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Dosimetric analysis | The DICOM files of submitted plans were imported into MIM software (Cleveland, OH) for review. All plans were reviewed by at least one experienced radiation oncologist and one specialized dosimetrist in the QA team. The plans were evaluated regarding the homogeneity and conformality of PTV, dose to OARs, beams arrangement, skin flash, inhomogeneity corrections, space for improvement, and the results of dosimetric verification. Major deviations such as inappropriate beam arrangement were defined by the radiation oncologists and dosimetrists during review. The protocol compliance and the actual value of each parameter were assessed in the first and final submission, respectively. Statistical analyses were computed using SPSS 22.0 (IBM Corporation, Armonk, NY, USA). Mc-Nemar test was used for paired differences between the first and final submission. Two-sided P < 0.05 indicated statistically significances. | PMC10685528 | ||
Discussion | volume and left-sided tumor | HOT SPOT, BREAST CANCER | To our best knowledge, this is the first study to evaluate the IMRT and VMAT plans regarding regional nodal irradiation including IMNI in the planning benchmark case, and is also the first study to compare the first and revised plans before enrolling patients. The results showed that a number of major deviations were found in the first submission. After revision, the major deviations were corrected; the protocol compliance was significantly improved and was of high level; and the inter-institutional consistency of planning quality was achieved in the revised plans in the benchmark case.Some previous studies showed that a variety of potential protocol deviations and heterogeneities were always detected in the pretrial benchmark case, and many of them could be improved during actual patient enrollment [In our study, the case used for the benchmark planning had a considerably large irradiated volume with left-sided breast cancer, including chest wall, supraclavicular fossa, axilla levels I-III, and IMN region, for which the plan design was very difficult. Various optimization strategies were used by the dosimetrists. In the first submission, insufficient target coverage, hot spot dose, and dose inhomogeneity in PTV were common major deviations. The protocol compliance rates were all low for first PTVcw, PTVsc + ax, and PTVim VGiven that increased radiation-induced heart and lung injury were the main concerns for IMNI [It is worth noting that the use of multi-beam IMRT and VMAT improves homogeneity and conformity at the expense of extending low-dose spread [This study has some limitations. First, there was only one benchmark case in this QA procedure, and the large irradiated volume and left-sided tumor resulted in difficulties for plan design, which might be unrepresentative, but was effective to improve the ability of dosimetrists in individual institutions. Second, owing to the close proximity between the IMN and chest wall, the unintentional IMN dose in the non-IMNI group is an important focus, which might affect the trial results. However, no benchmark case was provided for non-IMNI planning, and the unintentional IMN dose was not evaluated in this study, which would be assessed in individual case review. Third, electron beams were not used in this benchmark case; therefore, careful QA is warranted in subsequent individual case review for the actual enrolled patients. Last, the protocol compliance in other follow-up cases was not evaluated in this paper. We will report the results of subsequent individual case review in the near future and reflect upon the fact that this benchmark planning procedure provided a meaningful contribution to improving the plan qualities for actual enrolled patients. | PMC10685528 |
Conclusions | In this planning benchmark case, a number of major deviations were found in the first submission, and they were corrected after revision. The protocol compliance was significantly improved and was of high level in the final submission. The reduced variations will guarantee good RT plan quality and its inter-institutional consistency. The benchmark case results provided a valuable insight into the importance of pretrial QA, continuous education, communication through regular workshops, real-time central review, and feedback in multi-center clinical trials. | PMC10685528 | ||
Acknowledgements | The authors acknowledge the POTENTIAL trial QA team, collaborators, and investigators for their kind help and suggestions during the preparation of this manuscript. | PMC10685528 | ||
Author contributions | JJ | Conceptualization, YCS and SLW; methodology, YCS, ZHH, XNY and SLW; software, ZHH, XNY and KM; formal analysis, YCS and ZHH; investigation, ZHH and XNY; data curation, YCS, ZHH and XNY; resources, HF, YT, HJ, NZ, JZ, JJ, QZZ, JM, WFY, YHZ, LHD, XHW, HFW, XHD, XRH, JT, YFL, LNZ, YXL, and SLW; writing—original draft preparation, YCS; writing—review and editing, SLW, YXL, LNZ, YFL, JT, and XRH; supervision, SLW; project administration, SLW, YXL, LNZ, YFL, JT, and XRH; funding acquisition, SLW All authors read and approved the final manuscript. | PMC10685528 | |
Funding | Cancer | CANCER | This work was supported by the CAMS Innovation Fund for Medical Sciences (2020-I2M-C&T-B-075) and the Beijing Hope Run Special Fund of Cancer Foundation of China (LC2019L02). | PMC10685528 |
Availability of data and materials | The datasets used and analyzed during the current study are available from the corresponding author on reasonable request. | PMC10685528 | ||
Declarations | PMC10685528 | |||
Ethics approval and consent to participate | Cancer | CANCER | This study was approved by the ethics committee of the Cancer Hospital, Chinese Academy of Medical Sciences (approval number: 19/317–2101). | PMC10685528 |
Consent for publication | Not applicable. | PMC10685528 | ||
Competing interests | The authors declare that they have no competing interests. | PMC10685528 | ||
References | PMC10685528 | |||
Introduction | deaths, death | Countries without complete civil registration and vital statistics systems rely on retrospective full pregnancy history surveys (FPH) to estimate incidence of pregnancy and mortality outcomes, including stillbirth and neonatal death. Yet surveys are subject to biases that impact demographic estimates, and few studies have quantified these effects. We compare data from an FPH vs. prospective records from a population-based cohort to estimate validity for maternal recall of live births, stillbirths, and neonatal deaths in a rural population in Sarlahi District, Nepal. | PMC10709973 | |
