Flaxseed cotyledon fraction reduces tumour growth and sensitises tamoxifen treatment of human breast cancer xenograft (MCF-7) in athymic mice
Bibliographic record
Abstract
Dietary flaxseed (FS) inhibited the growth of human breast tumours and enhanced the effectiveness of tamoxifen (TAM) in athymic mice with low oestradiol (E2) levels. The present study determined whether the n-3 fatty acid-rich cotyledon fraction of FS (FC), alone or in combination with TAM, has a similar effect and thus can substitute for FS. In a 2 × 2 factorial design, ovariectomised mice with established oestrogen receptor (ER)-positive breast tumours (MCF-7) were treated as follows: groups 1 and 2 were fed the basal diet (BD, control) and FC diet (82 g FC/kg), respectively. Groups 3 and 4 with TAM implants (5 mg) were fed the BD and FC diet, respectively. At 8 weeks post-treatment, mice were euthanised, and tumours were analysed by immunohistochemistry and real-time PCR. BD, FC and FC/TAM groups significantly decreased tumour area, but the TAM group did not. Tumour regression in the FC/TAM group was greater compared to the TAM group. FC lowered cell proliferation but had no effect on apoptosis; the opposite was observed with TAM. FC suppressed mRNA expressions of pS2 and insulin-like growth factor 1 receptor (IGF-1R) and protein expressions of ERα, phosphospecific ERα, human epidermal growth factor receptor 2 (HER2), phosphospecific HER2 (pHER2) and amplified in breast 1 (AIB1), while TAM up-regulated mRNA expressions of Bcl2, progesterone receptor and IGF-1R and protein expression of pHER2, and down-regulated ERβ mRNA. FC modulated the effect of TAM on tumour growth biomarkers. In conclusion, FC reduced the growth of ER+ human breast tumours at low circulating E2, alone and combined with TAM, in part through modulation of ER- and growth factor-mediated signalling pathways; it may substitute for FS in increasing the effectiveness of TAM.
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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".