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Background: Vitamin D deficiency is common among children and adolescents and can be affected by several factors such as puberty and obesity. Objective: The aim of this study was to evaluate vitamin D status in children and adolescents and to analyse the influence of puberty and obesity on its level. Method: A cross-sectional study was carried-out, in which clinical and biochemical data were gathered from 384 healthy children and adolescents between May 2019 to May 2020. Results: 220 females and 164 males were enrolled (aged 7-16 years; mean ± SD: 11 ± 2.5). Vitamin D deficiency was found in 49% of the total cases and was significantly more prevalent in females than males (33.1% in female; 15.9% in male, P < .001). Mean vitamin D level was lower in obese children compared with non-obese ( P < .001). Non-obese group had significantly higher levels of vitamin D in Tanner stage IV of puberty than obese individuals (20.1 ± 17.0 vs 5.4 ± 2.0) ( P = .03). Vitamin D levels were significantly lower in females than males only in Tanner stage II (12.3 ± 9.0 vs 19.6 ± 16.6) ( P = .005). The lowest level of Vitamin D was in Tanner stage Ⅳ-Ⅴ in boys and in Tanner stage Ⅱ-Ⅲ in girls ( P < .001). Conclusion: Puberty is an additional risk factor for vitamin D deficiency especially in girls and obese children. This increased risk, together with the fact that most important time for building a proper skeleton is during childhood and adolescent, makes it essential to monitor vitamin D in these age groups.

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BackgroundAs the era of big data analytics unfolds, machine learning (ML) might be a promising tool for predicting clinical outcomes. This study aimed to evaluate the predictive ability of ML models for estimating mortality after coronary artery bypass grafting (CABG).Materials and methodsVarious baseline and follow-up features were obtained from the CABG data registry, established in 2005 at Tehran Heart Center. After selecting key variables using the random forest method, prediction models were developed using: Logistic Regression (LR), Support Vector Machine (SVM), Naïve Bayes (NB), K-Nearest Neighbors (KNN), Extreme Gradient Boosting (XGBoost), and Random Forest (RF) algorithms. Area Under the Curve (AUC) and other indices were used to assess the performance.ResultsA total of 16,850 patients with isolated CABG (mean age: 67.34 ± 9.67 years) were included. Among them, 16,620 had one-year follow-up, from which 468 died. Eleven features were chosen to train the models. Total ventilation hours and left ventricular ejection fraction were by far the most predictive factors of mortality. All the models had AUC &gt; 0.7 (acceptable performance) for 1-year mortality. Nonetheless, LR (AUC = 0.811) and XGBoost (AUC = 0.792) outperformed NB (AUC = 0.783), RF (AUC = 0.783), SVM (AUC = 0.738), and KNN (AUC = 0.715). The trend was similar for two-to-five-year mortality, with LR demonstrating the highest predictive ability.ConclusionVarious ML models showed acceptable performance for estimating CABG mortality, with LR illustrating the highest prediction performance. These models can help clinicians make decisions according to the risk of mortality in patients undergoing CABG.