Caihong Wang, Wen Zhang, Hong Li, Wenyue Wu, Ziyan Pan, Ruoyu Gao, Haodong Ma, Qiushuang Ji, Zhi Chen, Wei Chen, Hong You
{"title":"Non-invasive tests for early-stage liver fibrosis: Current advances and challenges.","authors":"Caihong Wang, Wen Zhang, Hong Li, Wenyue Wu, Ziyan Pan, Ruoyu Gao, Haodong Ma, Qiushuang Ji, Zhi Chen, Wei Chen, Hong You","doi":"10.1097/CM9.0000000000004311","DOIUrl":"https://doi.org/10.1097/CM9.0000000000004311","url":null,"abstract":"<p><strong>Abstract: </strong>Early-stage liver fibrosis (F1-F2) constitutes a clinically silent yet pivotal phase during which therapeutic intervention may halt or reverse disease progression. However, its timely identification remains challenging given the limited sensitivity of currently non-invasive tests (NITs), including routine serological panels and elastography-based techniques. This review provides a comprehensive and systematic evaluation of the evolving diagnostic landscape aimed at addressing this unmet need. Established approaches are critically appraised alongside emerging next-generation NITs, including ultrasensitive serum biomarkers, molecular imaging probes targeting fibrogenic activity, and artificial intelligence (AI)-driven analysis of conventional imaging modalities. In addition, this review examines innovative diagnostic strategies based on alternative biofluids, including urine, exhaled breath, and saliva, as well as gut microbiota-derived biomarkers and multiomics-integrated models, all of which offer distinct pathophysiological insights and potential advantages in large-scale screening. Despite their considerable promise for improving the precision of early-stage fibrosis detection, these approaches face substantial barriers to clinical implementation, including challenges related to validation, standardization, and integration into existing diagnostic pathways. Addressing these limitations will be essential for establishing a robust, multidimensional diagnostic framework capable of enabling early detection, guiding timely intervention, and advancing the paradigm of hepatology toward effective disease prevention.</p>","PeriodicalId":10183,"journal":{"name":"Chinese Medical Journal","volume":" ","pages":""},"PeriodicalIF":9.1,"publicationDate":"2026-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148967080","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Burden of mental disorders: Evidence from the Global Burden of Disease Study 2023.","authors":"Jianhui Guo, Huan Wang, Yanhua Chen, Yaqing Tan, Xindou Chen, Junyu Lu, Zhiyong Zou, Jing Li","doi":"10.1097/CM9.0000000000004280","DOIUrl":"https://doi.org/10.1097/CM9.0000000000004280","url":null,"abstract":"<p><strong>Background: </strong>Mental disorders contribute significantly to the global disease burden, with marked regional variations. This study provides the most up-to-date assessment of trends in the burden of mental disorders worldwide and in China and compares the burden of mental disorders between China and other regions.</p><p><strong>Methods: </strong>We conducted a descriptive secondary analysis of publicly available estimates from the updated Global Burden of Disease Study 2023 (GBD 2023) for the period 1990-2023, covering the 6 World Health Organization (WHO) regions and China. We summarized prevalence, disability-adjusted life years (DALYs), age-standardized prevalence rates (ASPRs), and age-standardized DALY rates (ASDRs) by year, region, age, and sex. Future trends were projected using a Bayesian age-period-cohort model with second-order random-walk priors, and uncertainty was described using 95% uncertainty or credible intervals.</p><p><strong>Results: </strong>In 2023, 1171.64 million people suffered from mental disorders globally, resulting in 171.27 million DALYs. China accounted for 16.32% of global cases and 15.39% of global DALYs, ranking second worldwide for both measures. In China, mental disorders were the fourth leading cause of DALYs. Among these, anxiety and depressive disorders were the most prevalent and the leading contributors to the burden. From 2010 to 2023, the ASDR increased by 16.18%, with the largest increase in anxiety disorders (38.37%), eating disorders (21.84%), and autism spectrum disorders (ASDs, 21.20%). Anxiety and depressive disorders were more prevalent in females, whereas attention-deficit/hyperactivity disorder (ADHD), ASDs, and conduct disorders were more prevalent in males. Although the burden was concentrated on older adults, notable increases were also observed among young people. By 2035, the numbers of mental disorder cases and associated DALYs in China are projected to reach 209.6 million and 28.7 million, respectively.</p><p><strong>Conclusions: </strong>China faces a growing burden of mental health issues, particularly anxiety disorders, eating disorders, and ASD. Immediate development and implementation of comprehensive strategies are crucial to address the escalating mental health crisis in China.</p>","PeriodicalId":10183,"journal":{"name":"Chinese Medical Journal","volume":" ","pages":""},"PeriodicalIF":9.1,"publicationDate":"2026-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148966936","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Quan Zhou, Shuo Chen, Ping Chen, Mian Chen, Yanfeng Song
