Co-encapsulation of gallium with gentamicin in liposomes enhances antimicrobial activity of gentamicin against Pseudomonas aeruginosa
Bibliographic record
Abstract
OBJECTIVES: The aim of this study was to enhance the antimicrobial efficacy of a liposomal gentamicin formulation with gallium metal (Lipo-Ga-GEN) against clinical isolates of Pseudomonas aeruginosa. METHODS: Sputum isolates of P. aeruginosa from cystic fibrosis patients were used to determine the MIC and MBC of Lipo-Ga-GEN. P. aeruginosa biofilms were formed and used to compare the minimum biofilm eradication concentration of the conventional drugs with that of Lipo-Ga-GEN. Quorum sensing (QS) molecule reduction of P. aeruginosa was determined by monitoring N-acyl homoserine lactone production using Agrobacterium tumefaciens reporter strain (A136). Viability of the cultured human lung epithelial cells (A549) was determined by Trypan Blue assay in order to assess Ga toxicity. RESULTS: MIC and MBC values indicated that gentamicin was more effective against a highly resistant strain of P. aeruginosa (PA-48913) when delivered as a Lipo-Ga-GEN formulation (256 mg/L free gentamicin versus 2 mg/L Lipo-Ga-GEN). Lipo-Ga-GEN was the only formulation that completely eradicated biofilms and blocked QS molecules at a very low concentration (0.94 mg/L gentamicin). The decrease in cell viability was less in A549 cells exposed to Lipo-Ga, suggesting that encapsulated Ga is safer. CONCLUSIONS: The results clearly indicate that the Lipo-Ga-GEN formulation is more effective than gentamicin alone in eradicating antibiotic-resistant P. aeruginosa isolates growing in a planktonic or biofilm community.
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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".