A Novel Approach of Determining the Risks for the Development of Hyperinsulinemia in the Children and Adolescent Population Using Radial Basis Function and Support Vector Machine Learning Algorithm

Hyperinsulinemia is a condition with extremely high levels of insulin in the blood. Various factors can lead to hyperinsulinemia in children and adolescents. Puberty is a period of significant change in children and adolescents. They do not have to have explicit symptoms for prediabetes, and certain health indicators may indicate a risk of developing this problem. The scientific study is designed as a cross-sectional study. In total, 674 children and adolescents of school age from 12 to 17 years old participated in the research. They received a recommendation from a pediatrician to do an OGTT (Oral Glucose Tolerance test) with insulinemia at a regular systematic examination. In addition to factor analysis, the study of the influence of individual factors was tested using RBF (Radial Basis Function) and SVM (Support Vector Machine) algorithm. The obtained results indicated statistically significant differences in the values of the monitored variables between the experimental and control groups. The obtained results showed that the number of adolescents at risk is increasing, and, in the presented research, it was 17.4%. Factor analysis and verification of the SVM algorithm changed the percentage of each risk factor. In addition, unlike previous research, three groups of children and adolescents at low, medium, and high risk were identified. The degree of risk can be of great diagnostic value for adopting corrective measures to prevent this problem and developing potential complications, primarily type 2 diabetes mellitus, cardiovascular disease, and other mass non-communicable diseases. The SVM algorithm is expected to determine the most accurate and reliable influence of risk factors. Using factor analysis and verification using the SVM algorithm, they significantly indicate an accurate, precise, and timely identification of children and adolescents at risk of hyperinsulinemia, which is of great importance for improving their health potential, and the health of society as a whole

Risk factors for the development of metabolic syndrome in obese children and adolescents

Introduction. High prevalence of metabolic syndrome (MetS) in children and adolescents is a great concern of the modern society. Objective. Our aim was to determine the influence of previously investigated, but also and potentially novel risk factors for the development of metabolic syndrome in children and adolescents. Methods. Observational case-control clinical study was conducted involving children and adolescents with obesity/metabolic syndrome, treated on inpatient basis from January 2008 to January 2012 at the Pediatric Clinic of the Clinical Centre Kragujevac, Kragujevac, Serbia. The group of “cases” (n=28) included patients aged 10-16 years with the diagnosis of metabolic syndrome according to the International Diabetes Federation (IDF) criteria, while the control group included twice as many obese patients (n=56) matched to the compared group. Results. Presence of maternal gestational diabetes (ORadjusted: 39.426; 95% CI: 1.822-853.271; p=0.019), and/or lack of breastfeeding in the first six months of life (ORadjusted: 0.079; 95% CI: 0.009-0.716; p=0.024) were significant predictors for developing MetS. Also, microalbuminuria is associated with MetS in obese children and adolescents (ORadjusted: 1.686; 95% CI: 1.188-2.393; p=0.003). Conclusion. Presence of maternal gestational diabetes and/or lack of infant breastfeeding are considered as relevant factors that may contribute to the increased risk of developing MetS syndrome, while microalbuminuria is frequently associated with MetS in obese children and adolescents. [Projekat Ministarstva nauke Republike Srbije, br. 175007

Hospital-Acquired Pneumonia in Newborns with Birth Weight Less Than 1500 Grams: Risk Factors and Causes

Low birth weight newborns (≤1500 grams) are at a high risk of acquiring hospital infections due to the immaturity of the immune system, lack of efficient structural barriers, and an incomplete development of endogenous microbial flora

A Novel Approach of Determining the Risks for the Development of Hyperinsulinemia in the Children and Adolescent Population Using Radial Basis Function and Support Vector Machine Learning Algorithm

Hyperinsulinemia is a condition with extremely high levels of insulin in the blood. Various factors can lead to hyperinsulinemia in children and adolescents. Puberty is a period of significant change in children and adolescents. They do not have to have explicit symptoms for prediabetes, and certain health indicators may indicate a risk of developing this problem. The scientific study is designed as a cross-sectional study. In total, 674 children and adolescents of school age from 12 to 17 years old participated in the research. They received a recommendation from a pediatrician to do an OGTT (Oral Glucose Tolerance test) with insulinemia at a regular systematic examination. In addition to factor analysis, the study of the influence of individual factors was tested using RBF (Radial Basis Function) and SVM (Support Vector Machine) algorithm. The obtained results indicated statistically significant differences in the values of the monitored variables between the experimental and control groups. The obtained results showed that the number of adolescents at risk is increasing, and, in the presented research, it was 17.4%. Factor analysis and verification of the SVM algorithm changed the percentage of each risk factor. In addition, unlike previous research, three groups of children and adolescents at low, medium, and high risk were identified. The degree of risk can be of great diagnostic value for adopting corrective measures to prevent this problem and developing potential complications, primarily type 2 diabetes mellitus, cardiovascular disease, and other mass non-communicable diseases. The SVM algorithm is expected to determine the most accurate and reliable influence of risk factors. Using factor analysis and verification using the SVM algorithm, they significantly indicate an accurate, precise, and timely identification of children and adolescents at risk of hyperinsulinemia, which is of great importance for improving their health potential, and the health of society as a whole

Preconditioning with PDE1 Inhibitors and Moderate-Intensity Training Positively Affect Systemic Redox State of Rats

Taken into consideration that oxidative stress response after preconditioning with phosphodiesterase inhibitors (PDEIs) and moderate physical activity has still not been clarified, the aim of this study was to assess the effects of PDEIs alone or in combination with physical activity, on systemic redox status. The study was carried out on 96 male Wistar albino rats classified into two groups. The first group included animals exposed only to pharmacological preconditioning (PreC) maneuver (sedentary control (CTRL, 1 ml/day saline, n=12), nicardipine (6 mg/kg/day of NIC, n=12), vinpocetine (10 mg/kg/day of VIN, n=12), and nimodipine (NIM 10 mg/kg/day of, n=12). The second included animals exposed to preconditioning with moderate-intensity training (MIT) on treadmill for 8 weeks. After 5 weeks from the start of training, the animals were divided into four subgroups depending on the medication to be used for pharmacological PreC: moderate-intensity training (MIT+ 1 ml/day saline, n=12), nicardipine (MIT+ 6 mg/kg/day of NIC, n=12), vinpocetine (MIT+ 10 mg/kg/day of VIN, n=12), and nimodipine (MIT+ 10 mg/kg/day of NIM, n=12). After three weeks of pharmacological preconditioning, the animals were sacrificed. The following oxidative stress parameters were measured spectrophotometrically: nitrites (NO2−), superoxide anion radical (O2−), hydrogen peroxide (H2O2), index of lipid peroxidation (TBARS), superoxide dismutase (SOD), catalase (CAT), and reduced glutathione (GSH). Our results showed that PDE1 and MIT preconditioning decreased the release of prooxidants and improved the activity of antioxidant enzymes thus preventing systemic oxidative stress