从Nelumbinis Receptaculum中发现血静成分组合,使用双重机器学习频谱效应分析
Qing Xu1, Wen-Shu Zou1, Hui Li2
1Jiangxi Province Key Laboratory of Traditional Chinese Medicine Pharmacology, Institute of Traditional Chinese Medicine Health Industry, China Academy of Chinese Medical Sciences, Nanchang, 330115, China; Jiangxi Health Industry Institute of Traditional Chinese Medicine, Nanchang, 330115, China.
Journal of ethnopharmacology
|December 4, 2025
概括
双重机器学习识别了来自Nelumbinis Receptaculum (NR) 的六种化合物组合,具有显著的静血效应. 这一发现有助于理解NRNR.
科学领域:
- 药理学和植物化学
- 计算化学和数据科学计算化学和数据科学
背景情况:
- 传统上 Nelumbinis Receptaculum (NR) 被用作各种出血情况的止血剂.
- 负责NR血静功效的特定化合物及其组合在很大程度上仍未被描述.
研究的目的:
- 确定Nelumbinis Receptaculum (NR) 传统的静血性质的物质基础.
- 使用基于双重机器学习的频谱效应关系 (DML-SER) 选静血组分组合 (HCC).
主要方法:
- 使用超高性能液体染色学-并联质谱法对NR提取物进行化学分析.
- 在simvastatin诱导的肠道出血斑马鱼模型中的静血评价.
- 使用人工神经网络 (ANN) 和支持向量回归 (SVR) 结合灰色关系分析的频谱效应关系建模.
主要成果:
- 在NR提取物中发现了266种化合物,其中42种被量化;NR提取物在斑马鱼中显示出显著的静血活性.
- 双机器学习模型 (ANN/SVR) 有效地建模了频谱效应关系,性能优于部分最小平方回归.
- 一种六种化合物的组合 (洛甲基,甲基,核,高酸,异基,奎尔丁-3-O-葡萄化物) 显示出与整个NR提取物相比的静血指标.
结论:
- 在Nelumbinis Receptaculum中使用DML-SER.确定了六种化合物的潜在静血成分组合 (HCC).
- 这一发现为进一步的机制研究,质量控制和NR的临床应用提供了科学基础.
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