Modeling Treatment of P. aeruginosa Biofilms in the Lungs Using Aerosolized Tobramycin
Bibliographic record
Abstract
The biofilms produced and maintained by Pseudomonas aeruginosa in the lungs of cystic fibrosis patients are difficult to treat and can have fatal effects. Antibiotics are necessary to control and eliminate these bacterial biofilms, but in vivo administration may not be the most effective means. Tobramycin, a commonly used antibiotic for treating cystic fibrosis patients, has been commercially developed into a solution that is inhalable via nebulizer. Inhaling this mist form of the antibiotic will allow administration of higher concentrations at the site of infection. The goal of this study was to develop a model using COMSOL Multiphysics to better understand the distribution of tobramycin to bacterial biofilms in the lungs. Like nearly all medications, tobramycin can become toxic at high concentrations. Since filtration from the blood stream is the only significant mechanism of tobramycin elimination, the kidneys are at the greatest risk for toxicity. Therefore the study focused on the possibility of maintaining safe blood serum concentrations while providing sufficient doses to inhibit the bacteria occupying the lungs. The model showed that the bacteria?s minimum inhibitory concentration was easily achievable throughout the biofilm while keeping the blood serum concentrations at a safe level.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".