Interactive effects of sesame seed and tamoxifen on estrogen dependent breast cancer in athymic nude mice
Bibliographic record
Abstract
Flaxseed (FS), the richest source of mammalian lignan precursor protects against breast cancer and increases the effectiveness of the breast cancer drug tamoxifen (TAM). Ingestion of sesame seed (SS) produces mammalian lignans comparable to FS but there is limited data on its anticancer potential. This study determined in an ovariectomized athymic nude mouse model, the interactive effects of SS and TAM on estrogen (E2) responsive breast cancer (MCF‐7) in the presence of high or low E2 levels. In study 1, mice (with low E2 to mimic post‐menopausal condition) with established MCF‐7 tumors were fed a basal diet and treated for 7 weeks as follows: 1) no treatment (–E2 control); 2) 10% SS; 3) TAM implant; 4) 10% SS + TAM; 5) E2 implant (+E2 control). Study 2 was similar to study 1 except that apart from the –E2 control, all groups received an E2 implant to mimic high E2 levels in pre‐menopausal women. Palpable tumor size was monitored weekly. Final tumor area, weight, cell proliferation, and apoptosis were also measured. In study 1, SS regressed tumor size similar to the –E2 control, while TAM and SS + TAM did not, resulting in larger tumors than –E2 control (P<0.05). In study 2, while SS did not, TAM regressed tumors to a final size that was 56% (p<0.05) of + E2 control. However, when SS was combined with TAM, TAM's inhibitory effect was negated resulting in tumors that did not differ in size from +E2 control. Unlike apoptosis, tumor cell proliferation related positively to palpable tumor area (p<0.05, r=0.52). In summary, 10% SS did not exert a protective effect nor strengthen the effect of TAM in MCF‐7 tumors, in part due to its effects on cell proliferation. Supported by 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.001 | 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".