Objective: The development, industrialization, the nature of work, technical and economic reasons, and increasing demand for services jobs have made shift work common. The aim of this study was investigation of association between Prevalence of Diabetes Mellitus and Coronary Heart Disease (CHD) including Angina and Heart Failure with Shift Work in Adults of Shahedieh cohort study in Yazd. Materials and Methods: The data of this study were from the cross-sectional phase of the Shahedieh Cohort Study. The data of 9513 participants in the study were used to analyze the data. Data were analyzed using SPSS 22 software. Chi-square test was used to examine the relationship between independent and dependent variables and t-test was used to compare means. Binary logistic regression was also used for statistical modeling. Significance level less than 0.05 were considered.

Objective: Knowledge of the current status of diabetes in Yazd is crucial for health policy planning, maintaining stable disease prevalence rates and risk factors, and establishing evaluation indicators. This study aimed to investigate the prevalence of diabetes in Yazd Province in accordance with acceptable international standards in 2018–2019. Materials and Methods: A cross-sectional descriptive study was conducted in Yazd Province in 2018-2019. The study population included all individuals living in the geographical areas of Yazd Province in 2018-2019 who had sought medical or preventive services at health centers or pharmacies. Data were obtained through collaboration with the Health Deputy (APPLE project), the Food and Drug Deputy (provincial pharmacies), insurance organizations (medical services, social security, relief committee, armed forces, banks, etc.), and the Diabetes Treatment Research Center. Results: After removing duplicate data, 95,798 diabetic patients were registered. Approximately 58% of the patients were female and 42% were male. The mean age of the patients was 59.2 ± 13.34 years. About 68% of the patients were receiving insulin treatment. Conclusion: Considering the importance of diabetes and its increasing prevalence globally and especially in Yazd Province, and in order to implement a monitoring and continuous care program for the disease, an effort was made to present the overall picture of diabetes in the province.

Objective: In addition to controlling seasonal and circadian rhythms, melatonin has been recognized as a potential gene associated with type 2 diabetes (T2D). This study aimed to investigate the impact of resistance and interval training on serum melatonin and its receptors expression (MTNR1A, MTNR1B) in pancreatic tissue of type 2 diabetic rats. Materials and Methods: To achieve this, T2D was established in 21 male Wistar rats through an 8-week high-fat diet followed by an intraperitoneal injection of STZ (25 ml/kg), then were divided to control (no exercise), resistance (resistance training), and interval (interval training) groups. Exercise interventions lasted 8 weeks (5 time/weekly). Fasting glucose, serum insulin and melatonin, beta cell function, MTNR1A and MTNR1B expression in pancreatic tissue were assessed 48 hours following lasting exercise and analyzed among groups using a one-way ANOVA test. Results: Both resistance and interval training led to significant increase in insulin and beta cell function and significant decrease in glucose, serum melatonin, MTNR1A and MTNR1B expression in pancreatic tissue compared with control group (P< 0.05). Significant difference were not observed in all variables between interval and resistance groups (P> 0.05). Conclusion: Resistance and interval training are associated with increased insulin in T2D rats, and this improvement may be attributed to decreased melatonin and its receptor expression in pancreatic beta cells. Additional research is required to elucidate alternative mechanisms that contribute to elevated insulin levels.

Objective: Among diabetic patients, diabetic retinopathy (DR) remains one of the most common causes of preventable blindness and vision loss, making its early detection crucial for preventing irreversible complications. Manual evaluation of fundus photographs is a lengthy process. Additionally, it requires specialized training that is not always available in all clinical settings. Consequently, artificial intelligence based automated retinal image analysis systems have emerged as complementary tools to enhance diagnostic accuracy and efficiency. This study proposes an ensemble learning based framework to improve the accuracy and robustness of automated DR detection. In the first stage, pretrained convolutional neural network (CNN) models extract high level features from fundus images, capturing complex patterns and DR related lesions. These features are then fed into several classical machine learning classifiers, including Support Vector Machine (SVM), Random Forest, and XGBoost. To further boost discriminative power and reduce classification errors, a stacking ensemble strategy integrates the predictions of the individual classifiers within a meta learning framework, enabling the model to learn the optimal combination for DR detection and grading. This hybrid approach effectively combines the strengths of deep learning and classical machine learning, yielding improved performance in DR detection and classification. Experimental results show that the stacking ensemble achieves higher accuracy and F1 score compared to individual models, underscoring its potential as an auxiliary tool for early diabetic retinopathy detection.

