Development and Initial Testing of EcoToxChip, a Novel Toxicogenomics Tool for Environmental Management and Chemical Risk Assessment
Bibliographic record
Abstract
New approach methods (NAMs) are increasingly important to help accelerate the pace of ecological risk assessment and offer more ethical, affordable, and efficient alternatives to traditional toxicity tests. In the present study, we describe the development, technical characterization, and initial testing of a toxicogenomics tool, EcoToxChip (384-well quantitative polymerase chain reaction [qPCR] array), to support chemical management and environmental monitoring for three laboratory model species-fathead minnow (Pimephales promelas), African clawed frog (Xenopus laevis), and Japanese quail (Coturnix japonica). Chip design, including gene selection, was informed by a diverse end-user group and quality control metrics (e.g., primer assay, reverse transcription, and PCR efficiency) performed well based on a priori established criteria. Correlation with RNA sequencing (seq) data provided additional confidence in this novel toxicogenomics tool. Although the present study represents an initial testing of only 24 EcoToxChips for each of the model species, the results provide increased confidence in the robustness/reproducibility of EcoToxChips for evaluating perturbations in gene expression associated with chemical exposure and thus, this NAM, combined with early-life stage toxicity testing, could augment current efforts for chemical prioritization and environmental management. Environ Toxicol Chem 2023;42:1763-1771. © 2023 SETAC.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".