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

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