In the context of the digital-intelligent era of traditional Chinese medicine （TCM）， the lack of clinical thinking is a pressing issue that limits the overall effectiveness of TCM and talent cultivation. The "disease-pulse-syndrome-treatment differentiation" thinking model， originally developed by ZHANG Zhongjing in the Treatise on Cold Damage and Miscellaneous Diseases （Shang Han Za Bing Lun）， has served as a guideline and paradigm followed by generations of medical practitioners. This study aims to construct a digitalized "disease-pulse-syndrome-treatment differentiation" thinking system， develop a digital assessment system， and implement it for practical application. The goal is to recommend a digitalized assessment model for TCM and provide a reference for the integrated innovation of talent cultivation in medicine， education， and research. First， based on the complex diagnostic and treatment framework of the Treatise on Cold Damage Diseases （Shang Han Lun）， the research team previously established a "process" + "result" thinking model that included four processes and ten steps. This study integrates knowledge unit theory and digital technology to create a digital system for "disease-pulse-syndrome-treatment differentiation" with a dual-control model of "process control" and "result control". The system consists of 46 items across three categories： knowledge body （W=20%）， knowledge element （W=30%）， and knowledge element associations （W=50%）. Second， a mixed-methods research design was employed， combining qualitative and quantitative approaches. The Delphi method was used to establish hierarchical levels and screen items， while the analytic hierarchy process （AHP） was used to assign weights. Expert surveys were conducted to reach a consensus and further validate the rationale and necessity of the system. Finally， based on the system architecture and integrating key computer technologies， a digital assessment system for "disease-pulse-syndrome-treatment differentiation" was developed. The Treatise on Cold Damage Diseases （Shang Han Lun） was used as a case study to validate the system's feasibility. Statistical results showed that the difficulty level of the assessment question bank was moderate （DL ranging from 0.65 to 0.89）， with good discrimination （D>0.4）， and reasonable reliability and validity （Cronbach's α=0.84， KMO=0.72， Bartlett's test P<0.01）. The system can perform process-oriented evaluations of candidates' thinking in "disease-pulse-syndrome-treatment differentiation" and effectively achieve the goal of clinical thinking assessment through a combination of "process control" and "result control". The examination system offers three major advantages. It standardizes， objectifies， and streamlines the assessment of thought processes， facilitates the organic transformation of TCM education from outcome-based education to thinking-based education， and from exam-oriented education to competency-oriented education， and promotes the practical transformation of TCM assessments from qualitative to quantitative evaluation， as well as from theory to practice. In summary， this system not only represents a technological upgrade to traditional examinations but also empowers the cultivation and assessment of clinical talent in the digital-intelligent era， demonstrating broad application prospects.

This study employed bioinformatics to screen the feature genes related to efferocytosis in diabetic kidney disease(DKD) and explores traditional Chinese medicine(TCM) regulating these feature genes. The GSE96804 and GSE30528 datasets were integrated as the training set, and the intersection of differentially expressed genes and efferocytosis-related genes(ERGs) was identified as DKD-ERGs. Subsequently, correlation analysis, protein-protein interaction(PPI) network construction, enrichment analysis, and immune infiltration analysis were performed. Consensus clustering was conducted on DKD patients based on the expression levels of DKD-ERGs, and the expression levels, immune infiltration characteristics, and gene set variations between different subtypes were explored. Eight machine learning models were constructed and their prediction performance was evaluated. The best-performing model was evaluated by nomograms, calibration curves, and external datasets, followed by the identification of efferocytosis-related feature genes associated with DKD. Finally, potential TCMs that can regulate these feature genes were predicted. The results showed that the training set contained 640 differentially expressed genes, and after intersecting with ERGs, 12 DKD-ERGs were obtained, which demonstrated mutual regulation and immune modulation effects. Consensus clustering divided DKD into two subtypes, C1 and C2. The support vector machine(SVM) model had the best performance, predicting that growth arrest-specific protein 6(GAS6), S100 calcium-binding protein A9(S100A9), C-X3-C motif chemokine ligand 1(CX3CL1), 5'-nucleotidase(NT5E), and interleukin 33(IL33) were the feature genes of DKD. Potential TCMs with therapeutic effects included Astragali Radix, Trionycis Carapax, Sargassum, Rhei Radix et Rhizoma, Curcumae Radix, and Alismatis Rhizoma, which mainly function to clear heat, replenish deficiency, activate blood, resolve stasis, and promote urination and drain dampness. Molecular docking revealed that the key components of these TCMs, including β-sitosterol, quercetin, and sitosterol, exhibited good binding activity with the five target genes. These results indicated that efferocytosis played a crucial role in the development and progression of DKD. The feature genes closely related to both DKD and efferocytosis, such as GAS6, S100A9, CX3CL1, NT5E, and IL33, were identified. TCMs such as Astragali Radix, Trionycis Carapa, Sargassum, Rhei Radix et Rhizoma, Curcumae Radix, and Alismatis Rhizoma may provide a new therapeutic strategy for DKD by regulating efferocytosis.
Humans ; Computational Biology ; Diabetic Nephropathies/physiopathology* ; Protein Interaction Maps ; Medicine, Chinese Traditional ; Drugs, Chinese Herbal ; Phagocytosis/genetics* ; Efferocytosis

