Atrial fibrillation (AF) is the most prevalent arrhythmia among adults worldwide, frequently co-occurring with comorbidities such as Heart Failure (HF) and Type 2 Diabetes Mellitus (T2DM). This association contributes to increased morbidity and mortality, elevated healthcare costs, and diminished quality of life. Consequently, preventing or delaying the onset and recurrence of AF is crucial for reducing the incidence of complications. Sodium-glucose cotransporter 2 inhibitors (SGLT2is), due to their multifaceted pharmacological actions, have been proposed as potential therapeutic agents in the management of AF. However, current evidence from both animal models and clinical studies remains inconclusive. This narrative literature review aims to provide a comprehensive analysis of existing evidence on the impact of SGLT2is on the prevalence, incidence of new-onset, and recurrence of AF in diabetic populations and patients with HF. Numerous observational studies, predominantly retrospective, suggest a consistent reduction in AF risk with SGLT2is, while randomized controlled trials (RCTs) have yielded mixed results, with some demonstrating benefits and others not reaching statistical significance. The heterogeneity in study outcomes, population characteristics, follow-up duration, and specific SGLT2is used, as well as potential biases, underscore the need for further extensive and rigorous RCTs to establish definitive conclusions and elucidate the underlying mechanisms.

Atrial fibrillation (AF) is the most common sustained arrhythmia and a growing global health burden, yet conventional monitoring with Holter devices, event recorders and implantable loop recorders often fails to adequately capture recurrence. Rapid advances in digital health, wearable biosensors and artificial intelligence (AI) have transformed consumer smartwatches and wearables into potential clinical tools capable of continuous, real-world rhythm surveillance. This narrative review synthesizes contemporary evidence on smartwatch-based AF monitoring, spanning core technologies—photoplethysmography, single-lead electrocardiography and AI fusion algorithms—and validation studies across post-ablation follow-up. Compared with traditional modalities, smartwatch-based AF monitoring demonstrates improved detection of AF recurrence, enhanced characterization of AF burden, symptom–rhythm correlation, and greater patient engagement. At the same time, key limitations are critically examined, including motion artifacts, false-positive alerts, short recording windows, adherence dependence, digital literacy and access gaps, as well as unresolved issues around regulation, interoperability and data privacy. By integrating engineering advances with guideline-directed care pathways, smartwatch-based AF monitoring holds promise to complement, rather than immediately replace, established diagnostic tools and to enable more proactive, individualized AF management. Future work must focus on robust clinical validation, equitable implementation and clear regulatory frameworks to safely scale these technologies.

During the COVID-19 pandemic, different SARS-CoV-2 variants of concern (VOC) with specific characteristics have emerged and spread worldwide. At the same time, clinicians routinely evaluate the results of certain blood tests upon patient admission as well as during hospitalization to assess disease severity and the overall patient status. In the present study, we searched for significant cell blood count and biomarker differences among patients affected with the Alpha, Delta and Omicron VOCs at admission. Data from 330 patients were retrieved regarding age, gender, VOC, cell blood count results (WBC, Neut%, Lymph%, Ig%, PLT), common biomarkers (D-dimers, urea, creatinine, SGOT, SGPT, CRP, IL-6, suPAR), ICU admission and death. Statistical analyses were performed using ANOVA, the Kruskal–Wallis test, two-way ANOVA, Chi-square, T-test, the Mann–Whitney test and logistic regression was performed where appropriate using SPSS v.28 and STATA 14. Age and VOC were significantly associated with hospitalization, whereas significant differences among VOC groups were found for WBC, PLT, Neut%, IL-6, creatinine, CRP, D-dimers and suPAR. Our analyses showed that throughout the current pandemic, not only the SARS-CoV-2 VOCs but also the laboratory parameters that are used to evaluate the patient’s status at admission are subject to changes.

Whereas hypertension is an established cardiovascular risk factor in the general population, the contribution of increased blood pressure (BP) to the huge burden of cardiovascular morbidity and mortality in patients receiving dialysis continues to be debated. In a large part, this controversy is attributable to particular difficulties in the accurate diagnosis of hypertension. The reverse epidemiology of hypertension in dialysis patients is based on evidence from large cohort studies showing that routine predialysis or postdialysis BP measurements exhibit a U-shaped or J-shaped association with cardiovascular or all-cause mortality. However, substantial evidence supports the notion that home or ambulatory BP measurements are superior to dialysis-unit BP recordings in diagnosing hypertension, in detecting evidence of target-organ damage and in prognosticating the all-cause death risk. In the first part of this article, we explore the accuracy of different methods of BP measurement in diagnosing hypertension among patients on dialysis. In the second part, we describe how the epidemiology of hypertension is modified when the assessment of BP is based on dialysis-unit versus home or ambulatory recordings.

The pathophysiology of human immunodeficiency virus (HIV)-associated bone loss is complex and to date largely unknown. In this study, we investigated serum expression of microRNAS (miRNAs) linked to bone metabolism in HIV-associated bone loss. This was a case-control study. Thirty male individuals with HIV infection (HIV+) and osteoporosis/osteopenia (HIV+/OP+) (cases) and 30 age-matched male HIV+ individuals with normal bone mass (HIV+/OP−) (controls) were included in the analysis. Thirty male individuals matched for age without HIV infection (HIV−), were also included as second controls. The selected panel of miRNAs was as follows: hsa-miRNA-21-5p; hsa-miRNA-23a-3p; hsa-miRNA-24-2-5p; hsa-miRNA-26a-5p; hsa-miRNA-29a-3p; hsa-miRNA-124-3p; hsa-miRNA-33a-5p; and hsa-miRNA-133a-3p. Within the cohort of HIV+ individuals, relative serum expression of miRNA-21-5p and miRNA-23a-3p was significantly lower (p < 0.001) while the expression of miRNA-24-2-5p was significantly higher (p = 0.030) in HIV+/OP+ compared to HIV+/OP−. Expression of miRNA-21-5p demonstrated a sensitivity of 84.6% and a specificity of 66.7 in distinguishing HIV+/OP+ individuals. Expression of circulating miRNAs related to bone metabolism; miRNA-23a-3p, miRNA-24-2-5p, and miRNA-21-5p is significantly altered in HIV+OP+ individuals, in line with data on other causes of osteoporosis, suggesting a common pattern of circulating miRNAs independent of the underlying cause.

Artificial neural networks are widely established models used to solve a variety of real-world problems in the fields of physics, chemistry, etc. These machine learning models contain a series of parameters that must be appropriately tuned by various optimization techniques in order to effectively address the problems that they face. Genetic algorithms have been used in many cases in the recent literature to train artificial neural networks, and various modifications have been made to enhance this procedure. In this article, the incorporation of a novel genetic operator into genetic algorithms is proposed to effectively train artificial neural networks. The new operator is based on the differential evolution technique, and it is periodically applied to randomly selected chromosomes from the genetic population. Furthermore, to determine a promising range of values for the parameters of the artificial neural network, an additional genetic algorithm is executed before the execution of the basic algorithm. The modified genetic algorithm is used to train neural networks on classification and regression datasets, and the results are reported and compared with those of other methods used to train neural networks.