医学
马森三色染色
再狭窄
纤维化
心肌纤维化
心脏病学
内科学
心肌梗塞
免疫印迹
冠状动脉
支架
动脉
化学
生物化学
基因
作者
Lulu Yan,Xiao‐Hong Wei,Qiu-Ping Shi,Chun‐Shui Pan,Kaiyin Li,Bin Zhang,Xingang Wang,B. Zheng,Mingxia Wang,Yan Li,Ping Huang,Jian Liu,Jing‐Yu Fan,Huan Li,Chuan‐She Wang,Ming Chen,Jing‐Yan Han
出处
期刊:Phytomedicine
[Elsevier]
日期:2022-08-23
卷期号:106: 154405-154405
被引量:4
标识
DOI:10.1016/j.phymed.2022.154405
摘要
Stent implantation has been increasingly applied for the treatment of obstructive coronary artery disease, which, albeit effective, often harasses patients by in-stent restenosis (ISR).The present study was to explore the role of compound Chinese medicine Cardiotonic Pills® (CP) in attenuating ISR-evoked myocardial injury and fibrosis.Chinese miniature pigs were used to establish ISR model by implanting obsolete degradable stents into coronary arteries. Quantitative coronary angiography (QCA) was performed to confirm the success of the model.CP was given at 0.2 g/kg daily for 30 days after ISR. On day 30 and 60 after stent implantation, the myocardial infarct and myocardial blood flow (MBF) were assessed. Myocardial histology was evaluated by hematoxylin-eosin and Masson's trichrome staining. The content of ATP, MPO, and the activity of mitochondrial respiratory chain complex Ⅳ were determined by ELISA. Western blot was performed to assess the expression of ATP5D and related signaling proteins, and the mediators of myocardial fibrosis.Treatment with CP diminished myocardial infarct size, retained myocardium structure, attenuated myocardial fibrosis, and restored MBF. CP ameliorated energy metabolism disorder, attenuated TGFβ1 up-regulation and reversed its downstream gene expression, such as Smad6 and Smad7, and inhibited the increased expression of MCP-1, PR S19, MMP-2 and MMP-9.CP effectively protects myocardial structure and function from ISR challenge, possibly by regulating energy metabolism via inactivation of RhoA/ROCK signaling pathway and inhibition of monocyte chemotaxis and TGF β1/Smads signaling pathway.
科研通智能强力驱动
Strongly Powered by AbleSci AI