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.

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

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*

OBJECTIVE: To explore the therapeutic effects and underlying mechanisms of Dendrobium officinale (Tiepi Shihu) extract (DOE) on insomnia. METHODS: Forty-two male Sprague-Dawley rats were randomly divided into 6 groups (n=7 per group): normal control, model control, melatonin (MT, 40 mg/kg), and 3-dose DOE (0.25, 0.50, and 1.00 g/kg) groups. Rats were raised in a strong-light (10,000 LUX) and -noise (>80 db) environment (12 h/d) for 16 weeks to induce insomnia, and from week 10 to week 16, MT and DOE were correspondingly administered to rats. The behavior tests including sodium pentobarbital-induced sleep experiment, sucrose preference test, and autonomous activity test were used to evaluate changes in sleep and emotions of rats. The metabolic-related indicators such as blood pressure, blood viscosity, blood glucose, and uric acid in rats were measured. The pathological changes in the cornu ammonis 1 (CA1) region of rat brain were evaluated using hematoxylin and eosin staining and Nissl staining. Additionally, the sleep-related factors gamma-aminobutyric acid (GABA), glutamate (GA), 5-hydroxytryptamine (5-HT), and interleukin-6 (IL-6) were measured using enzyme linked immunosorbent assay. Finally, we screened potential sleep-improving receptors of DOE using polymerase chain reaction (PCR) array and validated the results with quantitative PCR and immunohistochemistry. RESULTS: DOE significantly improved rats' sleep and mood, increased the sodium pentobarbital-induced sleep time and sucrose preference index, and reduced autonomic activity times (P<0.05 or P<0.01). DOE also had a good effect on metabolic abnormalities, significantly reducing triglyceride, blood glucose, blood pressure, and blood viscosity indicators (P<0.05 or P<0.01). DOE significantly increased the GABA content in hippocampus and reduced the GA/GABA ratio and IL-6 level (P<0.05 or P<0.01). In addition, DOE improved the pathological changes such as the disorder of cell arrangement in the hippocampus and the decrease of Nissel bodies. Seven differential genes were screened by PCR array, and the GABA_A receptors (Gabra5, Gabra6, Gabrq) were selected for verification. The results showed that DOE could up-regulate their expressions (P<0.05 or P<0.01). CONCLUSION DOE demonstrated remarkable potential for improving insomnia, which may be through regulating GABA_A receptors expressions and GA/GABA ratio.
Animals ; Dendrobium/chemistry* ; Rats, Sprague-Dawley ; Male ; Sleep Initiation and Maintenance Disorders/blood* ; Plant Extracts/therapeutic use* ; Receptors, GABA-A/metabolism* ; Noise/adverse effects* ; Light/adverse effects* ; gamma-Aminobutyric Acid/metabolism* ; Sleep/drug effects* ; Rats ; Receptors, GABA/metabolism*

Objective: To investigate the potential efficacy and safety of Lutai Danshen Baishao granules (LDBG) for treating female melasma associated with kidney deficiency and blood stasis patterns. Methods: A randomized, double-blind, placebo-controlled trial was conducted at the Third Central Hospital of Tianjin, China from March to December 2023. A total of 110 female patients with melasma linked to kidney deficiency and blood stasis were enrolled and treated with either LDBG or a placebo twice daily for 60 days. Efficacy was assessed through measures such as the total melasma area, reduced melasma area, reduction rate of melasma area, melasma color score, Melasma Area and Severity Index (MASI) score, and traditional Chinese medicine (TCM) symptom score scale. Safety assessments included routine blood and biochemical tests. Results: Participants in both groups were aged 52–63 years, with no significant differences. After the 2-month intervention, the total melasma area decreased in both groups; however, a greater reduction was observed in the test group [462.50 mm2 (12.81%) vs. 100.00 mm2 (3.11%), P < .001]. Moreover, LDBG treatment significantly reduced the MASI and melasma color scores in the test group (P < .05). The total TCM symptom evaluation score significantly decreased (test group: 6.00 vs. placebo group: 7.00, P = .001), with significant relief in symptoms such as improvement in dark lips, nails, and waist soreness in the test group, compared with that in the placebo group (P < .05). Within-group comparisons revealed that TCM syndrome was significantly alleviated in the test group (P < .05). Conclusion LDBG intervention shows promising effectiveness in reducing female melasma and alleviating TCM syndromes.

Background: Optimizing cultivation techniques for traditional Chinese medicine has become a crucial means to improve the quality of medicinal materials. Microbial agents, as environmentally friendly and efficient plant growth promoters and soil conditioners, have increasingly attracted attention in eco-agriculture research. Objective: Our understanding remains limited regarding how the application of microbial agents, alone or in combination, affects changes in the rhizosphere microbiome and its association with the bioactive components of medicinal materials. Methods: In this study, Epimedium pubescens Maxim. was employed as a model plant to examine the effects of 2 microbial agents(Paenibacillus mucilaginosus and Bacillus subtilis) applied individually and in combination on plant growth and the accumulation of bioactive components. Additionally, this study explored the relationship between the rhizosphere microbiome and plant development. Results: The application of microbial agents increased the yield of E. pubescens leaves by 20.30% to 33.66% and enhanced the total flavonol glycosides content by 11.40% to 29.94%. Meanwhile, microbial treatments reshaped the rhizosphere microbiome, promoted the enrichment of beneficial microorganisms (e.g., Frankia and Paenibacillus), suppressed phytopathogenic fungi such as Didymella and Scytalidium, and enhanced the stability of the soil microbial co-occurrence network. The partial least squares path model suggested that microbial agents not only directly impact the quality of medicinal herbs but also indirectly alter the accumulation of bioactive components by modulating the soil microbiome. Conclusion: These findings deepen our understanding of the relationship between medicinal plant quality and rhizosphere microbiomes as mediated by microbial agents. They also provide a basis for designing and manipulating synthetic microbial communities to promote sustainable development in eco-agriculture.