Real-time quantitative PCR for detection and identification of <i>Actinobacillus pleuropneumoniae</i> serotype 2
Bibliographic record
Abstract
Abstract Introduction: Porcine pleuropneumonia inflicts important economic losses on most commercial herds. Detection of subclinical or chronic infection in animals still remains a challenge, as isolation and identification of A. pleuropneumoniae serotypes is difficult and quantification of the bacteria on agar plates is often almost impossible. The aim of the study was to develop and evaluate a serotype-specific quantitative TaqMan probe-based PCR for detection of serotype 2 in pig lungs, tonsils, and nasal swabs. Material and Methods: The primers were designed from the capsular polysaccharide biosynthesis genes of A. pleuropneumoniae serotype 2. PCR specificity and sensitivity were evaluated using reference strains and several other bacterial species commonly isolated from pigs. Results: The real-time qPCR for detection of A. pleuropneumoniae serotype 2 was highly specific and gave no false positives with other serotypes or different bacterial species of pig origin. The detection limit for pure culture was 1.2 × 104 CFU/mL, for lung tissue and nasal swabs it was 1.2 × 105 CFU/mL, and for tonsils - 1.2 × 105 CFU/mL. Conclusion: The method can be used to serotype A. pleuropneumoniae isolates obtained during cultivation and to detect and identify A. pleuropneumoniae serotype 2 directly in nasal swabs and tonsil scrapings obtained from live pigs or lung tissue and tonsils collected post-mortem.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 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".