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

Entropy (Basel). 2025 Apr 27;27(5):473. doi: 10.3390/e27050473.

ABSTRACT

In this study, we propose a computational approach that applies text mining and deep learning to conduct controversy detection on social media platforms. Unlike previous research, our method integrates multidimensional and heterogeneous information from social media into a heterogeneous signed attributed network, encompassing various users' attributes, semantic information, and structural heterogeneity. We introduce a deep dual-layer self-supervised algorithm for community detection and analyze controversy within this network. A novel controversy metric is devised by considering three dimensions of controversy: community distinctions, betweenness centrality, and user representations. A comparison between our method and other classical controversy measures such as Random Walk, Biased Random Walk (BRW), BCC, EC, GMCK, MBLB, and community-based methods reveals that our model consistently produces more stable and accurate controversy scores. Additionally, we calculated the level of controversy and computed p-values for the detected communities on our crawled dataset Weibo, including #Microblog (3792), #Comment (45,741), #Retweet (36,126), and #User (61,327). Overall, our model had a comprehensive and nuanced understanding of controversy on social media platforms. To facilitate its use, we have developed a user-friendly web server.

PMID:40422428 | DOI:10.3390/e27050473

Ann Gen Psychiatry. 2025 May 23;24(1):32. doi: 10.1186/s12991-025-00568-8.

ABSTRACT

Adolescent suicide represents a critical global health issue. While research has identified numerous risk factors, the specific impact of parental dysfunction on adolescent suicide remains understudied, especially in Chinese contexts. This study explores how parental dysfunction manifests in suicide notes and affects adolescent mental health. We collected data from Chinese social media platforms using web crawlers, yielding 30 valid suicide notes for analysis. Using the AI-aided content analysis platform DiVoMiner®, we conducted high-frequency word and semantic network analyses. Our findings reveal that parents are a central concern for suicidal youth. We identified three primary patterns of parental dysfunction: excessive emphasis on instrumental goals, neglect of basic emotional needs, and inadequate protection from life traumas. These dysfunctions contribute to severe psychological distress, identity loss, and negative coping behaviors among youth. The research highlights two significant phenomena in contemporary Chinese family dynamics: the "short-sightedness" of prioritizing short-term instrumental goals over long-term social-emotional development, and the remarkably high prevalence of "lack of autonomy" in parenting approaches. Our study extends the literature by exploring mechanisms through which parental dysfunctions contribute to suicidal behaviors in young people. These findings emphasize the need for collaborative efforts among parents, educators, policymakers, and mental health professionals to foster nurturing environments characterized by emotional support, autonomy encouragement, and balanced academic expectations-all crucial for adolescent well-being.

PMID:40410879 | DOI:10.1186/s12991-025-00568-8

Eur Burn J. 2025 May 15;6(2):25. doi: 10.3390/ebj6020025.

ABSTRACT

Burn injuries lead to significant physical and psychological consequences, including chronic pain, post-traumatic stress, depression, and social isolation. Mindfulness-based interventions (MBIs) have been proposed as a holistic approach to address these challenges in burn rehabilitation. This systematic review evaluates the efficacy of dispositional mindfulness and MBIs, including mindfulness meditation, yoga, and self-compassion training, in managing pain, emotional distress, and psychosocial adaptation in burn survivors. A comprehensive literature search was conducted through MEDLINE and Web of Science, covering studies up to February 2025, with additional papers retrieved from Google Scholar and Semantic Scholar. Studies were included if they reported quantitative data on the effects of MBIs in burn patients and/or their families, excluding opinion pieces, editorials, reviews, and qualitative studies. After screening 91 studies retrieved from the databases and adding a compelling paper retrieved from the other sources explored, 12 studies were included in the final pool, categorized into cross-sectional studies (n = 6), and intervention studies (n = 6). The extracted data included publication year, research design, sample characteristics, intervention details, main findings, and data for quality assessment. The synthesis of the results suggests that mindfulness is associated with reduced psychological symptoms, improved emotional regulation, and enhanced self-compassion, leading to better coping strategies and social reintegration. However, the long-term efficacy of MBIs remains inconclusive, and further research is needed to differentiate mindfulness-specific effects from those of general physical exercise. Evidence also suggests that mindfulness interventions may reduce anxiety and secondary trauma in children with burns and their caregivers. This review highlights the potential of MBIs as adjuncts to conventional burn rehabilitation programs, but further high-quality trials are needed to establish their sustained efficacy and to understand the specific benefits of mindfulness.

