Mammary tumour development is dose-dependently inhibited by n-3 polyunsaturated fatty acids in the MMTV-neu(ndl)-YD5 transgenic mouse model
Bibliographic record
Abstract
BACKGROUND: Breast cancer is attributable to modifiable risk factors including the intake of dietary n-3 polyunsaturated fatty acids (PUFA). A key piece of evidence, yet to be addressed, that would demonstrate a causal relationship between n-3 PUFA and breast cancer, is a dose-dependent effect of n-3 PUFA on tumour outcomes. Thus, the objective of the present study was to determine whether n-3 PUFA reduces mammary gland tumor outcomes in a dose-dependent manner in female MMTV-neu(ndl)-YD5 transgenic mice, an aggressive model of human breast cancer. METHODS: Harems were provided one of three experimental diets comprised of 0, 3 or 9% (w/w) menhaden fish oil containing n-3 PUFA. Female offspring were weaned onto the same parental diet and maintained on their respective diet for 20 weeks. Tumour onset, size and multiplicity were measured throughout the study. Fatty acid composition of mammary gland and tumours were determined by gas-liquid chromatography. RESULTS: Tumour size was significantly (p < 0.05) reduced in a dose-dependent manner. n-3 PUFA were also incorporated in a dose-dependent manner; differential incorporation was observed for eicosapentaenoic and docosapentaenoic acids into mammary gland tissue, while docosahexaenoic acid was preferentially incorporated into tumours. CONCLUSION: Overall, the present study provides fundamental knowledge about the dose-dependent effect of n-3 PUFA on tumour outcomes in a pre-clinical model and also sheds light on the differential role of individual n-3 PUFA on tumour outcomes.
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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) | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".