期刊文献+

基于文献挖掘探析肝癌前病变的中医临床用药规律

Exploring the medication rules of hepatic precancerous lesions based on data mining
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摘要 目的收集并整理中医药治疗肝癌前病变临床用药经验的文献,通过分析其组方规律,归纳总结疾病特点和治疗思路,为该病的临床中医药治疗在遣方用药、经验加减上提供参考。方法经过系统性检索中国知网、维普中文科技期刊数据库、万方数据库、中国生物医学文献数据库、PubMed、Web of Science、EMBASE、Cochrane等8个电子数据库,纳入自建库起至2023年12月收录的中医药治疗肝癌前病变的临床研究及个人经验类文献,提取符合纳入标准的方药信息,基于“中医药数据挖掘系统TCM Miner”组成用药记录数据库来统计药频及性味归经,使用SPSS 25.0统计软件进行聚类分析,通过SPSS Modeler 14.1的Apriori模块完成关联规则分析,最后应用SPSS Modeler网络模块来完成对高频药物的复杂网络分析。结果最终达到标准纳入研究的共34篇文献,其中含有34首中药复方,共110味药物,涉及15类功效,总计291药物频次。频数分析显示,药物词频前8位的中药为黄芪、白术、丹参、白花蛇舌草、鳖甲、莪术、半枝莲、郁金;在“四气”中所占比重前3位的药物排名依次为寒、温、平;“五味”中所占比重前3位排名依次为苦、甘、辛;由多到少药物在归经频次中前4位排名依次为肝、脾、肾、肺。Apriori关联规则分析显示核心药对组合为“白花蛇舌草+半枝莲”;复杂网络分析得到核心处方为白术、白芍、白花蛇舌草、茯苓、莪术、郁金、鳖甲、黄芪、三棱、丹参、半枝莲。结论中医治疗肝癌前病变以健脾养肝、活血解毒为组方首选,配伍行气、清热、化瘀、软坚散结等药物。 Objective To collect and sort out the literature on the clinical medication experience of traditional Chinese medicine(TCM)in the treatment of hepatic precancerous lesions,and to summarize the characteristics of the disease and the treatment ideas by analyzing the rules of its formula,in order to provide instructions for the treatment of precancerous lesions of hepatocellular carcinoma with TCM in terms of medication and experience addition and subtraction.Methods CNKI,VIP,WanFang Data,CBM,PubMed,Web of Science,EMBASE,Cochrane databases were systematically searched,the clinical research and personal experience literature of TCM in the treatment of hepatic precancerous lesions from the establishment of the databases to December 2023 was included,and the prescription information that met the inclusion criteria was extracted.Based on“Traditional Chinese Medicine data Miner(TCM Miner)”,the medication database was established to analyze the frequency,properties and flavours of the drugs and meridian tropism.The cluster analysis were conducted by SPSS 25.0 statistical software,and the association rules were analyzed by using Apriori algorithm of SPSS Modeler 14.1,and the complex network analysis of high-frequency drugs was conducted by network module of SPSS Modeler.Results 34 researches including 34 formulas,110 drugs with 291 frequencies and 15 drug efficacy were included.Frequency analysis showed that the top eight types of frequently applied drugs were milkvetch root,largehead atractylodes rhizome,radix salviae miltiorrhhizae,hedyotis,carapax trionycis,zedorgy rhidoray,barbated skullcup herb,redix curcumae.The top three properties in the"four qi"were cold,warm and mild.The top three tastes of the"five flavors"were bitterness,sweetness and acridity.The top four in the frequency of meridian tropism were liver,spleen,kidney and lung.Apriori correlation analysis showed that the core drug pairs was"Hedyotis diffusa+Atractylodes".Complex network analysis showed that the core prescriptions were composed of Atractylodes,Radix Paeoniae Alba,Hedyotis diffusa,Poria cocos,Rhizoma Curcumae,Radix Curcumae Aromaticae,Trionycis Carapax,Radix Astragali,Sparganium stoloniferum,Salvia miltiorrhiza,Scutellaria barbata.Conclusion The TCM treatment of hepatic precancerous lesions should be based on tonifying spleen and nourishing liver,promoting blood circulation and detoxification,and combined with drugs that can promote qi-circulation,clear heat,remove blood-stasis,soften hard masses and resolve hard masses.
作者 苟思媛 杨先照 王龙珠 田羽佳 茹淑瑛 GOU Siyuan;YANG Xianzhao;WANG Longzhu;TIAN Yujia;RU Shuying(Department of Gastroenterology,Dongzhimen Hospital Affiliated to Beijing University of Chinese Medicine,Beijing 100700,China;Department of Infectious Diseases,Dongzhimen Hospital Affiliated to Beijing University of Chinese Medicine,Beijing 100700,China;Department of Gastroenterology,Tongzhou District,Dongzhimen Hospital Affiliated to Beijing University of Chinese Medicine,Beijing 101100,China)
出处 《药学前沿》 CAS 2024年第9期80-89,共10页 China Pharmacist
基金 国家自然科学基金青年科学基金项目(81603555)。
关键词 肝癌 癌前病变 中医药 数据挖掘 用药规律 黄芪 白术 丹参 Hepatocellular carcinoma Precancerous lesions Traditional Chinese medicine Data mining Medication rule Milkvetch root Largehead atractylodes rhizome Radix salviae miltiorrhhizae
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