OBJECTIVE:To screen Gongshisong's active sites for anti-sports fatigue. METHODS:Gongshisong extract was prepared with 80% ethanol extraction technology,and extracted with petroleum ether,chloroform,ethyl acetate and n-butyl alco-hol after dispersed with water to obtain the extract. 70 mice were randomly divided into blank control group(1% sodium carboxy-methylcellulose,CMC-Na),positive control group [Rhodiola wallichiana capsules,590 mg/(kg·d)],petroleum ether,chloro-form,ethyl acetate and n-butyl alcohol extracts and aqueous layer of Gongshisong groups(TS,TL,TY,TZ,TW groups). Gong-shisong extracts groups was given relevant medicine 2.5 g(crude drug)/(kg·d),ig,for consecutive 7 days. Exhaustion time of bur-den swimming test was detected. 70 mice were grouped according to above method,and the contents of liver glycogen,muscle gly-cogen and the coefficient of liver were tested in mice. 80 mice were grouped according to above method,and model group was es-tablished additionally(1% CMC-Na). The contents of lactic acid(LA),creatine kinase(CK)and urea nitrogen(BUN)in serum of mice were determined after 90 minutes of unburden swimming. RESULTS:Compared with blank control group,exhaustion time of burden swimming mice in TS,TY and TZ groups prolonged;the content of liver glycogen increased in TY,TZ and TW groups;the content of muscle glycogen increased in TS and TW groups;the contents of BUN,LA and CK in mice increased in model group (P<0.05 or P<0.01). Compared with model group,the serum content of BUN in mice decreased in TS and TY groups;that of LA in mice decreased in TZ and TW groups;that of CK in mice decreased in TS group (P<0.05 or P<0.01). CONCLUSIONS:The petroleum ether and n-butanol extract site and water layer of Gongshisong are good anti-fatigue active sites.

Objective To explore and analyze the prescription patterns of Professor Zhou Min in treating primary liver cancer at different stages according to the China Liver Cancer Staging (CNLC) system. Methods The clinical records of outpatients with primary liver cancer treated by Professor Zhou Min were collected and entered into the Traditional Chinese Medicine Inheritance Support System (Version 2.50) to establish a database. Data mining methods such as frequency analysis, drug association analysis, and cluster analysis were employed, the pathogenesis of primary liver cancer the prescription patterns at different stages was explored and medication rules were analyzed according to Professor Zhou Min's experience in treating liver cancer at various CNLC stages. Results A total of 202 prescriptions from 113 patients with primary liver cancer were collected, involving 230 traditional Chinese medicines. The high-frequency drugs and drug combinations at each stage were identified. The drugs with higher frequencies at each stage included Fuling, Chenpi, Yiyiren, fried Baishu, and Fabanxia. For stage Ⅰ, high-frequency drugs also included Zhongjiefeng, Xiangfu, Jiangcan, and Jianghuang. For stages Ⅱ and Ⅲ, high-frequency drugs further encompassed Zhongjiefeng, Xianhecao, Banzhilian, Baihua Sheshecao, Jiangcan, Zeqi, Xiangfu, and Maidong. For stage Ⅳ, high-frequency drugs also include Maydis stigma, Huoxiang, fried Maiya, Jineijin, and fried Guya. The majority of the drugs were cold in nature, with sweet and bitter tastes being the most common, and their meridian tropism were mostly distributed in the spleen and stomach meridians. The drug combinations with higher frequencies at each stage were mostly derived from Sijunzi Decoction and Erchen Decoction. The drug efficacies were mainly heat-clearing and dampness-resolving. Cluster analysis screened out new prescriptions with unique characteristics at each stage. Conclusion By performing data mining on the prescriptions used by Professor Zhou Min in treating primary liver cancer at various CNLC stages through the Traditional Chinese Medicine Inheritance Platform, combined with his understanding of the pathogenesis and clinical experience of the disease, the pathogenesis characteristics of primary liver cancer are summarized as dampness-heat, phlegm, and toxin accumulation, as well as qi and yin deficiency. The basic treatment methods established are heat-clearing and dampness-resolving, spleen-invigorating and yin-nourishing, with an emphasis on strengthening the body resistance to eliminate pathogenic factors and stage-based treatment. Flexible prescriptions and medications are used for different complications.

Objective:To extract the differentially expressed key genes of primary biliary cholangitis (PBC) using bioinformatics methods, so as to provide information for further study into the mechanism.Methods:The GSE119600 dataset was downloaded from the GEO database to obtain differentially expressed genes. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed for differentially expressed genes. Protein-protein interaction (PPI) network reconstruction, Cytoscape software visualization, and core gene screening were performed. The area under the receiver operating characteristic curve (ROC AUC) was used to assess the diagnostic effectiveness of genes and plot the pROC software package. The x-Cell software was used to calculate the enrichment score of 34 immune cells in each sample. Finally, four key genes (PSMA4, PSMA1, PSMB1, and PSMA3) were selected. Blood samples were analyzed using the qPCR method.Results::A total of 373 immune-related differentially expressed genes were identified. Eight genes (PSMC6, PSMB2, PSMB1, PSMA3, PSMA4, PSMA1, PSMD7, and PSMB5) were screened from the 178 nodes and 596 edges as hub genes of the PPI network, which were significantly related to amino acid metabolism, hematopoietic stem cell differentiation, cell cycle, and immune processes. PSMA4, PSMA1, PSMB1, and PSMA3 were defined as immunological biomarkers for PBC with an AUC value of the ROC curve > 0.7. Immunoinfiltrating cell analysis showed that the proportion of eosinophils was significantly higher in PBC patients compared to the control group, whereas the proportion of CD4+ memory T cells, plasma cells, Th2 cells, and cDC cells was significantly lower in PBC patients than the control group. Plasma cells were associated with all four immunological biomarkers. Seven PBC patients and seven healthy subjects were selected for peripheral blood qPCR validation, which demonstrates that PSMB1, PSMA3, PSMA1, and PSMA4 levels were significantly lower in PBC patients than healthy subjects, with a statistically significant difference.Conclusion::Bioinformatics screened eight key genes, of which four were key immunological markers and may serve as a basis for clinical diagnosis and mechanism exploration.