Origin, evolution, and virulence of porcine deltacoronaviruses in the United States. mBio
Bibliographic record
Abstract
ABSTRACT A novel porcine deltacoronavirus (PdCV) was first discovered in Ohio and Indiana in February 2014, rapidly spread to other states in the United States and Canada, and caused significant economic loss in the swine industry. The origin and viru-lence of this novel porcine coronavirus are not known. Here, we characterized U.S. PdCV isolates and determined their virulence in gnotobiotic and conventional piglets. Genome analyses revealed that U.S. PdCV isolates possess unique genetic characteristics and share a close relationship with Hong Kong and South Korean PdCV strains and coronaviruses (CoVs) of Asian leopard cats and Chinese ferret-badgers. The PdCV-positive intestinal content (Ohio CVM1) and the cell culture-adapted PdCVMichigan (MI) strain were orally inoculated into gnotobiotic and/or conventional piglets. Within 1 to 3 days postinfection, profuse watery diarrhea, vomiting, and dehydration were observed. Clinical signs were associated with epithelial necrosis in the gastric pits and small intestine, the latter resulting in severe villous atrophy. Mild interstitial pneumonia was identified in the lungs of PdCV-infected piglets. High levels of viral RNA (8 to 11 log RNA copies/g) were detected in intestinal tissues/luminal contents and feces of infected piglets, whereas moderate RNA levels (2 to 5 log RNA copies/g) were detected in blood, lung, liver, and kidney, indi-cating multisystemic dissemination of the virus. Polyclonal immune serum against PdCV but not immune serum against por-cine epidemic diarrhea virus (PEDV) reacted with PdCV-infected small-intestinal epithelial cells, indicating that PdCV is anti-genically distinct from PEDV. Collectively, we demonstrate for the first time that PdCV caused severe gastrointestinal diseases in swine.
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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 | 0.000 | 0.000 |
| 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".