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Heart disease is a malignant threat to human health. Electrocardiogram (ECG) tests are used to help diagnose heart disease by recording the heart’s activity. However, automated medical-aided diagnosis with computers usually requires a large volume of labeled clinical data without patients' privacy to train the model, which is an empirical problem that still needs to be solved. To address this problem, we propose a generative adversarial network (GAN), which is composed of a bidirectional long short-term memory(LSTM) and convolutional neural network(CNN), referred as BiLSTM-CNN,to generate synthetic ECG data that agree with existing clinical data so that the features of patients with heart disease can be retained. The model includes a generator and a discriminator, where the generator employs the two layers of the BiLSTM networks and the discriminator is based on convolutional neural networks. The 48 ECG records from individuals of the MIT-BIH database were used to train the model. We compared the performance of our model with two other generative models, the recurrent neural network autoencoder(RNN-AE) and the recurrent neural network variational autoencoder (RNN-VAE). The results showed that the loss function of our model converged to zero the fastest. We also evaluated the loss of the discriminator of GANs with different combinations of generator and discriminator. The results indicated that BiLSTM-CNN GAN could generate ECG data with high morphological similarity to real ECG recordings.

Background Large language models like ChatGPT have revolutionized the field of natural language processing with their capability to comprehend and generate textual content, showing great potential to play a role in medical education. This study aimed to quantitatively evaluate and comprehensively analysis the performance of ChatGPT on three types of national medical examinations in China, including National Medical Licensing Examination (NMLE), National Pharmacist Licensing Examination (NPLE), and National Nurse Licensing Examination (NNLE). Methods We collected questions from Chinese NMLE, NPLE and NNLE from year 2017 to 2021. In NMLE and NPLE, each exam consists of 4 units, while in NNLE, each exam consists of 2 units. The questions with figures, tables or chemical structure were manually identified and excluded by clinician. We applied direct instruction strategy via multiple prompts to force ChatGPT to generate the clear answer with the capability to distinguish between single-choice and multiple-choice questions. Results ChatGPT failed to pass the accuracy threshold of 0.6 in any of the three types of examinations over the five years. Specifically, in the NMLE, the highest recorded accuracy was 0.5467, which was attained in both 2018 and 2021. In the NPLE, the highest accuracy was 0.5599 in 2017. In the NNLE, the most impressive result was shown in 2017, with an accuracy of 0.5897, which is also the highest accuracy in our entire evaluation. ChatGPT’s performance showed no significant difference in different units, but significant difference in different question types. ChatGPT performed well in a range of subject areas, including clinical epidemiology, human parasitology, and dermatology, as well as in various medical topics such as molecules, health management and prevention, diagnosis and screening. Conclusions These results indicate ChatGPT failed the NMLE, NPLE and NNLE in China, spanning from year 2017 to 2021. but show great potential of large language models in medical education. In the future high-quality medical data will be required to improve the performance.

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. 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. 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. 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. 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.

Reproductive cancers are malignancies that develop in the reproductive organs. One of the leading cancers affecting the male reproductive system on a global scale is prostate cancer (PCa). The negative consequences of PCa metastases endure and are severe, significantly affecting mortality and life quality for those who are affected. The association between inflammation and PCa has captured interest for a while. Inflammatory cells, cytokines, CXC chemokines, signaling pathways, and other elements make up the tumor microenvironment (TME), which is characterized by inflammation. Inflammatory cytokines and CXC chemokines are especially crucial for PCa development and prognosis. Cytokines (interleukins) and CXC chemokines such as IL-1, IL-6, IL-7, IL-17, TGF-β, TNF-α, CXCL1–CXCL6, and CXCL8–CXCL16 are thought to be responsible for the pleiotropic effects of PCa, which include inflammation, progression, angiogenesis, leukocyte infiltration in advanced PCa, and therapeutic resistance. The inflammatory cytokine and CXC chemokines systems are also promising candidates for PCa suppression and immunotherapy. Therefore, the purpose of this work is to provide insight on how the spectra of inflammatory cytokines and CXC chemokines evolve as PCa develops and spreads. We also discussed recent developments in our awareness of the diverse molecular signaling pathways of these circulating cytokines and CXC chemokines, as well as their associated receptors, which may one day serve as PCa-targeted therapies. Moreover, the current status and potential of theranostic PCa therapies based on cytokines, CXC chemokines, and CXC receptors (CXCRs) are examined.

