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The aim of this study is to explore how patients experience the personal nature of a personalized patient leaflet (PPL), and the role of health literacy in patients' experience. A PPL was tailored on patients' characteristics and medical information. Semi-structured interviews were performed to capture patient experien...	Do patients experience a personalized patient leaflet as personal?
Poly (ADP-ribose) polymerase inhibitors (PARPi) serve as crucial therapeutic agents in solid tumor treatment. Preclinical investigations suggest a potential protective function of PARPi against endocrine and metabolic impairments. Nevertheless, the existing body of evidence remains inconclusive on this aspect. Our aim ...	Incidence and risk of endocrine and metabolic abnormalities linked to PARP inhibitors in solid tumors: a meta-analysis.
To investigate the attitudes of Chinese radiologists or interns towards generative pre-trained (GPT)-like technologies. A prospective survey was distributed to 1339 Chinese radiologists or interns via an online platform from October 2023 to May 2024. The questionnaire covered respondent characteristics, opinions on usi...	Attitudes of radiologists and interns toward the adoption of GPT-like technologies: a National Survey Study in China.
SLC6A1 (Solute Carrier Family 6 Member 1) variants are associated with SLC6A1-neurodevelopmental disorders (SLC6A1-NDD), which manifest as early-onset epilepsy, intellectual developmental disorder, and autism spectrum disorder. There have been over 300 reported cases so far. A retrospective analysis of 14 patients with...	Behavioral, neurodevelopmental profile, and epilepsy trajectory in two series of SLC6A1-NDD: A retrospective study with comprehensive assessment, and a participatory database study.
This study aimed to simulate diverse scenarios of students employing LLMs for CDLE examination preparation, providing a detailed evaluation of their performance in medical education. A stratified random sampling strategy was implemented to select and subsequently revise 200 questions from the CDLE. Seven LLMs, recognis...	Evaluating the Performance of Large Language Models (LLMs) in Answering and Analysing the Chinese Dental Licensing Examination.
Language models are transforming materials-aware natural-language processing by enabling the extraction of dynamic, context-rich information from unstructured text, thus, moving beyond the limitations of traditional information-extraction methods. Moreover, small language models are on the rise because some of them can...	MechBERT: Language Models for Extracting Chemical and Property Relationships about Mechanical Stress and Strain.
We investigated the prevalence of loneliness recorded during assessment of general hospital inpatients by older adult liaison psychiatry services and its associations with level of subsequent hospitalisation, emergency presentation and mortality. Data were drawn from a large south London mental healthcare provider of o...	Recorded Loneliness and Adverse Outcomes in Older Acute Care Inpatients Receiving Psychiatric Assessment.
VoxRad is an open-source application designed to enhance radiology reporting by leveraging generative AI. Utilizing locally hosted Automatic Speech Recognition (ASR) and Large Language Models (LLM), VoxRad enables continuous dictation, transcribing reports into standardized formats with high accuracy, efficiency, and d...	VoxRad: Building an open-source locally-hosted radiology reporting system.
Post-stroke aphasia is a network disorder characterized by language impairments and aberrant network activation. While patients with post-stroke aphasia recover over time, the dynamics of the underlying changes in the brain remain elusive. Neuroimaging work demonstrated that language recovery is a heterogeneous process...	Dynamic reorganization of task-related network interactions in post-stroke aphasia recovery.
Chat Generative Pre-Trained Transformer (ChatGPT) is a popular natural-language processor that is able to analyze and respond to a variety of prompts, providing eloquent answers based on a collection of Internet data. ChatGPT has been considered an avenue for the education of resident physicians in the form of board pr...	Performance of Chat Generative Pre-Trained Transformer on Personal Review of Learning in Obstetrics and Gynecology.
Analog In-memory Computing (IMC) has demonstrated energy-efficient and low latency implementation of convolution and fully-connected layers in deep neural networks (DNN) by using physics for computing in parallel resistive memory arrays. However, recurrent neural networks (RNN) that are widely used for speech-recogniti...	Efficient nonlinear function approximation in analog resistive crossbars for recurrent neural networks.
Automatic Compliance Checking (ACC) within the Architecture, Engineering, and Construction (AEC) sector necessitates automating the interpretation of building regulations to achieve its full potential. Converting textual rules into machine-readable formats is challenging due to the complexities of natural language and ...	CODE-ACCORD: A Corpus of building regulatory data for rule generation towards automatic compliance checking.
