Objective: Parkinson’s disease (PD) affects approximately 2% of individuals over the age of 60. With more than two billion Muslims observing Ramadan, individuals with PD encounter specific challenges, such as deteriorating motor skills, sleep disturbances, and an increased risk of falls during fasting. Methods: Our study focused on 75 patients with idiopathic PD divided into two groups: the Ramadan Regime group, which consisted of 50 patients whose medication was adjusted to twice daily at Suhoor and Iftar, and the Nontreatment group, which included 25 patients who abstained from medication for religious reasons. Both groups were instructed to wear a Parkinson’s KinetiGraph (PKG) wrist device. Results: The study findings revealed that motor function worsened in the Nontreatment group (p<0.001) but improved in the Ramadan Regime group (p=0.007). Daytime sleepiness also significantly increased in the Nontreatment group (p<0.001). Conclusion Overall, the findings suggest that the Ramadan regime significantly enhances patient health and quality of life.

Purpose: To create a computer-aided prediction (CAP) system to predict Wilms tumor (WT) responsiveness to preoperative chemotherapy (PC) using pre-therapy contrast-enhanced computed tomography (CECT). Materials and Methods: A single-center database was reviewed for children <18 years diagnosed with WT and received PC between 2001 and 2021. Patients were excluded if pre- and post-PC CECT were not retrievable. According to the Response Evaluation Criteria in Solid Tumors criteria, volumetric response was considered favorable if PC resulted in ≥30% tumor volume reduction.Histological response was considered favorable if post-nephrectomy specimens had ≥66% necrosis. Four steps were used to create the prediction model: tumor delineation; extraction of shape, texture and functionality-based features; integration of the extracted features and selection of the prediction model with the highest diagnostic performance. K-fold cross-validation allowed the presentation of all data in the training and testing phases. Results: A total of 63 tumors in 54 patients were used to train and test the prediction model. Patients were treated with 4–8 weeks of vincristine/actinomycin-D combination. Favorable volumetric and histologic responses were achieved in 46 tumors (73.0%) and 38 tumors (60.3%), respectively. Among machine learning classifiers, support vector machine had the best diagnostic performance with an accuracy, sensitivity, and specificity of 95.24%, 95.65%, and 94.12% for volumetric and 84.13%, 89.47%, 88% for histologic response prediction. Conclusions Based on pre-therapy CECT, CAP systems can help identify WT that are less likely to respond to PC with excellent accuracy. These tumors can be offered upfront surgery, avoiding the cons of PC.