Effectiveness of Topical Antibiotics on <i>Staphylococcus Aureus</i> Biofilm <i>in Vitro</i>
Bibliographic record
Abstract
BACKGROUND: In vitro biofilm-producing capacity in isolates of Staphylococcus aureus and Pseudomonas aeruginosa collected from the sinus cavities after endoscopic sinus surgery (ESS) are associated with a poor outcome in patients with chronic rhinosinusitis (CRS). However, conventional oral antibiotic therapy is frequently ineffective in eradicating bacteria in the biofilm form. Increasing the concentration of antibiotics may offer a means of countering this resistance. The aim of this study was to determine the in vitro activity of moxifloxacin (MOXI) against S. aureus in biofilm form (recovered from patients with CRS at least 1 year post-ESS). METHOD: This study was performed in a research microbiology laboratory, where five isolates of S. aureus with known biofilm-forming capacity were cultured in Tryptic Soy Broth 0.5% glucose in 96-well plates at 37 degrees C for 24 hours. After visual confirmation of biofilm formation, plates were incubated in phosphate-buffered saline (PBS) or with MOXI at concentrations of 0.1x, Ix, 100x, and 1000x minimal inhibitory concentration (MIC) for an additional 24 hours. Biofilm from 3 wells of each concentration were collected and sonicated and the number of viable bacteria was determined by serial dilution and plating. RESULTS: After incubation, the number of viable bacteria was similar for nontreated and MOXI-treated biofilms at MIC and sub-MIC levels. However, MOXI at 1000x (0.1-0.2 mg/mL) gave a 2 to 2.5 log reduction in number of viable bacteria. CONCLUSION: In vitro results show that increased concentrations of antibiotics, easily attainable in topical solutions, are effective in killing bacteria in bacterial biofilms. This suggests a role for topical antibiotic therapies in the treatment of biofilm 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 | 0.000 | 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) | 0.002 | 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".