Development and Application of a Competitive Enzyme-Linked Immunosorbent Assay for the Detection of Serum Antibodies to Porcine Circovirus Type 2
Bibliographic record
Abstract
We report the development of a competitive enzyme-linked immunosorbent assay (c-ELISA) for the detection of antibodies to porcine circovirus type 2 (PCV2), the agent associated with the recently described postweaning multisystemic wasting syndrome in pigs. At present, no method has been published describing a c-ELISA for the detection of antibodies to PCV2, and currently employed tests are impractical for use in some laboratories. The assay described here uses a cell culture isolate of porcine circovirus type 2 as antigen and a PCV2-specific monoclonal antibody as the competing reagent. Evaluation of the ELISA was performed by comparison with results obtained using an indirect immunofluorescent test on 484 sera from pig herds in the United Kingdom, Canada, France, and the USA and serial bleeds from pigs experimentally infected with porcine circoviruses. The sensitivity and specificity of the ELISA were determined as 99.58% and 97.14%, respectively, at 2 standard deviations (SD) from the mean or 95.81% and 100% at 3 SD from the mean. Using this ELISA, a serologic survey of 461 sera collected from commercial pig herds in Northern Ireland between 1973 and 1999 was undertaken. Analysis of the results of this survey demonstrated that the number of ELISA-positive sera detected in an individual year during this period ranged from 55% to 100%. This c-ELISA has applications for large-scale rapid diagnosis of PCV2 infection in pig populations worldwide and for immunoscreening of sera from other species for antibodies to PCV2.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".