Noncoding RNAs (ncRNAs) are pivotal for various pathological processes, impacting disease progression. The potential for leveraging ncRNAs to prevent or treat atherosclerosis and associated cardiovascular diseases is of great significance, especially given the increasing prevalence of atherosclerosis in an ageing and sedentary population. Together, these diseases impose a substantial socio-economic burden, demanding innovative therapeutic solutions. This review explores the potential of ncRNAs in atherosclerosis treatment. We commence by examining approaches for identifying and characterizing atherosclerosis-associated ncRNAs. We then delve into the functional aspects of ncRNAs in atherosclerosis development and progression. Additionally, we review current RNA and RNA-targeting molecules in development or under approval for clinical use, offering insights into their pharmacological potential. The importance of improved ncRNA delivery strategies is highlighted. Finally, we suggest avenues for advanced research to accelerate the use of ncRNAs in treating atherosclerosis and mitigating its societal impact.

Introduction: Hypertension is a major and modifiable risk factor for cardiovascular diseases. Essential, primary, or idiopathic hypertension accounts for 90–95% of all cases. Identifying novel biomarkers specific to essential hypertension may help in understanding pathophysiological pathways and developing personalized treatments. We tested whether the integration of circulating microRNAs (miRNAs) and clinical risk factors via machine learning modeling may provide useful information and novel tools for essential hypertension diagnosis and management. Materials and methods: In total, 174 participants were enrolled in the present observational case–control study, among which, there were 89 patients with essential hypertension and 85 controls. A discovery phase was conducted using small RNA sequencing in whole blood samples obtained from age- and sex-matched hypertension patients (n = 30) and controls (n = 30). A validation phase using RT-qPCR involved the remaining 114 participants. For machine learning, 170 participants with complete data were used to generate and evaluate the classification model. Results: Small RNA sequencing identified seven miRNAs downregulated in hypertensive patients as compared with controls in the discovery group, of which six were confirmed with RT-qPCR. In the validation group, miR-210-3p/361-3p/362-5p/378a-5p/501-5p were also downregulated in hypertensive patients. A machine learning support vector machine (SVM) model including clinical risk factors (sex, BMI, alcohol use, current smoker, and hypertension family history), miR-361-3p, and miR-501-5p was able to classify hypertension patients in a test dataset with an AUC of 0.90, a balanced accuracy of 0.87, a sensitivity of 0.83, and a specificity of 0.91. While five miRNAs exhibited substantial downregulation in hypertension patients, only miR-361-3p and miR-501-5p, alongside clinical risk factors, were consistently chosen in at least eight out of ten sub-training sets within the SVM model. Conclusions: This study highlights the potential significance of miRNA-based biomarkers in deepening our understanding of hypertension’s pathophysiology and in personalizing treatment strategies. The strong performance of the SVM model highlights its potential as a valuable asset for diagnosing and managing essential hypertension. The model remains to be extensively validated in independent patient cohorts before evaluating its added value in a clinical setting.

Aim of this paper was to determine the frequency of congenital anomalies in a sample of newborns of Tuzla Canton and as well as their distribution according to gender, mother’s age and marital distance category. Research was undertaken using the retrospective analysis on the Clinic for Gynecology and Obstetrics of University Clinical Center in Tuzla. By analyzing medical documentation of 17223 newborns, we determined the frequency of congenital anomalies of 5.24%. Highest frequency of congenital anomalies was found in the newborns whose mothers are older than 35. It was found that the frequency of congenital anomalies in the observed population is within the range of variation of this parameter with data from the literature.

Abstract The coronavirus disease 2019 (COVID-19) pandemic has been as unprecedented as unexpected, affecting more than 105 million people worldwide as of 8 February 2020 and causing more than 2.3 million deaths according to the World Health Organization (WHO). Not only affecting the lungs but also provoking acute respiratory distress, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is able to infect multiple cell types including cardiac and vascular cells. Hence a significant proportion of infected patients develop cardiac events, such as arrhythmias and heart failure. Patients with cardiovascular comorbidities are at highest risk of cardiac death. To face the pandemic and limit its burden, health authorities have launched several fast-track calls for research projects aiming to develop rapid strategies to combat the disease, as well as longer-term projects to prepare for the future. Biomarkers have the possibility to aid in clinical decision-making and tailoring healthcare in order to improve patient quality of life. The biomarker potential of circulating RNAs has been recognized in several disease conditions, including cardiovascular disease. RNA biomarkers may be useful in the current COVID-19 situation. The discovery, validation, and marketing of novel biomarkers, including RNA biomarkers, require multi-centre studies by large and interdisciplinary collaborative networks, involving both the academia and the industry. Here, members of the EU-CardioRNA COST Action CA17129 summarize the current knowledge about the strain that COVID-19 places on the cardiovascular system and discuss how RNA biomarkers can aid to limit this burden. They present the benefits and challenges of the discovery of novel RNA biomarkers, the need for networking efforts, and the added value of artificial intelligence to achieve reliable advances.

Cardiovascular disease (CVD) is the biggest cause of sickness and mortality worldwide in both males and females. Clinical statistics demonstrate clear sex differences in risk, prevalence, mortality rates, and response to treatment for different entities of CVD. The reason for this remains poorly understood. Non-coding RNAs (ncRNAs) are emerging as key mediators and biomarkers of CVD. Similarly, current knowledge on differential regulation, expression, and pathology-associated function of ncRNAs between sexes is minimal. Here, we provide a state-of-the-art overview of what is known on sex differences in ncRNA research in CVD as well as discussing the contributing biological factors to this sex dimorphism including genetic and epigenetic factors and sex hormone regulation of transcription. We then focus on the experimental models of CVD and their use in translational ncRNA research in the cardiovascular field. In particular, we want to highlight the importance of considering sex of the cellular and pre-clinical models in clinical studies in ncRNA research and to carefully consider the appropriate experimental models most applicable to human patient populations. Moreover, we aim to identify sex-specific targets for treatment and diagnosis for the biggest socioeconomic health problem globally.