and seizures.\nAnosmia and ageusia have recently been hinted as significant early symptoms in COVID-19.\nAs cases with neurological deficits in COVID-19 emerge, the overall prognosis is yet unknown.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 935}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Ambroxol hydrochloride inhibits the interaction between severe acute respiratory syndrome coronavirus 2 spike protein 's receptor binding domain and recombinant human ace2 .\n\nAbstract:\nSevere Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), the causative agent of coronavirus disease 2019 (COVID-19), enters the host cells through two main pathways, both involving key interactions between viral envelope-anchored spike glycoprotein of the novel coronavirus and the host receptor, angiotensin-converting enzyme 2 (ACE2).\nTo date, SARS-CoV-2 has infected up to 26 million people worldwide; yet, there is no clinically approved drug or vaccine available.\nTherefore, a rapid and coordinated effort to re-purpose clinically approved drugs that prevent or disrupt these critical entry pathways of SARS-CoV-2 spike glycoprotein interaction with human ACE2, could potentially accelerate the identification and clinical advancement of prophylactic and/or treatment options against COVID-19, thus providing possible countermeasures against viral entry, pathogenesis and survival.\nHerein, we discovered that Ambroxol hydrochloride (AMB), and its progenitor, Bromhexine hydrochloride (BHH), both clinically approved drugs are potent effective modulators of the key interaction between the receptor binding domain (RBD) of SARS-CoV-2 spike protein and human ACE2.\nWe also found that both compounds inhibited SARS-CoV-2 infection-induced cytopathic effect at micromolar concentrations.\nTherefore, in addition to the known TMPRSS2 activity of BHH; we report for the first time that the BHH and AMB pharmacophore has the capacity to target and modulate yet another key protein-protein interaction essential for the two known SARS-CoV-2 entry pathways into host cells.\nAltogether, the potent efficacy, excellent safety and pharmacologic profile of both drugs along with their affordability and availability, makes them promising candidates for drug repurposing as possible prophylactic and/or treatment options against SARS-CoV-2 infection.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Herein, we discovered that Ambroxol hydrochloride (AMB), and its progenitor, Bromhexine hydrochloride (BHH), both clinically approved drugs are potent effective modulators of the key interaction between the receptor binding domain (RBD) of SARS-CoV-2 spike protein and human ACE2.\"]}", "id": 936}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Covid-19 kills also young people\n\nAbstract:\nBackground: The coronavirus 2019 (COVID-19) pandemic has been spread-ing globally for months, yet the infection fatality ratio of the disease is still uncertain.\nThis is partly because of inconsistencies in testing and death reporting standards across countries.\nOur purpose is to provide accurate estimates which do not rely on testing and death count data directly but only use population level statistics.\nMethods: We collected demographic and death records data from the Italian Institute of Statistics.\nWe focus on the area in Italy that experienced the initial outbreak of COVID-19 and estimated a Bayesian model fitting age-stratified mortality data from 2020 and previous years.\nWe also assessed the sensitivity of our estimates to alternative assumptions on the proportion of population infected.\nFindings: We estimate an overall infection fatality rate of 1.29% (95% credible interval [CrI] 0.89 - 2.01), as well as large differences by age, with a low infection fatality rate of 0.05% for under 60 year old (CrI 0-.19) and a substantially higher 4.25% (CrI 3.01-6.39) for people above 60 years of age.\nIn our sensitivity analysis, we found that even under extreme assumptions, our method delivered useful information.\nFor instance, even if only 10% of the population were infected, the infection fatality rate would not rise above 0.2% for people under 60.\nInterpretation: Our empirical estimates based on population level data show a sharp difference in fatality rates between young and old people and firmly rule out overall fatality ratios below 0.5% in populations with more than 30% over 60 years old.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Findings: We estimate an overall infection fatality rate of 1.29% (95% credible interval [CrI] 0.89 - 2.01), as well as large differences by age, with a low infection fatality rate of 0.05% for under 60 year old (CrI 0-.19) and a substantially higher 4.25% (CrI 3.01-6.39) for people above 60 years of age.\"]}", "id": 937}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: A potently neutralizing antibody protects mice against sars-cov-2 infection\n\nAbstract:\nSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is responsible for millions of infections and hundreds of thousands of deaths globally.\nThere are no widely available licensed therapeutics against SARS-CoV-2, highlighting an urgent need for effective interventions.\nThe virus enters host cells through binding of a receptor-binding domain within its trimeric spike glycoprotein to human angiotensin-converting enzyme 2.\nIn this article, we describe the generation and characterization of a panel of murine mAbs directed against the receptor-binding domain.\nOne mAb, 2B04, neutralized wild-type SARS-CoV-2 in vitro with remarkable potency (half-maximal inhibitory concentration of <2 ng/ml).\nIn a murine model of SARS-CoV-2 infection, 2B04 protected challenged animals from weight loss, reduced lung viral load, and blocked systemic dissemination.\nThus, 2B04 is a promising candidate for an effective antiviral that can be used to prevent SARS-CoV-2 infection.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"In a murine model of SARS-CoV-2 infection, 2B04 protected challenged animals from weight loss, reduced lung viral load, and blocked systemic dissemination.\"]}", "id": 938}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: It does not make a lot of sense that if somebody is otherwise healthy and young and they have hypertension alone, that they should be at increased risk\n\nAbstract:\nOBJECTIVE: To investigate the association between hypertension and outcome in patients with Coronavirus Disease 2019 (COVID-19) pneumonia.\nMETHODS: We performed a systematic literature search from several databases on studies that assess hypertension and outcome in COVID-19.\nComposite of poor outcome, comprising of mortality, severe COVID-19, acute respiratory distress syndrome (ARDS), need for intensive care unit (ICU) care and disease progression were the outcomes of interest.\nRESULTS: A total of 6560 patients were pooled from 30 studies.\nHypertension was associated with increased composite poor outcome (risk ratio (RR) 2.11 (95% confidence interval (CI) 1.85, 2.40), p < 0.001; I2, 44%) and its sub-group, including mortality (RR 2.21 (1.74, 2.81), p < 0.001; I2, 66%), severe COVID-19 (RR 2.04 (1.69, 2.47), p < 0.001; I2 31%), ARDS (RR 1.64 (1.11, 2.43), p = 0.01; I2,0%, p = 0.35), ICU care (RR 2.11 (1.34, 3.33), p = 0.001; I2 18%, p = 0.30), and disease progression (RR 3.01 (1.51, 5.99), p = 0.002; I2 0%, p = 0.55).\nMeta-regression analysis showed that gender (p = 0.013) was a covariate that affects the association.\nThe association was stronger in studies with a percentage of males < 55% compared to \u00e2\u00a9\u00be 55% (RR 2.32 v. RR 1.79).\nCONCLUSION: Hypertension was associated with increased composite poor outcome, including mortality, severe COVID-19, ARDS, need for ICU care and disease progression in patients with COVID-19.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"CONCLUSION: Hypertension was associated with increased composite poor outcome, including mortality, severe COVID-19, ARDS, need for ICU care and disease progression in patients with COVID-19.\"]}", "id": 939}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Dexamethasone do not cure the new coronavirus\n\nAbstract:\nRecent announcements indicated, without sharing any distinct published set of results, that the corticosteroid dexamethasone may reduce mortality of severe COVID-19 patients only.\nThe recent Coronavirus [severe acute respiratory syndrome (SARS)-CoV-2]-associated multiorgan disease, called COVID-19, has high morbidity and mortality due to autoimmune destruction of the lungs stemming from the release of a storm of pro-inflammatory cytokines.\nDefense against this Corona virus requires activated T cells and specific antibodies.\nInstead, cytokines are responsible for the serious sequelae of COVID-19 that damage the lungs.\nDexamethasone is a synthetic corticosteroid approved by the FDA 1958 as a broad-spectrum immunosuppressor and it is about 30 times as active and with longer duration of action (2-3 days) than cortisone.\nDexamethasone would limit the production of and damaging effect of the cytokines, but will also inhibit the protective function of T cells and block B cells from making antibodies, potentially leading to increased plasma viral load that will persist after a patient survives SARS.\nMoreover, dexamethasone would block macrophages from clearing secondary, nosocomial, infections.\nHence, dexamethasone may be useful for the short-term in severe, intubated, COVID-19 patients, but could be outright dangerous during recovery since the virus will not only persist, but the body will be prevented from generating protective antibodies.\nInstead, a pulse of intravenous dexamethasone may be followed by administration of nebulized triamcinolone (6 times as active as cortisone) to concentrate in the lungs only.\nThese corticosteroids could be given together with the natural flavonoid luteolin because of its antiviral and anti-inflammatory properties, especially its ability to inhibit mast cells, which are the main source of cytokines in the lungs.\nAt the end, we should remember that \"The good physician treats the disease; the great physician treats the patient who has the disease\" [Sir William Osler's (1849-1919)].", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Hence, dexamethasone may be useful for the short-term in severe, intubated, COVID-19 patients, but could be outright dangerous during recovery since the virus will not only persist, but the body will be prevented from generating protective antibodies.\"]}", "id": 940}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: the virus can stay on surfaces long enough to be a source of transmission\n\nAbstract:\nPURPOSE OF REVIEW This article reviews 'no touch' methods for disinfection of the contaminated surface environment of hospitalized patients' rooms.\nThe focus is on studies that assessed the effectiveness of ultraviolet (UV) light devices, hydrogen peroxide systems, and self-disinfecting surfaces to reduce healthcare-associated infections (HAIs).\nRECENT FINDINGS The contaminated surface environment in hospitals plays an important role in the transmission of several key nosocomial pathogens including methicillin-resistant Staphylococcus aureus, vancomycin-resistant Enterococcus spp.\n, Clostridium difficile, Acinetobacter spp., and norovirus.\nMultiple clinical trials have now demonstrated the effectiveness of UV light devices and hydrogen peroxide systems to reduce HAIs.\nA limited number of studies have suggested that 'self-disinfecting' surfaces may also decrease HAIs.\nSUMMARY Many studies have demonstrated that terminal cleaning and disinfection with germicides is often inadequate and leaves environmental surfaces contaminated with important nosocomial pathogens. 'No touch' methods of room decontamination (i.e., UV devices and hydrogen peroxide systems) have been demonstrated to reduce key nosocomial pathogens on inoculated test surfaces and on environmental surfaces in actual patient rooms.\nFurther UV devices and hydrogen peroxide systems have been demonstrated to reduce HAI.\nA validated 'no touch' device or system should be used for terminal room disinfection following discharge of patients on contact precautions.\nThe use of a 'self-disinfecting' surface to reduce HAI has not been convincingly demonstrated.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"RECENT FINDINGS The contaminated surface environment in hospitals plays an important role in the transmission of several key nosocomial pathogens including methicillin-resistant Staphylococcus aureus, vancomycin-resistant Enterococcus spp.\", \", Clostridium difficile, Acinetobacter spp., and norovirus.\"]}", "id": 941}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Multivalency transforms sars-cov-2 antibodies into broad and ultrapotent neutralizers\n\nAbstract:\nThe novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes Coronavirus Disease 2019 (COVID-19), has caused a global pandemic.\nAntibodies are powerful biotherapeutics to fight viral infections; however, discovery of the most potent and broadly acting clones can be lengthy.\nHere, we used the human apoferritin protomer as a modular subunit to drive oligomerization of antibody fragments and transform antibodies targeting SARS-CoV-2 into exceptionally potent neutralizers.\nUsing this platform, half-maximal inhibitory concentration (IC50) values as low as 9 \u00d7 10\u221214 M were achieved as a result of up to 10,000-fold potency enhancements.\nCombination of three different antibody specificities and the fragment crystallizable (Fc) domain on a single multivalent molecule conferred the ability to overcome viral sequence variability together with outstanding potency and Ig-like in vivo bioavailability.\nThis MULTi-specific, multi-Affinity antiBODY (Multabody; or MB) platform contributes a new class of medical countermeasures against COVID-19 and an efficient approach to rapidly deploy potent and broadly-acting therapeutics against infectious diseases of global health importance.\nOne Sentence Summary multimerization platform transforms antibodies emerging from discovery screens into potent neutralizers that can overcome SARS-CoV-2 sequence diversity.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Here, we used the human apoferritin protomer as a modular subunit to drive oligomerization of antibody fragments and transform antibodies targeting SARS-CoV-2 into exceptionally potent neutralizers.\", \"This MULTi-specific, multi-Affinity antiBODY (Multabody; or MB) platform contributes a new class of medical countermeasures against COVID-19 and an efficient approach to rapidly deploy potent and broadly-acting therapeutics against infectious diseases of global health importance.\", \"One Sentence Summary multimerization platform transforms antibodies emerging from discovery screens into potent neutralizers that can overcome SARS-CoV-2 sequence diversity.\"]}", "id": 942}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Is there an airborne component to the use of covid-19?\n\nAbstract:\nObjectives While COVID-19 is known to be spread by respiratory droplets (which travel <2m horizontally), much less is known about its transmission via aerosols, which can become airborne and be widely distributed throughout room spaces.\nIn order to quantify the risk posed by COVID-19 infectors exhaling respiratory aerosols in enclosed spaces, we undertook a computer modelling study to simulate transmission in an office building.\nMethods Respiratory droplet data from four published datasets were analysed to quantify the number and volume of droplets <100m diameter produced by a typical cough and speaking event (i.e. counting from 1 to 100).\nThis was used in a stochastic model to simulate (10,000 simulations) the number of respiratory particles, originating from a COVID-19 infector, that would be inhaled in one hour by a susceptible individual practicing socially distancing in a 5 x 5 x 2.75m office space.