Rapid Detection and Typing of Actinobacillus pleuropneumoniae Serovars Directly From Clinical Samples: Combining FTA® Card Technology With Multiplex PCR
Bibliographic record
Abstract
Actinobacillus pleuropneumoniae (APP), the causative agent of porcine pleuropneumonia, is highly contagious and responsible for high morbidity, mortality, and economic losses in the swine industry worldwide, but quick serotyping and diagnosis are still not widely available. In this study, we sought to validate the use of Whatman FTA ® cards for collection and processing of A. pleuropneumoniae isolates, or porcine lung tissue samples, for direct use in diagnostic multiplex PCRs. We have optimized the processing of 3-mm discs punched from FTA ® cards loaded with cultured A. pleuropneumoniae , or imprinted on lesioned regions of lung tissue, with only three distilled water washes before addition into our APP-multiplex PCR (mPCR) assay for rapid, low-cost identification and serotyping. DNA captured on FTA ® cards generated the same diagnostic PCR results as DNA extracted using commercial kits for 85 A. pleuropneumoniae clinical isolate cultures and 22 lung samples. Additionally, bacterial DNA bound to FTA ® cards was detectable by PCR after 6 months of storage at 37°C. This study provides simple, efficient, rapid, and practical sample processing for detection and molecular serotyping of A. pleuropneumoniae .
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".