Testing the effects of two different zebrafish exposure paradigms on transcriptomic-based chemical risk assessment using the flame retardant triphenyl phosphate
Bibliographic record
Abstract
In the zebrafish larval toxicity model, phenotypic changes induced by chemical exposure can potentially be explained and predicted by the analysis of gene expression changes at sub-phenotypic concentrations. The increase in knowledge of gene pathway-specific effects arising from the zebrafish transcriptomic model has the potential to enhance the role of the larval zebrafish as a component of Integrated Approaches to Testing and Assessment (IATA). In this paper, we compared the transcriptomic responses to triphenyl phosphate between 2 standard exposure paradigms, the Zebrafish Embryo Toxicity (ZET) and General and Behavioral Toxicity (GBT) assays. The ZET assay represents a developmental model with chemical exposure from 6 to 120 h post fertilization (hpf), which covers organogenesis, whereas the GBT represents a juvenile model with exposure from 72 to 120 hpf, which occurs post-organogenesis. This comparison demonstrates both similarities and differences between the 2 assays. Although both models identified similar xenobiotic metabolism pathways, the difference in exposure window length and the time of transcriptomic sampling between the 2 methods also yielded unique sets of affected pathways, demonstrating their complimentary nature. Both data sets support previously described effects of triphenyl phosphate on aquatic and mammalian systems. This work validates and strengthens the use of both exposure paradigms and continues to demonstrate that zebrafish larvae are a valuable tool in the context of IATA toward reduced reliance on the use of higher vertebrate derived data for chemical 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| 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".