Use of genomics to design a diagnostic assay to discriminate between Streptococcus pneumoniae and Streptococcus pseudopneumoniae
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Distinuishing the species of mitis group streptococci is challenging due to ambiguous phenotypic characteristics and high degree of genetic similarity. This has been particularly true for resolving atypical Streptococcus pneumoniae and Streptococcus pseudopneumoniae. We used phylogenetic clustering to demonstrate specific and separate clades for both S. pneumoniae and S. pseudopneumoniae genomes. The genomes that clustered within these defined clades were used to extract species-specific genes from the pan-genome. The S. pneumoniae marker was detected in 8027 out of 8051 (>99.7 %) S. pneumoniae genomes. The S. pseudopneumoniae marker was specific for all genomes that clustered in the S. pseudopneumoniae clade, including unresolved species of the genus Streptococcus sequenced by the BC Centre for Disease Control Public Health Laboratory that previously could not be distinguished by other methods. Other than the presence of the S. pseudopneumoniae marker in six of 8051 (<0.08 %) S. pneumoniae genomes, both the S. pneumoniae and S. pseudopneumoniae markers showed little to no detectable cross-reactivity to the genomes of any other species of the genus Streptococcus or to a panel of over 46 000 genomes from viral, fungal, bacterial pathogens and microbiota commonly found in the respiratory tract. A real-time PCR assay was designed targeting these two markers. Genomics provides a useful technique for PCR assay design and development.
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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) | 0.001 | 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 it