Cardiovascular aging is a multifactorial and systemic process that contributes significantly to the global burden of cardiovascular disease, particularly in older populations. This review explores the molecular and cellular mechanisms underlying cardiovascular remodeling in age-related conditions such as hypertension, atrial fibrillation, atherosclerosis, and heart failure. Central to this process are chronic low-grade inflammation (inflammaging), oxidative stress, cellular senescence, and maladaptive extracellular matrix remodeling. These hallmarks of aging interact to impair endothelial function, promote fibrosis, and compromise cardiac and vascular integrity. Key molecular pathways—including the renin–angiotensin–aldosterone system, NF-κB, NLRP3 inflammasome, IL-6, and TGF-β signaling—contribute to the transdifferentiation of vascular cells, immune dysregulation, and progressive tissue stiffening. We also highlight the role of the senescence-associated secretory phenotype and mitochondrial dysfunction in perpetuating inflammatory and fibrotic cascades. Emerging molecular therapies offer promising strategies to reverse or halt maladaptive remodeling. These include senescence-targeting agents (senolytics), Nrf2 activators, RNA-based drugs, and ECM-modulating compounds such as MMP inhibitors. Additionally, statins and anti-inflammatory biologics (e.g., IL-1β inhibitors) exhibit pleiotropic effects that extend beyond traditional risk factor control. Understanding the molecular basis of remodeling is essential for guiding future research and improving outcomes in older adults at risk of CVD.

This narrative review explores emerging technologies in dyslipidemia management, focusing on nanotechnology and artificial intelligence (AI). It examines the current treatment recommendations and contrasts them with the future prospects enabled by these innovations. Nanotechnology shows significant potential in enhancing drug delivery systems, enabling more targeted and efficient lipid-lowering therapies. In parallel, AI offers advancements in diagnostics, cardiovascular risk prediction, and personalized treatment strategies. AI-based decision support systems and machine learning algorithms are particularly promising for analyzing large datasets and delivering evidence-based recommendations. Together, these technologies hold the potential to revolutionize dyslipidemia management, improving outcomes and optimizing patient care. In addition, this review covers key topics such as cardiovascular disease biomarkers and risk factors, providing insights into the current methods for assessing cardiovascular risk. It also discusses the current understanding of dyslipidemia, including pathophysiology and clinical management. Together, these insights and technologies hold the potential to revolutionize dyslipidemia management, improving outcomes and optimizing patient care.

Retinitis pigmentosa is a group of inherited retinal dystrophies characterized by progressive photoreceptor cell loss leading to irreversible vision loss. Affecting approximately 1 in 4000 individuals worldwide, retinitis pigmentosa exhibits significant genetic heterogeneity, with mutations in genes such as RHO, PRPF31, RPE65, USH2A, and NR2E3, which contribute to its diverse clinical presentation. This review outlines the genetic basis of retinitis pigmentosa and explores cutting-edge gene-based therapeutic strategies. Luxturna (voretigene neparvovec-rzyl), the first FDA-approved gene therapy targeting RPE65 mutations, represents a milestone in precision ophthalmology, while OCU400 is a gene-independent therapy that uses a modified NR2E3 construct to modulate retinal homeostasis across different RP genotypes. Additionally, CRISPR–Cas genome-editing technologies offer future potential for the personalized correction of specific mutations, though concerns about off-target effects and delivery challenges remain. The article also highlights MCO-010, a novel optogenetic therapy that bypasses defective phototransduction pathways, showing promise for patients regardless of their genetic profile. Moreover, QR-1123, a mutation-specific antisense oligonucleotide targeting the P23H variant in the RHO gene, is under clinical investigation for autosomal dominant RP and has shown encouraging preclinical results in reducing toxic protein accumulation and preserving photoreceptors. SPVN06, another promising candidate, is a mutation-agnostic gene therapy delivering RdCVF and RdCVFL via AAV to support cone viability and delay degeneration, currently being evaluated in a multicenter Phase I/II trial for patients with various rod–cone dystrophies. Collectively, these advances illustrate the transition from symptom management toward targeted, mutation-specific therapies, marking a major advancement in the treatment of RP and inherited retinal diseases.