{"title":"Trends and cross-country inequalities in the global burden of female genital prolapse from 1990 to 2021: A population-based study.","authors":"Quan Zhou, Shuo Chen, Ping Chen, Mian Chen, Yanfeng Song","doi":"10.1097/CM9.0000000000004297","DOIUrl":"10.1097/CM9.0000000000004297","url":null,"abstract":"","PeriodicalId":10183,"journal":{"name":"Chinese Medical Journal","volume":" ","pages":""},"PeriodicalIF":9.1,"publicationDate":"2026-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148967062","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yingjie Du, Xiangting Zhao, Quan Chen, Yafan Bai, Yue Zhang, Ruirong Chen, Guyan Wang
{"title":"5-HT2A receptor activation is dispensable for psilocybin-induced fear extinction and neuroplasticity in mice.","authors":"Yingjie Du, Xiangting Zhao, Quan Chen, Yafan Bai, Yue Zhang, Ruirong Chen, Guyan Wang","doi":"10.1097/CM9.0000000000004350","DOIUrl":"10.1097/CM9.0000000000004350","url":null,"abstract":"<p><strong>Background: </strong>Psilocybin facilitates fear extinction in rodents, but its clinical utility is hampered by 5-hydroxytryptamine (5-HT) 2A-receptor-mediated hallucinogenic effects. This study aimed to explore whether 5-HT2A receptor is obligatory for psilocybin's fear extinction and neuroplasticity effect.</p><p><strong>Methods: </strong>Male C57BL/6J mice underwent auditory-cue fear conditioning. Ketanserin (5-HT2A/2C antagonist) or MDL100907 (selective 5-HT2A antagonist) was administered 30 min before a single psilocybin (2.5 mg/kg, intraperitoneal) or vehicle injection. Mice were fear-conditioned 2 days before psilocybin administration, extinction training commenced 30 min after the psilocybin injection, and extinction testing was performed 24 h later. The mice were euthanized 1.5 h after behavioral testing in the MDL100907 antagonism experiment, and hippocampal and medial prefrontal cortex (mPFC) tissues were collected. Western blotting and enzyme-linked immunosorbent assay (ELISA) were used to detect brain-derived neurotrophic factor (BDNF) protein expression. Immunofluorescence staining was used to assess the number of doublecortin-positive cells in the hippocampal dentate gyrus (DG).</p><p><strong>Results: </strong>In extinction testing, mice that received psilocybin combined with either ketanserin or MDL100907 still exhibited a significantly lower freezing response than those treated with antagonist alone. MDL100907 pretreatment failed to block the psilocybin-evoked upregulation of BDNF in both hippocampus and mPFC. Likewise, the increase in doublecortin (DCX)-positive newborn neurons in the DG induced by psilocybin remained intact after MDL100907 blockade.</p><p><strong>Conclusions: </strong>The effects of psilocybin in facilitating fear extinction and increasing neuroplasticity in fear-conditioned mice are not dependent on 5-HT2A receptor activation, which supports that its therapeutic and hallucinogenic effects can be dissociated.</p>","PeriodicalId":10183,"journal":{"name":"Chinese Medical Journal","volume":" ","pages":""},"PeriodicalIF":9.1,"publicationDate":"2026-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148966980","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Shichang Zhang, Yingjian Zhan, Kun Xu, Hongwei Zhou
{"title":"Artificial intelligence in laboratory medicine: From machine learning to large language models.","authors":"Shichang Zhang, Yingjian Zhan, Kun Xu, Hongwei Zhou","doi":"10.1097/CM9.0000000000004291","DOIUrl":"10.1097/CM9.0000000000004291","url":null,"abstract":"<p><strong>Abstract: </strong>The integration of artificial intelligence (AI) into laboratory medicine has undergone a remarkable evolution over the past decade, shifting from traditional machine learning (ML) applied to structured laboratory data to the recent emergence of large language models (LLMs) capable of processing unstructured clinical text, interpreting complex laboratory findings, and supporting clinical decision-making. This narrative review traces that progression and critically compares four main AI paradigms accessible to laboratory professionals: conventional ML, deep learning, general-purpose LLMs, and laboratory-specific foundation models. We examine representative application domains-blood cell morphological analysis, autoverification, acute infectious risk stratification, interpretation of urinalysis data, clinical decision support, and report generation-and assess the maturity of the evidence for each domain. Laboratories contemplating AI adoption face a distinctive set of challenges: preanalytical variability, interplatform calibration differences, effects of the specimen quality, reagent-lot sensitivity, and the context dependence of result interpretation, all of which demand validation practices beyond those that are standard in general medical AI research. We conclude that AI is best positioned, in the near term, as an assistive layer rather than an autonomous agent. Its deployment requires a clear problem definition, disciplined data engineering, external validation, prospective silent trials, human-in-the-loop oversight, and postdeployment surveillance aligned with existing laboratory quality systems. We additionally discussed the rationale for developing vertically oriented, laboratory-specific foundation models that can integrate structured results, longitudinal patient data, quality-control metadata, instrument information, and clinical knowledge. Clinical accountability must remain with qualified laboratory professionals.</p>","PeriodicalId":10183,"journal":{"name":"Chinese Medical Journal","volume":" ","pages":""},"PeriodicalIF":9.1,"publicationDate":"2026-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148963017","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}