Abstract B33: Dissecting the role of Nrf3 (NF-E2-related factor 3) in chemical carcinogenesis using knock-out mice
Bibliographic record
Abstract
Abstract In this study, we investigated the role of the transcription factor Nrf3 in chemical carcinogenesis induced by benzo[a]pyrene, a common carcinogen found in cigarette smoke, car exhaust and charcoal-grilled meats. Wild type and Nrf3-deficient mice were treated weekly for 4 consecutive weeks with benzo[a]pyrene and then sacrificed 30 weeks after the first administration of carcinogen. Here, we report that mice exposed to benzo[a]pyrene rapidly exhibit labored breathing further accompanied with different signs of morbidity. Kaplan-Meyer survival rate analyses indicate that mice deficient for Nrf3 die earlier that wild type mice following benzo[a]pyrene exposure. Upon necropsy, gross morphology analyses showed the presence of an increased number of lung and thymus lesions in Nrf3-deficient mice compared to wild type mice whereas the number of stomach lesions is identical in both genotypes. Pathology analysis strongly suggests that observed lesions in lung and thymus are from lymphoma origin. Overall, our study reveals that mice deficient for Nrf3 are highly sensitive to chemicalinduced carcinogenesis suggesting a protective role of the transcription factor Nrf3 in this process. This provides new important insights into the molecular mechanisms involved in the development of lymphomas induced by chemical agents. We also propose the Nrf3-deficient mice as a new in vivo model to study tumor formation and response to chemoprotective agents. Citation Information: Cancer Res 2009;69(23 Suppl):B33.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".