This study provides a systematic comparison of the mathematical properties, strengths, and limitations of traditional statistical methods and machine learning models in diabetes forecasting. While classical approaches like logistic regression and ANOVA offer interpretability and simplicity, their reliance on linear assumptions and sensitivity to heteroscedasticity limit their utility in modeling complex, nonlinear relationships inherent in diabetes data. In contrast, machine learning techniques-including neural networks, random forests, and gradient boosting-excel in capturing high-dimensional interactions and nonlinear dynamics, achieving superior predictive accuracy. However, these gains come at the cost of computational complexity, black box interpretability challenges, and ethical concerns around algorithmic bias. Through a detailed analysis of mathematical frameworks (e.g., activation functions, regularization, ensemble methods), we demonstrate how hybrid approaches integrating explainable AI (XAI) can bridge the gap between statistical rigor and clinical usability. Our findings highlight the critical trade-offs between model interpretability, predictive power, and scalability, offering actionable insights for optimizing diabetes risk prediction in precision medicine.

Objective: The present study was conducted to make a comparison between the effectiveness of acceptance and commitment therapy and emotion-focused therapy on Physical-social anxiety in obese individuals. Materials and Methods: The research method was a quasi-experimental design with a pre-test/post-test design and follow-up with a control group. 45 women aged 20 to 30 were selected through convenience sampling and randomly assigned to two experimental groups and a control group (15 people in each group). Then, the experimental groups were treated for 8 treatment sessions, 90 minutes each week. Results: The results showed, there was a significant difference between the effectiveness of acceptance and commitment therapy and emotion- focused therapy on Physical-social anxiety in obese women. Accordingly, acceptance and commitment therapy was more effective than emotion-focused therapy in reducing Physical-social anxiety in obese individuals. Conclusion: Acceptance and commitment therapy and emotion-focused therapy can be effective strategies for reducing socio-physical anxiety in overweight women.

Obesity and inflammation are major risk factors for various diseases, and metabolic syndrome is a complex disorder characterized by insulin resistance, dyslipidemia, hypertension, and abdominal obesity. Aerobic exercise appears to reduce obesity-induced inflammation. The purpose of this study was to review the molecular mechanisms of aerobic exercise in modulating adipose tissue apoptosis and inflammation in individuals with metabolic syndrome. Studies related to the intrinsic apoptotic pathway in women with metabolic syndrome evaluated the effects of aerobic exercise on apoptotic signaling pathways and adipose tissue inflammation. Aerobic exercise appears to increase the expression of the BCL-2 gene and decrease the expression of BAX and caspase-3, thereby delaying the apoptotic process. In addition, aerobic exercise reduced body fat percentage, fasting insulin levels, insulin resistance, and was associated with decreased apoptosis and inflammation of adipose tissue in individuals with metabolic syndrome. The findings indicate that aerobic exercise exerts significant beneficial effects on factors associated with metabolic syndrome and may represent an appropriate and effective strategy for appetite regulation in individuals with metabolic syndrome.

Insulin resistance (IR) is a central pathophysiological hallmark of type 2 diabetes mellitus (T2D) and related cardio-metabolic disorders. This narrative review explores the impact of regular physical activity, particularly aerobic and resistance exercise, on the mitigation of IR through various molecular mechanisms. Genetic predispositions, chronic low-grade inflammation, dysregulated circulating metabolites, hormonal imbalances, oxidative stress, and abnormal enzymatic activities collectively contribute to the pathogenesis of IR. Physical activity, especially aerobic exercise, has significant anti-IR effects by modulating inflammatory processes. This includes suppression of pro-inflammatory cytokines (e.g., TNF-a, IL-6) and adipokines (e.g., resistin, visfatin), as well as an increase in anti-inflammatory myokines (e.g., irisin, muscle-derived IL-6) and adiponectin/emilin-1 profiles. These changes create an anti-inflammatory environment that enhances insulin signaling in skeletal muscle, liver, and adipose tissue. Hormonal adjustments, such as improved insulin secretion, beta-cell function, and tissue sensitivity, further support these metabolic adaptations. Additionally, exercise reduces oxidative stress by strengthening antioxidant defenses and inhibiting key IR-promoting enzymes like PTP1B and 11ß-HSD1. This preservation of tyrosine phosphorylation of the insulin receptor and downstream IRS-1/PI3K/Akt pathway activation leads to increased GLUT4 translocation and glucose uptake. In summary, regular exercise is a cost-effective, non-pharmacological intervention that targets interconnected molecular factors in the etiology of IR. These diverse effects highlight its therapeutic potential in personalized prevention and management strategies for IR and associated metabolic diseases, warranting its integration into clinical guidelines.