Exploring pathogen-specific somatic cell patterns in dairy ewes during early lactation
Bibliographic record
Abstract
The postpartum period is a critical period for mastitis susceptibility in dairy ewes, yet the species-specific effects of mastitis pathogens on milk SCC and differential inflammatory cell counts (DICC) remain largely unknown. Therefore, this study investigates pathogen-specific impacts on microscopic SCC and DICC in milk and colostrum from Lacaune dairy ewes during early lactation. A total of 586 colostrum and milk samples were collected from 50 ewes (100 udder halves) at 6 time points: on the day of parturition and at 1, 3, 7, 15, and 30 d postpartum. Milk and colostrum samples were examined through bacteriological analysis and species-level pathogen identification using MALDI-TOF MS, along with microscopic SCC and DICC measurements using precise DNA-specific cell counting procedures. Non-aureus staphylococci and mammaliicocci (NASM) were identified as the primary etiological agents of IMI, with Staphylococcus xylosus, Staphylococcus simulans, and Staphylococcus chromogenes being the most frequently isolated species. Among NASM species, S. simulans IMI resulted in the highest increases in SCC and neutrophil percentages, supporting its classification as a major mastitis pathogen in dairy ewes. In contrast, S. xylosus and S. chromogenes triggered mild or negligible inflammatory effects and could therefore be classified as minor mastitis pathogens. In addition, multiparous ewes had higher SCC than primiparous ewes, possibly due to the persistence of IMI. Furthermore, colostrum samples showed high SCC, predominantly composed of lymphocytes. Although our research provides insights on somatic and differential cell counts and the bacteria associated with IMI in dairy ewes during early lactation, its limitation to a single flock reduces generalizability. Further, comprehensive studies are needed to better estimate the effects on SCC, DICC, and their practical applications.
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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 | 0.000 |
| 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".