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基于机器学习的Artemisia argyi Folium评估策略:探索抗炎活性和化学成分之间的剂量效应关系

Wennan Nie¹, Shubo Guo¹, Cunhao Li¹

¹College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, China.

Journal of ethnopharmacology

|November 27, 2025

概括

来自不同地区的Artemisia argyi Folium (AAF) 显示出不同的抗炎作用. 六个关键化合物被确定为质量标志物,以确保AAF.

科学领域:

植物化学植物化学
药理学药理学是指药理学的学科.
传统中国医药传统中国医药

背景情况:

阿尔提米西亚阿尔吉叶 (AAF) 具有传统的药用.
了解AAF抗炎活性的区域差异对于草药应用至关重要.

研究的目的:

评估来自不同地理来源的AAF的抗炎活性.
确定质量标志物 (Q-标志物),反映AAF的区域化学和疗效差异.

主要方法:

来自九个地区的AAF中的十个化学成分的HPLC分析.
使用免疫光学,流细胞计和炎症因子测量 (TNF-α,IL-1β,IL-6,NO) 评估LPS诱导的RAW 264.7巨细胞中的抗炎作用.
使用PCR,双矩阵相关性和机器学习 (Lasso,Ridge,Elastic Net) 来将化学特征与抗炎活性联系起来.

主要成果:

六种化合物被确定为AAF的Q标志物.
在AAF的化学成分,地理来源和抗炎功效之间发现了显著的相关性.
为AAF建立了一个基于面板的综合质量控制框架.

结论:

该研究为AAF质量控制和临床使用提供了科学依据.
开发的综合战略提供了一种对天然药物的系统评估方法.

关键词:

阿尔特梅西亚阿尔吉叶 (Artemisia argyi Folium Folium) 是一种植物.剂量与效果的关系.机器学习机器学习质量评估质量评价质量评价这是一种抗炎药物.

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