Toxicity of Aqueous L-Selenomethionine and Tert-Butyl Hydroperoxide Exposure to Zebrafish (Danio rerio) Embryos Following Tert-Butyl Hydroquinone Treatment
Bibliographic record
Abstract
Aqueous L-selenomethionine (SeMet) embryo exposures represent a rapid and simplified method for investigating the embryotoxic effects of SeMet. Using zebrafish (Danio rerio) as a model organism, the objective of the present study was to characterize the effects of waterborne exposure to both SeMet and tert-butyl hydroperoxide (tBOOH) to early life stages of zebrafish pre-treated with the antioxidant tert-butyl hydroquinone (tBHQ) in an attempt to investigate the mechanism of Se toxicity as it relates to oxidative stress. During the initial concentration range finding experiment, recently fertilized embryos were exposed for five days to 5, 25, 125, and 625 µg Se/L (as SeMet). These exposures informed the second experiment in which embryos were exposed to two concentrations of SeMet (25 and 125 µg Se/L) and 75 mg/L tBOOH either with (tBOOH-t, 25-t, 125-t) or without (tBOOH, 25, 125) a 4 h 100 µg/L tBHQ pre-treatment. Survival, hatchability, time to hatch, the frequency and severity of deformities (total and type), and changes in the expression of seven antioxidant-associated genes were determined. Exposures to SeMet and tBOOH reduced hatchability, increased time to hatch, decreased survival, increased the incidence and severity of deformities, and increased glutathione-disulfide reductase (gsr) expression in the pre-treated tBOOH treatment group.
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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 | 0.000 |
| 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".