Anal Chem. 2025 May 6. doi: 10.1021/acs.analchem.5c00390. Online ahead of print.

ABSTRACT

The interpretation of proteomics data often relies on functional enrichment analysis, such as Gene Ontology (GO) enrichment, to uncover the biological functions of proteins, as well as the examination of protein expression patterns across data sets like the Clinical Proteomic Tumor Analysis Consortium (CPTAC) database. However, conventional approaches to functional enrichment frequently produce extensive and redundant term lists, complicating interpretation and synthesis. Moreover, the absence of specialized tools tailored to proteomics researchers limits the efficient exploration of protein expression within specific biological contexts. To address these challenges, we developed Ontolomics-P, a user-friendly web-based tool designed to advance proteomics data interpretation. Ontolomics-P integrates topic modeling using latent Dirichlet allocation (LDA) with GO semantic similarity analysis, enabling the consolidation of redundant terms into coherent topics. These topics are further refined and reannotated using the GPT-4o language model, creating a novel topics database that provides precise and interpretable insights into shared biological functions. Additionally, Ontolomics-P incorporates quantitative proteomic data from 10 diverse cancer types archived in the CPTAC database, allowing for a comprehensive exploration of protein expression profiles from a data-driven perspective. Through detailed case studies, we demonstrate the tool's capacity to streamline workflows, simplify interpretation, and provide actionable biological insights. Ontolomics-P represents a significant advancement in proteomics data analysis, offering innovative solutions for functional annotation, quantitative exploration, and visualization, ultimately empowering researchers to accelerate discoveries in systems biology and beyond.

PMID:40326493 | DOI:10.1021/acs.analchem.5c00390

Data Brief. 2025 Apr 2;60:111535. doi: 10.1016/j.dib.2025.111535. eCollection 2025 Jun.

ABSTRACT

There has been a recent push to make public, aggregate, and increase coverage of bibliographic citation data. Here we describe uCite, a citation dataset containing 564 million PubMed citation pairs aggregated from the following nine sources: PubMed Central, iCite, OpenCitations, Dimensions, Microsoft Academic Graph, Aminer, Semantic Scholar, Lens, and OpCitance. Of these, 51 million (9%) were labeled unreliable, as determined by patterns of source discrepancies explained by ambiguous metadata, crosswalk, and typographical errors, citing future publications, and multi-paper documents. Each source contributes to improved coverage and reliability, but varies dramatically in precision and recall, estimates of which are contrasted with the Web of Science and Scopus herein.

PMID:40322502 | PMC:PMC12049819 | DOI:10.1016/j.dib.2025.111535

Brain Sci. 2025 Apr 20;15(4):419. doi: 10.3390/brainsci15040419.

ABSTRACT

BACKGROUND: Cognitive stimulation programs typically consist of task collections ("bundles") designed to cover various aspects of a cognitive domain and/or sustain user engagement. However, task order is often overlooked, despite variations in difficulty based on structure or mode of implementation. This study examined users' performance accuracy across the eight tasks that comprise the BOX semantic-based program, adapted for the Cerup/CQ online platforms. Our ultimate goal was to map the tasks onto increasing levels of challenge within thematic clusters to provide guidance for personalized task selection.

METHODS: After adapting the program into Portuguese using original materials based on BOX task descriptions, we made Cerup and CQ (which share the same content but have different layouts) available as free web-based tools. Participants, primarily older adults without dementia, were invited to use these platforms for cognitive stimulation. We analyzed accuracy data as a function of activity-related characteristics (complexity scores, sentence- vs. word-level) as well as participants' spontaneous task selection.

RESULTS: Task characteristics influenced performance accuracy, indicating different levels of challenge across activities. However, spontaneous task selection did not follow any discernible pattern beyond the spatial contiguity of activity buttons, which was unrelated to participants' likelihood of success. Based on these findings, we defined optimal navigation paths for the eight tasks.

