Multiple Bioassays and Targeted and Nontargeted Analyses to Characterize Potential Toxicological Effects Associated with Sediments of Masan Bay: Focusing on AhR-Mediated Potency
Bibliographic record
Abstract
Abstract An enhanced, multiple lines of evidence approach was applied to assess potential toxicological effects associated with polluted sediments. Two in vitro bioassays (H4IIE-luc and Vibrio fischeri) and three in vivo bioassays (microalgae: Isochrysis galbana and Phaeodactylum tricornutum; zebrafish embryo: Danio rerio) were applied. To identify causative chemicals in samples, targeted analyses (polycyclic aromatic hydrocarbons (PAHs), styrene oligomers (SOs), and alkylphenols) and nontargeted full-scan screening analyses (FSA; GC- and LC-QTOFMS) were performed. First, great AhR-mediated potencies were observed in midpolar and polar fractions of sediment extracts, but known and previously characterized AhR agonists, including PAHs and SOs could not fully explain the total potencies of samples. Enoxolone was identified as a novel AhR agonist in a highly potent sediment fraction by use of FSA. Enoxolone has a relative potency of 0.13 compared to benzo[a]pyrene (1.0) in the H4IIE-luc bioassay. Nonylphenols associated with membrane damage that influenced the viability of the microalgae were also observed. Finally, inhibitions of bioluminescence of V. fischeri and lethality of D. rerio embryos were strongly related to nonpolar compounds. Overall, the present work addressed assay- and end point-specific variations and sensitivities for potential toxicities of mixture samples, warranting a significant utility of the “multiple lines of evidence” approach in ecological risk assessment.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".