Molecular methods enhance the detection of pyoderma-related Streptococcus pyogenes and emm-type distribution in children
Bibliographic record
Abstract
Background: Streptococcus pyogenes-related skin infections are increasingly implicated in the development of rheumatic heart disease (RHD) in lower-resourced settings, where they are often associated with scabies. The true prevalence of S. pyogenes-related pyoderma may be underestimated by bacterial culture. Methods: A multiplex qPCR for S. pyogenes, Staphylococcus aureus and Sarcoptes scabiei was applied to 250 pyoderma swabs from a cross-sectional study of children <5 years in The Gambia. Direct PCR-based emm-typing was used to supplement previous whole genome sequencing (WGS) of cultured isolates. Results: Pyoderma lesions with S. pyogenes increased from 51% (127/250) using culture to 80% (199/250) with qPCR. Compared to qPCR, the sensitivity of culture was 95.4% for S. pyogenes (95% CI 77.2-99.9) in samples with S. pyogenes alone (22/250, 9%), but 59.9% (95% CI 52.3-67.2) for samples with S. aureus co-infection (177/250, 71%). Direct PCR-based emm-typing was successful in 50% (46/92) of cases, identifying 27 emm-types, including six not identified by WGS (total 52 emm-types). Conclusions: Bacterial culture significantly underestimates the burden of S. pyogenes in pyoderma, particularly when co-infected with S. aureus. Molecular methods should be used to enhance the detection of S. pyogenes in surveillance studies and clinical trials of preventative measures in RHD-endemic settings.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".