CONCLUSIONS: Challenge-based, active guidance for task selection appears justified and necessary within the BOX/Cerup/CQ programs. Additionally, the method we developed may help other programs enhance user experience and optimize task progression.

PMID:40309900 | DOI:10.3390/brainsci15040419

J Clin Res Pediatr Endocrinol. 2025 Apr 30. doi: 10.4274/jcrpe.galenos.2025.2024-11-11. Online ahead of print.

ABSTRACT

This systematic review and meta-analysis aim to synthesize the existing literature to clarify the role of amino acids as potential indicators or contributors to childhood obesity. The study follows the PRISMA 2020 guidelines. A comprehensive search was conducted across multiple electronic databases, including PubMed, Cochrane Library, Embase, Web of Science, Google Scholar, Semantic Scholar, and ResearchRabbit, using relevant keywords such as "childhood obesity," "amino acids," and "branched-chain amino acids (BCAAs)."Heterogeneity among studies was assessed using the chi-square test and the I² statistic. Publication bias was evaluated using funnel plots and Egger's test. Five studies involving a total of 1,229 participants met the inclusion criteria. A significant association was observed between amino acid levels and obesity in children. Specifically, glutamine was inversely associated with obesity (SMD = -0.48, 95% CI: -0.85 to -0.11), while leucine (SMD = 0.79, 95% CI: 0.20 to 1.38) and valine (SMD = 0.67, 95% CI: 0.18 to 1.15) were positively associated. Additionally, odds ratio analysis indicated that higher glutamine levels were associated with 56% lower odds of obesity (OR = 0.44, 95% CI: 0.21-0.94, P < .01), suggesting a potential protective role. Elevated levels of specific amino acids, particularly BCAAs, were consistently linked to increased body mass index (BMI) and other obesity-related indicators in children. Future research should focus on longitudinal and interventional studies to better understand these associations and explore targeted strategies involving amino acid metabolism to help prevent and manage childhood obesity.

PMID:40304146 | DOI:10.4274/jcrpe.galenos.2025.2024-11-11

Nucleic Acids Res. 2025 Apr 29:gkaf337. doi: 10.1093/nar/gkaf337. Online ahead of print.

ABSTRACT

Biomedical ontologies, such as Gene Ontology (GO), Disease Ontology (DO), and the Human Phenotype Ontology (HPO), have been extensively applied to characterize molecular roles and their semantic relationships in biomedical research and clinical practice. Although numerous algorithms have been developed to quantify relationships between ontology terms or to explore molecular functions, the absence of a comprehensive tool to integrate these algorithms has limited effective ontology applications. To address this, we developed OntoTiger, a platform of Ontology-based application Tools for InteGrativE biomedical exploRation. OntoTiger combines >20 classic algorithms, supporting six prevalent molecular types as well as five widespread biomedical ontologies. The platform comprises four modules: (i) Annotation module, which qualifies the relationships between ontology terms and molecules; (ii) Similarity module, quantifying functional similarity between/across pairwise ontology terms or between molecules; (iii) Prediction module, characterizing the molecular roles from an ontological perspective; and (iv) Enrichment module, elucidating the potential biological significance of a particular list of molecules. OntoTiger provides a freely accessible, user-friendly web server dedicated to enabling one-stop ontology-based applications and is freely available at https://bio-computing.hrbmu.edu.cn/OntoTiger.

PMID:40297993 | DOI:10.1093/nar/gkaf337

JMIR Form Res. 2025 Apr 28;9:e59640. doi: 10.2196/59640.

ABSTRACT

BACKGROUND: Adolescents increasingly communicate through text-based messaging platforms such as SMS and social media messaging. These are now the dominant platforms for communication between adolescents, and adolescents use them to obtain emotional support from parents and other adults. The absence of nonverbal cues can make it challenging to communicate emotions on these platforms, however, so users rely on emojis to communicate sentiment or imbue messages with emotional tone. While research has investigated the functions of emojis in adult communication, less is known about adolescent emoji use.