PMID:40407681 | DOI:10.3390/ebj6020025

Database (Oxford). 2025 May 20;2025:baaf008. doi: 10.1093/database/baaf008.

ABSTRACT

We previously described Graph2VR, a prototype that enables researchers to use virtual reality (VR) to explore and navigate through Linked Data graphs using SPARQL queries (see https://doi.org/10.1093/database/baae008). Here we evaluate the use of Graph2VR in three realistic life science use cases. The first use case visualizes metadata from large-scale multi-center cohort studies across Europe and Canada via the EUCAN Connect catalogue. The second use case involves a set of genomic data from synthetic rare disease patients, which was processed through the Variant Interpretation Pipeline and then converted into Resource Description Format for visualization. The third use case involves enriching a graph with additional information, in this case, the Dutch Anatomical Therapeutic Chemical code Ontology with the DrugID from Drugbank. These examples collectively showcase Graph2VR's potential for data exploration and enrichment, as well as some of its limitations. We conclude that the endless three-dimensional space provided by VR indeed shows much potential for the navigation of very large knowledge graphs, and we provide recommendations for data preparation and VR tooling moving forward. Database URL: https://doi.org/10.1093/database/baaf008.

PMID:40402773 | DOI:10.1093/database/baaf008

J Cheminform. 2025 May 21;17(1):79. doi: 10.1186/s13321-025-01017-0.

ABSTRACT

Named entities, such as chemicals/drugs, genes/proteins, and diseases, and their associations are not only important components of biomedical literature, but also the foundation of creating biomedical knowledgebases and knowledge graphs. This work addresses the challenges of expressing co-occurrence associations between named entities extracted from a biomedical literature corpus in a machine-readable format. We developed a Resource Description Framework (RDF) data model and integrated it into the PubChemRDF resource, which is freely accessible and publicly available. The developed co-occurrence data model was populated into a triplestore with named entities and their associations derived from text mining of millions of biomedical references found in PubMed. The utility of the data model was demonstrated through multiple use cases. Together with meta-data modeling of the references including the information about the author, journal, grant, and funding agency, this data model allows researchers to address pertinent biomedical questions through SPARQL queries and helps to exploit biomedical knowledge in various user perspectives and use cases.

PMID:40399973 | DOI:10.1186/s13321-025-01017-0

Database (Oxford). 2025 May 20;2025:baaf008. doi: 10.1093/database/baaf008.

ABSTRACT

We previously described Graph2VR, a prototype that enables researchers to use virtual reality (VR) to explore and navigate through Linked Data graphs using SPARQL queries (see https://doi.org/10.1093/database/baae008). Here we evaluate the use of Graph2VR in three realistic life science use cases. The first use case visualizes metadata from large-scale multi-center cohort studies across Europe and Canada via the EUCAN Connect catalogue. The second use case involves a set of genomic data from synthetic rare disease patients, which was processed through the Variant Interpretation Pipeline and then converted into Resource Description Format for visualization. The third use case involves enriching a graph with additional information, in this case, the Dutch Anatomical Therapeutic Chemical code Ontology with the DrugID from Drugbank. These examples collectively showcase Graph2VR's potential for data exploration and enrichment, as well as some of its limitations. We conclude that the endless three-dimensional space provided by VR indeed shows much potential for the navigation of very large knowledge graphs, and we provide recommendations for data preparation and VR tooling moving forward. Database URL: https://doi.org/10.1093/database/baaf008.

PMID:40392751 | DOI:10.1093/database/baaf008

Gigascience. 2025 Jan 6;14:giaf045. doi: 10.1093/gigascience/giaf045.

