Background: Large language models (LLMs) are increasingly integrated into medical education, with transformative potential for learning and assessment. However, their performance across diverse medical exams globally has remained underexplored. Objective: This study aims to introduce MedExamLLM, a comprehensive platform designed to systematically evaluate the performance of LLMs on medical exams worldwide. Specifically, the platform seeks to (1) compile and curate performance data for diverse LLMs on worldwide medical exams; (2) analyze trends and disparities in LLM capabilities across geographic regions, languages, and contexts; and (3) provide a resource for researchers, educators, and developers to explore and advance the integration of artificial intelligence in medical education. Methods: A systematic search was conducted on April 25, 2024, in the PubMed database to identify relevant publications. Inclusion criteria encompassed peer-reviewed, English-language, original research articles that evaluated at least one LLM on medical exams. Exclusion criteria included review articles, non-English publications, preprints, and studies without relevant data on LLM performance. The screening process for candidate publications was independently conducted by 2 researchers to ensure accuracy and reliability. Data, including exam information, data process information, model performance, data availability, and references, were manually curated, standardized, and organized. These curated data were integrated into the MedExamLLM platform, enabling its functionality to visualize and analyze LLM performance across geographic, linguistic, and exam characteristics. The web platform was developed with a focus on accessibility, interactivity, and scalability to support continuous data updates and user engagement. Results: A total of 193 articles were included for final analysis. MedExamLLM comprised information for 16 LLMs on 198 medical exams conducted in 28 countries across 15 languages from the year 2009 to the year 2023. The United States accounted for the highest number of medical exams and related publications, with English being the dominant language used in these exams. The Generative Pretrained Transformer (GPT) series models, especially GPT-4, demonstrated superior performance, achieving pass rates significantly higher than other LLMs. The analysis revealed significant variability in the capabilities of LLMs across different geographic and linguistic contexts. Conclusions: MedExamLLM is an open-source, freely accessible, and publicly available online platform providing comprehensive performance evaluation information and evidence knowledge about LLMs on medical exams around the world. The MedExamLLM platform serves as a valuable resource for educators, researchers, and developers in the fields of clinical medicine and artificial intelligence. By synthesizing evidence on LLM capabilities, the platform provides valuable insights to support the integration of artificial intelligence into medical education. Limitations include potential biases in the data source and the exclusion of non-English literature. Future research should address these gaps and explore methods to enhance LLM performance in diverse contexts.

Background: Sepsis is a severe syndrome of organ dysfunction caused by infection; it has high heterogeneity and high in-hospital mortality, representing a grim clinical challenge for precision medicine in critical care. Objective: We aimed to extract reported sepsis biomarkers to provide users with comprehensive biomedical information and integrate retrieval augmented generation (RAG) and prompt engineering to enhance the accuracy, stability, and interpretability of clinical decisions recommended by large language models (LLMs). Methods: To address the challenge, we established and updated the first knowledge-enhanced platform, MetaSepsisKnowHub, comprising 427 sepsis biomarkers and 423 studies, aiming to systematically collect and annotate sepsis biomarkers to guide personalized clinical decision-making in the diagnosis and treatment of human sepsis. We curated a tailored LLM framework incorporating RAG and prompt engineering and incorporated 2 performance evaluation scales: the System Usability Scale and the Net Promoter Score. Results: The overall quantitative ratings of expert-reviewed clinical recommendations based on RAG surpassed baseline responses generated by 4 LLMs and showed a statistically significant improvement in textual questions (GPT-4: mean 75.79, SD 7.11 vs mean 81.59, SD 9.87; P=.02; GPT-4o: mean 70.36, SD 7.63 vs mean 77.98, SD 13.26; P=.02; Qwen2.5-instruct: mean 77.08 SD 3.75 vs mean 85.46, SD 7.27; P<.001; and DeepSeek-R1: mean 77.67, SD 3.66 vs mean 86.42, SD 8.56; P<.001), but no significant statistical differences could be measured in clinical scenarios. The RAG assessment score comparing RAG-based responses and expert-provided benchmark answers illustrated prominent factual correctness, accuracy, and knowledge recall compared to the baseline responses. After use, the average the System Usability Scale score was 82.20 (SD 14.17) and the Net Promoter Score was 72, demonstrating high user satisfaction and loyalty. Conclusions: We highlight the pioneering MetaSepsisKnowHub platform, and we show that combining MetaSepsisKnowHub with RAG can minimize limitations on precision and maximize the breadth of LLMs to shorten the bench-to-bedside distance, serving as a knowledge-enhanced paradigm for future application of artificial intelligence in critical care medicine.

Introduction: Variation in melanin deposition profoundly influences the economic value of Sichuan mountainous black-boned chickens; however, the contribution of the gut microbiome in modulating this process remains poorly understood. This study aimed to characterize the gut microbiome in Sichuan mountainous black-boned chickens with distinct skin color brightness (quantified by colorimeter) and to explore its association with melanin deposition. Methods: Cecal microbiota composition was profiled using 16S rRNA sequencing in dark-skinned (BlackD) and light-skinned (BlackL) groups. Serum metabolic profiles were determined via untargeted metabolomics. Differential abundance of bacterial genera was assessed, followed by pathway enrichment analysis (KEGG and MetaCyc). Associations between microbiome, metabolites, and melanin-related pathways were evaluated. Results: The BlackD group exhibited higher alpha diversity and significant alterations in 10 bacterial genera (primarily linked to short-chain fatty acid metabolism and melanin-related metabolites) compared to the BlackL group. Pathway enrichment showed upregulation of energy metabolism-related KEGG pathways (AMPK signaling, insulin signaling, thyroid hormone signaling) and MetaCyc pathways in BlackD. Untargeted serum metabolomics revealed elevated melanin-related metabolites in BlackD, including L-Tyrosine, L-DOPA, and Dopaquinone. Gut microbiome and serum metabolite profiles in BlackD were strongly correlated with enhanced energy metabolism. Discussion: The gut microbiome may influence melanin deposition by modulating host metabolic activity, with microbiome-derived metabolites supporting the high energy demands of melanocyte activity. These findings uncover a potential mechanism linking microbial composition to phenotypic variation in melanin deposition.