The rapid evolution of large language models (LLMs), such as Bidirectional Encoder Representations from Transformers (BERT; Google) and GPT (OpenAI), has introduced significant advancements in natural language processing. These models are increasingly integrated into various applications, including mental health suppor...	Exploring the Credibility of Large Language Models for Mental Health Support: Protocol for a Scoping Review.
Investigations on identifying the nature of stuttering present varying views. The argument remains whether the stuttering dysfluencies have a motor or a linguistic foundation. Though stuttering is considered a speech-motor disorder, linguistic factors are increasingly reported to play a role in stuttering. Current lite...	Exploring the nature of stuttering through a behavioral-neuro-modulation intervention program in bilinguals with stuttering.
Natural language processing can be used to identify patient symptoms from the electronic health records with good performance when compared with manual chart review. Natural language processing–extracted patient symptom burden does not reflect patient burden due to under-recognition and underdocumentation by health car...	Natural Language Processing Identifies Underdocumentation of Symptoms in Patients on Hemodialysis.
<b>BACKGROUND.</b> Automated extraction of actionable details of recommendations for additional imaging (RAIs) from radiology reports could facilitate tracking and timely completion of clinically necessary RAIs and thereby potentially reduce diagnostic delays. <b>OBJECTIVE.</b> The purpose of the study was to assess th...	Use of ChatGPT Large Language Models to Extract Details of Recommendations for Additional Imaging From Free-Text Impressions of Radiology Reports.
The Sexual Abuse History Questionnaire (SAHQ), a widely used screening tool for childhood sexual abuse (CSA) and adolescent/adult sexual assault (AASA) experiences, has limited examination of its psychometric properties in diverse populations. Our study assessed the SAHQ's psychometric properties (i.e., structural vali...	A short screen for lifetime sexual victimization experiences: Expanding research on the Sexual Abuse History Questionnaire (SAHQ) across cultures, genders, and sexual identities.
Patient-reported outcomes (PROs) are vital in assessing disease activity and treatment outcomes in inflammatory bowel disease (IBD). However, manual extraction of these PROs from the free-text of clinical notes is burdensome. We aimed to improve data curation from free-text information in the electronic health record, ...	Large Language Models Outperform Traditional Natural Language Processing Methods in Extracting Patient-Reported Outcomes in Inflammatory Bowel Disease.
The digitization of healthcare records has revolutionized medical research and patient care, with electronic health records (EHRs) containing a wealth of structured and unstructured data. Extracting valuable information from unstructured clinical text presents a significant challenge, necessitating automated tools for ...	Clinical entity-aware domain adaptation in low resource setting for inflammatory bowel disease.
Assessing the quality of life (QoL) in patients with various diseases is essential for understanding their well-being and guiding clinical management. Dermatological conditions, particularly those affecting the scalp, significantly impact QoL due to their visible nature, which can affect appearance and social interacti...	SCALPDEX Questionnaire: creation and validation of the Polish language version.
Large language models (LLMs) are artificial intelligence tools that have the prospect of profoundly changing how we practice all aspects of medicine. Considering the incredible potential of LLMs in medicine and the interest of many health care stakeholders for implementation into routine practice, it is therefore essen...	The Clinicians' Guide to Large Language Models: A General Perspective With a Focus on Hallucinations.
EEG involves recording electrical activity generated by the brain through electrodes placed on the scalp. Imagined speech classification has emerged as an essential area of research in brain-computer interfaces (BCIs). Despite significant advances, accurately classifying imagined speech signals remains challenging due ...	Classification of Imagined Speech Signals Using Functional Connectivity Graphs and Machine Learning Models.
This nationwide Danish cohort study compared overall survival (OS) between non-Western immigrant patients and Danish-born patients with lymphoma in Denmark. Furthermore, differences in clinical and socioeconomic variables were compared, and mediators of OS differences were explored to explain possible outcome differenc...	Similar Survival Between Non-Western Immigrant Patients and Danish-Born Patients with Lymphoma: A Danish Population-Based Study.
Urban sensing has become increasingly important as cities evolve into the centers of human activities. Large language models (LLMs) offer new opportunities for urban sensing based on commonsense and worldview that emerged through their language-centric framework. This paper illustrates the transformative impact of LLMs...	Urban sensing in the era of large language models.
