Alpha ‐ linolenic acid (ALA)‐ rich flaxseed oil (FO) enhances the effectiveness of trastuzumab (TRAS) in reducing the growth of HER2 overexpressing human breast cancer tumors (BT‐474)
Bibliographic record
Abstract
TRAS is the primary drug for breast tumors that overexpress epidermal growth factor receptor 2 (HER2) but tumors develop resistance within 1 year of treatment. Our objective is to determine whether combining dietary ALA‐rich FO with TRAS treatment will enhance TRAS effectiveness. HER2 overexpressing breast tumor cells (BT‐474) were tested for proliferation and HER2 level in vitro after treatment with ALA (50µM), TRAS (10µg/ml) or their combination. Compared with untreated control, ALA, TRAS and combination caused a 35%, 54% and 61% reduction in proliferation and a 23% increase and 19% and 88% reduction in HER 2 expression (p<0.05), respectively. Ovariectomized mice with BT‐474 tumors were fed the basal diet (BD) (control) or treated with TRAS (2.5 or 5mg/kg) and fed either the BD or BD supplemented with 8% FO for 4 weeks. Compared to control, both TRAS and FO+TRAS treatments caused significant reductions in tumor growth over time (P<0.05). Control tumors increased in size by 63% while tumor size with FO+TRAS2.5 was reduced by 85% which is greater than that with TRAS2.5 (16%) (p<0.05) and not significantly different from that with TRAS5, alone or with FO. FO+TRAS2.5 caused greater reduction ( 85%) in cell proliferation (Ki67 labeling index) than with TRAS2.5 alone (59%; p<0.05). In conclusion, ALA and ALA‐rich FO enhanced TRAS effects and combined FO and low dose TRAS was just as effective as high dose TRAS. Grant Funding Source NSERC
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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) | 0.001 | 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".