Role of Machine Learning and Artificial Intelligence in Arrhythmias and Electrophysiology.

Machine learning (ML), a subset of artificial intelligence (AI) centered on machines learning from extensive datasets, stands at the forefront of a technological revolution shaping various facets of society. Cardiovascular medicine has emerged as a key domain for ML applications, with considerable efforts to integrate these innovations into routine clinical practice. Within cardiac electrophysiology, ML applications, especially in the automated interpretation of electrocardiograms, have garnered substantial attention in existing literature. However, less recognized are the diverse applications of ML in cardiac electrophysiology and arrhythmias, spanning basic science research on arrhythmia mechanisms, both experimental and computational, as well as contributions to enhanced techniques for mapping cardiac electrical function and translational research related to arrhythmia management. This comprehensive review delves into various ML applications within the scope of this journal, organized into 3 parts. The first section provides a fundamental understanding of general ML principles and methodologies, serving as a foundational resource for readers interested in exploring ML applications in arrhythmia research. The second part offers an in-depth review of studies in arrhythmia and electrophysiology that leverage ML methodologies, showcasing the broad potential of ML approaches. Each subject is thoroughly outlined, accompanied by a review of notable ML research advancements. Finally, the review delves into the primary challenges and future perspectives surrounding ML-driven cardiac electrophysiology and arrhythmias research.

Individual and interactive effects of amino acid and paracetamol on growth, physiological and biochemical aspects of Brassica napus L. under drought conditions.

Drought stress poses a significant threat to Brassica napus (L.), impacting its growth, yield, and profitability. This study investigates the effects of foliar application of individual and interactive pharmaceutical (Paracetamol; 0 and 250 mg L-1) and amino acid (0 and 4 ml/L) on the growth, physiology, and yield of B. napus under drought stress. Seedlings were subjected to varying levels of drought stress (100% field capacity (FC; control) and 50% FC). Sole amino acid application significantly improved chlorophyll content, proline content, and relative water contents, as well as the activities of antioxidative enzymes (such as superoxide dismutase and catalase) while potentially decreased malondialdehyde and hydrogen peroxide contents under drought stress conditions. Pearson correlation analysis revealed strong positive correlations between these parameters and seed yield (R2 = 0.8-1), indicating their potential to enhance seed yield. On the contrary, sole application of paracetamol exhibited toxic effects on seedling growth and physiological aspects of B. napus. Furthermore, the combined application of paracetamol and amino acids disrupted physio-biochemical functions, leading to reduced yield. Overall, sole application of amino acids proves to be more effective in ameliorating the negative effects of drought on B. napus.