Mitral annular disjunction (MAD) is associated with malignant ventricular arrhythmias and progressive mitral regurgitation. We describe a 35-year-old man with a history of sudden cardiac death who presented with palpitations and recurrent ventricular arrhythmias. Remote transthoracic echocardiography showed no structural abnormality, with repeated transthoracic echocardiography demonstrating only mild mitral regurgitation. During this admission, transesophageal echocardiography revealed severe mitral regurgitation from Barlow disease with profound MAD. Port-access robotic mitral valve repair eliminated mitral regurgitation and corrected MAD. This case underscores that ventricular arrhythmias may precede valve disease, warranting high suspicion for MAD in young patients with arrhythmias and mild mitral regurgitation.

Hypertrophic cardiomyopathy (HCM) is a heritable cardiac disorder characterized by increased left ventricular (LV) wall thickness, often leading to heart failure (HF). HF represents a significant burden and is a challenging condition to diagnose in individuals with HCM. Cardiovascular magnetic resonance (CMR) imaging provides detailed insights into cardiac assessment, but its role in predicting new onset of HF symptoms in patients with HCM remains unknown. This study aimed to identify CMR predictors associated with the development of HF symptoms in individuals with HCM.

Malignant ventricular arrhythmias (MVA) post myocardial infarction (MI) is a leading cause of sudden death in affected patients. Hyperactivation of the sympathetic nervous system plays a crucial role in the onset and persistence of MVA after MI. While percutaneous stellate ganglion block with local anesthetics is an effective minimally invasive approach for transient MVA attenuation, achieving prolonged sympathetic blockade remains challenging. This study developed a novel injectable thermosensitive hydrogel-lignin-microsphere system for the sustained release of lidocaine (Gel-LMS Lido), aiming at long-term suppression of sympathetic nerve activity. Lignin-modified PLGA microspheres were used as the drug carrier, encapsulating lidocaine for sustained release. These microspheres were incorporated into a PLGA-PEG-PLGA hydrogel matrix, making up a triple-release system. The designed hydrogel-microsphere system demonstrated excellent injectability, controlled drug release profiles, prolonged pharmacological efficacy, robust in situ stability, and biocompatibility. In a rat MI model induced by left anterior descending coronary ligation, the materials were injected near the cervical stellate ganglion. Up to 2 weeks post-infarction, the sympathetic nerve activity was continuously suppressed and both the incidence and the duration of MVA were notably reduced in the Gel-LMS Lido group. Moreover, the Gel-LMS Lido was successfully extended to the treatment of sciatica, delivering a single injection with therapeutic effects lasting up to one week, highlighting the broad potential of present system for peripheral neuromodulation. In summary, this study is the first to demonstrate the use of a tailored Gel-LMS Lido triple-release system for cardiac neural modulation, significantly enhancing therapeutic outcomes in ischemic ventricular arrhythmias.

Sudden cardiac death after myocardial infarction (MI) remains a major clinical problem, partly driven by complex electromechanical feedback mechanisms that are not fully understood. In particular, the interplay between scar stiffness, border zone (BZ) remodeling, mechano-electric feedback (MEF) through stretch-activated channels (SACs), and cross-bridge formation, can strongly influence ventricular function and arrhythmogenic risk. In this study, we developed a 3D finite element model of human ventricular electromechanics coupled with a closed-loop circulation model to investigate the impact of scar size on cardiac function. Before addressing scar effects, we first analyzed the spatial resolution of the mesh to ensure reliable electromechanical predictions. Within the present electromechanical framework and mechanical discretization, spatial resolutions of approximately 1.0-1.3 mm were required to capture SAC-driven depolarizations in the BZ. Using this confirmed to be suitable resolution, we examined infarct volume fractions (IVFs) from 5-20% of the left ventricle (LV). Increasing IVF progressively impaired contractility and ejection fraction, with a nonlinear threshold emerging between 10% and 15% IVF in the primary models studied here, above which SACs elicited premature activations which disrupted sinus-driven dynamics, and produced irregular multi-beat PV-loop patterns. These findings highlight two key insights: spatial resolution is critical for capturing SAC-mediated feedback accurately, and scar extent strongly influences the severity of this feedback, with larger scars significantly altering ventricular dynamics. Together, this work provides mechanistic understanding of how scar enlargement can accelerate heart failure progression and arrhythmic risk through nonlinear MEF.

Cardiovascular diseases, especially arrhythmias, are a leading cause of death worldwide. This highlights the need for automated systems that can detect and diagnose these conditions early. This research introduces a deep learning model that identifies arrhythmias using electrocardiogram (ECG) signals. The model focuses on five main types of heartbeats: Normal (N), Left Bundle Branch Block (L), Right Bundle Branch Block (R), Atrial Premature Beat (A), and Premature Ventricular Contraction (V). The system uses Lead I signals from several databases, including MIT-BIH Arrhythmia, Supraventricular, INCART 12-lead, and Sudden Cardiac Death Holter. This provides more than 3.9 million training segments and 112,575 testing segments. The data is preprocessed by dividing it into fixed windows of 180 samples, scaling it using Min-Max normalization, and balancing the classes with the Synthetic Minority Over-sampling Technique. The model combines 1D Convolutional Neural Networks to extract spatial features and transformer layers to capture time-based patterns. It uses the Adam optimizer and includes dropout and batch normalization to enhance performance. The system achieves 99.99% accuracy, precision, and F1-score across all classes, which is better than the TN4 model and other top-performing models. The use of Convolutional Neural Networks and deep hybrid architectures improves the robustness of features. This model shows great potential for scalable and real-time arrhythmia detection and contributes to the advancement of AI-driven, personalized digital healthcare.

Long QT syndrome (LQTS) predisposes to syncope and sudden cardiac death. Type 5 LQTS, linked to KCNE1 variants, is rare. A teenage female presented with recurrent syncope. ECG showed QTc 485 ms. Genetic testing identified KCNE1 and RYR2 variants. Beta-blockers and ICD prevented events. The case highlights diagnostic and genotype-phenotype challenges.