AIMC Topic: Environmental Pollutants

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Investigating the potential risk of cadmium exposure on Osteoporosis: An integrated multi-omics approach.

Ecotoxicology and environmental safety
Osteoporosis (OP) is a chronic progressive bone disease, and its occurrence and development under cadmium exposure remain unclear. This study aims to explore the role of cadmium exposure in the pathogenesis of OP through a comprehensive analysis of m...

Association between metal mixture in urine and abnormal blood pressure and mediated effect of oxidative stress based on BKMR and Machine learning method.

Ecotoxicology and environmental safety
BACKGROUND: Exposure to heavy metals represents a significant risk factor for hypertension and blood pressure disorders. Notably, current evidence indicates that the key biological processes of oxidative stress, inflammation, and endothelial dysfunct...

A negative combined effect of exposure to maternal Mn-Cu-Rb-Fe metal mixtures on gestational anemia, and the mediating role of creatinine in the Guangxi Birth Cohort Study (GBCS): Twelve machine learning algorithms.

Ecotoxicology and environmental safety
The link between individual metals and gestational anemia has been established, but the impact of metal mixtures and the mediating role of renal function on gestational anemia remain inconclusive. The concentrations of 20 blood essential trace and no...

Application of a metabolic network-based graph neural network for the identification of toxicant-induced perturbations.

Toxicological sciences : an official journal of the Society of Toxicology
Transcriptomic analyses have been an effective approach to investigate the biological responses and metabolic perturbations by environmental contaminants in rodent models. However, it is well recognized that metabolic networks are highly connected an...

Letter to the Editor regarding "Prediction of PFAS bioaccumulation in different plant tissues with machine learning models based on molecular fingerprints" by Song et al. (2024), Sci. Total Environ. 950 175091.

The Science of the total environment
Song et al. (2024), "Prediction of PFAS bioaccumulation in different plant tissues with machine learning models based on molecular fingerprints," employed machine learning methods, such as XGBoost and SHapley Additive exPlanations (SHAP), to predict ...

Development and validation of an interpretable machine learning model for predicting hyperuricemia risk: Based on environmental chemical exposure.

Ecotoxicology and environmental safety
Hyperuricemia is a global health concern, with environmental chemicals as risk factors. This study used data of multiple environmental chemical exposures from the 2011-2012 cycle of the National Health and Nutrition Examination Survey (NHANES) to dev...

Advancements in artificial intelligence-based technologies for PFAS detection, monitoring, and management.

The Science of the total environment
Per- and polyfluoroalkyl substances (PFAS) are persistent environmental contaminants with strong carbon‑fluorine (CF) bonds that contribute to bioaccumulation and long-term environmental and health risks. Traditional PFAS detection and treatment meth...

Development of an Interpretable Machine Learning Model for Neurotoxicity Prediction of Environmentally Related Compounds.

Environmental science & technology
The rising prevalence of nervous system disorders has become a significant global health challenge, with environmental pollutants identified as key contributors. However, the large number of environmental related compounds, combined with the low effi...

Exploring the toxicological impact of bisphenol a exposure on psoriasis through network toxicology, machine learning, and multi-dimensional bioinformatics analysis.

Journal of environmental management
Psoriasis is a common immune - mediated skin disease, the pathogenesis of which is not completely elucidated. Environmental factors are key to its onset and progression. Bisphenol A (BPA) is a ubiquitous environmental pollutant that endangers human h...

XenoBug: machine learning-based tool to predict pollutant-degrading enzymes from environmental metagenomes.

NAR genomics and bioinformatics
Application of machine learning-based methods to identify novel bacterial enzymes capable of degrading a wide range of xenobiotics offers enormous potential for bioremediation of toxic and carcinogenic recalcitrant xenobiotics such as pesticides, pla...