Dietary Vitamin E Affects Neutrophil Distribution and Genetic Instability in Murine Mutatect Tumors
Bibliographic record
Abstract
Vitamin E is best known for its ability to scavenge reactive oxygen and nitrogen species. Solid tumors are frequently infiltrated with leukocytes, a potential source of these reactive species. The Mutatect tumor model is a fibrosarcoma that can be grown subcutaneously in syngeneic C57BL/6 mice. We previously showed that these tumors are infiltrated with neutrophils and that the number of neutrophils correlates with the number of hypoxanthine phosphoribosyl transferase (hprt) mutations and loss of an interleukin-8 (IL-8) transgene. Neutrophils are a source of nitric oxide, and tumors contain nitrotyrosine, a marker of damage by nitric oxide-related species. We also showed previously that dietary vitamin E supplements markedly lower the frequency of hprt mutants and the level of myeloperoxidase (a neutrophil marker) in a tumor fraction containing "loosely bound" cells. In the present report, we examine the effect of dietary vitamin E in greater detail. No effect on inducible nitric oxide synthase expression or nitrotyrosine levels was observed. However, dietary vitamin E induced a major redistribution of neutrophils from the loosely bound cellular fraction to the "stromal" fraction, while the total number of neutrophils in tumors was essentially unchanged. The loss of the IL-8 transgene seen earlier in Mutatect tumors was largely prevented. Vitamin E also prevented the large increase in hprt mutants (in the cellular and stromal fractions). Thus vitamin E appears to be protective against genotoxicity by scavenging reactive species, but also its ability to affect the distribution of neutrophils within tumors may be important.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".