scholarly journals Traditional Chinese medicine enhances myocardial metabolism during heart failure

2022 ◽  
Vol 146 ◽  
pp. 112538
Author(s):  
Wang Shao-mei ◽  
Ye Li-fang ◽  
Wang Li-hong
Medicine ◽  
2020 ◽  
Vol 99 (30) ◽  
pp. e21091
Author(s):  
Hui Wang ◽  
Jun Zhang ◽  
Chun-fang Shi ◽  
Jing Jia ◽  
Zhi-min Zhang ◽  
...  

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Hui Guan ◽  
Guo-Hua Dai ◽  
Wu-Lin Gao ◽  
Xue Zhao ◽  
Zhen-Hao Cai ◽  
...  

Objective. This study aimed to construct a 5-year survival prediction model of coronary heart disease (CHD) induced chronic heart failure (CHF), which is supported by the traditional Chinese medicine (TCM) factor, and to verify the model. Methods. Inpatients from January 1, 2012, to December 31, 2017, in seven hospitals in Shandong Province were studied. The random number table was used to randomly divide the seven hospitals into two groups (training set and verification set). In the training set, the least absolute shrinkage selection operator regression was first used to screen the independent variables. Logistic regression was then applied to construct a survival prediction model. The following nomogram visualizes the prediction model results. Finally, C-indices, calibration curves, and decision curves were used to discriminate and calibrate the established model and evaluate its practicability in the clinic. Bootstrap resampling and the verification set were used for internal and external verification, respectively. Results. A total of 424 eligible patients were included in the model construction and verification. In this 5-year survival prediction model of patients with CHF induced by CHD, eight independent predictors were included. The series of C-indices for the training set, bootstrap resamples, and verification set was 0.885, 0.867, and 0.835, respectively, demonstrating the credibility of our model. Additionally, the receiver operating characteristic curve, calibration curve, and clinical decision curve analysis of the training and verification sets showed that this 5-year survival prediction model was good in discrimination, calibration, and clinical practicability. Conclusion. This work highlights eight independent factors affecting 5-year mortality in patients with CHF induced by CHD after discharge and further helps reallocate medical resources rationally by precisely identifying high-risk groups. The constructed prediction model not only plays a credible role in prediction but also demonstrates TCM intervention as a protective factor for the 5-year death of patients with CHF induced by CHD, thereby advancing the use of TCM in CHF.


2021 ◽  
Vol 5 (3) ◽  
Author(s):  
Feifei Lei ◽  
Mingjun Zhao ◽  
Haifang Wang ◽  
Chao Pan ◽  
Xiaoya Shi

Objective: To explore the target and mechanism of Astragalus membranaceus, poria, salvia miltiorrhiza and semen leiocarpa in the treatment of heart failure by network pharmacology. Methods: The active components of traditional Chinese medicine and the target of heart failure were screened by multi-platform, and the standard gene was transformed by Uniprot. CytoCasp 3.6.1 was used to draw the network diagram of traditional Chinese medicine - component - target. Go and KEGG analysis were performed by Metascape. Results: A total of 36 predictive target sites of Radix Astragalus, Fuling poria, Salvia miltiorrhiza and Draba nemorosa were screened for treatment of heart failure, mainly involving nerve and factor pathways: ADRB2, ADRA1B and AChE. Cancer pathway: TP53, TNF; Pathways of inflammation: IL1B, PTSG2, PTSG1; Sex hormone pathway: ESR1, AR, PGR; Others: SCN5A, HIF1A, etc. The results of GO and KEGG enrichment suggested that the treatment of heart failure with the top four drugs involved cancer pathway, calcium signaling pathway, HIF-1 signaling pathway, and involved in blood circulation, cell proliferation and other processes. Conclusion: This study combines the pharmacological studies of Chinese medicine and western medicine to reveal the mechanism of multi-target and multi-channel regulation of body balance in Chinese medicine treatment.


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