ABSTRACT

BACKGROUND: In recent decades, several life science resources have structured data using the same framework and made these accessible using the same query language to facilitate interoperability. Knowledge graphs have seen increased adoption in bioinformatics due to their advantages for representing data in a generic graph format. For example, yummydata.org catalogs more than 60 knowledge graphs accessible through SPARQL, a technical query language. Although SPARQL allows powerful, expressive queries, even across physically distributed knowledge graphs, formulating such queries is a challenge for most users. Therefore, to guide users in retrieving the relevant data, many of these resources provide representative examples. These examples can also be an important source of information for machine learning (for example, machine-learning algorithms for translating natural language questions to SPARQL), if a sufficiently large number of examples are provided and published in a common, machine-readable, and standardized format across different resources.

FINDINGS: We introduce a large collection of human-written natural language questions and their corresponding SPARQL queries over federated bioinformatics knowledge graphs (KGs) collected for several years across different research groups at the SIB Swiss Institute of Bioinformatics. The collection comprises more than 1,000 example questions and queries, including almost 100 federated queries. We propose a methodology to uniformly represent the examples with minimal metadata, based on existing standards. Furthermore, we introduce an extensive set of open-source applications, including query graph visualizations and smart query editors, easily reusable by KG maintainers who adopt the proposed methodology.

CONCLUSIONS: We encourage the community to adopt and extend the proposed methodology, towards richer KG metadata and improved Semantic Web services. URL: https://github.com/sib-swiss/sparql-examples.

PMID:40378136 | DOI:10.1093/gigascience/giaf045

Urologiia. 2025 May;(2):121-127.

ABSTRACT

OBJECTIVE: to assess the needs and attitudes of urologists regarding the use of technologies related to artificial intelligence, particularly the web platform "Sechenov.AI_nephro", in the surgical treatment of patients with renal parenchymal neoplasms.

MATERIALS AND METHODS: a qualitative study was conducted through in-depth interviews. A questionnaire was developed for the interviews, including 14 categories of questions covering various aspects of the use of artificial intelligence (AI) aimed at optimizing preoperative planning for patients with renal parenchymal neoplasms. The study involved 8 urologists with extensive experience in the surgical treatment of patients with renal parenchymal neoplasms.

RESULTS: the survey results highlight the growing interest in the implementation of AI technologies in medical practice.

CONCLUSION: in-depth interviews among urologists in Russia showed that there is a high interest in AI developments in urological practice. At the same time, successful integration of technologies requires overcoming several obstacles, including training specialists and ensuring data security. The "Sechenov.AI_nephro" platform has the potential to become an important tool in optimizing preoperative planning, but its success will depend on the readiness of physicians for new technologies and support from the medical community.

PMID:40377592

J Med Internet Res. 2025 May 14;27:e70733. doi: 10.2196/70733.

ABSTRACT

BACKGROUND: Online health information is widely available, but a substantial portion of it is inaccurate or misleading, including exaggerated, incomplete, or unverified claims. Such misinformation can significantly influence public health decisions and pose serious challenges to health care systems. With advances in artificial intelligence and natural language processing, pretrained large language models (LLMs) have shown promise in identifying and distinguishing misleading health information, although their effectiveness in this area remains underexplored.

OBJECTIVE: This study aimed to evaluate the performance of 4 mainstream LLMs (ChatGPT-3.5, ChatGPT-4, Ernie Bot, and iFLYTEK Spark) in the identification of online health information, providing empirical evidence for their practical application in this field.

METHODS: Web scraping was used to collect data from rumor-refuting websites, resulting in 2708 samples of online health information, including both true and false claims. The 4 LLMs' application programming interfaces were used for authenticity verification, with expert results as benchmarks. Model performance was evaluated using semantic similarity, accuracy, recall, F1-score, content analysis, and credibility.

RESULTS: This study found that the 4 models performed well in identifying online health information. Among them, ChatGPT-4 achieved the highest accuracy at 87.27%, followed by Ernie Bot at 87.25%, iFLYTEK Spark at 87%, and ChatGPT-3.5 at 81.82%. Furthermore, text length and semantic similarity analysis showed that Ernie Bot had the highest similarity to expert texts, whereas ChatGPT-4 showed good overall consistency in its explanations. In addition, the credibility assessment results indicated that ChatGPT-4 provided the most reliable evaluations. Further analysis suggested that the highest misjudgment probabilities with respect to the LLMs occurred within the topics of food and maternal-infant nutrition management and nutritional science and food controversies. Overall, the research suggests that LLMs have potential in online health information identification; however, their understanding of certain specialized health topics may require further improvement.

