BMJ Open. 2025 Aug 3;15(8):e093124. doi: 10.1136/bmjopen-2024-093124.

ABSTRACT

OBJECTIVE: This systematic review and meta-analysis aimed to determine the pooled prevalence of and factors associated with work-related musculoskeletal disorders (WMSDs) among low- and middle-income countries.

METHODS AND DESIGN: Databases such as PubMed/MEDLINE, CINAHL, LIVIVO, African Journals Online, African Index Medicus (AIM), HINARI, Science Direct, Web of Science, Cochrane Library, Google Scholar, Semantic Scholar and Google were used to retrieve all the relevant articles. The search was carried out from 22 April 2024 to 26 June 2024. Data were analysed via STATA 17 software. With a 95% CI, this meta-analysis with a random-effects model was carried out to determine the pooled prevalence.

SETTING: The study was conducted in low- and middle-income countries.

PARTICIPANTS: Weavers of low- and middle-income countries.

OUTCOME MEASURES: The primary outcome of this study was the prevalence of WMSD.

RESULT: In this meta-analysis, a total of 21 articles with 7322 study participants were included. The pooled prevalence of WMSDs was 72.20%. Working more than 8 hours per day, working in a chair with no back support, working in an uncomfortable posture, not performing regular physical exercise, lacking knowledge of the causes of WMSD and lacking job satisfaction were factors significantly associated with WMSDs.

CONCLUSION: A high prevalence of WMSDs among weavers in low- and middle-income countries was recorded. This indicates the need to take effective intervention measures. Rigorous ergonomic training, providing lengthy breaks and building centres for physical exercise, improving workplace ergonomic design and increasing job satisfaction are recommended.

PROSPERO REGISTRATION NUMBER: CRD42024561064.

PMID:40754326 | DOI:10.1136/bmjopen-2024-093124

BMC Musculoskelet Disord. 2025 Aug 2;26(1):744. doi: 10.1186/s12891-025-08967-4.

ABSTRACT

INTRODUCTION: Low back pain, one of the musculoskeletal disorders is among the major global public health problems and contributors to disability and workers' absence in occupational areas which certainly disrupts work productivity and the expected results. Though various studies investigated low back pain, the results were inconsistent and inconclusive enough, and there is no representative data in low- and middle-income countries on this public health concern. This in turn hinders the efforts of various intervention activities. Therefore, this systematic review and meta-analysis was conducted to determine the pooled prevalence of low back pain and its associated factors among weavers of low- and middle-income countries.

METHODS: All the relevant articles were retrieved from databases such as PubMed/MEDLINE, CINAHL, LIVIVO, African Journals Online, African Index Medicus (AIM), HINARI, Science Direct, Web of Science, Cochrane Library, Google Scholar, Semantic Scholar, and Google. The Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guideline was followed for this study. The extracted data were analyzed using STATA 17 software. With a 95% confidence interval, this meta-analysis with the random-effects model was conducted to determine the pooled prevalence. RESULT: A total of twenty articles with 7211 study participants were included in this meta-analysis. The pooled prevalence of low back pain was 55.81%. Age, working in a chair with no back support, working in an uncomfortable posture, work experience, and job stress were the factors significantly associated with low back pain.

CONCLUSION: A high prevalence of low back pain among weavers in low-and middle-income countries was registered. This indicates the need to take effective intervention measures. Rigorous provision of ergonomic training, providing lengthy breaks, improving workplace ergonomic design, and increasing job satisfaction are recommended.

PMID:40753385 | DOI:10.1186/s12891-025-08967-4

Int J Pharm. 2025 Jul 28:125981. doi: 10.1016/j.ijpharm.2025.125981. Online ahead of print.

