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

Data Brief. 2025 May 14;60:111648. doi: 10.1016/j.dib.2025.111648. eCollection 2025 Jun.

ABSTRACT

Scene Text Recognition (STR) has advanced significantly in recent years, yet languages utilizing Arabic-based scripts, such as Kurdish, remain underrepresented in existing datasets. This paper introduces KSTRV1, the first large-scale dataset designed for Kurdish Scene Text Recognition (KSTR), addressing the lack of resources for non-Latin scripts. The dataset comprises 1,420 natural scene images and 19,872 cropped word samples, covering Kurdish (Sorani and Badini dialects), Arabic, and English. Additionally, 20,000 synthetic text instances have been generated to enhance the dataset's diversity, quantity, and quality by incorporating varied fonts, orientations, distortions, and background complexities. KSTRV1 captures the multilingual landscape of the Kurdistan Region while addressing real-world challenges like occlusion, lighting variations, and script complexity. The dataset includes detailed annotations with bounding boxes, language identification, and text orientation labels, ensuring comprehensive support for training and evaluating STR models. By providing both natural and synthetic data, KSTRV1 enables the development of robust text recognition models, particularly for Central Kurdish, a low-resource language. The KSTRV1 dataset is publicly available at https://doi.org/10.5281/zenodo.15038953 and is expected to significantly contribute to research in multilingual STR, document analysis, and optical character recognition (OCR), facilitating more inclusive and accurate text recognition systems.

PMID:40496736 | PMC:PMC12151206 | DOI:10.1016/j.dib.2025.111648

BMC Med Inform Decis Mak. 2025 Jun 10;25(Suppl 1):211. doi: 10.1186/s12911-025-03022-7.

ABSTRACT

BACKGROUND: Use of the FAIR principles (Findable, Accessible, Interoperable and Reusable) allows the rapidly growing number of biomedical datasets to be optimally (re)used. An important aspect of the FAIR principles is metadata. The FAIR Data Point specifications and reference implementation have been designed as an example on how to publish metadata according to the FAIR principles. Metadata can be added to a FAIR Data Point with the FDP's web interface or through its API. However, these methods are either limited in scalability or only usable by users with a background in programming. We aim to provide a new tool for populating FDPs with metadata that addresses these limitations with the FAIR Data Point Populator.

RESULTS: The FAIR Data Point Populator consists of a GitHub workflow together with Excel templates that have tooltips, validation and documentation. The Excel templates are targeted towards non-technical users, and can be used collaboratively in online spreadsheet software. A more technical user then uses the GitHub workflow to read multiple entries in the Excel sheets, and transform it into machine readable metadata. This metadata is then automatically uploaded to a connected FAIR Data Point. We applied the FAIR Data Point Populator on the metadata of two datasets, and a patient registry. We were then able to run a query on the FAIR Data Point Index, in order to retrieve one of the datasets.

CONCLUSION: The FAIR Data Point Populator addresses the limitations of the other metadata publication methods by allowing the bulk creation of metadata entries while remaining accessible for users without a background in programming. Additionally, it allows efficient collaboration. As a result of this, the barrier of entry for FAIRification is lower, which allows the creation of FAIR data by more people.

PMID:40495132 | DOI:10.1186/s12911-025-03022-7

Int J Cardiol. 2025 Jun 5:133483. doi: 10.1016/j.ijcard.2025.133483. Online ahead of print.

ABSTRACT

BACKGROUND: Takotsubo cardiomyopathy (TTC) is a stress-induced condition with limited evidence-based treatment options. Beta-blockers are commonly used, yet their efficacy remains uncertain. This meta-analysis evaluates the impact of beta-blocker therapy on mortality and recurrence in TTC patients.

METHODS: We systematically searched PubMed, EMBASE, Cochrane Library, Web of Science, Google Scholar, and Semantic Scholar, alongside trial registries and grey literature, for studies from inception to March 2025. Included studies examined adult TTC patients treated with beta-blockers versus controls, reporting all-cause mortality and recurrence. Odds ratios (ORs) with 95 % confidence intervals (CIs) were pooled using a random-effects model. Heterogeneity was assessed with I2 statistics, and publication bias was evaluated via funnel plots. Subgroup analyses stratified studies by design (retrospective, prospective, mixed) to assess methodological heterogeneity. A meta-regression explored ejection fraction (EF) as a moderator of mortality outcomes.

