The Effect Of Flaxseed And Soy, and their phytoestrogens, On MCF‐7 Tumor Growth Biomarkers In Ovariectomized Athymic Nude Mice
Bibliographic record
Abstract
Combining flaxseed (FS) and soy protein isolate (SPI), or their respective phytoestrogens (PEs), lignans enterolactone (ENL) or enterodiol (END) and isoflavone genistein (GEN), can more effectively reduce the growth of estrogen receptor (ER) positive human breast cancer in ovariectomized mice (simulate postmenopausal situation) than SPI or GEN alone. This study analyzed tumor growth biomarkers with specific focus on signaling pathways involved in cell proliferation and apoptosis. Mice with established MCF‐7 tumors were fed a basal diet (BD), 20%SPI, 10%FS, or 20%SPI + 10%FS for 25 weeks. Tumors were analyzed by immunohistochemistry for proliferation (Ki67 labeling index (LI)), ERα, cyclin D1, apoptosis, and pAkt. SPI enhanced Ki67 LI, ERα, and cyclin D1. SPI also enhanced apoptosis by inhibiting pAkt, but its effects on proliferation were stronger, leading to enhanced tumor growth. FS alone did not alter expression of proteins measured, however, when combined with SPI, a reduction in Ki67 LI, and an attenuation of cyclin D1 were observed with no effect on ERα, compared to SPI alone. The combination also enhanced apoptosis, however, a non‐significant reduction in pAkt was observed. Using the same study model, GEN, like SPI, enhanced Ki67, ERα, and cyclin D1, while the lignans did not; however, its effects were attenuated when combined with the lignans. This indicates that like SPI, the tumor promoting effects of GEN were induced by an increase in cell proliferation mediated by the ER. Since combining lignans or FS with GEN or SPI reduces these effects, it may be better to consume PEs in combination under postmenopausal situation. (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) | 0.000 | 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".