Narrative medicine focuses on empathy， relevance， and emotion， precisely aligning with the elements of “clown doctor” such as compassion， interaction， and pain relief. From the perspective of narrative medicine， the practice of “clown doctors” not only focuses on the emotional changes of patients but also enhances their sense of belonging by recreating their experiences. The key element for the success of “clown doctors” lies in establishing a multi-dimensional trust relationship among medical workers， patients， colleagues， and society， while ensuring their practice adheres to medical ethics norms. “Clown doctors” should concentrate on dimensions such as concept dissemination， clinical application， social recognition， and ethical practice of narrative medicine. They should also constantly optimize narrative techniques， deepen the understanding of patients’ stories， and intervene in the medical process in a more delicate and comprehensive way， thereby fostering in-depth communication and understanding between doctors and patients.

Intelligent prescription recommendation has become an important research direction in traditional Chinese medicine (TCM), offering new opportunities to support clinical decision-making and promote the modernization of TCM practice. With the rapid development of artificial intelligence (AI), a variety of computational approaches have been proposed to learn prescription patterns from clinical data and generate personalized treatment recommendations. However, despite increasing research activity, systematic and comprehensive reviews of AI-driven methods for TCM prescription recommendation remain limited. In this study, we present a comprehensive review of computational approaches for herbal prescription recommendation (HPR) in TCM. Existing methods are systematically categorized into several major paradigms, including traditional machine learning methods, topic model methods, sequential generative methods, deep learning and graph-based methods, and large language model–based frameworks. In addition to summarizing methodological developments, we also review commonly used public datasets and evaluation metrics in this field. Furthermore, representative models with publicly available implementations are experimentally evaluated on multiple benchmark datasets to provide a comparative analysis of their performance on the HPR task. Finally, we discuss the key challenges that hinder the practical deployment of intelligent prescription recommendation systems, including data heterogeneity, limited interpretability, and insufficient integration of TCM domain knowledge. Future research directions are outlined to facilitate the development of more reliable, interpretable, and clinically applicable AI-assisted HPR systems for TCM.

Growth and development-related diseases result from the interplay of biological, psychological, and social factors. The collaboration between healthcare, sports, and education sectors integrates multidisciplinary resources and strengths to promote standardized diagnostic and therapeutic processes. This approach establishes a comprehensive closed-loop system encompassing early screening and referral, diagnosis and comprehensive evaluation, intervention and support plan formulation, as well as long-term management andoutcome assessment. It provides systematic scientific support for the healthy growth of children and adolescents, shifting disease intervention to the subclinical stage. Against the backdrop of societal informatization and intelligent development, this diagnostic and therapeutic model not only safeguards the holistic health of children and adolescents but also offers novel perspectives and feasible pathways for managing growth and development-related diseases. The implementation of this systematic diagnostic and therapeutic paradigm presents an innovative solution with Chinese characteristics for addressing such conditions, while injecting new vitality into the advancement of national health initiatives.

To investigate the correlation between vitamin D nutritional status and insulin resistance in pubertal adolescents.

To investigate the mediating role of vitamin D in the association between exerciseand triglyceride among adolescents, as well as its potential molecular mechanisms.

To investigate body composition and associated factors in adolescents undergoing long-term regular sports training.

Objective To develop and validate data extraction and patient identification algorithms for cutaneous lupus erythematosus(CLE)and its two subtypes,discoid lupus erythematosus(DLE)and subacute cutaneous lupus erythematosus(SCLE),and to enable high-efficiency patient identification in large-scale electronic health databases.Methods This study utilized data from the 2013-2017 National Insurance Claims for Epidemiological Research(NICER)to construct data extraction and rapid patient identification algorithms.The manual verification results were used as gold standard to assess the sensitivity and specificity of the algorithms.Additionally,the basic characteristics of the identified patients were analyzed.Results Initially,standardized expressions were developed based on medical terminology and diagnostic codes.These were further refined with input from clinicians to include potential synonyms and common misspellings,improving the preliminary screening expressions.Through iterative verification by clinicians and data management engineers,a final disease-specific screening algorithm was established.The developed extraction and identification algorithms for all 3 targeted disease demonstrated strong performance,with sensitivity values of 0.985,1.000,and 0.991,and specificity values of 0.997,0.999,and 0.998 for CLE,DLE,and SCLE,respectively.A total of 34,554 CLE cases,including 2,879 DLE cases,and 623 SCLE cases were identified between 2013 and 2017,with a higher prevalence among females than males.Conclusion This study developed and validated an identification algorithm for CLE patients based on medical insurance databases,demonstrating high performance.The proposed algorithm provides a methodological framework and empirical evidence for designing and optimizing big data-driven rapid patient identification algorithms in dermatology research.

