Development of a Rapid and Sensitive Test for Identification of Major Pathogens in Bovine Mastitis by PCR
Bibliographic record
Abstract
Bovine mastitis is the most important source of loss for the dairy industry. A rapid and specific test for the detection of the main pathogens of bovine mastitis is not actually available. Molecular probes reacting in PCR with bacterial DNA from bovine milk, providing direct and rapid detection of Escherichia coli, Staphylococcus aureus, Streptococcus agalactiae, Streptococcus dysgalactiae, Streptococcus parauberis, and Streptococcus uberis, have been developed. Two sets of specific primers were designed for each of these microorganisms and appeared to discriminate close phylogenic bacterial species (e.g., S. agalactiae and S. dysgalactiae). In addition, two sets of universal primers were designed to react as positive controls with all major pathogens of bovine mastitis. The sensitivities of the test using S. aureus DNA extracted from milk with and without a pre-PCR enzymatic lysis step of bacterial cells were compared. The detection limit of the assay was 3.125 x 10(2) CFU/ml of milk when S. aureus DNA was extracted with the pre-PCR enzymatic step compared to 5 x 10(3) CFU/ml of milk in the absence of the pre-PCR enzymatic step. This latter threshold of sensitivity is still compatible with its use as an efficient tool of diagnosis in bovine mastitis, allowing the elimination of expensive reagents. The two PCR tests avoid cumbersome and lengthy cultivation steps, can be performed within hours, and are sensitive, specific, and reliable for the direct detection in milk of the six most prevalent bacteria causing bovine mastitis.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".