<i>Macrococcus canis</i> and <i>M. caseolyticus</i> in dogs: occurrence, genetic diversity and antibiotic resistance
Bibliographic record
Abstract
BACKGROUND: The discovery of a new Macrococcus canis species isolated from skin and infection sites of dogs led us to question if Macrococcus spp. are common in dogs and are resistant to antibiotics. HYPOTHESIS/OBJECTIVES: To evaluate the occurrence of Macrococcus spp. in dogs, determine antibiotic resistance profiles and genetic relationships. ANIMALS: One hundred and sixty two dogs (mainly West Highland white terriers and Newfoundland dogs) were screened for the presence of Macrococcus, including six dogs with Macrococcus infections. METHODS: Samples were taken from skin, ear canal and oral mucosa using swabs. Macrococci were identified by matrix-assisted laser desorption ionization-time of flight mass spectrometry, 16S rRNA sequencing and nuc-PCR. Minimal inhibitory concentrations of 19 antibiotics were determined using broth microdilution. Resistance mechanisms were identified by microarray and sequencing of the fluoroquinolone-determining region of gyrA and grlA. Sequence type (ST) was determined by multilocus sequence typing. RESULTS: Out of the 162 dogs, six harboured M. caseolyticus (n = 6) and 13 harboured M. canis (n = 16). Six isolates of M. canis and one of M. caseolyticus were obtained from infection sites. The 22 M. canis strains belonged to 20 different STs and the seven M. caseolyticus strains to three STs. Resistance to antibiotics was mostly associated with the detection of known genes, with mecB-mediated meticillin resistance being the most frequent. CONCLUSION AND CLINICAL IMPORTANCE: This study gives some insights into the occurrence and genetic characteristics of antibiotic-resistant Macrococcus from dogs. Presence of M. canis in infection sites and resistance to antibiotics emphasized that more attention should be paid to this novel bacteria species.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".