OBJECTIVE: This study sought to understand whether the pragmatic functions of adolescent emoji use resemble those of adults, and to gain insight into the semantic meanings of emojis sent by adolescents.

METHODS: Web-based focus groups were conducted with a convenience sample of adolescents, in which participants responded to questions about their use and interpretation of emojis and engaged in unstructured interactions with one another. Two trained coders analyzed transcripts using a constant comparative coding procedure to identify themes in the discussion.

RESULTS: A total of 6 focus groups were conducted with 31 adolescent participants (mean age 16.2, SD 1.5 years). Discussion in the groups generally fell into 4 themes: emojis as humorous or absurd, emokis as insincere or complex expressions of setiment, emojis as straightforward experssions of sentiment, and emojis as having context-dependent meanings. Across themes, participants often described important differences between their own emoji use and emoji use by adults.

CONCLUSIONS: Adolescent focus group participants described patterns of emoji use that largely resembled those observed in studies of adults. Like adults, our adolescent participants described emojis' semantic meanings as being highly flexible and context-dependent. They also described both phatic and emotive functions of emoji use but described both functions in ways that differed from the patterns of emoji use described in adult samples. Adolescents described their phatic emoji use as absurd and described their emotive emoji use as most often sarcastic. These findings suggest that emoji use serves similar pragmatic functions for both adolescents and adults, but that adolescents see their emoji use as more complex than adult emoji use. This has important implications for adults who communicate with adolescents through text-based messaging and for researchers interested in adolescents' text-based communication.

PMID:40294434 | DOI:10.2196/59640

Sensors (Basel). 2025 Apr 8;25(8):2362. doi: 10.3390/s25082362.

ABSTRACT

Traditional farming has evolved from standalone computing systems to smart farming, driven by advancements in digitalization. This has led to the proliferation of diverse information systems (IS), such as IoT and sensor systems, decision support systems, and farm management information systems (FMISs). These systems often operate in isolation, limiting their overall impact. The integration of IS into connected smart systems is widely addressed as a key driver to tackle these issues. However, it is a complex, multi-faceted issue that is not easily achievable. Previous studies have offered valuable insights, but they often focus on specific cases, such as individual IS and certain integration aspects, lacking a comprehensive overview of various integration dimensions. This systematic review of 74 scientific papers on IS integration addresses this gap by providing an overview of the digital technologies involved, integration levels and types, barriers hindering integration, and available approaches to overcoming these challenges. The findings indicate that integration primarily relies on a point-to-point approach, followed by cloud-based integration. Enterprise service bus, hub-and-spoke, and semantic web approaches are mentioned less frequently but are gaining interest. The study identifies and discusses 27 integration challenges into three main areas: organizational, technological, and data governance-related challenges. Technologies such as blockchain, data spaces, AI, edge computing and microservices, and service-oriented architecture methods are addressed as solutions for data governance and interoperability issues. The insights from the study can help enhance interoperability, leading to data-driven smart farming that increases food production, mitigates climate change, and optimizes resource usage.

PMID:40285052 | DOI:10.3390/s25082362

IEEE Trans Image Process. 2025 Apr 25;PP. doi: 10.1109/TIP.2025.3562740. Online ahead of print.

ABSTRACT

Webly-supervised fine-grained visual classification (WSL-FGVC) aims to learn similar sub-classes from cheap web images, which suffers from two major issues: label noises in web images and subtle differences among fine-grained classes. However, existing methods for WSL-FGVC only focus on suppressing noise at image-level, but neglect to mine cues at pixel-level to distinguish the subtle differences among fine-grained classes. In this paper, we propose a bag-level top-down attention framework, which could tackle label noises and mine subtle cues simultaneously and integrally. Specifically, our method first extracts high-level semantic information from a bag of images belonging to the same class, and then uses the bag-level information to mine discriminative regions in various scales of each image. Besides, we propose to derive attention weights from attention maps to weight the bag-level fusion for a robust supervision. We also propose an attention loss on self-bag attention and cross-bag attention to facilitate the learning of valid attention. Extensive experiments on four WSL-FGVC datasets, i.e., Web-Aircraft, Web-Bird, Web-Car, and WebiNat-5089, demonstrate the effectiveness of our method against the state-of-the-art methods.

