A low-carbohydrate diet containing soy protein and fish oil reduces breast but not prostate cancer in C3(1)/Tag mice
Bibliographic record
Abstract
We recently showed that a low-carbohydrate (CHO) diet containing soy protein and fish oil dramatically reduces lung nodules in a mouse model of lung cancer when compared to a Western diet. To explore the universality of this finding, we herein compared this low-CHO diet to a Western diet on in preventing breast and prostate cancer using a mouse model that expresses the SV40 large T-antigen specifically in breast epithelia in females and prostate epithelia in males. We found that breast cancer was significantly reduced with this low-CHO diet and this correlated with a reduction in plasma levels of glucose, insulin, IL-6, TNFα and prostaglandin E2 (PGE2). This also corresponded with a reduction in the Ki67 proliferation index within breast tumors. On the other hand, this low-CHO diet did not reduce the incidence of prostate cancer in the male mice. Although it reduced both blood glucose and insulin to the same extent as in the female mice, there was no reduction in plasma IL-6, TNFα or PGE2 levels, or in the Ki67 proliferation index in prostate lesions. Based on immunohistochemistry studies with antibodies to 6-phosphofructo-2-kinase/fructose-2,6-biphosphatase 3 (PFKFB3), carnitine palmitoyltransferase Ia (CPT1a) and fatty acid synthase (FAS), it is likely that this difference in response of the two cancer types to this low-CHO diet reflects differences in the glucose dependence of breast and prostate cancer, with the former being highly dependent on glucose for energy and the latter being more dependent on fatty acids.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".