Development of a one-run real-time PCR detection system for pathogens associated with porcine respiratory diseases
Bibliographic record
Abstract
The etiology of Porcine respiratory disease complex is complicated by infections with multiple pathogens, and multiple infections increase the difficulty in identifying the causal pathogen. In this present study, we developed a detection system of microbes from porcine respiratory by using TaqMan real-time PCR (referred to as Dempo-PCR) to screen a broad range of pathogens associated with porcine respiratory diseases in a single run. We selected 17 porcine respiratory pathogens (Actinobacillus pleuropneumoniae, Boldetella bronchiseptica, Haemophilus parasuis, Pasteurella multocida, Pasteurella multocida toxin, Streptococcus suis, Mycoplasma hyopneumoniae, Mycoplasma hyorhinis, Mycoplasma hyosynovie, porcine circovirus 2, pseudorabies virus, porcine cytomegalovirus, swine influenza A virus, porcine reproductive and respiratory virus US strain, EU strain, porcine respiratory coronavirus and porcine hemagglutinating encephalomyelitis virus) as detection targets and designed novel specific primer-probe sets for seven of them. In sensitivity test by using standard curves from synthesized DNA, all primer-probe sets showed high sensitivity. However, porcine reproductive and respiratory virus is known to have a high frequency of genetic mutations, and the primer and probe sequences will need to be checked at a considerable frequency when performing Dempo-PCR from field samples. A total of 30 lung samples from swine showing respiratory symptoms on six farms were tested by the Dempo-PCR to validate the assay’s clinical performance. As the results, 12 pathogens (5 virus and 7 bacteria) were detected and porcine reproductive and respiratory virus US strain, Mycoplasma hyorhinis, Haemophilus parasuis, and porcine cytomegalovirus were detected at high frequency. These results suggest that Dempo-PCR assay can be applied as a screening system with wide detection targets.
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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 | 0.004 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".