\nSeveral scenarios were simulated that mimicked the presence of both symptomatic and asymptomatic COVID-19 infectors.\nResults On average, each cough and speaking event produced similar numbers of droplets <100 m diameter (median range = 955-1010).\nComputer simulations (at ventilation rate = 2AC/h) revealed that sharing the office space with a symptomatic COVID-19 infector (4 coughs per hour) for one hour resulted in the inhalation of 187.3 (median value) respiratory droplets, whereas sharing with an asymptomatic COVID-19 positive person (10 speaking events per hour) resulted in the inhalation of 482.9 droplets.\nIncreasing the ventilation rate resulted in only modest reductions in particle numbers inhaled.\nConclusions Given that live SARS-CoV-2 virions are known to be shed in high concentrations from the nasal cavity of both symptomatic and asymptomatic COVID-19 patients, the results suggest that individuals who share enclosed spaces with an infector may be at risk of contracting COVID-19 by the aerosol route, even when practicing social distancing.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"In order to quantify the risk posed by COVID-19 infectors exhaling respiratory aerosols in enclosed spaces, we undertook a computer modelling study to simulate transmission in an office building.\"]}", "id": 943}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: However, some children can get severely ill from COVID-19. They might require hospitalization, intensive care, or a ventilator to help them breathe.\n\nAbstract:\nBACKGROUND Novel coronavirus disease (COVID-19) is spreading globally.\nLittle is known about the risk factors for the clinical outcomes of COVID-19 in children.\nMETHODS A retrospective case-control study was taken in children with severe acute respiratory syndrome coronary virus-2 infection in Wuhan Children's Hospital.\nRisk factors associated with the development of COVID-19 and progression were collected and analyzed.\nRESULTS Eight out of 260 children diagnosed with severe COVID-19 pneumonia were included in the study.\nThirty-five children with COVID-19 infection matched for age, sex and date of admission, and who classified as non-severe type, were randomly selected from the hospital admissions.\nFor cases with severe pneumonia caused by COVID-19, the most common symptoms were dyspnea (87.5%), fever (62.5%) and cough (62.5%).\nIn laboratory, white blood cells count was significantly higher in severe children than non-severe children.\nLevels of inflammation bio-makers such as hsCRP, IL-6, IL-10 and D-dimer elevated in severe children compared with non-severe children on admission.\nThe level of total bilirubin and uric acid clearly elevated in severe children compared with non-severe children on admission.\nAll of severe children displayed the lesions on chest CT, more lung segments were involved in severe children than in non-severe children, which was only risk factor associated with severe COVID-19 pneumonia in multivariable analysis.\nCONCLUSIONS More than 3 lung segments involved were associated with greater risk of development of severe COVID-19 in children.\nMoreover, the possible risk of the elevation of IL-6, high total bilirubin and D-dimer with univariable analysis could identify patients to be severe earlier.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 944}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Diabetes is generally known to weaken the immune system, making it harder to protect against viral infections like COVID-19.\n\nAbstract:\nCoronavirus disease 2019 (COVID-19) has become a global concern and public health issue due to its higher infection and mortality rate; particularly, the risk is very higher among the patients who have cardiovascular diseases (CVD) and/or diabetes mellitus (DM).\nIn this review, we analyzed the recently published literature on CVD and DM associated with COVD-19 infections and highlight their association with potential mechanisms.\nThe findings revealed that without any previous history of CVD, the COVID-19 patients have developed some CVD complications like myocardial injury, cardiomyopathy, and venous thromboembolism after being infected with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and required for those patients an emergency clinical support to be aware to manage those complications.\nThough the association between DM and COVID-19-induced severe complications is still unclear, the limited data predict that different markers like interleukin (IL)-1, IL-6, C-reactive protein, and D-dimer linked with the severity of COVID-19 infection in diabetic individuals.\nFurther studies on a large scale are urgently needed to explore the underlying mechanisms between CVD, DM, and COVID-19 for better treatment.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 945}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Scientists are endeavoring to find antivirals specific to the virus. Several drugs such as chloroquine, arbidol, remdesivir, and favipiravir are currently undergoing clinical studies to test their efficacy and safety in the treatment of coronavirus disease 2019 (COVID-19) in China; some promising results have been achieved thus far.\n\nAbstract:\nHere we report on the most recent updates on experimental drugs successfully employed in the treatment of the disease caused by SARS CoV-2 coronavirus, also referred to as COVID-19 (COronaVIrus Disease 19).\nIn particular, several cases of recovered patients have been reported after being treated with lopinavir/ritonavir (which is widely used to treat human immunodeficiency virus (HIV) infection) in combination with the anti-flu drug oseltamivir.\nIn addition, remdesivir, which has been previously administered to Ebola virus patients, has also proven effective in the U.S. against coronavirus, while antimalarial chloroquine and hydroxychloroquine, favipiravir and co-administered darunavir and umifenovir (in patient therapies) were also recently recorded as having anti-SARS CoV-2 effects.\nSince the recoveries/deaths ratio in the last weeks significantly increased, especially in China, it is clear that the experimental antiviral therapy, together with the availability of intensive care unit beds in hospitals and rigorous government control measures, all play an important role in dealing with this virus.\nThis also stresses the urgent need for the scientific community to devote its efforts to find other more specific antiviral strategies.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"In addition, remdesivir, which has been previously administered to Ebola virus patients, has also proven effective in the U.S. against coronavirus, while antimalarial chloroquine and hydroxychloroquine, favipiravir and co-administered darunavir and umifenovir (in patient therapies) were also recently recorded as having anti-SARS CoV-2 effects.\"]}", "id": 946}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Blood pressure drugs may improve COVID-19 survival\n\nAbstract:\nAngiotensin-converting enzyme (ACE) inhibitors (ACEIs) and angiotensin II type\u00ad1 receptor blockers (ARBs) are among the most widely prescribed drugs for the treatment of arterial hypertension, heart failure and chronic kidney disease.\nA number of studies, mainly in animals and not involving the lungs, have indicated that these drugs can increase expression of angiotensin-converting enzyme 2 (ACE2).\nACE2 is the cell entry receptor of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of coronavirus disease 2019 (COVID-19) that is currently battering the globe.\nThis has led to the hypothesis that use of ACEIs and ARBs may increase the risk of developing severe COVID-19.\nIn this point of view paper, possible scenarios regarding the impact of ACEI/ARB pharmacotherapy on COVID-19 are discussed in relation to the currently available evidence.\nAlthough further research on the influence of blood-pressure-lowering drugs, including those not targeting the renin-angiotensin system, is warranted, there are presently no compelling clinical data showing that ACEIs and ARBs increase the likelihood of contracting COVID-19 or worsen the outcome of SARS-CoV\u00ad2 infections.\nThus, unless contraindicated, use of ACEIs/ARBs in COVID-19 patients should be continued in line with the recent recommendations of medical societies.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"This has led to the hypothesis that use of ACEIs and ARBs may increase the risk of developing severe COVID-19.\"]}", "id": 947}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Cats may be able to spread coronavirus to humans despite showing no symptoms\n\nAbstract:\nCoronaviruses, which were generally considered harmless to humans before 2003, have appeared again with a pandemic threatening the world since December 2019 after the epidemics of SARS and MERS.\nIt is known that transmission from person to person is the most important way to spread.\nHowever, due to the widespread host diversity, a detailed examination of the role of animals in this pandemic is essential to effectively fight against the outbreak.\nAlthough coronavirus infections in pets are known to be predominantly related to the gastrointestinal tract, it has been observed that there are human-to-animal transmissions in this outbreak and some animals have similar symptoms to humans.\nAlthough animal-to-animal transmission has been shown to be possible, there is no evidence of animal-to-human transmission.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Although coronavirus infections in pets are known to be predominantly related to the gastrointestinal tract, it has been observed that there are human-to-animal transmissions in this outbreak and some animals have similar symptoms to humans.\", \"Although animal-to-animal transmission has been shown to be possible, there is no evidence of animal-to-human transmission.\"]}", "id": 948}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: No drug is approved by the Food and Drug Administration (FDA) for COVID-19, although dozens of candidates - including drugs used to treat rheumatoid arthritis, parasites, cancer, and HIV - have been proposed\n\nAbstract:\nThe current pandemic of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has presented unprecedented challenges to the healthcare systems in almost every country around the world.\nCurrently, there are no proven effective vaccines or therapeutic agents against the virus.\nCurrent clinical management includes infection prevention and control measures and supportive care including supplemental oxygen and mechanical ventilatory support.\nEvolving research and clinical data regarding the virologic SARS-CoV-2 suggest a potential list of repurposed drugs with appropriate pharmacological effects and therapeutic efficacies in treating COVID-19 patients.\nIn this review, we will update and summarize the most common and plausible drugs for the treatment of COVID-19 patients.\nThese drugs and therapeutic agents include antiviral agents (remdesivir, hydroxychloroquine, chloroquine, lopinavir, umifenovir, favipiravir, and oseltamivir), and supporting agents (Ascorbic acid, Azithromycin, Corticosteroids, Nitric oxide, IL-6 antagonists), among others.\nWe hope that this review will provide useful and most updated therapeutic drugs to prevent, control, and treat COVID-19 patients until the approval of vaccines and specific drugs targeting SARS-CoV-2.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Currently, there are no proven effective vaccines or therapeutic agents against the virus.\", \"In this review, we will update and summarize the most common and plausible drugs for the treatment of COVID-19 patients.\", \"These drugs and therapeutic agents include antiviral agents (remdesivir, hydroxychloroquine, chloroquine, lopinavir, umifenovir, favipiravir, and oseltamivir), and supporting agents (Ascorbic acid, Azithromycin, Corticosteroids, Nitric oxide, IL-6 antagonists), among others.\"]}", "id": 949}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: The ground level ozone concentration is greaterly correlated with the number of covid-19 cases in warsaw, poland\n\nAbstract:\nCOVID-19, which is a consequence of infection with the novel viral agent SARS-CoV-2, first identified in China (Hubei Province), has been declared a pandemic by the WHO.\nAs of September 10, 2020, over 70,000 cases and over 2,000 deaths have been recorded in Poland.\nOf the many factors contributing to the level of transmission of the virus, the weather appears to be significant.\nIn this work we analyse the impact of weather factors such as temperature, relative humidity, wind speed and ground level ozone concentration on the number of COVID-19 cases in Warsaw, Poland.\nThe obtained results show an inverse correlation between ground level ozone concentration and the daily number of COVID-19 cases.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"The obtained results show an inverse correlation between ground level ozone concentration and the daily number of COVID-19 cases.\"]}", "id": 950}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Masks can protect you from more severe COVID-19\n\nAbstract:\nEvidence that face masks provide effective protection against respiratory infections in the community is scarce.\nHowever, face masks are widely used by health workers as part of droplet precautions when caring for patients with respiratory infections.\nIt would therefore be reasonable to suggest that consistent widespread use of face masks in the community could prevent further spread of the Severe Acute Respiratory Syndrome-Coronavirus 2 (SARS-CoV-2).\nIn this study we examine public face mask wearing in Uganda where a proportion wears masks to protect against acquiring, and the other to prevent from transmitting SARS-CoV-2.\nThe objective of this study was to determine what percentage of the population would have to wear face masks to reduce susceptibility to and infectivity of COVID-19 in Uganda, keeping the basic reproduction number below unity and/or flattening the curve.\nWe used an SEIAQRD model for the analysis.\nResults show that implementation of facemasks has a relatively large impact on the size of the coronavirus epidemic in Uganda.\nWe find that the critical mask adherence is 5 per 100 when 80% wear face masks.\nA cost-effective analysis shows that utilizing funds to provide 1 public mask to the population has a per capita compounded cost of USD 1.34.\nIf provision of face masks is done simultaneously with supportive care, the per capita compounded cost is USD 1.965, while for the case of only treatment and no provision of face masks costs each Ugandan USD 4.0579.\nWe conclude that since it is hard to achieve a 100% adherence to face masks, government might consider provision of face masks in conjunction with provision of care.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Evidence that face masks provide effective protection against respiratory infections in the community is scarce.\", \"However, face masks are widely used by health workers as part of droplet precautions when caring for patients with respiratory infections.\", \"It would therefore be reasonable to suggest that consistent widespread use of face masks in the community could prevent further spread of the Severe Acute Respiratory Syndrome-Coronavirus 2 (SARS-CoV-2).\"]}", "id": 951}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Orf8 and orf3b antibodies are accurate serological markers of early and acute sars-cov-2 infection\n\nAbstract:\nThe SARS-CoV-2 virus emerged in December 2019 and has caused a worldwide pandemic due to the lack of any pre-existing immunity.\nAccurate serology testing is urgently needed to help diagnose infection, determine past exposure of populations and assess the response to a future vaccine.\nThe landscape of antibody responses to SARS-CoV-2 is unknown.\nIn this study, we utilized the luciferase immunoprecipitation system to assess the antibody responses to 15 different SARS-CoV-2 antigens in patients with COVID-19.