Objective:To establish tumor specific protein (SP70) targeted tumor cell enrichment technology and to assess applicational value of next-generation sequencing (NGS) analysis for enriched circulating tumor cell (CTC) in precision medicines of non-small cell lung cancer (NSCLC).Methods:The monoclonal antibody NJ001 was covalently coupled to the surface of magnetic beads to build targeted magnetic bead enrichment technology based on SP70. The limit of detection, coincidence rate, interference experiment, recovery test and clinical performance were evaluated. From March 2016 to August 2017, NGS analysis with or without pre-treatment of targeted enrichment for serous fluids of 43 NSCLC in the First Affiliated Hospital with Nanjing Medical University were compared (Kappa or Fisher exact test).Results:The CTC enrichment technology based on SP70 targeted immunomagnetic beads can specifically enrich tumor cells. The limit of detection was 10 4 SPC-A1 cells/L, and the coincidence rate, sensitivity and specificity were 100% (3/3). The endogenous interfering substances such as red blood cells, hemoglobin, white blood cells, epithelial cells and triglycerides had no interfering effects, as well as the exogenous interfering substances such as EDTA-K2, cefoxitin, carboplatin and paclitaxel. The recovery rate was 56.0% (56 000/100 000). A total of 30 gene mutations including 65 loci were found in 43 NSCLC under SP70 targeted enrichment, with a higher detection rate compared with unenrichment method [95.0% (19/20) vs 65.0% (13/20), χ 2=5.625, P=0.044]. Conclusion:In this study, SP70-targeted enriched CTC liquid biopsy method was established, with higher sensitivity and specificity of NGS detection than unenrichment method.

Cancer incidence in China has been rising steadily,with a particularly heavy burden from several high-prevalence malignancies.Medical examination for cancer plays a critical role in the early detection of cancer,precancerous lesions,and precursor conditions,thereby facilitating timely diagnosis and intervention.Such examination also addresses the growing demand for person-alized cancer screening services among diverse population groups.The development of evidence-based,context-specific cancer screening guidelines is essential to enhance the standardization,quality,and equity of preventive screening practices across the country,ultimately improving out-comes in early cancer detection and treatment.Guided by the Department of Medical Emergency Response of the National Health Commission,the Guidelines for Medical Examination for Cancer in Health Examination Agency(2025 Edition)were developed under the leadership of the National Cancer Center.A multidisciplinary panel of experts formulated the guidelines in accordance with the principles and methodology of the World Health Organization Handbook for Guideline Deve-lopment.The guidelines provide evidence-based recommendations on key clinical domains:target cancers and populations,overall screening workflow,screening protocols,diagnostic technolo-gies,result interpretation,follow-up procedures,and quality control.The primary objective is to standardize cancer screening practices in health examination agency and strengthen China's ca-pacity for prevention and control of high-burden cancers.

Cancer stands as a significant global public health challenge,and cancer screening serves as a pivotal strategy for reducing its mortality.Presently,only a limited number of cancer types have appropriate screening methods available.Traditional single-cancer screen-ing approaches are fraught with limitations,including invasiveness,low accuracy,and poor patient compliance.Multi-cancer early detection(MCED)leveraging liquid biopsy technology enables non-invasive and efficient early detection of multiple cancers by analyzing biomarkers such as cell-free DNA,cell-free RNA,proteins,and metabolites in blood and other bodily fluids.This innovative approach substantially broadens the spectrum of detectable cancers and enhances population coverage,showcasing immense potential for improving existing can-cer screening strategies.This expert consensus comprehensively reviews the progress of liquid biopsy-based MCED,biomarker selection and detection technologies,the criteria for cancer type selection,research design and clinical utility evaluation,as well as implementation path-ways.The overarching goal of this consensus is to offer scientific guidance for further research and the widespread adoption of MCED,thereby facilitating the continuous optimization of cancer screening strategies.