CONCLUSIONS: The results demonstrate that, while these models show potential in providing assistance, their performance varies significantly in terms of accuracy, semantic understanding, and cultural adaptability. The principal findings highlight the models' ability to generate accessible and context-aware explanations; however, they fall short in areas requiring specialized medical knowledge or updated data, particularly for emerging health issues and context-sensitive scenarios. Significant discrepancies were observed in the models' ability to distinguish scientifically verified knowledge from popular misconceptions and in their stability when processing complex linguistic and cultural contexts. These challenges reveal the importance of refining training methodologies to improve the models' reliability and adaptability. Future research should focus on enhancing the models' capability to manage nuanced health topics and diverse cultural and linguistic nuances, thereby facilitating their broader adoption as reliable tools for online health information identification.

PMID:40367512 | DOI:10.2196/70733

J Clin Med. 2025 Apr 29;14(9):3063. doi: 10.3390/jcm14093063.

ABSTRACT

Background: Primary progressive aphasia (PPA) is a neurodegenerative disorder that worsens over time without appropriate treatment. Although referral to a speech and language pathologist is essential for diagnosing language deficits and developing effective treatment plans, there is no scientific consensus regarding the most effective treatment. Thus, our study aims to assess the efficacy and safety of various therapeutic interventions for PPA. Methods: Google Scholar, PubMed, Web of Science, and the Cochrane Library databases were systematically searched to identify articles assessing different therapeutic interventions for PPA. To ensure comprehensive coverage, the search strategy employed specific medical subject headings. The primary outcome measure was language gain; the secondary outcome assessed overall therapeutic effects. Data on study characteristics, patient demographics, PPA subtypes, therapeutic modalities, and treatment patterns were collected. Results: Fifty-seven studies with 655 patients were included. For naming and word finding, errorless learning therapy, lexical retrieval cascade (LRC), semantic feature training, smartphone-based cognitive therapy, picture-naming therapy, and repetitive transcranial magnetic stimulation (rTMS) maintained effects for up to six months. Repetitive rTMS, video-implemented script training for aphasia (VISTA), and structured oral reading therapy improved speech fluency. Sole transcranial treatments enhanced auditory verbal comprehension, whereas transcranial direct current stimulation (tDCS) combined with language or cognitive therapy improved repetition abilities. Phonological and orthographic treatments improved reading accuracy across PPA subtypes. tDCS combined with speech therapy enhanced mini-mental state examination (MMSE) scores and cognitive function. Several therapies, including smartphone-based cognitive therapy and VISTA therapy, demonstrated sustained language improvements over six months. Conclusions: Various therapeutic interventions offer potential benefits for individuals with PPA. However, due to the heterogeneity in study designs, administration methods, small sample sizes, and lack of standardized measurement methods, drawing a firm conclusion is difficult. Further studies are warranted to establish evidence-based treatment protocols.

PMID:40364094 | DOI:10.3390/jcm14093063

Stud Health Technol Inform. 2025 May 12;326:27-32. doi: 10.3233/SHTI250230.

ABSTRACT

We evaluated the performance of Semantic Clinical Artificial Intelligence (SCAI, pronounced Sky), a large language model (LLM) knowledge resource through usability testing. This pretest-intervention-posttest mixed-methods user interface (UI) design study investigates usability to determine whether the LLM provides a more comprehensive, efficient, and enhanced user-friendly means of delivering end user information as compared to using primary sources of information from the Internet (Web). Our analysis focused on assessing the LLM's efficiency and usability in helping users arrive at accurate and reliable outcomes, to ultimately determine its value as an innovative tool for packaging and presenting information. Usability test sessions were conducted using the cognitive walkthrough approach, via Zoom. Participants were asked to respond to scenarios using only the LLM, and then only the web, and vice versa. These sessions were followed by user feedback sessions where participants rated their experiences and responded to open-ended questions related to the overall usability and satisfaction with SCAI.

