Seroprevalence of Turkey Coronavirus in North American Turkeys Determined by a Newly Developed Enzyme-Linked Immunosorbent Assay Based on Recombinant Antigen
Bibliographic record
Abstract
Turkey coronavirus (TCoV) causes diarrhea in young turkey poults, but little is known about its prevalence in the field. To address this, the complete nucleocapsid gene was cloned and expressed in Escherichia coli. Expressed nucleocapsid gene produced two distinct proteins (52 and 43 kDa); their specificity was confirmed by Western blotting using two different monoclonal antibodies. Recombinant N protein was purified and used as an antigen to develop an enzyme-linked immunosorbent assay (ELISA) for the serological detection of TCoV that was then validated using experimentally derived turkey serum. The N-based ELISA showed (97%) sensitivity and (93%) specificity for TCoV, which was significantly higher than an infectious bronchitis coronavirus-based commercial test for TCoV. To assess the utility of this recombinant ELISA, 360 serum samples from turkey farms in Ontario, Canada, and 81 serum samples from farms in Arkansas were tested for TCoV-specific antibodies. A high seroprevalence of TCoV was found in turkeys from the Ontario farms with 73.9% of breeders and 60.0% of meat turkeys testing seropositive using the N-based ELISA. Similarly, a high field prevalence was found in the turkeys from Arkansas, for which 64.2% of the serum samples tested seropositive.
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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 | 0.000 |
| 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".