AI Medical Compendium Journal:
Journal of hazardous materials

Showing 1 to 10 of 97 articles

PM pollution characteristics, drivers, and regional transport during different pollution levels in Linyi, China: An integrated PMF-ML-SHAP framework and transport models.

Journal of hazardous materials
Despite significant progress in air quality improvement, heavy fine particulate matter (PM) pollution events persist in China. The pollution characteristics of PM vary during different pollution levels, highlighting the necessity for a deeper underst...

A framework for spatial correlations between industrial pollution sources and groundwater vulnerabilities based on machine learning and spatial cluster analysis: Implications for risk control.

Journal of hazardous materials
The efficacy of groundwater pollution risk control is often limited by a lack of information on spatial correlations between industrial pollution sources and groundwater vulnerabilities. To overcome this limitation, a novel data-driven framework was ...

Advancing wetland groundwater pollution zoning: A novel integration of Monte Carlo health risk modeling and machine learning.

Journal of hazardous materials
Wetlands serve as crucial water reservoirs, providing essential water resources for the surrounding regions. However, elevated ion concentrations in wetland groundwater may pose health risks to local populations. This study focused on Judian Lake and...

Mitigation of the toxic effects of nitrite: Role and mechanism of isoleucine in mitigating mitochondrial DNA leakage-induced inflammation in grass carp (Ctenopharyngodon idella) under nitrite exposure.

Journal of hazardous materials
The physiological and growth processes of fish are closely associated with their surrounding environment. This study investigated the role and underlying mechanisms of isoleucine (Ile) in alleviating mitochondrial DNA (mtDNA) leakage-induced inflamma...

Unveiling the systemic impact of airborne microplastics: Integrating breathomics and machine learning with dual-tissue transcriptomics.

Journal of hazardous materials
Airborne microplastics (MPs) pose significant respiratory and systemic health risks upon inhalation; however, current assessment methods remain inadequate. This study integrates breathomics and transcriptomics to establish a non-invasive approach for...

Development of a deep neural network model based on high throughput screening data for predicting synergistic estrogenic activity of binary mixtures for consumer products.

Journal of hazardous materials
A paradigm of chemical risk assessment is continuously extending from focusing on 'single substances' to more comprehensive approaches that examines the combined toxicity among different components in 'mixtures.' This change aims to account for the c...

Efficient detection of foodborne pathogens via SERS and deep learning: An ADMIN-optimized NAS-Unet approach.

Journal of hazardous materials
Amid the increasing global challenge of foodborne diseases, there is an urgent need for rapid and precise pathogen detection methods. This study innovatively integrates surface-enhanced Raman Spectroscopy (SERS) with deep learning technology to devel...

Web server-based deep learning-driven predictive models for respiratory toxicity of environmental chemicals: Mechanistic insights and interpretability.

Journal of hazardous materials
Respiratory toxicity of chemicals is a common clinical and environmental health concern. Currently, most in silico prediction models for chemical respiratory toxicity are often based on a single or vague toxicity endpoint, and machine learning models...

Integration of machine learning and meta-analysis reveals the behaviors and mechanisms of antibiotic adsorption on microplastics.

Journal of hazardous materials
Microplastics (MPs) can adsorb antibiotics (ATs) to cause combined pollution in the environment. Research on this topic has been limited to specific types of MPs and ATs, resulting in inconsistent findings, particularly for the influencing factors an...

Untargeted metabolomics and machine learning unveil the exposome and metabolism linked with the risk of early pregnancy loss.

Journal of hazardous materials
Early pregnancy loss (EPL) may result from exposure to emerging contaminants (ECs), although the underlying mechanisms remain poorly understood. This case-control study measured over 2000 serum features, including 37 ECs, 6 biochemicals, and 2057 end...