Low carbohydrate diets containing soy protein and fish oil slow the growth of established NNK-induced lung tumors
Bibliographic record
Abstract
We recently found that a diet composed of 15% of total calories as carbohydrate (CHO), primarily as amylose, 35% soy protein and 50% fat, primarily as fish oil (FO) (15%Amylose/Soy/FO) was highly effective at preventing lung nodule formation in a nicotine-derived nitrosamine ketone (NNK)-induced lung cancer model. We asked herein whether adopting such a diet once cancers are established might also be beneficial. To test this, NNK-induced lung nodules were established in mice on a Western diet and the mice were then either kept on a Western diet or switched to various low CHO diets. Since we previously found that sedentary mice develop more lung nodules than active mice, we also compared the effect of exercise in this cancer progression model. We found that switching to a 15%Amylose/Soy/FO diet reduced lung nodules and slowed tumor growth with both 'active' and 'sedentary' mice. Ki67, cleaved caspase 3 and Terminal Deoxynucleotidyl Transferase-Mediated dUTP Nick End Labeling assays suggested that the efficacy of the 15%Amylose/Soy/FO in lowering tumor nodule count and size was not due to a reduction in tumor cell proliferation, but to an increase in apoptosis. The 15%Amylose/Soy/FO diet also significantly lowered liver fatty acid synthase and 6-phosphofructo-2-kinase/fructose-2,6-biphosphatase 3 expression, pointing to a global metabolic switch from glycolysis to fatty acid oxidation. Mice fed the 15%Amylose/Soy/FO diet also had significantly reduced plasma levels of interleukin (IL)-1β, IL-6 and tumor necrosis factor α. These results suggest that the 15%Amylose/Soy/FO diet may slow tumor growth by suppressing proinflammatory cytokines, inducing a metabolic switch away from glycolysis and inducing apoptosis in tumors.
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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".