Atorvastatin, etanercept and the nephrogenic cardiac sympathetic remodeling in chronic renal failure rats

BACKGROUND Chronic renal failure(CRF) patients are predisposed to arrhythmias, while the detailed mechanisms are unclear. We hypothesized the chronic inflammatory state of CRF patients may lead to cardiac sympathetic remodeling, increasing the incidence of ventricular arrhythmia(VA) and sudden cardiac death. And explored the role of atorvastatin and etanercept in this process.METHODS A total of 48 rats were randomly divided into sham operation group(Sham group), CRF group, CRF + atorvastatin group(CRF + statin group), and CRF + etanercept group(CRF + rhTNFR-Fcgroup). Sympathetic nerve remodeling was assessed by immunofluorescence of growth-associated protein 43(GAP-43) and tyrosine hydroxylase positive area fraction. Electrophysiological testing was performed to assess the incidence of VA by assessing the ventricular effective refractory period and ventricular fibrillation threshold. The levels of tumor necrosis factor-alpha(TNF-α) and interleukin-1beta were determined by Western blotting and enzyme-linked immunosorbent assay.RESULTS Echocardiogram showed that compared with the Sham group, left ventricular end-systolic diameter and ventricular weight/body weight ratio were significantly higher in the CRF group. Hematoxylin-eosin and Masson staining indicated that myocardial fibers were broken, disordered, and fibrotic in the CRF group. Western blotting, enzyme-linked immunosorbent assay,immunofluorescence and electrophysiological examination suggested that compared with the Sham group, GAP-43 and TNF-α proteins were significantly upregulated, GAP-43 and tyrosine hydroxylase positive nerve fiber area was increased, and ventricular fibrillation threshold was significantly decreased in the CRF group. The above effects were inhibited in the CRF + statin group and the CRF + rhTNFR-Fcgroup.CONCLUSIONS In CRF rats, TNF-α was upregulated, cardiac sympathetic remodeling was more severe, and the nephrogenic cardiac sympathetic remodeling existed. Atorvastatin and etanercept could downregulate the expression of TNF-α or inhibit its activity, thus inhibited the above effects, and reduced the occurrence of VA and sudden cardiac death.

Impact of old age on clinical and angiographic characteristics of coronary artery spasm as assessed by acetylcholine provocation test

Background Smoking and other risk factors have been well known as important factors of variant angina or coronary artery spasm （CAS）. However, clinical features related to age on coronary artery spasm have been rarely evaluated. Methods We evaluated 3155 consecutive patients with insignificant coronary artery lesion. Patients underwent Acetylcholine （Ach） provocation test for induction of CAS. CAS was defined as 〉 70% luminal narrowing of coronary arteries during Ach provocation test. The results of Ach provocation test were compared among age groups; 〈 45 years （Group 1）, 45-54 years （Group 2）, 55-64 years （Group 3）, and 〉_ 65 years （Group 4）. Results Older patients had higher incidence of hypertension, diabetes, but lower incidence rate of current smoking, male sex compared with younger patients. Positive Ach provocation test finding was frequently showed with aging （47.36% vs. 58.3% vs. 62.6% vs. 61.5%; P 〈 0.001）. Multivariate logistic analysis showed that age, male, and myocardial bridge were independent predictors of CAS induced by Ach provocation test. Conclusion Our present study showed that old age was independent predictor for Ach-induced significant coronary artery spasm.

Electrocardiogram-based artificial intelligence for the diagnosis of heart failure:a systematic review and meta-analysis

BACKGROUND The electrocardiogram(ECG)is an inexpensive and easily accessible investigation for the diagnosis of cardiovascular diseases including heart failure(HF).The application of artificial intelligence(AI)has contributed to clinical practice in terms of aiding diagnosis,prognosis,risk stratification and guiding clinical management.The aim of this study is to systematically review and perform a meta-analysis of published studies on the application of AI for HF detection based on the ECG.METHODS We searched Embase,PubMed and Web of Science databases to identify literature using AI for HF detection based on ECG data.The quality of included studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2(QUADAS-2)criteria.Random-effects models were used for calculating the effect estimates and hierarchical receiver operating characteristic curves were plotted.Subgroup analysis was performed.Heterogeneity and the risk of bias were also assessed.RESULTS A total of 11 studies including 104,737 subjects were included.The area under the curve for HF diagnosis was 0.986,with a corresponding pooled sensitivity of 0.95(95%CI:0.86–0.98),specificity of 0.98(95%CI:0.95–0.99)and diagnostic odds ratio of 831.51(95%CI:127.85–5407.74).In the patient selection domain of QUADAS-2,eight studies were designated as high risk.CONCLUSIONS According to the available evidence,the incorporation of AI can aid the diagnosis of HF.However,there is heterogeneity among machine learning algorithms and improvements are required in terms of quality and study design.