Effects of Flaxseed Lignan and Oil on Bone Health of Breast-Tumor-Bearing Mice Treated With or Without Tamoxifen
Bibliographic record
Abstract
Previous studies showed that flaxseed lignan (secoisolariciresinol diglucoside, SDG) and oil (FO) inhibit established breast tumor growth in athymic mice with or without tamoxifen (TAM) treatment. TAM was found to increase bone mineral content (BMC) and density (BMD) in breast cancer patients. It is not known whether SDG or FO alone or combined with TAM affects bone health. Hence, the effects of SDG and FO, alone or in combination, on BMC, BMD, and biomechanical bone strength in ovariectomized athymic mice with established human breast tumors (MCF-7) treated with or without TAM were studied. In a factorial design, mice were divided into four non-TAM and four TAM groups. Each group consisted of mice fed a basal diet (BD), SDG (1 g/kg), FO (38.5 g/kg) or SDG + FO (combination) diets. The TAM group had TAM implants that provide a 5-mg TAM dose released over 60 d. TAM exerted an overall significant effect in increasing BMC, BMD, and biomechanical strength in femurs and lumbar vertebra. Without TAM treatment, SDG produced significant lower femur BMD (6%) while FO produced lower vertebrae BMC (8%) and BMD (6%). With TAM treatment, SDG and FO did not exert an effect on BMC and BMD at the femur or vertebra. SDG and FO produced no marked effect on biomechanical bone strength with or without TAM treatment. In conclusion, FS components did not significantly attenuate the positive effects on bone induced by TAM in this model system, indicating no apparent adverse effects on bone health.
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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".