Can the combination of flaxseed and its lignans with soy and its isoflavones reduce the growth stimulatory effect of soy and its isoflavones on established breast cancer?
Bibliographic record
Abstract
Consumption of phytoestrogen (PE)-rich foods (i. e., soy and flaxseed (FS)) is increasing because of their suggested health benefits. However, recent studies raise concern over the safety of soy and its isoflavones, particularly genistein (GEN), for postmenopausal breast cancer (BC), due to their potential stimulatory effects on human breast tissue and on the growth of existing tumors in rodents. FS, rich in PE lignans, which is metabolized to the mammalian lignans enterolactone (ENL) and enterodiol (END), has consistently been shown to have tumor inhibitory effects in a human clinical trial as well as rodent BC models. Using the preclinical athymic mouse postmenopausal BC model, combining FS with soy protein or GEN with END and ENL, was found to negate the tumor stimulatory effects of soy protein or GEN alone. The mechanism may be related to the modulation of estrogen receptor and MAPK signaling pathways. If these studies can be confirmed in clinical trials, then consumption of combined soy and FS, or their PEs, may reduce the tumor growth stimulatory effect of soy or GEN. This may indicate that if soy is consumed with lignan-rich foods, it may continue to induce its other beneficial health effects, without inducing adverse effect on postmenopausal BC.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".