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In today’s world, wound healing is a growing serious problem for clinical institutes. In this study, nanofibrous scaffolds were prepared using polyurethane as a mat scaffold. Also, by immersing curcumin as an antibacterial component another scaffold was fabricated using the electrospinning technique. The obtained scaffolds were characterized by means of scanning electron microscopy, tensile analysis, porosity, and water vapor transmission rate. MTT and DAPI staining were used to prove the biocompatibility and cell attachment of the nanofibers. The curcumin incorporated into the PU scaffold can stop both the Gram-negative and Gram-positive bacteria activities through direct contact with them. Studies showed that the PU/Curcumin scaffold has considerable ability to play a key role in wound healing applications.

Background: Good response to ovulation induction and in turn improving pregnancyrates represents a big challenge in patients with PCOS. Aim: To assess the effectiveness of adjuvant therapy to clomiphene citrate and metformin (in theform of N-acetyl cysteine versus Chromium Picolinate) as regards response to ovulation inductionand metabolic syndrome improvement in patients with PCOS and infertility. Materials and Methods: This randomized controlled trial involved 108 women diagnosed withPCOS and infertility and were divided into 3 groups; according to the treatment protocol; group 1received clomiphene citrate plus metformin only, group2 received clomiphene citrate and metforminplus N-acetyl cysteine and Group 3 received clomiphene citrate and metformin plus chromiumpicolinate. Results: There was a statistically significant difference as regards good ovulation in the form of maturefollicles (≥18 mm in diameter), with the best results in group 3, followed by group 2. Both Nacetylcysteine and chromium picolinate, as adjuvant therapies, were more effective in reducing thefasting insulin levels than clomiphene citrate plus metformin alone. Conclusion: Chromium picolinate and N-acetyl cysteine are effective adjuvant therapies for improvingovulation, insulin resistance, and endocrinal outcomes in women with PCOS.

With the surge in global population and economic expansion, there's been a marked increase in electricity demand. This necessitates the efficient distribution of electricity to both residential and industrial sectors to minimize energy loss. Smart Grids (SG) emerge as a promising solution to reduce power dissipation in distribution networks. The application of machine learning and artificial intelligence in SGs has significantly improved the precision of predicting consumer electricity needs. This paper presents a novel approach to improving the stability prediction of Internet of Things (IOT)-driven SGs using different advanced machine learning models. This study explores multiple advanced machine-learning techniques, including Gradient Boosting (GB), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Neural Networks, and the Decision Tree classifier, focusing on the stability prediction of SGs. This study explores the efficiency of hyperparameter-optimized GB models in predicting SG dynamic stability that encompasses the ability of the system to return to a stable operating point following a disturbance. Focusing on various models, it identifies the Dipper Throated Optimization Algorithm DTO+GB model as the standout, exhibiting unparalleled accuracy and reliability across critical performance metrics such as accuracy (99.32%), sensitivity (99.16%), and specificity (99.54%). Diagnostic and regression analyses further emphasize its better predictive power and the need for hyperparameter optimization to improve the model. This paper highlights the capabilities of advanced machine learning algorithms in conjunction with tactical hyperparameter optimization in enhancing SG stability prediction, introducing a new baseline for future technological and methodological developments in this application.

Background & Objective: Exercise and dietary interventions are used to control dyslipidemiaand depression in obese individuals, whilst rare investigations have examined the concurrenteffects of a low-fat diet and moderate-intensity aerobic exercise training (MIAET) on dyslipidemiaand depression in obese patients. Hence, we assessed the potential influences of a low-fat diet combinedwith MIAET on blood lipids and depression in those individuals. Methods: Forty-two obese patients aged 30-50 years have been enrolled in this randomized controlledtrial. They have been randomized equally into MIAET group (n=14, 60-70% of the maximumheart rate (Max HR), three sessions a week), a low-fat diet group (n=14, fat, 30% Kcal/day),and a low-fat diet plus MIAET (n=14) for 10 consecutive weeks. Body mass index (BMI), lipidprofile, and Hamilton depression rating scale (HDRS) have been assessed on two occasions, preand post- 10 weeks. Results: It was demonstrated that a low-fat diet group showed an improvement in total cholesterol(T-Ch), p=0.046, with no changes in triglycerides (TGs), p=0.343, low-density lipoproteins (LDLs),p=0.187, and high-density lipoproteins (HDLs), p=0.224; however, MIAET group showed animprovement in TGs, p=0.042, HDLs, p=0.038 with no changes in T-Ch, p=0.126, and LDLs,p=0.368. Regarding the low-fat diet plus MIAET group, significant improvements were identifiedin TGs, p=0.003, T-Ch, p<0.001, LDLs, p=0.004, and HDLs, p<0.001. For the depression status,all groups showed a significant improvement in HDRS, p<0.001, with a low-fat diet plus MIAETgroup showing greater advantages, p<0.05. Conclusion: The results of the current trial suggest an important implication for promoting improvementin blood lipids and a reduction in depression status in obese patients with dyslipidemiafollowing 10-week of a concurrent low-fat diet and moderate-intensity aerobic exercise more thanlow-fat diet or MIAET alone.