PMID:40357596 | DOI:10.3233/SHTI250230

Eur Urol Oncol. 2025 May 9:S2588-9311(25)00121-X. doi: 10.1016/j.euo.2025.04.024. Online ahead of print.

ABSTRACT

Several large analyses have revealed contradictory results regarding the association between prostate cancer (PC) survival and the use of statins prescribed for prevention of dyslipidaemia or atherosclerosis complications, or of metformin prescribed for type 2 diabetes (T2D). Using data collected between 2006 and 2018 in French national health databases for 521 052 men with PC and 1 827 345 men without PC, we evaluated current evidence regarding overall survival for men with PC according to statin and/or metformin use. The highest mortality was observed in PC patients exposed to both statins and metformin (hazard ratio [HR] 2.29, 95% confidence interval [CI] 2.25-2.33). However, for patients whose first PC treatment was androgen deprivation therapy, a protective effect was observed for statin alone exposure (HR 0.91, 95% CI 0.88-0.93) and combined statin and metformin exposure (HR 0.86, 95% CI 0.85-0.87), whereas men with metformin exposure alone had higher mortality (HR 1.07, 95% CI 1.03-1.11) in comparison to non-users. This protective effect of statins was not observed for PC patients treated with radical prostatectomy. The result was confirmed using causal analysis in a Bayesian network, followed by semantic elicitation using generative artificial intelligence that compiles web-based human knowledge and dedicated literature.

PMID:40348654 | DOI:10.1016/j.euo.2025.04.024

CNS Neurosci Ther. 2025 May;31(5):e70411. doi: 10.1111/cns.70411.

ABSTRACT

BACKGROUND: Research on animal models of neurological diseases has primarily focused on understanding pathogenic mechanisms, advacing diagnostic strateggies, developing pharmacotherapies, and exploring preventive interventions. To facilitate comprehensive and systematic studies in this filed, we have developed the Neurological Disease Animal Model Database (ND-AMD), accessible at https://www.uc-med.net/NDAMD. This database is signed around the central theme of "Big Data - Neurological Diseases - Animal Models - Mechanism Research," integrating large-scale, multi-dimensional, and multi-scale data to facilitate in-depth analyses. ND-AMD serves as a resource for panoramic studies, enabling comparative and mechanistic research across diverse experimental conditions, species, and disease models.

METHOD: Data were systematically retrieved from PubMed, Web of Science, and other relevant databases using Boolean search strategies with standardized MeSH terms and keywords. The collected data were curated and integrated into a structured SQL-based framework, ensuring consistency through automated validation checks and manual verification. Heterogeneity and sensitivity analyses were conducted using Cochran's Q test and the I2 statistic to assess variability across studies. Statistical workflows were implemented in Python (SciPy, Pandas, NumPy) to support multi-scale data integration, trend analysis, and model validation. Additionally, a text co-occurrence network analysis was performed using Natural Language Processing (TF-IDF and word embeddings) to identify key conceptual linkages and semantic structures across studies.

RESULTS: ND-AMD integrates data from 483 animal models of neurological diseases, covering eight disease categories, 21 specific diseases, 13 species, and 152 strains. The database provides a comprehensive repository of experimental and phenotypic data, covering behavioral, physiological, biochemical, molecular pathology, immunological, and imaging characteristics. Additionally, it incorporates application-oriented data, such as drug evaluation outcomes. To enhance data accessibility and facilitate in-depth analysis, ND-AMD features three custom-developed online tools: Model Frequency Analysis, Comparative Phenotypic Analysis, and Bibliometric Analysis, enabling systematic comparison and trend identification across models and experimental conditions.

CONCLUSIONS: The centralized feature of ND-AMD enables comparative analysis across different animal models, strains, and experimental conditions. It helps capture intricate interactions between biological systems at different levels, ranging from molecular mechanisms to cellular processes, neural networks, and behavioral outcomes. These models play a vital role as tools in replicating pathological conditions of neurological diseases. By offering users convenient, efficient, and intuitive access to data, ND-AMD enables researchers to identify patterns, trends, and potential therapeutic targets that may not be apparent in individual studies.

