The purpose of this manuscript is to review the effects of sodium-glucose cotransport protein 2 inhibitors (SGLT2is) in patients with chronic kidney disease according to basic mechanisms, current recommendations, and future perspectives. Based on growing evidence from randomized, controlled trials, SGLT2is have proven their benefit on cardiac and renal adverse complications, and their indications expanded into the following five categories: glycemic control, reduction in atherosclerotic cardiovascular disease (ASCVD), heart failure, diabetic kidney disease, and nondiabetic kidney disease. Although kidney disease accelerates the progression of atherosclerosis, myocardial disease, and heart failure, so far, no specific drugs were available to protect renal function. Recently, two randomized trials, the DAPA-CKD and EMPA-Kidney, demonstrated the clinical benefit of the SGLT2is dapagliflozin and empagliflozin in improving the outcome in patients with chronic kidney disease. For the consistently positive results in cardiorenal protection, the SGLT2i represents an effective treatment to reduce the progression of kidney disease or death from cardiovascular causes in patients with and without diabetes mellitus.

Artificial intelligence (AI) applied to cardiovascular disease (CVD) is enjoying great success in the field of scientific research. Electrocardiograms (ECGs) are the cornerstone form of examination in cardiology and are the most widely used diagnostic tool because they are widely available, inexpensive, and fast. Applications of AI to ECGs, especially deep learning (DL) methods using convolutional neural networks (CNNs), have been developed in many fields of cardiology in recent years. Deep learning methods provide valuable support for rapid ECG interpretation, demonstrating a diagnostic capability overlapping with specialists in the diagnosis of CVD by a classical analysis of macroscopic changes in the ECG trace. Through photoplethysmography, wearable devices can obtain single-derivative ECGs for the recognition of AI-diagnosed arrhythmias. In addition, CNNs have been developed that recognize no macroscopic electrocardiographic changes and can predict, from a 12-lead ECG, atrial fibrillation, even from sinus rhythm; left and right ventricular function; hypertrophic cardiomyopathy; acute coronary syndromes; or aortic stenosis. The fields of application are many, but numerous are the limitations, mainly associated with the reliability of the acquired data, an inability to verify black box processes, and medico-legal and ethical problems. The challenge of modern medicine is to recognize the limitations of AI and overcome them.

This review aims to examine the complex interaction between dyslipidemia, platelet function, and related drug treatments. In particular, the manuscript provides an overview of the effects of major hypolipidemic drugs on platelet function. Indeed, growing evidence supports the view that statins, ezetimibe, PCSK9 inhibitors, inclisiran, and icosapent ethyl also act as antithrombotics. It is known that platelets play a key role not only in the acute phase of coronary syndromes but also in the early phase of atherosclerotic plaque formation. The goal of cholesterol-lowering therapy is to reduce cardiovascular events. The direct effects of cholesterol-lowering drugs are widely described in the literature. Lowering LDL-c (low-density lipoprotein cholesterol) by 1 mmol/L results in a 22–23% reduction in cardiovascular risk. Numerous studies have examined the direct antithrombotic effects of these drugs on platelets, endothelium, monocytes, and smooth muscle cells, and thus, potentially independent of blood LDL-cholesterol reduction. We reviewed in vitro and in vivo studies evaluating the complex interaction between hypercholesterolemia, hypertriglyceridemia, platelet function, and related drug treatments. First, we discussed the role of statins in modulating platelet activation. Discontinuation of statin therapy was associated with increased cardiovascular events with increased ox-LDL, P-selectin, and platelet aggregation. The effect of PCSK9-I (inhibitors of proprotein convertase subtilisin/kexin type 9, PCSK9 involved in the degradation of LDL receptors in the liver) was associated with a statistically significant reduction in platelet reactivity, calculated in P2Y12 reaction units (PRU), in the first 14 days and no difference at 30 days compared to placebo. Finally, in patients with hypertriglyceridemia, the REDUCE-IT study showed that icosapent ethyl (an ethyl ester of eicosapentaenoic acid that reduces triglyceride synthesis and improves triglyceride clearance) resulted in a 25% reduction in ischemic events and cardiovascular death. However, to date, there is not yet clear clinical evidence that the direct antithrombotic effects of the drugs may have a beneficial impact on outcomes independently from the reduction in LDL-C or triglycerides.

