A new serotyping method of S. pneumoniae using an automated microarray-based assay
Bibliographic record
Abstract
Autolysin, pneumolysin and eight other genes located in the capsular operon were first amplified using multiplex PCR. This step was followed by a tagged primer extension step targeting serotype-specific polymorphisms. The tagged primers were then assigned to a specific spot on a microarray, and processed and scanned in an ISO-certified automated molecular diagnostic system, using a confocal laser microscope. Results from the assay were exported to the analysis software/expert system that transformed genetic typing data into capsular serotype identification. Using this new technology, 51 serotypes of S. pneumoniae can be precisely and uniquely identified, including the 13 types present in the new conjugate vaccine. The remaining 39 are assigned to a serogroup. Blood, CSF and nasopharyngeal samples from children with S. pneumoniae infection or carriage were tested and serotype was confirmed by sequence analysis. 26 different serotypes were detected and concordance between both methods was greater than 96%. This automated microarray assay is robust and could identify precise serotypes of S. pneumoniae directly from clinical samples. It is easy to handle and will be most useful in clinical settings and for the evaluation of serotype prevalence changes.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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".