AIMC Topic: Environmental Exposure

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Exploring the potential associations between single and mixed volatile compounds and preserved ratio impaired spirometry using five different approaches.

Ecotoxicology and environmental safety
BACKGROUND: Although the relationship between environmental pollutants and respiratory health has received widespread attention, no studies have explored the association between volatile organic compounds (VOCs) and preserved ratio impaired spirometr...

Distribution mapping and risk assessment of lead in topsoil across the Tibetan Plateau.

Ecotoxicology and environmental safety
Lead exposure poses substantial long-term health risks, accounting for over 900,000 annual deaths worldwide and impairing cognitive development in more than 800 million children. Recent studies have indicated elevated soil lead contamination levels o...

Effect of the exposure to brominated flame retardants on hyperuricemia using interpretable machine learning algorithms based on the SHAP methodology.

PloS one
BACKGROUND: Brominated flame retardants (BFRs) are classified as important endocrine disruptors and persistent organic pollutants; nevertheless, there is no comprehensive investigation to evaluate the association between BFRs and hyperuricemia, and t...

Extreme urban temperature exposure and preterm birth: Spatial-temporal risk zone prediction using machine learning models.

Environmental research
This study investigates temperature impacts on preterm birth (PTB) using residential address GPS coordinates for 311,972 pregnant women in Wuhan, China, coupled with daily environmental data. We developed a machine learning model to analyze the impac...

The Cost Outcome Pathway framework: Integrating socio-economic impacts to Adverse Outcome Pathways for supporting policy makers.

Toxicology
The Adverse Outcome Pathway (AOP) concept leverages existing data to formalize and disseminate knowledge and is a well-accepted concept in chemical risk assessment. However, it does not handle the socio-economic impact that environmentally-induced di...

Exploring the association between volatile organic compound exposure and chronic kidney disease: evidence from explainable machine learning methods.

Renal failure
BACKGROUND: Chronic Kidney Disease (CKD) affects approximately 697.5 million people worldwide. Volatile organic compounds (VOCs) are emerging as potential risk factors, but their complex relationships with CKD may be underestimated by traditional lin...

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...

Air Pollution and Autism Spectrum Disorder: Unveiling Multipollutant Risks and Sociodemographic Influences in California.

Environmental health perspectives
BACKGROUND: Autism spectrum disorder (ASD) is a neurodevelopmental condition with increasing prevalence worldwide. Air pollution may be a major contributor to the rise in ASD cases. This study investigated how the risk of ASD associated with prenatal...

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...

Urban-rural inequality in soil heavy metal health risks: Insights from Baoding, China.

Ecotoxicology and environmental safety
Soil heavy metal contamination poses serious health risks, but few studies have quantitatively assessed disparities in these risks between urban and rural populations. To address this gap, we introduce a novel framework integrating machine learning a...