To date, no medical therapy can slow the progression of aortic stenosis. Fibrocalcific stenosis is the most frequent form in the general population and affects about 6% of the elderly population. Over the years, diagnosis has evolved thanks to echocardiography and computed tomography assessments. The application of artificial intelligence to electrocardiography could further implement early diagnosis. Patients with severe aortic stenosis, especially symptomatic patients, have valve repair as their only therapeutic option by surgical or percutaneous technique (TAVI). The discovery that the pathogenetic mechanism of aortic stenosis is similar to the atherosclerosis process has made it possible to evaluate the hypothesis of medical therapy for aortic stenosis. Several drugs have been tested to reduce low-density lipoprotein (LDL) and lipoprotein(a) (Lp(a)) levels, inflammation, and calcification. The Proprotein Convertase Subtilisin/Kexin type 9 inhibitors (PCSK9-i) could decrease the progression of aortic stenosis and the requirement for valve implantation. Great interest is related to circulating Lp(a) levels as causally linked to degenerative aortic stenosis. New therapies with ASO (antisense oligonucleotides) and siRNA (small interfering RNA) are currently being tested. Olpasiran and pelacarsen reduce circulating Lp(a) levels by 85–90%. Phase 3 studies are underway to evaluate the effect of these drugs on cardiovascular events (cardiovascular death, non-fatal myocardial injury, and non-fatal stroke) in patients with elevated Lp(a) and CVD (cardiovascular diseases). For instance, if a reduction in Lp(a) levels is associated with aortic stenosis prevention or progression, further prospective clinical trials are warranted to confirm this observation in this high-risk population.

Artificial intelligence (AI) is transforming cardiac electrophysiology across the entire care pathway, from arrhythmia detection on 12-lead electrocardiograms (ECGs) and wearables to the guidance of catheter ablation procedures, through to outcome prediction and therapeutic personalization. End-to-end deep learning (DL) models have achieved cardiologist-level performance in rhythm classification and prognostic estimation on standard ECGs, with a reported arrhythmia classification accuracy of ≥95% and an atrial fibrillation detection sensitivity/specificity of ≥96%. The application of AI to wearable devices enables population-scale screening and digital triage pathways. In the electrophysiology (EP) laboratory, AI standardizes the interpretation of intracardiac electrograms (EGMs) and supports target selection, and machine learning (ML)-guided strategies have improved ablation outcomes. In patients with cardiac implantable electronic devices (CIEDs), remote monitoring feeds multiparametric models capable of anticipating heart-failure decompensation and arrhythmic risk. This review outlines the principal modeling paradigms of supervised learning (regression models, support vector machines, neural networks, and random forests) and unsupervised learning (clustering, dimensionality reduction, association rule learning) and examines emerging technologies in electrophysiology (digital twins, physics-informed neural networks, DL for imaging, graph neural networks, and on-device AI). However, major challenges remain for clinical translation, including an external validation rate below 30% and workflow integration below 20%, which represent core obstacles to real-world adoption. A joint clinical engineering roadmap is essential to translate prototypes into reliable, bedside tools.

Echocardiography is a cornerstone technique for evaluating cardiac function in preclinical research using murine models. This review provides a comprehensive overview of the echocardiographic approaches employed to assess ventricular function in mouse models of heart disease, highlighting methodological principles, technical challenges, and the translational relevance of findings. Various echocardiographic modalities enable the precise evaluation of systolic and diastolic function. This article emphasizes standardization in image acquisition and analysis to minimize inter-operator variability and ensure reproducibility. It details echocardiographic parameters and strain imaging across commonly used mouse models of non-ischemic dilated cardiomyopathy, diabetic cardiomyopathy, hypertensive heart disease, and ischemic heart disease. Furthermore, it explores the advantages and limitations of anesthesia, probe positioning, and physiological monitoring during imaging. The integration of advanced imaging technologies such as Speckle-Tracking Echocardiography (STE), Three-Dimensional (3-D), and Four-Dimensional (4-D) echocardiography is discussed as a promising avenue for enhancing data quality and improving the translational potential of preclinical cardiac studies.