Aims Hypertension is a significant risk for the development of left ventricular hypertrophy, diastolic dysfunction, followed by heart failure and sudden cardiac death. While therapy with sacubitril/valsartan (SV) reduces the risk of sudden cardiac death in patients with heart failure and systolic dysfunction, the effect on those with diastolic dysfunction remains unclear. We hypothesized that, in the animal model of hypertensive heart disease, treatment with SV reduces the susceptibility to ventricular arrhythmia. Methods and results Young adult female spontaneous hypertensive rats (SHRs) were randomly separated into three groups, which were SHRs, SHRs treated with valsartan, and SHRs treated with SV. In addition, the age-matched and weight-matched Wistar Kyoto rats were considered as controls, and there were 12 rats in each group. In vivo ventricular tachyarrhythmia induction and in vitro optical mapping were used to measure the inducibility of ventricular arrhythmias and to characterize the dynamic properties of electrical propagation. The level of small-conductance Ca 2+-activated potassium channel type 2 (KCNN2) was analysed in cardiac tissue. Compared with SHR with left ventricular hypertrophy, treatment with SV significantly improved cardiac geometry (relative wall thickness, 0.68 ± 0.11 vs. 0.76 ± 0.13, P < 0.05) and diastolic dysfunction (isovolumetric relaxation time, 59.4 ± 3.2 vs. 70.5 ± 4.2 ms, P < 0.05; deceleration time of mitral E wave, 46 ± 4.8 vs. 42 ± 3.8, P < 0.05). The incidence of induced ventricular arrhythmia was significantly reduced in SHR treated with SV compared with SHR (ventricular tachycardia, 1.14 ± 0.32 vs. 2.91 ± 0.5 episodes per 10 stimuli, P < 0.001; ventricular fibrillation, 1.72 ± 0.31 vs. 5.81 ± 0.42 episodes per 10 stimuli, P < 0.001). The prolonged action potential duration (APD) and increase of the maximum slope of APD restitution were observed in SHR, while the treatment of SV improved the arrhythmogeneity (APD, 37.12 ± 6.18 vs. 92.41 ± 10.71 ms at 250 ms pacing cycle length, P < 0.001; max slope 0.29 ± 0.01 vs. 1.48 ± 0.04, P < 0.001). These effects were strongly associated with down-regulation of KCNN2 (0.38 ± 0.07 vs. 0.74 ± 0.12 ng/ml, P < 0.001). The treatment of SV also decreased the level of N-terminal pro-B-type natriuretic peptide, cardiac bridging integrator-1, and intramyocardial fibrosis of SHR. Conclusions In conclusion, synergistic blockade of the neprilysin and the renin-angiotensin system by SV in SHRs results in KCNN2-associated electrical remodelling in ventricle, which stabilizes electrical dynamics and attenuates arrhythmogenesis.

Background Patients with rheumatoid arthritis are at twice the risk of ventricular arrhythmia and sudden cardiac death as the general population. We hypothesize that β‐blocker treatment of rheumatoid arthritis is antiarrhythmic by producing synergistic anticatecholaminergic and anti‐inflammatory effects. Methods and Results Collagen‐induced arthritis (CIA) was induced in Lewis rats by immunization with type II collagen in Freund's incomplete adjuvant. The treatment with propranolol (4 mg/kg) started on the first day of immunization. We evaluated the ventricular vulnerability to ventricular arrhythmia using in vivo programmed stimulation and performed ex vivo optical mapping to measure the electrical remodeling of the heart. The ventricular tissue was further processed for immunohistochemical staining and protein array analysis. The assessment of ventricular vulnerability showed that the number and duration of the induced ventricular arrhythmia episodes were increased in CIA rats, which were improved with propranolol treatment. The sympathovagal index and the plasma level of catecholamines significantly increased in CIA rats, whereas the use of propranolol attenuated sympathetic hyperactivity. In the optical mapping study, electrical remodeling, characterized by prolonged action potential duration, slow conduction velocity, and steepened action‐potential duration restitution, were noted in CIA rats and reversed in the propranolol‐treatment group. The propranolol treatment was associated with decreases in paw thickness, fewer inflammatory cell infiltrations in the heart, reduced levels of cardiac inflammatory cytokines, and less cardiac fibrosis as compared with the CIA group. Conclusions CIA increased ventricular arrhythmia vulnerability through sympathetic hyperinnervation and proarrhythmic ventricular electrophysiological remodeling. Treatment with propranolol in CIA rats was both anti‐inflammatory and antiarrhythmic.

Continuous blood pressure (BP) measurement is crucial for long-term cardiovascular monitoring, especially for prompt hypertension detection. However, most of the continuous BP measurements rely on the pulse transit time (PTT) from multiple-channel physiological acquisition systems that impede wearable applications. Recently, wearable and smart health electronics have become significant for next-generation personalized healthcare progress. This study proposes an intelligent single-channel bio-impedance system for personalized BP monitoring. Compared to the PTT-based methods, the proposed sensing configuration greatly reduces the hardware complexity, which is beneficial for wearable applications. Most of all, the proposed system can extract the significant BP features hidden from the measured bio-impedance signals by an ultra-lightweight AI algorithm, implemented to further establish a tailored BP model for personalized healthcare. In the human trial, the proposed system demonstrates the BP accuracy in terms of the mean error (ME) and the mean absolute error (MAE) within 1.7 ± 3.4 mmHg and 2.7 ± 2.6 mmHg, respectively, which agrees with the criteria of the Association for the Advancement of Medical Instrumentation (AAMI). In conclusion, this work presents a proof-of-concept for an AI-based single-channel bio-impedance BP system. The new wearable smart system is expected to accelerate the artificial intelligence of things (AIoT) technology for personalized BP healthcare in the future.

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Electrocardiogram (ECG)-based intelligent screening for systolic heart failure (HF) is an emerging method that could become a low-cost and rapid screening tool for early diagnosis of the disease before the comprehensive echocardiographic procedure. We collected 12-lead ECG signals from 900 systolic HF patients (ejection fraction, EF < 50%) and 900 individuals with normal EF in the absence of HF symptoms. The 12-lead ECG signals were converted by continuous wavelet transform (CWT) to 2D spectra and classified using a 2D convolutional neural network (CNN). The 2D CWT spectra of 12-lead ECG signals were trained separately in 12 identical 2D-CNN models. The 12-lead classification results of the 2D-CNN model revealed that Lead V6 had the highest accuracy (0.93), sensitivity (0.97), specificity (0.89), and f1 scores (0.94) in the testing dataset. We designed four comprehensive scoring methods to integrate the 12-lead classification results into a key diagnostic index. The highest quality result among these four methods was obtained when Leads V5 and V6 of the 12-lead ECG signals were combined. Our new 12-lead ECG signal–based intelligent screening method using straightforward combination of ECG leads provides a fast and accurate approach for pre-screening for systolic HF.