Suppression of Colorectal Oncogenesis by Selenium-Enriched Milk Proteins: Apoptosis and <i>K-ras</i> Mutations
Bibliographic record
Abstract
The chemical form and bioavailability of dietary selenium may influence its protectiveness against colorectal cancer. Selenium is readily incorporated into milk proteins by feeding cows with selenized-yeast. This study examined whether a dairy source of organic selenium (as milk proteins) is more effective than a yeast source at inhibiting oncogenesis in carcinogen-treated mice and whether it regulates the homeostatic response to carcinogen-induced DNA damage. Dietary interventions are as follows: selenium-enriched milk protein isolate (Tatura-Bio Se; 0.5 or 1 ppm selenium) or milk protein control and selenized-yeast (Sel-Plex; 1 or 4 ppm selenium) with casein or casein alone as control. After 4 weeks on diet, mice received a single azoxymethane (10 mg/kg) injection to induce mutations and were killed 6 hours later. Measures were as follows: plasma selenium, cell proliferation, and acute apoptotic response to azoxymethane (AARGC). Separate groups of mice on the same diets were given 4 weekly azoxymethane (15 mg/kg) injections to induce oncogenesis. Mice were killed 6 or 30 weeks after the last azoxymethane injection. Measures were as follows: aberrant crypt foci (ACF), cancers, and K-ras mutations. Dairy-selenium at 1 ppm significantly suppressed ACF and cancers, whereas yeast-selenium at an equivalent selenium intake had no effect. Dairy-selenium significantly increased plasma selenium levels and AARGC, and reduced cell proliferation and frequency of K-ras mutations in ACF relative to an equivalent dose of selenium from yeast. Selenium-enriched milk protein isolate is superior to selenized-yeast in terms of its bioavailability and capacity to suppress oncogenesis. Suppression may be a consequence of enhanced apoptotic deletion of azoxymethane-induced DNA lesions and the subsequent reduction in frequency of K-ras mutations.
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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".