Identification of AhR agonists in sediments of the Bohai and Yellow Seas using advanced effect-directed analysis and in silico prediction
Bibliographic record
Abstract
Novel aryl hydrocarbon receptor (AhR) agonists were identified in coastal sediments in the Yellow and Bohai Seas by use of a combination of effect-directed analysis (EDA) and in silico prediction. A total of 125 sediments were screened for AhR-mediated potencies using H4IIE-luc bioassay. Great potencies were observed in organic extracts, mid-polar fraction (F2), and subfractions of F2 (F2.6-F2.9) of sediments collected from Nantong, Qinhuangdao, and Yancheng. Less than 15% AhR potencies could be explained by detected dioxin-like PAHs. Full-scan screening analysis was conducted for the more potent fractions using GC-QTOFMS to investigate the presence of unmonitored AhR agonists. A five-step prioritization strategy was applied; 92 candidate compounds satisfied all criteria. Among these chemicals, thirteen were evaluated for AhR efficacy. Six compounds; benz[b]anthracene, 6-methylchrysene, 2-methylbenz[a]anthracene, 1-methylbenz[a]anthracene, 1,12-dimethylbenzo[c]phenanthrene, and indeno[1,2,3-cd]fluoranthene, exhibited significant AhR-mediated efficacies. 1,12-dimethylbenzo[c]phenanthrene and indeno[1,2,3-cd]fluoranthene were identified as novel AhR agonists. Potency balance analysis showed that the six newly identified AhR agonists explained 0.4-100% of the total AhR-mediated potencies determined. Overall, combining EDA and in silico prediction applied in this study demonstrated the benefits of assessing the potential toxic effects of previously unidentified AhR agonists in sediments from the coasts of China and Korea.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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".