C-type natriuretic peptide suppresses ventricular arrhythmias in rats with acute myocardial ischemia

This study aimed to investigate the effects of C-type natriuretic peptide (CNP) on ventricular arrhythmias in rats with acute myocardial ischemia (AMI). Forty male Sprague-Dawley rats were randomly divided into sham group (n = 10), AMI group (n = 15) and AMI + CNP group (n = 15). AMI model was induced by ligating the left anterior descending branch of the coronary artery, and CNP was pumped through the femoral vein starting 30 min before ischemia and continuing until 1 h after AMI. The occurrence of ventricular arrhythmias after ischemia and heart rate variability (HRV) were recorded and analyzed. The plasma norepinephrine level was detected at 15 min after AMI. Ventricular electrophysiological parameters including ventricular effective refractory period (ERP), ERP dispersion, ventricular action potential duration (APD) alternans and ventricular fibrillation threshold (VFT) were measured one hour after AMI. Then, the expressions of cyclic guanosine monophosphate in myocardial tissue and left stellate ganglion were examined. Compared to sham group, AMI significantly shortened the ERP, augmented ERP dispersion, elevated APD alternans cycle length, reduced VFT, and increased the incidence of ventricular arrhythmias. Moreover, AMI increased the sympathetic component of HRV, raised plasma norepinephrine levels, and decreased the cyclic guanosine monophosphate levels in myocardium and left stellate ganglion. All those changes were attenuated by CNP treatment. These findings suggest that CNP protected against ventricular arrhythmias in rats with AMI, potentially by inhibiting ischemia-induced cardiac sympathetic hyperactivity and cardiac electrophysiology instability.

A Novel and Effective Method for Congestive Heart Failure Detection and Quantification Using Dynamic Heart Rate Variability Measurement

Risk assessment of congestive heart failure (CHF) is essential for detection, especially helping patients make informed decisions about medications, devices, transplantation, and end-of-life care. The majority of studies have focused on disease detection between CHF patients and normal subjects using short-/long-term heart rate variability (HRV) measures but not much on quantification. We downloaded 116 nominal 24-hour RR interval records from the MIT/BIH database, including 72 normal people and 44 CHF patients. These records were analyzed under a 4-level risk assessment model: no risk (normal people, N), mild risk (patients with New York Heart Association (NYHA) class I-II, P1), moderate risk (patients with NYHA III, P2), and severe risk (patients with NYHA III-IV, P3). A novel multistage classification approach is proposed for risk assessment and rating CHF using the non-equilibrium decision-tree-based support vector machine classifier. We propose dynamic indices of HRV to capture the dynamics of 5-minute short term HRV measurements for quantifying autonomic activity changes of CHF. We extracted 54 classical measures and 126 dynamic indices and selected from these using backward elimination to detect and quantify CHF patients. Experimental results show that the multistage risk assessment model can realize CHF detection and quantification analysis with total accuracy of 96.61%. The multistage model provides a powerful predictor between predicted and actual ratings, and it could serve as a clinically meaningful outcome providing an early assessment and a prognostic marker for CHF patients.