Sequential distribution, potential sources, and health risk assessment of persistent toxic substances in sewage sludge used as organic fertilizer in Indo-Gangetic region.

This study evaluates the environmental and human health impact of sewage sludge generated in the Indo-Gangetic region (Uttarakhand and Uttar Pradesh) used as organic fertilizer and landfill disposal. The research conducts a comprehensive risk assessment, including physicochemical and heavy metals analysis, on triplicate sludge samples obtained from 30 sewage treatment plants. The study provides both qualitative and quantitative insights into potential hazards associated with sewage sludge.

Solid dispersion of alectinib HCl: preclinical evaluation for improving bioavailability and establishing an IVIVC model.

Objective: Alectinib HCl (ALB-HCl) is a BCS class IV molecule with low solubility and low oral bioavailability. Owing to its low bioavailability, a high dose of ALB-HCl is recommended with food to meet clinical efficacy. Thus, there is a need for a delivery system to overcome the bioavailability concerns.

Flunarizine Versus Propranolol in Prophylaxis of Pediatric Migraine: An Open-Label Randomized Trial.

Background Pediatric migraine is a primary headache affecting daily activities and causing significant disability among children. However, clarity on the usage of prophylactic medications in children is yet to be established. This study was conducted with the aim of comparing the efficacy and safety of flunarizine and propranolol in the prophylaxis of pediatric migraine.

Ensemble deep learning and EfficientNet for accurate diagnosis of diabetic retinopathy.

Diabetic Retinopathy (DR) stands as a significant global cause of vision impairment, underscoring the critical importance of early detection in mitigating its impact. Addressing this challenge head-on, this study introduces an innovative deep learning framework tailored for DR diagnosis. The proposed framework utilizes the EfficientNetB0 architecture to classify diabetic retinopathy severity levels from retinal images.

Advance drought prediction through rainfall forecasting with hybrid deep learning model.

Drought is a natural disaster that can affect a larger area over time. Damage caused by the drought can only be reduced through its accurate prediction. In this context, we proposed a hybrid stacked model for rainfall prediction, which is crucial for effective drought forecasting and management.