Bibliographic record
Abstract
Mechanistic information is essential to screen and predict the adverse effects of a large number of chemicals during early-life exposure. Concentration-dependent omics can capture the extent of perturbations of biological pathways or processes and provide information on the mechanism of toxicity. However, the application of concentration-dependent transcriptome to assess the developmental toxicity of environmental chemicals is still limited. Here, twelve chemicals representing five different modes of action (MOAs) were tested by the concentration-dependent reduced zebrafish transcriptome approach (CRZT) in combination with a phenotype-based high content screen (PHCS). The responsiveness, sensitivity and mechanistic differentiation of CRZT were validated in comparison with PHCS. First, PHCS identified 10 chemicals with obvious embryotoxicity (LD<sub>50</sub> range: 2.11-70.68 μM), while the potencies of the biological pathways perturbed by 12 chemicals (POD<sub>path20</sub> range: 0.002-2.1 μM) were demonstrated by CRZT. Second, although the potency of the transcriptome perturbations was positively correlated with lethality (LD<sub>50</sub>) (R<sup>2</sup> = 0.64, P-value < 0.05) for most tested chemicals, BbF was non-embryotoxic but was the most potent on the perturbance of biological pathways. Finally, the profiles of the perturbed biological processes and the transcriptome potency (POD<sub>path20</sub>) captured by CRZT could effectively classify most chemicals corresponding to their known MOAs. In summary, CRZT could significantly improve testing the developmental toxicity of environmental chemicals.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 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 teacher head, 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".