Correlation between the Resistance Genotype Determined by Multiplex PCR Assays and the Antibiotic Susceptibility Patterns of <i>Staphylococcus aureus</i> and <i>Staphylococcus epidermidis</i>
Bibliographic record
Abstract
Clinical isolates of Staphylococcus aureus (a total of 206) and S. epidermidis (a total of 188) from various countries were tested with multiplex PCR assays to detect clinically relevant antibiotic resistance genes associated with staphylococci. The targeted genes are implicated in resistance to oxacillin (mecA), gentamicin ¿aac(6')-aph(2"), and erythromycin (ermA, ermB, ermC, and msrA). We found a nearly perfect correlation between genotypic and phenotypic analysis for most of these 394 strains, showing the following correlations: 98% for oxacillin resistance, 100% for gentamicin resistance, and 98.5% for erythromycin resistance. The discrepant results were (i) eight strains found to be positive by PCR for mecA or ermC but susceptible to the corresponding antibiotic based on disk diffusion and (ii) six strains of S. aureus found to be negative by PCR for mecA or for the four erythromycin resistance genes targeted but resistant to the corresponding antibiotic. In order to demonstrate in vitro that the eight susceptible strains harboring the resistance gene may become resistant, we subcultured the susceptible strains on media with increasing gradients of the antibiotic. We were able to select cells demonstrating a resistant phenotype for all of these eight strains carrying the resistance gene based on disk diffusion and MIC determinations. The four oxacillin-resistant strains negative for mecA were PCR positive for blaZ and had the phenotype of beta-lactamase hyperproducers, which could explain their borderline oxacillin resistance phenotype. The erythromycin resistance for the two strains found to be negative by PCR is probably associated with a novel mechanism. This study reiterates the usefulness of DNA-based assays for the detection of antibiotic resistance genes associated with staphylococcal infections.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".