A low carbohydrate, high protein diet combined with celecoxib markedly reduces metastasis
Bibliographic record
Abstract
We recently demonstrated that both murine and human carcinomas grow significantly slower in mice on low carbohydrate (CHO), high protein diets than on isocaloric Western diets and that a further reduction in tumor growth rates occur when the low CHO diets are combined with the cyclooxygenase-2 inhibitor, celecoxib. Following upon these studies, we asked herein what effect low CHO, high protein diets, with or without celecoxib, might have on tumor metastasis. In the highly metastatic 4T1 mouse mammary tumor model, a 15% CHO, high protein diet supplemented with celecoxib (1 g/kg chow) markedly reduced lung metastases. Moreover, in longer-term studies using male Transgenic Adenocarcinoma of the Mouse Prostate mice, which are predisposed to metastatic prostate cancer, the 15% CHO diet, with and without celecoxib (0.3 g/kg chow), gave the lowest incidence of metastases, but a more moderate 25% CHO diet containing celecoxib led to the best survival. Metabolic studies with 4T1 tumors suggested that the low CHO, high protein diets may be forcing tumors to become dependent on amino acid catabolism for survival/growth. Taken together, our results suggest that a combination of a low CHO, high protein diet with celecoxib substantially reduces metastasis.
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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".