ABSTRACT

The digital transformation of pharmaceutical industry is a challenging task due to the high complexity of involved elements and the strict regulatory compliance. Maintenance activities in the pharmaceutical industry play an essential role in ensuring product quality and integral functioning of equipment and premises. This paper first identifies the key challenges of digitalization in pharmaceutical industry and creates the corresponding problem space for key involved elements. A semantic-driven digitalization framework is proposed aiming to improve the digital continuity of digital resources and technologies for maintenance activities. This framework aligns with Quality 4.0 principles and supports the industry's pursuit of zero manufacturing defects. A case study is conducted to verify the feasibility of the proposed framework based on the water sampling activities in Merck Serono facility in Switzerland. A tool-chain is presented to enable the functional modules of the framework. Some of the key functional modules within the framework are implemented and have demonstrated satisfactory performance. As one of the outcomes, a digital sampling assistant with web-based services is created to support the automated workflow of water sampling activities. The implementation result proves the potential of the proposed framework to solve the identified problems of maintenance digitalization in the pharmaceutical industry.

PMID:40738268 | DOI:10.1016/j.ijpharm.2025.125981

Methods Mol Biol. 2025;2947:241-268. doi: 10.1007/978-1-0716-4662-5_14.

ABSTRACT

Proteins play a crucial role in almost all biological processes, serving as the building blocks of life and mediating various cellular functions, from enzymatic reactions to immune responses. Accurate annotation of protein functions is essential for advancing our understanding of biological systems and developing innovative biotechnological applications and therapeutic strategies. To predict protein function, researchers primarily rely on classical homology-based methods, which use evolutionary relationships, and increasingly on machine learning (ML) approaches. Lately, protein language models (PLMs) have gained prominence; these models leverage specialized deep learning architectures to effectively capture intricate relationships between sequence, structure, and function. We recently conducted a comprehensive benchmarking study to evaluate diverse protein representations (i.e., classical approaches and PLMs) and discuss their trade-offs. The current work introduces the Protein Representation Benchmark-PROBE tool, a benchmarking framework designed to evaluate protein representations on function-related prediction tasks. Here, we provide a detailed protocol for running the framework via the GitHub repository and accessing our newly developed user-friendly web service. PROBE encompasses four core tasks: semantic similarity inference, ontology-based function prediction, drug target family classification, and protein-protein binding affinity estimation. We demonstrate PROBE's usage through a new use case evaluating ESM2 and three recent multimodal PLMs-ESM3, ProstT5, and SaProt-highlighting their ability to integrate diverse data types, including sequence and structural information. This study underscores the potential of protein language models in advancing protein function prediction and serves as a valuable tool for both PLM developers and users.

PMID:40728618 | DOI:10.1007/978-1-0716-4662-5_14

Int J Surg. 2025 Jul 15. doi: 10.1097/JS9.0000000000003059. Online ahead of print.

ABSTRACT

BACKGROUND: Timely detection and intervention for pulmonary nodules play a vital role in decreasing lung cancer-related deaths. Nevertheless, the precise differentiation between benign and malignant nodules continues to face a major clinical challenge. With the rapid progress of artificial intelligence (AI), significant improvements have been made in the detection, classification, and clinical decision-making related to pulmonary nodules. Although scholarly interest in this domain has surged in recent years, there is still a lack of comprehensive bibliometric studies that systematically map its current landscape and evolution. This study seeks to explore emerging research trends, highlight thematic focus areas, and analyze patterns of collaboration within the field of AI-assisted pulmonary nodule research over the past 20 years.

METHODS: A literature search was conducted in the Web of Science Core Collection to collect relevant studies published from 2005 to 2024 concerning the application of AI in pulmonary nodules. Bibliometric analysis was carried out using tools such as CiteSpace, VOSviewer, and the Online Analysis Platform of Literature Metrology to examine contributions from countries, institutions, authors, journals, keywords, and references.

RESULTS: A total of 1,657 relevant publications were retrieved, reflecting a consistent upward trend in research output over the past two decades, with a marked acceleration observed after 2014. The leading contributors in terms of publication volume were China, the United States, and India. Shanghai Jiao Tong University stood out as the most prolific research institution. Analysis of keyword co-occurrence revealed several prominent thematic clusters, notably centered around Deep Convolutional Neural Network models, major diameter, lung nodule detection, false-positive reduction, cancer diagnosis, quantitative-semantic models, double reading, and clinical utility studies.