PMID:40279222 | DOI:10.1109/TIP.2025.3562740

Stud Health Technol Inform. 2025 Apr 24;324:234-239. doi: 10.3233/SHTI250194.

ABSTRACT

BACKGROUND: Standardizing laboratory data is essential for interoperability and secondary use in clinical research and healthcare. However, many laboratory information systems (LIS) still rely on internal codes rather than internationally recognized terminologies, hindering data exchange, queryability, and integration into health data infrastructures.

OBJECTIVES: This study aimed to automate the extraction and mapping of internal lab codes to LOINC to improve structured data integration by utilizing web scraping and terminology mapping, we sought to create a FHIR-compliant ConceptMap.

METHODS: Guided by key requirements for structured data integration, we developed a Python-based workflow to extract and process laboratory data from an internal lab index. Using Selenium, BeautifulSoup, and Pandas, the extracted data was mapped to LOINC codes and transformed into a FHIR-compliant ConceptMap.

RESULTS: The workflow extracted 2,870 analytes, mapping 768 (27%) to LOINC. The automated process demonstrated feasibility and scalability.

CONCLUSION: The approach enables structured laboratory data integration but highlights the need for direct LIS integration and expanded LOINC coverage for legacy data.

PMID:40270418 | DOI:10.3233/SHTI250194

Stroke. 2025 Apr 24. doi: 10.1161/STROKEAHA.124.050207. Online ahead of print.

ABSTRACT

BACKGROUND: Women less frequently receive secondary prevention medications at discharge poststroke than men. It is unclear whether similar sex differences exist in the long term poststroke, after accounting for age and clinical characteristics. We aimed to evaluate sex differences in medication prescription, initiation, and discontinuation poststroke or transient ischemic attack.

METHODS: A retrospective cohort study using person-level linked data from the Australian Stroke Clinical Registry (42 hospitals; Victoria and Queensland; 2012-2016). We included all adults with first-ever ischemic stroke, intracerebral hemorrhage, or transient ischemic attack who survived >60 days post-discharge. For each major class of secondary prevention medication (antihypertensive, antithrombotic, or lipid lowering), we evaluated sex differences in prescription at hospital discharge, initiation within 60 days, and discontinuation within 2 years post-discharge. Sex differences were assessed using multivariable models, adjusted for sociodemographics and comorbidities. Where effect modification by age was found (Pinteraction≤0.05), age-specific odds ratios were reported.

RESULTS: Among 8108 women (median age, 74.3 years) and 10 344 men (median age, 70.5 years) with first-ever stroke (≈8% intracerebral hemorrhage) or transient ischemic attack, women were less likely to be prescribed antihypertensive medications on discharge (odds ratio, 0.82 [95% CI, 0.74-0.91]). Women were less likely to initiate antihypertensive (odds ratio, 0.76 [95% CI, 0.69-0.84]) and antithrombotic (odds ratio, 0.89 [95% CI, 0.82-0.96]) medications within 60 days than men. While there was no overall difference in discontinuation between men and women, interactions were observed with age (Pinteraction, all <0.002). Younger women (aged <45 years) and older women (aged >90 years) were more likely to discontinue secondary prevention medications than men of equivalent age.

CONCLUSIONS: Sex differences exist for prescription, initiation, and discontinuation of secondary prevention medications poststroke. With many sex differences being age specific, there is a critical need for targeted interventions to improve prevention medication use in these patient subgroups.

