Unraveling microbial landscapes: high-throughput amplicon sequencing reveals distinct bacterial communities and potential health risks in potable springs of the Indian Himalayas.

Spring water is a vital drinking resource for residents in the Eastern Himalayas' Sikkim, India. While our initial investigations into spring water quality highlighted concerning levels of fecal coliform bacteria, the bacterial community composition (BCC) of these springs remains largely unexplored. This study sought to elucidate the BCC of Himalayan spring water, exploring its effects on water quality and delving into the unique bacterial ecology of these high-altitude springs.

Machine learning models with innovative outlier detection techniques for predicting heavy metal contamination in soils.

Machine learning (ML) models for accurately predicting heavy metals with inconsistent outputs have improved owing to dataset outliers, which influence model reliability and accuracy. A comprehensive technique that combines machine learning and advanced statistical methods was applied to assess data outlier's effects on ML models. Ten ML models with three outlier detection methods predicted Cr, Ni, Cd, and Pb in Narayanganj soils.

Electro-anatomically confirmed sites of origin of ventricular tachycardia and premature ventricular contractions and occurrence of R wave in lead aVR: A proof of concept study.

Background: The R wave in lead aVR, during the wide QRS complex tachycardia, is commonly observed in a ventricular tachycardia (VT) arising from certain locations in the ventricles.

Objectives: This was a retrospective and a proof of concept study to correlate the sites of origin of VT and premature ventricular contractions (PVCs) with the occurrence of R wave in lead aVR.

Methods: Patients with VT or high density of PVCs were included.

Pollution area identification, receptor model-oriented sources and probabilistic health hazards to prioritize control measures for heavy metal management in soil.

Identifying the primary source of heavy metals (HMs) pollution and the key pollutants is crucial for safeguarding eco-health and managing risks in industrial vicinity. For this purpose, this investigation was carried out to investigate the pollution area identification with soil static environmental capacity (QI), receptor model-oriented critical sources, and Monte Carlo simulation (MCS) based probabilistic environmental and human health hazards associated with HMs in agricultural soils of Narayanganj, Bangladesh. The average concentration of Cr, Ni, Cu, Cd, Pb, Co, Zn, and Mn were 98.