AIMC Topic: Environmental Pollutants

Clear Filters Showing 21 to 30 of 147 articles

Triclosan exposure potentiates ischemic stroke risk: Multi-omics integration and molecular docking unveil neurotoxic mechanisms.

Ecotoxicology and environmental safety
This study applied network toxicology and multimodal biological approaches integrated with machine learning to systematically identify four TCS-IS-related genes, providing a comprehensive understanding of the pathophysiological relationship between t...

Machine learning-powered fluorescent sensor arrays for rapid detection of heavy metals and pesticides in complex environments.

Biosensors & bioelectronics
The co-contamination of multiple pollutants in complex environmental matrices poses a significant threat to ecosystems and public health, necessitating advanced detection methods. In this study, we developed a machine learning-powered chemical sensor...

A Machine Learning-Based Clustering Analysis to Explore Bisphenol A and Phthalate Exposure from Medical Devices in Infants with Congenital Heart Defects.

Environmental health perspectives
BACKGROUND: Plastic-containing medical devices are commonly used in critical care units and other patient care settings. Patients are often exposed to xenobiotic agents that are leached out from plastic-containing medical devices, including bisphenol...

Unraveling the ecotoxicity of micro(nano)plastics loaded with environmental pollutants using ensemble machine learning.

Journal of hazardous materials
Micro(nano)plastics are ubiquitous and pose a severe threat to the environment and human health. Despite increasing research, most existing studies have focused on the toxicity of micro(nano)plastics as individual pollutants. Furthermore, fragmented ...

Single-cell sequencing and machine learning reveal the role of dioxin-interacting genes in HCC prognosis and immune microenvironment.

Ecotoxicology and environmental safety
Dioxins are persistent environmental pollutants that bioaccumulate in the food chain, posing significant risks to human health. Despite their low environmental concentrations, dioxins accumulate in tissues, particularly in top predators and humans, r...

Evaluating the relationship between environmental chemicals and obesity: Evidence from a machine learning perspective.

Ecotoxicology and environmental safety
Environmental chemicals are increasingly recognized as important contributors to obesity, yet the number of studies evaluating this relationship remains insufficient. This study aimed to investigate these associations using interpretable machine lear...

Impact of e-waste pollutant exposure on renal injury and oxidative stress biomarkers: Evidence from causal machine learning.

Journal of hazardous materials
Global electronification has driven an unprecedented surge in electronic and electrical waste (e-waste), with approximately 75 % of this e-waste informally managed, releasing hazardous chemicals. Traditional association analyses have limited ability ...

Emerging technologies for assessing the occurrence, fate, effects, and remediation of plastics in the environment.

Environmental monitoring and assessment
Plastic pollution and contamination originates from raw material handling, polymerization, compounding, and fabrication, contributing to environmental accumulation. Advanced analytical techniques such as Fourier transform infrared, Raman spectroscopy...

ExpoPath: A method for identifying and annotating exposure pathways from chemical co-occurrence networks.

The Science of the total environment
Improving risk evaluation for environmental and human health is of paramount concern for the U.S. Environmental Protection Agency (EPA). This includes the identification and assessment of chemical transport from commercial and industrial sources to e...

Automated detection and recognition of oocyte toxicity by fusion of latent and observable features.

Journal of hazardous materials
Oocyte quality is essential for successful pregnancy, yet no discriminant criterion exists to assess the effects of environmental pollutants on oocyte abnormalities. We developed a stepwise framework integrating deep learning-extracted latent feature...