Altering cytochrome P4501A activity affects polycyclic aromatic hydrocarbon metabolism and toxicity in rainbow trout (<i>Oncorhynchus mykiss</i>)
Bibliographic record
Abstract
The polycyclic aromatic hydrocarbons (PAHs) phenanthrene and retene (7-isopropyl-1-methyl phenanthrene) are lethal to rainbow trout (Oncorhynchus mykiss) larvae during chronic exposures. Phenanthrene is a low-toxicity, non-cytochrome P4501A (CYP1A)-inducing compound that accumulates in fish tissues during exposure to lethal concentrations in water. Retene is a higher toxicity CYP1A-inducing compound that is not detectable in tissue at lethal exposure concentrations. The metabolism, excretion, and toxicity of retene and phenanthrene were examined in juvenile and larval rainbow trout during coexposure to the model CYP1A inducer beta-naphthoflavone (betaNF), or to the inducer-inhibitor piperonyl butoxide to determine if modulating CYP1A activity affected PAH metabolism and toxicity. Phenanthrene metabolism, excretion rate, and toxicity increased with coexposure to betaNE Piperonyl butoxide inhibited phenanthrene metabolism and reduced the excretion of all phenanthrene metabolites. As a consequence, embryo mortality rates increased but rates of sublethal effects did not. Coexposure of trout to retene and betaNF caused no change in retene metabolism and excretion, but retene toxicity increased, perhaps due to additivity. Piperonyl butoxide inhibited retene metabolism, decreased the excretion of some retene metabolites while increasing the excretion of others, and increased the toxicity of retene. These results support the role of CYP1A activity in PAH metabolism and excretion, and the role ofthe CYP1A-generatedmetabolites of PAHs in chronic toxicity to larval fish.
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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".