In vitro biotransformation rates in fish liver S9: Effect of dosing techniques
Bibliographic record
Abstract
In vitro biotransformation assays are currently being explored to improve estimates of bioconcentration factors of potentially bioaccumulative organic chemicals in fish. The present study compares thin-film and solvent-delivery dosing techniques as well as single versus multiple chemical dosing for measuring biotransformation rates of selected polycyclic aromatic hydrocarbons in rainbow trout (Oncorhynchus mykiss) liver S9. The findings show that biotransformation rates of very hydrophobic substances can be accurately measured in thin-film sorbent-dosing assays from concentration-time profiles in the incubation medium but not from those in the sorbent phase because of low chemical film-to-incubation-medium mass-transfer rates at the incubation temperature of 13.5 °C required for trout liver assays. Biotransformation rates determined by thin-film dosing were greater than those determined by solvent-delivery dosing for chrysene (octanol-water partition coefficient [KOW ] =10(5.60) ) and benzo[a]pyrene (KOW =10(6.04) ), whereas there were no statistical differences in pyrene (KOW =10(5.18) ) biotransformation rates between the 2 methods. In sorbent delivery-based assays, simultaneous multiple-chemical dosing produced biotransformation rates that were not statistically different from those measured in single-chemical dosing experiments for pyrene and benzo[a]pyrene but not for chrysene. In solvent-delivery experiments, multiple-chemical dosing produced biotransformation rates that were much smaller than those in single-chemical dosing experiments for all test chemicals. While thin-film sorbent-phase and solvent delivery-based dosing methods are both suitable methods for measuring biotransformation rates of substances of intermediate hydrophobicity, thin-film sorbent-phase dosing may be more suitable for superhydrophobic chemicals.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.001 | 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".