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Background: Severe psychological or mental pain is an experience of discomfort that can be associated with mental illness (such as major depression) or loss (such as the death of a child). Objectives: The aim of this study is to understand the pathology of major depression using a non-clinical student sample by assessing the roles of mental pain, cognitive emotion regulation, self-compassion, and anxiety. Methods: This cross-sectional study selected a sample (n = 300) using a multi-stage random cluster sampling method. Data was collected using the Orbach & Mikulincer Mental Pain Questionnaire (OMMP), the Cognitive Emotion Regulation Questionnaire (CERQ), the Self-Compassion Scale-Short Form (SCS-SF), the Beck Anxiety Inventory (BAI), and the Beck Depression Inventory-II (BD-II). Results: The results of the forward multiple linear regression model showed significant standardized beta coefficients for the following variables: Anxiety and depression (β = 0.21, P = 0.002), mental pain and depression (β = 0.436, P < 0.001), maladaptive cognitive emotion regulation strategies and depression (β = 0.21, P = 0.002), negative dimensions of self-compassion and depression (β = 0.082, p = 0.041), adaptive cognitive emotion regulation strategies and depression (β = -0.135, P = 0.031), and positive dimensions of self-compassion and depression (β = -0.078, P = 0.042). Additionally, the results indicated that 56% of the variance in depression is explained by mental pain, cognitive emotion regulation, self-compassion, and anxiety (P < 0.001). Conclusions: The results of this study indicate that therapies focused on emotional regulation and self-compassion can effectively address emotional problems, anxiety, and depression in individuals with depression. [ABSTRACT FROM AUTHOR]

Introduction: Serum vitamin D levels are related to a wide spectrum of factors including low sunlight exposure, high oxidative stress, low physical activity and sleep disorders. In this paper we are going to investigate the most crucial parameters associated with serum vitamin D levels in survey of ultraviolet intake by nutritional approach (SUVINA) study with a data mining approach. Material and Methods: Data including demographic, anthropometric, clinical and laboratory information were extracted from the SUVINA dataset comprising 289 subjects who were enrolled into our study. The XGBoost algorithm was used to define the most important features related to vitamin D level in our population. Results: Applying XGBoost modeling for vitamin D level showed that the presented scheme can determine the most important determinants of serum vitamin D level with an accuracy of 91%. Pro-oxidant anti-oxidant balance (PAB), body fat percentage, physical activity level (PAL), age, restless leg syndrome (RLS), and dietary inflammatory index (DII) density were the most important variables correlated with vitamin D deficiency. Conclusion: Using XGBoost and with an accuracy of more than 90%, we showed that the six most important risk factors for vitamin D deficiency are PAB, PAL, age, body fat percentage, RLS and DII density, respectively. [ABSTRACT FROM AUTHOR]