Selenomethionine Mitigates Effects of Nocardia cyriacigeorgica-Induced Inflammation, Oxidative Stress, and Apoptosis in Bovine Mammary Epithelial Cells
Bibliographic record
Abstract
Nocardia cyriacigeorgica causes bovine mastitis, reduces milk quantity and quality, and is often resistant to antimicrobials. Selenomethionine (SeMet) is a form of selenium, which reduces reactive oxygen species (ROS)-mediated apoptosis and intramammary infections. However, the protective effects of SeMet on N. cyriacigeorgica-infected bovine mammary epithelial cells (bMECs) are unclear. The objective of this study was to evaluate whether SeMet mitigated N. cyriacigeorgica-induced inflammatory injury, oxidative damage and apoptosis in bMECs. Cells were cultured with or without being pretreated with 40 µM of SeMet for 12 h, then challenged with N. cyriacigeorgica (multiplicity of infection = 5:1) for 6 h. Although N. cyriacigeorgica was resistant to lincomycin, erythromycin, enrofloxacin, penicillin, amoxicillin, cephalonium, cephalexin, and ceftriaxone, 40 μM SeMet increased cell viability and inhibited lactate dehydrogenase release in infected bMECs. Furthermore, N. cyriacigeorgica significantly induced mRNA production and protein expression of TNF-α, IL-1β, IL-6, and IL-8 at 6 h. Cell membrane rupture, cristae degeneration and mitochondria swelling were evident with transmission electron microscopy. Superoxide dismutase (SOD) and glutathione peroxidase (GSH-px) activities were down-regulated after 3, 6, or 12 h, whereas malondialdehyde (MDA) and ROS contents were significantly upregulated, with cell damage and apoptosis rapidly evident (the latter increased significantly in a time-dependent manner). In contrast, bMECs pretreated with 40 μM SeMet before infection, SOD, and GSH-px activities were upregulated (p < 0.05); MDA and ROS concentrations were downregulated (p < 0.05), and apoptosis was reduced (p < 0.05). In conclusion, 40 μM SeMet alleviated inflammation, oxidative stress and apoptosis induced by N. cyriacigeorgica in bMECs cultured in vitro.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".