Application of the ethoxyresorufin‐<i>O</i>‐deethylase (EROD) assay to mixtures of halogenated aromatic compounds
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The ethoxyresorufin-O-deethylase (EROD) assay monitors the induction of the xenobiotic-metabolizing enzyme cytochrome P-450 (CYP) 1A1 and is a widely used biomarker for exposure of wildlife to substances that bind the aryl hydrocarbon (Ah) receptor. In this work the induction of EROD activity by single compounds and binary mixtures in primary rat hepatocytes was compared with the predictions of a kinetic model involving mixtures of inducers. The inducing agents were also examined for their ability to activate the Ah receptor to its DNA-binding form and for their ability to act as competitive inhibitors for CYP 1A1. Xenobiotics that failed to activate the Ah receptor did not induce EROD activity. Competitive inhibition for CYP 1A1 between the xenobiotic and 7-ethoxyresorufin caused EROD activity to fall at high xenobiotic concentrations. Competition for a limited number of Ah receptor sites depressed the EROD activity of a strong inducer such as 2,3,7,8-tetrachlorodibenzo-p-dioxin at high concentrations of a weak inducer. Application of the kinetic model to the example of a mixture of low concentrations of dibenzo-p-dioxins and much higher concentrations of polychlorinated biphenyls indicated that EROD assays often seriously underestimate the true potency of an environmental sample. Hence the EROD assay cannot be used for determining dioxin equivalent concentrations using the toxic equivalence factor approach.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it