Epidemiologic investigation and genetic characterization of canine respiratory coronavirus in the Southeastern United States
Bibliographic record
Abstract
Canine respiratory coronavirus (CRCoV) is one of the main causative agents of canine infectious respiratory disease (CIRD), an illness whose epidemiology is poorly understood. We assessed the prevalence, risk factors, and genetic characterization of CRCoV in privately owned dogs in the Southeastern United States. We PCR-screened 189 nasal swabs from dogs with and without CIRD clinical signs for 9 CIRD-related pathogens, including CRCoV; 14% of dogs, all diagnosed with CIRD, were positive for CRCoV, with a significantly higher rate of cases in younger dogs and during warmer weather. Notably, the presence of CRCoV, alone or in coinfection with other CIRD pathogens, was statistically associated with a worse prognosis. We estimated a CRCoV seroprevalence of 23.7% retrospectively from 540 serum samples, with no statistical association to dog age, sex, or season, but with a significantly higher presence in urban counties. Additionally, the genomes of 6 CRCoVs were obtained from positive samples using an in-house developed targeted amplicon-based approach specific to CRCoV. Subsequent phylogeny clustered their genomes in 2 distinct genomic groups, with most isolates sharing a higher similarity with CRCoVs from Sweden and only 1 more closely related to CRCoVs from Asia. We provide new insights into CIRD and CRCoV epidemiology in the Southeastern United States and further support the association of CRCoV with more severe cases of CIRD. Additionally, we developed and successfully tested a new amplicon-based approach for whole-genome sequencing of CRCoV that can be used to further investigate the genetic diversity within CRCoVs.
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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 | 0.000 | 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".