Methods | EARLY PREGNANCY | We used prospective data, collected through frequent visits of women from early pregnancy through the neonatal period, from a population-based randomized trial spanning 2010–2017. We randomly selected 76 trial participants from three pregnancy outcome groups: live birth ( | PMC10709973 | |
Results | death | Among 76 participants, we recorded 122 pregnancy outcomes in the prospective data and 104 outcomes in the FPH within ± 30 days of each woman’s total observation period in the trial. Among 226 outcomes, we observed 65 live births that survived to 28 days, 25 stillbirths, and 32 live births followed by neonatal death in the prospective data and participants reported 63 live births that survived to 28 days, 15 stillbirths, and 26 live births followed by neonatal death in the pregnancy history survey. Sixty-two FPH outcomes were matched by date within ± 30 days to an outcome in prospective data. Stillbirth, neonatal death, higher parity, and delivery at a health facility were associated with likelihood of a non-matched pregnancy outcome. | PMC10709973 | |
Conclusions | deaths | Stillbirth and neonatal deaths were underestimated overall by the FPH, potentially underestimating the burden of mortality in this population. There is a need to develop tools to reduce or adjust for biases and errors in retrospective surveys to improve reporting of pregnancy and mortality outcomes. | PMC10709973 | |
Supplementary Information | The online version contains supplementary material available at 10.1186/s41043-023-00472-5. | PMC10709973 | ||
Introduction | abortions, deaths, death | EVENTS, MISCARRIAGES | In 2020, more than 5 million children under the age of five died, with almost half occurring in the first 28 days of life (neonatal deaths) [Complete civil registration and vital statistics systems (CRVS systems) that collect prospective data on vital events are the ideal source of such mortality data [The DHS-VIII woman’s questionnaire reproductive section includes an full pregnancy history with questions about pregnancy characteristics, outcomes, and timing, including for live births, miscarriages, abortions, stillbirths, and neonatal deaths, spanning the entire pregnancy history [The goal of this study is to compare pregnancy and neonatal mortality outcome data from a full pregnancy history survey against prospectively collected data from a population-based randomized trial that utilized high-frequency follow-up of women in pregnancy through the infant’s first 28 days of life. Specifically, we will compare numbers of pregnancy and neonatal outcomes and calculate measures of validity for live birth that survived to 28 days, stillbirth, and neonatal death events. | PMC10709973 |
Methods | In this study, we compare data on pregnancy and neonatal outcomes collected through a full pregnancy history survey questionnaire against prospectively collected data in a sub-area of Sarlahi District, Nepal. This study was conducted at a population-based field site for maternal, newborn, and child health and nutrition research operated by the Nepal Nutrition Intervention Project Sarlahi (NNIPS) since 1989. Specifically, we evaluate the following research aims:Compare numbers of pregnancy and neonatal outcomes and other characteristics reported in the full pregnancy history survey to those recorded in the prospective data.Compare, among pregnancy outcomes matched by delivery date (± 30 days) in the two data sources, measures of agreement and validity for pregnancy and neonatal outcomes, using the prospective data as the reference. | PMC10709973 | ||
Prospective data | death, abortion, miscarriages, stillbirths, deaths | MISCARRIAGES, MISCARRIAGE | We used prospectively collected data from a large, population-based randomized trial of topical applications for newborn massage, the Nepal Oil Massage Study (NOMS) (NCT01177111), which enrolled and followed pregnant women and their infants in 34 Village Development Committees (VDCs) in Sarlahi District, Nepal, between November 2010 and January 2017. At the start of the trial, data collectors conducted a census activity to systematically visit all households in the study area to update existing project maps and database. During the trial, data collectors monitored all married women 15–35 years through a pregnancy surveillance system. Local-resident-female data collectors visited women every 5 weeks to ask about the date of last menstrual period (LMP) and offer a pregnant test if needed. Pregnant women were enrolled in the study and followed monthly in pregnancy, as soon as possible after delivery, and through the neonatal period with visits on days 1, 3, 7, 10, 14, 21, and 28. The trial recorded 32,114 live births, 865 stillbirths, and 998 neonatal deaths.The trial collected baseline data on participants’ demographic characteristics, socioeconomic status, and pregnancy history. At the study visit following delivery, data collectors recorded the date/time of the pregnancy outcome, location of delivery, labor and delivery characteristics, health status of the mother and infant, and infant anthropometry (weight, length, head circumference, temperature). Data collectors asked participants if each pregnancy outcome was a miscarriage, abortion, live birth only, stillbirth only, or live birth(s) and stillbirth(s) (in cases of multiple gestation). We made a final classification between live birth and stillbirth based upon three questions: Did the baby ever cry? Did the baby ever move? Did the baby ever breathe? If the mother or caregiver answered yes to any of these questions, we classified the outcome as live birth; otherwise, we classified it as a fetal loss. We made a classification between miscarriages (< 28 weeks) and stillbirths (≥ 28 weeks) among fetal losses using the gestational age at delivery calculated from the LMP date, which was obtained at the time of enrollment, early in pregnancy. Data collectors administered a stillbirth verbal autopsy module to the mother or caregiver for this outcome. In cases of death of a liveborn infant, data collectors administered a neonatal verbal autopsy module. | PMC10709973 |
Pregnancy history survey data | We randomly selected participants from an eligible list of women from the population-based trial participants, including 25 individuals from three pregnancy and neonatal outcome groups: live birth that survived to 28 days ( | PMC10709973 | ||
