Real-Time PCR Assay for Detection of Fluoroquinolone Resistance Associated with <i>grlA</i> Mutations in <i>Staphylococcus aureus</i>
Bibliographic record
Abstract
Resistance to fluoroquinolones among clinical isolates of Staphylococcus aureus has become a clinical problem. Therefore, a rapid method to identify S. aureus and its susceptibility to fluoroquinolones could provide clinicians with a useful tool for the appropriate use of these antimicrobial agents in the health care settings. In this study, we developed a rapid real-time PCR assay for the detection of S. aureus and mutations at codons Ser-80 and Glu-84 of the grlA gene encoding the DNA topoisomerase IV, which are associated with decreased susceptibility to fluoroquinolones. The detection limit of the assay was 10 genome copies per reaction. The PCR assay was negative with DNA from all 26 non-S. aureus bacterial species tested. A total of 85 S. aureus isolates with various levels of fluoroquinolone resistance was tested with the PCR assay. The PCR assay correctly identified 100% of the S. aureus isolates tested compared to conventional culture methods. The correlation between the MICs of ciprofloxacin, levofloxacin, and gatifloxacin and the PCR results was 98.8%. The total time required for the identification of S. aureus and determination of its susceptibility to fluoroquinolones was about 45 min, including DNA extraction. This new rapid PCR assay represents a powerful method for the detection of S. aureus and its susceptibility to fluoroquinolones.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".