Transcriptomic Dose-Response Analysis in Zebrafish Embryos to Estimate Aquatic Toxicity of Plasticizers and Plastic Monomers
Bibliographic record
Abstract
Current toxicity tests have high monetary costs, efficiency issues and ethical concerns.New approach methodologies, such as transcriptomic dose response modelling (TDRM), are increasingly being used to address these issues.This thesis aimed to combine the TDRM method with an acute zebrafish embryo model exposure to determine effective concentrations for chemicals of concern (plasticizers and plastic monomers).We hypothesized that the TDRM methodology would be more sensitive and informative than traditional apical endpoints derived from acute fish exposures.We found that transcriptomic endpoints were more sensitive than apical endpoints for the majority of chemicals tested.However, several challenges related to experimental design and RNA sequencing were encountered and recommendations were given for future studies to address these challenges.Overall, the TDRM methodology, when combined with the zebrafish embryo model, shows promise as an effective tool for screening and prioritizing chemicals of concern.Appendix D. GO terms for TGSH samples.Red GO terms indicate GO terms that are related to cholesterol or estrogen fuction............
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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 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".