CONCLUSIONS: This bibliometric study offers a thorough assessment of the scholarly landscape concerning AI applications in pulmonary nodule research, underscoring major developments and key contributors. The insights gained may serve as a strategic reference for researchers in the medical and AI fields, facilitating informed future directions. Notably, the intersection of AI and pulmonary nodule research is concentrated in the following areas: 1. Application of AI in pulmonary nodule detection and classification; 2. AI in malignancy risk prediction and growth modeling; 3.AI-driven development of drug efficacy evaluation metrics may be a future direction for pulmonary nodule treatment research.

PMID:40717586 | DOI:10.1097/JS9.0000000000003059

BMC Health Serv Res. 2025 Jul 23;25(1):971. doi: 10.1186/s12913-025-12980-7.

ABSTRACT

INTRODUCTION: Patient satisfaction reflects the discrepancy between anticipated and actual healthcare service delivery, serving as a pivotal metric for strategic healthcare decision-making. This systematic review and meta-analysis aimed to assess the magnitude of patient satisfaction with pharmacy services and its determinants in Ethiopia.

METHODS: A systematic search was performed across multiple electronic databases, including PubMed, Hinari, Semantic Scholar, EMBASE, Scopus, Web of Science, and Google Scholar, to identify both published and unpublished relevant studies. Methodological quality and risk of bias were assessed using the Joanna Briggs Institute (JBI) critical appraisal tools and in accordance with the PRISMA 2020 guidelines. Statistical analyses were conducted using Stata version 17.

RESULTS: In total, 19 articles were included in the qualitative synthesis, of which 11 were selected for the quantitative analysis. The pooled prevalence of patient satisfaction with pharmacy services in Ethiopia was 56% (95% CI: 50-62), with significant associations observed with sociodemographic, socioeconomic, provider communication, and healthcare facility-related factors.

CONCLUSION: Approximately 40% of patients expressed dissatisfaction with pharmacy services, underscoring significant systemic deficiencies. To improve healthcare quality, policymakers and healthcare administrators should prioritize the optimization of pharmacy service delivery by implementing evidence-based interventions targeting the key contributing factors identified in this study.

PMID:40702479 | DOI:10.1186/s12913-025-12980-7

J Chem Inf Model. 2025 Jul 22. doi: 10.1021/acs.jcim.5c01237. Online ahead of print.

ABSTRACT

The 5-methyluridine (m5U) modification in RNA is vital for numerous biological processes, making its precise identification a key focus in computational biology. However, traditional wet-lab detection methods are cumbersome and time-consuming, whereas existing machine learning and deep learning computational prediction models still have room for improvement. Consequently, this study introduces m5U-HybridNet, an innovative framework that strategically integrates an RNA foundation model (RNA-FM) for deep semantic feature extraction with convolutional neural network-derived characteristics, attaining unparalleled success in identifying RNA m5U modification sites. Simultaneously, when compared with other existing models across different cell types and experimental techniques, it exhibits outstanding generalization capabilities. The m5U-HybridNet web server, accessible at http://www.bioai-lab.com/m5U, offers an effective and reliable platform for predicting RNA modification sites. It not only implies the diverse potential applications of pretrained models in the analysis of biological sequences but also enhances the application of data-driven machine intelligence in the realm of molecular biophysics principles.

PMID:40693567 | DOI:10.1021/acs.jcim.5c01237

BMC Cardiovasc Disord. 2025 Jul 18;25(1):519. doi: 10.1186/s12872-025-04956-6.

