Hydrogeochemical characterization of shallow and deep groundwater for drinking and irrigation water quality index of Kathmandu Valley, Nepal.

A comprehensive hydrogeochemical analysis of 156 groundwater samples (106 shallow and 50 deep) was conducted in the Kathmandu Valley, Nepal. This study addresses a significant research gap by focusing on the hydro-geochemical composition and contamination of groundwater in the Kathmandu Valley, an area with limited detailed assessments. The novelty of this work lies in its comprehensive analysis of both shallow and deep groundwater, particularly concerning the high concentration of contaminants like arsenic, microbial pathogens, and ammonium, which are critical for public health.

An augmented transformer model trained on protein family specific variant data leads to improved prediction of variants of uncertain significance.

Variants of uncertain significance (VUS) represent variants that lack sufficient evidence to be confidently associated with a disease, thus posing a challenge in the interpretation of genetic testing results. Here we report an improved method for predicting the VUS of Arylsulfatase A (ARSA) gene as part of the Critical Assessment of Genome Interpretation challenge (CAGI6). Our method uses a transfer learning approach that leverages a pre-trained protein language model to predict the impact of mutations on the activity of the ARSA enzyme, whose deficiency is known to cause a rare genetic disorder, metachromatic leukodystrophy.

The role of mitochondrial dysfunction in Huntington's disease: Implications for therapeutic targeting.

Huntington's disease (HD) is a progressive, autosomal dominant neurodegenerative disorder characterized by cognitive decline, motor dysfunction, and psychiatric disturbances. A common feature of neurodegenerative disorders is mitochondrial dysfunction, which affects the brain's sensitivity to oxidative damage and its high oxygen demand. This dysfunction may plays a significant role in the pathogenesis of Huntington's disease.

Robotic versus manual disinfection of global priority pathogens at COVID-19 dedicated hospitals.

Background: Twelve bacterial families were identified as global priority pathogens by the World Health Organization in 2017, recognizing the greatest threat they pose to human health and the declining antibiotic efficacy. Robotics has emerged as a swift and contactless tool for disinfecting bacterial surface contamination in healthcare facilities, however, head-to-head comparison of disinfection efficacy of robotic versus manual disinfections is limited. This study aimed at comparing how robotic disinfection performs over manual disinfection against the global priority pathogens in the healthcare setting.

Development of an algorithm impacting COPD care through personalized nutrition and IoT-based monitoring.

Background: Chronic obstructive pulmonary disease (COPD) is a chronic respiratory condition characterized by high morbidity and mortality rates. This study aims to assess the clinical outcomes of COPD patients after implementing an algorithm within the MyTatva app.

Methods: The study involved a sample of 10 COPD patients, evaluating key parameters such as Forced Expiratory Volume in 1 s (FEV1), Forced Vital Capacity, Weight, Body Mass Index (BMI), Fat-Free Mass Index, and Distance Covered during the 6-Minute Walk Test (6MWT) before and after the algorithm's implementation in the MyTatva app.