In this article, we introduce a sociolinguistic perspective on language modeling. We claim that language models in general are inherently modeling <i>varieties of language</i>, and we consider how this insight can inform the development and deployment of language models. We begin by presenting a technical definition of...	The sociolinguistic foundations of language modeling.
A vast amount of potentially useful information such as description of patient symptoms, family, and social history is recorded as free-text notes in electronic health records (EHRs) but is difficult to reliably extract at scale, limiting their utility in research. This study aims to assess whether an "out of the box" ...	Scalable information extraction from free text electronic health records using large language models.
Natural products have long been a rich source of diverse and clinically effective drug candidates. Non-ribosomal peptides (NRPs), polyketides (PKs), and NRP-PK hybrids are three classes of natural products that display a broad range of bioactivities, including antibiotic, antifungal, anticancer, and immunosuppressant a...	Interpretable adenylation domain specificity prediction using protein language models.
[This corrects the article DOI: 10.3389/fpsyg.2024.1375353.].	Corrigendum: The influence of temperament and perinatal factors on language development: a longitudinal study.
The L2 Motivational Self System (L2MSS) determines an individual's motivation in second language learning and influences the learning experience and intended effort. Although physical activity (PA) has been shown to enhance academic efficacy, the role of PA in whether it promotes second language learning efficacy has n...	Chain-mediated effect of physical activity between Chinese language-based L2 motivational self-system and intended effort.
Prediction models have demonstrated a range of applications across medicine, including using electronic health record (EHR) data to identify hospital readmission and mortality risk. Large language models (LLMs) can transform unstructured EHR text into structured features, which can then be integrated into statistical p...	Classifying Unstructured Text in Electronic Health Records for Mental Health Prediction Models: Large Language Model Evaluation Study.
Generative artificial intelligence (GenAI) shows potential for personalized care, psychoeducation, and even crisis prediction in mental health, yet responsible use requires ethical consideration and deliberation and perhaps even governance. This is the first published theme issue focused on responsible GenAI in mental ...	Responsible Design, Integration, and Use of Generative AI in Mental Health.
Vestibular neuritis (VN) is a common cause of vertigo with significant impact on patients' quality of life. This study aimed to analyze global research trends in VN using bibliometric methods to identify key themes, influential authors, institutions, and countries contributing to the field. We conducted a comprehensive...	Bibliometric analysis of global research trends in vestibular neuritis (1980-2024).
Although the Transformer architecture has established itself as the industry standard for jobs involving natural language processing, it still has few uses in computer vision. In vision, attention is used in conjunction with convolutional networks or to replace individual convolutional network elements while preserving...	Leveraging two-dimensional pre-trained vision transformers for three-dimensional model generation via masked autoencoders.
Extracting named entities from clinical free-text presents unique challenges, particularly when dealing with discontinuous entities-mentions that are separated by unrelated words. Traditional NER methods often struggle to accurately identify these entities, prompting the development of specialised computational solutio...	Discontinuous named entities in clinical text: A systematic literature review.
Electronic health record (EHR) systems contain a wealth of clinical data stored as both codified data and free-text narrative notes (NLP). The complexity of EHR presents challenges in feature representation, information extraction, and uncertainty quantification. To address these challenges, we proposed an efficient Ag...	ARCH: Large-scale knowledge graph via aggregated narrative codified health records analysis.
In recent years, advancements in the interaction and collaboration between humans and have garnered significant attention. Social intelligence plays a crucial role in facilitating natural interactions and seamless communication between humans and Artificial Intelligence (AI). To assess AI's ability to understand human ...	A Comprehensive Analysis of a Social Intelligence Dataset and Response Tendencies Between Large Language Models (LLMs) and Humans.
Green infrastructure (GI) plays a crucial role in sustainable urban development, but effective mapping and analysis of such features requires a detailed understanding of the materials and state-of-the-art methods. This review presents the current landscape of green infrastructure mapping, focusing on the various sensor...	Mapping the Green Urban: A Comprehensive Review of Materials and Learning Methods for Green Infrastructure Mapping.