In order to support the implementation of the Opinions on Improving the Quality of Traditional Chinese Medicine and Promoting the High-Quality Development of the Traditional Chinese Medicine Industry and fundamentally promote the high-quality development of Chinese materia medica(CMM) industry, this article analyzed the quality and safety issues arising during the transition of CMM from wild harvesting to cultivation. Root causes of these issues were identified, including changes in the habitats of medicinal plants caused by inappropriate field cultivation patterns, excessive use of chemical inputs such as fertilizers and pesticides, and shortened cultivation periods due to rising economic costs. To address the above issues, the following countermeasures and suggestions were proposed to advance the high-quality development of CMM:(1) comprehensively adjust the cultivation patterns, vigorously promote ecological cultivation of CMM, and ensure production quality and safety of CMM from the source;(2) strengthen the breeding of high-quality, stress-resistant CMM varieties, improve cultivation techniques to reduce the use of fertilizers and pesticides, and improve the quality and efficiency of ecological cultivation of CMM;(3) systematically design the production, operation, and supervision models for ecological cultivation of CMM, carry out demonstrations of "high quality with fair price", and ensure the sustainable development of ecological cultivation of CMM.
Drugs, Chinese Herbal/standards* ; Quality Control ; Plants, Medicinal/chemistry* ; Plant Roots/chemistry* ; China ; Fertilizers/analysis* ; Materia Medica/standards* ; Medicine, Chinese Traditional/standards*

Large language models (LLMs) such as ChatGPT, Claude, Llama, and Qwen are emerging as transformative technologies for the diagnosis and treatment of various diseases. With their exceptional long-context reasoning capabilities, LLMs are proficient in clinically relevant tasks, particularly in medical text analysis and interactive dialogue. They can enhance diagnostic accuracy by processing vast amounts of patient data and medical literature and have demonstrated their utility in diagnosing common diseases and facilitating the identification of rare diseases by recognizing subtle patterns in symptoms and test results. Building on their image-recognition abilities, multimodal LLMs (MLLMs) show promising potential for diagnosis based on radiography, chest computed tomography (CT), electrocardiography (ECG), and common pathological images. These models can also assist in treatment planning by suggesting evidence-based interventions and improving clinical decision support systems through integrated analysis of patient records. Despite these promising developments, significant challenges persist regarding the use of LLMs in medicine, including concerns regarding algorithmic bias, the potential for hallucinations, and the need for rigorous clinical validation. Ethical considerations also underscore the importance of maintaining the function of supervision in clinical practice. This paper highlights the rapid advancements in research on the diagnostic and therapeutic applications of LLMs across different medical disciplines and emphasizes the importance of policymaking, ethical supervision, and multidisciplinary collaboration in promoting more effective and safer clinical applications of LLMs. Future directions include the integration of proprietary clinical knowledge, the investigation of open-source and customized models, and the evaluation of real-time effects in clinical diagnosis and treatment practices.
Humans ; Large Language Models ; Tomography, X-Ray Computed

Fermented traditional Chinese medicine(TCM) has a long history of medicinal use, such as Sojae Semen Praeparatum, Arisaema Cum Bile, Pinelliae Rhizoma Fermentata, red yeast rice, and Jianqu. Fermentation technology was recorded in the earliest TCM work, Shen Nong's Classic of the Materia Medica. Microorganisms are essential components of the fermentation process. However, the contamination of fermented TCM by toxigenic fungi and mycotoxins due to unstandardized fermentation processes seriously affects the quality of TCM and poses a threat to the life and health of consumers. In this paper, the characteristics, microbial composition, and mycotoxin profile of fermented TCM are systematically summarized to provide a theoretical basis for its quality and safety control.
Fermentation ; Mycotoxins/analysis* ; Drugs, Chinese Herbal/analysis* ; Fungi/classification* ; Bacteria/genetics* ; Drug Contamination ; Medicine, Chinese Traditional