RESULTS: Nineteen studies (n = 11,167 patients, predominantly female, mean age 59-74 years) were included. Beta-blocker therapy significantly reduced all-cause mortality by 28 % (OR 0.72, 95 % CI: 0.62-0.84, p < 0.001; I2 = 30 %) with consistent effects across study designs (between-subgroup heterogeneity p = 0.86). Subgroup analyses showed a non-significant 21 % reduction in 1-year mortality (OR 0.79, 95 % CI: 0.54-1.16, p = 0.23; I2 = 52 %) and a significant 29 % reduction in 2-5-year mortality (OR 0.71, 95 % CI: 0.61-0.82, p < 0.001; I2 = 7 %). Recurrence decreased by 29 % overall (OR 0.71, 95 % CI: 0.52-0.97, p = 0.03; I2 = 57 %), with significant protective effects in mixed (OR 0.595) and retrospective (OR 0.485) studies but not prospective studies (OR 0.842), demonstrating significant between-subgroup heterogeneity (p = 0.01). Meta-regression showed no significant moderation of mortality by EF (p = 0.64), suggesting consistent benefits across cardiac function levels.

CONCLUSIONS: Beta-blockers significantly reduce long-term mortality and recurrence in TTC. While mortality benefits are consistent across study designs, recurrence outcomes show methodological sensitivity, with stronger evidence from mixed and retrospective studies. Benefits are more pronounced with sustained therapy, with no variation by EF. These findings support beta-blocker use in long-term TTC management, though randomized trials are needed to confirm causality and optimize protocols.

PMID:40482835 | DOI:10.1016/j.ijcard.2025.133483

Front Neurosci. 2025 May 21;19:1591410. doi: 10.3389/fnins.2025.1591410. eCollection 2025.

ABSTRACT

BACKGROUNDS: This study innovatively enhances personalized emotional responses and user experience quality in traditional Chinese folding armchair (Jiaoyi chair) design through an interdisciplinary methodology.

GOAL: To systematically extract user emotional characteristics, we developed a hybrid research framework integrating web-behavior data mining.

METHODS: 1) the KJ method combined with semantic crawlers extracts emotional descriptors from multi-source social data; 2) expert evaluation and fuzzy comprehensive assessment reduce feature dimensionality; 3) random forest and K-prototype clustering identify three core emotional preference factors: "Flexible Refinement," "Uncompromising Quality," and "ergonomic stability."

DISCUSSION: A CNN-GRU-Attention hybrid deep learning model was constructed, incorporating dynamic convolutional kernels and gated residual connections to address feature degradation in long-term semantic sequences. Experimental validation demonstrated the superior performance of our model in three chair design preference prediction tasks (RMSE = 0.038953, 0.066123, 0.0069777), outperforming benchmarks (CNN, SVM, LSTM). Based on the top-ranked preference encoding, we designed a new Jiaoyi chair prototype, achieving significantly reduced prediction errors in final user testing (RMSE = 0.0034127, 0.0026915, 0.0035955).

CONCLUSION: This research establishes a quantifiable intelligent design paradigm for modernizing cultural heritage through computational design.

PMID:40470295 | PMC:PMC12133947 | DOI:10.3389/fnins.2025.1591410

Arch Suicide Res. 2025 Jun 2:1-20. doi: 10.1080/13811118.2025.2509653. Online ahead of print.

ABSTRACT

OBJECTIVE: Suicide rates continue to rise, particularly among young adults, with college student-athletes representing a specific subgroup of concern. The aim of this systematic review was to clarify the individual and environmental risk factors for suicide specific to U.S. college student-athletes.

METHOD: Databases searched included the State University of New York (SUNY) libraries, Google Scholar, Web of Science, PsychINFO, Semantic Scholar, and PubMed. No date restrictions were applied, resulting in 112 articles and reports included in this review. Studies examining U.S. student-athletes participating in intercollegiate athletics within the context of suicide, including ideation, actions, or attempts, met the inclusion criteria for this thematic review. The PRISMA framework guided the literature selection and content review.

RESULTS: Risk factors included the convergence of academic and athletic pressure, toxic team culture, barriers to accessing services, complexities of the athlete identity, and experiences of injury.

CONCLUSION: Given these unique risk factors, approaches to suicide prevention, intervention, and postvention for U.S. college student-athletes should include mandated suicide training for college athletic department personnel, routine mental health screening for athletes, improved access to mental health services, and the implementation of collaborative multidisciplinary care.

PMID:40454444 | DOI:10.1080/13811118.2025.2509653

Discov Oncol. 2025 Jun 1;16(1):978. doi: 10.1007/s12672-025-02716-8.

ABSTRACT

BACKGROUND: Cancer poses a significant health threat, causing millions of deaths annually. Although chemotherapy-based comprehensive therapies are common, their low accuracy and severe side effects limit effectiveness. Metal-organic frameworks (MOFs), with their superior biocompatibility and stability, show great promise for drug delivery and cancer treatment. This study aims to explore the potential and developmental trajectories of MOFs in cancer research through a bibliometric analysis.

