Selenomethionine stimulates expression of glutathione peroxidase 1 and 3 and growth of bovine mammary epithelial cells in primary culture
Bibliographic record
Abstract
This study examined the localization of cellular glutathione peroxidase (GPx1) and extracellular glutathione peroxidase (GPx3) in lactating mammary tissue and in primary cultures of bovine mammary epithelial cells (BMEC). The effect of selenium as selenomethionine (SeMet) on the growth and viability of BMEC and GPx protein expression and activity were also studied. Single mammary epithelial cells were recovered by serial collagenase/hyaluronidase digestion from lactating bovine mammary tissue and cultured in a low-serum collagen gel system enriched with lactogenic hormones and 0, 10, 20, or 50 nM SeMet. Positive immunostaining with anti-cytokeratin and bovine anti-casein confirmed the epithelial nature and differentiated state of BMEC. Addition of SeMet to media facilitated rapid confluence of BMEC and formation of dome structures. Immunohistochemical and immunocytochemical staining revealed that both GPx1 and GPx3 are synthesized by BMEC and localized in the cytoplasm and nucleus. Up to 50 nM SeMet linearly increased BMEC number and viability over 5 d of culture. Bovine mammary epithelial cells cultured in SeMet-supplemented medium also exhibited markedly elevated GPx activity and linear increases in abundance of GPx1 and GPx3 proteins. It is apparent that SeMet degradation to release Se for synthesis of selenoproteins is carried out by BMEC. Results indicate that bovine mammary epithelial cells express GPx1 and GPx3 in vivo and in vitro; SeMet enhances expression of these selenoproteins in vitro and the growth and viability of BMEC.
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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 | 0.001 |
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".