Genetic Toxicity of Complex Mixtures of Polycyclic Aromatic Hydrocarbons: Evaluating Dose-Additivity in a Transgenic Mouse Model
Bibliographic record
Abstract
This study evaluates the risk assessment approach currently employed for polycyclic aromatic hydrocarbon (PAH)-contaminated media, wherein carcinogenic hazards are evaluated using a dose-addition model that employs potency equivalency factors (PEFs) for targeted carcinogenic PAHs. Here, MutaMouse mice were subchronically exposed to PAH mixtures (p.o.), and mutagenic potency (MP) values were determined for five tissues. Predicted dose-additive mixture MPs were generated by summing the products of the concentrations and MPs of the individual targeted PAHs; values were compared to the experimental MPs of the mixtures to evaluate dose-additivity. Additionally, the PEF-determined BaP-equivalent concentrations were compared to those determined using a bioassay-derived method (BDM) (i.e., an additivity-independent approach). In bone marrow, mixture mutagenicity was less than dose-additive and the PEF-method provided higher estimates of BaP-equivalents than the BDM. Conversely, mixture mutagenicity in site-of-contact tissues (e.g., small intestine) was generally more than dose-additive and the PEF-method provided lower estimates of BaP-equivalents than the BDM. Overall, this study demonstrates that dose-additive predictions of mixture mutagenic potency based on the concentrations and potencies of a small number of targeted PAHs results in values that are surprisingly close to those determined experimentally, providing support for the dose-additive assumption employed for human health risk assessment of PAH mixtures.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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 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".