Developmental toxicology of cadmium in living embryos of a stable transgenic zebrafish line.
Bibliographic record
Abstract
The toxic effects of cadmium and other heavy metals have been well established, and many of these and other environmental pollutants are known to be embryotoxic or teratogenic. However, it has proven difficult to identify individual cells that respond to toxicants among the wide range of cell populations in an intact animal, particularly during early development when cells are continually changing their molecular and physiologic characteristics as they differentiate. Here we report the establishment of an in vivo system that uses hsp70 gene activation as a measure of cadmium toxicity in living early larvae of transgenic zebrafish carrying a stably integrated hsp70-enhanced green fluorescent protein (eGFP) reporter gene. We demonstrate that eGFP expression in this strain of fish acts as an accurate and reproducible indicator of cell-specific induction of hsp70 gene expression. Furthermore, the transgene responds in a dose-dependent manner at concentrations similar to those observed for morphologic indicators of early-life-stage toxicity and is sensitive enough to detect cadmium at doses below the median combined adverse effect concentration and the median lethal concentration. The stable nature of this transgenic line should allow for extremely rapid and reproducible toxicologic profiling of embryos and larvae throughout development.
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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 | 0.000 |
| 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 | 0.000 |
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".