Background: ChatGPT, an artificial intelligence (AI) based on large-scale language models, has sparked interest in the field of health care. Nonetheless, the capabilities of AI in text comprehension and generation are constrained by the quality and volume of available training data for a specific language, and the performance of AI across different languages requires further investigation. While AI harbors substantial potential in medicine, it is imperative to tackle challenges such as the formulation of clinical care standards; facilitating cultural transitions in medical education and practice; and managing ethical issues including data privacy, consent, and bias. Objective: The study aimed to evaluate ChatGPT’s performance in processing Chinese Postgraduate Examination for Clinical Medicine questions, assess its clinical reasoning ability, investigate potential limitations with the Chinese language, and explore its potential as a valuable tool for medical professionals in the Chinese context. Methods: A data set of Chinese Postgraduate Examination for Clinical Medicine questions was used to assess the effectiveness of ChatGPT’s (version 3.5) medical knowledge in the Chinese language, which has a data set of 165 medical questions that were divided into three categories: (1) common questions (n=90) assessing basic medical knowledge, (2) case analysis questions (n=45) focusing on clinical decision-making through patient case evaluations, and (3) multichoice questions (n=30) requiring the selection of multiple correct answers. First of all, we assessed whether ChatGPT could meet the stringent cutoff score defined by the government agency, which requires a performance within the top 20% of candidates. Additionally, in our evaluation of ChatGPT’s performance on both original and encoded medical questions, 3 primary indicators were used: accuracy, concordance (which validates the answer), and the frequency of insights. Results: Our evaluation revealed that ChatGPT scored 153.5 out of 300 for original questions in Chinese, which signifies the minimum score set to ensure that at least 20% more candidates pass than the enrollment quota. However, ChatGPT had low accuracy in answering open-ended medical questions, with only 31.5% total accuracy. The accuracy for common questions, multichoice questions, and case analysis questions was 42%, 37%, and 17%, respectively. ChatGPT achieved a 90% concordance across all questions. Among correct responses, the concordance was 100%, significantly exceeding that of incorrect responses (n=57, 50%; P<.001). ChatGPT provided innovative insights for 80% (n=132) of all questions, with an average of 2.95 insights per accurate response. Conclusions: Although ChatGPT surpassed the passing threshold for the Chinese Postgraduate Examination for Clinical Medicine, its performance in answering open-ended medical questions was suboptimal. Nonetheless, ChatGPT exhibited high internal concordance and the ability to generate multiple insights in the Chinese language. Future research should investigate the language-based discrepancies in ChatGPT’s performance within the health care context.

With the increasing incidence of metabolic diseases year by year and their impact on the incidence of cardiovascular diseases, metabolic diseases have attracted great attention as a major health care problem, but there is still no effective treatment. Oxidative stress and inflammation are the main mechanisms leading to metabolic diseases. T cells are involved in the inflammatory response, which can also regulate the development of metabolic diseases, CD4+ T cells and CD8+ T cells are mainly responsible for the role. Th1 and Th17 differentiated from CD4+ T promote inflammation, while Th2 and Treg inhibit inflammation. CD8+ T cells also contribute to inflammation. The severity and duration of inflammatory reactions can also lead to different degrees of progression of metabolic diseases. Moreover, mTOR, PI3K-Akt, and AMPK signaling pathways play unique roles in the regulation of T cells, which provide a new direction for the treatment of metabolic diseases in the future. In this review, we will elaborate on the role of T cells in regulating inflammation in various metabolic diseases, the signaling pathways that regulate T cells in metabolic diseases, and the latest research progress.

Microplastics and nanoplastics are ubiquitous environmental pollutants that pose significant health risks. Several studies have reported the presence of these particles in various human tissues, including the heart, arterial plaques, and bloodstream of patients undergoing percutaneous coronary interventions. Emerging research suggests an association between these particles and an increased risk of cardiovascular diseases. In vitro and in vivo studies reveal that micro- and nanoplastics induce endothelial cell toxicity, disrupt lipid metabolism, promote oxidative stress, and facilitate foam cell formation, thereby exacerbating atherosclerosis. Furthermore, they activate inflammatory pathways, compromising vascular barrier integrity. These particles also influence coagulation dynamics by altering clotting cascade activation and promoting thrombus formation. Additionally, they induce haemolysis, alter red blood cell morphology, and impair immune cell functions, including those of macrophages and lymphocytes, by modulating cytokine secretion and inflammatory responses. Understanding these mechanisms is essential for devising effective public health strategies to mitigate the cardiovascular impact of micro- and nanoplastics.