PMID:40344361 | DOI:10.1111/cns.70411

J Biomed Semantics. 2025 May 8;16(1):9. doi: 10.1186/s13326-025-00330-9.

ABSTRACT

BACKGROUND: The Swiss Personalized Health Network (SPHN) adopted the Resource Description Framework (RDF), a core component of the Semantic Web technology stack, for the formal encoding and exchange of healthcare data in a medical knowledge graph. The SPHN RDF Schema defines the semantics on how data elements should be represented. While RDF is proven to be machine readable and interpretable, it can be challenging for individuals without specialized background to read and understand the knowledge represented in RDF. For this reason, the semantics described in the SPHN RDF Schema are primarily defined in a user-accessible tabular format, the SPHN Dataset, before being translated into its RDF representation. However, this translation process was previously manual, time-consuming and labor-intensive.

RESULT: To automate and streamline the translation from tabular to RDF representation, the SPHN Schema Forge web service was developed. With a few clicks, this tool automatically converts an SPHN-compliant Dataset spreadsheet into an RDF schema. Additionally, it generates SHACL rules for data validation, an HTML visualization of the schema and SPARQL queries for basic data analysis.

CONCLUSION: The SPHN Schema Forge significantly reduces the manual effort and time required for schema generation, enabling researchers to focus on more meaningful tasks such as data interpretation and analysis within the SPHN framework.

PMID:40341005 | DOI:10.1186/s13326-025-00330-9

JMIR Serious Games. 2025 May 6;13:e66781. doi: 10.2196/66781.

ABSTRACT

BACKGROUND: The problem of how to deal with waste is a global issue all countries face. Like many developing countries, Indonesia has inadequate infrastructure to process the extremely high volume of waste produced throughout the country and minimal public participation in proper waste management. Although the Indonesian government regulates waste banks as a community-based waste management solution, there is a lack of integrated technological innovations to support waste banks. This study fills the gap by developing Circonomy, a model combining Internet of Things, gamification, and semantic web technologies to advance community-based circular waste management.

OBJECTIVE: The aim of this study is to develop Circonomy as a circular waste model that integrates an Internet of Things-based smart bin, semantic web, and gamification as an innovative technological solution.

METHODS: We identified the problem by observing Indonesian waste banks at 3 locations in Jakarta and Yogyakarta to define and design Circonomy. The Circonomy prototype was developed using the Design Science Research Methodology and evaluated based on technical performance and user experience. The technical performance has three indicators: bin capacity accuracy with a minimum of 80% precision, bin lid response time <5 seconds for a minimum of 80% of trials, and data transmission success rate of at least 80%. The user experience metric has two indicators: a minimum of 80% reporting high usability and ease of use, and at least 80% of users reporting that they feel more motivated using the prototype than the traditional waste bank. We selected 10 random participants aged 18-60 years to perform a user experience evaluation of our prototype.

RESULTS: The Circonomy prototype demonstrated sound and stable performances related to technical performance and user experience. Circonomy achieved at least 80% technical performance accuracy, comparable to industry standards. The accuracy problem lies in the placement of the ultrasonic sensor. The waste should be placed directly under the ultrasonic sensor to ensure the bin's capacity measurement accuracy. The user experience testing results from 10 participants indicated that Circonomy has excellent user engagement, and 100% felt motivated by gamification and 80% found the mobile app easy to use.

CONCLUSIONS: The testing results showed that Circonomy has acceptable performances for early-stage prototyping, with at least an 80% accuracy rate in technical performance and user experience. This ensures that Circonomy operates effectively in real-world conditions while remaining cost-efficient and scalable. For future development, Circonomy will prioritize enhancing the accuracy and reliability of sensor-based occupancy detection through improved sensor placement, the integration of multiple sensors, and an exploration of alternative technologies for regions with limited IT resources. In addition, more gamification features such as challenges and quizzes should be added to improve user experience and motivation.

PMID:40327891 | DOI:10.2196/66781