PMID:40270283 | DOI:10.1161/STROKEAHA.124.050207

J Alzheimers Dis. 2025 Apr 21:13872877251331222. doi: 10.1177/13872877251331222. Online ahead of print.

ABSTRACT

BackgroundConvolutional neural network (CNN) based volumetry of MRI data can help differentiate Alzheimer's disease (AD) and the behavioral variant of frontotemporal dementia (bvFTD) as causes of cognitive decline and dementia. However, existing CNN-based MRI volumetry tools lack a structured hierarchical representation of brain anatomy, which would allow for aggregating regional pathological information and automated computational inference.ObjectiveDevelop a computational ontology pipeline for quantifying hierarchical pathological abnormalities and visualize summary charts for brain atrophy findings, aiding differential diagnosis.MethodsUsing FastSurfer, we segmented brain regions and measured volume and cortical thickness from MRI scans pooled across multiple cohorts (N = 3433; ADNI, AIBL, DELCODE, DESCRIBE, EDSD, and NIFD), including healthy controls, prodromal and clinical AD cases, and bvFTD cases. Employing the Web Ontology Language (OWL), we built a semantic model encoding hierarchical anatomical information. Additionally, we created summary visualizations based on sunburst plots for visual inspection of the information stored in the ontology.ResultsOur computational framework dynamically estimated and aggregated regional pathological deviations across different levels of neuroanatomy abstraction. The disease similarity index derived from the volumetric and cortical thickness deviations achieved an AUC of 0.88 for separating AD and bvFTD, which was also reflected by distinct atrophy profile visualizations.ConclusionsThe proposed automated pipeline facilitates visual comparison of atrophy profiles across various disease types and stages. It provides a generalizable computational framework for summarizing pathologic findings, potentially enhancing the physicians' ability to evaluate brain pathologies robustly and interpretably.

PMID:40255031 | DOI:10.1177/13872877251331222

Sci Data. 2025 Apr 15;12(1):628. doi: 10.1038/s41597-025-04938-5.

ABSTRACT

Ontologies have gained popularity in the scientific community as a way to standardize terminologies in organizations' data. Although certain cohorts have created frameworks with rules and guidelines on creating ontologies, there exist significant variations in how Materials Science ontologies are currently developed. We seek to provide guidance in the form of a unified automated framework for developing interoperable and modular ontologies for Materials Data Science that simplifies the ontology terms matching by establishing a semantic bridge up to the Basic Formal Ontology(BFO). This framework provides key recommendations on how ontologies should be positioned within the semantic web, what knowledge representation language is recommended, and where ontologies should be published online to boost their findability and interoperability. Two fundamental components of the MDS-Onto framework are the bilingual package called FAIRmaterials for ontology creation and FAIRLinked, for FAIR data creation. To showcase the practical capabilities of FAIRmaterials, we present two exemplar domain ontologies of MDS-Onto: Synchrotron X-Ray Diffraction and Photovoltaics.

PMID:40234492 | DOI:10.1038/s41597-025-04938-5

Front Artif Intell. 2025 Mar 26;8:1451865. doi: 10.3389/frai.2025.1451865. eCollection 2025.

ABSTRACT

As we become increasingly dependent on technology in our daily lives, the usability of HCIs is a key driver of individual empowerment for us all. A primary focus of AI systems has been to make HCIs easier to use by identifying what users need and agentively taking over some of the cognitive work users would have otherwise performed, as such, they are becoming our delegates. To become effective and reliable delegates, AI agents need to understand all relevant situational semantic context surrounding a user's need and how the tools of the HCI can be leveraged. Current ML systems have fundamental semantic gaps in bespoke human context, real-time world knowledge, and how those relate to HCI tooling. These challenges are difficult to close due factors such as privacy, continual learning, access to real-time context, and how deeply integrated the semantics are with in-context learning. As such, we need to research and explore new ways to safely capture, compactly model, and incrementally evolve semantics in ways that can efficiently integrate into how AI systems act on our behalf. This article presents a thought experiment called the Game of Delegation as a lens to view the effectiveness of delegation and the semantic efficiency with which the delegation was achieved.

