Dietary flaxseed lignan or oil combined with tamoxifen treatment affects MCF‐7 tumor growth through estrogen receptor‐ and growth factor‐signaling pathways
Bibliographic record
Abstract
This study aimed to elucidate which component of flaxseed, i.e. secoisolariciresinol diglucoside (SDG) lignan or flaxseed oil (FO), makes tamoxifen (TAM) more effective in reducing growth of established estrogen receptor positive breast tumors (MCF-7) at low circulating estrogen levels, and potential mechanisms of action. In a 2 x 2 factorial design, ovariectomized athymic mice with established tumors were treated for 8 wk with TAM together with basal diet (control), or basal diet supplemented with SDG (1 g/kg diet), FO (38.5 g/kg diet), or combined SDG and FO. SDG and FO were at levels in 10% flaxseed diet. Palpable tumors were monitored and after animal sacrifice, analyzed for cell proliferation, apoptosis, ER-mediated (ER-alpha, ER-beta, trefoil factor 1, cyclin D1, progesterone receptor, AIBI), growth factor-mediated (epidermal growth factor receptor, human epidermal growth factor receptor-2, insulin-like growth factor receptor-1, phosphorylated mitogen activated protein kinase, PAKT, BCL2) signaling pathways and angiogenesis (vascular endothelial growth factor). All treatments reduced the growth of TAM-treated tumors by reducing cell proliferation, expression of genes, and proteins involved in the ER- and growth factor-mediated signaling pathways with FO having the greatest effect in increasing apoptosis compared with TAM treatment alone. SDG and FO reduced the growth of TAM-treated tumors but FO was more effective. The mechanisms involve both the ER- and growth factor-signaling pathways.
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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".