在生-Eucommiae皮层中含有的烯基 aldehyde 通过调节 ALDOA 和 H3K23la 基因素乳化来增强骨关节炎治疗
Yijing Pan1, Xinyue Liu1, Shunshun Wang1
1Hubei Provincial Engineering Technology Research Center for Chinese Medicine Processing, School of Pharmacy, Hubei University of Chinese Medicine, Wuhan 430065, China; Hubei Shizhen Laboratory, Wuhan 430065, China.
概括
来自加工的Eucommiae皮层 (EC) 的烯酸 (CFA) 通过调节糖解和乳化来治疗骨关节炎 (OA). 一个机器学习模型有助于使用乳酸水平快速诊断OA.
科学领域:
- 生物化学 生物化学
- 药理学 药理学是指药理学的学科.
- 计算生物学 计算生物学
背景情况:
- 骨关节炎 (OA) 与代谢障碍有关,特别是乳酸积累.
- 传统的中国草药Eucommiae Cortex (EC) 用于治疗OA,其疗效通过加工生汁来增强.
- 在使用生汁进行EC加工后,烯酸化物 (CFA) 度显著增加.
研究的目的:
- 调查CFA是否通过向乳化来改善OA.
- 开发一种非侵入性机器学习模型,用于使用乳酸等代谢标记物的快速OA诊断.
主要方法:
- 非向的代谢学和网络药理学确定了CFA的抗炎作用.
- 机器学习,RNA测序和细胞内西部涂抹分析了CFA对炎症和H3基因素乳化的影响.
- 在体内研究中使用小鼠中介半月体 (DMM) 模型的不稳定性来评估CFA的有效性.
主要成果:
- CFA在体外和体外表现出显著的抗炎作用.
- 使用随机森林和夏普利添加剂扩展的机器学习模型将乳酸确定为关键的OA诊断代谢物.
- 代谢分析显示,CFA影响了体细胞中的糖解路径.
结论:
- 这项研究阐明了生汁对EC的协同作用和CFA的抗OA机制.
- 通过影响糖解和调节H3K23的乳化,CFA改善了OA.
- 一种非侵入性机器学习模型能够根据代谢标志物快速预测OA风险.
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