Traditional Vision-and-Language Navigation (VLN) tasks require an agent to navigate static environments using natural language instructions. However, real-world road conditions such as vehicle movements, traffic signal fluctuations, pedestrian activity, and weather variations are dynamic and continually changing. These...	DynamicVLN: Incorporating Dynamics into Vision-and-Language Navigation Scenarios.
The coal mining industry in Northern Shaanxi is robust, with a prevalent use of the local dialect, known as "Shapu", characterized by a distinct Northern Shaanxi accent. This study addresses the practical need for speech recognition in this dialect. We propose an end-to-end speech recognition model for the North Shaanx...	An End-To-End Speech Recognition Model for the North Shaanxi Dialect: Design and Evaluation.
Stigmatising language is used commonly in healthcare, affecting healthcare providers' perceptions of patients and care delivery. Using person-first language is best practice, however, it does not reflect reality. This study examined medical students' perspectives on stigmatising language in healthcare. Twenty-one medic...	"You're not taught to think about the words you use and then it just perpetuates"- a qualitative examination of medical students' perspectives of stigmatising language in healthcare.
Named Entity Recognition (NER) is an essential component of numerous Natural Language Processing (NLP) systems, with the aim of identifying and classifying entities that have specific meanings in raw text, such as person (PER), location (LOC), and organization (ORG). Recently, Deep Neural Networks (DNNs) have been exte...	Attention-based interactive multi-level feature fusion for named entity recognition.
Online child sexual exploitation and abuse (OCSEA) is a global health issue. The aim of this global systematic review and meta-analysis was to better understand the prevalence and nature of OCSEA on a global scale. Comprehensive literature searches were done in six UN languages (Arabic, Chinese, English, French, Russia...	Prevalence estimates and nature of online child sexual exploitation and abuse: a systematic review and meta-analysis.
Vision-language models are pre-trained by aligning image-text pairs in a common space to deal with open-set visual concepts. Recent works adopt fixed or learnable prompts, i.e., classification weights are synthesized from natural language descriptions of task-relevant categories, to reduce the gap between tasks during ...	Supporting vision-language model few-shot inference with confounder-pruned knowledge prompt.
Conversational artificial agents such as ChatGPT are commonly used by people seeking healthcare information. This study investigates whether ChatGPT exhibits distinct communicative behaviors in healthcare settings based on the nature of the disorder (medical or psychological) and the user communication style (neutral v...	Exploring ChatGPT's communication behaviour in healthcare interactions: A psycholinguistic perspective.
Virtual patients (VPs) are computer screen-based simulations of patient-clinician encounters. VP use is limited by cost and low scalability. We aimed to show that VPs powered by large language models (LLMs) can generate authentic dialogues, accurately represent patient preferences, and provide personalized feedback on ...	Virtual Patients Using Large Language Models: Scalable, Contextualized Simulation of Clinician-Patient Dialogue With Feedback.
Literature on how to translate information extracted from clinical progress notes into numeric scores for 3-step theory of suicide (3ST) factors is nonexistent. We determined which scoring option would best discriminate between patients who will attempt or die by suicide and patients with neither suicidal ideation nor ...	Computing 3-Step Theory of Suicide Factor Scores From Veterans Health Administration Clinical Progress Notes.
Experiencing music often entails the perception of a periodic beat. Despite being a widespread phenomenon across cultures, the nature and neural underpinnings of beat perception remain largely unknown. In the last decade, there has been a growing interest in developing methods to probe these processes, particularly to ...	Measuring self-similarity in empirical signals to understand musical beat perception.
Large language models (LLMs) have gained significant attention for their capabilities in natural language understanding and generation. However, their widespread adoption potentially raises public mental health concerns, including issues related to inequity, stigma, dependence, medical risks, and security threats. This...	Chain of Risks Evaluation (CORE): A framework for safer large language models in public mental health.
As psychological research progresses, the issue of concept overlap becomes increasing evident, adding to participant burden and complicating data interpretation. This study introduces an Embedding-based Semantic Analysis Approach (ESAA) for detecting redundancy in psychological concepts, which are operationalized throu...	An Embedding-Based Semantic Analysis Approach: A Preliminary Study on Redundancy Detection in Psychological Concepts Operationalized by Scales.
In the Kolmogorov Theory of Consciousness, algorithmic agents utilize inferred compressive models to track coarse-grained data produced by simplified world models, capturing regularities that structure subjective experience and guide action planning. Here, we study the dynamical aspects of this framework by examining h...	Structured Dynamics in the Algorithmic Agent.
