Comparative Effects of Sesame Seed Lignan and Flaxseed Lignan in Reducing the Growth of Human Breast Tumors (MCF-7) at High Levels of Circulating Estrogen in Athymic Mice
Bibliographic record
Abstract
Flaxseed (FS) has a breast tumor-reducing effect, possibly because of its high content of secoisolariciresinol diglucoside (SDG) lignan. Sesame seed (SS) is rich in the lignan sesamin (SES) but is non-protective. Both lignans are metabolized to estrogen-like enterodiol and enterolactone. The objective of this study was to differentiate the effects of SDG and SES on established human estrogen receptor-positive breast tumors (MCF-7) in athymic mice with high serum estrogen to help explain the different effects of FS and SS. Mice were fed for 8 wk the basal diet (BD, control) or BD supplemented with 1 g/kg SDG or SES. SES reduced palpable tumor size by 23% compared to control, whereas SDG did not differ from SES or control. Both treatments reduced tumor cell proliferation, but only SES increased apoptosis. SDG and SES reduced human epidermal growth factor receptor 2 and endothelial growth factor receptor expressions, but only SES reduced downstream pMAPK. Neither treatment affected IGF-1R, vascular endothelial growth factor receptor-2, Akt, pAkt, or MAPK of the growth factor signaling pathway. Thus, at high serum estrogen levels, SDG may not account for the tumor reducing effect of FS. SES was more effective than SDG in reducing breast tumor growth, but its effect may have been lost when consumed as a component of SS.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".