Niacin Supplementation Decreases the Incidence of Alkylation-Induced Nonlymphocytic Leukemia in Long-Evans Rats
Bibliographic record
Abstract
Niacin deficiency impairs poly(ADP-ribose) formation and enhances ethylnitrosourea (ENU)-induced carcinogenesis. Previous experiments were compromised by rapid progression of cancer, and the current study was designed with half the number of ENU doses. Weanling male Long-Evans rats were fed niacin deficient (ND), pair-fed (PF) control (30 mg nicotinic acid/kg), or pharmacological niacin (NA; 4 g nicotinic acid/kg) diets. After 2 wk, rats were gavaged every other day with ENU [30 mg/kg body weight (bw)] or vehicle (6 doses). Four days after the last dose of ENU, all rats were switched to AIN-93M diet and mildly feed restricted to maintain a constant food intake per bw. Rats were monitored for termination criteria and assessed for cancer development. Total cancers developed more rapidly in rats on the ND diet compared to those receiving high dose supplements of NA (P = 0.02; Gehan's generalized Wilcoxon test). Importantly, all of these differences occurred in the leukemias, especially the nonlymphocytic leukemia fraction (P = 0.008; Gehan's generalized Wilcoxon test), with incidences of 36%, 17%, and 11% in ND, PF, and NA rats, respectively. Because nonlymphocytic leukemias represent the majority of secondary cancers, these data support the concept that niacin supplementation may help protect cancer patients from the deleterious side effects of chemotherapy.
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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) | 0.001 | 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 | 0.000 | 0.001 |
| 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".