A Novel Assay for Detection of Methicillin-Resistant Staphylococcus aureus Directly From Clinical Samples
Bibliographic record
Abstract
The timely detection of Methicillin-resistant Staphylococcus aureus (MRSA) is crucial for antimicrobial therapy and a key factor to limit the hospital spread of MRSA. Currently available commercial MRSA detection assays target the 3’ end of the orfX gene and the right extremity of Staphylococcal Cassette Chromosome mec (SCCmec). These assays suffer from both false positive due to SCC-like elements that lack mecA and false negative results due to the inability to detect new or variant SCCmec cassettes with the existing primers. We developed a novel MRSA detection scheme, designed to circumvent issues present in the existing commercial assays. Our assay demonstrated specificity and accuracy, capable of detecting prototypic strains of SCCmec types I-XIII [C(t) values ranged 8.58-26.29]. Previous false positive isolates (N=19) by Xpert MRSA nasal assay were accurately classified with our assay. Further validation with 218 randomly selected clinical isolates (73 MRSA, 75 MSSA, 43 MR-CoNS, and 27 MS-CoNS) confirmed its feasibility and practicality. Testing assay performance with 88 direct clinical swabs from 33 patients showed that the assay was 96.6% in agreement with clinical culture results. Our novel MRSA detection assay targets both the S. aureus specific sequence and the mecA/mecC genes simultaneously to overcome the false positive and false negative deficits of currently available commercial assays. The results validate our assay and confirmed its feasibility and practicality. The assay is not affected by SCCmec types and only needs modification if new mec homologues emerge and establishes a new platform for other emerging SCCmec types.
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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 | 0.002 | 0.002 |
| 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".