AIMC Topic: Endocrine Disruptors

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Graphene oxide enhanced the endocrine disrupting effects of bisphenol A in adult male zebrafish: Integrated deep learning and metabolomics studies.

The Science of the total environment
In our previous studies, it was found that graphene oxide (GO) reduced the endocrine disruption of bisphenol A (BPA) in zebrafish embryo and larvae, but through different mechanisms. In this study, adult male zebrafish were selected to further unders...

Deep Learning Model for Identifying Critical Structural Motifs in Potential Endocrine Disruptors.

Journal of chemical information and modeling
This paper aims to identify structural motifs within a molecule that contribute the most toward a chemical being an endocrine disruptor. We have developed a deep neural network-based toolkit toward this aim. The trained model can virtually assess a s...

Predictive modeling of estrogen receptor agonism, antagonism, and binding activities using machine- and deep-learning approaches.

Laboratory investigation; a journal of technical methods and pathology
As defined by the World Health Organization, an endocrine disruptor is an exogenous substance or mixture that alters function(s) of the endocrine system and consequently causes adverse health effects in an intact organism, its progeny, or (sub)popula...

A curated knowledgebase on endocrine disrupting chemicals and their biological systems-level perturbations.

The Science of the total environment
Human well-being can be affected by exposure to several chemicals in the environment. One such group is endocrine disrupting chemicals (EDCs) that can perturb the hormonal homeostasis leading to adverse health effects. In this work, we have developed...

Deep learning driven QSAR model for environmental toxicology: Effects of endocrine disrupting chemicals on human health.

Environmental pollution (Barking, Essex : 1987)
Over 80,000 endocrine-disrupting chemicals (EDCs) are considered emerging contaminants (ECs), which are of great concern due to their effects on human health. Quantitative structure-activity relationship (QSAR) models are a promising alternative to i...

Machine Learning Consensus To Predict the Binding to the Androgen Receptor within the CoMPARA Project.

Journal of chemical information and modeling
The nuclear androgen receptor (AR) is one of the most relevant biological targets of Endocrine Disrupting Chemicals (EDCs), which produce adverse effects by interfering with hormonal regulation and endocrine system functioning. This paper describes n...

Machine learning models for predicting endocrine disruption potential of environmental chemicals.

Journal of environmental science and health. Part C, Environmental carcinogenesis & ecotoxicology reviews
We introduce here ML4Tox, a framework offering Deep Learning and Support Vector Machine models to predict agonist, antagonist, and binding activities of chemical compounds, in this case for the estrogen receptor ligand-binding domain. The ML4Tox mode...

Determination of 17 potential endocrine-disrupting chemicals in human saliva by dispersive liquid-liquid microextraction and liquid chromatography-tandem mass spectrometry.

Talanta
Endocrine-disrupting chemicals are a group of emerging contaminants that alters the function of the endocrine system, causing possible adverse health effects. In this study, a dispersive liquid-liquid microextraction method coupled with liquid chroma...

Maternal urinary paraben levels and offspring size at birth from a Chinese birth cohort.

Chemosphere
BACKGROUND: Parabens are suspected to impair fetal growth because of their endocrine disrupting effects. Epidemiological studies regarding the effects of prenatal exposure to parabens on birth outcomes are limited.

A ternary classification using machine learning methods of distinct estrogen receptor activities within a large collection of environmental chemicals.

The Science of the total environment
Endocrine-disrupting chemicals (EDCs), which can threaten ecological safety and be harmful to human beings, have been cause for wide concern. There is a high demand for efficient methodologies for evaluating potential EDCs in the environment. Herein ...