Activity of Tobramycin against Cystic Fibrosis Isolates of Burkholderia cepacia Complex Grown as Biofilms
Bibliographic record
Abstract
Pulmonary infection with Burkholderia cepacia complex in cystic fibrosis (CF) patients is associated with more-rapid lung function decline and earlier death than in CF patients without this infection. In this study, we used confocal microscopy to visualize the effects of various concentrations of tobramycin, achievable with systemic and aerosolized drug administration, on mature B. cepacia complex biofilms, both in the presence and absence of CF sputum. After 24 h of growth, biofilm thickness was significantly reduced by exposure to 2,000 μg/ml of tobramycin for Burkholderia cepacia, Burkholderia multivorans, and Burkholderia vietnamiensis; 200 μg/ml of tobramycin was sufficient to reduce the thickness of Burkholderia dolosa biofilm. With a more mature 48-h biofilm, significant reductions in thickness were seen with tobramycin at concentrations of ≥100 μg/ml for all Burkholderia species. In addition, an increased ratio of dead to live cells was observed in comparison to control with tobramycin concentrations of ≥200 μg/ml for B. cepacia and B. dolosa (24 h) and ≥100 μg/ml for Burkholderia cenocepacia and B. dolosa (48 h). Although sputum significantly increased biofilm thickness, tobramycin concentrations of 1,000 μg/ml were still able to significantly reduce biofilm thickness of all B. cepacia complex species with the exception of B. vietnamiensis. In the presence of sputum, 1,000 μg/ml of tobramycin significantly increased the dead-to-live ratio only for B. multivorans compared to control. In summary, although killing is attenuated, high-dose tobramycin can effectively decrease the thickness of B. cepacia complex biofilms, even in the presence of sputum, suggesting a possible role as a suppressive therapy in CF.
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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) | 0.001 | 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".