The role of lifestyle in the impact of constant phthalate exposure on overweight and obesity: A longitudinal cohort study in China

Pediatric Obesity, Год журнала: 2025, Номер unknown

Опубликована: Март 17, 2025

Summary Background and Objectives To explore the relationship between constant exposure to phthalates (PAEs) overweight/obesity role of lifestyle in children. Methods This study conducted five repeated follow‐up visits with 829 children analysed data from 740 Logistic regression models were used evaluate association PAE exposure, overweight/obesity. Results The found that high levels PAEs may increase risk obesity girls, is higher girls an unhealthy lifestyle. In group compared low (CL) PAEs, odds ratios (ORs) for (CH) 2.99 (1.11, 8.05) 11.58 (1.38, 96.87), respectively. addition, interaction effect was observed on girls. Conclusion These results suggest importance reducing reduce obesity, especially individuals lifestyles. government should strengthen formulation regulations standards while guiding parents use fewer plastic products.

Язык: Английский

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The intestinal toxicity mechanisms of Triclosan and Triclocarban and their possible clinical nutritional intervention mechanisms

Environmental Pollution, Год журнала: 2025, Номер unknown, С. 126396 - 126396

Опубликована: Май 1, 2025

Язык: Английский

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Prediction of reproductive and developmental toxicity using an attention and gate augmented graph convolutional network

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Май 25, 2025

Due to the diverse molecular structures of chemical compounds and their intricate biological pathways toxicity, predicting reproductive developmental toxicity remains a challenge. Traditional Quantitative Structure-Activity Relationship models that rely on descriptors have limitations in capturing complexity achieve high predictive performance. In this study, we developed descriptor-free deep learning model by constructing Graph Convolutional Network designed with multi-head attention gated skip-connections predict toxicity. By integrating structural alerts directly related into model, enabled more effective toxicologically relevant substructures. We built dataset 4,514 compounds, including both organic inorganic substances. The was trained validated using stratified 5-fold cross-validation. It demonstrated excellent performance, achieving an accuracy 81.19% test set. To address interpretability identified subgraphs corresponding known alerts, providing insights model's decision-making process. This study conducted accordance OECD principles for reliable modeling contributes development robust silico prediction.

Язык: Английский

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Parabens, bisphenols, and triclosan in coral polyps, algae, and sediments from Sanya, China: Occurrence, profiles, and environmental implications

Environmental Pollution, Год журнала: 2024, Номер 361, С. 124839 - 124839

Опубликована: Авг. 28, 2024

Язык: Английский

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