ABSTRACT

In the healthcare industry, the Semantic Web offers to manage a huge amount of medical data which is machine-readable and machine-understandable as well. This domain incorporates ontologies, linked data, and semantic web technologies to promote healthcare data interoperability and facilitate the effective and precise exchange of medical expertise, medical records, clinical recommendations, and research. It is tracked down that knowledge is better comprehended after undergoing an ontological analysis. An ontology serves as the basis of any knowledge representation system for a certain domain and eliminates inconsistencies in data to ensure its validity. The necessity of establishing healthcare systems for heart diseases is emphasized by the significant lack of awareness among the general public. A heart disease ontology needed to be created as it is not as thoroughly considered and explored (like, the classes of main heart diseases have not been structured) as it should have. In this research, we intend to develop a comprehensive ontology i.e. Heart Disease Ontology (HDO) that serves as a knowledge gateway for knowledge concerning major heart diseases. This ontology provides precise, comprehensive, and reliable data on prevalent heart diseases, including their causes, risk factors, symptoms, diagnosis, and treatment. HDO represents an extensive ontology structure consisting of 104 classes, 20 object properties, 14 data properties, and 808 instances. To ensure the interoperability of HDO, we have utilized some schema classes and properties and also reused classes from existing medical standardized ontologies. From the metrics of HDO, 22 classes, 5 object properties, and 12 data properties were reused from schema.org. Other than that, we have incorporated international medical standards by utilizing 18 classes from existing medical ontologies including, SNOMED CT, ICD10, FHIR, NCIT, SCDO, and some others. HDO is evaluated using OOPs Pitfall Scanner and Hermit Reasoner. Its functionality is validated by populating a use case and executing SPARQL queries. The domain knowledge and use case of HDO was validated through a domain expert study conducted in association with a cardiologist. HDO is developed by integrating Ontology Web Language (OWL) and RDF (Resource Description Framework) within the Protégé environment.

PMID:40681965 | DOI:10.1186/s12872-025-04956-6

Artif Intell Med. 2025 Jul 10;168:103187. doi: 10.1016/j.artmed.2025.103187. Online ahead of print.

ABSTRACT

BACKGROUND: The automatic International Classification of Diseases (ICD) coding task assigns unique medical codes to diseases in clinical texts for further data statistics, quality control, billing and other tasks. The efficiency and accuracy of medical code assignment is a significant challenge affecting healthcare. However, in clinical practice, Electronic Health Records (EHRs) data are usually complex, heterogeneous, non-standard and unstructured, and the manual coding process is time-consuming, laborious and error-prone. Traditional machine learning methods struggle to extract significant semantic information from clinical texts accurately, but the latest progress in Deep Learning (DL) has shown promising results to address these issues.

OBJECTIVE: This paper comprehensively reviewed recent advancements in utilizing deep learning for automatic ICD coding, which aimed to reveal prominent challenges and emerging development trends by summarizing and analyzing the model's year, design motivation, deep neural networks, and auxiliary data.

METHODS: This review introduced systematic literature on automatic ICD coding methods based on deep learning. We screened 5 online databases, including Web of Science, SpringerLink, PubMed, ACM, and IEEE digital library, and collected 53 published articles related to deep learning-based ICD coding from 2017 to 2023.

RESULTS: These deep neural network methods aimed to overcome some challenges, such as lengthy and noisy clinical text, high dimensionality and functional relationships of medical codes, and long-tail label distribution. The Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), attention mechanisms, Transformers, Pre-trained Language Models (PLMs), etc, have become popular to address prominent issues in ICD coding. Meanwhile, introducing medical ontology within the ICD coding system (code description and code hierarchy) and external knowledge (Wikipedia articles, tabular data, Clinical Classification Software (CCS), fine-tuning PLMs based on biomedical corpus, entity recognition and concept extraction) has become an emerging trend for automatic ICD coding.

CONCLUSION: This paper provided a comprehensive review of recent literature on applying deep learning technology to improve medical code assignment from a unique perspective. Multiple neural network methods (CNNs, RNNs, Transformers, PLMs, especially attention mechanisms) have been successfully applied in ICD tasks and achieved excellent performance. Various medical auxiliary data has also proven valuable in enhancing model feature representation and classification performance. Our in-depth and systematic analysis suggested that the automatic ICD coding method based on deep learning has a bright future in healthcare. Finally, we discussed some major challenges and outlined future development directions.

PMID:40664094 | DOI:10.1016/j.artmed.2025.103187

ChemSusChem. 2025 Jul 15:e2500458. doi: 10.1002/cssc.202500458. Online ahead of print.