Cholesterol acts as a metabolic cue that reshapes diverse signaling networks, including hedgehog and several sterol-regulated pathways orchestrated by key proteins, including sterol regulatory element-binding protein 2 (SREBP2), sterol O-acyltransferase 1 (SOAT1), Niemann–Pick type C1 (NPC1), and proprotein convertase subtilisin/kexin type 9 (PCSK9). Research over the past decade has highlighted cholesterol metabolism as a key modulator of cancer development and a promising therapeutic target. By integrating mechanistic and translational evidence, this review seeks to clarify how cholesterol metabolism interfaces with oncogenic signaling and set directions for future investigation. Accumulating preclinical and clinical data suggest that dysregulated cholesterol levels, often associated with high-fat diets, may contribute to tumorigenesis and malignant transformation. Implicated pathways, such as SREBP, NPC1, PCSK9, and SOAT1, orchestrate various processes of lipid metabolism, including cholesterol synthesis, esterification, receptor degradation, and transport, that harbor a tumorigenic environment and promote oncogenic processes. Additionally, these enzymes and corresponding pathways provide a promising direction for developing metabolism-oriented anticancer strategies. Cholesterol metabolism dysregulation serves as a major avenue for cancer signaling and growth, but studies also highlight key molecular mechanisms and targets for future treatments. Future studies should focus on expanding studies into further cancer types, investigating combination therapies, and developing novel inhibitors of key molecular targets.

Inflammatory bowel disease (IBD) is an immunologically complex disorder involving genetic, microbial, and environmental risk factors. Its global burden has continued to rise since industrialization, with epidemiological studies suggesting that ambient particulate matter (PM) in air pollution could be a contributing factor. Prior animal studies have shown that oral PM₁₀ exposure promotes intestinal inflammation in a genetic IBD model and that PM_2.5 inhalation exposure can increase intestinal levels of pro-inflammatory cytokines. PM₁₀ and PM_2.5 include ultrafine particles (UFP), which have an aerodynamic diameter of <0.10 μm and biophysical and biochemical properties that promote toxicity. UFP inhalation, however, has not been previously studied in the context of murine models of IBD. Here, we demonstrated that ambient PM is toxic to cultured Caco-2 intestinal epithelial cells and examined whether UFP inhalation affected acute colitis induced by dextran sodium sulfate and 2,4,6-trinitrobenzenesulfonic acid. C57BL/6J mice were exposed to filtered air (FA) or various types of ambient PM reaerosolized in the ultrafine size range at ~300 μg/m³, 6 h/day, 3–5 days/week, starting 7–10 days before disease induction. No differences in weight change, clinical disease activity, or histology were observed between the PM and FA-exposed groups. In conclusion, UFP inhalation exposure did not exacerbate intestinal inflammation in acute, chemically-induced colitis models.

BACKGROUND Large language models (LLMs) are increasingly integrated into medical education, with transformative potential for learning and assessment. However, their performance across diverse medical exams globally has remained underexplored. OBJECTIVE This study aims to introduce MedExamLLM, a comprehensive platform designed to systematically evaluate the performance of LLMs on medical exams worldwide. Specifically, the platform seeks to (1) compile and curate performance data for diverse LLMs on worldwide medical exams; (2) analyze trends and disparities in LLM capabilities across geographic regions, languages, and contexts; and (3) provide a resource for researchers, educators, and developers to explore and advance the integration of artificial intelligence in medical education. METHODS A systematic search was conducted on April 25, 2024, in the PubMed database to identify relevant publications. Inclusion criteria encompassed peer-reviewed, English-language, original research articles that evaluated at least one LLM on medical exams. Exclusion criteria included review articles, non-English publications, preprints, and studies without relevant data on LLM performance. The screening process for candidate publications was independently conducted by 2 researchers to ensure accuracy and reliability. Data, including exam information, data process information, model performance, data availability, and references, were manually curated, standardized, and organized. These curated data were integrated into the MedExamLLM platform, enabling its functionality to visualize and analyze LLM performance across geographic, linguistic, and exam characteristics. The web platform was developed with a focus on accessibility, interactivity, and scalability to support continuous data updates and user engagement. RESULTS A total of 193 articles were included for final analysis. MedExamLLM comprised information for 16 LLMs on 198 medical exams conducted in 28 countries across 15 languages from the year 2009 to the year 2023. The United States accounted for the highest number of medical exams and related publications, with English being the dominant language used in these exams. The Generative Pretrained Transformer (GPT) series models, especially GPT-4, demonstrated superior performance, achieving pass rates significantly higher than other LLMs. The analysis revealed significant variability in the capabilities of LLMs across different geographic and linguistic contexts. CONCLUSIONS MedExamLLM is an open-source, freely accessible, and publicly available online platform providing comprehensive performance evaluation information and evidence knowledge about LLMs on medical exams around the world. The MedExamLLM platform serves as a valuable resource for educators, researchers, and developers in the fields of clinical medicine and artificial intelligence. By synthesizing evidence on LLM capabilities, the platform provides valuable insights to support the integration of artificial intelligence into medical education. Limitations include potential biases in the data source and the exclusion of non-English literature. Future research should address these gaps and explore methods to enhance LLM performance in diverse contexts.