Objective: To develop a clinical decision and prescription generation system (CDPGS) specifically for diarrhea in traditional Chinese medicine (TCM), utilizing a specialized large language model (LLM), Qwen-TCM-Dia, to standardize diagnostic processes and prescription generation. Methods: Two primary datasets were constructed: an evaluation benchmark and a fine-tuning dataset consisting of fundamental diarrhea knowledge, medical records, and chain-of-thought (CoT) reasoning datasets. After an initial evaluation of 16 open-source LLMs across inference time, accuracy, and output quality, Qwen2.5 was selected as the base model due to its superior overall performance. We then employed a two-stage low-rank adaptation (LoRA) fine-tuning strategy, integrating continued pre-training on domain-specific knowledge with instruction fine-tuning using CoT-enriched medical records. This approach was designed to embed the clinical logic (symptoms → pathogenesis → therapeutic principles → prescriptions) into the model’s reasoning capabilities. The resulting fine-tuned model, specialized for TCM diarrhea, was designated as Qwen-TCM-Dia. Model performance was evaluated for disease diagnosis and syndrome type differentiation using accuracy, precision, recall, and F1-score. Furthermore, the quality of the generated prescriptions was compared with that of established open-source TCM LLMs. Results: Qwen-TCM-Dia achieved peak performance compared to both the base Qwen2.5 model and five other open-source TCM LLMs. It achieved 97.05% accuracy and 91.48% F1-score in disease diagnosis, and 74.54% accuracy and 74.21% F1-score in syndrome type differentiation. Compared with existing open-source TCM LLMs (BianCang, HuangDi, LingDan, TCMLLM-PR, and ZhongJing), Qwen-TCM-Dia exhibited higher fidelity in reconstructing the “symptoms → pathogenesis → therapeutic principles → prescriptions” logic chain. It provided complete prescriptions, whereas other models often omitted dosages or generated mismatched prescriptions. Conclusion By integrating continued pre-training, CoT reasoning, and a two-stage fine-tuning strategy, this study establishes a CDPGS for diarrhea in TCM. The results demonstrate the synergistic effect of strengthening domain representation through pre-training and activating logical reasoning via CoT. This research not only provides critical technical support for the standardized diagnosis and treatment of diarrhea but also offers a scalable paradigm for the digital inheritance of expert TCM experience and the intelligent transformation of TCM.

Medical institutions, with their clinical practice foundation and abundant human use experience data, have become important carriers for the inheritance and innovation of traditional Chinese medicine(TCM) and the "cradles" of the preparation of new TCM. To effectively promote the transformation of new TCM originating from the TCM clinical practice in medical institutions and establish an effective evaluation index system for the transformation of new TCM conforming to the characteristics of TCM, consensus experts adopted the literature research, questionnaire survey, Delphi method, etc. By focusing on the policy and technical evaluation of new TCM originating from the TCM clinical practice in medical institutions, a comprehensive evaluation from the dimensions of drug safety, efficacy, feasibility, and characteristic advantages was conducted, thus forming a comprehensive evaluation system with four primary indicators and 37 secondary indicators. The expert consensus reached aims to encourage medical institutions at all levels to continuously improve the high-quality research and development and transformation of new TCM originating from the TCM clinical practice in medical institutions and targeted at clinical needs, so as to provide a decision-making basis for the preparation, selection, cultivation, and transformation of new TCM for medical institutions, improve the development efficiency of new TCM, and precisely respond to the public medication needs.
Medicine, Chinese Traditional/standards* ; Humans ; Consensus ; Drugs, Chinese Herbal/therapeutic use* ; Surveys and Questionnaires

