Carotid artery stenosis remains a major cause of ischemic stroke worldwide, and its management continues to evolve in parallel with advances in surgical, endovascular, and medical therapies. Carotid endarterectomy (CEA) was established as the standard of care for symptomatic high-grade stenosis following landmark randomized trials, while carotid artery stenting (CAS) subsequently emerged as a less invasive alternative for appropriately selected patients. This review aims to summarize the historical evolution of carotid artery stenting, critically appraise evidence from major clinical trials comparing CAS and CEA, and examine contemporary practice patterns in the era of intensive medical therapy. A comprehensive review of randomized trials, registries, guideline statements, and recent literature was performed to synthesize current evidence regarding procedural outcomes, patient selection, and emerging technologies, including transcarotid artery revascularization (TCAR). Large, randomized trials have demonstrated comparable long-term composite outcomes between CAS and CEA in selected patients, although peri-procedural risk profiles differ, with higher stroke risk observed after CAS and higher myocardial infarction rates after CEA. Technological advancements in embolic protection devices, stent platforms, and alternative access strategies have further refined endovascular approaches. Concurrently, improvements in intensive medical therapy—including lipid-lowering, antiplatelet therapy, blood pressure control, smoking cessation, and lifestyle modification—have substantially reduced overall stroke risk, particularly in asymptomatic patients. In the contemporary era, optimal stroke prevention requires individualized, multidisciplinary decision-making that integrates symptom status, anatomical complexity, comorbid conditions, procedural expertise, and sustained long-term vascular risk factor management following revascularization.

Background: Electrical and dielectric properties of the myocardium depend on its composition, fibrosis, edema, ischemia, and heat damage, and hence may play an important role in defining the electrophysiologic substrate. Local impedance and other impedance-based methods give information about these parameters that is available to clinicians, but not direct or substitutable for intrinsic conductivity and permittivity of the tissue. The use of artificial intelligence has been thoroughly studied for the ECGs, electrograms, images, and computational data analysis, but not for dielectric or impedance measurements. Objective: The study aims to review the scientific basis of dielectric properties of heart muscle critically in cardiology and electrophysiology, to differentiate between existing clinical and conceptual applications of myocardial dielectrics, and to estimate current and potential contributions of artificial intelligence to their interpretation. Data Sources and Study Selection: The current work represents a structured narrative review in PubMed, Scopus, and Google Scholar databases from inception until May 2026 based on various combinations of keywords that refer to myocardial impedance, dielectric properties, conductivity, permittivity, local impedance, dielectric imaging, cardiac electrophysiology, catheter ablation, machine learning, and artificial intelligence. This review included experimental, ex vivo, animal, computational, and human studies related to myocardial dielectric properties and their use in electrophysiology. Evidence was classified according to four categories: direct human clinical evidence; preclinical/translation evidence; indirect evidence from related techniques; and extrapolation concept. As a result of heterogeneity between studies, sensing technologies, and outcomes, the results have been analyzed narratively. Key Findings: Evidence regarding the usage of AI algorithms, which have been primarily trained on myocardial dielectric or catheter-based local impedance data, is extremely scarce and mostly consists of preclinical or ex vivo data. On the contrary, the majority of clinically used AI algorithms in the field of electrophysiology make use of ECG data, intracardiac electrogram data, image processing, or computational modeling without any inputs related to dielectrics or local impedance. Conclusions and Implications for Practice: Dielectric sensing is an attractive supplement to traditional methods of voltage mapping and imaging, although the clinical readiness of dielectric sensing greatly varies depending on the application. At the present time, the existing data provide a basis for utilizing the local impedance measurement in the ablation procedure and evaluation of the myocardium properties, while the combination of artificial intelligence and dielectric sensing is still experimental. Before being introduced into the clinical electrophysiology practice, signal acquisition standardization and prospective multicenter trials are needed.

Haematological malignancies comprise a diverse group of life-threatening systemic diseases in the blood, bone marrow, and lymphoid tissues, and their progression is strongly influenced by a supportive tumour microenvironment that drives proliferation and drug resistance. Drug development and personalised treatment using patient-derived cells are a tedious and expensive process, often conducted in 2D cell culture, which has many uncertainties and limitations due to poor mimicry of the microenvironment, cellular mechanisms, and morphological changes. Animal models also fail to fully replicate human pathobiology and are time-consuming. Alternatively, three-dimensional (3D) in vitro tissue modelling techniques can replicate the complex microenvironment, resembling the morphology, cell-cell interactions, and extracellular matrix (ECM) through methods like organoids, spheroids, tumoroids, and scaffold-based cultures. High-throughput drug screening in edited 3D cultures allows analysis of tumour interactions in haematological malignancies, for example, assessing gene-mediated drug resistance in AML within the marrow niche, depending on specific myeloma plasma cell cultures and their interactions with mesenchymal stem cells, along with susceptibility to CAR-T regulators and clinical translation in patient-derived therapies. The clinical translation of 3D culture and gene-editing–based in vitro models in leukemia, lymphoma, and multiple myeloma predicts cellular interactions with the tumour microenvironment, thereby enabling targeted treatment options for patients. This review highlights current progress in integrating gene editing with 3D culture system and emphasises their potential to enhance personalised drug discovery and preclinical testing in haematological malignancies.