72 A One-health Perspective on S. aureus Induced Mastitis
Bibliographic record
Abstract
Abstract Bovine mastitis, commonly caused by bacteria, is one of the most devastating diseases for the dairy industry worldwide, with high economic, social, and public health impacts. Among the pathogenic bacteria, Staphylococcus aureus is one of the leading pathogens in most countries. Success rate of antibiotic treatment is low such that effective control of S. aureus induced mastitis is currently only possible through prevention of new infections and culling of infected animals. The infection is usually subclinical, causing elevated somatic cell counts but no detectable changes in milk or the udder. S. aureus persists in mammary glands, teat canals, and teat lesions of infected cows and is transmitted to other cows during milking. The direct evidence of cross-species transmission of S. aureus is still scarce. Nevertheless, accumulating indirect evidence supports the zoonotic potential for S. aureus from bovine mastitis to humans. In addition to direct contact, aerosol exposures may also be an important mechanism for the transmission. While animal-adapted livestock-associated methicillin-resistant S. aureus has been known for many years, most S. aureus isolated from mastitic milk in Canada are methicillin-sensitive. S. aureus induced mastitis is a common reason for therapeutic and/or prophylactic use of antibiotics on dairy farms. Occurrence of multidrug resistant, especially methicillin-resistant, S. aureus has been a major public health concern. In this talk, we will address the pathogenesis and strain spectrum of S. aureus induced bovine mastitis, review existing evidence for inter-species transmission, and discuss possible transmission of methicillin-resistance determinants. This talk aims to emphasize the need for the one-health approach for prevention and treatment of S. aureus induced mastitis, in order to provide safe and nutritious milk and milk products to consumers.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".