Background Academic paper writing holds significant importance in the education of medical students, and poses a clear challenge for those whose first language is not English. This study aims to investigate the effectiveness of employing large language models, particularly ChatGPT, in improving the English academic writing skills of these students. Methods A cohort of 25 third-year medical students from China was recruited. The study consisted of two stages. Firstly, the students were asked to write a mini paper. Secondly, the students were asked to revise the mini paper using ChatGPT within two weeks. The evaluation of the mini papers focused on three key dimensions, including structure, logic, and language. The evaluation method incorporated both manual scoring and AI scoring utilizing the ChatGPT-3.5 and ChatGPT-4 models. Additionally, we employed a questionnaire to gather feedback on students’ experience in using ChatGPT. Results After implementing ChatGPT for writing assistance, there was a notable increase in manual scoring by 4.23 points. Similarly, AI scoring based on the ChatGPT-3.5 model showed an increase of 4.82 points, while the ChatGPT-4 model showed an increase of 3.84 points. These results highlight the potential of large language models in supporting academic writing. Statistical analysis revealed no significant difference between manual scoring and ChatGPT-4 scoring, indicating the potential of ChatGPT-4 to assist teachers in the grading process. Feedback from the questionnaire indicated a generally positive response from students, with 92% acknowledging an improvement in the quality of their writing, 84% noting advancements in their language skills, and 76% recognizing the contribution of ChatGPT in supporting academic research. Conclusion The study highlighted the efficacy of large language models like ChatGPT in augmenting the English academic writing proficiency of non-native speakers in medical education. Furthermore, it illustrated the potential of these models to make a contribution to the educational evaluation process, particularly in environments where English is not the primary language.

Sepsis is a complex, life-threatening condition characterized by significant heterogeneity and vast amounts of unstructured data, posing substantial challenges for traditional knowledge graph construction methods. The integration of large language models (LLMs) with real-world data offers a promising avenue to address these challenges and enhance the understanding and management of sepsis. This study aims to develop a comprehensive sepsis knowledge graph by leveraging the capabilities of LLMs, specifically GPT-4.0, in conjunction with multicenter clinical databases. The goal is to improve the understanding of sepsis and provide actionable insights for clinical decision-making. We also established a multicenter sepsis database (MSD) to support this effort. We collected clinical guidelines, public databases, and real-world data from 3 major hospitals in Western China, encompassing 10,544 patients diagnosed with sepsis. Using GPT-4.0, we used advanced prompt engineering techniques for entity recognition and relationship extraction, which facilitated the construction of a nuanced sepsis knowledge graph. We established a sepsis database with 10,544 patient records, including 8497 from West China Hospital, 690 from Shangjin Hospital, and 357 from Tianfu Hospital. The sepsis knowledge graph comprises of 1894 nodes and 2021 distinct relationships, encompassing nine entity concepts (diseases, symptoms, biomarkers, imaging examinations, etc) and 8 semantic relationships (complications, recommended medications, laboratory tests, etc). GPT-4.0 demonstrated superior performance in entity recognition and relationship extraction, achieving an F -score of 76.76 on a sepsis-specific dataset, outperforming other models such as Qwen2 (43.77) and Llama3 (48.39). On the CMeEE dataset, GPT-4.0 achieved an F -score of 65.42 using few-shot learning, surpassing traditional models such as BERT-CRF (62.11) and Med-BERT (60.66). Building upon this, we compiled a comprehensive sepsis knowledge graph, comprising of 1894 nodes and 2021 distinct relationships. This study represents a pioneering effort in using LLMs, particularly GPT-4.0, to construct a comprehensive sepsis knowledge graph. The innovative application of prompt engineering, combined with the integration of multicenter real-world data, has significantly enhanced the efficiency and accuracy of knowledge graph construction. The resulting knowledge graph provides a robust framework for understanding sepsis, supporting clinical decision-making, and facilitating further research. The success of this approach underscores the potential of LLMs in medical research and sets a new benchmark for future studies in sepsis and other complex medical conditions.

