Methicillin-Resistant Staphylococcus aureus (MRSA) Prevalence in Bovine Mastitis Milk
Bibliographic record
Abstract
We are investigating the prevalence of methicillin-resistant Staphylococcus aureus (MRSA) in mastitic dairy cows from San Bernardino County, CA. Although there are many types of bacteria that cause bovine mastitis, an inflammation of the udder in dairy cows, we are focusing on S. aureus because it causes "'30% of mastitis cases. This is of concern due to its economic and public health significance. Mastitis costs the U.S. dairy industry about $1.7-2 billion/year as a result of reduced milk production, discarded milk, treatment, labor, and veterinary costs (Jones and Bailey, 2009). \n \nThese mastitiscausing pathogens present a public health concern due to possible transmission of bacteria from mastitic cows to milk. Although pasteurization of milk does not produce a bacteria-free product, it significantly decreases the risk of pathogens in the milk. Raw dairy products are a major concern due to the lack of pasteurization (Oliver et al., 2009). \nThe study includes isolating and culturing Staphylococcus sp. and S. aureus from milk samples. The milk samples were collected from fifteen mastitic dairy cows from two dairy farms in Chino and Ontario, San Bernardino County, CA in 2012. The confirmation of desired bacterial isolates is done through PCR techniques targeting genus-and species-specific DNA sequences. S. aureus isolates are tested for methicillin resistance to identify MRSA. PCR techniques are used to detect presence of mecA, a gene associated with resistance to penicillin-type drugs. \n \nAn additional goal is to test each isolate's resistance to cefoxitin, oxacillin, methicillin, and mupirocin using the Kirby Bauer disk diffusion method. PCR techniques will be used to tests for the gene that encodes resistance to mupirocin (mupA). Most research suggests that there is a low prevalence of MRSA. However, comprehensive surveys of MRSA in livestock species are lacking in the US. Thus, we expect our results to encourage further studies so that there may be an accurate assessment of the incidence of MRSA on dairy farms in California and the rest of the country. This will allow us to determine whether MRSA poses a threat to consumers of raw bovine milk products.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".