Her-2 Breast Cancer Outcomes Are Mitigated by Consuming n-3 Polyunsaturated, Saturated, and Monounsaturated Fatty Acids Compared to n-6 Polyunsaturated Fatty Acids
Bibliographic record
Abstract
Lifestyle habits, such as the consumption of a healthy diet, may prevent up to 30-50% of breast cancer (BC) cases. Dietary fats are of specific interest, as research provides strong evidence regarding the association of dietary fats and BC. However, there is limited research on the role of different types of fats including polyunsaturated (PUFA), monounsaturated (MUFA), and saturated fatty acids (SFA). The objective of this study was to determine the effects of lifelong exposure to various dietary fats on mammary tumour development over a 20-week period. Female heterozygous MMTV-neu (ndl) YD5 mouse models were fed five maternal diets containing (1) 10% safflower oil (n-6 PUFA, control), (2) 3% menhaden oil + 7% safflower oil (marine n-3 PUFA, control), (3) 3% flaxseed + 7% safflower oil (plant-based n-3 PUFA), (4) 10% olive oil (MUFA), or (5) 10% lard (SFA). The primary measures, tumour latency, volume, and multiplicity differed by diet treatment in the following general order, n-6 PUFA > plant n-3 PUFA, SFA, MUFA > marine n-3 PUFA. Overall, these findings show that the quality of the diet plays a significant role influencing mammary 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) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".