Rodent Models Assessing Mammary Tumor Prevention by Soy or Soy Isoflavones
Bibliographic record
Abstract
While epidemiological studies performed in Asian countries generally show that high levels of dietary soy are associated with reduced breast cancer risk, studies in Western countries have typically failed to show this correlation. In an attempt to model the preventative actions of soy on mammary tumor development, rodent models have been employed. Thirty-four studies were identified that evaluated the impact of soy products or purified soy isoflavones on mammary tumor initiation (studies evaluating established mammary tumors or mammary tumor cell lines were not included) and these studies were separated into mammary tumors induced by chemical carcinogens or transgenic expression of oncogenes based on the timing of soy administration. Regardless of when soy-based diets or purified isoflavones were administered, no consistent protective effects were observed in either carcinogen-induced or oncogene-induced mammary tumors. While some studies demonstrated that soy or purified isoflavones could reduce mammary tumor incidence, other studies showed either no effect or tumor promoting effects of soy products or isoflavones. Most importantly, only five studies found a decrease in mammary tumor incidence and six studies observed a decrease in tumor multiplicity, two relevant measures of the tumor preventative effects of soy or isoflavones. The variable outcomes of the studies examined were not completely surprising given that few studies employed the same experimental design. Future studies should be carefully designed to more accurately emulate soy consumption observed in Asian cultures including lifetime exposure to less refined soy products and potentially the incorporation of multigenerational feeding studies.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.002 | 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".