Based on the interleukin-17(IL-17) signaling pathway, this study explores the effect and mechanism of Bufei Decoction on Klebsiella pneumoniae pneumonia in rats. SD rats were randomly divided into the control group, model group, Bufei Decoction low-dose group(6.68 g·kg~(-1)·d~(-1)), Bufei Decoction high-dose group(13.36 g·kg~(-1)·d~(-1)), and dexamethasone group(1.04 mg·kg~(-1)·d~(-1)), with 10 rats in each group. A pneumonia model was established by tracheal drip injection of K. pneumoniae. After successful model establishment, the improvement in lung tissue damage was observed following drug administration. Core targets and signaling pathways were screened using transcriptomics techniques. Real-time fluorescence quantitative polymerase chain reaction was used to detect the mRNA expression of core targets interleukin-6(IL-6), interleukin-1β(IL-1β), tumor necrosis factor-α(TNF-α), and chemokine CXC ligand 6(CXCL6). Western blot was used to assess key proteins in the IL-17 signaling pathway, including interleukin-17A(IL-17A), nuclear transcription factor-κB activator 1(Act1), tumor necrosis factor receptor-associated factor 6(TRAF6), and downstream phosphorylated p38 mitogen-activated protein kinase(p-p38 MAPK), and phosphorylated nuclear factor-κB p65(p-NF-κB p65). Apoptosis of lung tissue cells was detected by terminal deoxynucleotidyl transferase-mediated dUTP-biotin nick end labeling(TUNEL). The results showed that, compared with the control group, the model group exhibited significant pathological damage in lung tissue. The mRNA expression of IL-6, IL-1β, TNF-α, and CXCL6, as well as the protein levels of IL-17A, Act1, TRAF6, p-p38 MAPK/p38 MAPK, and p-NF-κB p65/NF-κB p65, were significantly increased, and the number of apoptotic cells was notably higher, indicating successful model establishment. Compared with the model group, both low-and high-dose groups of Bufei Decoction showed reduced pathological damage in lung tissue. The mRNA expression levels of IL-6, IL-1β, TNF-α, and CXCL6, and the protein levels of IL-17A, Act1, TRAF6, p-p38 MAPK/p38 MAPK, and p-NF-κB p65/NF-κB p65, were significantly decreased, with a significant reduction in apoptotic cells in the high-dose group. In conclusion, Bufei Decoction can effectively improve lung tissue damage and reduce inflammation in rats with K. pneumoniae. The mechanism may involve the regulation of the IL-17 signaling pathway and the reduction of apoptosis.
Animals ; Interleukin-17/metabolism* ; Drugs, Chinese Herbal/administration & dosage* ; Rats, Sprague-Dawley ; Signal Transduction/drug effects* ; Rats ; Male ; Klebsiella pneumoniae/physiology* ; Klebsiella Infections/immunology* ; Humans ; Lung/drug effects*

Traditional Chinese medicine(TCM) processing is a specialized pharmaceutical technique with the primary objective of reducing the toxicity of medicinal substances. Euphorbia pekinensis, E. ebracteolata, and E. fischeriana, all belonging to Euphorbiaceae, are classified as drastic purgative herbs, traditionally used for eliminating retained water, reducing swelling, resolving toxicity, and dispersing masses. However, these herbs are also associated with adverse effects such as abdominal pain and diarrhea. Accordingly, they are commonly processed with vinegar, milk, or Terminalia chebula decoction to reduce the toxicity. This review summarizes the chemical constituents, pharmacological activities, historical evolution of processing methods, and detoxification mechanisms of the three toxic Euphorbia species. The primary toxic constituents are terpenoids. Specifically, E. ebracteolata and E. fischeriana are rich in diterpenoids, while E. pekinensis contains diterpenoids, triterpenoids, and sesquiterpenoids. Studies have shown that vinegar processing promotes structural transformations of diterpenoids, including ether bond hydrolysis, lactone ring opening, esterification, oxidation, and epoxide ring cleavage, thereby reducing the content and toxicity of these compounds. Milk processing facilitates the dissolution of toxic components into the residual liquid of excipients, leading to decreases in their concentrations in the final decoction pieces. Processing with T. chebula decoction raises the levels of tannin-derived phenolic acids, which antagonize the adverse effects of the intestine. These findings reveal a shared detoxification pattern among the three toxic herbs. Accordingly, this review proposes the concept of a shared detoxification mechanism for toxic herbs belonging to the same family or genus. That is, toxic herbs belonging to the same taxon often exhibit similar toxicological profiles and can undergo detoxification through the same processing methods, reflecting common underlying mechanisms. Investigating such shared mechanisms across multiple species of the same genus offers a promising research strategy. Ultimately, the research into processing-induced detoxification mechanisms provides both theoretical and practical support for ensuring the safety of toxic TCM.
Euphorbia/classification* ; Drugs, Chinese Herbal/metabolism* ; Humans ; Animals ; Inactivation, Metabolic ; Medicine, Chinese Traditional