Non-alcoholic fatty liver disease (NAFLD) is a metabolic disease spectrum associated with insulin resistance (IR), from non-alcoholic fatty liver (NAFL) to non-alcoholic steatohepatitis (NASH), cirrhosis, and hepatocellular carcinoma (HCC). O-GlcNAcylation is a posttranslational modification, regulated by O-GlcNAc transferase (OGT) and O-GlcNAcase (OGA). Abnormal O-GlcNAcylation plays a key role in IR, fat deposition, inflammatory injury, fibrosis, and tumorigenesis. However, the specific mechanisms and clinical treatments of O-GlcNAcylation and NAFLD are yet to be elucidated. The modification contributes to understanding the pathogenesis and development of NAFLD, thus clarifying the protective effect of O-GlcNAcylation inhibition on liver injury. In this review, the crucial role of O-GlcNAcylation in NAFLD (from NAFL to HCC) is discussed, and the effect of therapeutics on O-GlcNAcylation and its potential mechanisms on NAFLD have been highlighted. These inferences present novel insights into the pathogenesis and treatments of NAFLD.

BACKGROUND ChatGPT, an artificial intelligence (AI) based on large-scale language models, has sparked interest in the field of health care. Nonetheless, the capabilities of AI in text comprehension and generation are constrained by the quality and volume of available training data for a specific language, and the performance of AI across different languages requires further investigation. While AI harbors substantial potential in medicine, it is imperative to tackle challenges such as the formulation of clinical care standards; facilitating cultural transitions in medical education and practice; and managing ethical issues including data privacy, consent, and bias. OBJECTIVE The study aimed to evaluate ChatGPT’s performance in processing Chinese Postgraduate Examination for Clinical Medicine questions, assess its clinical reasoning ability, investigate potential limitations with the Chinese language, and explore its potential as a valuable tool for medical professionals in the Chinese context. METHODS A data set of Chinese Postgraduate Examination for Clinical Medicine questions was used to assess the effectiveness of ChatGPT’s (version 3.5) medical knowledge in the Chinese language, which has a data set of 165 medical questions that were divided into three categories: (1) common questions (n=90) assessing basic medical knowledge, (2) case analysis questions (n=45) focusing on clinical decision-making through patient case evaluations, and (3) multichoice questions (n=30) requiring the selection of multiple correct answers. First of all, we assessed whether ChatGPT could meet the stringent cutoff score defined by the government agency, which requires a performance within the top 20% of candidates. Additionally, in our evaluation of ChatGPT’s performance on both original and encoded medical questions, 3 primary indicators were used: accuracy, concordance (which validates the answer), and the frequency of insights. RESULTS Our evaluation revealed that ChatGPT scored 153.5 out of 300 for original questions in Chinese, which signifies the minimum score set to ensure that at least 20% more candidates pass than the enrollment quota. However, ChatGPT had low accuracy in answering open-ended medical questions, with only 31.5% total accuracy. The accuracy for common questions, multichoice questions, and case analysis questions was 42%, 37%, and 17%, respectively. ChatGPT achieved a 90% concordance across all questions. Among correct responses, the concordance was 100%, significantly exceeding that of incorrect responses (n=57, 50%; P<.001). ChatGPT provided innovative insights for 80% (n=132) of all questions, with an average of 2.95 insights per accurate response. CONCLUSIONS Although ChatGPT surpassed the passing threshold for the Chinese Postgraduate Examination for Clinical Medicine, its performance in answering open-ended medical questions was suboptimal. Nonetheless, ChatGPT exhibited high internal concordance and the ability to generate multiple insights in the Chinese language. Future research should investigate the language-based discrepancies in ChatGPT’s performance within the health care context.