Proximity effects, exchange bias and magnetic relaxation in γ-FeO nanoparticles.

Carbon-encapsulated γ-FeO nanoparticles (NPs) with emerging proximity effects were synthesized using a single-step solid-state pyrolysis at 750 °C. The morphology and size distribution of the NPs were investigated using high-resolution transmission and scanning electron microscopies revealing that the γ-FeO NPs, with an average diameter of 9 nm, are embedded in the amorphous porous carbon matrix. In addition, other trace phases (FeC and metallic-Fe) were also detected through X-ray absorption spectroscopy and Mössbauer spectrometry.

Artificial Intelligence in Cardiac Electrophysiology: Enhancing Mapping and Ablation Precision.

Artificial intelligence (AI) is rapidly reshaping cardiac electrophysiology (EP), offering new avenues for arrhythmia detection, procedural planning, and outcome prediction. This review synthesizes recent advances in AI applications across EP workflows, emphasizing model validation, clinical performance, and implementation challenges. Early studies employed internal cross-validation, while more recent work favors external and multicenter validation strategies, enhancing generalizability.

AI-Guided Decision Support in Acute Cardiac Care: From Chest Pain to STEMI.

Artificial intelligence (AI) is rapidly transforming the landscape of acute cardiac care, offering novel opportunities to enhance diagnostic accuracy, risk stratification, and clinical decision-making. This literature review explores the current and emerging applications of AI in managing acute cardiovascular conditions, including myocardial infarction, arrhythmias, and heart failure. Methods such as machine learning, deep learning, and natural language processing have demonstrated potential in analyzing electrocardiograms, imaging, electronic health records, and wearable data to support timely and individualized care.

Artificial Intelligence in Predicting Sudden Cardiac Death.

Sudden cardiac death (SCD) remains a significant global health challenge, with existing risk stratification tools such as left ventricular ejection fraction demonstrating limited predictive accuracy. Recent advancements in artificial intelligence (AI) have enabled the development of novel predictive models capable of integrating high-dimensional clinical, electrocardiographic, imaging, genetic, and wearable device data. This review examines the performance of various AI architectures-particularly convolutional neural networks and multimodal ensemble models-in improving SCD prediction and risk stratification.

Targeting Corin and Furin in Hypertension and Heart Failure: A New Therapeutic Frontier in Cardiovascular Therapeutics.

Corin and furin are protetic enzymes central to the activation of natriuretic peptides (NPs), which regulate cardiovascular homeostasis. Recent insights suggest that disruptions in the Corin-Furin axis-via genetic polymorphisms, aberrant post-translational modifications, or disease-associated downregulation-contribute to the pathogenesis of hypertension, heart failure, and myocardial fibrosis. This study examines current challenges in enzymatic stability, pharmacodynamics, and delivery of corin- and furin-based therapies, emphasizing translational barriers and the need for precision medicine.