Characterization of Staphylococcus Species Isolated from Bovine Quarter Milk Samples
Bibliographic record
Abstract
Staphylococcus (S.) aureus is considered as a major mastitis pathogen, with considerable epidemiological information on such infections while the epidemiology of coagulase-negative staphylococci (CNS) is more controversial. The aim of this study was to use matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) technology for identification of staphylococci isolated from bovine milk at species level and to characterize them in reference to presentation, somatic cell count (SCC), bacterial shedding (cfu) and antimicrobial resistance patterns. A total of 200 staphylococcal isolates (S. aureus n = 100; CNS n = 100) originating from aseptically collected quarter milk samples from different quarters of dairy cows were included in the study. They originated from cases of clinical (CM) and subclinical mastitis (SCM) or were isolated from milk with SCC ≤ 100,000 cells/mL in pure culture. We found staphylococci predominantly in cases of SCM (n = 120). In low-SCC cows, 12 S. aureus and 32 CNS isolates were detected. Eighteen percent of each were associated with CM. Eleven CNS species were identified, S. chromogenes (n = 26) and S. xylosus (n = 40) predominated. CNS, particularly those in low-SCC cows, showed higher MIC90 (minimal inhibitory concentration) values for penicillin, ampicillin, cefoperazone, pirlimycin and marbofloxacin. Based on the present results, a careful interpretation of laboratory results is recommended to avoid antimicrobial therapy of staphylococci without clinical relevance and to ensure prudent use of antimicrobials.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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".