Characterization of meticillin-resistant and meticillin-susceptible isolates of Staphylococcus pseudintermedius from cases of canine pyoderma in Australia
Bibliographic record
Abstract
Meticillin-resistant Staphylococcus pseudintermedius (MRSP) has recently emerged as a worldwide cause of canine pyoderma. In this study, we characterized 22 S. pseudintermedius isolates cultured from 19 dogs with pyoderma that attended a veterinary dermatology referral clinic in Australia in 2011 and 2012. Twelve isolates were identified as MRSP by mecA real-time PCR and phenotypic resistance to oxacillin. In addition to β-lactam resistance, MRSP isolates were resistant to erythromycin (91.6 %), gentamicin (83.3 %), ciprofloxacin (83.3 %), chloramphenicol (75 %), clindamycin (66 %), oxytetracycline (66 %) and tetracycline (50 %), as shown by disc-diffusion susceptibility testing. Meticillin-susceptible S. pseudintermedius isolates only showed resistance to penicillin/ampicillin (90 %) and tetracycline (10 %). PFGE using the SmaI restriction enzyme was unable to type nine of the 12 MRSP isolates. However the nine isolates provided the same PFGE pulsotype using the Cfr91 restriction enzyme. Application of the mec-associated direct repeat unit (dru) typing method identified the nine SmaI PFGE-untypable isolates as dt11cb, a dru type that has only previously been associated with MRSP sequence type (ST)45 isolates that possess a unique SCCmec element. The dt11cb isolates shared a similar multidrug-resistant antibiogram phenotype profile, whereas the other MRSP isolates, dt11a, dt11af (dt11a-associated) and dt10h, were resistant to fewer antibiotic classes and had distinct PFGE profiles. This is the first report of MRSP causing pyoderma in dogs from Australia. The rapid intercontinental emergence and spread of multidrug-resistant MRSP strains confirms the urgent need for new treatment modalities for recurrent canine pyoderma in veterinary practice.
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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 | 0.002 |
| 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 | 0.001 | 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".