The burden of atherosclerotic disease worldwide necessitates implementing the treatment of its risk factors. Among them, hypercholesterolemia has a central role. In addition to conventional small organic compounds and the recently introduced monoclonal antibodies, new technologies are arising such as the antisense oligonucleotides and small interfering RNAs (siRNAs) that operate upstream, blocking the mRNA translation of the proteins specifically involved in lipid metabolism. In this review, we briefly explain the mechanisms of action of these molecules and discuss the difficulties related to their in vivo use as therapeutical agents. We go over the oligonucleotides tested in clinical trials that could potentially revolutionize the care of patients by acting on proteins involved in the lipoprotein metabolism and regulation, namely: angiopoietin-like protein 3 (ANGPTL3); lipoprotein a (Lp(a)); apolipoprotein B (Apo B); apolipoprotein C III (Apo C-III); and proprotein convertase subtilisin–kexin type 9 (PCSK9). Finally, the differences between ASOs and siRNAs, their future possible clinical applications, and the role of Inclisiran, a siRNA direct against PCSK9 to reduce LDL-C, were reviewed in detail.

Artificial intelligence (AI) is being intensively applied to cardiology, particularly in diagnostics, risk prediction, treatment planning, and invasive procedures. While AI-driven advancements have demonstrated promise, their real-world implementation remains constrained by critical challenges. Current AI applications, such as electrocardiogram interpretation and automated imaging analysis, have improved diagnostic accuracy and workflow efficiency, yet generalizability, regulatory hurdles, and integration into existing clinical workflows remain major obstacles. Algorithmic bias and the lack of explainable AI further complicate widespread adoption, potentially leading to disparities in healthcare outcomes. In interventional cardiology, robotic-assisted percutaneous coronary intervention has emerged as a technological innovation, but comparative clinical evidence supporting its superiority (or even non-inferiority) over conventional approaches is still limited. Additionally, AI-based decision support systems in high-risk cardiovascular procedures require rigorous validation to ensure safety and reliability. Ethical considerations, including patient data security and region-specific regulatory frameworks, also pose significant barriers. Addressing these challenges requires interdisciplinary collaboration, robust external validation, and the development of transparent, interpretable AI models. This review provides a critical appraisal of the current role of AI in cardiology, emphasizing both its potential and its limitations, and outlines future directions to facilitate its responsible integration into clinical practice.

Acute hyperglycemia is a transient increase in plasma glucose level (PGL) frequently observed in patients with ST-elevation myocardial infarction (STEMI). The aim of this review is to clarify the molecular mechanisms whereby acute hyperglycemia impacts coronary flow and myocardial perfusion in patients with acute myocardial infarction (AMI) and to discuss the consequent clinical and prognostic implications. We conducted a comprehensive literature review on the molecular causes of myocardial damage driven by acute hyperglycemia in the context of AMI. The negative impact of high PGL on admission recognizes a multifactorial etiology involving endothelial function, oxidative stress, production of leukocyte adhesion molecules, platelet aggregation, and activation of the coagulation cascade. The current evidence suggests that all these pathophysiological mechanisms compromise myocardial perfusion as a whole and not only in the culprit coronary artery. Acute hyperglycemia on admission, regardless of whether or not in the context of a diabetes mellitus history, could be, thus, identified as a predictor of worse myocardial reperfusion and poorer prognosis in patients with AMI. In order to reduce hyperglycemia-related complications, it seems rational to pursue in these patients an adequate and quick control of PGL, despite the best pharmacological treatment for acute hyperglycemia still remaining a matter of debate.

Transcatheter aortic valve implantation (TAVI) requires thorough preprocedural planning with non-invasive imaging, including computed tomography (CT). The plethora of details obtained with thoraco-abdominal CT represents a challenge for accurate and synthetic decision-making. We devised and tested a comprehensive score suitable to summarize CT exams when planning TAVI. An original comprehensive scoring system (TAVI-CT score) was devised, including details on cardiac, aortic, iliac and femoral artery features. The score was applied to a prospectively collected series of patients undergoing TAVI at our institution, driving decision making on access and prosthesis choice. Different TAVI-CT score groups were compared in terms of procedural success, acute complications, and early clinical outcomes. We included a total of 200 undergoing TAVI between February 2020 and May 2021, with 74 (37.0%) having a low (0–2) TAVI-CT score, 50 (25.0%) having a moderate (3) TAVI-CT score, and 76 (38.0%) having a high (≥ 4) TAVI-CT score. Male gender was the only non-CT variable significantly associated with the TAVI-CT score (p = 0.001). As expected, access choice differed significantly across TAVI-CT scores (p = 0.009), as was device choice, with Portico more favored and Allegra less favored in the highest TAVI-CT score group (p = 0.036). Acute outcomes were similar in the 3 groups, including device and procedural success rates (respectively p = 0.717 and p = 1). One-month follow-up showed similar rates of death, myocardial infarction, stroke, and bleeding, as well as of a composite safety endpoint (all p > 0.05). However, vascular complications were significantly more common in the highest TAVI-CT score group (p = 0.041). The TAVI-CT score is a simple scoring system that could be routinely applied to CT imaging for TAVI planning, if the present hypothesis-generating findings are confirmed in larger prospective studies.