\nWe identified new targets of the immune response to SARS-CoV-2 and show that nucleocapsid, open reading frame (ORF)8 and ORF3b elicit the strongest specific antibody responses.\nORF8 and ORF3b antibodies, taken together as a cluster of points, identified 96.5% of COVID-19 samples at early and late time points of disease with 99.5% specificity.\nOur findings could be used to develop second-generation diagnostic tests to improve serological assays for COVID-19 and are important in understanding pathogenicity.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"We identified new targets of the immune response to SARS-CoV-2 and show that nucleocapsid, open reading frame (ORF)8 and ORF3b elicit the strongest specific antibody responses.\"]}", "id": 952}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Susceptible supply limits the role of climate in the early sars-cov-2 pandemic\n\nAbstract:\nPreliminary evidence suggests that climate may modulate the transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).\nYet it remains unclear whether seasonal and geographic variations in climate can substantially alter the pandemic trajectory, given that high susceptibility is a core driver.\nHere, we use a climate-dependent epidemic model to simulate the SARS-CoV-2 pandemic by probing different scenarios based on known coronavirus biology.\nWe find that although variations in weather may be important for endemic infections, during the pandemic stage of an emerging pathogen, the climate drives only modest changes to pandemic size.\nA preliminary analysis of nonpharmaceutical control measures indicates that they may moderate the pandemic-climate interaction through susceptible depletion.\nOur findings suggest that without effective control measures, strong outbreaks are likely in more humid climates and summer weather will not substantially limit pandemic growth.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"A preliminary analysis of nonpharmaceutical control measures indicates that they may moderate the pandemic-climate interaction through susceptible depletion.\"]}", "id": 953}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: The antiviral compound remdesivir potently inhibits rna-dependent rna polymerase from middle east respiratory syndrome coronavirus\n\nAbstract:\nAntiviral drugs for managing infections with human coronaviruses are not yet approved, posing a serious challenge to current global efforts aimed at containing the outbreak of severe acute respiratory syndrome-coronavirus 2 (CoV-2).\nRemdesivir (RDV) is an investigational compound with a broad spectrum of antiviral activities against RNA viruses, including severe acute respiratory syndrome-CoV and Middle East respiratory syndrome (MERS-CoV).\nRDV is a nucleotide analog inhibitor of RNA-dependent RNA polymerases (RdRps).\nHere, we co-expressed the MERS-CoV nonstructural proteins nsp5, nsp7, nsp8, and nsp12 (RdRp) in insect cells as a part a polyprotein to study the mechanism of inhibition of MERS-CoV RdRp by RDV.\nWe initially demonstrated that nsp8 and nsp12 form an active complex.\nThe triphosphate form of the inhibitor (RDV-TP) competes with its natural counterpart ATP.\nOf note, the selectivity value for RDV-TP obtained here with a steady-state approach suggests that it is more efficiently incorporated than ATP and two other nucleotide analogs.\nOnce incorporated at position i, the inhibitor caused RNA synthesis arrest at position i + 3.\nHence, the likely mechanism of action is delayed RNA chain termination.\nThe additional three nucleotides may protect the inhibitor from excision by the viral 3'-5' exonuclease activity.\nTogether, these results help to explain the high potency of RDV against RNA viruses in cell-based assays.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Remdesivir (RDV) is an investigational compound with a broad spectrum of antiviral activities against RNA viruses, including severe acute respiratory syndrome-CoV and Middle East respiratory syndrome (MERS-CoV).\"]}", "id": 954}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: UVC wands kill viruses. They're also a 'major safety issue\n\nAbstract:\nInfection by coronavirus (CoV-19) has led to emergence of a pandemic called as Coronavirus Disease (COVID-19) that has so far affected about 210 countries.\nThe dynamic data indicate that the pandemic by CoV-19 so far has infected 2,403,963 individuals, and among these 624,698 have recovered while, it has been fatal for 165,229.\nWithout much experience, currently, the medicines that are clinically being evaluated for COVID-19 include chloroquine, hydroxychloroquine, azithromycin, tocilizumab, lopinavir, ritonavir, tocilizumab and corticosteroids.\nTherefore, countries such as Italy, USA, Spain and France with the most advanced health care system are partially successful to control CoV-19 infection.\nIndia being the 2nd largest populous country, where, the healthcare system is underdeveloped, major portion of population follow unhygienic lifestyle, is able to restrict the rate of both infection and death of its citizens from COVID-19.\nIndia has followed an early and a very strict social distancing by lockdown and has issued advisory to clean hands regularly by soap and/or by alcohol based sterilizers.\nRolling data on the global index of the CoV infection is 13,306, and the index of some countries such as USA (66,148), Italy (175,055), Spain (210,126), France (83,363) and Switzerland (262,122) is high.\nThe index of India has remained very low (161) so far, mainly due to early implementation of social lockdown, social distancing, and sanitizing hands.\nHowever, articles on social lockdown as a preventive measure against COVID-19 in PubMed are scanty.\nIt has been observed that social lockdown has also drastic impacts on the environment especially on reduction of NO2 and CO2 emission.\nSlow infection rate under strict social distancing will offer time to researchers to come up with exact medicines/vaccines against CoV-19.\nTherefore, it is concluded that stringent social distancing via lockdown is highly important to control COVID-19 and also to contribute for self-regeneration of nature.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 955}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: N95 masks are more effective than regular masks.\n\nAbstract:\nEvidence that face masks provide effective protection against respiratory infections in the community is scarce.\nHowever, face masks are widely used by health workers as part of droplet precautions when caring for patients with respiratory infections.\nIt would therefore be reasonable to suggest that consistent widespread use of face masks in the community could prevent further spread of the Severe Acute Respiratory Syndrome-Coronavirus 2 (SARS-CoV-2).\nIn this study we examine public face mask wearing in Uganda where a proportion wears masks to protect against acquiring, and the other to prevent from transmitting SARS-CoV-2.\nThe objective of this study was to determine what percentage of the population would have to wear face masks to reduce susceptibility to and infectivity of COVID-19 in Uganda, keeping the basic reproduction number below unity and/or flattening the curve.\nWe used an SEIAQRD model for the analysis.\nResults show that implementation of facemasks has a relatively large impact on the size of the coronavirus epidemic in Uganda.\nWe find that the critical mask adherence is 5 per 100 when 80% wear face masks.\nA cost-effective analysis shows that utilizing funds to provide 1 public mask to the population has a per capita compounded cost of USD 1.34.\nIf provision of face masks is done simultaneously with supportive care, the per capita compounded cost is USD 1.965, while for the case of only treatment and no provision of face masks costs each Ugandan USD 4.0579.\nWe conclude that since it is hard to achieve a 100% adherence to face masks, government might consider provision of face masks in conjunction with provision of care.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 956}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Like other viruses with a lipid envelope, SARS-CoV-2 is probably sensitive to temperature, humidity, and solar radiation\n\nAbstract:\nThe coronavirus disease 2019 (COVID-19) outbreak has become a severe public health issue.\nThe novelty of the virus prompts a search for understanding of how ecological factors affect the transmission and survival of the virus.\nSeveral studies have robustly identified a relationship between temperature and the number of cases.\nHowever, there is no specific study for a tropical climate such as Brazil.\nThis work aims to determine the relationship of temperature to COVID-19 infection for the state capital cities of Brazil.\nCumulative data with the daily number of confirmed cases was collected from February 27 to April 1, 2020, for all 27 state capital cities of Brazil affected by COVID-19.\nA generalized additive model (GAM) was applied to explore the linear and nonlinear relationship between annual average temperature compensation and confirmed cases.\nAlso, a polynomial linear regression model was proposed to represent the behavior of the growth curve of COVID-19 in the capital cities of Brazil.\nThe GAM dose-response curve suggested a negative linear relationship between temperatures and daily cumulative confirmed cases of COVID-19 in the range from 16.8 \u00b0C to 27.4 \u00b0C.\nEach 1 \u00b0C rise of temperature was associated with a -4.8951% (t = -2.29, p = 0.0226) decrease in the number of daily cumulative confirmed cases of COVID-19.\nA sensitivity analysis assessed the robustness of the results of the model.\nThe predicted R-squared of the polynomial linear regression model was 0.81053.\nIn this study, which features the tropical temperatures of Brazil, the variation in annual average temperatures ranged from 16.8 \u00b0C to 27.4 \u00b0C.\nResults indicated that temperatures had a negative linear relationship with the number of confirmed cases.\nThe curve flattened at a threshold of 25.8 \u00b0C.\nThere is no evidence supporting that the curve declined for temperatures above 25.8 \u00b0C.\nThe study had the goal of supporting governance for healthcare policymakers.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Results indicated that temperatures had a negative linear relationship with the number of confirmed cases.\"]}", "id": 957}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Attenuated influenza virions expressing the sars- cov-2 receptor-binding domain induce neutralizing antibodies in humans\n\nAbstract:\nAn effective vaccine is essential for controlling the spread of the SARS-CoV-2 virus.\nHere, we describe an influenza virus-based vaccine for SARS-CoV-2.\nWe incorporated a membrane-anchored form of the SARS-CoV-2 spike receptor binding domain (RBD) in place of the neuraminidase (NA) coding sequence in an influenza virus also possessing a mutation that reduces the affinity of hemagglutinin for its sialic acid receptor.\nThe resulting \u0394NA(RBD)-Flu virus can be generated by reverse genetics and grown to high titers in cell culture.\nA single-dose intranasal inoculation of mice with \u0394NA(RBD)-Flu elicits serum neutralizing antibody titers against SAR-CoV-2 comparable to those observed in humans following natural infection (~1:200).\nFurthermore, \u0394NA(RBD)-Flu itself causes no apparent disease in mice.\nIt might be possible to produce a vaccine similar to \u0394NA(RBD)-Flu at scale by leveraging existing platforms for the production of influenza vaccines.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"The resulting \\u0394NA(RBD)-Flu virus can be generated by reverse genetics and grown to high titers in cell culture.\", \"A single-dose intranasal inoculation of mice with \\u0394NA(RBD)-Flu elicits serum neutralizing antibody titers against SAR-CoV-2 comparable to those observed in humans following natural infection (~1:200).\"]}", "id": 958}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: N95 masks are more effective than regular masks.\n\nAbstract:\nFace mask use by the general public for limiting the spread of the COVID-19 pandemic is controversial, though increasingly recommended, and the potential of this intervention is not well understood.\nWe develop a compartmental model for assessing the community-wide impact of mask use by the general, asymptomatic public, a portion of which may be asymptomatically infectious.\nModel simulations, using data relevant to COVID-19 dynamics in the US states of New York and Washington, suggest that broad adoption of even relatively ineffective face masks may meaningfully reduce community transmission of COVID-19 and decrease peak hospitalizations and deaths.\nMoreover, mask use decreases the effective transmission rate in nearly linear proportion to the product of mask effectiveness (as a fraction of potentially infectious contacts blocked) and coverage rate (as a fraction of the general population), while the impact on epidemiologic outcomes (death, hospitalizations) is highly nonlinear, indicating masks could synergize with other non-pharmaceutical measures.\nNotably, masks are found to be useful with respect to both preventing illness in healthy persons and preventing asymptomatic transmission.\nHypothetical mask adoption scenarios, for Washington and New York state, suggest that immediate near universal (80%) adoption of moderately (50%) effective masks could prevent on the order of 17--45% of projected deaths over two months in New York, while decreasing the peak daily death rate by 34--58%, absent other changes in epidemic dynamics.\nEven very weak masks (20% effective) can still be useful if the underlying transmission rate is relatively low or decreasing: In Washington, where baseline transmission is much less intense, 80% adoption of such masks could reduce mortality by 24--65% (and peak deaths 15--69%), compared to 2--9% mortality reduction in New York (peak death reduction 9--18%).\nOur results suggest use of face masks by the general public is potentially of high value in curtailing community transmission and the burden of the pandemic.\nThe community-wide benefits are likely to be greatest when face masks are used in conjunction with other non-pharmaceutical practices (such as social-distancing), and when adoption is nearly universal (nation-wide) and compliance is high.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 959}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: there is no evidence that garlic can protect you against the COVID-19 virus.\n\nAbstract:\nOBJECTIVE To analyze the characteristics of YouTube videos in Spanish on the basic measures to prevent coronavirus disease 2019 (COVID-19).\nMETHODS On 18 March 2020, a search was conducted on YouTube using the terms \"Prevencion Coronavirus\" and \"Prevencion COVID-19\".\nWe studied the associations between the type of authorship and the country of publication with other variables (such as the number of likes and basic measures to prevent COVID-19 according to the World Health Organization, among others) with univariate analysis and a multiple logistic regression model.\nRESULTS A total of 129 videos were evaluated; 37.2% were produced in Mexico (25.6%) and Spain (11.6%), and 56.6% were produced by mass media, including television and newspapers.\nThe most frequently reported basic preventive measure was hand washing (71.3%), and the least frequent was not touching the eyes, nose, and mouth (24.0%).\nHoaxes (such as eating garlic or citrus to prevent COVID-19) were detected in 15 videos (10.9%).\nIn terms of authorship, papers produced by health professionals had a higher probability of reporting hand hygiene (OR (95% CI) = 4.20 (1.17-15.09)) and respiratory hygiene (OR (95% CI) = 3.05 (1.22-7.62)) as preventive measures.\nCONCLUSION Information from YouTube in Spanish on basic measures to prevent COVID-19 is usually not very complete and differs according to the type of authorship.