ABSTRACT

Semantic technology is revolutionizing how the battery research community collaborates. It is becoming even more important as artificial intelligence agents emerge in the field. This article explores the role of semantic resources in advancing battery research by enabling the formalization of knowledge in a way that can be understood by both people and computers. Domain-specific ontologies provide definitive frameworks for structuring knowledge, while open-source software packages enable the creation, validation, manipulation, and sharing of data. To link ontologies with other resources, articles, and multimedia content, a new web-based platform called the Battery Knowledge Base, which provides a centralized hub to enhance knowledge sharing and collaboration, is introduced. In this article, how these semantic tools address critical challenges in knowledge and data management, driving progress in the field, are highlighted.

PMID:40663482 | DOI:10.1002/cssc.202500458

Front Public Health. 2025 Jun 23;13:1609615. doi: 10.3389/fpubh.2025.1609615. eCollection 2025.

ABSTRACT

INTRODUCTION: Infectious diseases pose a significant global health threat, exacerbated by factors like globalization and climate change. Artificial intelligence (AI) offers promising tools to enhance crucial early warning systems (EWS) for disease surveillance. This systematic review evaluates the current landscape of AI applications in EWS, identifying key techniques, data sources, benefits, and challenges.

METHODS: Following PRISMA guidelines, a systematic search of Semantic Scholar (2018-onward) was conducted. After screening 600 records and removing duplicates and non-relevant articles, the search yielded 67 relevant studies for review.

RESULTS: Key findings reveal the prevalent use of machine learning (ML), deep learning (DL), and natural language processing (NLP), which often integrate diverse data sources (e.g., epidemiological, web, climate, wastewater). The major benefits identified include earlier outbreak detection and improved prediction accuracy. However, significant challenges persist regarding data quality and bias, model transparency (the "black box" issue), system integration difficulties, and ethical considerations such as privacy and equity.

DISCUSSION: AI demonstrates considerable potential to strengthen infectious disease EWS. Realizing this potential, however, requires concerted efforts to address data limitations, enhance model explainability, ensure ethical implementation, improve infrastructure, and foster collaboration between AI developers and public health experts.

PMID:40626156 | PMC:PMC12230060 | DOI:10.3389/fpubh.2025.1609615

J Cheminform. 2025 Jul 6;17(1):99. doi: 10.1186/s13321-025-01037-w.

ABSTRACT

In this work, a concept for an open chemistry knowledge base was developed to integrate chemical research results into a collaboratively usable platform. To achieve this, we enhanced Semantic MediaWiki (SMW) to support the collection and structured summary of chemical data contained in publications. We implemented tools for capturing chemical structures in machine-readable formats and designed data forms along with a data model to ensure standardized input and organization of research results. These enhancements allow for effective data comparison and contextual analysis within an expandable Wiki environment. The use of the platform was specifically demonstrated by organizing and comparing research in the area of "CO2 reduction in homogeneous photocatalytic systems," showcasing its potential to significantly enhance the collaborative collection of research outcomes.Scientific contributionThis work shows ways to collaboratively collect and manage subject-specific knowledge in the domain of chemistry via an open database. By integrating cheminformatic tools into Semantic Mediawiki, an established technology for building knowledge databases is made systematically usable for the chemical community. The integration of chemistry-specific workflows and forms allows the mapping of data from current research with links to the original sources. This work is intended to show how gaps in the information system of scientists can be closed without having to use commercial systems.

PMID:40619391 | DOI:10.1186/s13321-025-01037-w

Accid Anal Prev. 2025 Jul 1;220:108152. doi: 10.1016/j.aap.2025.108152. Online ahead of print.

ABSTRACT

BACKGROUND: The decision-making processes of automated vehicles (AVs) can confuse users and reduce trust, highlighting the need for clear and human-centric explanations. Such explanations can help users understand AV actions, facilitate smooth control transitions and enhance transparency, acceptance, and trust. Critically, such explanations could improve situational awareness and support timely, appropriate human responses, thereby reducing the risk of misuse, unexpected automated decisions, and delayed reactions in safety-critical scenarios. However, current literature offers limited insight into how different types of explanations impact drivers in diverse scenarios and the methods for evaluating their quality. This paper systematically reviews what, when and how to provide human-centric explanations in AV contexts.

