Polycyclic aromatic hydrocarbons as inducers of cytochrome P4501A enzyme activity in the rainbow trout liver cell line, RTL-W1, and in primary cultures of rainbow trout hepatocytes
Bibliographic record
Abstract
In order to investigate cell-specific differences in the response of in vitro models to environmental toxicants, we compared the capacity of nine polycyclic aromatic hydrocarbons (PAHs) to induce cytochrome P4501A (CYPIA) in primary rainbow trout (Oncorhynchus mykiss) hepatocytes and a rainbow trout liver cell line, RTL-W1. Induction of CYPIA was estimated from the catalytic activity of 7-ethoxyresorufin-O-deethylase (EROD) and compared by median effective concentration (EC50) values, induction spans, and benzo[a]pyrene induction equivalency factors for inducing PAHs. The influence of culture conditions was investigated with respect to the presence or absence of serum and varying exposure times. Both in vitro systems lead to an identical classification of the PAHs in noninducing (anthracene, fluoranthene, phenanthrene, and pyrene) and inducing compounds with a similar ranking of inducing PAHs. Mean EC50 values in RTL-W1 cells were, respectively, 343 and 266 nM for benzo[a]anthracene, 57 and 92 nM for BaP, 134 and 283 nM for benzo[b]fluoranthene, 455 and 270 nM for chrysene, and 98 and 116 nM for 3-methylcholanthrene. Compared to primary hepatocytes, the RTL-W1 cell line was more sensitive in its EROD response to the presence or absence of serum and to the increase in exposure time, which led to higher EC50 values.
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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 | 0.001 |
| 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) | 0.001 | 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 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".