Based on the thinking of programmatization and proceduralization， this study integrated traditional Chinese medicine （TCM） classic theories with modern knowledge expression technologies to construct a "formula-symptom" syndrome differentiation thinking model centered on "symptom clustering-main syndrome screening-formula adaptation"， and explored the standardization and intelligentization path of TCM syndrome differentiation and treatment. By establishing the mapping relationship model between formulas and syndromes including quantitative weight analysis of chief， deputy， assistant and envoy medicines， designing the logical hierarchical structure of formula-syndrome decision tree （application of three-level decision tree and fuzzy logic）， and formulating the procedural design of four diagnostic methods （structured collection， correlation model， and dynamic correction mechanism）， the standardization and visualization of the syndrome differentiation process are realized. This model can be transformed into the core data set for artificial intelligence training. Through ternary knowledge graph and machine learning algorithms， it can improve the repeatability of syndrome differentiation and the efficiency of diagnosis and treatment， and implement the strategy of "group model + individual modification" to balance the conflict between quantification and individualization. The core value of this model lies in promoting the objectification and precision development of TCM syndrome differentiation and treatment through the integration of traditional syndrome differentiation thinking and modern system science.

Perimenopause raises the risk and incidence of depression, whereas the underlying molecular mechanism remains unclear. Disturbed glucose regulation has been widely documented in depressive disorders, which renders the brain susceptible to various stresses such as estrogen depletion. However, whether and how glucose dysfunction regulates depression-like behaviors and neuronal damage in perimenopausal transition remains unexplored. Here, a prominent depressive phenotype was found in perimenopausal mice induced by the ovarian toxin 4-vinylcyclohexene diepoxide (VCD). The VCD depression susceptible group (VCD^SS) and the VCD depression resilient group (VCD^RES) were determined using a ROC-based behavioral screening approach. We found that the hippocampus, a crucial region linked to depression, had hyperglycemia and mitochondrial abnormalities. Interestingly, oral administration of the SGLT2 inhibitor empagliflozin (EMPA) and intrahippocampal glucose infusion suggest a close relationship between hyperglycemia in the hippocampus and the susceptibility to depression. We verified that cytochrome c oxidase 7c (COX7C) downregulation is a potential cause of the high glucose-induced neuronal injury using proteomic screening and biochemical validations. High glucose causes COX7C to be ubiquitinated in a S-phase kinase associated protein 1 (SKP1)-dependent manner. According to these results, SKP1/COX7C represents a unique therapeutic target and a novel molecular route for treating perimenopausal depression.

Knee osteoarthritis is a common joint disease within osteoarthritis,characterized by pain,swelling,and limited functionality as the main clinical manifestations.In severe cases,it affects daily life and falls under the category of"impediment syndrome"or"bone impediment"in TCM.The author believes that the theory of"bones,tendons,and muscles"is closely related to this disease.Treatment should focus on simultaneously nourishing the liver,spleen and kidneys,considering tendons,bones and muscles,while also dispelling wind,cold and dampness.The clinical application of Sanbi Decoction has shown good efficacy,and this discussion aimed to provide ideas for the diagnosis and treatment of knee osteoarthritis.

Knee osteoarthritis is a common joint disease within osteoarthritis,characterized by pain,swelling,and limited functionality as the main clinical manifestations.In severe cases,it affects daily life and falls under the category of"impediment syndrome"or"bone impediment"in TCM.The author believes that the theory of"bones,tendons,and muscles"is closely related to this disease.Treatment should focus on simultaneously nourishing the liver,spleen and kidneys,considering tendons,bones and muscles,while also dispelling wind,cold and dampness.The clinical application of Sanbi Decoction has shown good efficacy,and this discussion aimed to provide ideas for the diagnosis and treatment of knee osteoarthritis.

Hand osteoarthritis(HOA)is a disease of hand joint disorders,mainly manifested by hand interphalangeal joint and thumb carpal metacarpal joint pain,swelling,morning stiffness,limited movement,and even deformity,belonging to the category of TCM"bone arthralgia".The authors believe that HOA is more common with Taiyang Shaoyang disease,suitable for simultaneous treatment for Taiyang and Shaoyang,to operate the cardinal,regulate qi,blood,nutritive and defensive levels,dispel wind and cold,remove dampness and arthralgia,using modified Chaihu Guizhi Decoction,with confirmed efficacy.

Lumbar disc herniation is mainly manifested as lower back pain,numbness,weakness,and radiating pain in the lower limbs,which seriously affects the patients'work and quality of life.In clinical practice,it has been found that this disease always belongs to the category of deficiency in healthy qi and excess in pathogenic factors,often accompanied by kidney deficiency and cold dampness.Kidney deficiency is the root cause,while cold dampness is the symptoms.The two factors interact with each other and cause back pain.The treatment is based on dispersing cold and dampness,tonifying the kidneys and strengthening the waist,and the classic ancient formula Shenzhuo Powder is safe and effective.

The development of large language models in the field of Traditional Chinese Medicine(TCM)using artificial intelli-gence has significantly contributed to both the innovation and preservation of TCM.This paper outlines the current research status and process of large language models in TCM,focusing on three key tasks:collecting TCM data,fine-tuning models with specific instruc-tions,and using different methods to evaluate model performance.It also highlights cutting-edge techniques,such as prompt engineer-ing,retrieval-augmented generation,and reinforcement learning from human feedback,which have enhanced the models'adaptability in various TCM applications.The challenges faced by TCM language models,such as data privacy,ethical biases,model interpret-ability,technical difficulties,and evaluation standards,are also analyzed,indicating areas for further improvement.Looking ahead,the combination of these models with advanced AI techniques like deep learning,and the integration of multimodal information such as TCM diagnostic data and herbal images,can open new possibilities for large language models in TCM.This will enhance their applica-tion in areas like syndrome differentiation diagnosis,prescription recommendations,TCM knowledge graph construction,and TCM edu-cation.

