中医药临床杂志2026，Vol.38Issue(5)：977-986,10.DOI:10.16448/j.cjtcm.2026.0524
蒲辅周治疗乙脑的用药规律及机制探讨
Based on Data Mining and Network Pharmacology,this Study Explores the Medication Patterns and Mechanisms of Pu Fuzhou in the Treatment of Japanese Encephalitis
摘要
Abstract
Objective:To explore the patterns and mechanisms of traditional Chinese medicine(TCM)use in treating Japanese encephalitis(JE)by PU Fuzhou using data mining and network pharmacology techniques.Methods:Relevant medical records on JE treatment were manually retrieved from publications such as"Pu Fuzhou's Medical Experi-ence,""One Hundred Famous TCM Clinicians in China's Century-Old Series—PU Fuzhou,"and"Pu Fuzhou's True Medical Transmission—Insights into the Inheritance of Exogenous Febrile Diseases."Literature on TCM treatment of JE that met the criteria was included.The properties,flavors,and meridian tropism of TCM herbs were statistically analyzed using Excel software.Using the R 4.2.1 platform,the Apriori algorithm(support threshold≥0.15,confidence≥0.75)was employed to mine association rules for the top 20 high-frequency drugs,and hierarchical cluster analysis(Euclidean distance method)was implemented to extract the core drug groups.The core formula obtained from cluster analysis was selected.A dataset of core formula compounds was obtained from the TCMSP v2.3 database(OB≥30%,DL≥0.18).Target correction was performed using the UniPort database.Disease genes related to Japanese encepha-litis were retrieved from the GeneCards database and the Human Mendelian Inheritance Database(OMIM).Intersection analysis was then performed to obtain common genes.A protein-protein interaction(PPI)network for the core target was constructed using Cytoscape 3.10.0(STRING confidence≥0.7).Key targets were identified using the CytoHubba plugin(MCC algorithm TOP10).GO/KEGG multi-level annotation was performed using the Metascape platform(P<0.01),constructing a multi-dimensional network model of traditional Chinese medicine,components,targets,and pathways.Results:A total of 30 prescriptions containing 167 Chinese medicinal herbs were included.The top 20 most frequently used herbs included licorice root(36 times),forsythia(25 times),magnolia bark(25 times),talc(25 times),and rhubarb(22 times).The properties of the herbs were mainly cold,warm,and neutral,and the flavors were mainly sweet,pun-gent,and bitter.The main focus of the study was on the lung,spleen,and stomach meridians.Association rule analysis revealed combinations such as cinnabar and musk.Cluster analysis identified three new drug combinations.The first combination(cinnabar,musk,rhinoceros horn,coptis,scutellaria,gardenia,rhubarb,and mirabilite)may be the core prescription for treating TI with Chinese medicine.Quercetin,kaempferol,and apigenin are key components of the core formula for treating Japanese encephalitis(TI).These components may act on targets such as RAC-α serine/amino acid-protein kinase(TNF),interleukin-6(IL6),protein kinase B(AKT1),interleukin-1B(IL1B),prostaglandin G/H synthase 2(PTGS2),tumor protein P53(TP53),and proto-oncogenes(JUN),and exert their effects through signaling pathways such as the cancer core pathway,AGE-RAGE-mediated glucose metabolism disorders,and lipid abnormal-ity-related pathology(atherosclerosis).Conclusion:Clinical treatment of Japanese encephalitis often uses traditional Chinese medicine with pungent and cooling properties to dispel pathogens,clear heat and detoxify,and open the orific-es to resolve phlegm.The core formula may exert its therapeutic effect on Japanese encephalitis by participating in the regulation of oxidative stress and inflammatory responses through signaling pathways such as the cancer core pathway,AGE-RAGE-mediated glucose metabolism disorders,and lipid abnormality-related pathology(atherosclerosis).关键词
蒲辅周/乙脑/数据挖掘/网络药理学Key words
PU Fuzhou/Japanese encephalitis/Data mining/Network pharmacology分类
医药卫生引用本文复制引用
董宇昂,林峙豪,叶海鹏,张冬阳,曹宇翔,张珺..蒲辅周治疗乙脑的用药规律及机制探讨[J].中医药临床杂志,2026,38(5):977-986,10.基金项目
国家自然科学基金委员会,面上项目(82174160) （82174160）
安徽省高等学校科学研究项目(2023AH050752) （2023AH050752）
安徽省高等学校科学研究项目(2024AH051038) （2024AH051038）