METHODS: The Web of Science Core Collection was searched for documents from its inception in 2009 to December 31, 2023. We analyzed and visualized document types, countries, institutions, authors, journals, references, and keywords using the Bibliometrix package, dplyr, sankeywheel, term extraction, and ggplot2. Additionally, the Latent Dirichlet Allocation (LDA) algorithm was employed for detailed semantic analysis, uncovering latent thematic distributions.

RESULTS: A total of 7106 authors from 1591 institutions across 45 countries contributed 1955 papers on MOFs in cancer research, published in 327 journals. China leads in research output and international collaboration, with the Chinese Academy of Sciences as the top institution. Lin Wenbin from the University of Chicago is the most influential author, and ACS Applied Materials & Interfaces is the most active journal. MOFs are predominantly studied for breast cancer, followed by lung and liver cancers. Drug delivery remains a focal point for future research.

CONCLUSIONS: This study provides a comprehensive overview of the research landscape on MOFs in cancer treatment, offering insights into key trends and future directions, particularly in drug delivery and disease-specific applications.

PMID:40450655 | DOI:10.1007/s12672-025-02716-8

J Cheminform. 2025 May 31;17(1):91. doi: 10.1186/s13321-025-01025-0.

ABSTRACT

Persistent air quality pollution poses a serious threat to human health, and is one of the action points that policy makers should monitor according to the Directive 2008/50/EC. While deploying a massive network of hyperlocal sensors could provide extensive monitoring, this approach cannot generate geospatial continuous data and present several challenges in terms of logistics. Thus, developing accurate and trustable expert systems based on chemistry transport models is a key strategy for environmental protection. However, chemistry transport models present an important lack of standardization, and the formats are not interoperable between different systems, which limits the use for different stakeholders. In this context, semantic technologies provide methods and standards for scientific data and make information readable for expert systems. Therefore, this paper proposes a novel methodology for an ontology driven transformation for CHIMERE simulations, a chemistry transport model, allowing to generate knowledge graphs representing air quality information. It enables the transformation of netCDF files into RDF triples for short term air quality forecasting. Concretely, we utilize the Semantic Web Integration Tool (SWIT) framework for mapping individuals using an ontology as a template. Then, a new ontology for CHIMERE has been defined in this work, reusing concepts for other standards in the state of the art. Our approach demonstrates that RDF files can be created from netCDF in a linear computational time, allowing the scalability for expert systems. In addition, the ontology complains with the OQuaRE quality metrics and can be extended in future extensions to be applied to other chemistry transport models. SCIENTIFIC CONTRIBUTIONS: Development of the first ontology for a chemistry transport model. FAIRification of physical models thanks to the generation of knowledge graphs from netCDF files. The ontology proposed is published in PURL ( https://purl.org/chimere-ontology ) and the knowledge graph generated for a 72-h simulation can be accessed in the following repository: https://doi.org/10.5281/zenodo.13981544 .

PMID:40450355 | DOI:10.1186/s13321-025-01025-0

Brain Sci. 2025 May 18;15(5):515. doi: 10.3390/brainsci15050515.

ABSTRACT

Background/Objectives: Autobiographical memory (AM) is typically viewed in terms of comprising episodic (EAM) and semantic (SAM) components. Despite the emergence of numerous meta-analyses, the literature on these constructs remains fragmented. We aimed to summarize neural activations and to discuss the relations between constructs based on theory and experimentation, while evaluating the consistency between literature sources and discussing the critical issues and challenges of current research. Methods: We conducted a scoping meta-review on AM, EAM, and SAM based on meta-analytic studies in five scientific databases (PubMed, Web of Science, Scopus, PsychInfo, and PsychArticles). No temporal or language limits were applied. Results: We included twelve meta-analyses on AM, EAM and SAM in healthy populations. The meta-analyses of AM and EAM actually investigated the same construct, leading to misinterpretation. The two available meta-analyses on SAM used two different operationalizations of the construct. Neural data about EAM were analyzed via mean rank classification, finding the most relevant areas in the posterior cingulate cortex, hippocampus, precuneus, temporo-parietal junction, angular gyrus, and medial prefrontal cortex. SAM was linked to the posterior and anterior cingulate cortexes, middle and inferior frontal gyri, thalamus, middle and superior temporal gyri, inferior frontal and fusiform gyri, and parahippocampal cortex. Conclusions: Variability in reported activation patterns persists, reflecting differences in methodology and assumptions. We propose the homogenization the notations of EAM and AM based on experimental practice. In this notation, AM does not have a separate experimental task nor activation pattern and may not indicate a separate construct but an array of its components.

PMID:40426686 | DOI:10.3390/brainsci15050515