Investigating the endocrine disruption effects of four disinfection byproducts on zebrafish estrogen receptor-α
Bibliographic record
Abstract
Reports have shown an increase in the use of disinfectants in wastewater treatment plants, prompted by the detection of residual viruses in sewage. However, the release of disinfection byproducts (DBPs) in final effluents has raised concerns about their potential adverse effects, such as endocrine disruption, on aquatic environments. Despite these concerns, few studies have examined the endocrine-disrupting effects of DBPs on fish, which may be vulnerable to DBPs. The aim of this case study was to investigate the endocrine-disrupting properties of four commonly formed DBPs: chloroiodomethane (CIM), dibromochloromethane (DBCM), bromodichloromethane (BDCM), and trichloroacetic acid (TCA) on the estrogen receptor-α in zebrafish (zERα). The results indicated that all four DBPs have high anti-estrogenic activity against zERα; with CIM, BDCM, DBCM, and TCA yielding 80.8%, 78.4%, 49.0%, and 64.1% anti-estrogenic effects on zERα, respectively. Moreover, all DBPs demonstrated negligible estrogenic effects on zERα. Our study sheds new light on the adverse effects of DBPs, particularly the endocrine-disrupting activity of CIM, which, as part of the dihalomethanes group, has received limited research attention in the past. This study shows the molecular interactions in terms of the endocrine disruption of DBP on zERα, warranting further studies to understand the overall impact of fish in affected aquatic ecosystems.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".