Use of a Multiplex Molecular Beacon Platform for Rapid Detection of Methicillin and Vancomycin Resistance in <i>Staphylococcus aureus</i>
Bibliographic record
Abstract
Drug resistance, particularly vancomycin and methicillin resistance, in Staphylococcus aureus continues to emerge as a significant public health threat in both the hospital and community settings. In addition to the limited treatment options, S. aureus strains acquire and express numerous virulence factors that continue to increase its ability to cause a wide spectrum of human disease. As a result, empirical treatment decisions are confounded and there is a heightened need for a diagnostic test (or assay) to rapidly identify antibiotic resistance and specific virulence determinants and indicate the appropriate treatment. To that end we developed a platform using multiplex molecular beacon probes with real-time PCR for the rapid detection of drug resistance-determining genes and virulence factors in S. aureus. In this study, we demonstrate the specificity and sensitivity of our platform for detection of the genes conferring methicillin (mecA) and vancomycin (vanA) resistance as well as a gene encoding the virulence factor Panton-Valentine leucocidin (lukF) in S. aureus isolates.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".