Graph anomaly detection is crucial in many high-impact applications across diverse fields. In anomaly detection tasks, collecting plenty of annotated data tends to be costly and laborious. As a result, few-shot learning has been explored to address the issue by requiring only a few labeled samples to achieve good perfo...	Few-Shot Graph Anomaly Detection via Dual-Level Knowledge Distillation.
The diagnostic boundaries between schizophrenia and bipolar disorder are controversial due to the ambiguity of psychiatric nosology. From this perspective, it is noteworthy that formal thought disorder has historically been considered pathognomonic of schizophrenia. Given that human thought is partially based on langua...	Semantic abnormalities in schizophrenia and bipolar disorder: A natural language processing approach.
Evidence regarding the safety of early discharge following transcatheter aortic valve implantation (TAVI) is limited. The aim of this study was to evaluate the safety of very early (<24) and early discharge (24-48 h) as compared to standard discharge (>48 h), supported by the implementation of a voice-based virtual ass...	Early discharge programme after transcatheter aortic valve implantation based on close follow-up supported by telemonitoring using artificial intelligence: the TeleTAVI study.
The integration of large language models (LLMs) in healthcare has generated significant interest due to their potential to improve diagnostic accuracy, personalization of treatment, and patient care efficiency. This study aims to conduct a comprehensive bibliometric analysis to identify current research trends, main th...	Large Language Models in Healthcare: A Bibliometric Analysis and Examination of Research Trends.
Multiple imputation (MI) models can be improved with auxiliary covariates (AC), but their performance in high-dimensional data remains unclear. We aimed to develop and compare high-dimensional MI (HDMI) methods using structured and natural language processing (NLP)-derived AC in studies with partially observed confound...	High-dimensional multiple imputation (HDMI) for partially observed confounders including natural language processing-derived auxiliary covariates.
Clathrin proteins, key elements of the vesicle coat, play a crucial role in various cellular processes, including neural function, signal transduction, and endocytosis. Disruptions in clathrin protein functions have been associated with a wide range of diseases, such as Alzheimer's, neurodegeneration, viral infection, ...	TargetCLP: clathrin proteins prediction combining transformed and evolutionary scale modeling-based multi-view features via weighted feature integration approach.
The potential of Large Language Models (LLMs) in enhancing a variety of natural language tasks in clinical fields includes medical imaging reporting. This pilot study examines the efficacy of a retrieval-augmented generation (RAG) LLM system considering zero-shot learning capability of LLMs, integrated with a comprehen...	Empowering PET imaging reporting with retrieval-augmented large language models and reading reports database: a pilot single center study.
Missed critical imaging findings, particularly those indicating cancer, are a common issue that can result in delays in patient follow-up and treatment. To address this, we developed a rule-based natural language processing (NLP) algorithm to detect cancer-suspicious findings from Japanese radiology reports. The datase...	Automated Detection of Cancer-Suspicious Findings in Japanese Radiology Reports with Natural Language Processing: A Multicenter Study.
Modelling the prodrome to severe mental disorders (SMD), including unipolar mood disorders (UMD), bipolar mood disorders (BMD) and psychotic disorders (PSY), should consider both the evolution and interactions of symptoms and substance use (prodromal features) over time. Temporal network analysis can detect causal depe...	Longitudinal evolution of the transdiagnostic prodrome to severe mental disorders: a dynamic temporal network analysis informed by natural language processing and electronic health records.
Health research that significantly impacts global clinical practice and policy is often published in high-impact factor (IF) medical journals. These outlets play a pivotal role in the worldwide dissemination of novel medical knowledge. However, researchers identifying as women and those affiliated with institutions in ...	Diversity in the medical research ecosystem: a descriptive scientometric analysis of over 49 000 studies and 150 000 authors published in high-impact medical journals between 2007 and 2022.
Artificial intelligence (AI), including its subfields of machine learning and deep learning, is a branch of computer science and engineering focused on creating machines capable of tasks requiring human-like intelligence, such as visual perception, decision-making, and natural language processing. AI applications have ...	Applications of Artificial Intelligence in Dental Medicine: A Critical Review.
