Modified multiplex PCR for serotyping and pathotyping of Streptococcus suis
Bibliographic record
Abstract
Introduction. Streptococcus suis is a zoonotic pathogen that causes invasive infections in humans who have been in close contact with infected pigs or contaminated pork-derived products. There is currently no consensus on the universal virulence factors or markers that can differentiate pathogenic from non-pathogenic or commensal S. suis isolates. Gap statement. A diagnostic tool for serotyping and pathotyping of S. suis is required for active public health surveillance and the One-Health approach. Aim. To improve the former multiplex PCR to serotyping all 29 recognized ‘true’ serotypes and distinguish pathogenic pathotypes using primers targeting the capsule and ROK pathogenic marker genes. Methodology. Four sets of multiplex PCRs were modified and improved to detect all 29 recognized serotypes of S. suis and distinguish their pathogenic pathotypes using the ROK gene. Results. This multiplex PCR allowed for the simultaneous amplification of S. suis -specific, serotype-specific and pathogenic pathotypes from the DNA of each serotype in each reaction. The accuracy, sensitivity, specificity, positive predictive value and negative predictive value of the pathogenic ROK marker genes were 84.7% (625/738), 96.4% (423/439), 67.6% (202/299), 81.4% (423/520) and 92.7% (202/218), respectively. There was a significant ( P -value <0.001), high positive likelihood ratio [2.9 with 2.5–3.5 of 95% confidence interval (CI)] and a significant odds ratio (55.1 with 31.6–95.9 of 95 % CI), which indicated that the ROK gene could be used as the pathogenic pathotype marker. No cross-reactions were observed with other bacterial species. Conclusion. This modified multiplex PCR was able to distinguish 29 well-known serotypes and predicted the pathogenic pathotypes of S. suis isolates from humans and pigs in a single assay. It is useful for One-Health surveillance of human and pig isolates of S. suis .
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| 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".