Comparison of pregnancy and neonatal outcomes | MAY | We compared participant characteristics, as recorded in the prospective data, stratified by the three pregnancy and neonatal outcome groups using Chi-squared tests among the 76 women enrolled in the pregnancy history survey. To compare pregnancy and neonatal outcomes in the two data sources, we included all pregnancy outcomes in the prospective data observed among the 76 enrolled women (which occurred between May 25, 2011, and May 25, 2017) and pregnancy outcomes reported in the pregnancy history survey that occurred during the prospective follow-up period for each participant plus 30 days before and after their observation. Specifically, this period was defined for each individual participant as the time from 30 days before their enrollment date in the prospective study and 30 days after either the last birth visit or last pregnancy surveillance visit (whichever was later) in the prospective study. This yielded an analytic dataset, referred to as the “complete dataset,” of 226 pregnancy outcomes, including 122 from the prospective data and 104 from the pregnancy history survey. | PMC10709973 | |
Validity analysis | REGRESSION | We aimed to match the known pregnancy outcome dates in the prospective data (We presented a table to compare agreement by date, sex, and pregnancy and neonatal outcomes stratified by pregnancy and neonatal outcome according to the prospective data. We assessed the individual validity of the three pregnancy and neonatal outcomes reported in the pregnancy history survey by estimating sensitivity, specificity, positive predictive value, negative predictive value, and proportion correctly classified for each outcome. We used a logistic regression model with generalized estimating equations, to account for correlation associated with reporting of ≥ 1 singleton pregnancies for each woman, to estimate adjusted odds ratios and 95% confidence intervals of non-matched pregnancy outcomes by participant characteristics.We graphed pregnancy outcomes that did not match within ± 30 days from both data sources ( | PMC10709973 | |
Stillbirth and neonatal outcome counts | deaths, death, stillbirths | We plotted live births that survived to 28 days, stillbirths, and live births followed by neonatal death by time of birth for each participant according to the two data sources among pregnancy outcomes in the complete dataset. We presented the following from each data source: total pregnancy outcomes, median pregnancy outcomes per participant, duration and time between pregnancies, and numbers of pregnancy and neonatal outcomes. Specifically, we compared the number of stillbirths and neonatal deaths reported in the complete dataset of 226 pregnancy outcomes, including the 122 outcomes from the prospective data and 104 outcomes from the pregnancy history survey, and the date matched dataset of 124 pregnancy outcomes, including 62 outcomes from each source. | PMC10709973 | |
Ethical approval | The trial and the pregnancy history survey received ethical approval from the Johns Hopkins Bloomberg School of Public Health Institutional Review Board, Baltimore, MD, USA. The trial received ethical approval from the Institutional Review Board of Tribhuvan University Institute of Medicine, Kathmandu, Nepal, and the pregnancy history survey received ethical approval from the Ethical Review Board of the Nepal Health Research Council (Kathmandu, Nepal). | PMC10709973 | ||
Results | PMC10709973 | |||
Participant selection | We contacted 95 potential participants from the trial, of whom 19 (20.0%) could not be contacted or had moved away from the study area, and 76 (80.0%) were met in-person, consented, and enrolled into the pregnancy history survey between October 22, 2021, and November 18, 2021 (Fig. Participant flowchart and outcomes by data sourceThe characteristics of the 76 enrolled participants are presented in Table Participant characteristics by infant outcome from NOMS trial prospective data*All data presented in this table are from the NOMS trial~Variable missingness: Delivery location: | PMC10709973 | ||
Number of pregnancy outcomes | MAY | In the prospective data, the 76 participants had a total of 122 pregnancy outcomes (median: 2, range: 1 to 4) between May 25, 2011, and May 25, 2017, during their follow-up in the NOMS trial (Table Pregnancy outcomes recorded in prospective data and pregnancy history survey*Chi-squared test for comparison of the overall distribution of the three outcomes between prospective data and pregnancy history survey was | PMC10709973 | |
Completeness of pregnancy outcome dates | All pregnancy outcome dates in the prospective data were fully recorded. In the pregnancy history survey, one-third of women ( | PMC10709973 | ||
Infant sex and name match | Among 62 pregnancy outcomes with a matched date, most ( | PMC10709973 | ||
Stillbirth and neonatal outcome counts and measures of validity | deaths, death | Among the 226 outcomes in the complete dataset, a similar number of live births that survived to 28 days (Among the date matched dataset of 124 outcomes, only half (Age at death for the 10 neonatal deaths among the 62 matches ranged from 0.02 to 27.1 days (median 2.2 days) according to the prospective data. The difference in dates of death for the 9 correctly classified neonatal deaths ranged from − 2.0 days to 1.0 day (pregnancy history survey minus prospective data).Among the 60 prospective non-date matched outcomes, the most likely errors, according to the criteria outlined in Additional file | PMC10709973 | |
Discussion | deaths, death, DHS, stillbirths | SENSITIVITY | Few studies have compared pregnancy dates and outcomes from the DHS full pregnancy history survey to prospective, population-based data at aggregate or individual levels. Our validity study, conducted in a rural Nepali community, found that a DHS full pregnancy history survey reported a similar number of live births that survived to 28 days but fewer stillbirths and live births followed by neonatal death, compared to prospectively collected trial data. Other studies have similarly reported that births of children who died were less likely to be reported in survey data than in longitudinal estimates generated from regular home visits at Health and Demographic Surveillance Systems (HDSS) sites, although these sites differ from population-based cohort studies like our trial in important ways [The Nepal DHS 2016 neonatal mortality rate estimate of 30 deaths per 1,000 live births for Nepal’s Province 2 is similar to that observed in the prospective trial data over the 2010–2017 follow-up period (31.2). However, the NMR in the study population decreased during this period from 34.7 in 2011 to 27.5 in 2016 [About half of the 122 prospectively observed