Although cell-based therapies show potential antiarrhythmic effects that could be mediated by their paracrine action, the mechanisms and the extent of these effects were not deeply explored. We investigated the antiarrhythmic mechanisms of extracellular vesicles secreted by cardiosphere-derived cell extracellular vesicles (CDC-EVs) on the electrophysiological properties and gene expression profile of HL1 cardiomyocytes. HL-1 cultures were primed with CDC-EVs or serum-free medium alone for 48 h, followed by optical mapping and gene expression analysis. In optical mapping recordings, CDC-EVs reduced the activation complexity of the cardiomyocytes by 40%, increased rotor meandering, and reduced rotor curvature, as well as induced an 80% increase in conduction velocity. HL-1 cells primed with CDC-EVs presented higher expression of SCN5A, CACNA1C, and GJA1, coding for proteins involved in INa, ICaL, and Cx43, respectively. Our results suggest that CDC-EVs reduce activation complexity by increasing conduction velocity and modifying rotor dynamics, which could be driven by an increase in expression of SCN5A and CACNA1C genes, respectively. Our results provide new insights into the antiarrhythmic mechanisms of cell therapies, which should be further validated using other models.

Although Atrial Fibrillation (AF) is the most common cardiac arrhythmia, its early identification, diagnosis, and treatment is still challenging. Due to its heterogeneous mechanisms and risk-factors, targeting an individualized treatment of AF demands a large amount of patient data to identify specific patterns. Artificial Intelligence (AI) algorithms are particularly well suited for treating high-dimensional data, predicting outcomes and, eventually, optimizing strategies for patient management. The analysis of large patient samples combining different sources of information such as blood biomarkers, electrical signals and medical images opens a new paradigm for improving diagnostic algorithms. In this review, we summarize suitable AI techniques for this purpose. In particular, we describe potential applications for understanding the structural and functional bases of the disease, as well as for improving early noninvasive diagnosis, developing more efficient therapies, and predicting long-term clinical outcomes of AF patients.