METHODS: The systematic review followed PRISMA guidelines, and covered five databases-Scopus, Web of Science, IEEE Xplore, TRID, and Semantic Scholar-from 2000 to April 2024. Out of 266 identified articles, 59 met the inclusion criteria.

RESULTS: Providing a detailed content explanation following AV's driving actions in real time does not always increase user trust and acceptance. Explanations that clarify the reasoning behind actions are more effective than those merely describing actions. Providing explanations before action is recommended, though the optimal timing remains uncertain. Multimodal explanations (visual and audio) are most effective when each mode conveys unique information; otherwise, visual-only explanations are preferred. The narrative perspective (first-person vs. third-person) also impacts user trust differently across scenarios.

CONCLUSIONS: The review underscores the importance of tailoring human-centric explanations to specific driving contexts. Future research should address explanation length, timing, and modality coordination and focus on real-world studies to enhance generalisability. These insights are vital for advancing the research of human-centric explanations in AV systems and fostering safer, more trustworthy human-vehicle interactions, ultimately reducing the risk of inappropriate reactions, delayed responses, or user error in traffic settings.

PMID:40602017 | DOI:10.1016/j.aap.2025.108152

Sci Rep. 2025 Jul 1;15(1):21219. doi: 10.1038/s41598-025-08356-w.

ABSTRACT

As global energy demands rise, the advancement of new energy technologies increasingly relies on the development of metals that can endure extreme pressures, temperatures, and fluxes of energetic particles and photons, as well as aggressive chemical reactions. One way to assist in the design and manufacturing of metals for the future is by learning from their past. Here we track the progress of metallic materials for extreme environments in the past 35 years using the text mining method, which allows us to discover patterns from a large scale of literature in the field. Specifically, we leverage transfer learning and dynamic word embeddings. Approximately one million relevant abstracts ranging from 1989 to 2023 were collected from the Web of Science. The literature was then mapped to a 200-dimensional vector space, generating time-series word embeddings across six time periods. Subsequent orthogonal Procrustes analysis was employed to align and compare vectors across these periods, overcoming challenges posed by training randomness and the non-uniqueness of singular value decomposition. This enabled the comparison of the semantic evolution of terms related to metals under extreme conditions. The model's performance was evaluated using inputs categorized into materials, properties, and applications, demonstrating its ability to identify relevant metallic materials to the three input categories. The study also revealed the temporal changes in keyword associations, indicating shifts in research focus or industrial interest towards high-performance alloys for applications in aerospace and biomedical engineering, among others. This showcases the model's capability to track the progress in metallic materials for extreme environments over time.

PMID:40593280 | DOI:10.1038/s41598-025-08356-w

Nutr Rev. 2025 Jun 30:nuaf106. doi: 10.1093/nutrit/nuaf106. Online ahead of print.

ABSTRACT

BACKGROUND: Nephrotic syndrome (NS) is a chronic pathology, with an undulating evolution, that reduces the quality of life among children.

OBJECTIVE: In this meta-analysis we sought to assess the reliability of zinc supplementation in reducing the relapse prevalence and morbidity associated with pediatric NS.

DATA SOURCES: We searched the PubMed, Web of Science, Scopus, Embase, Google Scholar, and Semantic Scholar databases for the period of January 2000 toDecember 2023.

DATA EXTRACTION: Inclusion criteria were articles reporting investigations of children younger than 18 years who were diagnosed with NS and would benefit from zinc supplementation compared with a similar control group. The studies were verified according to the PRISMA criteria. In our initial screening we identified 1063 articles. After screening, 25 full-text articles were analyzed. After we applied the inclusion/exclusion criteria, 6 articles (484 patients) were included in the review.

