Selenoproteins reduce susceptibility to DMBA-induced mammary carcinogenesis
Bibliographic record
Abstract
Selenium is an essential micronutrient in the diet of humans and other mammals. Based largely on animal studies and epidemiological evidence, selenium is purported to be a promising cancer chemopreventive agent. However, the biological mechanisms by which chemopreventive activity takes place are poorly understood. It remains unclear whether selenium acts in its elemental form, through incorporation into organic compounds, through selenoproteins or any combination of these. The purpose of this study was to determine whether selenoproteins mitigate the risk of developing chemically induced mammary cancer. Selenoprotein expression was ablated in mouse mammary epithelial cells through genetic deletion of the selenocysteine (Sec) tRNA gene (Trsp), whose product, designated selenocysteine tRNA, is required for selenoprotein translation. Trsp floxed and mouse mammary tumor virus (MMTV)-cre mice were crossed to achieve tissue-specific excision of Trsp in targeted mammary glands. Eight- to twelve-week-old second generation Trsp(fl/+);wt, Trsp(fl/+);MMTV-cre, Trsp(fl/fl);wt and Trsp(fl/fl);MMTV-cre female mice were administered standard doses of the carcinogen, 7,12-dimethylbenzylbenz[a]antracene. Our results revealed that heterozygous, Trsp(fl/+);MMTV-cre mice showed no difference in tumor incidence, tumor rate and survival compared with the Trsp(fl/+);wt mice. However, 54.8% of homozygous Trsp(fl/f)(l);MMTV-cre mice developed mammary tumors and exhibited significantly shorter survival than the corresponding Trsp(fl/fl);wt mice, where only 36.4% developed tumors. Loss of the homozygous Trsp alleles was associated with the reduction of selenoprotein expression. The results suggest that mice with reduced selenoprotein expression have increased susceptibility to developing carcinogen-induced mammary tumors and that a major protective mechanism against carcinogen-induced mammary cancer requires the expression of these selenoproteins.
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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 | 0.001 | 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) | 0.002 | 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".