Ibuprofen and Antibiotic Co-Delivery via Nanoparticles: A Novel Approach to Treating Pseudomonas aeruginosa Infections in Cystic Fibrosis - A Protocol Study
Bibliographic record
Abstract
Introduction: Cystic fibrosis (CF) is a genetic disorder that severely affects the lungs, leading to chronic infections primarily by Pseudomonas aeruginosa (P. aeruginosa). This pathogen adapts to the CF lung environment, forming biofilms that contribute to persistent infections and antibiotic resistance. ibuprofen, a non-steroidal anti-inflammatory drug, has shown potential in disrupting biofilms and enhancing antibiotic efficacy against P. aeruginosa. Recent studies focus on delivering ibuprofen via nebulization, using nanoparticles to improve targeting and reduce drug dosage. Methods: This paper explores the in vitro and in vivo effects of nebulized ibuprofen nanoparticles combined with the antibiotic ceftazidime. The study hypothesizes that this co-delivery system can reduce bacterial load, inflammation, and biofilm formation more effectively than standard treatments. Results: Results are anticipated to show that ibuprofen enhances antibiotic penetration and disrupts biofilm structure, thereby improving therapeutic outcomes in CF patients. Discussion: This research suggests a promising direction for CF treatment, leveraging advanced drug delivery systems to combat bacterial resistance and improve patient prognosis.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".