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Environmental Exposure

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Spatiotemporal analysis of airborne pollutants and health risks in Mashhad metropolis: enhanced insights through sensitivity analysis and machine learning.

Environmental geochemistry and health
The study delved into an extensive assessment of outdoor air pollutant levels, focusing specifically on PM, SO, NO, and CO, across the Mashhad metropolis from 2017 to 2021. In tandem, it explored their intricate correlations with meteorological condi...

Predicting the risk of cardiovascular disease in adults exposed to heavy metals: Interpretable machine learning.

Ecotoxicology and environmental safety
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 ...

De Novo exposomic geospatial assembly of chronic disease regions with machine learning & network analysis.

EBioMedicine
BACKGROUND: Determining spatial relationships between diseases and the exposome is limited by available methodologies. aPEER (algorithm for Projection of Exposome and Epidemiological Relationships) uses machine learning (ML) and network analysis to f...

Exposure experiments and machine learning revealed that personal care products can significantly increase transdermal exposure of SVOCs from the environment.

Journal of hazardous materials
We investigated the impacts of personal care products (PCPs) on dermal exposure to semi-volatile organic compounds (SVOCs), including phthalates, organophosphate esters, polycyclic aromatic hydrocarbons (PAHs), ultraviolet filters, and p-phenylenedia...

Prediction of p-phenylenediamine antioxidant concentrations in human urine using machine learning models.

Journal of hazardous materials
p-phenylenediamine antioxidants (PPDs) are extensively used in rubber manufacturing for their potent antioxidative properties, but PPDs and 2-anilino-5-[(4-methylpentan-2yl)amino]cyclohexa-2,5-diene-1,4-dione (6PPDQ) pose potential environmental and ...

Assessing chemical exposure risk in breastfeeding infants: An explainable machine learning model for human milk transfer prediction.

Ecotoxicology and environmental safety
Breast milk is essential for infant health, but the transfer of xenobiotic chemicals poses significant risks. Ethical challenges in clinical trials necessitate the use of in vitro predictive models to assess chemical exposure risks in breastfeeding i...

Blood metal levels predict digestive tract cancer risk using machine learning in a U.S. cohort.

Scientific reports
BACKGROUND: Environmental metal exposure has been implicated in the development of digestive tract cancers, although the specific associations remain poorly defined. This study aimed to investigate the relationship between blood metal levels and the ...

Machine learning prediction of glaucoma by heavy metal exposure: results from the National Health and Nutrition Examination Survey 2005 to 2008.

Scientific reports
Using follow-up data from the National Health and Nutrition Examination Survey (NHANES) database, we have collected information on 2572 subjects and used generalized linear model to investigate the association between urinary heavy metal levels and g...

An Enhanced Protocol to Expand Human Exposome and Machine Learning-Based Prediction for Methodology Application.

Environmental science & technology
The human exposome remains limited due to the challenging analytical strategies used to reveal low-level endocrine-disrupting chemicals (EDCs) and their metabolites in serum and urine. This limits the integrity of the EDC exposure assessment and hind...

Development of a machine learning model related to explore the association between heavy metal exposure and alveolar bone loss among US adults utilizing SHAP: a study based on NHANES 2015-2018.

BMC public health
BACKGROUND: Alveolar bone loss (ABL) is common in modern society. Heavy metal exposure is usually considered to be a risk factor for ABL. Some studies revealed a positive trend found between urinary heavy metals and periodontitis using multiple logis...