Lifelong n-3 Polyunsaturated Fatty Acid Exposure Modulates Size of Mammary Epithelial Cell Populations and Expression of Caveolae Resident Proteins in Fat-1 Mice
Bibliographic record
Abstract
Omega-3 polyunsaturated fatty acids (n-3 PUFA) have been associated with reduced breast cancer risk; however, the exact mechanism remains elusive. Female wildtype (WT) and fat-1 mice were fed a 10% safflower diet until 6 weeks of age. Mammary gland epithelial cells (EC) were isolated and EC populations were determined by CD24 surface expression. Fat-1 mice expressed 65%, 20%, and 15% while WT mice expressed 65%, 26% and 9% for non-, myo- and luminal ECs, respectively. The luminal EC population was significantly greater in fat-1 mice (p ≤ 0.05), while the total number of mammary ECs were similar between groups (p = 0.79). Caveolae was isolated from ECs and Her-2/neu, ER-α and cav-1 protein expression was determined by Western blotting. Fat-1 mice had a two-fold greater ER-α (p ≤ 0.05) and a 1.5-fold greater cav-1 (p ≤ 0.05) expression than WT with a similar amount of Her-2/neu protein (p = 0.990) between groups. Overall, this study provides novel mechanistic evidence by which n-3 PUFA modifies early mammary gland development that may potentially reduce breast cancer risk later in life.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.000 |
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".