Occurrence of Canine parvovirus-2 and Canine adenovirus-1 Infections in Dogs: A Hospital Based Study
Bibliographic record
Abstract
Canine parvovirus-2 (CPV-2) and canine adenovirus-1 (CAV-1) are the major viral agents causing enteric diseases in dogs worldwide despite several control measures. The objective of the present study was to investigate the hospital based occurrence of CPV-2 and CAV-1 in dogs in teaching veterinary clinical complex of northern India from August, 2017 to March, 2019. Faecal samples were collected from the dogs with gastroenteritis and screened for CPV-2 and CAV-1 infections by polymerase chain reaction (PCR). Simultaneously, data regarding age, breed, sex and vaccination status were also recorded from the affected dogs. A total of 216 faecal samples were collected during the study period out of which 90 (41.67%), 12 (5.56%) and 26 (12.04%) samples were found to be positive for CPV-2, CAV-1 and co-infection, respectively. The analysis of positive samples revealed that the occurrence of CPV-2 and CAV-1 infection was highest in young dogs of the age group 0-3 months (CPV-2=44.44% and CAV-1=33.33%), Labrador Retriever breed (CPV-2=35.55% and CAV-1=50.0%) and unvaccinated dogs (CPV-2=68.88% and CAV-1=83.33%). Sex wise analysis revealed that the positivity of CPV-2 infection was higher in female (51.11%) than male dogs (48.88%) but, no such difference was noticed with respect to CAV-1 infection. It is concluded that the occurrence of CPV-2 and CAV-1 infections was very high in the study region and proper control measures are recommended. However, analysis of large number of samples including those from the field are required for further validation of the study as well as sequential analysis of CPV-2 to ensure the prevalent field antigenic variant of CPV-2 in the region.
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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".