Synergy of ultrasound microbubbles and vancomycin against Staphylococcus epidermidis biofilm
Bibliographic record
Abstract
OBJECTIVES: Device-associated biofilm infections primarily caused by Staphylococcus epidermidis are difficult to treat effectively with conventional antibiotics. The aim of this study was to investigate the anti-biofilm effect of ultrasound-mediated microbubbles combined with vancomycin and to explore underlying mechanisms. METHODS: Twenty-four hour S. epidermidis biofilms were established in OptiCell(TM) chambers to facilitate ultrasound exposure. Microbubbles were prepared and diluted to concentrations of 1% and 4% (v/v). Ultrasound was applied for 5 min at 300 kHz and 0.5 W/cm(2), with a 50% duty cycle. Vancomycin at the peak serum concentration of 32 mg/L was used on preformed biofilms for 24 h. Antibiotic susceptibility tests were conducted on biofilms to confirm the synergy between ultrasound and vancomycin. Biofilms exposed to ultrasound-mediated microbubbles combined with vancomycin were subjected to plate counting and microscopic examinations. A vancomycin penetration test was also performed. RESULTS: Ultrasound and ultrasound-mediated microbubbles both enhanced biofilm susceptibility to vancomycin. Ultrasound-mediated microbubbles without vancomycin could exert a bactericidal effect on biofilms. A bubble dose-dependent bioeffect was also observed. In the presence of vancomycin, biofilms exposed to ultrasound-mediated microbubbles exhibited significantly more micropores and more reduction in biofilm thickness than other treatment groups (P<0.05). The transportation of vancomycin through S. epidermidis biofilms was significantly enhanced by ultrasound, and microbubbles could further increase biofilm permeability to vancomycin. CONCLUSIONS: Ultrasound-mediated microbubbles may provide an efficient and non-invasive alternative to treat device-related biofilm infections. Future research is needed to optimize ultrasound parameters and microbubble concentrations so that this technology can be both effectively and safely applied in clinical practice.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.000 | 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 teacher head, 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".