LLMs like GPT-4, despite their advancements, often produce hallucinations and struggle with integrating external knowledge effectively. While Retrieval-Augmented Generation (RAG) attempts to address this by incorporating external information, it faces significant challenges such as context length limitations and imprec...	ESCARGOT: an AI agent leveraging large language models, dynamic graph of thoughts, and biomedical knowledge graphs for enhanced reasoning.
Valid scalable biomarkers for predicting longitudinal clinical outcomes in psychiatric research are crucial for optimizing intervention and prevention efforts. Here, we recorded spontaneous speech from initially abstinent individuals with cocaine use disorder (iCUDs) for use in predicting drug use outcomes. At baseline...	Speak and You Shall Predict: Evidence That Speech at Initial Cocaine Abstinence Is a Biomarker of Long-Term Drug Use Behavior.
This is the first Malaysian machine learning model to detect and disambiguate abbreviations in clinical notes. The model has been designed to be incorporated into MyHarmony, a natural language processing system, that extracts clinical information for health care management. The model utilizes word embedding to ensure ...	Deciphering Abbreviations in Malaysian Clinical Notes Using Machine Learning.
Ontologies and knowledge graphs (KGs) are general-purpose computable representations of some domain, such as human anatomy, and are frequently a crucial part of modern information systems. Most of these structures change over time, incorporating new knowledge or information that was previously missing. Managing these c...	A change language for ontologies and knowledge graphs.
The general aim of the present study was to analyse eight mother-child interactions during shared reading with children and to assess the efficacy of a brief intervention with the mothers to promote changes in the strategies they used to develop their children's oral language. The specific objectives were to work colla...	The effects of a brief intervention at home based on shared reading to promote children's oral language.
The Satisfaction With Life Scale (SWLS) is a widely used self-report measure of subjective well-being, but studies of its measurement invariance across a large number of nations remain limited. Here, we utilised the Body Image in Nature (BINS) dataset-with data collected between 2020 and 2022 -to assess measurement inv...	Life satisfaction around the world: Measurement invariance of the Satisfaction With Life Scale (SWLS) across 65 nations, 40 languages, gender identities, and age groups.
Population level tracking of post-stroke functional outcomes is critical to guide interventions that reduce the burden of stroke-related disability. However, functional outcomes are often missing or documented in unstructured notes. We developed a natural language processing (NLP) model that reads electronic health rec...	Automated extraction of post-stroke functional outcomes from unstructured electronic health records.
Data extraction from the published literature is the most laborious step in conducting living systematic reviews (LSRs). We aim to build a generalizable, automated data extraction workflow leveraging large language models (LLMs) that mimics the real-world 2-reviewer process. A dataset of 10 trials (22 publications) fro...	Collaborative large language models for automated data extraction in living systematic reviews.
Speech comprehension involves the dynamic interplay of multiple cognitive processes, from basic sound perception, to linguistic encoding, and finally to complex semantic-conceptual interpretations. How the brain handles the diverse streams of information processing remains poorly understood. Applying Hidden Markov Mode...	Tripartite organization of brain state dynamics underlying spoken narrative comprehension.
Science is crucial for evidence-based decision-making. Public trust in scientists can help decision makers act on the basis of the best available evidence, especially during crises. However, in recent years the epistemic authority of science has been challenged, causing concerns about low public trust in scientists. We...	Trust in scientists and their role in society across 68 countries.
Online reviews significantly influence consumer purchasing decisions and serve as a vital reference for product improvement. With the surge of generative artificial intelligence (AI) technologies such as ChatGPT, some merchants might exploit them to fabricate deceptive positive reviews, and competitors may also fabrica...	A multigrained preference analysis method for product iterative design incorporating AI-generated review detection.
Science is integral to society because it can inform individual, government, corporate, and civil society decision-making on issues such as public health, new technologies or climate change. Yet, public distrust and populist sentiment challenge the relationship between science and society. To help researchers analyse t...	Perceptions of science, science communication, and climate change attitudes in 68 countries - the TISP dataset.
Speech-to-speech translation (S2ST) has evolved from cascade systems which integrate Automatic Speech Recognition (ASR), Machine Translation (MT), and Text-to-Speech (TTS), to end-to-end models. This evolution has been driven by advancements in model performance and the expansion of cross-lingual speech datasets. Despi...	Tibetan-Chinese speech-to-speech translation based on discrete units.
