Molecular detection of bovine mastitis pathogens in refrigerated raw milk samples: Linking pathogen profiles with somatic cell count pattern
Bibliographic record
Abstract
ABSTRACT Bovine mastitis remains a significant global challenge in dairy production, adversely affecting animal health, milk quality, and economic viability. Hence, this study presents the development and validation of a molecular diagnostic assay capable of identifying 14 mastitis-associated pathogens in refrigerated raw milk using pathogen-specific primers optimized for specificity and sensitivity. Among 39 raw milk samples analyzed from dairy farms in southern Brazil, somatic cell count (SCC) and total bacterial count were assessed via flow cytometry and DNA quantification, respectively. The assay demonstrated robust sensitivity (detecting as few as 200 DNA copies per reaction), with no cross-reactivity. Elevated SCC levels were observed in 82.1% of samples, yet only 52.3% showed high-risk pathogen presence-suggesting a partial decoupling between SCC elevation and active infection. The most prevalent species were Streptococcus dysgalactiae, Enterococcus faecalis, Staphylococcus aureus, and coagulase-negative staphylococci. This molecular tool offers a rapid, accurate alternative to traditional culturing, suitable for early detection of both clinical and subclinical mastitis, with potential applicability across diverse dairy systems globally.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 teacher head, 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".