DATA ANALYSIS: The data considered eligible, regardless of the duration of supplementation, were entered into Review Manager software for analysis using random effects models. Analysis of the causes of heterogeneity revealed that zinc supplementation has shown a beneficial effect on widening the disease-free interval, reducing the number of patients experiencing relapses (relative risk [RR]: 0.70, 95% CI, 0.58 to 0.84, P = .0001; NNT = 5), the mean relapse rate (SMD, -0,41, 95% CI, -0.78 to -0.04, P = .03) and of the need for hospitalization (SMD: -1.35, 95% CI -1.78 to -0.91, P < 00001) depending on the duration of supplementation. In accordance with the Grading of Recommendations Assessment, Development and Evaluation (GRADE) guidelines, we found a medium level of certainty for the interrelationship of zinc supplementation-with reduction in the number of relapses/mean relapses and a low level between zinc supplementation and the reduction in time spent hospitalized.

CONCLUSIONS: Zinc supplementation seems to be beneficial in improving the course of pediatric NS. Due to the limited amount of evidence included in the present study, additional studies are needed in order to establish the true size of the effect.

SYSTEMATIC REVIEW REGISTRATION: PROSPERO registration No. [CRD42024527895].

PMID:40587383 | DOI:10.1093/nutrit/nuaf106

Adv Health Sci Educ Theory Pract. 2025 Jun 25. doi: 10.1007/s10459-025-10426-7. Online ahead of print.

ABSTRACT

Clinical reasoning skills develop through increased knowledge acquisition, greater clinical experience, and continued practice over time. Yet, across undergraduate and graduate medical education, it is inconsistently taught. As progressive clinical reasoning curricula emerge, research is needed to help inform the content and activities appropriate for different learner levels. While much is understood about the clinical reasoning skills of novices and experts, less has been theorized about students in between those two extremes. Our study explores the clinical reasoning skills of medical students in their final year of medical school, informed by clinical reasoning models and information processing theories. We conducted think-aloud interviews with 18 4th-year medical students tasked with completing a novel web-based assessment. Students reviewed simulated patient charts, answered clinically relevant questions, and justified their thinking and responses. Using a qualitative data collection and analysis framework, we coded interviews for clinical reasoning elements and emergent themes. Our findings present an initial framework for understanding the clinical reasoning skills of 4th-year medical students. The framework includes four high-level skills that we defined as interpreting, framing, generating, and justifying. These skills reflect elements of nonanalytic and analytic thinking in that students used semantic qualifiers, partially activated illness scripts, and engaged in aspects of hypothetical-deductive reasoning. Our framework can help shape how best to structure clinical reasoning instruction in medical education across the novice-to-expert continuum, as well as aid in the development of clinical reasoning theories that incorporate a range of learner levels.

PMID:40560425 | DOI:10.1007/s10459-025-10426-7

Neural Comput Appl. 2025;37(18):11737-11753. doi: 10.1007/s00521-024-10674-5. Epub 2024 Dec 14.

ABSTRACT

The research in Materials Science and Engineering focuses on the design, synthesis, properties, and performance of materials. An important class of materials that is widely investigated are crystalline materials, including metals and semiconductors. Crystalline material typically contains a specific type of defect called "dislocation". This defect significantly affects various material properties, including bending strength, fracture toughness, and ductility. Researchers have devoted a significant effort in recent years to understanding dislocation behaviour through experimental characterization techniques and simulations, e.g., dislocation dynamics simulations. This paper presents how data from dislocation dynamics simulations can be modelled using semantic web technologies through annotating data with ontologies. We extend the dislocation ontology by adding missing concepts and aligning it with two other domain-related ontologies (i.e., the Elementary Multi-perspective Material Ontology and the Materials Design Ontology), allowing for efficiently representing the dislocation simulation data. Moreover, we present a real-world use case for representing the discrete dislocation dynamics data as a knowledge graph (DisLocKG) which can depict the relationship between them. We also developed a SPARQL endpoint that brings extensive flexibility for querying DisLocKG.