\nOur findings make it possible to guide Spanish-speaking users on the characteristics of the videos to be viewed in order to obtain reliable information.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Hoaxes (such as eating garlic or citrus to prevent COVID-19) were detected in 15 videos (10.9%).\"]}", "id": 960}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Residual sars-cov-2 viral antigens detected in gastrointestinal and hepatic tissues from all recovered covid-19 patients\n\nAbstract:\nResidual SARS-CoV-2 RNA has been detected in stool samples and gastrointestinal tissues during the convalescence phase of COVID-19 infection.\nThis raises concern for persistence of SARS-CoV-2 virus particles and faecal-oral transmissibility in recovered COVID-19 patients.\nUsing multiplex immunohistochemistry, we unexpectedly detected SARS-CoV-2 viral antigens in intestinal and liver tissues, in surgical samples obtained from two patients who recovered from COVID-19.\nWe further validated the presence of virus by RT-PCR and flow cytometry to detect SARS-CoV-2-specific immunity in the tissues.\nThese findings might have important implications in terms of disease management and public health policy regarding transmission of COVID-19 via faecal-oral and iatrogenic routes during the convalescence phase.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"This raises concern for persistence of SARS-CoV-2 virus particles and faecal-oral transmissibility in recovered COVID-19 patients.\", \"Using multiplex immunohistochemistry, we unexpectedly detected SARS-CoV-2 viral antigens in intestinal and liver tissues, in surgical samples obtained from two patients who recovered from COVID-19.\", \"We further validated the presence of virus by RT-PCR and flow cytometry to detect SARS-CoV-2-specific immunity in the tissues.\", \"These findings might have important implications in terms of disease management and public health policy regarding transmission of COVID-19 via faecal-oral and iatrogenic routes during the convalescence phase.\"]}", "id": 961}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Serological surveys in reunion island of the first hospitalized patients revealed that long-lived immunoglobulin g antibodies specific against sars-cov2 virus are rapidly circulating in severe cases\n\nAbstract:\nBoth cellular and humoral immunities are critically important to control COVID19 infection but little is known about the kinetics of those responses and, in particular, in patients who will go on to develop a severe form of the disease over several weeks.\nWe herein report the first set of data of our prospective cohort study of 90 hospitalized cases.\nSerological surveys were thoroughly performed over 2 month period by assessing IgG and IgM responses by immunofluorescence, immunoblot, Western blot and conventional ELISA using clinical RUN isolates of SARS-CoV-2 immobilized on 96 well plates.\nWhile the IgM and, unexpectedly, the IgG responses were readily detected early during the course of the disease (5-7 days post-first symptoms), our results (n=3-5 and over the full dilution set of the plasma 1/200 to 1/12800) demonstrated a significant decrease (over 2.5-fold) of IgG levels in severe (ICU) hospitalized patients (exemplified in patient 1) by WB and ELISA.\nIn contrast, mild non-ICU patients had a steady and yet robust rise in their specific IgG levels against the virus.\nInterestingly, both responses (IgM and IgG) were initially against the nucleocapsid (50kDa band on the WB) and spreading to other major viral protein S and domains (S1 and S2.\nIn conclusion, serological testing may be helpful for the diagnosis of patients with negative RT-PCR results and for the identification of asymptomatic cases.\nMoreover, medical care and protections should be maintained particularly for recovered patients (severe cases) who may remain at risk of relapsing or reinfection.\nExperiments to ascertain T cell responses but although their kinetics overtime are now highly warranted.\nAll in all, these studies will help to delineate the best routes for vaccination.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Serological surveys were thoroughly performed over 2 month period by assessing IgG and IgM responses by immunofluorescence, immunoblot, Western blot and conventional ELISA using clinical RUN isolates of SARS-CoV-2 immobilized on 96 well plates.\", \"While the IgM and, unexpectedly, the IgG responses were readily detected early during the course of the disease (5-7 days post-first symptoms), our results (n=3-5 and over the full dilution set of the plasma 1/200 to 1/12800) demonstrated a significant decrease (over 2.5-fold) of IgG levels in severe (ICU) hospitalized patients (exemplified in patient 1) by WB and ELISA.\", \"In conclusion, serological testing may be helpful for the diagnosis of patients with negative RT-PCR results and for the identification of asymptomatic cases.\"]}", "id": 962}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Wearing a surgical face mask probably won't keep you from getting sick\n\nAbstract:\nEvidence that face masks provide effective protection against respiratory infections in the community is scarce.\nHowever, face masks are widely used by health workers as part of droplet precautions when caring for patients with respiratory infections.\nIt would therefore be reasonable to suggest that consistent widespread use of face masks in the community could prevent further spread of the Severe Acute Respiratory Syndrome-Coronavirus 2 (SARS-CoV-2).\nIn this study we examine public face mask wearing in Uganda where a proportion wears masks to protect against acquiring, and the other to prevent from transmitting SARS-CoV-2.\nThe objective of this study was to determine what percentage of the population would have to wear face masks to reduce susceptibility to and infectivity of COVID-19 in Uganda, keeping the basic reproduction number below unity and/or flattening the curve.\nWe used an SEIAQRD model for the analysis.\nResults show that implementation of facemasks has a relatively large impact on the size of the coronavirus epidemic in Uganda.\nWe find that the critical mask adherence is 5 per 100 when 80% wear face masks.\nA cost-effective analysis shows that utilizing funds to provide 1 public mask to the population has a per capita compounded cost of USD 1.34.\nIf provision of face masks is done simultaneously with supportive care, the per capita compounded cost is USD 1.965, while for the case of only treatment and no provision of face masks costs each Ugandan USD 4.0579.\nWe conclude that since it is hard to achieve a 100% adherence to face masks, government might consider provision of face masks in conjunction with provision of care.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"It would therefore be reasonable to suggest that consistent widespread use of face masks in the community could prevent further spread of the Severe Acute Respiratory Syndrome-Coronavirus 2 (SARS-CoV-2).\", \"We find that the critical mask adherence is 5 per 100 when 80% wear face masks.\"]}", "id": 963}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Activation of sars-cov-2 orf8, a rapidly evolving coronavirus protein implicated in immune evasion\n\nAbstract:\nThe molecular basis for the severity and rapid spread of the COVID-19 disease caused by SARS-CoV-2 is largely unknown.\nORF8 is a rapidly evolving accessory protein that has been proposed to interfere with immune responses.\nThe crystal structure of SARS-CoV-2 ORF8 was determined at 2.04 \u00c5 resolution by x-ray crystallography.\nThe structure reveals a ~60 residue core similar to SARS-CoV ORF7a with the addition of two dimerization interfaces unique to SARS-CoV-2 ORF8.\nA covalent disulfide-linked dimer is formed through an N-terminal sequence specific to SARS-CoV-2, while a separate non-covalent interface is formed by another SARS-CoV-2-specific sequence, (73)YIDI(76).\nTogether the presence of these interfaces shows how SARS-CoV-2 ORF8 can form unique large-scale assemblies not possible for SARS-CoV, potentially mediating unique immune suppression and evasion activities.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Together the presence of these interfaces shows how SARS-CoV-2 ORF8 can form unique large-scale assemblies not possible for SARS-CoV, potentially mediating unique immune suppression and evasion activities.\"]}", "id": 964}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Although smoking is the most common way to use marijuana, some people bake it into a brownie or other food. Eating pot might spare you the lung effects of this drug, but that doesn't mean it's safe.\n\nAbstract:\nThe recently discovered novel coronavirus, SARS-CoV-2 (COVID-19 virus), has brought the whole world to standstill with critical challenges, affecting both health and economic sectors worldwide.\nAlthough initially, this pandemic was associated with causing severe pulmonary and respiratory disorders, recent case studies reported the association of cerebrovascular-neurological dysfunction in COVID-19 patients, which is also life-threatening.\nSeveral SARS-CoV-2 positive case studies have been reported where there are mild or no symptoms of this virus.\nHowever, a selection of patients are suffering from large artery ischemic strokes.\nAlthough the pathophysiology of the SARS-CoV-2 virus affecting the cerebrovascular system has not been elucidated yet, researchers have identified several pathogenic mechanisms, including a role for the ACE2 receptor.\nTherefore, it is extremely crucial to identify the risk factors related to the progression and adverse outcome of cerebrovascular-neurological dysfunction in COVID-19 patients.\nSince many articles have reported the effect of smoking (tobacco and cannabis) and vaping in cerebrovascular and neurological systems, and considering that smokers are more prone to viral and bacterial infection compared to non-smokers, it is high time to explore the probable correlation of smoking in COVID-19 patients.\nHerein, we have reviewed the possible role of smoking and vaping on cerebrovascular and neurological dysfunction in COVID-19 patients, along with potential pathogenic mechanisms associated with it.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Since many articles have reported the effect of smoking (tobacco and cannabis) and vaping in cerebrovascular and neurological systems, and considering that smokers are more prone to viral and bacterial infection compared to non-smokers, it is high time to explore the probable correlation of smoking in COVID-19 patients.\"]}", "id": 965}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Virus shedding affected early covid-19 spread\n\nAbstract:\nAs the SARS-Cov-2 virus spreads around the world afflicting millions of people, it has undergone divergent genetic mutations.\nAlthough most of these mutations are expected to be inconsequential, some mutations in the spike protein structure have been hypothesized to affect the critical stage at which the virus invades human cells, which could affect transmission probability and disease expression.\nIf true, then we expect an increased growth rate of reported COVID-19 cases in regions dominated by viruses with these altered proteins.\nWe modeled early global infection dynamics based on clade assignment along with other demographic and meteorological factors previously found to be important.\nClade, but not variant D614G which has been associated with increased viral load, enhanced our ability to describe early COVID-19 growth dynamics.\nIncluding clade identity in models significantly improved predictions over earlier work based only on weather and demographic variables.\nIn particular, higher proportions of clade 19A and 19B were negatively correlated with COVID-19 growth rate, whereas higher proportions of 20A and 20C were positively correlated with growth rate.\nA strong interaction between the prevalence of clade 20C and relative humidity suggests that the impact of clade identity might be more important when coupled with certain weather conditions.\nIn particular, 20C an 20A generate the highest growth rates when coupled with low humidity.\nProjections based on data through April 2020 suggest that, without intervention, COVID-19 has the potential to grow more quickly in regions dominated by the 20A and 20C clades, including most of South and North America.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"In particular, higher proportions of clade 19A and 19B were negatively correlated with COVID-19 growth rate, whereas higher proportions of 20A and 20C were positively correlated with growth rate.\"]}", "id": 966}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Selenium deficiency is associated with mortality risk from covid-19\n\nAbstract:\nSARS-CoV-2 infections underlie the current coronavirus disease (COVID-19) pandemic and are causative for a high death toll particularly among elderly subjects and those with comorbidities.\nSelenium (Se) is an essential trace element of high importance for human health and particularly for a well-balanced immune response.\nThe mortality risk from a severe disease like sepsis or polytrauma is inversely related to Se status.\nWe hypothesized that this relation also applies to COVID-19.\nSerum samples (n = 166) from COVID-19 patients (n = 33) were collected consecutively and analyzed for total Se by X-ray fluorescence and selenoprotein P (SELENOP) by a validated ELISA.\nBoth biomarkers showed the expected strong correlation (r = 0.7758, p < 0.001), pointing to an insufficient Se availability for optimal selenoprotein expression.\nIn comparison with reference data from a European cross-sectional analysis (EPIC, n = 1915), the patients showed a pronounced deficit in total serum Se (mean \u00b1 SD, 50.8 \u00b1 15.7 vs. 84.4 \u00b1 23.4 \u00b5g/L) and SELENOP (3.0 \u00b1 1.4 vs. 4.3 \u00b1 1.0 mg/L) concentrations.\nA Se status below the 2.5th percentile of the reference population, i.e., [Se] < 45.7 \u00b5g/L and [SELENOP] < 2.56 mg/L, was present in 43.4% and 39.2% of COVID samples, respectively.\nThe Se status was significantly higher in samples from surviving COVID patients as compared with non-survivors (Se; 53.3 \u00b1 16.2 vs. 40.8 \u00b1 8.1 \u00b5g/L, SELENOP; 3.3 \u00b1 1.3 vs. 2.1 \u00b1 0.9 mg/L), recovering with time in survivors while remaining low or even declining in non-survivors.\nWe conclude that Se status analysis in COVID patients provides diagnostic information.\nHowever, causality remains unknown due to the observational nature of this study.\nNevertheless, the findings strengthen the notion of a relevant role of Se for COVID convalescence and support the discussion on adjuvant Se supplementation in severely diseased and Se-deficient patients.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"In comparison with reference data from a European cross-sectional analysis (EPIC, n = 1915), the patients showed a pronounced deficit in total serum Se (mean \\u00b1 SD, 50.8 \\u00b1 15.7 vs. 84.4 \\u00b1 23.4 \\u00b5g/L) and SELENOP (3.0 \\u00b1 1.4 vs. 4.3 \\u00b1 1.0 mg/L) concentrations.\"]}", "id": 967}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: The Weather Network - Wildfire smoke could worsen COVID-19\n\nAbstract:\nThe coronavirus disease 2019 (COVID-19) outbreak has become a severe public health issue.\nThe novelty of the virus prompts a search for understanding of how ecological factors affect the transmission and survival of the virus.\nSeveral studies have robustly identified a relationship between temperature and the number of cases.\nHowever, there is no specific study for a tropical climate such as Brazil.\nThis work aims to determine the relationship of temperature to COVID-19 infection for the state capital cities of Brazil.\nCumulative data with the daily number of confirmed cases was collected from February 27 to April 1, 2020, for all 27 state capital cities of Brazil affected by COVID-19.