pregnancy outcomes in our study were matched by date within one month in the pregnancy history survey. The high proportion of unmatched dates could be due in part to the large number of partial dates reported in the pregnancy history survey. This, in turn, could be a result of the long recall times (median 6.2 years, range: 4.4–10.5 years) between the prospective outcome and the pregnancy history survey administration date, which is longer than the average recall in the five years preceding the survey range used by DHS. Due to high missingness of infant names or other identifying data for matching participants between these two data sources, there was no definitive method for differentiating omissions from date displacements. However, visual observation of the pregnancy outcomes in both sources that did not match within ± 30 days (Additional file Among pregnancy outcomes matched by date, live births that survived to 28 days were well classified; however, four of nine stillbirths were misreported as live births followed by neonatal death and one in ten neonatal deaths as stillbirths. This misclassification of stillbirths as neonatal deaths resulted in a neonatal mortality rate higher than the prospective data within the outcomes matched by date. Sensitivity analyses allowing for matching of pregnancy outcome dates within wider date ranges (i.e., ± 60 days, ± 100 days, ± 365 days, and unrestricted), found generally lower, but not markedly different agreement, for pregnancy outcomes in the two data sources as the date ranges widened. Again, it must be noted that a high proportion of mismatched outcomes could be due to the large number of partial dates reported in the pregnancy history survey. Underreporting and misclassification of stillbirths and neonatal deaths have been observed in other settings. A study in Bangladesh comparing HDSS data and a DHS pregnancy history survey from the 1990s found that the completeness of neonatal death reporting was 83% [Examination of participant characteristics provided some insights into the drivers of reporting errors in this population. Women with higher parity in our study were more likely to omit or displace an outcome compared to the prospective data, which was similar to the study in Bangladesh that found higher parity was associated with missed live birth in surveys [Women who delivered at a health facility in our study were more likely to not report an outcome in the pregnancy history survey that matched with the prospective data. A study in this same population in Sarlahi District observed lower recall reliability for receipt of several labor and delivery and immediate newborn care interventions among women who delivered at a health facility compared to those that delivered at home [Age at death was fairly accurate for neonatal deaths with pregnancy outcome dates that were correctly recalled, although due to our small sample, there were few late neonatal deaths that would be more subject to age errors resulting in misclassification as postnatal deaths. Date errors and age under- or over-statements are common causes of age error that can impact neonatal mortality rates. The study in Guinea-Bissau reported a large number of postnatal deaths were transferred to the neonatal period, which could lead to overestimation of the neonatal mortality rate [Our study had limitations—including a small sample size, high number of partial dates in the pregnancy history survey, and a small number of pregnancy outcome date matches, yielding fewer participants for analysis and limiting the precision of our estimates—so our findings should be interpreted with caution. The long recall period, relative to the DHS five years preceding the survey time frame, may have contributed to poor date recall and subsequent failure to match more pregnancy outcome dates. An attempt to match pregnancy outcomes by name and sex in this community was not useful, given how few infants are named at birth, which is common in South Asia and other settings. There were few participants with less common but important demographic and socioeconomic characteristics, for example, higher maternal age, Pahadi ethnicity, or more education, limiting investigations of maternal recall by these factors. Both maternal recall and pregnancy outcome incidence may also differ across by factors that we did not consider in this analysis, such as seasonality of birth [Our findings have implications for fertility and mortality estimation in the Terai region of Nepal and similar settings. Stillbirth and neonatal deaths were underestimated by the full pregnancy history survey, which would potentially misrepresent the burden of mortality in this population. This indicates a need to design and evaluate survey measurement tools and techniques to reduce biases and errors or statistical approaches to adjust for these issues. | PMC10709973 |
Acknowledgements | Thank you to all of the women, infants, and their families who participated in the studies included in this analysis. | PMC10709973 | ||
Author contributions | MG and JK conceptualized the study and obtained funding. DE, TPL, SS, and JK contributed to the study design and implemented data collection in the field. DE and SS conducted the analysis. AV provided analysis and programming support. DE wrote the initial manuscript. All authors reviewed results, discussed interpretations, and contributed to development and revision of the manuscript. | PMC10709973 | ||
Funding | This work was supported by the National Institute for Child Health and Human Development (NICHD 1R01HD090082-01). NOMS was supported by the National Institutes for Child Health and Development (HD060712) and the Bill & Melinda Gates Foundation (OPP1084399). The funders did not have a role in the study, data collection, analysis, interpretation, or writing of the manuscript. | PMC10709973 | ||
Availability of data and materials | Not applicable. | PMC10709973 | ||
Declarations | PMC10709973 | |||
Ethics approval and consent to participate | The trial and the pregnancy history survey received ethical approval from the Johns Hopkins Bloomberg School of Public Health Institutional Review Board, Baltimore, MD, USA. The trial received ethical approval from the Institutional Review Board of Tribhuvan University Institute of Medicine, Kathmandu, Nepal, and the pregnancy history survey received ethical approval from the Ethical Review Board of the Nepal Health Research Council (Kathmandu, Nepal). | PMC10709973 | ||
Consent for publication | Not applicable. | PMC10709973 | ||
