Development of Real-Time PCR Assays for Detection of the <i>Streptococcus milleri</i> Group from Cystic Fibrosis Clinical Specimens by Targeting the <i>cpn60</i> and 16S rRNA Genes
Bibliographic record
Abstract
Cystic fibrosis (CF) is a multiorgan disease, with the majority of mortalities resulting from pulmonary failure due to repeated pulmonary exacerbations. Recently, members of the Streptococcus anginosus group (S. anginosus, S. constellatus, and S. intermedius), herein referred to as the "Streptococcus milleri group" (SMG) have been implicated as important etiological pathogens contributing to pulmonary exacerbations in CF patients. This is partly due to better microbiological detection of the SMG species through the development of a novel specific medium termed "McKay agar." McKay agar demonstrated that SMG has been an underreported respiratory pathogen contributing to lung exacerbations. Our aim was to develop a real-time PCR assay to expedite the detection of SMG within diagnostic samples. The cpn60 gene was chosen as a target, with all three members amplified using a single hybridization probe set. SMG strain analysis showed that speciation based on melting curve analysis allowed for the majority of the S. constellatus (96%), S. intermedius (94%), and S. anginosus (60%) strains to be correctly identified. To increase specificity for S. anginosus, two 16S rRNA real-time PCR assays were developed targeting the 16S rRNA gene. The 16s_SA assay is specific for S. anginosus (100%), while the 16s_SCI assay is specific for S. constellatus and S. intermedius (100%). These assays can detect <10 genome equivalents in pure culture and >10(4) genome equivalents in sputum samples, making this a great tool for assessment of the presence of SMG in complex polymicrobial samples. Novel molecular methods were developed providing detection ability for SMG, an emerging opportunistic pathogen.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".