Evaluation of the Uptake, Metabolism, and Secretion of Toxicants by Zebrafish Larvae
Bibliographic record
Abstract
Zebrafish larvae have classically been used as a high-throughput model with which to test both the bioactivity and toxicity of known and novel compounds, making them a promising whole organism New Approach Method in the context of the international momentum to eliminate animal testing. Larvae are generally exposed to the chemicals being tested in a static environment and the concentration-response patterns are calculated based on the initial bath concentrations of the compounds. This approach rarely takes into account the absorption, distribution, metabolism, and excretion of the compounds being tested, which can have a significant effect on the toxicokinetic profiles of the compounds and thus impact the predictive ability of the model. In this study, we have evaluated the toxicokinetic profile of 5 known toxicants, 3 phenolic compounds, along with thiabendazole and 3,4-dicholoronalanine, at 6, 8, 24, 72, and 120 h postfertilization in order to match the exposure timelines of a standard in vitro fish embryo toxicity test. It was revealed that in addition to bioaccumulation effects, the compounds were all actively metabolized and excreted by the larvae. Importantly, comparisons between the toxicants revealed that the patterns of uptake and metabolism were varied and could often partially explain the differences in their concentration-response patterns. The findings of this study are significant as they highlight the requirement for an assessment of the stability and toxicokinetic profile of chemicals tested using standard zebrafish larval toxicity assays in order to better understand and compare their toxicity profiles.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".