Patients or public involvement | Patients or the public were not involved in the design, conduct, reporting, or dissemination plans of our research. | PMC10709973 | ||
Competing interests | The authors declare that they have no competing interests. | PMC10709973 | ||
References | PMC10709973 | |||
Background | This study was designed to examine the possible efficacy of the probiotic strain | PMC10636814 | ||
Methods | ADHD | CARD | In this randomized controlled trial, 80 children and adolescents with ADHD diagnosis, aged 6–16 years, were included. The participants were randomly assigned to two groups: one group received probiotics plus atomoxetine, whereas the other group received atomoxetine only. ADHD symptomatology was assessed using the Conners Parent Rating Scale–Revised Long Version (CPRS-R-L) and Child Behavioral Checklist (CBCL/6–18). The participants were evaluated for their vigilance and executive function using Conner’s Continuous Performance Test (CPT) and Wisconsin Card Sort Test (WCST). Both groups were assessed at the beginning of the study and the end of the twelve weeks. | PMC10636814 |
Results | The probiotic group comprised 36 patients, whereas the control group comprised 40 patients in the final analysis after four patients dropped out of the trial. After 3 months of probiotic supplementation, a significant improvement in the CPRS-R-L and CBCL total T scores was observed compared with those in the control group ( | PMC10636814 | ||
Trial registration | ClinicalTrials.gov (identifier: NCT04167995). Registration date: 19–11-2019. | PMC10636814 | ||
Keywords | Open access funding provided by The Science, Technology & Innovation Funding Authority (STDF) in cooperation with The Egyptian Knowledge Bank (EKB). | PMC10636814 | ||
Introduction | hyperactivity, Attention-deficit/hyperactivity disorder, impulsivity, ADHD, neurodevelopmental disorder, neurodevelopmental disorders | PATHOPHYSIOLOGY | Attention-deficit/hyperactivity disorder (ADHD) is the most common early-onset neurodevelopmental disorder in the pediatric population, affecting 7.2% of school-age children globally. It is characterized by deficits in the cognitive functioning pattern, with hyperactivity, impulsivity, and attention problems that are developmentally inappropriate and significantly impairing symptoms [Recently, a growing interest has been directed to the bidirectional pathway between the gut and brain—the gut–brain axis (GBA). Alterations and imbalances in the gut microbiota may play a role in developing and progressing neurodevelopmental disorders like ADHD. [Furthermore, several reports showed an association between ADHD and low levels of brain-derived neurotrophic factor (BDNF), which is essential for neuronal development, suggesting that BDNF contributes to its pathophysiology [Probiotics are bacteria that benefit the host body [ | PMC10636814 |
Patients and methods | PMC10636814 | |||
Study design | ADHD | This study was a 12-week randomized controlled trial set as a prospective, parallel, open-label study conducted from June 2020 to October 2021 on pediatric and adolescent outpatients with ADHD. The trial was registered at ClinicalTrials.gov (identifier: NCT04167995, on 19/11/2019). The study’s reporting complies with the Consolidated Standards of Reporting Trials 2010 statement [ | PMC10636814 | |
Participants | DSM-5, ADHD | DISORDERS | Eighty children and adolescents aged 6–16 were recruited from the Developmental and Behavioral Pediatrics Clinic Children’s Hospital and Psychiatry Institute, Faculty of Medicine, Ain-Shams University, Cairo, Egypt. The participants fulfilled the diagnostic criteria for ADHD according to the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) [ | PMC10636814 |
Randomization | A statistician, independent from the study investigators, randomly allocated the enrolled participants into the probiotic group ( | PMC10636814 | ||
Intervention | ADHD | The participants with ADHD randomized to the study (Probiotic) group received a probiotic preparation (Lacteol Fort®; lyophilized heat-killed | PMC10636814 | |
Child Behavioral Checklist (CBCL/6–18) | The CBCL/school-aged (6–18 years) is a parent-rated questionnaire containing 113 items subdivided into three dimensions, noted as internalizing, externalizing, and total behavior problems, quantitatively assessing and providing dimensional insights concerning children’s psychopathology and behavioral functioning [ | PMC10636814 | ||
Primary and secondary outcomes | inattentiveness, Inattentive, Hyperactive/impulsive, impulsivity, ADHD | SYNDROME, DYSFUNCTION | PEBL version 2.0 of the CPT measures sustained attention and impulsivity in a 14-min computerized task. Participants are instructed to hit the button whenever any alphabet letter other than the X letter is shown. The test measures selective inattentiveness (missing target stimuli: omission errors), impulsivity (false responding to non-target stimuli: commission errors), and sustained attention (reaction time and reaction time variability).PEBL version 2.0 of the WCST measures executive functioning, cognitive flexibility, and set-shifting abilities. It comprises two sets of cards: 64 reaction cards and four stimulus cards. Based on the patterns present on the cards, participants were instructed to categorize them. The rule for properly sorting the stimuli shifts regularly, and the ability to change strategies that vary according to the stimuli's color, number, or shape is recorded. The participant should first choose the proper sorting principle and stick with it throughout the test to perform successfully. Shifting the matching rule to another category occurs after ten successively correct matches in one category (e.g., matching numbers). The main outcome parameters are the correct responses, categories completed, perseverative errors, non-perseverative errors, total errors, and failure to maintain a set. Executive dysfunction is assumed to be reflected in preservative and non-preservative errors [Both groups were assessed at baseline and follow-up at twelve weeks. The primary outcomes were changes in the severity of ADHD symptoms and associated behavioral problems assessed using the CPRS-R-L (Inattentive, Hyperactive/impulsive, and Total score) and CBCL (Syndrome scale and Total score), respectively. Secondary outcomes were improvements in sustained and focused attention, impulsivity, executive functioning, and set-shifting abilities based on the CPT and WSCT tasks. | PMC10636814 |