\nA generalized additive model (GAM) was applied to explore the linear and nonlinear relationship between annual average temperature compensation and confirmed cases.\nAlso, a polynomial linear regression model was proposed to represent the behavior of the growth curve of COVID-19 in the capital cities of Brazil.\nThe GAM dose-response curve suggested a negative linear relationship between temperatures and daily cumulative confirmed cases of COVID-19 in the range from 16.8 \u00b0C to 27.4 \u00b0C.\nEach 1 \u00b0C rise of temperature was associated with a -4.8951% (t = -2.29, p = 0.0226) decrease in the number of daily cumulative confirmed cases of COVID-19.\nA sensitivity analysis assessed the robustness of the results of the model.\nThe predicted R-squared of the polynomial linear regression model was 0.81053.\nIn this study, which features the tropical temperatures of Brazil, the variation in annual average temperatures ranged from 16.8 \u00b0C to 27.4 \u00b0C.\nResults indicated that temperatures had a negative linear relationship with the number of confirmed cases.\nThe curve flattened at a threshold of 25.8 \u00b0C.\nThere is no evidence supporting that the curve declined for temperatures above 25.8 \u00b0C.\nThe study had the goal of supporting governance for healthcare policymakers.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 968}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Hydroxychloroquine is an Effective Treatment for COVID-19\n\nAbstract:\nBackground: Coronavirus pandemic is currently a global public health emergency.\nAt present, no pharmacological treatment is known to treat this condition, and there is a need to review the available treatments.\nObjective: While there have been studies to describe the role of chloroquine and hydroxychloroquine in various viral conditions, there is limited information about the use of them in COVID-19.\nThis systematic review aims to summarize the available evidence regarding the role of chloroquine in treating coronavirus infection.\nMethods: The preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines were used for this review.\nA literature search was performed using PUBMED & Google Scholar to find articles about the role of CQ in COVID-19 patients.\nResults: We included 19 publications (Five published articles, three letters/correspondence, one commentary, five pre-proofs of accepted articles, one abstract of yet to be published article, and four were pre-prints (not yet peer-reviewed) articles) in this systematic review.\nAll the articles mentioned about the role of chloroquine and /or hydroxychloroquine in limiting the infection with SARS-CoV-2 (the virus causing COVID-19).\nConclusions: There is theoretical, experimental, preclinical and clinical evidence of the effectiveness of chloroquine in patients affected with COVID-19.\nThere is adequate evidence of drug safety from the long-time clinical use of chloroquine and hydroxychloroquine in other indications.\nMore data from ongoing and future trials will add more insight into the role of chloroquine and hydroxychloroquine in COVID-19 infection.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Conclusions: There is theoretical, experimental, preclinical and clinical evidence of the effectiveness of chloroquine in patients affected with COVID-19.\"]}", "id": 969}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: cloth face mask with a filter can help prevent the spread of COVID-19\n\nAbstract:\nMa's research shows N95 masks, medical masks, even homemade masks could block at least 90% of the virus in aerosols(1).\nThis study puts the debate on whether the public wear masks back on the table.\nRecently Science interviewed Dr. Gao, director\u2010general of Chinese Center for Disease Control and Prevention (CDC).\nThis article is protected by copyright.\nAll rights reserved.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Ma's research shows N95 masks, medical masks, even homemade masks could block at least 90% of the virus in aerosols(1).\"]}", "id": 970}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Many readers have written in to ask whether ibuprofen or other non-steroidal anti-inflammatory drugs, or NSAIDs, can worsen COVID-19, the disease caused by the novel coronavirus.\n\nAbstract:\n: The COVID-19 pandemic is challenging our cardiovascular care of patients with heart diseases.\nIn the setting of pericardial diseases, there are two possible different scenarios to consider: the patient being treated for pericarditis who subsequently becomes infected with SARS-CoV-2, and the patient with COVID-19 who develops pericarditis or pericardial effusion.\nIn both conditions, clinicians may be doubtful regarding the safety of nonsteroidal anti-inflammatory drugs (NSAIDs), corticosteroids, colchicine, and biological agents, such as anti-IL1 agents (e.g. anakinra), that are the mainstay of therapy for pericarditis.\nFor NSAIDs, there is no clear scientific evidence linking ibuprofen and other NSAIDs to worsening of COVID-19; however, it seems prudent to continue them, if necessary to control pericarditis, and on the other hand, to prefer paracetamol for fever and systemic symptoms related to COVID-19.\nTreatments with corticosteroids, colchicine, and anakinra appear well tolerated in the context of COVID-19 infection and are currently actively evaluated as potential therapeutic options for COVID infection at different stages of the disease.\nOn this basis, currently most treatments for pericarditis do not appear contraindicated also in the presence of possible COVID-19 infection and should not be discontinued, and some (corticosteroids, colchicine, and anakinra) can be considered to treat both conditions.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 971}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: hydroxychloroquine cures covid-19.\n\nAbstract:\nHydroxychloroquine has been promoted for its use in treatment of COVID-19 patients based on in-vitro evidences.\nWe searched the databases to include randomized and observational studies evaluating the effect of Hydroxychloroquine on mortality in COVID-19 patients.\nThe outcome was summarized as odds ratios (OR) with a 95% confidence interval (CI).We used the inverse-variance method with a random effect model and assessed the heterogeneity using I2 test.\nWe used ROBINS-I tool to assess methodological quality of the included studies.\nWe performed the meta-analysis using 'Review manager software version 5.3'.\nWe identified 6 observationalstudies satisfying the selection criteria.\nIn all studies, Hydroxychloroquine was given as add on to the standard care and effect was compared with the standard care alone.\nA pooled analysis observed 251 deaths in 1331 participants of the Hydroxychloroquine arm and 363 deaths in 1577 participants of the control arm.\nThere was no difference in odds of mortality events amongst Hydroxychloroquine and supportive care arm [1.25 (95% CI: 0.65, 2.38); I2 = 80%].\nA similar trend was observed with moderate risk of bias studies [0.95 (95% CI: 0.44, 2.06); I2 = 85%].\nThe odds of mortality were significantly higher in patients treated with Hydroxychloroquine + Azithromycin than supportive care alone [2.34 (95% CI: 1.63, 3.34); I2 = 0%].\nA pooled analysis of recently published studies suggests no additional benefit for reducing mortality in COVID-19 patients when Hydroxychloroquine is given as add-on to the standard care.\nGraphical Abstract.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"The odds of mortality were significantly higher in patients treated with Hydroxychloroquine + Azithromycin than supportive care alone [2.34 (95% CI: 1.63, 3.34); I2 = 0%].\", \"A pooled analysis of recently published studies suggests no additional benefit for reducing mortality in COVID-19 patients when Hydroxychloroquine is given as add-on to the standard care.\"]}", "id": 972}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: To help slow the spread and reduce your risk of COVID-19, stay at least 6 feet away from others. Keeping physical distance is important, even if you are not sick.\n\nAbstract:\nSocial distancing measures, with varying degrees of restriction, have been imposed around the world in order to stem the spread of COVID-19.\nIn this work we analyze the effect of current social distancing measures in the United States.\nWe quantify the reduction in doubling rate, by state, that is associated with social distancing.\nWe find that social distancing is associated with a statistically-significant reduction in the doubling rate for all but three states.\nAt the same time, we do not find significant evidence that social distancing has resulted in a reduction in the number of daily confirmed cases.\nInstead, social distancing has merely stabilized the spread of the disease.\nWe provide an illustration of our findings for each state, including point estimates of the effective reproduction number, R, both with and without social distancing.\nWe also discuss the policy implications of our findings.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"We find that social distancing is associated with a statistically-significant reduction in the doubling rate for all but three states.\"]}", "id": 973}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: The drugs have anti-inflammatory effects \"in addition to their blood pressure benefits.\n\nAbstract:\nCoronavirus disease 2019 (COVID-19) is a viral pandemic precipitated by the severe acute respiratory syndrome coronavirus 2.\nSince previous reports suggested that viral entry into cells may involve angiotensin converting enzyme 2, there has been growing concern that angiotensin converting enzyme inhibitor (ACEI) and angiotensin II receptor blocker (ARB) use may exacerbate the disease severity.\nIn this retrospective, single-center US study of adult patients diagnosed with COVID-19, we evaluated the association of ACEI/ARB use with hospital admission.\nSecondary outcomes included: ICU admission, mechanical ventilation, length of hospital stay, use of inotropes, and all-cause mortality.\nPropensity score matching was performed to account for potential confounders.\nAmong 590 unmatched patients diagnosed with COVID-19, 78 patients were receiving ACEI/ARB (median age 63 years and 59.7% male) and 512 patients were non-users (median age 42 years and 47.1% male).\nIn the propensity matched population, multivariate logistic regression analysis adjusting for age, gender and comorbidities demonstrated that ACEI/ARB use was not associated with hospital admission (OR 1.2, 95% CI 0.5-2.7, p = 0.652).\nCAD and CKD/ESRD remained independently associated with admission to hospital.\nAll-cause mortality, ICU stay, need for ventilation, and inotrope use was not significantly different between the 2 study groups.\nIn conclusion, among patients who were diagnosed with COVID-19, ACEI/ARB use was not associated with increased risk of hospital admission.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 974}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Coronavirus disease (COVID-19) is an infectious disease caused by a newly discovered coronavirus\n\nAbstract:\nCoronavirus disease 2019 (COVID-19) originated in the city of Wuhan, Hubei Province, Central China, and has spread quickly to 72 countries to date.\nCOVID-19 is caused by a novel coronavirus, named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) [previously provisionally known as 2019 novel coronavirus (2019-nCoV)].\nAt present, the newly identified SARS-CoV-2 has caused a large number of deaths with tens of thousands of confirmed cases worldwide, posing a serious threat to public health.\nHowever, there are no clinically approved vaccines or specific therapeutic drugs available for COVID-19.\nIntensive research on the newly emerged SARS-CoV-2 is urgently needed to elucidate the pathogenic mechanisms and epidemiological characteristics and to identify potential drug targets, which will contribute to the development of effective prevention and treatment strategies.\nHence, this review will focus on recent progress regarding the structure of SARS-CoV-2 and the characteristics of COVID-19, such as the aetiology, pathogenesis and epidemiological characteristics.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 975}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: There's been much speculation about whether vitamin D might prevent or help survival with COVID-19, and two new studies appear to underscore the link.\n\nAbstract:\nThe severity of coronavirus 2019 infection (COVID-19) is determined by the presence of pneumonia, severe acute respiratory distress syndrome (SARS-CoV-2), myocarditis, microvascular thrombosis and/or cytokine storms, all of which involve underlying inflammation.\nA principal defence against uncontrolled inflammation, and against viral infection in general, is provided by T regulatory lymphocytes (Tregs).\nTreg levels have been reported to be low in many COVID-19 patients and can be increased by vitamin D supplementation.\nLow vitamin D levels have been associated with an increase in inflammatory cytokines and a significantly increased risk of pneumonia and viral upper respiratory tract infections.\nVitamin D deficiency is associated with an increase in thrombotic episodes, which are frequently observed in COVID-19.\nVitamin D deficiency has been found to occur more frequently in patients with obesity and diabetes.\nThese conditions are reported to carry a higher mortality in COVID-19.\nIf vitamin D does in fact reduce the severity of COVID-19 in regard to pneumonia/ARDS, inflammation, inflammatory cytokines and thrombosis, it is our opinion that supplements would offer a relatively easy option to decrease the impact of the pandemic.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 976}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: covid-19 increases risk of heart attacks and stroke\n\nAbstract:\nBACKGROUND: The 2019 novel coronavirus has caused the outbreak of the acute respiratory disease in Wuhan, Hubei Province of China since December 2019.\nThis study was performed to analyze the clinical characteristics of patients who succumbed to and who recovered from 2019 novel coronavirus disease (COVID-19).\nMETHODS: Clinical data were collected from two tertiary hospitals in Wuhan.\nA retrospective investigation was conducted to analyze the clinical characteristics of fatal cases of COVID-19 (death group) and we compare them with recovered patients (recovered group).\nContinuous variables were analyzed using the Mann-Whitney U test.\nCategorical variables were analyzed by &#967; test or Fisher exact test as appropriate.\nRESULTS: Our study enrolled 109 COVID-19 patients who died during hospitalization and 116 recovered patients.\nThe median age of the death group was older than the recovered group (69 [62, 74] vs. 40 [33, 57] years, Z\u00e2\u0080\u008a=\u00e2\u0080\u008a9.738, P\u00e2\u0080\u008a<\u00e2\u0080\u008a0.001).\nMore patients in the death group had underlying diseases (72.5% vs. 41.4%, &#967;\u00e2\u0080\u008a=\u00e2\u0080\u008a22.105, P\u00e2\u0080\u008a<\u00e2\u0080\u008a0.001).\nPatients in the death group had a significantly longer time of illness onset to hospitalization (10.0 [6.5, 12.0] vs. 7.0 [5.0, 10.0] days, Z\u00e2\u0080\u008a=\u00e2\u0080\u008a3.216, P\u00e2\u0080\u008a=\u00e2\u0080\u008a0.001).\nOn admission, the proportions of patients with symptoms of dyspnea (70.6% vs. 19.0%, &#967;\u00e2\u0080\u008a=\u00e2\u0080\u008a60.905, P\u00e2\u0080\u008a<\u00e2\u0080\u008a0.001) and expectoration (32.1% vs. 12.1%, &#967;\u00e2\u0080\u008a=\u00e2\u0080\u008a13.250, P\u00e2\u0080\u008a<\u00e2\u0080\u008a0.001) were significantly higher in the death group.\nThe blood oxygen saturation was significantly lower in the death group (85 [77, 91]% vs. 97 [95, 98]%, Z\u00e2\u0080\u008a=\u00e2\u0080\u008a10.625, P\u00e2\u0080\u008a<\u00e2\u0080\u008a0.001).