Transcriptomics Points-of-Departure (tPODs) to Support Hazard Assessment of Benzo[<i>a</i>]pyrene in Early-Life-Stage Rainbow Trout
Bibliographic record
Abstract
New approach methods (NAMs) are urgently needed to address the significant ethical and economic concerns associated with live animal testing as well as the low throughput associated with current toxicity testing frameworks. NAMs such as rapid mechanistic early-life-stage fish assays are promising alternatives to current hazard assessment approaches, as they can be used to derive toxicity thresholds and guide decision-makers on identifying or prioritizing chemicals of concern. This study aimed to derive benchmark concentrations from RNaseq data (transcriptomic points-of-departure; tPOD) from a short-term exposure study with early life stages of rainbow trout (RBT; Oncorhynchus mykiss ) using benzo[ a ]pyrene (B[ a ]P) as the model compound. tPODs were then calibrated with higher organizational-level responses observed during an extended 28 day exposure period. RBT were exposed from 1 to 28 days post-hatch (dph) to 0.079, 0.35, 1.5, 7.4, and 29 μg/L (28 d time weighted average measured) B[ a ]P, as well as 0.05% dimethyl sulfoxide and water only controls. Benchmark concentration analysis of transcriptomic responses at 4 dph, based on the most sensitive transcriptomic features, yielded tPODs between 0.028 and 0.47 μg/L B[ a ]P. At 28 dph, Cyp1a1 exhibited significantly increased catalytic activity, with biochemical POD, bPOD EROD,28dph of 0.599 μg/L B[ a ]P, while morphometric analysis showed significant growth inhibition in terms of length, with apical POD, aPOD length,28dph of 1.77 μg/L B[ a ]P, with a notable decreasing trend in body weight. A toxicity pathway model constructed from genes and apical end points exhibiting concentration-dependent responses provided further evidence supporting the utility of tPODs from short-term RBT early-life-stage assay to support chemical risk assessment to guide decision-makers in chemical testing prioritization.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".