Statistical analysis | Using G*Power, the alpha error and study power were set at 5% and 80%, respectively. Assuming an effect size of 0.7 (Cohen’s d), a sample size of 40 cases per group was required, considering a dropout rate of 20%.The collected data were revised, coded, tabulated, and introduced to a personal computer using Statistical Package for the Social Sciences, version 25.Student’s t-test was used to evaluate the statistical significance of the mean difference between the two study groups. The chi-square test was used to compare the two study groups. The | PMC10636814 | ||
Adverse events | decreased appetite, diarrhea | ADVERSE EFFECTS | An analysis of the medication’s potential adverse effects revealed no notable undesirable symptoms in either group. Seven participants (three in the probiotics group and four in the control group) reported decreased appetite. One patient from the probiotics group reported having diarrhea, which cleared up after a few days. | PMC10636814 |
Discussion | Anxiety, anxiety, Lactobacillus acidophilus, acidophilus, deficit in the cognitive function, ADHD, depression | CORTEX, SECONDARY, PATHOPHYSIOLOGY | As knowledge of the GBA has risen with emerging research highlighting this bidirectional relationship with a theoretical translation of animal models to human analyses, the core mechanism and which probiotics have a promising or negative result remain ambiguous. To the best of our knowledge, this study is the first randomized controlled trial to use a Twelve-week supplementation with L. acidophilus LB combined with a weight-dependent dose of atomoxetine could improve the symptoms and behavioral problems of ADHD, according to the parental reports of the CPRS-R-L and CBCL, respectively, relative to the control group.Our findings support the results of a recent trial by Ghanaatgar et al. [Another study investigated the effects of probiotics on the psychological health of children with ADHD. The study found that probiotic treatment for 8 weeks, using four bacterial strains, including Lactobacillus acidophilus, significantly improved the severity of ADHD symptoms and anxiety compared to a placebo. The improvement was measured using the ADHD and Hamilton Anxiety Rating scales. However, probiotics did not have an impact on depression. [Contradictory to our findings, Kumperscak et al. [Available reviewing research has broadened our understanding of the link between probiotic supplementation and its impact on ADHD clinical symptoms. However, their findings remain inconclusive to formulate any clinical recommendation or approaches. As the underlying etiopathogenesis of ADHD is still unclear, investigating the role of the complex messaging system between the microbiota, gut, and brain has drawn much attention [Considering that the GBA hypothesis has been linked to the pathophysiological pathways underlying ADHD [Regarding the secondary outcomes under study, our current analysis found that the probiotic group revealed improvement in the target accuracy rate and omission errors compared with the control group (medium effect size), suggesting that Mounting evidence suggests that the cholinergic and dopaminergic systems contribute to the pathophysiology of selective attention [Our results showed improvement in the executive functions in the probiotic group reflected in both perseverative and non-perseverative errors on WCST performance (small-medium effect size); however, this did not reach a significant level of differences between the two groups.Impairments in executive functions (i.e., cognitive flexibility, working memory, sustained attention, inhibitory control, and planning) are considered a core deficit in the cognitive function of ADHD, which may play a crucial role in the challenging adaptation of ADHD [The cognitive regulation of behavior and reward perception is modulated via the norepinephrine and dopamine circuits, which connect to the prefrontal cortex and striatum, and these pathways are considered fundamental in the pathophysiology of ADHD [Until now, limited randomized trials have investigated the impact of probiotic supplementation on cognitive performance [The linkage between ADHD and the microbiota can be understood in terms of how neurotransmitters function in cognition. A recent study has provided insights into the GBA and introduced a new strain that improves cognitive function through this axis. Using healthy mice, Jeon et al. [In the present study, most participants were males. The finding aligns with previous studies indicating that ADHD is more common in boys [ | PMC10636814 |
Limitations | This study is an open-label study without a placebo intervention. Given that the parents were knowledgeable of the treatment their child had experienced, this potentially may have affected how they responded to the questionnaires. However, parallel to the parent responses, we also included various objective measure evaluations (i.e., the CPT and WCST), and the results showed the advantage of adding probiotics to the standard treatment alone. While our findings showed that a 3-month probiotic intervention was beneficial, there were no observable changes in some cognitive functions measured by the neuropsychological assessment battery, leading us to believe that the study’s duration was insufficient to track the improvements. Therefore, further research with a longer intervention time is needed. In this study, we did not investigate the socioeconomic status of the groups; however, SES should be assessed in microbiome studies, given that it can be an influential confounding variable that impacts the analysis of the study results [ | PMC10636814 | ||
Conclusion | In conclusion, this study has demonstrated that 3 months’ supplementation of oral probiotics, such as | PMC10636814 | ||
Acknowledgements | We would like to thank all children and adolescents who participated in the study. | PMC10636814 | ||
Authors’ contributions | RA | RE and HE were responsible for the determination of the study topic and the design of the study. RA contributed to the first draft of the manuscript writing. RE and HE revised the manuscript. RA and HM were responsible for the research activity planning and execution. All authors contributed to the article and approved the submitted version. | PMC10636814 | |
Funding | Open access funding provided by The Science, Technology & Innovation Funding Authority (STDF) in cooperation with The Egyptian Knowledge Bank (EKB). | PMC10636814 | ||
Availability of data and materials | All data generated or analyzed during this study are included in this published article or are available from the corresponding author on reasonable request. | PMC10636814 | ||
Declarations | PMC10636814 | |||
