Diagnostics Evaluation of Smart Sure™ Mycobacterium tuberculosis Screening Kit and Smart Sure™ Multidrug-resistant Tuberculosis Detection Kit on Nonsputum Specimens at a Tertiary Care Center of North India.

Background: Tuberculosis (TB) is caused due to the infection of Mycobacterium tuberculosis (MTB) and it can infect the various parts of the human body. The disease is highly prevalent and is the second most common cause of death worldwide after COVID-19. Apart from sputum specimen, it is exceedingly difficult to diagnose due to its paucibacillary nature.

Evaluation of GeneNAT Real-Time Polymerase Chain Reaction Analyzer and Pre-loaded Chip-Based Mycobacterium tuberculosis and Multidrug-Resistant Tuberculosis Detection in the Diagnosis of Pulmonary Tuberculosis.

Background: Tuberculosis (TB) is still the second causative agent of death worldwide after COVID-19. It is caused by (MTB) infection.

Objective: The aim of the current study was to compare the performance of GeneNAT real-time polymerase chain reaction analyzer and pre-loaded chip-based MTB screening and multidrug-resistant tuberculosis (MDR-TB) detection kit (Smart Sure MTB & MDR-TB, Genetix Biotech Asia Pvt.

Effect of Yoga among Children and Adolescents Diagnosed with Psychiatric Disorders: A Scoping Review.

Background: Depression has been expected to be the second-leading cause of disability, followed by autism, attention and hyperactivity disorder, and learning disorder. Yoga therapy has found to be beneficial in managing psychiatric disorders.

Aim: The present study undertakes a scoping review of research on yoga therapy in psychiatric disorders among children and adolescents.

Prediction of Individual Gas Yields of Supercritical Water Gasification of Lignocellulosic Biomass by Machine Learning Models.

Supercritical water gasification (SCWG) of lignocellulosic biomass is a promising pathway for the production of hydrogen. However, SCWG is a complex thermochemical process, the modeling of which is challenging via conventional methodologies. Therefore, eight machine learning models (linear regression (LR), Gaussian process regression (GPR), artificial neural network (ANN), support vector machine (SVM), decision tree (DT), random forest (RF), extreme gradient boosting (XGB), and categorical boosting regressor (CatBoost)) with particle swarm optimization (PSO) and a genetic algorithm (GA) optimizer were developed and evaluated for prediction of H, CO, CO, and CH gas yields from SCWG of lignocellulosic biomass.