The reliability of general-purpose large language models (LLMs) for complex clinical tasks in specialized domains such as microsatellite instability (MSI) cancers remains critically uncharacterized. The absence of a domain-specific benchmark to evaluate and guide the optimization of their capabilities across diverse clinical tasks poses unevaluated risks to patient safety. This study aimed to develop and validate Microsatellite Instability Cancer Benchmark (MSIC-Bench), a novel, two-tiered benchmark for MSI cancer, covering both consensus and frontier knowledge. Using this framework, we aimed to systematically assess LLM performance across various prompting strategies, identify task-specific weaknesses, and reveal effective pathways for performance improvement. We developed MSIC-Bench, a 511-question benchmark derived from clinical guidelines and a curated knowledge base. Three state-of-the-art LLMs (GPT-4o, [OpenAI], Gemini 2.5 Pro [Google], and Claude Opus 4 [Anthropic]) were evaluated using 4 prompting strategies, including vanilla, chain-of-thought, reflection of thoughts, and retrieval-augmented generation (RAG), under both multiple-choice and open-ended modalities. Performance was assessed on accuracy, safety (honesty), error composition, and token usage. LLMs demonstrated a significant \"scaffolding effect,\" with accuracy dropping substantially in open-ended scenarios. For non-RAG strategies, the primary failure mode was an internal knowledge deficit. The integration of RAG proved to be the most effective intervention. A domain-aligned RAG strategy not only significantly improved accuracy in complex decision-making tasks but also fundamentally shifted the system's primary bottleneck from knowledge deficits to retrieval failures. In terms of safety, RAG induced a favorable shift from high-risk fabrications to safer refusals, though this introduced a safety-utility trade-off in the form of false refusals. Notably, our hybrid-RAG configuration, which combined both knowledge sources, demonstrated the most robust and generalizable performance across all tasks. Current LLMs lack the specialized knowledge required for reliable application in MSI oncology. A well-designed RAG architecture is the pivotal intervention to address this gap. However, its success is not automatic; it transforms the nature of system failure, making retrieval precision and knowledge base quality the new critical determinants of performance and safety. Our findings establish a clear directive for developing trustworthy clinical artificial intelligence: focus must shift toward optimizing the retrieval component and curating high-quality, comprehensive knowledge sources. MSIC-Bench provides a robust framework to guide these future efforts.

Hepatocellular Carcinoma (HCC) is one of the most common malignant tumors with high incidence and mortality rate. Precision and effective biomarkers are therefore urgently needed for the early diagnosis and prognostic estimation. MicroRNAs (miRNAs) are important regulators which play functions in various cellular processes and biological activities. Accumulating evidence indicated that the abnormal expression of miRNAs are closely associated with HCC initiation and progression. Recently, many biomarker miRNAs for HCC have been identified from blood or tissues samples, however, the universality and specificity on clinicopathological features of them are less investigated. In this review, we comprehensively surveyed and compared the diagnostic, prognostic, and therapeutic roles of HCC biomarker miRNAs in blood and tissues based on the cancer hallmarks, etiological factors as well as ethnic groups, which will be helpful to the understanding of the pathogenesis of biomarker miRNAs in HCC development and further provide accurate clinical decisions for HCC diagnosis and treatment.

Pelvic organ prolapse (POP) and urinary incontinence (UI) are common disorders that significantly impact women’s quality of life. Studies have demonstrated that cytokines, including pro- and anti-inflammatory immune mediators, play a role in illness genesis and progression. Research on the inflammatory milieu of the pelvic floor has shown that POP patients have increased inflammation in vaginal tissues. This evidence revealed that significant changes in the inflammatory milieu of the pelvic floor are an aspect of the pathogenesis of POP. POP patients exhibit increased levels of inflammatory cytokines (IL-1, TNF, IFN, and others) in the front vaginal wall, which may alter collagen metabolism and contribute to POP. Studies indicate that cytokines such as IL-6, IL-10, and TGF, which are involved in inflammation, remodelling, and repair, have dual effects on POP and UI. They can promote tissue healing and regeneration but also exacerbate inflammation and fibrosis, contributing to the progression of these conditions. Understanding the dual roles of these cytokines could help us improve the vaginal microenvironment of women and treat POP and UI. Given the considerable changes in these cytokines, this review addresses studies published between 2000 and 2024 on the molecular mechanisms by which pro- and anti-inflammatory cytokines affect women with POP and UI. Furthermore, we explain novel therapeutic strategies for cytokine regulation, emphasizing the possibility of personalized treatments that address the underlying inflammatory milieu of the vagina in POP and UI patients. This thorough analysis aims to establish a foundation for future research and clinical applications, ultimately improving patient outcomes via designed cytokine-based therapies.

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. 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). 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. 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. 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.