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The aim of this study was to investigate the efficiency of the automated DNA extraction method using the EZ2 Connect Fx instrument (QIAGEN), showcasing its ability to achieve swift and reliable results from bone samples. Genomic DNA extraction from 16 archaelogical and recent bone samples was performed with a commercial EZ1&2® DNA Investigator® Kit (QIAGEN). Prior to PCR amplification, DNA concentration was quantified using Qubit™ Fluorometer (Thermo Fisher Scientific). Amplification was carried out with the Investigator 24plex Kit (QIAGEN). DNA profiles were generated using 3500 Genetic Analyzer (Applied Biosystems) and analysed with GeneMapper™ ID-X 1.6 Software (Applied Biosystems). For nine archaeological dental samples, eight exhibited partial profiles, and one showed no amplified loci. Among the seven recent femoral bone samples, one exhibited a partial profile, while six presented complete DNA profiles. In this study, we investigated the benefits of automated DNA extraction, particularly its ability to generate timely and reliable results. Additionally, this method reduces pipette and tip usage, almost completely minimising the risk of human error, exogenous DNA contamination and cross-contamination. Such efficiency is of paramount importance, especially when working with skeletal remains, namely archaeological skeletal remains that pose a challenge for molecular genetic analyses.

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. LINKED ARTICLES: This article is part of a themed issue Non‐coding RNA Therapeutics. To view the other articles in this section visit http://onlinelibrary.wiley.com/doi/10.1111/bph.v182.2/issuetoc [ABSTRACT FROM AUTHOR]

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.