Ethics approval and consent to participate | This study was approved by the ethics committee of Ain-Shams University Hospitals (Ethical Committee No. FMASU 158). All methods were carried out in accordance with the Declaration of Helsinki.Informed consent was obtained from the legal guardians of the participants. | PMC10636814 | ||
Consent for publication | Not applicable. | PMC10636814 | ||
Competing interests | The authors declare no competing interests. | PMC10636814 | ||
References | PMC10636814 | |||
Background | Subjective perception of coercion has gained attention as an important outcome. However, little is known about its relation to patients’ appraisal of the justification of coercive measures. The present study aims to analyze the relationship between patients’ appraisal of the justification of coercive measures and their level of perceived coercion. | PMC10546675 | ||
Methods | REGRESSION, SECONDARY | This study presents a secondary analysis of the results of a multi-center RCT conducted to evaluate the effects of post-coercion review. Patients who experienced at least one coercive measure during their hospital stay were included in the trial. Participants’ appraisal of the justification of coercive measures was categorized into patient-related and staff-related justifications. Subjective coercion was assessed using the Coercion Experience Scale (CES) and used as dependent variable in a multivariate regression model. | PMC10546675 | |
Results | 97 participants who completed the CES were included in the analysis. CES scores were significantly associated with the perception of the coercive measure as justified by staff-related factors (B = 0,540, p < 0,001), as well as with higher level of negative symptoms (B = 0,265, p = 0,011), and with mechanical restraint compared to seclusion (B=-0,343, p = 0,017). | PMC10546675 | ||
Conclusions | arbitrariness | INCOMPETENCE | Patients’ perceptions of coercive measures as justified by staff-related factors such as arbitrariness or incompetence of staff are related to higher levels of perceived coercion. Multiprofessional efforts must be made to restrict the use of coercive measures and to ensure a transparent and sustainable decision-making process, particularly with patients showing high levels of negative symptoms. Such key elements should be part of all coercion reduction programs. | PMC10546675 |
Keywords | Open access funding provided by University of Geneva | PMC10546675 | ||
Introduction | delirium, post-traumatic symptoms, psychiatric | Although coercive measures are ethically justified in life-threatening clinical situations (such as severe delirium) or in most serious endangerment of others, their use is restricted to those situations where no other alternatives can be employed. Their potentially severe consequences, including a deterioration of the therapeutic relationship and the treatment course, subjective feelings of punishment and distress, the development of post-traumatic symptoms or physical injuries must be acknowledged and considered in decision-making processes [Besides the objective use of coercive measures, the aspect of subjective or perceived coercion has been highlighted as an important outcome in the context of psychiatric care. Subjective coercion can be defined as the patients’ perceptions, views and feelings related to their experience of coercion. High levels of perceived coercion are related to low patient satisfaction and negative attitudes towards hospital treatment [Coercive measures are often perceived by patients as extremely humiliating and dehumanizing [The present work aims at investigating the potential relationship between patients’ appraisal of the justification of coercive measures and the level of subjective distress they experienced during their application. It was hypothesized that the perception of coercive measures as justified by arbitrariness or other factors solely related to staff members or structural issues would increase the experienced distress. | PMC10546675 | |
Methods | SECONDARY | We performed a secondary analysis of a multi-center, two-armed, randomized controlled trial assessing the effect of standardized post-coercion review on posttraumatic symptoms and subjective coercion (ClinicalTrials.gov ID NCT03512925). The specific design of this RCT has been described in detail in previous publications [ | PMC10546675 | |
Coercive measures | Coercive measures were defined as mechanical restraint, seclusion or forced medication. In Germany, these measures can be applied by psychiatrists in case of acute endangerment of self or others. Seclusion or restraint measures lasting longer than 18 h must be authorized by the Civil Court. As to forced medication outside of an emergency, it must also be authorized by the Court and must be limited in time. | PMC10546675 | ||
Participants | psychotic disorder, psychiatric, cognitive deficits, F31.2, ICD-10 | Participants were recruited in six psychiatric hospitals providing acute psychiatric care for a defined catchment area, on inpatient wards where coercive measures were performed. Inclusion criteria were: age between 18 and 65, diagnosis of a psychotic disorder (ICD-10: F1x.5, F2x, F30.2, F31.2), and documented experience of at least one coercive measure such as mechanical restraint, seclusion or coerced medication on court order during their hospital stay. We excluded patients who were discharged within 24 h, who presented severe cognitive deficits, or who only had limited knowledge in German.As specified in the previous articles, potential participants, who were not able to give their consent when the first coercive measure took place, were included in the trial following a Zelen’s design, which allows to randomize participants without their explicit consent when the foreseen intervention only minimally differs from routine care, as was the case here [ | PMC10546675 | |
Measures | PMC10546675 | |||
Socio-demographic and clinical data | The following variables were obtained during assessment by the interviewers: age, gender, level of education, history of migration, if the person receives incapacity benefits, and past experiences of coercive measures. As to the clinical variables, level of functioning was assessed with the GAF (Global Assessment of Functioning) and global severity of symptoms with CGI-S (Clinical Global Impression - Severity scale). The level of positive and negative symptoms as well as the level of insight were assessed using four-point Likert scales (absent, mild, moderate, severe) to simplify the evaluation of symptoms and limit missing data. All clinical variables were obtained from the psychiatrists in charge of the patients at the time of discharge.
