Use of Calgary and Microfluidic BioFlux Systems To Test the Activity of Fosfomycin and Tobramycin Alone and in Combination against Cystic Fibrosis Pseudomonas aeruginosa Biofilms
Bibliographic record
Abstract
ABSTRACT Pseudomonas aeruginosa is a major cause of morbidity and mortality in chronically infected cystic fibrosis patients. Novel in vitro biofilm models which reliably predict the therapeutic success of antimicrobial therapies against biofilm bacteria should be implemented. The activity of fosfomycin, tobramycin, and the fosfomycin-tobramycin combination against 6 susceptible P. aeruginosa strains isolated from respiratory samples from cystic fibrosis patients was tested by using two in vitro biofilm models: a closed system (Calgary device) and an open model based on microfluidics (BioFlux). All but one of the isolates formed biofilms. The fosfomycin and tobramycin minimal biofilm inhibitory concentrations (MBIC) were 1,024 to >1,024 μg/ml and 8 to 32 μg/ml, respectively. According to fractional inhibitory concentration analysis, the combination behaved synergistically against all the isolates except the P. aeruginosa ATCC 27853 strain. The dynamic formation of the biofilm was also studied with the BioFlux system, and the MIC and MBIC of each antibiotic were tested. For the combination, the lowest tobramycin concentration that was synergistic with fosfomycin was used. The captured images were analyzed by measuring the intensity of the colored pixels, which was proportional to the biofilm biomass. A statistically significant difference was found when the intensity of the inoculum was compared with the intensity of the microchannel in which the MBIC of tobramycin, fosfomycin, or their combination was used ( P < 0.01) but not when the MIC was applied ( P > 0.01). Fosfomycin-tobramycin was demonstrated to be synergistic against cystic fibrosis P. aeruginosa strains in the biofilm models when both the Calgary and the microfluidic BioFlux systems were tested. These results support the clinical use of this combination.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 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".