\nThe white blood cell (WBC) in death group was significantly higher on admission (7.23 [4.87, 11.17] vs. 4.52 [3.62, 5.88] \u00d710/L, Z\u00e2\u0080\u008a=\u00e2\u0080\u008a7.618, P\u00e2\u0080\u008a<\u00e2\u0080\u008a0.001).\nPatients in the death group exhibited significantly lower lymphocyte count (0.63 [0.40, 0.79] vs. 1.00 [0.72, 1.27] \u00d710/L, Z\u00e2\u0080\u008a=\u00e2\u0080\u008a8.037, P\u00e2\u0080\u008a<\u00e2\u0080\u008a0.001) and lymphocyte percentage (7.10 [4.45, 12.73]% vs. 23.50 [15.27, 31.25]%, Z\u00e2\u0080\u008a=\u00e2\u0080\u008a10.315, P\u00e2\u0080\u008a<\u00e2\u0080\u008a0.001) on admission, and the lymphocyte percentage continued to decrease during hospitalization (7.10 [4.45, 12.73]% vs. 2.91 [1.79, 6.13]%, Z\u00e2\u0080\u008a=\u00e2\u0080\u008a5.242, P\u00e2\u0080\u008a<\u00e2\u0080\u008a0.001).\nAlanine transaminase (22.00 [15.00, 34.00] vs. 18.70 [13.00, 30.38] U/L, Z\u00e2\u0080\u008a=\u00e2\u0080\u008a2.592, P\u00e2\u0080\u008a=\u00e2\u0080\u008a0.010), aspartate transaminase (34.00 [27.00, 47.00] vs. 22.00 [17.65, 31.75] U/L, Z\u00e2\u0080\u008a=\u00e2\u0080\u008a7.308, P\u00e2\u0080\u008a<\u00e2\u0080\u008a0.001), and creatinine levels (89.00 [72.00, 133.50] vs. 65.00 [54.60, 78.75] \u00b5mol/L, Z\u00e2\u0080\u008a=\u00e2\u0080\u008a6.478, P\u00e2\u0080\u008a<\u00e2\u0080\u008a0.001) were significantly higher in the death group than those in the recovered group.\nC-reactive protein (CRP) levels were also significantly higher in the death group on admission (109.25 [35.00, 170.28] vs. 3.22 [1.04, 21.80] mg/L, Z\u00e2\u0080\u008a=\u00e2\u0080\u008a10.206, P\u00e2\u0080\u008a<\u00e2\u0080\u008a0.001) and showed no significant improvement after treatment (109.25 [35.00, 170.28] vs. 81.60 [27.23, 179.08] mg/L, Z\u00e2\u0080\u008a=\u00e2\u0080\u008a1.219, P\u00e2\u0080\u008a=\u00e2\u0080\u008a0.233).\nThe patients in the death group had more complications such as acute respiratory distress syndrome (ARDS) (89.9% vs. 8.6%, &#967;\u00e2\u0080\u008a=\u00e2\u0080\u008a148.105, P\u00e2\u0080\u008a<\u00e2\u0080\u008a0.001), acute cardiac injury (59.6% vs. 0.9%, &#967;\u00e2\u0080\u008a=\u00e2\u0080\u008a93.222, P\u00e2\u0080\u008a<\u00e2\u0080\u008a0.001), acute kidney injury (18.3% vs. 0%, &#967;\u00e2\u0080\u008a=\u00e2\u0080\u008a23.257, P\u00e2\u0080\u008a<\u00e2\u0080\u008a0.001), shock (11.9% vs. 0%, &#967;\u00e2\u0080\u008a=\u00e2\u0080\u008a14.618, P\u00e2\u0080\u008a<\u00e2\u0080\u008a0.001), and disseminated intravascular coagulation (DIC) (6.4% vs. 0%, &#967;\u00e2\u0080\u008a=\u00e2\u0080\u008a7.655, P\u00e2\u0080\u008a=\u00e2\u0080\u008a0.006).\nCONCLUSIONS: Compared to the recovered group, more patients in the death group exhibited characteristics of advanced age, pre-existing comorbidities, dyspnea, oxygen saturation decrease, increased WBC count, decreased lymphocytes, and elevated CRP levels.\nMore patients in the death group had complications such as ARDS, acute cardiac injury, acute kidney injury, shock, and DIC.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"CONCLUSIONS: Compared to the recovered group, more patients in the death group exhibited characteristics of advanced age, pre-existing comorbidities, dyspnea, oxygen saturation decrease, increased WBC count, decreased lymphocytes, and elevated CRP levels.\"]}", "id": 977}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Imbalanced host response to sars-cov-2 drives development of covid-19\n\nAbstract:\nViral pandemics, such as the one caused by SARS-CoV-2, pose an imminent threat to humanity.\nBecause of its recent emergence, there is a paucity of information regarding viral behavior and host response following SARS-CoV-2 infection.\nHere we offer an in-depth analysis of the transcriptional response to SARS-CoV-2 compared with other respiratory viruses.\nCell and animal models of SARS-CoV-2 infection, in addition to transcriptional and serum profiling of COVID-19 patients, consistently revealed a unique and inappropriate inflammatory response.\nThis response is defined by low levels of type I and III interferons juxtaposed to elevated chemokines and high expression of IL-6.\nWe propose that reduced innate antiviral defenses coupled with exuberant inflammatory cytokine production are the defining and driving features of COVID-19.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Cell and animal models of SARS-CoV-2 infection, in addition to transcriptional and serum profiling of COVID-19 patients, consistently revealed a unique and inappropriate inflammatory response.\", \"We propose that reduced innate antiviral defenses coupled with exuberant inflammatory cytokine production are the defining and driving features of COVID-19.\"]}", "id": 978}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Sterilizing immunity against sars-cov-2 in hamsters conferred by a single recombinant subunit vaccine.\n\nAbstract:\nA safe and effective SARS-CoV-2 vaccine is essential to avert the on-going COVID-19 pandemic.\nHere, we developed a subunit vaccine, which is comprised of CHO-expressed spike ectodomain protein (StriFK) and nitrogen bisphosphonates-modified zinc-aluminum hybrid adjuvant (FH002C).\nThis vaccine candidate rapidly elicited the robust humoral response, Th1/Th2 balanced helper CD4 T cell and CD8 T cell immune response in animal models.\nIn mice, hamsters, and non-human primates, 2-shot and 3-shot immunization of StriFK-FH002C generated 28-to 38-fold and 47-to 269-fold higher neutralizing antibody titers than the human COVID-19 convalescent plasmas, respectively.\nMore importantly, the StriFK-FH002C immunization conferred sterilizing immunity to prevent SARS-CoV-2 infection and transmission, which also protected animals from virus-induced weight loss, COVID-19-like symptoms, and pneumonia in hamsters.\nVaccine-induced neutralizing and cell-based receptor-blocking antibody titers correlated well with protective efficacy in hamsters, suggesting vaccine-elicited protection is immune-associated.\nThe StriFK-FH002C provided a promising SARS-CoV-2 vaccine candidate for further clinical evaluation.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"More importantly, the StriFK-FH002C immunization conferred sterilizing immunity to prevent SARS-CoV-2 infection and transmission, which also protected animals from virus-induced weight loss, COVID-19-like symptoms, and pneumonia in hamsters.\", \"Vaccine-induced neutralizing and cell-based receptor-blocking antibody titers correlated well with protective efficacy in hamsters, suggesting vaccine-elicited protection is immune-associated.\"]}", "id": 979}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Scientists are endeavoring to find antivirals specific to the virus. Several drugs such as chloroquine, arbidol, remdesivir, and favipiravir are currently undergoing clinical studies to test their efficacy and safety in the treatment of coronavirus disease 2019 (COVID-19) in China; some promising results have been achieved thus far.\n\nAbstract:\nThe current pandemic of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has presented unprecedented challenges to the healthcare systems in almost every country around the world.\nCurrently, there are no proven effective vaccines or therapeutic agents against the virus.\nCurrent clinical management includes infection prevention and control measures and supportive care including supplemental oxygen and mechanical ventilatory support.\nEvolving research and clinical data regarding the virologic SARS-CoV-2 suggest a potential list of repurposed drugs with appropriate pharmacological effects and therapeutic efficacies in treating COVID-19 patients.\nIn this review, we will update and summarize the most common and plausible drugs for the treatment of COVID-19 patients.\nThese drugs and therapeutic agents include antiviral agents (remdesivir, hydroxychloroquine, chloroquine, lopinavir, umifenovir, favipiravir, and oseltamivir), and supporting agents (Ascorbic acid, Azithromycin, Corticosteroids, Nitric oxide, IL-6 antagonists), among others.\nWe hope that this review will provide useful and most updated therapeutic drugs to prevent, control, and treat COVID-19 patients until the approval of vaccines and specific drugs targeting SARS-CoV-2.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Currently, there are no proven effective vaccines or therapeutic agents against the virus.\"]}", "id": 980}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: COVID-19 and kids: What can happen when children get the coronavirus. A rare but sometimes deadly syndrome poses extra risk for COVID's youngest victims.\n\nAbstract:\nThe novel severe acute respiratory syndrome coronavirus 2 (SARS-COV 2) has rapidly spread worldwide with increasing hospitalization and mortality rate.\nOngoing studies and accumulated data are de- tailing the features and the effects of the new coronavirus disease 19 (COVID 19) in the adult population, and cardiovascular involvement is emerging as the most significant and life-threatening complication, with an in- creased risk of morbidity and mortality in patients with underlying cardiovascular disease.\nAt present, though the limited data on the effects of COVID 19 in pediatric patients, children seem to count for a little proportion of SARS-COV 2 infection, and present with less severe disease and effects However infants and toddlers are at risk of developing critical course.\nThe disease has a range of clinical presentations in children, for which the potential need for further investigation of myocardial injury and cardiovascular issues should be kept in mind to avoid misdiagnosing severe clinical entities.\nOverlapping with Kawasaki disease is a concern, particularly the incomplete and atypical form.\nWe aim to summarize the initial considerations and potential cardiovascular implications of COVID-19 for children and patients with congenital heart disease.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"At present, though the limited data on the effects of COVID 19 in pediatric patients, children seem to count for a little proportion of SARS-COV 2 infection, and present with less severe disease and effects However infants and toddlers are at risk of developing critical course.\", \"The disease has a range of clinical presentations in children, for which the potential need for further investigation of myocardial injury and cardiovascular issues should be kept in mind to avoid misdiagnosing severe clinical entities.\"]}", "id": 981}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: vitamin D might be able to protect people from the coronavirus (COVID-19).\n\nAbstract:\nImportance: Vitamin D treatment has been found to decrease incidence of viral respiratory tract infection, especially in vitamin D deficiency.\nIt is unknown whether COVID-19 incidence is associated with vitamin D deficiency and treatment.\nObjective: To examine whether vitamin D deficiency and treatment are associated with testing positive for COVID-19.\nDesign: Retrospective cohort study Setting: University of Chicago Medicine Participants: Patients tested for COVID-19 from 3/3/2020-4/10/2020.\nVitamin D deficiency was defined by the most recent 25-hydroxycholecalciferol <20ng/ml or 1,25-dihydroxycholecalciferol <18pg/ml within 1 year before COVID-19 testing.\nTreatment was defined by the most recent vitamin D type and dose, and treatment changes between the time of the most recent vitamin D level and time of COVID-19 testing.\nVitamin D deficiency and treatment changes were combined to categorize vitamin D status at the time of COVID-19 testing as likely deficient(last-level-deficient/treatment-not-increased), likely sufficient(last-level-not-deficient/treatment-not-decreased), or uncertain deficiency(last-level-deficient/treatment-increased or last-level-not-deficient/treatment-decreased).\nMain Outcomes and Measures: The main outcome was testing positive for COVID-19.\nMultivariable analysis tested whether the most recent vitamin D level and treatment changes after that level were associated with testing positive for COVID-19 controlling for demographic and comorbidity indicators.\nBivariate analyses of associations of treatment with vitamin D deficiency and COVID-19 were performed.\nResults: Among 4,314 patients tested for COVID-19, 499 had a vitamin D level in the year before testing.\nVitamin D status at the time of COVID-19 testing was categorized as likely deficient for 127(25%) patients, likely sufficient for 291(58%) patients, and uncertain for 81(16%) patients.\nIn multivariate analysis, testing positive for COVID-19 was associated with increasing age(RR(age<50)=1.05,p<0.021;RR(age[\u2265]50)=1.02,p<0.064)), non-white race(RR=2.54,p<0.01) and being likely vitamin D deficient (deficient/treatment-not-increased:RR=1.77,p<0.02) as compared to likely vitamin D sufficient(not-deficient/treatment-not-decreased), with predicted COVID-19 rates in the vitamin D deficient group of 21.6%(95%CI[14.0%-29.2%] ) versus 12.2%(95%CI[8.9%-15.4%]) in the vitamin D sufficient group.\nVitamin D deficiency declined with increasing vitamin D dose, especially of vitamin D3.\nVitamin D dose was not significantly associated with testing positive for COVID-19.\nConclusions and Relevance: Vitamin D deficiency that is not sufficiently treated is associated with COVID-19 risk.\nTesting and treatment for vitamin D deficiency to address COVID-19 warrant aggressive pursuit and study.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Conclusions and Relevance: Vitamin D deficiency that is not sufficiently treated is associated with COVID-19 risk.\"]}", "id": 982}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Personalized prediction approach for hospitalized patients with covid-19\n\nAbstract:\nHospitalized patients with COVID-19 experiencing respiratory symptoms have different complications (inflammatory, co-infection and thrombotic) that are identifiable by analytics patterns.\nPersonalized treatment decisions decreased early mortality (OR 0.144, CI 0.03-0.686; p=0.015).\nIncreasing age (OR 1.06; p=0.038) and therapeutic effort limitation (OR 9.684; p<0.001) were associated with higher mortality.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Personalized treatment decisions decreased early mortality (OR 0.144, CI 0.03-0.686; p=0.015).\"]}", "id": 983}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Diabetes is generally known to weaken the immune system, making it harder to protect against viral infections like COVID-19.\n\nAbstract:\nAIMS: To describe characteristics of COVID-19 patients with type 2 diabetes and to analyze risk factors for severity.\nMETHODS: Demographics, comorbidities, symptoms, laboratory findings, treatments and outcomes of COVID-19 patients with diabetes were collected and analyzed.\nRESULTS: Seventy-fourCOVID-19 patients with diabetes were included.\nTwenty-seven patients (36.5%) were severe and 10 patients (13.5%) died.\nHigher levels of blood glucose, serum amyloid A (SAA), C reactive protein and interleukin 6 were associated with severe patients compared to non-severe ones (P<0.05).\nLevels of albumin, cholesterol, high density lipoprotein, small and dense low density lipoprotein and CD4+T lymphocyte counts in severe patients were lower than those in non-severe patients (P<0.05).\nLogistic regression analysis identified decreased CD4+T lymphocyte counts (odds ratio [OR]=0.988, 95%Confidence interval [95%CI] 0.979-0.997) and increased SAA levels (OR=1.029, 95%CI 1.002-1.058) as risk factors for severity of COVID-19 with diabetes (P<0.05).\nCONCLUSIONS: Type 2 diabetic patients were more susceptible to COVID-19 than overall population, which might be associated with hyperglycemia and dyslipidemia.\nAggressive treatment should be suggested, especially when these patients had low CD4+T lymphocyte counts and high SAA levels.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"CONCLUSIONS: Type 2 diabetic patients were more susceptible to COVID-19 than overall population, which might be associated with hyperglycemia and dyslipidemia.