Dry eye disease (DED) is a complex, multifactorial, progressive disease that has consequences both for individuals and society. Symptoms reported by patients include discomfort in the eye and periodic blurred vision, while in the broader perspective, the disease is associated with economic burdens and challenges for healthcare systems. Globally, dry eye disease remains a growing problem observed in many countries. It is estimated that symptoms of dry eye syndrome occur in approximately 10 to 20 per cent of people over the age of 40. This prevalence is on the rise, which is associated with both the aging population and increased incidence among younger adults. In this group, factors such as contact lens wear and prolonged use of digital devices are considered to be contributing factors. Further epidemiological studies, conducted in different regions of the world, covering diverse populations and a wide range of age groups, with a particular focus on younger cohorts, may contribute to a more accurate understanding of the prevalence of dry eye disease. There are more and more methods of diagnosing DED. In addition to well-known procedures like the Schirmer test or tear break-up time, there are also methods that focus on the evaluation of the tear film or imaging of the ocular surface. Moreover, usage of artificial intelligence is also playing a significant role in it. However, the key issue in individual cases is introducing the most effective treatment based on combining available substances, including corticosteroids, antibiotics and supplements, which leads to a reduction in inflammation and improvement in visual comfort.

The coronavirus disease 2019 (COVID-19) pandemic has revealed a close and multifaceted relationship between viral infection, systemic inflammation, and cardiovascular health. Among the cardiac complications of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), atrial fibrillation (AF)—especially new-onset atrial fibrillation (NOAF)—has emerged as a major determinant of disease severity and prognosis. Clinical studies and meta-analyses show that 5–10% of hospitalized COVID-19 patients develop AF, with markedly higher rates in critically ill individuals. Both pre-existing and NOAF are independently associated with increased risks of intensive care admission, mechanical ventilation, thromboembolic events, and mortality. The underlying mechanisms involve a combination of cytokine-mediated inflammation, endothelial dysfunction, microvascular injury, and dysregulation of the renin–angiotensin–aldosterone system (RAAS). Viral downregulation of angiotensin-converting enzyme 2 (ACE2) receptors contributes to myocardial fibrosis, while hypoxia, oxidative stress, and autonomic imbalance further promote electrical remodeling and arrhythmogenesis. Post-infectious studies indicate that atrial structural changes and autonomic dysfunction may persist for months, predisposing survivors to recurrent arrhythmias. Technological advances in telecardiology and digital medicine have provided new tools for early detection and long-term monitoring. Wearable electroencephalography (ECG) devices, implantable loop recorders (ILRs), and artificial intelligence (AI)-based diagnostic algorithms enable continuous rhythm surveillance and individualized management, improving outcomes in post-COVID patients. This review summarizes current evidence on the epidemiology, pathophysiology, clinical implications, and monitoring strategies of AF in COVID-19. It underscores the importance of integrating telemedicine and AI-assisted diagnostics into cardiovascular care to mitigate the long-term arrhythmic and systemic consequences of SARS-CoV-2 infection.

Background: Cardiovascular diseases (CVDs) remain the leading global cause of mortality, yet a critical “translational gap” persists: Conventional biomarkers often fail to detect subclinical stages or predict individual disease trajectories. While single-omics studies have proliferated, the field lacks a unified framework synthesizing these molecular layers with advanced computational intelligence. Aim: This review addresses this gap by evaluating the synergistic integration of multi-omics and Artificial Intelligence (AI) to transition from descriptive markers toward predictive precision cardiology. Scope: Evidence from non-coding RNA networks (miRNAs, lncRNAs) and exosomal trafficking is synthesized alongside a critical assessment of Machine Learning (ML) architectures, including supervised, unsupervised, and deep learning (DL) models. Findings: Unlike traditional reviews, this work delineates the specific pipelines required to deconvolute high-dimensional signatures—such as TMAO, acylcarnitines, and cardiac-enriched miRNAs—into actionable risk models for heart failure (HF) and post-infarction outcomes. The primary barrier to clinical translation is identified not as data scarcity but as the lack of standardized bioinformatic workflows and model interpretability. Conclusions: This review distinguishes itself by proposing an integrated molecular–computational framework that prioritizes Explainable AI (XAI) and standardized multi-omic protocols. Such a shift is essential to bridge the gap between high-dimensional biological insights and routine clinical decision-making.