Characterization of T-cell immune responses against Staphylococcus chromogenes naturally-induced subclinical mastitis in dairy cows
Bibliographic record
Abstract
Introduction Subclinical mastitis in dairy cows is an asymptomatic inflammatory condition of the mammary gland during lactation. It is frequently associated with reduced milk yield, impaired milk quality, and an elevated risk of progression to clinical mastitis. The dysregulation of immune homeostasis characterizing this subclinical state represents a significant yet often overlooked concern in dairy herds. Methods We characterized T cell subsets in cows with Staphylococcus chromogenes-induced subclinical mastitis and healthy controls. Results We found that S. chromogenes infection induced an increase in the percentage of γδ T cells and a decrease in CD4+CD8+ double-positive (DP) T cells within milk cells, while significantly elevating the percentage of γδ T cells expressing CD44. Cell sorting combined with quantitative RT-PCR has indicated that infection by S. chromogenes may activate γδ T cells. Specifically, in subclinical mastitis milk, the expression levels of five cytokines—IFN-γ, IL-2, TNF-α, IL-17, and GZMB—were significantly higher in WC1+ γδ T cells compared to healthy cows. Furthermore, these cells released elevated levels of PRF1, which directly killed target cells infected with bacteria compared to the levels observed in healthy cows. Discussion Our results reveal, for the first time, the significant role of γδ T cells in S. chromogenes -induced subclinical mastitis, providing new insights into the immune protective mechanisms against subclinical mastitis in cows.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".