\"]}", "id": 984}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Exploratory analysis of immunization records highlights increased sars-cov-2 rates in individuals with recent non-covid-19 vaccinations\n\nAbstract:\nMultiple clinical studies are ongoing to assess whether existing vaccines may afford protection against SARS-CoV-2 infection through trained immunity.\nIn this exploratory study, we analyze immunization records from 137,037 individuals who received SARS-CoV-2 PCR tests.\nWe find that polio, Hemophilus influenzae type-B (HIB), measles-mumps-rubella (MMR), varicella, pneumococcal conjugate (PCV13), geriatric flu, and hepatitis A / hepatitis B (HepA-HepB) vaccines administered in the past 1, 2, and 5 years are associated with decreased SARS-CoV-2 infection rates, even after adjusting for geographic SARS-CoV-2 incidence and testing rates, demographics, comorbidities, and number of other vaccinations.\nFurthermore, age, race/ethnicity, and blood group stratified analyses reveal significantly lower SARS-CoV-2 rate among black individuals who have taken the PCV13 vaccine, with relative risk of 0.45 at the 5 year time horizon (n: 653, 95% CI: (0.32, 0.64), p-value: 6.9e-05).\nThese findings suggest that additional pre-clinical and clinical studies are warranted to assess the protective effects of existing non-COVID-19 vaccines and explore underlying immunologic mechanisms.\nWe note that the findings in this study are preliminary and are subject to change as more data becomes available and as further analysis is conducted.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"We find that polio, Hemophilus influenzae type-B (HIB), measles-mumps-rubella (MMR), varicella, pneumococcal conjugate (PCV13), geriatric flu, and hepatitis A / hepatitis B (HepA-HepB) vaccines administered in the past 1, 2, and 5 years are associated with decreased SARS-CoV-2 infection rates, even after adjusting for geographic SARS-CoV-2 incidence and testing rates, demographics, comorbidities, and number of other vaccinations.\", \"Furthermore, age, race/ethnicity, and blood group stratified analyses reveal significantly lower SARS-CoV-2 rate among black individuals who have taken the PCV13 vaccine, with relative risk of 0.45 at the 5 year time horizon (n: 653, 95% CI: (0.32, 0.64), p-value: 6.9e-05).\", \"These findings suggest that additional pre-clinical and clinical studies are warranted to assess the protective effects of existing non-COVID-19 vaccines and explore underlying immunologic mechanisms.\"]}", "id": 985}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Seasonal human coronavirus antibodies are boosted upon sars-cov-2 infection but not associated with protection\n\nAbstract:\nSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has rapidly spread within the human population.\nAlthough SARS-CoV-2 is a novel coronavirus, most humans had been previously exposed to other antigenically distinct common seasonal human coronaviruses (hCoVs) before the COVID-19 pandemic.\nHere, we quantified levels of SARS-CoV-2-reactive antibodies and hCoV-reactive antibodies in serum samples collected from 204 humans before the COVID-19 pandemic.\nWe then quantified pre-pandemic antibody levels in serum from a separate cohort of 252 individuals who became PCR-confirmed infected with SARS-CoV-2.\nFinally, we longitudinally measured hCoV and SARS-CoV-2 antibodies in the serum of hospitalized COVID-19 patients.\nOur studies indicate that most individuals possessed hCoV-reactive antibodies before the COVID-19 pandemic.\nWe determined that ~23% of these individuals possessed non-neutralizing antibodies that cross-reacted with SARS-CoV-2 spike and nucleocapsid proteins.\nThese antibodies were not associated with protection against SARS-CoV-2 infections or hospitalizations, but paradoxically these hCoV cross-reactive antibodies were boosted upon SARS-CoV-2 infection.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Although SARS-CoV-2 is a novel coronavirus, most humans had been previously exposed to other antigenically distinct common seasonal human coronaviruses (hCoVs) before the COVID-19 pandemic.\", \"We then quantified pre-pandemic antibody levels in serum from a separate cohort of 252 individuals who became PCR-confirmed infected with SARS-CoV-2.\", \"These antibodies were not associated with protection against SARS-CoV-2 infections or hospitalizations, but paradoxically these hCoV cross-reactive antibodies were boosted upon SARS-CoV-2 infection.\"]}", "id": 986}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Mutation of sars-cov-2 orf8, a rapidly evolving coronavirus protein implicated in immune evasion\n\nAbstract:\nThe molecular basis for the severity and rapid spread of the COVID-19 disease caused by SARS-CoV-2 is largely unknown.\nORF8 is a rapidly evolving accessory protein that has been proposed to interfere with immune responses.\nThe crystal structure of SARS-CoV-2 ORF8 was determined at 2.04 \u00c5 resolution by x-ray crystallography.\nThe structure reveals a ~60 residue core similar to SARS-CoV ORF7a with the addition of two dimerization interfaces unique to SARS-CoV-2 ORF8.\nA covalent disulfide-linked dimer is formed through an N-terminal sequence specific to SARS-CoV-2, while a separate non-covalent interface is formed by another SARS-CoV-2-specific sequence, (73)YIDI(76).\nTogether the presence of these interfaces shows how SARS-CoV-2 ORF8 can form unique large-scale assemblies not possible for SARS-CoV, potentially mediating unique immune suppression and evasion activities.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Together the presence of these interfaces shows how SARS-CoV-2 ORF8 can form unique large-scale assemblies not possible for SARS-CoV, potentially mediating unique immune suppression and evasion activities.\"]}", "id": 987}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Tobacco kills more than 8 million people globally every year. More than 7 million of these deaths are from direct tobacco use and around 1.2 million are due to non-smokers being exposed to second-hand smoke. \n\nAbstract:\nImportance.\nCovid-19 infection has major international health and economic impacts and risk factors for infection are not completely understood.\nCannabis smoking is linked with poor respiratory health, immunosuppression and multiple contaminants.\nPotential synergism between the two epidemics would represent a major public health convergence.\nCigarettes were implicated with disease severity in Wuhan, China.\nObjective.\nIs cannabis use epidemiologically associated with coronavirus incidence rate (CVIR)?\nDesign.\nCross-sectional state-based multivariable study.\nSetting.\nUSA.\nPrimary and Secondary Outcome Measures.\nCVIR.\nMultivariable-adjusted geospatially-weighted regression models.\nAs the American cannabis epidemic is characterized by a recent doubling of daily cannabis use it was considered important to characterize the contribution of high intensity use.\nResults.\nSignificant associations of daily cannabis use quintile with CVIR were identified with the highest quintile having a prevalence ratio 5.11 (95%C.I. 4.90-5.33), an attributable fraction in the exposed (AFE) 80.45% (79.61-81.25%) and an attributable fraction in the population of 77.80% (76.88-78.68%) with Chi-squared-for-trend (14,782, df=4) significant at P<10-500.\nSimilarly when cannabis legalization was considered decriminalization was associated with an elevated CVIR prevalence ratio 4.51 (95%C.I. 4.45-4.58), AFE 77.84% (77.50-78.17%) and Chi-squared-for-trend (56,679, df=2) significant at P<10-500.\nMonthly and daily use were linked with CVIR in bivariate geospatial regression models (P=0.0027, P=0.0059).\nIn multivariable additive models number of flight origins and population density were significant.\nIn interactive geospatial models adjusted for international travel, ethnicity, income, population, population density and drug use, terms including last month cannabis were significant from P=7.3x10-15, daily cannabis use from P=7.3x10-11 and last month cannabis was independently associated (P=0.0365).\nConclusions and Relevance.\nData indicate CVIR demonstrates significant trends across cannabis use intensity quintiles and with relaxed cannabis legislation.\nRecent cannabis use is independently predictive of CVIR in bivariate and multivariable adjusted models and intensity of use is interactively significant.\nCannabis thus joins tobacco as a SARS2-CoV-2 risk factor.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Cannabis smoking is linked with poor respiratory health, immunosuppression and multiple contaminants.\", \"Potential synergism between the two epidemics would represent a major public health convergence.\", \"Cigarettes were implicated with disease severity in Wuhan, China.\"]}", "id": 988}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Many readers have written in to ask whether ibuprofen or other non-steroidal anti-inflammatory drugs, or NSAIDs, can worsen COVID-19, the disease caused by the novel coronavirus.\n\nAbstract:\nOBJECTIVE: It was recently suggested that ibuprofen might increase the risk for severe and fatal coronavirus disease 2019 (COVID-19) and should therefore be avoided in this patient population.\nWe aimed to evaluate whether ibuprofen use in individuals with COVID-19 was associated with more severe disease, compared with individuals using paracetamol or no antipyretics.\nMETHODS: In a retrospective cohort study of patients with COVID-19 from Shamir Medical Centre, Israel, we monitored any use of ibuprofen from a week before diagnosis of COVID-19 throughout the disease.\nPrimary outcomes were mortality and the need for respiratory support, including oxygen administration and mechanical ventilation.\nRESULTS: The study included 403 confirmed cases of COVID-19, with a median age of 45 years.\nOf the entire cohort, 44 patients (11%) needed respiratory support and 12 (3%) died.\nOne hundred and seventy-nine (44%) patients had fever, with 32% using paracetamol and 22% using ibuprofen, for symptom-relief.\nIn the ibuprofen group, 3 (3.4%) patients died, whereas in the non-ibuprofen group, 9 (2.8%) patients died (p 0.95).\nNine (10.3%) patients from the ibuprofen group needed respiratory support, compared with 35 (11%) from the non-ibuprofen group (p 1).\nWhen compared with exclusive paracetamol users, no differences were observed in mortality rates or the need for respiratory support among patients using ibuprofen.\nCONCLUSIONS: In this cohort of COVID-19 patients, ibuprofen use was not associated with worse clinical outcomes, compared with paracetamol or no antipyretic.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 989}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: COVID-19 spreads by contact with respiratory droplets that spread when an infected person coughs or sneezes.\n\nAbstract:\nPURPOSE: To share a useful intervention to minimize risk of COVID-19 infection to both healthcare workers and patients in the eye clinic.\nMETHODS: We present our experience of virtual, within-clinic remote visual acuity assessment to reduce the risk of infection with COVID-19.\nRESULTS: Along with standard recommendations for personal protective equipment and hand hygiene to contain viral spread and treating only urgent cases, remote within-clinic visual acuity testing and consultations can be undertaken with minimal specialist equipment and appears to provide useful information whilst being acceptable to patients.\nCONCLUSION: Ophthalmology practice must adapt in order to combat COVID-19.\nThis measure can easily be incorporated into daily practice to reduce both patient footfall within the department and close contact between patient and healthcare practitioners.", "answer": "{\"verdict\": \"NEI\", \"evidence\": []}", "id": 990}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Some herbal medicine advocates believe that the combination of garlic, ginger and some herbs can cure Coronavirus.\n\nAbstract:\nThe severity of coronavirus disease 2019 (COVID-19) infection is quite variable and the manifestations varies from asymptomatic disease to severe acute respiratory infection.\nFever, dry cough, dyspnea, myalgia, fatigue, loss of appetite, olfactory and gustatory dysfunctions are the most prevalent general symptoms.\nDecreased immune system cells such as suppressed regulatory T cells, cytotoxic and helper T cells, natural killer cells, monocytes/macrophages and increased proinflammatory cytokines are the characteristic features.\nCompounds derived from Allium sativum (garlic) have the potential to decrease the expression of proinflammatory cytokines and to reverse the immunological abnormalities to more acceptable levels.\nAllium sativum is suggested as a beneficial preventive measure before being infected with SARS-CoV-2 virus.\nAllium sativum is a functional food well-known for its immunomodulatory, antimicrobial, antiinflammatory, antimutagenic, antitumor properties.\nIts antiviral efficiency was also demonstrated.\nSome constituents of this plant were found to be active against protozoan parasites.\nWithin this context, it appears to reverse most immune system dysfunctions observed in patients with COVID-19 infection.\nThe relations among immune system parameters, leptin, leptin receptor, adenosin mono phosphate-activated protein kinase, peroxisome proliferator activated receptor-gamma have also been interpreted.\nLeptin's role in boosting proinflammatory cytokines and in appetite decreasing suggest the possible beneficial effect of decreasing the concentration of this proinflammatory adipose tissue hormone in relieving some symptoms detected during COVID-19 infection.\nIn conclusion, Allium sativum may be an acceptable preventive measure against COVID-19 infection to boost immune system cells and to repress the production and secretion of proinflammatory cytokines as well as an adipose tissue derived hormone leptin having the proinflammatory nature.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"In conclusion, Allium sativum may be an acceptable preventive measure against COVID-19 infection to boost immune system cells and to repress the production and secretion of proinflammatory cytokines as well as an adipose tissue derived hormone leptin having the proinflammatory nature.\"]}", "id": 991}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: hydroxychloroquine cures covid-19.\n\nAbstract:\nBackgrounds.\nSince COVID-19 outbreak, various agents have been tested but no proven effective therapies have been identified.\nThis has led to a lot of controversies among associated researches.\nHence, in order to address the issue of using hydroxychloroquine in treating COVID-19 patients, we conducted a systematic review and meta-analysis.\nMethods.\nA thorough search was carried out to find relevant studies in MEDLINE, medRxiv, PubMed, Cochrane Database, China Academic Journals Full-text Database and Web of Science.\nTwo investigators independently reviewed 274 abstracts and 23 articles.\nThe trials which evaluated hydroxychloroquine for treatment of COVID-19 were included for this systematic review.\nTwo investigators assessed quality of the studies and data extraction was done by one reviewer and cross checked by the other.\nResults.\nFive trials involving 677 patients were included while conducting the meta-analysis.\nCompared with the control group, hydroxychloroquine with or without azithromycin showed benefits in positive-to-negative conversion of SARS-CoV-2 (odds ratio [OR], 1.95 [95% CI,0.19 to 19.73] and a reduction in progression rate (OR, 0.89 [95% CI, 0.58 to 1.37]), but without demonstrating any statistical significance.\nThis systematic review has also suggested a possible synergistic effect of the combination therapy which included hydroxychloroquine and azithromycin.\nHowever, the use of hydroxychloroquine alone was associated with increased mortality in COVID-19 patients.