Interaction of sesame seed and tamoxifen on tumor growth and bone health in athymic mice.
Bibliographic record
Abstract
Some premenopausal breast cancer patients use phytoestrogen-rich soy and flaxseed to alleviate side effects induced by drugs such as tamoxifen (TAM). Lignan-rich flaxseed protects against breast cancer and increases the effectiveness of TAM. This study determined the interactive effects of lignan-rich sesame seed (SS) and TAM on estrogen-responsive MCF-7 breast tumor growth and bone health in ovariectomized athymic mice under premenopausal-simulated conditions. Ovariectomized mice with an estrogen implant and established MCF-7 tumors were treated for 8 weeks as follows: (i) positive control fed basal diet (BD), (ii) SS group fed BD supplemented with 10% ground SS, (iii) TAM group with TAM implant fed BD, (iv) SS + TAM group with TAM implant fed BD supplemented with 10% SS, and (v) negative control fed BD with no estrogen implant. Palpable tumor data, adjusted for body weight, showed that SS does not inhibit MCF-7 tumor growth and tends to negate the tumor inhibitory effect of TAM by increasing cell proliferation and reducing apoptosis. SS alone and combined with TAM enhanced femur biomechanical strength but caused no differences in bone mineral content or bone mineral density in either the femur or lumbar vertebrae. SS is not protective and interacts adversely with TAM in MCF-7 breast tumors but induces beneficial effects on bone both alone and when combined with 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) | 0.001 | 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) | 0.002 | 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".