A robust PCR for the differentiation of potential virulent strains of Haemophilus parasuis
Bibliographic record
Abstract
BACKGROUND: Haemophilus parasuis is the etiological agent of Glässer's disease in swine. H. parasuis comprises strains with heterogeneous virulence capacity, from non-virulent to highly virulent. Determination of the pathogenic potential of the strains is important for diagnosis and disease control. The virulence-associated trimeric autotransporters (vtaA) genes have been used to predict H. parasuis virulence by PCR amplification of their translocator domains. Here, we report a new and improved PCR designed to detect a different domain of the vtaA genes, the leader sequence (LS) as a diagnostic tool to predict virulence. METHODS: A collection of 360 H. parasuis strains was tested by PCR with LS specific primers. Results of the PCR were compared with the clinical origin of the strains and, for a subset of strains, with their phagocytosis and serum resistance using a Chi-square test. RESULTS: LS-PCR was specific to H. parasuis, and allowed the differential detection of the leader sequences found in clinical and non-clinical isolates. Significant correlation was observed between the results of the LS-PCR and the clinical origin (organ of isolation) of the strains, as well as with their phagocytosis and serum susceptibility, indicating that this PCR is a good predictor of the virulence of the strains. In addition, this new PCR showed a full correlation with the previously validated PCR based on the translocator domain. LS-PCR could be performed in a wide range of annealing temperatures without losing specificity. CONCLUSION: This newly described PCR based on the leader sequence of the vtaA genes, LS-PCR, is a robust test for the prediction of the virulence potential of H. parasuis strains.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".