\nConclusion.\nThe use of hydroxychloroquine with or without azithromycin for treatment of COVID-19 patients, seems to be effective.\nThe combination of hydroxychloroquine and azithromycin has shown synergic effects.\nHowever, mortality rate was increased when the treatment was conducted with hydroxychloroquine.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Since COVID-19 outbreak, various agents have been tested but no proven effective therapies have been identified.\", \"However, the use of hydroxychloroquine alone was associated with increased mortality in COVID-19 patients.\", \"However, mortality rate was increased when the treatment was conducted with hydroxychloroquine.\"]}", "id": 992}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Ferrets can catch the coronavirus and might give it to other ferrets. But poultry and pigs don't appear to be at risk.\n\nAbstract:\nOn April 22, CDC and the U.S. Department of Agriculture (USDA) reported cases of two domestic cats with confirmed infection with SARS-CoV-2, the virus that causes coronavirus disease 2019 (COVID-19).\nThese are the first reported companion animals (including pets and service animals) with SARS-CoV-2 infection in the United States, and among the first findings of SARS-CoV-2 symptomatic companion animals reported worldwide.\nThese feline cases originated from separate households and were epidemiologically linked to suspected or confirmed human COVID-19 cases in their respective households.\nNotification of presumptive positive animal test results triggered a One Health* investigation by state and federal partners, who determined that no further transmission events to other animals or persons had occurred.\nBoth cats fully recovered.\nAlthough there is currently no evidence that animals play a substantial role in spreading COVID-19, CDC advises persons with suspected or confirmed COVID-19 to restrict contact with animals during their illness and to monitor any animals with confirmed SARS-CoV-2 infection and separate them from other persons and animals at home (1).", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Although there is currently no evidence that animals play a substantial role in spreading COVID-19, CDC advises persons with suspected or confirmed COVID-19 to restrict contact with animals during their illness and to monitor any animals with confirmed SARS-CoV-2 infection and separate them from other persons and animals at home (1).\"]}", "id": 993}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: it is safe take Advil to bring down your temperature caused by covid-19\n\nAbstract:\nIbuprofen is an over-the-counter medication that is used widely for the treatment of pain and fever during COVID-19 pandemic.\nA concern was raised regarding the safety of ibuprofen use because of its role in increasing ACE2 levels within the Renin-Angiotensin-Aldosterone system.\nACE2 is the coreceptor for the entry of SARS-CoV-2 into cells, and so, a potential increased risk of contracting COVID-19 disease and/or worsening of COVID-19 infection was feared with ibuprofen use.\nHowever, available data from limited studies show administration of recombinant ACE2 improves lung damage caused by respiratory viruses, suggesting ibuprofen use may be beneficial in COVID-19 disease.\nAt this time, there is no supporting evidence to discourage the use of ibuprofen.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Ibuprofen is an over-the-counter medication that is used widely for the treatment of pain and fever during COVID-19 pandemic.\", \"However, available data from limited studies show administration of recombinant ACE2 improves lung damage caused by respiratory viruses, suggesting ibuprofen use may be beneficial in COVID-19 disease.\", \"At this time, there is no supporting evidence to discourage the use of ibuprofen.\"]}", "id": 994}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: SARS-CoV-2 and COVID-19 are the same thing\n\nAbstract:\nThe recent global outbreak of viral pneumonia designated as Coronavirus Disease 2019 (COVID-19) by coronavirus (SARS-CoV-2) has threatened global public health and urged to investigate its source.\nWhole genome analysis of SARS-CoV-2 revealed ~96% genomic similarity with bat CoV (RaTG13) and clustered together in phylogenetic tree.\nFurthermore, RaTGl3 also showed 97.43% spike protein similarity with SARS-CoV-2 suggesting that RaTGl3 is the closest strain.\nHowever, RBD and key amino acid residues supposed to be crucial for human-to-human and cross-species transmission are homologues between SARS-CoV-2 and pangolin CoVs.\nThese results from our analysis suggest that SARS-CoV-2 is a recombinant virus of bat and pangolin CoVs.\nMoreover, this study also reports mutations in coding regions of 125 SARS-CoV-2 genomes signifying its aptitude for evolution.\nIn short, our findings propose that homologous recombination has been occurred between bat and pangolin CoVs that triggered cross-species transmission and emergence of SARS-CoV-2, and, during the ongoing outbreak, SARS-CoV-2 is still evolving for its adaptability.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"The recent global outbreak of viral pneumonia designated as Coronavirus Disease 2019 (COVID-19) by coronavirus (SARS-CoV-2) has threatened global public health and urged to investigate its source.\"]}", "id": 995}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Several people, including the US president, have suggested that the novel coronavirus SARS-CoV-2 and the disease it causes, COVID-19, will go away on its own in the warmer weather that will come.\n\nAbstract:\nBackground: Understanding and projecting the spread of COVID-19 requires reliable estimates of how weather components are associated with the transmission of the virus.\nPrior research on this topic has been inconclusive.\nIdentifying key challenges to reliable estimation of weather impact on transmission we study this question using one of the largest assembled databases of COVID-19 infections and weather.\nMethods: We assemble a dataset that includes virus transmission and weather data across 3,739 locations from December 12, 2019 to April 22, 2020.\nUsing simulation, we identify key challenges to reliable estimation of weather impacts on transmission, design a statistical method to overcome these challenges, and validate it in a blinded simulation study.\nUsing this method and controlling for location-specific response trends we estimate how different weather variables are associated with the reproduction number for COVID-19.\nWe then use the estimates to project the relative weather-related risk of COVID-19 transmission across the world and in large cities.\nResults: We show that the delay between exposure and detection of infection complicates the estimation of weather impact on COVID-19 transmission, potentially explaining significant variability in results to-date.\nCorrecting for that distributed delay and offering conservative estimates, we find a negative relationship between temperatures above 25 degrees Celsius and estimated reproduction number ([R]), with each degree Celsius associated with a 3.1% (95% CI, 1.5% to 4.8%) reduction in [R].\nHigher levels of relative humidity strengthen the negative effect of temperature above 25 degrees.\nMoreover, one millibar of additional pressure increases [R] by approximately 0.8 percent (95% CI, 0.6% to 1%) at the median pressure (1016 millibars) in our sample.\nWe also find significant positive effects for wind speed, precipitation, and diurnal temperature on [R].\nSensitivity analysis and simulations show that results are robust to multiple assumptions.\nDespite conservative estimates, weather effects are associated with a 43% change in [R] between the 5th and 95th percentile of weather conditions in our sample.\nConclusions: These results provide evidence for the relationship between several weather variables and the spread of COVID-19.\nHowever, the (conservatively) estimated relationships are not strong enough to seasonally control the epidemic in most locations.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Conclusions: These results provide evidence for the relationship between several weather variables and the spread of COVID-19.\", \"However, the (conservatively) estimated relationships are not strong enough to seasonally control the epidemic in most locations.\"]}", "id": 996}
{"query": "You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\".\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.\n\nFor instance, if the model were given the claim \"the COVID vaccine is safe for healthy adults\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults.\"]\n}\n\nClaim: Phylogenetic modeling explains differential sars cov-2 kinetics in lung and nasal passages in remdesivir treated rhesus macaques\n\nAbstract:\nRemdesivir was recently demonstrated to decrease recovery time in hospitalized patients with SARS-CoV-2 infection.\nIn rhesus macaques, early initiation of remdesivir therapy prevented pneumonia and lowered viral loads in the lung, but viral loads increased in the nasal passages five days after therapy.\nWe developed mathematical models to explain these results.\nWe identified that 1) drug potency is slightly higher in nasal passages than in lungs, 2) viral load decrease in lungs relative to nasal passages during therapy because of infection-dependent generation of refractory cells in the lung, 3) incomplete drug potency in the lung that decreases viral loads even slightly may allow substantially less lung damage, and 4) increases in nasal viral load may occur due to a slight blunting of peak viral load and subsequent decrease of the intensity of the innate immune response, as well as a lack of refractory cells.\nWe also hypothesize that direct inoculation of the trachea in rhesus macaques may not recapitulate natural infection as lung damage occurs more abruptly in this model than in human infection.\nWe demonstrate with sensitivity analysis that a drug with higher potency could completely suppress viral replication and lower viral loads abruptly in the nasal passages as well as the lung.\nOne Sentence Summary We developed a mathematical model to explain why remdesivir has a greater antiviral effect on SARS CoV-2 in lung versus nasal passages in rhesus macaques.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"In rhesus macaques, early initiation of remdesivir therapy prevented pneumonia and lowered viral loads in the lung, but viral loads increased in the nasal passages five days after therapy.\", \"One Sentence Summary We developed a mathematical model to explain why remdesivir has a greater antiviral effect on SARS CoV-2 in lung versus nasal passages in rhesus macaques.\"]}", "id": 997}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: clinical trials are needed to determine if endogenously created vitamin D could be effective for the prevention and treatment of COVID-19\n\nAbstract:\nThe outbreak of COVID-19 has created a global public health crisis.\nLittle is known about the protective factors of this infection.\nTherefore, preventive health measures that can reduce the risk of infection, progression and severity are desperately needed.\nThis review discussed the possible roles of vitamin D in reducing the risk of COVID-19 and other acute respiratory tract infections and severity.\nMoreover, this study determined the correlation of vitamin D levels with COVID-19 cases and deaths in 20 European countries as of 20 May 2020.\nA significant negative correlation (p=0.033) has been observed between mean vitamin D levels and COVID-19 cases per one million population in European countries.\nHowever, the correlation of vitamin D with COVID-19 deaths of these countries was not significant.\nSome retrospective studies demonstrated a correlation between vitamin D status and COVID-19 severity and mortality, while other studies did not find the correlation when confounding variables are adjusted.\nSeveral studies demonstrated the role of vitamin D in reducing the risk of acute viral respiratory tract infections and pneumonia.\nThese include direct inhibition with viral replication or with anti-inflammatory or immunomodulatory ways.\nIn the meta-analysis, vitamin D supplementation has been shown as safe and effective against acute respiratory tract infections.\nThus, people who are at higher risk of vitamin D deficiency during this global pandemic should consider taking vitamin D supplements to maintain the circulating 25(OH)D in the optimal levels (75-125nmol/L).\nIn conclusion, there is not enough evidence on the association between vitamin D levels and COVID-19 severity and mortality.\nTherefore, randomized control trials and cohort studies are necessary to test this hypothesis.", "answer": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"A significant negative correlation (p=0.033) has been observed between mean vitamin D levels and COVID-19 cases per one million population in European countries.\", \"Several studies demonstrated the role of vitamin D in reducing the risk of acute viral respiratory tract infections and pneumonia.\", \"These include direct inhibition with viral replication or with anti-inflammatory or immunomodulatory ways.\", \"In the meta-analysis, vitamin D supplementation has been shown as safe and effective against acute respiratory tract infections.\", \"Thus, people who are at higher risk of vitamin D deficiency during this global pandemic should consider taking vitamin D supplements to maintain the circulating 25(OH)D in the optimal levels (75-125nmol/L).\"]}", "id": 998}
{"query": "You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:\n\n- \"verdict\": The fact-checking verdict. If the information in the abstract supports the claim, write \"SUPPORT\". If the abstract contradicts the claim, write \"CONTRADICT\". If the abstract does not provide enough information to arrive at a verdict, write \"NEI\" (for \"not enough information\").\n- \"evidence\": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was \"NEI\", then return an empty array.\n\nFor instance, if the model were given the claim \"wearing masks can prevent the spread of COVID\", the output might be\n{\n  \"verdict\": \"SUPPORT\",\n  \"evidence\": [\"Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19.\"]\n}\n\nClaim: Some models show that if people wear masks, death rates from COVID-19 stay very low.\n\nAbstract:\nIn the context of Coronavirus Disease (2019) (COVID-19) cases globally, there is a lack of consensus across cultures on whether wearing face masks is an effective physical intervention against disease transmission.\nThis study 1) illustrates transmission routes of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2); 2) addresses controversies surrounding the mask from perspectives of attitude, effectiveness, and necessity of wearing the mask with evidence that the use of mask would effectively interrupt the transmission of infectious diseases in both hospital settings and community settings; and 3) provides suggestion that the public should wear the mask during COVID-19 pandemic according to local context.\nTo achieve this goal, government should establish a risk adjusted strategy of mask use to scientifically publicize the use of masks, guarantee sufficient supply of masks, and cooperate for reducing health resources inequities.", "answer": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"This study 1) illustrates transmission routes of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2); 2) addresses controversies surrounding the mask from perspectives of attitude, effectiveness, and necessity of wearing the mask with evidence that the use of mask would effectively interrupt the transmission of infectious diseases in both hospital settings and community settings; and 3) provides suggestion that the public should wear the mask during COVID-19 pandemic according to local context.\"]}", "id": 999}