Development and Application of ApxIV-ELISA Differentiating Diagnostic Kit for Actinobacillus pleuropneumoniae
Bibliographic record
Abstract
Actinobacillus pleuropneumoniae (APP) is the causative agent of porcine contagious pleuropneumonia which causes great economic losses in the pig industry worldwide. There are at least 15 serotypes of APP so that it is very difficult to diagnose and control this disease. An ApxⅣ-ELISA which could distinguish the vaccinated pigs from APP-infected ones was established based on the fact that the toxin ApxⅣ expresses only in vivo in APP-infected pigs. A differentiating diagnostic kit was developed by the optimization of conditions, whose specificity, sensitivity, repeatability and stability were evaluated and compared with an analogous ELISA kit (CHEKIT-APP-APXⅣ) from the company IDEXX and the indirect haemagglutination assay (IHA). A total of 1 453 clinical sera from China, UK, Denmark, USA, Canada and Australia were screened using the ApxⅣ-ELISA kit. The results showed that the ApxⅣ-ELISA kit had very high specificity, sensitivity, repeatability [the inter- and intrabatch imprecision (CV%) 15%] and stability (very stable at 4~8 ℃ for 9 months). Compared with CHEKIT-APP-APXⅣ, the overall agreement rate was up to 91.37%. The ApxⅣ antibody positive ratios of sows from China Farm 1 (CN1), China Farm 2 (CN2), USA, Canada, Denmark, Australia and UK were 30.05%, 70.69%, 39.72%, 0.64%, 42.22%, 1.85% and 93.94%, respectively; The positive ratios of piglets were 21.77% (CN1) and 27.15% (CN2), and those of finishing pigs were 5.51% (CN1) and 11.07% (CN2). The data confirm that the ApxⅣ-ELISA kit can be used to distinguish the vaccinated pigs from APP-infected ones.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 |
| 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 teacher head, 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".