Development and Use of a Multiplex Real-Time Quantitative Polymerase Chain Reaction Assay for Detection and Differentiation of <i>Porcine Circovirus</i> -2 Genotypes 2a and 2b in an Epidemiological Survey
Bibliographic record
Abstract
By the end of 2004, the Canadian swine population had experienced a severe increase in the incidence of Porcine circovirus-associated disease (PCVAD), a problem that was associated with the emergence of a new Porcine circovirus-2 genotype (PCV-2b), previously unrecovered in North America. Thus, it became important to develop a diagnostic tool that could differentiate between the old and new circulating genotypes (PCV-2a and PCV-2b, respectively). Consequently, a multiplex real-time quantitative polymerase chain reaction (mrtqPCR) assay that could sensitively and specifically identify and differentiate PCV-2 genotypes was developed. A retrospective epidemiologic survey that used the mrtqPCR assay was performed to determine if cofactors could affect the risk of PCVAD. From 121 PCV-2-positive cases gathered for this study, 4.13%, 92.56%, and 3.31% were positive for PCV-2a, PCV-2b, and both genotypes, respectively. In a data analysis using univariate logistic regressions, the PCVAD-compatible (PCVAD/c) score was significantly associated with the presence of Porcine reproductive and respiratory syndrome virus (PRRSV), PRRSV viral load, PCV-2 viral load, and PCV-2 immunohistochemistry (IHC) results. Polytomous logistic regression analysis revealed that PCVAD/c score was affected by PCV-2 viral load (P = 0.0161) and IHC (P = 0.0128), but not by the PRRSV variables (P > 0.9), which suggests that mrtqPCR in tissue is a reliable alternative to IHC. Logistic regression analyses revealed that PCV-2 increased the odds ratio of isolating 2 major swine pathogens of the respiratory tract, Actinobacillus pleuropneumoniae and Streptococcus suis serotypes 1/2, 1, 2, 3, 4, and 7, which are serotypes commonly associated with clinical diseases.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".