Current studies leveraging social media data for disease monitoring face challenges like noisy colloquial language and insufficient tracking of user disease progression in longitudinal data settings. This study aims to develop a pipeline for collecting, cleaning, and analyzing large-scale longitudinal social media data...	Analysis of longitudinal social media for monitoring symptoms during a pandemic.
Protein-protein interactions within a cell are essential for various fundamental biological processes. Computational techniques have arisen in bioinformatics due to the challenging and resource-intensive nature of experimental protein pair interaction studies. This research seeks to create a cutting-edge machine learni...	PPILS: Protein-protein interaction prediction with language of biological coding.
Recent advances in natural language processing (NLP), particularly in language processing methods, have opened new avenues in semantic data analysis. A promising application of NLP is data harmonization in questionnaire-based cohort studies, where it can be used as an additional method, specifically when only different...	Semantic search helper: A tool based on the use of embeddings in multi-item questionnaires as a harmonization opportunity for merging large datasets - A feasibility study.
Accurately assessing temporal order of cognitive decline across multiple domains is critical in Alzheimer's disease (AD). Existing literature presented controversial conclusions likely due to the use of a single cohort and different analytical strategies. Harmonized composite cognitive measures in memory, language and ...	The Dynamics of Cognitive Decline towards Alzheimer's Disease Progression: Results from ADSP-PHC's Harmonized Cognitive Composites.
Creating an ontology is the essential step in natural language processing (NLP). To improve patient safety in this era of generative AI, it is crucial to develop a standards-driven, ontology-based architecture for patient safety that can seamlessly integrate with health systems, thereby facilitating effective detection...	A pathway from fragmentation to interoperability through standards-based enterprise architecture to enhance patient safety.
Biological invasions are a major threat to biodiversity, ecosystem functioning and nature's contributions to people worldwide. However, the effectiveness of invasive alien species (IAS) management measures and the progress toward achieving biodiversity targets remain uncertain due to limited and nonuniform data availab...	Management Measures and Trends of Biological Invasions in Europe: A Survey-Based Assessment of Local Managers.
Vitamin D deficiency is a major public health concern, affecting approximately half of the world's population, partly due to limited public knowledge about vitamin D sources. However, there is lack of data on awareness, knowledge, attitudes, and practices regarding vitamin D in high-risk countries like Ghana. We invest...	Knowledge, attitude and practices regarding vitamin D among adults in Ghana: a cross-sectional study.
We introduce a sentence corpus with eye-movement data in traditional Chinese (TC), based on the original Beijing Sentence Corpus (BSC) in simplified Chinese (SC). The most noticeable difference between TC and SC character sets is their visual complexity. There are reaction time corpora in isolated TC character/word lex...	The Beijing Sentence Corpus II: A cross-script comparison between traditional and simplified Chinese sentence reading.
Generative large language models (LLMs) like ChatGPT can quickly produce informative essays on various topics. However, the information generated cannot be fully trusted, as artificial intelligence (AI) can make factual mistakes. This poses challenges for using such tools in college classrooms. To address this, an adap...	The ChatGPT Fact-Check: exploiting the limitations of generative AI to develop evidence-based reasoning skills in college science courses.
Studying the impact of COVID-19 on mental health is both compelling and imperative for the health care system's preparedness development. Discovering how pandemic conditions and governmental strategies and measures have impacted mental health is a challenging task. Mental health issues, such as depression and suicidal ...	Explainable Predictive Model for Suicidal Ideation During COVID-19: Social Media Discourse Study.
Extracting PICO elements-Participants, Intervention, Comparison, and Outcomes-from clinical trial literature is essential for clinical evidence retrieval, appraisal, and synthesis. Existing approaches do not distinguish the attributes of PICO entities. This study aims to develop a named entity recognition (NER) model t...	Semi-supervised learning from small annotated data and large unlabeled data for fine-grained Participants, Intervention, Comparison, and Outcomes entity recognition.
To determine if fatigue systematically effects the timing of swallowing events and to discuss underlying causes of fatigue other than peripheral neuromuscular fatigue. Pre-post within-subject repeated-measures design. General acute care hospital and designated stroke center. Thirteen patients (10 males and 3 females) a...	Assessing the Effect of Fatigue on Swallowing Function in Adults with Acute Stroke. A Pilot Study.