PMID:40206708 | PMC:PMC11979188 | DOI:10.3389/frai.2025.1451865

J Insect Sci. 2025 Mar 14;25(2):8. doi: 10.1093/jisesa/ieaf019.

ABSTRACT

Taxonomy provides a general foundation for research on insects. Using stored product pest (SPP) arthropods as a model group, this article overviews the historical impacts of taxonomy on applied entomology. The article surveys the dynamics of historical descriptions of new species in various SPP taxa; the majority of all species (90%) were described prior to 1925, while the key pests were described prior to 1866. The review shows that process of describing new SPP species is not random but is influenced by following factors: (i) larger species tend to be described earlier than smaller and SPP moths and beetles are described earlier than psocids and mites; (ii) key economic pests are on average described earlier than less significant ones. Considering a species name as a "password" to unique information resources, this review also assesses the historical number of synonymous or duplicate names of SPP species. Pests belonging to some higher taxa Lepidoptera and Coleoptera has accumulated more scientific synonyms than those others belonging to Psocoptera and Acari. Number of synonyms positively correlated with the economic importance of SPP species. The review summarized semantic origin of SPP names showing minor proportion of names (17.6%) are toponyms (geography) or eponyms (people), while the majority (82.4%) fall into other categories (descriptive, etc.). It is concluded that awareness of taxonomic advances, including changes to species and higher taxa names, should be effectively communicated to pest control practitioners and applied entomology students, and specifically addressed in relevant textbooks, web media, and databases.

PMID:40178352 | DOI:10.1093/jisesa/ieaf019

JMIR Serious Games. 2025 Mar 29. doi: 10.2196/66781. Online ahead of print.

ABSTRACT

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

OBJECTIVE: The proposed model Circonomy is inspired by the Waste Bank, the Indonesian Government's community-based waste management initiative. This research has objective to develop Circonomy as a circular waste model that integrate IoT-based smart-bin, semantic web, and gamification as an innovative technological solution.

METHODS: We identify the problem faced by the Indonesian Waste Bank from three locations in Jakarta and Yogyakarta as a basis for the Circonomy model and prototype development. The evaluation of the model focuses on Technical Performance and User Experience. The Technical Performance has three indicators, i.e., Bin Capacity Accuracy with a minimum of 80% precision, Bin Lid Response Time should be less than 5 seconds at a minimum of 80% of trials, and Data Transmission Success Rate at a minimum of 80%. While User Experience Metrics has two indicators, i.e., a minimum of 80% reported high usability and ease of use, and at least 80% of users feel more motivated using the prototype than the traditional Waste Bank. We select 10 random participants from ages 18 to 60 to perform User Experience evaluation on our prototype.

RESULTS: The Circonomy prototype demonstrates sound and stable performances related to Technical Performance and User Experience. Circonomy performs with 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 indicate that Circonomy has excellent user engagement, whereas 100% felt motivated by gamification, and 80% found the mobile application easy to use.

CONCLUSIONS: The testing result shows that Circonomy has acceptable performances for early-stage prototyping with at least 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, multiple sensor integration, and exploring alternative technologies for regions with limited IT resources. In addition, more gamification features such as challenges and quiz should be added to improve the user experience and motivation.

PMID:40157387 | DOI:10.2196/66781

PeerJ Comput Sci. 2025 Mar 5;11:e2664. doi: 10.7717/peerj-cs.2664. eCollection 2025.

