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 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) | 0.000 | 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".