New Real-Time PCR Assay for Rapid Detection of Methicillin- Resistant<i>Staphylococcus aureus</i>Directly from Specimens Containing a Mixture of Staphylococci
Bibliographic record
Abstract
Molecular methods for the rapid identification of methicillin-resistant Staphylococcus aureus (MRSA) are generally based on the detection of an S. aureus-specific gene target and the mecA gene. However, such methods cannot be applied for the direct detection of MRSA from nonsterile specimens such as nasal samples without the previous isolation, capture, or enrichment of MRSA because these samples often contain both coagulase-negative staphylococci (CoNS) and S. aureus, either of which can carry mecA. In this study, we describe a real-time multiplex PCR assay which allows the detection of MRSA directly from clinical specimens containing a mixture of staphylococci in <1 h. Five primers specific to the different staphylococcal cassette chromosome mec (SCCmec) right extremity sequences, including three new sequences, were used in combination with a primer and three molecular beacon probes specific to the S. aureus chromosomal orfX gene sequences located to the right of the SCCmec integration site. Of the 1,657 MRSA isolates tested, 1,636 (98.7%) were detected with the PCR assay, whereas 26 of 569 (4.6%) methicillin-susceptible S. aureus (MSSA) strains were misidentified as MRSA. None of the 62 nonstaphylococcal bacterial species or the 212 methicillin-resistant or 74 methicillin-susceptible CoNS strains (MRCoNS and MSCoNS, respectively) were detected by the assay. The amplification of MRSA was not inhibited in the presence of high copy numbers of MSSA, MRCoNS, or MSCoNS. The analytical sensitivity of the PCR assay, as evaluated with MRSA-negative nasal specimens containing a mixture of MSSA, MRCoNS, and MSCoNS spiked with MRSA, was approximately 25 CFU per nasal sample. This real-time PCR assay represents a rapid and powerful method which can be used for the detection of MRSA directly from specimens containing a mixture of staphylococci.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".