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 re-sistant to antimicrobials. Selenomethionine (SeMet) is a form of selenium, an essential trace el-ement. Although SeMet reduced ROS-mediated apoptosis and intramammary infections. How-ever, protective effects of SeMet on N. cyriacigeorgica-infected bovine mammary epithelial cells (bMECs) are unclear. The objective was to evaluate whether SeMet mitigated N. cyriacigeorgi-ca-induced inflammatory injury, oxidative damage and apoptosis in bMECs. Cells were cultured with or without being pretreated 40 µM of SeMet for 12 h, then challenged N. cyriacigeorgica (MOI=5:1) for 6 h. Although N. cyriacigeorgica was resistant to lincomycin, erythromycin, en-rofloxacin, penicillin, amoxicillin, cephalonium, cephalexin, and ceftriaxone, 40 μM SeMet in-creased cell viability and inhibited LDH release in infected bMECs. Furthermore, N. cyriacigeor-gica 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 evi-dent with transmission electron microscopy. SOD and GSH-px activities were down regulated after 3, 6 or 12 h, whereas 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 ac-tivities 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, oxi-dative 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".