Three taraxerane nortriterpenoids were isolated from mastic by using various modern chromatographic separation techniques. They were identified as(5R,8R,9R,10S,11S,12R,13S,17R,18R)-28-norlupa-11,12-epoxy-14-taraxerene-3,16-dione(1),(5R,8R,9R,10S,11S,12R,13S,17S,18S)-17-hydroxy-28-norlupa-11,12-epoxy-14-taraxerene-3-one(2), and(5R,8R,9R,10R,11S,12R,13R,14S,17S,18S)-14,17-epoxy-28-norlupa-11,12-oxidotaraxerone(3) through the high-resolution electrospray ionization mass spectrometry(HR-ESI-MS), infrared(IR), ultraviolet(UV), nuclear magnetic resonance(NMR), and single-crystal X-ray diffraction techniques as well as comparison with literature data. Compounds 1-3 were C-28 nortriterpenoids and isolated from mastic for the first time, and compounds 1-2 were new ones. In the model for RAW264.7 cell anti-inflammation induced by lipopolysaccharide(LPS), compound 1 demonstrates an inhibitory effect on nitric oxide(NO) [IC_(50)=(13.38±0.68) μmol·L~(-1)], comparable to the activity of the positive control dexamethasone [IC_(50)=(14.59±1.49) μmol·L~(-1)]. Compounds 2 and 3 exhibit weaker inhibitory effects, with IC_(50) values of(24.17±2.56) and(22.25±2.84) μmol·L~(-1), respectively.
Animals ; Mice ; Triterpenes/isolation & purification* ; Drugs, Chinese Herbal/isolation & purification* ; Mastic Resin/chemistry* ; Nitric Oxide ; Molecular Structure ; Macrophages/immunology* ; RAW 264.7 Cells

OBJECTIVE To investigate the effects of alirocumab combined with atorvastatin on clinical efficacy and safety of patients with acute coronary syndrome （ACS） who underwent percutaneous coronary intervention （PCI）. METHODS A total of 207 patients with ACS who underwent PCI in our hospital from January 2021 to December 2023 were randomly divided into alirocumab group， ezetimibe group and control group， with 69 cases in each group. All patients received routine thrombosis prevention and antihypertensive treatment after PCI. On this basis， patients in the control group were treated with atorvastatin （20 mg/time， once a day）； patients in the ezetimibe group were treated with ezetimibe （10 mg/time， once a day） + atorvastatin （20 mg/time， once a day）； patients in the alirocumab group were treated with alirocumab （75 mg/time， once every 2 weeks） + atorvastatin （20 mg/time， once a day）. All patients in the three groups were treated for 8 weeks and followed up for another 6 months after treatment. The levels of cardiac function and lipid metabolism indices before and after treatment， as well as the occurrence of major adverse cardiovascular event （MACE） and other adverse drug reaction （ADR） during the follow-up period were compared among the three groups. RESULTS After treatment for 8 weeks， the levels of cardiac function and lipid metabolism indices in the three groups were significantly improved compared with those before treatment （P＜0.05）. Compared with the control group and ezetimibe group， the left ventricular ejection fraction in the alirocumab group was significantly increased， and the left ventricular end-diastolic diameter （LVEDD） was significantly shortened （P＜0.05）. Compared with control group， LVEDD of ezetimibe group was significantly shortened （P＜0.05）， the levels of total cholesterol， triglyceride and low-density lipoprotein cholesterol in the alirocumab group and ezetimibe group were significantly decreased （P＜0.05）. During the follow-up period， there was no significant difference in the total incidence of MACE and the total incidence of other ADR such as headache and abdominal pain among the three groups （P＞0.05）. CONCLUSIONS Alirocumab combined with atorvastatin can significantly improve cardiac function and regulate lipid metabolism indices in patients with ACS after PCI without increasing the risk of MACE or other ADR.

Objective To construct large medical model named by "Huaxi HongYi"and explore its application effectiveness in assisting medical record generation. Methods By the way of a full-chain medical large model construction paradigm of "data annotation - model training - scenario incubation", through strategies such as multimodal data fusion, domain adaptation training, and localization of hardware adaptation, "Huaxi HongYi" with 72 billion parameters was constructed. Combined with technologies such as speech recognition, knowledge graphs, and reinforcement learning, an application system for assisting in the generation of medical records was developed. Results Taking the assisted generation of discharge records as an example, in the pilot department, after using the application system, the average completion times of writing a medical records shortened (21 min vs. 5 min) with efficiency increased by 3.2 time, the accuracy rate of the model output reached 92.4%. Conclusion It is feasible for medical institutions to build independently controllable medical large models and incubate various applications based on these models, providing a reference pathway for artificial intelligence development in similar institutions.

