AIMC Topic: Endocrine Disruptors
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Machine Learning-Based Screening of Cosmetic Ingredients Identifies Vat Blue 6 as a Thyroid Hormone Receptor β Disruptor.
Environmental science & technology
Jul 16, 2025
Thyroid disorders are among the most prevalent endocrine conditions worldwide, exhibiting a rising incidence and disproportionately affecting women. In this study, we hypothesized that cosmetics may contain previously unidentified thyroid-disrupting ...
From Nuclear Receptors to GPCRs: a Deep Transfer Learning Approach for Enhanced Environmental Estrogen Recognition.
Environmental science & technology
Apr 30, 2025
Environmental estrogens (EEs), as typical endocrine-disrupting chemicals (EDCs), can bind to classic estrogen receptors (ERs) to induce genomic effects, as well as to G protein-coupled estrogen receptor (GPER) located on the membrane, thereby inducin...
Interpretable machine learning-based insights into early-life endocrine disruptor exposure and small vulnerable newborns.
Journal of hazardous materials
Mar 26, 2025
Early-life exposure to endocrine-disrupting chemicals (EDCs) may contribute to small vulnerable newborns, including conditions such as being small for gestational age (SGA) and preterm birth (PTB), yet evidence remains limited. This study, which is b...
Leveraging AlphaFold models to predict androgenic effects of endocrine-disrupting chemicals through zebrafish androgen receptor analysis.
Toxicology mechanisms and methods
Mar 12, 2025
The androgen receptor (AR) activation by androgens is vital for tissue development, sexual differentiation, and reproductive attributes in zebrafish (). However, our understanding of the molecular mechanisms behind their activation remains limited. I...
Development of a deep neural network model based on high throughput screening data for predicting synergistic estrogenic activity of binary mixtures for consumer products.
Journal of hazardous materials
Feb 22, 2025
A paradigm of chemical risk assessment is continuously extending from focusing on 'single substances' to more comprehensive approaches that examines the combined toxicity among different components in 'mixtures.' This change aims to account for the c...
Screening of estrogen receptor activity of per- and polyfluoroalkyl substances based on deep learning and in vivo assessment.
Environmental pollution (Barking, Essex : 1987)
Feb 11, 2025
Over the past decades, exposure to per- and polyfluoroalkyl substances (PFAS), a group of synthetic chemicals notorious for their environmental persistence, has been shown to pose increased health risks. Despite that some PFAS were reported to have e...
An Enhanced Protocol to Expand Human Exposome and Machine Learning-Based Prediction for Methodology Application.
Environmental science & technology
Feb 10, 2025
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...
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...
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...
Leveraging new approach methodologies: ecotoxicological modelling of endocrine disrupting chemicals to Danio rerio through machine learning and toxicity studies.
Toxicology mechanisms and methods
Sep 10, 2024
New approach methodologies (NAMs) offer information tailored to the intended application while reducing the use of animals. NAMs aim to develop quantitative structure-activity relationship (QSAR) and quantitive-Read-Across structure-activity relation...
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