Comparative potency analysis of whole smoke solutions in the bacterial reverse mutation test
Bibliographic record
Abstract
Short-term in vitro genotoxicity assays are useful tools to assess whether new and emerging tobacco products potentially have reduced toxicity. We previously demonstrated that potency ranking by benchmark dose (BMD) analysis quantitatively identifies differences among several known carcinogens and toxic chemicals representing different chemical classes found in cigarette smoke. In this study, six whole smoke solution (WSS) samples containing both the particulate and gas phases of tobacco smoke were generated from two commercial cigarette brands under different smoking-machine regimens. Sixty test cigarettes of each brand were machine-smoked according to the International Organization for Standardization (ISO) puffing protocol. In addition, either 60 or 20 test cigarettes of each brand were machine-smoked with the Canadian Intense (CI) puffing protocol. All six WSSs were evaluated in the bacterial reverse mutation (Ames) test using Salmonella typhimurium strains, in the presence or absence of S9 metabolic activation. The resulting S9-mediated mutagenic concentration-responses for the four WSSs from 60 cigarettes were then compared using BMD modelling analysis and the mutagenic potency expressed as number of revertants per μl of the WSS. The quantitative approaches resulted in a similar rank order of mutagenic potency for the Ames test in both TA98 and TA100. Under the conditions of this study, these results indicate that quantitative analysis of the Ames test data can discriminate between the mutagenic potencies of WSSs on the basis of smoking-machine regimen (ISO vs. CI), and cigarette product (differences in smoke chemistry).
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".