Huoxin pill protects verapamil-induced zebrafish heart failure through inhibition of oxidative stress-triggered inflammation and apoptosis.

Heart failure (HF) is a major and growing public health concern. Although advances in medical and surgical therapies have been achieved over the last decades, there is still no firmly evidence-based treatment with many traditional Chinese medicines (TCMs) for HF. Huoxin Pill (HXP), a TCM, has been widely used to treat patients with coronary heart disease and angina pectoris. However, the underlying molecular mechanism is poorly understood. In this study, using a verapamil-induced zebrafish HF model, we validated the efficacy and revealed the underlying mechanism of HXP in the treatment of HF. Zebrafish embryos were pretreated with different concentrations of HXP followed by verapamil administration, and we found that HXP significantly improved cardiac function in HF zebrafish, such as by effectively alleviating venous congestion and increasing heart rates. Mechanistically, HXP evidently inhibited verapamil-induced ROS and H2O2 production and upregulated CAT activity in HF zebrafish. Moreover, transgenic lines Tg(mpx:EGFP) and Tg(nfkb:EGFP) were administered for inflammation evaluation, and we found that neutrophil infiltration in HF zebrafish hearts and the activated NF-kB level could be reduced by HXP. Furthermore, HXP significantly downregulated the level of cell apoptosis in HF zebrafish hearts, as assessed by AO staining. Molecularly, RT‒qPCR results showed that pretreatment with HXP upregulated antioxidant-related genes such as gpx-1a and gss and downregulated the expression of the stress-related gene hsp70, proinflammatory genes such as tnf-α, il-6 and lck, and apoptosis-related indicators such as apaf1, puma and caspase9. In conclusion, HXP exerts a protective effect on verapamil-induced zebrafish HF through inhibition of oxidative stress-triggered inflammation and apoptosis.

Comparison of LASSO and random forest models for predicting the risk of premature coronary artery disease.

PURPOSE: With the change of lifestyle, the occurrence of coronary artery disease presents a younger trend, increasing the medical and economic burden on the family and society. To reduce the burden caused by this disease, this study applied LASSO Logistic Regression and Random Forest to establish a risk prediction model for premature coronary artery disease(PCAD) separately and compared the predictive performance of the two models. METHODS: The data are obtained from 1004 patients with coronary artery disease admitted to a third-class hospital in Liaoning Province from September 2019 to December 2021. The data from 797 patients were ultimately evaluated. The dataset of 797 patients was randomly divided into the training set (569 persons) and the validation set (228 persons) scale by 7:3. The risk prediction model was established and compared by LASSO Logistic and Random Forest. RESULT: The two models in this study showed that hyperuricemia, chronic renal disease, carotid artery atherosclerosis were important predictors of premature coronary artery disease. A result of the AUC between the two models showed statistical difference (Z = 3.47, P < 0.05). CONCLUSIONS: Random Forest has better prediction performance for PCAD and is suitable for clinical practice. It can provide an objective reference for the early screening and diagnosis of premature coronary artery disease, guide clinical decision-making and promote disease prevention.