Chemoprotection Against N-Nitrosomethylbenzylamine-Induced Mutation in the Rat Esophagus
Bibliographic record
Abstract
Prevention of esophageal cancer may be possible through dietary modification or supplementation. In this study we have investigated the mutation preventive properties of ellagic acid, green tea, and diallyl sulfide (DAS) against the mutagenicity of the nitrosamine N-nitrosomethylbenzylamine (NMBA) in the esophagus of the rat. In addition, the effect of the consumption of ethanol on the mutagenicity of NMBA was examined. NMBA is specific in inducing tumors in the rat esophagus and has been used in many studies investigating the mechanism and the prevention of this cancer. We found that the type of mutations induced by two 2-mg/kg subcutaneous injections of NMBA in the lacI gene of "Big Blue" rats is consistent with that found previously for nitrosamines in other systems and consists of G:C-->A:T transitions. We report that the addition of ellagic acid to the feed, replacing drinking water with green tea, and gavage with DAS significantly reduced the mutagenicity of NMBA. In contrast, the addition of 5% ethanol to the drinking water increased the mutagenicity of NMBA. This is consistent with findings that these compounds modulate NMBA-induced carcinogenesis in the rat.
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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) | 0.000 | 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 | 0.000 |
| 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".