Antimicrobial Sensitivity Profile of Eye Infection in Dogs
Bibliographic record
Abstract
In the present study 88 number of corneal swabs were collected from 8 different breeds of dog suffering from corneal diseases. All the dogs were presented to Teaching Veterinary Clinical Complex of Odisha Veterinary College for treatment during the period from December 2018 to June 2019. The breeds consists of Non-descript (n=18), Pug (n=11), Labrador (n=10), German shepherd (n=12), Spitz (n=14), Golden retriever (n=10), Dalmatian (n=7), and Mastiff (n=6). There were no history of injury of the eye prior to infection and all most all the dogs were naturally infected with various microbial agents. In order to identify the microbial isolates and its antimicrobial susceptibility profile all the isolates were subjected to routine microbial procedure. Out of 246 number of bacterial isolates, the most commonly isolated bacteria were Staphylococcus spp. (35%) followed by Streptococcus spp (27%), Pseudomonas spp. (26%), and E. Coli (10%). The antimicrobial susceptibility test was done by disk diffusion method in which ciprofioxacin, cephalexin, neomycin and amoxycillin/clavulanic acid was sensitive to Staphylococcus spp., cephalexin, chloramphenicol and amoxycillin/clavulanic acid was sensitive to Streptococcus spp. and amikacin, gentamicin and tobramycin was found to be highly sensitive to Pseudomonas spp. Similarly, out of 115 fungal isolates, the most commonly fungal isolates were found to be Aspergillus spp. (40%) and Candida spp (59%), whereas mixed fungal infection were found to be prominant. Antifungals like fluconazole, voriconazole and miconazole were found to be sensitive to Aspergillus spp. as well as Candida spp.
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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 | 0.000 |
| 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".