Enhanced Biofilm Disruption in Methicillin-Resistant Staphylococcus aureus Using Rifampin and Fluoroquinolone Combinations
Bibliographic record
Abstract
Staphylococcus aureus biofilms complicate the treatment of device-related infections. We hypothesized that combining rifampin with fluoroquinolones could eradicate biofilms even in antimicrobial-resistant S. aureus strains. We determined the synergistic interactions of these combinations in a biofilm model. Thirty methicillin-resistant S. aureus (MRSA) isolates with varying susceptibility profiles were evaluated. Minimum biofilm eradication concentrations (MBECs) were determined using the Calgary Biofilm Device, and the synergy was assessed using the fractional biofilm eradication concentration (FBEC) index. Scanning electron microscopy (SEM) was performed on one strain, and confocal laser scanning microscopy (CLSM) was conducted on four strains for visualizing and evaluating the biofilm viability. The MBEC90 for rifampin and levofloxacin were 512 mg/L and 256 mg/L, respectively, and exceeded 1024 mg/L for ciprofloxacin. Synergy was observed in 56.7% of strains for both the rifampin + ciprofloxacin and rifampin + levofloxacin combinations, with no difference between the combinations. A higher ciprofloxacin MBEC (≥16 mg/L) increased the likelihood of synergy with rifampin by 18-fold. SEM and CLSM analyses in a subset of strains confirmed the enhanced biofilm disruption with rifampin + ciprofloxacin compared to ciprofloxacin alone. Our findings suggest that rifampin combined with ciprofloxacin or levofloxacin may synergistically eradicate MRSA biofilms, offering a potential treatment option for device-related infections when alternatives are limited.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".