Amyotrophic lateral sclerosis (ALS) is a neurodegenerative malady that causes progressive degeneration and loss of motor neuron function in the brain and spinal cord, eventually resulting in muscular atrophy, paralysis, and death. Neural stem/progenitor cell (NSPC) transplantation can improve bodily function in animals...	Neural Stem/Progenitor Cell Therapy in Patients and Animals with Amyotrophic Lateral Sclerosis: A Systematic Review and Meta-analysis.
The Añana Salt Valley (northern Spain) is a continental saltern consisting of a series of natural springs that have been used for salt production for at least 7000 years. This habitat has been relatively understudied; therefore, prokaryotic diversity was investigated through Illumina-based 16S rRNA gene sequencing to d...	Prokaryotic Diversity and Community Distribution in the Complex Hydrogeological System of the Añana Continental Saltern.
Intercellular mitochondria transfer is an evolutionarily conserved process in which one cell delivers some of their mitochondria to another cell in the absence of cell division. This process has diverse functions depending on the cell types involved and physiological or disease context. Although mitochondria transfer w...	Recommendations for mitochondria transfer and transplantation nomenclature and characterization.
Electronic health records (EHRs) secondary usage with large language models (LLMs) raise privacy challenges. National regulations like GDPR and HIPAA offer protection frameworks, but specific strategies are needed to mitigate risk in generative AI. Risks can be reduced by using strategies like privacy-preserving locall...	Privacy preserving strategies for electronic health records in the era of large language models.
We examined the association between social determinants of health and the likelihood of sustaining a concussion among adolescents. Participants in this cross-sectional study were 7164 high school students who completed the 2021 Adolescent Behaviors and Experiences Survey (52.7% girls; mean age = 16.0 years, SD = 1.2; a...	Association Between Social Determinants of Health and Concussion Among High School Students in the United States.
Previous research on attitudes towards mathematics has mostly been assessed in a single language. We examined whether math attitudes differ by language in multilingual younger adults (ages 18-25). Furthermore, we evaluated the relationships between math attitudes, verbal memory, and calculation fluency in this sample. ...	Math attitudes and verbal memory in multilingual younger adults.
More than 3 billion years of evolution have produced an image of biology encoded into the space of natural proteins. Here, we show that language models trained at scale on evolutionary data can generate functional proteins that are far away from known proteins. We present ESM3, a frontier multimodal generative language...	Simulating 500 million years of evolution with a language model.
Analogue series (AS) are generated during compound optimization in medicinal chemistry and are the major source of structure-activity relationship (SAR) information. Pairs of active AS consisting of compounds with corresponding substituents and comparable potency progression represent SAR transfer events for the same t...	Context-dependent similarity analysis of analogue series for structure-activity relationship transfer based on a concept from natural language processing.
miRNA, circRNA, and lncRNA play crucial roles in the pathogenesis and progression of myocardial ischemia-reperfusion injury (MI/RI). This study aims to provide valuable insights into miRNA, circRNA, lncRNA, and MI/RI from a bibliometric standpoint, with the goal of fostering further advancements in this area. The relev...	Evidence and perspectives on miRNA, circRNA, and lncRNA in myocardial ischemia-reperfusion injury: a bibliometric study.
Retrieval-augmented generation (RAG) involves a solution by retrieving knowledge from an established database to enhance the performance of large language models (LLM). , these models retrieve information at the sentence or paragraph level, potentially introducing noise and affecting the generation quality. To address ...	BiomedRAG: A retrieval augmented large language model for biomedicine.
Community isolation of patients with communicable infectious diseases limits spread of pathogens but our understanding of isolated patients' needs and challenges is incomplete. Rwanda deployed a digital health service nationally to assist public health clinicians to remotely monitor and support SARS-CoV-2 cases via the...	Natural language processing to evaluate texting conversations between patients and healthcare providers during COVID-19 Home-Based Care in Rwanda at scale.
Patients with synucleinopathies such as multiple system atrophy (MSA) and Parkinson's disease (PD) frequently display speech and language abnormalities. We explore the diagnostic potential of automated linguistic analysis of natural spontaneous speech to differentiate MSA and PD. Spontaneous speech of 39 participants w...	Automated analysis of spoken language differentiates multiple system atrophy from Parkinson's disease.

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