ABSTRACT

The internet has been inundated with an ocean of information, and hence, information retrieval systems are failing to provide optimal results to the user. In order to meet the challenge, query expansion techniques have emerged as a game-changer and are improving the results of information retrieval significantly. Of late, semantic query expansion techniques have attracted increased interest among researchers since these techniques offer more pertinent and practical results to the users. These allow the user to retrieve more meaningful and useful information from the web. Currently, few research works provide a comprehensive review on semantic query expansion; usually, they cannot provide a full view on recent advances, diversified data application, and practical challenges. Therefore, it is imperative to go deep in review in order to explain these advances and assist researchers with concrete insights for future development. This article represents the comprehensive review of the query expansion methods, with a particular emphasis on semantic approaches. It overviews the recent frameworks that have been developed within a period of 2015-2024 and reviews the limitations of each approach. Further, it discusses challenges that are inherent in the semantic query expansion field and identifies some future research directions. This article emphasizes that the linguistic approach is the most effective and flexible direction for researchers to follow, while the ontology approach better suits domain-specific search applications. This, in turn, means that development of the ontology field may further open new perspectives for semantic query expansion. Moreover, by employing artificial intelligence (AI) and making most of the query context without relying on user intervention, improvements toward the optimal expanded query can be achieved.

PMID:40134880 | PMC:PMC11935759 | DOI:10.7717/peerj-cs.2664

Clin Pract. 2025 Mar 7;15(3):54. doi: 10.3390/clinpract15030054.

ABSTRACT

Background/Objectives: During the last decade, there has been an increased interest in phenolic compound-rich natural products as natural therapies for regulating the molecular pathways behind central obesity and associated metabolic disorders. The present scoping review presents the outcomes of clinical and preclinical studies examining the anti-obesity effects of high phenolic extra virgin olive oil (HP-EVOO) and its possible underlying molecular mechanisms. Methods: Studies published between 2014 and 2024 were searched via MEDLINE, Scopus, Cochrane, the Web of Science, Semantic Scholar, Google Scholar, Science.gov, and Clinicaltrials.gov databases. A combination of keywords and Boolean logic was used to search throughout the last decade in all databases, including "hyperglycemia" or "hypertension" or "metabolic syndrome" or "dyslipidemia" or "hyperlipidemia" or "hypoglycemia" or "obesity" or "macrovascular diabetic complications" or "microvascular diabetic complications" or "cardiovascular disease" or "overweight" or "insulin sensitivity" or "insulin resistance" and "extra virgin olive oil" or "high phenolic olive oil" and "human" or "animal model". Results: The 10-year literature survey identified 21 studies in both animal models and humans, indicating that HP-EVOO improves inflammation, glycemic control, oxidative stress and endothelial function, potentially protecting against metabolic syndrome, hypertension and type 2 diabetes, even compared to EVOO. Moreover, HP-EVOO's antiplatelet effect and improvement in HDL functionality reduce cardiovascular risk. Conclusions: The evidence presented in this study demonstrates that HP-EVOO represents an effective preventive and therapeutic dietary approach to cardiometabolic diseases.

PMID:40136590 | DOI:10.3390/clinpract15030054

Sci Rep. 2025 Mar 19;15(1):9516. doi: 10.1038/s41598-025-92192-5.

ABSTRACT

In recent years, there has been a growing field of research focused on comprehending complexity in relation to web platform accessibility. It has shown that it is quite difficult to accurately assess and identify web accessibility concerns while taking multifaceted factors into account. It is imperative to prioritize multi-dimensional characteristics as they facilitate the integration of many aspects into the assessment process, which is a critical component in enhancing the accessibility evaluation process. Although many existing solutions with varying degrees of computational success have been proposed by scholars, they are confined to (1) following a certain set of rules of a specific guideline; (2) limited evaluation properties; (3) disregard for user criteria; and (4) complex functional properties or architectural design. To address these problems, we present in this work a straightforward yet precise model that assesses webpage accessibility by taking into account common features of the structural and visual elements of webpages that are part of the HTML Document Object Model (DOM) structure. In order to predict a webpage's accessibility status, we implemented three distinct algorithms to analyze web features/objects considering both semantic and non-semantic aspects. We performed experimental work to validate 20 university webpages in Hungary through our developed tool. The computed result of the developed tool was assessed by comparing the result with a user study where we performed user testing that included 40 users' 80 reviews on the same 20 university webpages in Hungary. Additionally, we compared our developed tool with other scientific models (that already exist) and existing ten open-source commercial automated testing tools considering several functional characteristics or properties. This two-phase assessment result shows that the developed tool has several advanced properties and the potential to predict the accessibility issues of the tested webpages.

