Current Status of Emerging Contaminant Models and Their Applications Concerning the Aquatic Environment: A Review

Water, Journal Year: 2025, Volume and Issue: 17(1), P. 85 - 85

Published: Jan. 1, 2025

Increasing numbers of emerging contaminants (ECs) detected in water environments require a detailed understanding these chemicals’ fate, distribution, transport, and risk aquatic ecosystems. Modeling is useful approach for determining ECs’ characteristics their behaviors environments. This article proposes systematic taxonomy EC models addresses gaps the comprehensive analysis applications. The reviewed include conventional quality models, multimedia fugacity machine learning (ML) models. Conventional have higher prediction accuracy spatial resolution; nevertheless, they are limited functionality can only be used to predict contaminant concentrations Fugacity excellent at depicting how travel between different environmental media, but cannot directly analyze variations parts same media because model assumes that constant within compartment. Compared other ML applied more scenarios, such as identification assessments, rather than being confined concentrations. In recent years, with rapid development artificial intelligence, surpassed becoming one newest hotspots study ECs. primary challenge faced by outcomes difficult interpret understand, this influences practical value an some extent.

Language: Английский

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Benzimidazole Fungicide Carbendazim Induces Gut Inflammation through the TLR5/NF-κB Pathway in Grass Carp

Environmental Science & Technology, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 2, 2025

Fungicides have been increasingly used across various sectors, including agriculture and textiles. The biocidal properties of fungicides may negatively impact the stability intestinal microbiota, thereby posing a threat to health. In this study, we investigated mechanisms damage functional abnormalities in grass carp following 42-day exposure widely fungicide carbendazim at environmentally relevant concentrations (0.2 20 μg/L). Histopathological observations, mRNA protein expression analyses, biochemical analysis, quantification short-chain fatty acids (SCFAs), cytokines, lipopolysaccharide (LPS), 16S ribosomal ribonucleic acid (rRNA), as well internal transcribed spacer (ITS) sequencing, were performed. At concentrations, strongly induced inflammation, leading increased transcriptional translational levels genes involved toll-like receptor five (TLR5)/nuclear factor kappa B (NF-κB) pathway, TLR5, NF-κB, interleukin-1 beta (IL-1β), tumor necrosis factor-alpha (TNFα). Additionally, damaged barriers reduced tight junction proteins (e.g., occludin zonula occludens-1/2), goblet cells, immunoglobulin M levels, while also disrupting gut microbiome, metabolic disorders, particularly decreases SCFAs increases LPS. Treatment with TLR5 antagonist TH1020 mitigated inflammation caused by carbendazim, subsequently improving mechanical barrier function. Overall, our findings provide new insights into toxicological underlying carp, indicating that poses significant nontarget organisms. Given its widespread detection environment, these results underscore substantial ecological risks health fish living carbendazim-contaminated water bodies.

Language: Английский

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Interactions of Potential Endocrine-Disrupting Chemicals with Whole Human Proteome Predicted by AlphaFold2 Using an In Silico Approach

Environmental Science & Technology, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 11, 2024

Binding with proteins is a critical molecular initiating event through which environmental pollutants exert toxic effects in humans. Previous studies have been limited by the availability of three-dimensional (3D) protein structures and focused on only small set contaminants. Using highly accurate 3D structure predicted AlphaFold2, this study explored over 60 million interactions obtained docking between 20,503 human 1251 potential endocrine-disrupting chemicals. A total 66,613,773 results were obtained, 1.2% considered to be high binding, as their scores lower than -7. Monocyte macrophage differentiation factor 2 (MMD2) was interact highest number (526), polychlorinated biphenyls dibenzofurans accounting for significant proportion. Dimension reduction clustering analysis revealed distinct profiles characterized binding affinities perfluoroalkyl polyfluoroalkyl substances (PFAS), phthalate-like chemicals, other pollutants, consistent uniquely enriched pathways. Further structural indicated that pockets proportion charged amino acid residues, relatively low α-helix content, β-sheet content more likely bind PFAS others. This provides insights into toxicity pathways various impacting health offers novel perspectives establishment expansion adverse outcome pathway-based models.

Language: Английский

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Predicting estrogen receptor agonists from plastic additives across various aquatic species using machine learning and AlphaFold2

Journal of Hazardous Materials, Journal Year: 2025, Volume and Issue: 494, P. 138629 - 138629

Published: May 14, 2025

Language: Английский

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Predicting the new psychoactive substance activity of antitussives and evaluating their ecotoxicity to fish

The Science of The Total Environment, Journal Year: 2024, Volume and Issue: 932, P. 172872 - 172872

Published: April 29, 2024

Language: Английский

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Endocrine disruptor identification and multitoxicity level assessment of organic chemicals: an example of multiple machine learning models

Journal of Hazardous Materials, Journal Year: 2024, Volume and Issue: 485, P. 136896 - 136896

Published: Dec. 15, 2024

Language: Английский

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