Development of a Rapid PCR Assay Specific forStaphylococcus saprophyticus and Application to Direct Detection from Urine Samples
Bibliographic record
Abstract
Staphylococcus saprophyticus is one of the most frequently encountered microorganisms associated with acute urinary tract infections (UTIs) in young, sexually active female outpatients. Conventional identification methods based on biochemical characteristics can efficiently identify S. saprophyticus, but the rapidities of these methods need to be improved. Rapid and direct identification of this bacterium from urine samples would be useful to improve time required for the diagnosis of S. saprophyticus infections in the clinical microbiology laboratory. We have developed a PCR-based assay for the specific detection of S. saprophyticus. An arbitrarily primed PCR amplification product of 380 bp specific for S. saprophyticus was sequenced and used to design a set of S. saprophyticus-specific PCR amplification primers. The PCR assay was specific for S. saprophyticus when tested with DNA from 49 gram-positive and 31 gram-negative bacterial species. This assay was also able to amplify efficiently DNA from all 60 strains of S. saprophyticus from various origins tested. This assay was adapted for direct detection from urine samples. The sensitivity levels achieved with urine samples was 19 CFU with 30 cycles of amplification and 0.5 CFU with 40 cycles of amplification. This PCR assay for the specific detection of S. saprophyticus is simple and rapid (approximately 90 min, including the time for urine specimen preparation).
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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 | 0.002 |
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".