PMID:40108199 | DOI:10.1038/s41598-025-92192-5

Mol Divers. 2025 Mar 14. doi: 10.1007/s11030-025-11164-z. Online ahead of print.

ABSTRACT

Biomedical knowledge graphs have emerged as powerful tools for drug discovery, but existing platforms often suffer from outdated information, limited accessibility, and insufficient integration of complex data. This study presents MedKG, a comprehensive and continuously updated knowledge graph designed to address these challenges in precision medicine and drug discovery. MedKG integrates data from 35 authoritative sources, encompassing 34 node types and 79 relationships. A Continuous Integration/Continuous Update pipeline ensures MedKG remains current, addressing a critical limitation of static knowledge bases. The integration of molecular embeddings enhances semantic analysis capabilities, bridging the gap between chemical structures and biological entities. To demonstrate MedKG's utility, a novel hybrid Relational Graph Convolutional Network for disease-drug link prediction, MedLINK was developed and used in case studies on clinical trial data for disease drug link prediction. Furthermore, a web-based application with user-friendly APIs and visualization tools was built, making MedKG accessible to both technical and non-technical users, which is freely available at http://pitools.niper.ac.in/medkg/.

PMID:40085402 | DOI:10.1007/s11030-025-11164-z

Syst Rev. 2025 Mar 1;14(1):52. doi: 10.1186/s13643-025-02793-4.

ABSTRACT

INTRODUCTION: Empathy is a crucial skill that enhances the quality of patient care, reduces burnout among healthcare professionals, and fosters professionalism in medical students. Clinical practice and standardized patient-based education provide opportunities to enhance empathy, but a lack of consistency and reproducibility as well as significant dependency on resources are impediments. The COVID-19 pandemic has further restricted these opportunities, highlighting the need for alternative approaches. Virtual patients through standardized scenarios ensure consistency and reproducibility while offering safe, flexible, and repetitive learning opportunities unconstrained by time or location. Empathy education using virtual patients could serve as a temporary alternative during the COVID-19 pandemic and address the limitations of traditional face-to-face learning methods. This review aims to comprehensively map existing literature on the use of virtual patients in empathy education and identify research gaps.

METHODS: This scoping review will follow the Joanna Briggs Institute's guidelines and be reported according to PRISMA-P. The search strategy includes a comprehensive search across databases such as PubMed (MEDLINE), CINAHL, Web of Science, Scopus, ERIC, Google, Google Scholar, and Semantic Scholar, covering both published and gray literature without language restrictions. Both quantitative and qualitative studies will be included. Two independent researchers will screen all titles/abstracts and full texts for eligibility. Data will be extracted to summarize definitions of empathy, characteristics of virtual patient scenarios, and methods for measuring their impact on empathy development. Results will be presented in narrative and tabular formats to highlight key findings and research gaps.

DISCUSSION: As this review analyzes existing literature, ethical approval is not required. Findings will be actively disseminated through academic conferences and peer-reviewed publications, providing educators and researchers with valuable insights into the potential of virtual patients to enhance empathy in medical education. This study goes beyond the mere synthesis of academic knowledge by contributing to the advancement of medical education and clinical practice by clarifying virtual patient scenario design and evaluation methods in empathy education. The findings provide a critical foundation for our ongoing development of a medical education platform aimed at enhancing empathy through the use of virtual patients.

PMID:40025554 | DOI:10.1186/s13643-025-02793-4