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Abstract Limited research has explored the effectiveness of insulin resistance (IR) in forecasting metabolic syndrome (MetS) risk, especially within the Iranian population afflicted with type 2 diabetes mellitus (T2DM). The present investigation aimed to assess the efficacy of IR indices in predicting the risk of MetS among T2DM patients. Convenient sampling was utilized to select four hundred subjects with T2DM. Metabolic factors and IR indices, including the Waist Circumference-Triglyceride Index (WTI), Triglyceride and Glucose Index (TyG index), the product of TyG index and abdominal obesity indices, and the Metabolic Score for Insulin Resistance (METS-IR), were evaluated. Logistic regression, coupled with modeling, was employed to explore the risk of MetS. The predictive performance of the indices for MetS stratified by sex was evaluated via receiver operating characteristic (ROC) curve analysis and estimation of the area under the curve (AUC) values. The TyG-Waist Circumference (TyG-WC) index exhibited the largest AUCs in both males (0.91) and females (0.93), while the TyG-Body Mass Index (TyG-BMI) demonstrated the smallest AUCs (0.77 in males and 0.74 in females). All indices significantly predicted the risk of MetS in all subjects before and after adjustment (p

Background: Some recommendations and health protocols were presented to control COVID-19 after the outbreak, such as the use of face masks, observing social distancing, closure of schools, etc. Despite these protocols, we witnessed different peaks and variants of COVID-19 for more than two years. This study investigated some risky behaviors, such as not wearing face masks, violating social distancing, and attendance at crowded places. Methods: We used a checklist containing some demographic, caring behaviors, and survey questions. Data were collected from four universities in Iran. Patients with positive PCR results for COVID-19 were included in the study. The minimum sample size required for this study was estimated to be 407, which were selected from the universities by proportional allocation. Results: The use of face mask proportion was different between the upper and lower age groups of 50 years (P=0.005). Also, this proportion was different in the subgroups of educational level, job status, income, and living area. Conclusion: The space of most crowded places was confined and many patients did not use face masks and did not observe social distancing in these places. Hence, social distancing and face mask use can be considered the most important caring behaviors to deal with COVID-19.

Abstract In the present study, the magnetic NH2-MIL-101(Al)/chitosan nanocomposite (MIL/Cs@Fe3O4 NCs) was synthesized and used in the removal of azithromycin (AZT) from an aqueous solution for the first time. The as-synthesized MIL/Cs@Fe3O4 NCs was characterized by SEM, TEM, XRD, FTIR, BET, and VSM techniques. The effect of various key factors in the AZT adsorption process was modeled and optimized using response surface methodology based on central composite design (RSM-CCD). The low value of p-value (1.3101e−06) and RSD (1.873) parameters, along with the coefficient of determination > 0.997 implied that the developed model was well fitted with experimental data. Under the optimized conditions, including pH: 7.992, adsorbent dose: 0.279 g/L, time: 64.256 min and AZT concentration: 10.107 mg/L, removal efficiency and AZT adsorption capacity were obtained as 98.362 ± 3.24% and 238.553 mg/g, respectively. The fitting of data with the Langmuir isotherm (R2: 0.998, X2: 0.011) and Pseudo-second-order kinetics (R2: 0.999, X2: 0.013) showed that the adsorption process is monolayer and chemical in nature. ΔH° > 0, ΔS° > 0, and ∆G°