Correlation between PFGE Groups and<i>mrp/epf/sly</i>Genotypes of Human<i>Streptococcus suis</i>Serotype 2 in Northern Thailand
Bibliographic record
Abstract
Streptococcus suis infection is a severe zoonotic disease commonly found in Northern Thailand where people often consume raw pork and/or pig's blood. The most frequent clinical presentations are meningitis, sepsis, and endocarditis with higher rate of mortality and hearing loss sequelae. To clarify the correlation between pulsed-field gel electrophoresis (PFGE) groups and mrp/epf/sly genotypes of S. suis serotype 2, 62 patient and 4 healthy pig isolates from Northern Thailand were studied. By PFGE analysis, at 66% homology, most human isolates (69.4%) and 1 pig isolate were in group A, whereas 14.5% of human isolates and 3 out of 4 pig isolates were in group D. According to mrp/epf/sly genotypes, 80.6% of human isolates were identified in mrp (+) epf (-) sly (-) and only 12.9% were in mrp (-) epf (-) sly (+) genotypes; in contrast, 1 and 3 pig isolates were detected in these two genotypes, respectively. Interestingly, all isolates of S. suis serotype 2 classified in PFGE groups A, B, and E were set in mrp (+) epf (-) sly (-) genotypes. These data show a close correlation between PFGE groups and mrp/epf/sly genotypes of human S. suis serotype 2.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".