OBJECTIVE To systematically evaluate the clinical efficacy of dapagliflozin in the treatment of heart failure with diabetes mellitus type 2. METHODS The clinical trials of dapagliflozin in the treatment of heart failure with diabetes mellitus type 2 were searched in Embase, PubMed, Web of Science, Cochrane Library, VIP, CNKI and Wanfang databases from the establishment of the database to March 18, 2022. The RevMan 5.3 software was used for meta-analysis, and the TSA 0.9 software was used for sequential analysis. RESULTS The 31 RCT studies meeting the criteria were finally included, involving 2 906 patients. Meta-analysis showed that compared with the control group, the experimental group significantly improved LVEF[MD=4.43, 95% CI(3.35, 5.50), P<0.000 01], total effective rate[MD=4.19, 95%CI(2.52, 6.99), P<0.000 01], and reduced NT-proBNP[MD=–451.84, 95%CI(–608.09, –295.60), P<0.000 01], LVEDD[MD=–2.74, 95%CI(–3.67, –1.82), P<0.000 01, Hb1ac[MD=–0.88, 95%CI(–1.19, –0.57), P<0.000 01], FPG[MD=–1.10, 95%CI(–1.45, –0.75), P<0.000 01], 2hPG[MD=–2.52, 95%CI(–3.37, –1.66), P<0.000 01] and the incidence of adverse reactions[MD=0.63, 95%CI(0.47, 0.83), P=0.001]. Sequential analysis showed that the effect of dapagliflozin on LVEF in patients with heart failure with type 2 diabetes was accurate, and the possibility of excluding false positive was possible. CONCLUSION The treatment of heart failure with diabetes mellitus type 2 with good efficacy and safety is achieved by dapagliflozin, but it still needs to be included in more high-quality RCT studies for further demonstration.

Objective To screen and validate the key genes involved in hypoxia-induced inflammatory reactions in mice using transcrip-tome sequencing and bioinformatics. Methods C57/BL6 mice were bred at altitudes of 4200 m and 400 m,and mouse models were con-structed for the plateau hypoxia (HKT) group and the plain normoxia (PKC) group. Kidney tissues were aseptically removed after 30 days,renal pathological changes were analyzed by HE staining,blood gas analysis and renal index changes were measured in the mice under hypoxia. The kidney tissues of mice in the HKT and PKC groups were analyzed using transcriptome sequencing,key genes were screened using bioinformatics technology,and these genes were verified using real-time quantitative reverse transcription PCR (RT-qPCR) and Western blotting. Results HE staining showed glomerular atrophy in mice in the HKT group compared with the PKC group,and a decrease in blood gas analysis and renal index occurred in mice exposed to hypoxia. Transcriptome sequencing analysis revealed 3007 differentially expressed genes (DEGs) in the HKT group,of which 123 were inflammation-related DEGs (IR-DEGs). GO and KEGG enrichment analyses of IR-DEGs showed significant enrichment in inflammation-related signaling pathways,such as cytokine-cytokine receptor interactions and chemokines. The results of the protein-protein interaction (PPI) network construction of IR-DEGs showed that six hub genes,STAT3,TLR7,CD68,NFKBIA,LEP,and APOE,were identified,and the mRNA expression of these six genes was upregulated according to RT-qPCR results,which was in agreement with the results of transcriptome sequencing. Western blotting showed that CD68,NFKBIA,LEP,TLR7,and APOE expression was upregulated while STAT3 expression was downregulated. Conclusion STAT3,CD68,NFKBIA,LEP,TLR7,and APOE are the key genes involved in hypoxia-induced inflammatory reactions. A hypoxic environment induced inflammatory reactions in mouse kidney tissues by upregulating the expression of TLR7,CD68,STAT3,LEP,and APOE.

Objective:To identify factors influencing orthostatic intolerance (OI) in patients during initial ambulation following minimally invasive lung surgery and develop and validate a risk prediction model to assist clinical practitioners in screening high-risk patients.Methods:Totally 1 000 patients who underwent minimally invasive lung surgery at the Department of Thoracic Surgery of Peking University Third Hospital from March 2022 to November 2023 were recruited by convenience sampling. Patients were randomly divided into a modeling group ( n=800) and an internal validation group ( n=200) in an 8∶2 ratio. Univariate analysis and logistic regression were applied to determine risk factors for OI in the modeling group. R software was utilized to construct a nomogram model. The model's predictive performance was assessed using the area under the ROC curve ( AUC) for both the modeling and validation groups. Calibration curves were plotted to evaluate consistency, and the Hosmer-Lemeshow test was conducted to confirm model fit. Results:The incidence of OI during initial ambulation was 37.2% (372/1 000). Logistic regression identified BMI, postoperative day 1 drainage volume, postoperative use of nonsteroidal anti-inflammatory drugs (NSAIDs), and initial ambulation pain score as independent risk factors for OI ( P<0.05). The AUC for the nomogram model in the modeling group was 0.645, and 0.694 in the validation group, indicating good predictive accuracy. Calibration curves showed strong agreement between predicted and observed outcomes ( P>0.05) . Conclusions:The constructed risk prediction model demonstrates good predictive ability for OI risk during initial ambulation following minimally invasive lung surgery, which can support clinical identification of high-risk patients. This tool may provide valuable guidance for implementing early, targeted preventive measures.