Saponins in soy reduce NNK-induced lung cancer by increasing plasma isoflavone levels
Bibliographic record
Abstract
Recently, we found, using a cigarette carcinogen-induced lung cancer model, that soy protein isolate (SPI) was superior to casein in preventing lung cancer. In this study, we have attempted to identify the component(s) within SPI responsible for this chemopreventive effect. We fractionated the SPI using ethanol to separate the ethanol-soluble fraction (ESF) and the washed SPI and compared their efficacy to diets made with amino acids that comprise soy protein or casein, in preventing lung tumor formation in A/J mice. Only the ethanol-soluble fraction was as effective as SPI in preventing lung tumor formation. Since isoflavones and saponins are known ethanol-soluble bioactives from soy, we added isoflavones, or saponins or both to casein and found that isoflavones or saponins alone did not reduce lung nodule formation. However, when we combined soy saponins and isoflavones, we saw a significant (P < 0.05) reduction in NNK-induced lung nodules, and an increase in plasma isoflavone levels, suggesting that the saponins may enhance the bioavailability of the isoflavones in these mice. Taken together, we suggest that the superior efficacy of SPI over casein could be attributed, at least in part, to the synergistic effect of the soy saponins and isoflavones.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".