AI Medical Compendium Journal:
Journal of hazardous materials

Showing 31 to 40 of 97 articles

Integrating external stressors in supervised machine learning algorithm achieves high accuracy to predict multi-species biological integrity index of aquaculture wastewater.

Journal of hazardous materials
Monitoring and predicting the environmental impact of wastewater is essential for sustainable aquaculture. The environmental DNA metabarcoding-integrated supervised machine learning (SML) algorithm is an alternative method for ecological quality asse...

Deep learning-assisted detection of psychoactive water pollutants using behavioral profiling of zebrafish embryos.

Journal of hazardous materials
Water pollution poses a significant risk to the environment and human health, necessitating the development of innovative detection methods. In this study, a series of representative psychoactive compounds were selected as model pollutants, and a new...

Using the super-learner to predict the chemical acute toxicity on rats.

Journal of hazardous materials
With the rapid increase in the number of commercial chemicals, testing methods regarding on median lethal dose (LD) relying animal experiments face challenges such as high costs and ethical concerns. Classical quantitative structure-activity relation...

High-throughput prediction of oral acute toxicity in Rat and Mouse of over 100,000 polychlorinated persistent organic pollutants (PC-POPs) by interpretable data fusion-driven machine learning global models.

Journal of hazardous materials
This study utilized available oral acute toxicity data in Rat and Mouse for polychlorinated persistent organic pollutants (PC-POPs) to construct data fusion-driven machine learning (ML) global models. Based on atom-centered fragments (ACFs), the coll...

Machine learning models to predict the bioaccessibility of parent and substituted polycyclic aromatic hydrocarbons (PAHs) in food: Impact on accurate health risk assessment.

Journal of hazardous materials
Food intake is the primary pathway for polycyclic aromatic hydrocarbons (PAHs) to enter the human body. Once ingested, PAHs tend to accumulate, posing health risks. To accurately assess the risk of PAHs from food, concentrations of 10 parent PAHs (PP...

Rapid and noninvasive estimation of human arsenic exposure based on 4-photo-set of the hand and foot photos through artificial intelligence.

Journal of hazardous materials
Chronic exposure to arsenic is linked to the development of cancers in the skin, lungs, and bladder. Arsenic exposure manifests as variegated pigmentation and characteristic pitted keratosis on the hands and feet, which often precede the onset of int...

Machine learning-assisted laccase-like activity nanozyme for intelligently onsite real-time and dynamic analysis of pyrethroid pesticides.

Journal of hazardous materials
The intelligently efficient, reliable, economical and portable onsite assay toward pyrethroid pesticides (PPs) residues is critical for food safety analysis and environmental pollution traceability. Here, a fluorescent nanozyme Cu-ATP@ [Ru(bpy)] with...

Deep learning models for predicting plant uptake of emerging contaminants by including the role of plant macromolecular compositions.

Journal of hazardous materials
Deep learning models can predict uptake of emerging contaminants in plants with improved accuracy because they leverage advanced data-driven approaches to capture non-linear relationships that traditional models struggle to address. Traditional model...

Ultralow-resistance and self-sterilization biodegradable nanofibrous membranes for efficient PM removal and machine learning-assisted health management.

Journal of hazardous materials
The development of multifunctional nanofibrous membranes (NFMs) that enable anti-viral protection during air purification and respiratory disease diagnosis for health management is of increasing importance. Herein, we unraveled a heterostructure-enha...

Machine learning to guide the use of plasma technology for antibiotic degradation.

Journal of hazardous materials
Antibiotics are misused and discharged into environmental water, posing a constant potential threat to the ecosystem. Utilising plasma's physical and chemical effects to remove antibiotics has emerged as a promising wastewater treatment technology. H...