Staphylococcus pseudintermedius as the Principal Pathogen in Canine Pyoderma: A molecular and phenotypic study in North-Eastern Karnataka
Bibliographic record
Abstract
Pyoderma is among the most frequently encountered dermatological disorders in small animal practice, commonly linked with bacterial infections, particularly those caused by Staphylococci. The present investigation aimed to determine the major bacterial pathogens responsible for canine pyoderma, identify the predominant Staphylococcal species through molecular assays in the North-Eastern Karnataka region. Dogs showing clinical signs of pyoderma presented to the Veterinary Clinical Complex, Veterinary College Bidar, as well as nearby District Polyclinics, were examined, and skin swabs were collected aseptically for bacteriological evaluation. Isolation and identification of bacteria were performed using standard cultural, morphological and biochemical methods. Polymerase Chain Reaction (PCR) assays targeting the pta gene (genus-specific) and nuc gene (species-specific) were employed to confirm Staphylococcus spp. and Staphylococcus pseudintermedius, respectively. Of the 118 clinically affected dogs, 86 (73%) samples yielded bacterial isolates. Staphylococcus spp. formed the majority (52; 60.46%), followed by Escherichia spp. (11; 13%), Pseudomonas spp. (9; 11%), Klebsiella spp. (8; 9%), and Proteus spp. (1; ~1%). Labrador Retrievers showed the highest occurrence (37%), followed by Non-Descript dogs (20%). Pyoderma was more common in young adults aged 6-12 months (38%) and dogs above 24 months (34%), with a higher proportion in males (62%). Molecular confirmation showed that 92% of Staphylococcal isolates belonged to S. pseudintermedius. The study identifies S. pseudintermedius as the key pathogen of canine pyoderma in this region and highlights the need for routine culture and identification to guide appropriate diagnosis, therapy and mitigate the rise of resistant strains.
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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 | 0.000 |
| 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".