AIMC Topic: Environmental Pollutants
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Predicting the risk of cardiovascular disease in adults exposed to heavy metals: Interpretable machine learning.
Ecotoxicology and environmental safety
Dec 24, 2024
Machine learning exhibits excellent performance in terms of predictive power. We aimed to construct an interpretable machine learning model utilizing National Health and Nutrition Examination Survey data to investigate the relationship between heavy ...
Endocrine disruptor identification and multitoxicity level assessment of organic chemicals: An example of multiple machine learning models.
Journal of hazardous materials
Dec 15, 2024
Endocrine-disrupting chemicals (EDCs) pollution is a major global environmental issue. Assessing the multiple toxic effects of EDCs is key to managing their risks. This study successfully developed an EDCs classification and recognition model based o...
ChemNTP: Advanced Prediction of Neurotoxicity Targets for Environmental Chemicals Using a Siamese Neural Network.
Environmental science & technology
Dec 11, 2024
Environmental chemicals can enter the human body through various exposure pathways, potentially leading to neurotoxic effects that pose significant health risks. Many such chemicals have been identified as neurotoxic, but the molecular mechanisms und...
Effective analysis of thyroid toxicity and mechanisms of acetyltributyl citrate using network toxicology, molecular docking, and machine learning strategies.
Toxicology
Dec 8, 2024
The growing prevalence of environmental pollutants has raised concerns about their potential role in thyroid dysfunction and related disorders. Previous research suggests that various chemicals, including plasticizers like acetyl tributyl citrate (AT...
Machine Learning-Assisted Surface-Enhanced Raman Spectroscopy Detection for Environmental Applications: A Review.
Environmental science & technology
Nov 13, 2024
Surface-enhanced Raman spectroscopy (SERS) has gained significant attention for its ability to detect environmental contaminants with high sensitivity and specificity. The cost-effectiveness and potential portability of the technique further enhance ...
Trends in the prevalence of osteoporosis and effects of heavy metal exposure using interpretable machine learning.
Ecotoxicology and environmental safety
Oct 28, 2024
There is limited evidence that heavy metals exposure contributes to osteoporosis. Multi-parameter scoring machine learning (ML) techniques were developed using National Health and Nutrition Examination Survey data to predict osteoporosis based on hea...
Identifying cardiovascular disease risk in the U.S. population using environmental volatile organic compounds exposure: A machine learning predictive model based on the SHAP methodology.
Ecotoxicology and environmental safety
Oct 23, 2024
BACKGROUND: Cardiovascular disease (CVD) remains a leading cause of mortality globally. Environmental pollutants, specifically volatile organic compounds (VOCs), have been identified as significant risk factors. This study aims to develop a machine l...
Machine learning models reveal how polycyclic aromatic hydrocarbons influence environmental bacterial communities.
The Science of the total environment
Oct 22, 2024
Polycyclic aromatic hydrocarbons (PAHs) are harmful and widespread pollutants in the environment, posing an ecological threat. However, exploring the influence of PAHs on environmental bacterial communities in different habitats (soil, water, and sed...
Identification of endocrine-disrupting chemicals targeting key OP-associated genes via bioinformatics and machine learning.
Ecotoxicology and environmental safety
Oct 9, 2024
Osteoporosis (OP), a metabolic disorder predominantly impacting postmenopausal women, has seen considerable progress in diagnosis and treatment over the past few decades. However, the intricate interplay between genetic factors and endocrine disrupto...
Deciphering the cytotoxicity of micro- and nanoplastics in Caco-2 cells through meta-analysis and machine learning.
Environmental pollution (Barking, Essex : 1987)
Sep 16, 2024
Plastic pollution, driven by micro- and nanoplastics (MNPs), poses a major environmental threat, exposing humans through various routes. Despite human colorectal adenocarcinoma Caco-2 cells being used as an in vitro model for studying the intestinal ...
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