Dietary Docosahexaenoic Acid Levels Influence the Outcome of Arabinosylcytosine Chemotherapy in L1210 Leukemic Mice
Bibliographic record
Abstract
The purpose of this study was to investigate whether dietary supplementation with the n-3 fatty acid docosahexaenoic acid (DHA) in combination with arabinosylcytosine (AraC) chemotherapy could prolong the life expectancy of mice bearing L1210 leukemia. The four control diets included rodent chow, a diet containing 5% of a blended oil mimicking the fatty acid composition of rodent chow, and diets containing 5% or 10% fat with safflower oil as the main oil source. The two DHA-supplemented diets provided 1.5% or 3.5% DHA and 5% or 10% total fat, respectively. After tumor cell inoculation, mice were treated with AraC for 10 days. Mice fed the 5% safflower oil diet (30.1 -/+ 4.1 days), but not those fed the 10% safflower oil diet, survived longer than the chow-fed animals (22.1 -/+ 3.1 days, P = 0.05). The 1.5%-/+ DHA diet (average intake 1.8 g DHA/kg/day) was associated with a longer life span (33.3 -/+ 3.4 days, P < 0.01 vs. chow-fed) and no incidence of death due to drug toxicity. Further increasing DHA intake (4.5 g DHA/kg/day) resulted in shortened survival time (26.5 -/+ 2.0 days), increased circulating tumor cell burden, and lowered red blood cell concentrations. These data suggest that a modest level of dietary DHA or linoleic acid supplementation may improve the antineoplastic efficacy of AraC. However, overconsumption of DHA reverses the beneficial effect of DHA intake on drug sensitivity.
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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 | 0.001 |
| 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".