The spontaneous rhythmic action potentials generated by the sinoatrial node (SAN), the primary pacemaker in the heart, dictate the regular and optimal cardiac contractions that pump blood around the body. Although the heart rate of humans is substantially slower than that of smaller experimental animals, current perspectives on the biophysical mechanisms underlying the automaticity of sinoatrial nodal pacemaker cells (SANCs) have been gleaned largely from studies of animal hearts. Using human SANCs, we demonstrated that spontaneous rhythmic local Ca²⁺ releases generated by a Ca²⁺ clock were coupled to electrogenic surface membrane molecules (the M clock) to trigger rhythmic action potentials, and that Ca²⁺–cAMP–protein kinase A (PKA) signaling regulated clock coupling. When these clocks became uncoupled, SANCs failed to generate spontaneous action potentials, showing a depolarized membrane potential and disorganized local Ca²⁺ releases that failed to activate the M clock. β-Adrenergic receptor (β-AR) stimulation, which increases cAMP concentrations and clock coupling in other species, restored spontaneous, rhythmic action potentials in some nonbeating “arrested” human SANCs by increasing intracellular Ca²⁺ concentrations and synchronizing diastolic local Ca²⁺ releases. When β-AR stimulation was withdrawn, the clocks again became uncoupled, and SANCs reverted to a nonbeating arrested state. Thus, automaticity of human pacemaker cells is driven by a coupled-clock system driven by Ca²⁺-cAMP-PKA signaling. Extreme clock uncoupling led to failure of spontaneous action potential generation, which was restored by recoupling of the clocks. Clock coupling and action potential firing in some of these arrested cells can be restored by β-AR stimulation–induced augmentation of Ca²⁺-cAMP-PKA signaling.

Coronavirus disease (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is rapidly spreading across the globe. The clinical spectrum of SARS-CoV-2 pneumonia requires early detection and monitoring, within a clinical environment for critical cases and remotely for mild cases, with a large spectrum of symptoms. The fear of contamination in clinical environments has led to a dramatic reduction in on-site referrals for routine care. There has also been a perceived need to continuously monitor non-severe COVID-19 patients, either from their quarantine site at home, or dedicated quarantine locations (e.g. hotels). In particular, facilitating contact tracing with proximity and location tracing apps was adopted in many countries very rapidly. Thus, the pandemic has driven incentives to innovate and enhance or create new routes for providing healthcare services at distance. In particular, this has created a dramatic impetus to find innovative ways to remotely and effectively monitor patient health status. In this paper, we present a review of remote health monitoring initiatives taken in 20 states during the time of the pandemic. We emphasize in the discussion particular aspects that are common ground for the reviewed states, in particular the future impact of the pandemic on remote health monitoring and consideration on data privacy.

Deep learning models for detecting episodes of atrial fibrillation (AF) using rhythm information in long-term ambulatory ECG recordings have shown high performance. However, the rhythm-based approach does not take advantage of the morphological information conveyed by the different ECG waveforms, particularly the f-waves. As a result, the performance of such models may be inherently limited. To address this limitation, we have developed a deep learning model, named RawECGNet, to detect episodes of AF and atrial flutter (AFl) using the raw, single-lead ECG. We compare the generalization performance of RawECGNet on two external data sets that account for distribution shifts in geography, ethnicity, and lead position. RawECGNet is further benchmarked against a state-of-the-art deep learning model, named ArNet2, which utilizes rhythm information as input. Using RawECGNet, the results for the different leads in the external test sets in terms of the F1 score were 0.91-0.94 in RBDB and 0.93 in SHDB, compared to 0.89-0.91 in RBDB and 0.91 in SHDB for ArNet2. The results highlight RawECGNet as a high-performance, generalizable algorithm for detection of AF and AFl episodes, exploiting information on both rhythm and morphology.

Cardioembolic stroke accounts for over 20% of ischemic strokes and is associated with worse outcomes than other types of strokes. Atrial fibrillation (AF) is the most common risk factor for cardioembolic stroke. In this narrative review, we present an update about cardioembolic stroke mainly related to AF and atrial cardiopathy. Direct oral anticoagulants (DOACs) have revolutionized stroke prevention in patients with AF; however, their efficacy in preventing recurrent embolic stroke of unknown source remains uncertain. Various cardiac monitoring methods are used to detect AF, which is crucial for preventing stroke recurrence. DOACs are preferred over warfarin for AF-related stroke prevention; however, the timing of initiation after acute ischemic stroke is debated. Resuming anticoagulation after intracerebral hemorrhage in AF patients requires careful assessment of the risks. While catheter ablation may reduce the incidence of cardiovascular events, its effect on stroke prevention is unclear, especially in heart failure patients. Atrial cardiopathy is the emerging cause of embolic stroke of unknown source, which indicates atrial structural and functional disorders that can precede AF. Future research should focus on refining stroke risk prediction models, optimizing AF detection, understanding the roles of ablation and anticoagulation in stroke prevention, and establishing atrial cardiopathy as a therapeutic target, which could significantly reduce the burden of stroke.

Despite the high accuracy of AI-based automated analysis of 12-lead ECG images for classification of cardiac conditions, clinical integration of such tools is hindered by limited interpretability of model recommendations. We aim to demonstrate the feasibility of a generic, clinical resource interpretability tool for AI models analyzing digitized 12-lead ECG images. To this end, we utilized the sensitivity of the Jacobian matrix to compute the gradient of the classifier for each pixel and provide medical relevance interpretability. Our methodology was validated using a dataset consisting of 79,226 labeled scanned ECG images, 11,316 unlabeled and 1807 labeled images obtained via mobile camera in clinical settings. The tool provided interpretability for both morphological and arrhythmogenic conditions, highlighting features in terms understandable to physician. It also emphasized significant signal features indicating the absence of certain cardiac conditions. High correlation was achieved between our method of interpretability and gold standard interpretations of 3 electrophysiologists.

Background: Agents with α-2 adrenoreceptor (AR) agonistic action have reportedly suppressed tachyarrhythmias. Methods and Results: We hypothesized that α-2 AR agonists would have an inhibitory effect on abnormal repolarization-related ventricular tachyarrhythmias (VTs). To test this hypothesis, the effects of 2 clinically available α-2 AR agonists (dexmedetomidine and clonidine) on the occurrence of VTs were assessed in a methoxamine-sensitized rabbit model of acquired long QT syndrome (Study 1: n=45). In control rabbits, administration of methoxamine and nifekalant almost invariably caused VTs (14/15). In contrast, incidence of VT significantly decreased during the treatment with dexmedetomidine (1μg·kg–1·min–1: 5/12 [P<0.01 vs. control]) or with clonidine (33.3μg·kg–1·min–1: 10/18 [P<0.01]). To verify that VTs in this animal model are triggered by early afterdepolarization (EAD), the monophasic action potential on the left ventricular surface was recorded in 28 open-chest rabbits (Study 2). EAD-like hump was less frequently detected during treatment with clonidine or dexmedetomidine (2/14) than in saline-treated rabbits (9/10, P<0.005). Presence of a hump was significantly related to the advent of VTs (P<0.05). Conclusions: Agents with α-2 AR agonistic action have an inhibitory effect on VTs in a rabbit model of long QT syndrome. Alpha-2 AR agonists, especially dexmedetomidine, may be a therapeutic choice for abnormal repolarization-related VTs that are resistant to conventional treatment. (Circ J 2012; 76: 2343–2347)