目的：利用网络药理学和分子对接技术探讨黄精治疗糖尿病的潜在活性成分及作用机制。方法：通过TCMSP中草药系统药理学平台和DisGeNET数据库、NCB数据库、TTD数据库分别获取药物的化学成分、作用靶点及疾病的相关靶点；利用Cytoscape 3.8.0软件对数据进行药物-活性成分-作用靶点的网络关系图的构建；使用String数据库构建PPI网络模型；运用Metascape平台进行GO和KEGG功能分析；通过Pubchem数据库、PDB数据库、Chem Bio3D Ultra 14.0软件、PyMOL 2.4.0软件和Auto Dock Tools 1.5.6软件对进行分子对接。结果：共筛选出黄精活性成分8个，成分作用靶点75个，黄精与糖尿病共有靶点共29个；主要活性成分为DFV、β-谷甾醇、薯蓣皂苷元和黄芩素等，从PPI网络中得到核心靶点为AKT1、TP53、HIF1A等；KEGG富集有癌症、肝炎、lipid and atherosclerosis等多条信号通路；分子对接结果显示，关键活性成分与核心靶点均能自由结合，β-谷甾醇与AKT1有较强的相互作用。结论：黄精治疗糖尿病具有多成分、多靶点、多通路的特点，其作用机制可能与癌症、肝炎、lipid and atherosclerosis等多条信号通路有关。
Huangjing(Polygonatum sibiricum) is a dual-purpose natural plant used extensively in traditional Chinese medicine and folk herbal remedies for treating various diseases. Its rhizomes contain rich chemical components a...
Huangjing(Polygonatum sibiricum) is a dual-purpose natural plant used extensively in traditional Chinese medicine and folk herbal remedies for treating various diseases. Its rhizomes contain rich chemical components and exhibit multiple pharmacological activities, which has garnered increasing attention in recent years. Modern research has revealed that Huang Jing contains numerous beneficial nutrients for human health and has demonstrated therapeutic potential in the treatment of infectious diseases, diabetes, hyperlipidemia, osteoporosis, and more. This article focuses on Huang Jing as the research subject, providing an exposition of its chemical composition, nutritional value,pharmacological effects, and underlying mechanisms in recent years. This deeper understanding of its pharmacology and clinical applications aims to provide a solid theoretical basis for comprehensive future development of Huang Jing.
中英对照中医药术语数据集基于人民卫生出版社(PMPH)制定的《中医英语术语(内部草案)》、世界卫生组织(WHO)制定的《WHO International Standard Terminologies on Traditional Medicine in the Western Pacific Region》和世界中医药...
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中英对照中医药术语数据集基于人民卫生出版社(PMPH)制定的《中医英语术语(内部草案)》、世界卫生组织(WHO)制定的《WHO International Standard Terminologies on Traditional Medicine in the Western Pacific Region》和世界中医药学会联合会(WFCMS)制定的《International Standard Chinese-English Basic Nomenclature of Chinese Medicine》3个权威术语标准整合而成,旨在促进中医药术语标准化和中医药国际交流。本数据集通过Python pandas包及OCR技术将数据进行采集、清洗、整理、合并,最终分为56类,共整理数据16189条,经合并为8975条。本数据集促进了中医术语的规范化,方便了学术交流和中医的继承发扬,同时有助于中医药信息化建设。
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