Objective To mine and analyze the routine blood test data of children with allergic rhinitis (AR), identify routine blood parameters related to childhood allergic rhinitis, establish an effective diagnostic model, and evaluate the performance of the model. Methods This study was a retrospective study of clinical cases. The experimental group comprised a total of 1110 children diagnosed with AR at the First Affiliated Hospital of Air Force Medical University during the period from December 12, 2020 to December 12, 2021, while the control group included 1109 children without a history of allergic rhinitis or other allergic diseases who underwent routine physical examinations during the same period. Information such as age, sex and routine blood test results was collected for all subjects. The levels of routine blood test indicators were compared between AR children and healthy children using comprehensive intelligent baseline analysis, with indicators of P≥0.05 excluded; variables were screened by Lasso regression. Binary Logistic regression was used to further evaluate the influence of multiple routine blood indexes on the results. Five kinds of machine model algorithms were used, namely extreme value gradient lift (XGBoost), logistic regression (LR), gradient lift decision tree (LGBMC), Random forest (RF) and adaptive lift algorithm (AdaBoost), to establish the diagnostic models. The receiver operating characteristic (ROC) curve was used to screen the optimal model. The best LightGBM algorithm was used to build an online patient risk assessment tool for clinical application. Results Statistically significant differences were observed between the AR group and the control group in the following routine blood test indicators: mean cellular hemoglobin concentration (MCHC), hemoglobin (HGB), absolute value of basophils (BASO), absolute value of eosinophils (EOS), large platelet ratio (P-LCR), mean platelet volume (MPV), platelet distribution width (PDW), platelet count (PLT), absolute values of leukocyte neutrophil (W-LCC), leukocyte monocyte (W-MCC), leukocyte lymphocyte (W-SCC), and age. Lasso regression identified these variables as important predictors, and binary Logistic regression further analyzed the significant influence of these variables on the results. The optimal machine learning algorithm LightGBM was used to establish a multi-index joint detection model. The model showed robust prediction performance in the training set, with AUC values of 0.8512 and 0.8103 in the internal validation set. Conclusion The identified routine blood parameters can be used as potential biomarkers for early diagnosis and risk assessment of AR, which can improve the accuracy and efficiency of diagnosis. The established model provides scientific basis for more accurate diagnostic tools and personalized prevention strategies. Future studies should prospectively validate these findings and explore their applicability in other related diseases.
Humans ; Male ; Female ; Rhinitis, Allergic/blood* ; Child ; Biomarkers/blood* ; Retrospective Studies ; Early Diagnosis ; Child, Preschool ; ROC Curve ; Logistic Models ; Hematologic Tests ; Algorithms ; Adolescent ; Machine Learning

OBJECTIVE: To explore a predictive model that can better predict the prognosis of patients with diffuse large B-cell lymphoma (DLBCL), and validate its clinical value. METHODS: Clinical data of 134 newly treated DLBCL patients were collected from Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine from January 2015 to January 2020. Several risk factors of the patients were screened and analyzed, a novel prognostic model were then established based on this, and its clinical application potential was validated. RESULTS: In the novel model, predicting progression-free survival (PFS) based on the age at initial treatment, albumin level, Hans classification, Ann Arbor stage, and BCL2 expression showed better predictive performance than International Prognostic Index (IPI) score (AUC: 0.788 vs 0.620，P <0.001). Predicting overall survival (OS) based on the age at initial treatment, albumin level, lactate dehydrogenase (LDH) level, and expressions of BCL2 and MUM1 proteins also showed better predictive performance for mortality risk than IPI score (AUC: 0.817 vs 0.624，P <0.001). CONCLUSION This novel prognostic model can better predict the survival prognosis of DLBCL patients compared to the IPI scoring system.
Humans ; Lymphoma, Large B-Cell, Diffuse/diagnosis* ; Prognosis ; Proto-Oncogene Proteins c-bcl-2/metabolism* ; Risk Factors ; Male ; Female ; Middle Aged