PMID:40538984 | PMC:PMC12174205 | DOI:10.1007/s00521-024-10674-5

Res Integr Peer Rev. 2025 Jun 18;10(1):10. doi: 10.1186/s41073-025-00166-y.

ABSTRACT

BACKGROUND: Although commonly used to evaluate health interventions, cluster randomized trials raise difficult ethical issues. Recognizing this, the Ottawa Statement on the Ethical Design and Conduct of Cluster Randomized Trials, published in 2012, provides 15 recommendations to address ethical issues across seven domains. But due to several developments in the design and implementation of cluster randomized trials, there are new issues requiring guidance. To inform the forthcoming update of the Ottawa Statement, we aimed to identify any gaps in the Ottawa Statement discussed within the literature.

METHODS: We searched Google Scholar, Scopus, and Web of Science using the 'cited by' function on 11 November 2022.We included all types of publications, including articles, book chapters, commentaries, editorials, ethics guidelines, theses and trial-related publications (i.e., primary reports, protocols, and secondary analyses), that cited and engaged with the Ottawa Statement, the Ottawa Statement précis, or one or more of its four background papers. Data were extracted by four reviewers working in rotating pairs. Reviewers captured relevant text verbatim and recorded whether it reflected a gap relating to one or more of the Ottawa Statement domains. Using a thematic analysis approach, semantic coding was used to summarize the content of the data into distinct gaps within the Ottawa Statement domains, which was subsequently expanded in an inductive manner through discussion.

RESULTS: The qualitative analysis of the text from 53 articles resulted in the identification of 24 distinct gaps in the Ottawa Statement: 4 gaps about justifying the cluster randomized design; 2 gaps about research ethics committee review; 3 gaps about identifying research participants; 4 gaps about obtaining informed consent; 3 gaps about gatekepeers; 6 gaps about assessing benefits and harms; 1 gap about protecting vulnerable participants; and 1 gap about equity-related issues in cluster randomized trials.

CONCLUSION: Identifying 24 gaps reveals a need to update the Ottawa Statement. Alongside additional gaps identified in ongoing empirical work and through engagement with our patient and public partners, the gaps identified through this citation analysis should be considered in the forthcoming Ottawa Statement update.

PMID:40528254 | DOI:10.1186/s41073-025-00166-y

AMIA Jt Summits Transl Sci Proc. 2025 Jun 10;2025:46-55. eCollection 2025.

ABSTRACT

We developed a simulated process to show a software implementation to facilitate an approach to integrate the Informed Consent Ontology, a reference ontology of informed consent information, to express implicit description and implement conceptual permission from informed consent life cycle. An early study introduced an experimental method to use Semantic Web Rule Language (SWRL) to describe and represent permissions to computational deduce more information from the Informed Consent Ontology (ICO), demonstrated by the use of the All of Us informed consent documents. We show how incomplete information in informed consent documents can be elucidated using a computational model of permissions toward health information technology that integrates ontologies. Future goals entail applying our computational approach for specific sub-domains of the informed consent life cycle, specifically for vaccine informed consent.

PMID:40502263 | PMC:PMC12150727

AMIA Jt Summits Transl Sci Proc. 2025 Jun 10;2025:300-311. eCollection 2025.

ABSTRACT

Rare diseases affect approximately 1 in 11 Americans, yet their diagnosis remains challenging due to limited clinical evidence, low awareness, and lack of definitive treatments. Our project aims to accelerate rare disease diagnosis by developing a comprehensive informatics framework leveraging data mining, semantic web technologies, deep learning, and graph-based embedding techniques. However, our on-premises computational infrastructure faces significant challenges in scalability, maintenance, and collaboration. This study focuses on developing and evaluating a cloud-based computing infrastructure to address these challenges. By migrating to a scalable, secure, and collaborative cloud environment, we aim to enhance data integration, support advanced predictive modeling for differential diagnoses, and facilitate widespread dissemination of research findings to stakeholders, the research community, and the public and also proposed a facilitated through a reliable, standardized workflow designed to ensure minimal disruption and maintain data integrity for existing research project.

PMID:40502250 | PMC:PMC12150693