Molecular dynamics simulations, essential dynamics and MMPBSA to evaluate natural compounds as potential inhibitors for AccD6, a key drug target in the fatty acid biosynthesis pathway in Mycobacterium tuberculosis.

Antibiotic resistance in Mycobacterium tuberculosis, the primary causative agent of the tuberculosis disease is an ever growing threat especially in developing and underdeveloped countries. Isoniazid is a commonly used first line anti-tuberculosis drug used during the first phase of tuberculosis treatment. However, due to its improper use, many strains of Mycobacterium tuberculosis have acquired resistance to the drug.

Prediction of urban surface water quality scenarios using hybrid stacking ensembles machine learning model in Howrah Municipal Corporation, West Bengal.

In the pursuit of understanding surface water quality for sustainable urban management, we created a machine learning modeling framework that utilized Random Forest (RF), Cubist, Extreme Gradient Boosting (XGB), Multivariate Adaptive Regression Splines (MARS), Gradient Boosting Machine (GBM), Support Vector Machine (SVM), and their hybrid stacking ensemble RF (SE-RF), as well as stacking Cubist (SE-Cubist), to predict the distribution of water quality in the Howrah Municipal Corporation (HMC) area in West Bengal, India. Additionally, we employed the ReliefF and Shapley Additive exPlanations (SHAP) methods to elucidate the underlying factors driving water quality. We first estimated the water quality index (WQI) to model seven water quality parameters: total hardness (TH), pH, total dissolved solids (TDS), dissolved oxygen (DO), biochemical oxygen demand (BOD), calcium (Ca), magnesium (Mg).

Integrating machine learning and geospatial data analysis for comprehensive flood hazard assessment.

Flooding is a major natural hazard worldwide, causing catastrophic damage to communities and infrastructure. Due to climate change exacerbating extreme weather events robust flood hazard modeling is crucial to support disaster resilience and adaptation. This study uses multi-sourced geospatial datasets to develop an advanced machine learning framework for flood hazard assessment in the Arambag region of West Bengal, India.

Mapping groundwater potential zone in the subarnarekha basin, India, using a novel hybrid multi-criteria approach in Google earth Engine.

Assessing groundwater potential for sustainable resource management is critically important. In addressing this concern, this study aims to advance the field by developing an innovative approach for Groundwater potential zone (GWPZ) mapping using advanced techniques, such as FuzzyAHP, FuzzyDEMATEL, and Logistic regression (LR) models. GWPZ was carried out by integrating various primary factors, such as hydrologic, soil permeability, morphometric, terrain distribution, and anthropogenic influences, incorporating twenty-seven individual criteria using multi-criteria decision models along with a hybrid approach for the Subarnarekha River basin, India, in Google earth engine (GEE).

A Case of Congenital Coronary Ostial Stenosis with Systemic Lupus Erythematosus.

Congenital coronary ostial stenosis or atresia (COSA) is a spectrum of rare developmental conditions with different pathophysiologic mechanisms and clinical implications. Although COSA includes various entities, these entities have two features in common. First the defect is congenital, although it may progress during pre-natal and post natal life.