Socio-demographic and clinical characteristics of the studied sampleCGI-S: Clinical Global Impression – Severity Scale; CGI-I: Clinical Global Impression – Improvement Scale; GAF: Global Assessment of Functioning; M: mean; SD: standard deviation | PMC10546675 | ||
Subjective coercion experienced during coercive measures | The Coercion experience Scale (CES) [ | PMC10546675 | ||
Justification of coercive measures | Participants of the trial were asked to rate the reasons they retrospectively thought to have motivated the use of the first coercive measure. Participants were presented with 11 possible justifications for the use of coercion. These were derived from a previous study investigating patients’ views of the reasons leading to coercive interventions [Both CES and rating of the justification for the experienced coercive measure were assessed together at discharge. | PMC10546675 | ||
Statistical analysis | REGRESSION | Potential influencing factors of the level of perceived coercion as measured by the CES were tested in bivariate linear regression analysis. Factors correlating with CES scores at a p < 0,1 level were included as predictors in a multiple linear regression model using CES scores as dependent variable.Because of the small number of participants (n = 8; 7,3% of the total sample) who were submitted to forced medication, we chose to exclude these from analysis.Statistical analysis was performed using IBM SPPS Statistics 25. Significance was defined at a two-sided | PMC10546675 | |
Results | REGRESSION | The description of the sample’s socio-demographic and clinical variables is summarized in Tables
Participants’ appraisal of the justification for the use of coercionResults of the bivariate regression analyses are displayed in Table
Results of the bivariate regression analysesCI: Confidence Interval; CGI-S: Clinical Global Impression – Severity Scale; GAF: Global Assessment of Functioning. *p < 0.1A significant regression equation was found (
Results of the multiple linear regression modelCI: Confidence Interval; .CGI-S: Clinical Global Impression – Severity Scale. *p < 0.05Perception of the coercive measure as justified by staff-related factors (B = 0,540, p < 0,001) was significantly associated with CES scores, which confirmed our initial hypothesis. The type of coercive measures (B=-0,343, p = 0,017) and the severity of negative symptoms (B = 0,265, p = 0,011) were also significant predictors of the CES scores. The perception of the coercive measure as motivated by staff-related reasons was associated with higher CES scores. Mechanical restraint was also associated with higher CES scores, as was a higher severity of negative symptoms. Severity of symptoms as measured by the CGI-S and age were not significantly associated with CES scores. Level of insight was not correlated as well with CES scores. | PMC10546675 | |
Acknowledgements | None. | PMC10546675 | ||
Authors’ contributions | CM | AW coordinated the original trial and took part in study conception, data collection, curation, analysis and interpretation and drafted the manuscript. AV and JM took part in the study conception, data collection and manuscript revision. AH and FB made substantial contributions to the interpretation of data and mnuscript revision. LM and CM contributed significantly to the conception, data analysis and interpretation as well as the revision of the manuscript. All authors approved the present manuscript. | PMC10546675 | |
Funding | psychiatric | The study is part of the project “Prevention of coercion in psychiatric care” (“Zwangsvermeidung im psychiatrischen Hilfesystem”, ZVP) and was funded by the German Federal Ministry of Health.Open access funding provided by University of Geneva | PMC10546675 | |
Data Availability | Orignial data of the present study is available from the corresponding author upon reasonable, motivated request. | PMC10546675 | ||
Declarations | PMC10546675 | |||
Ethics approval and consent to participate | The study was conducted in accordance with the principles of the Declaration of Helsinki. The project was approved by the ethics committee of the Charité Universitätsmedizin Berlin (No. EA1/158/17). All participants have given their written informed consent. | PMC10546675 | ||
Consent for publication | Not applicable. | PMC10546675 | ||
Competing interests | The authors declare no competing interests. | PMC10546675 | ||
References | PMC10546675 | |||
Purpose | No large-scale prospective randomized study with a long-term follow-up period has evaluated the survival outcomes of preconcurrent chemoradiotherapy (CCRT) 18-fluorodeoxyglucose positron emission tomography–computed tomography ( | PMC9926577 | ||
Patients and Methods | OPSCC | We included patients with stage I–IVA p16-negative OPSCC receiving definitive CCRT and categorized them into two groups according to pre-CCRT | PMC9926577 | |
Results | REGRESSION | The final cohort consisted of 3942 patients (1663 and 2279 in the case and comparison groups, respectively). According to multivariable Cox regression analysis, pre-CCRT | PMC9926577 | |
Conclusions | Routine use of pre-CCRT | PMC9926577 | ||
Condensed abstract | No large-scale prospective randomized study with a long-term follow-up period has evaluated the survival outcomes of preconcurrent chemoradiotherapy (CCRT) 18-fluorodeoxyglucose positron emission tomography–computed tomography ( | PMC9926577 | ||
Keywords | PMC9926577 | |||
Introduction | malignancy, oropharyngeal and hypopharyngeal cancer, OPSCC, deaths | OROPHARYNGEAL SQUAMOUS CELL CARCINOMA | Oropharyngeal squamous cell carcinoma (OPSCC) is a relatively uncommon malignancy, with approximately 123,000 cases of oropharyngeal and hypopharyngeal cancer being diagnosed and approximately 79,000 deaths occurring worldwide each year [18-Fluorodeoxyglucose (Early-stage OPSCC can be treated with either primary surgery or definitive radiotherapy (RT) plus chemotherapy or not as a therapeutic modality [The role of a routine | PMC9926577 |
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