Importance of DNase and alginate lyase for enhancing free and liposome encapsulated aminoglycoside activity against Pseudomonas aeruginosa
Bibliographic record
Abstract
OBJECTIVES: This study evaluated the potential of DNase, alginate lyase (AlgL) and N-acetylcysteine (NAC) in enhancing the in vitro bactericidal activity of conventional (free) and vesicle-entrapped (liposomal) gentamicin, amikacin and tobramycin. METHODS: The MICs and biofilm eradication for two clinical isolates of Pseudomonas aeruginosa (a mucoid strain and a non-mucoid strain) were determined in the presence and absence of AlgL. The co-activity of aminoglycosides with DNase and/or AlgL against endogenous P. aeruginosa in cystic fibrosis (CF) sputum was also measured. The inhibitory effects of mucin in the presence and absence of the mucolytic agent NAC on aminoglycosidic activity were also examined. RESULTS: The MIC values of the liposomal aminoglycosides were similar to or lower than those of free aminoglycosides. Biofilm formation increased the bactericidal concentrations of these drugs by 8- to 256-fold and treatment with AlgL improved killing of the mucoid strain. The activity of some aminoglycosides against the sputum was increased by the addition of DNase or AlgL (P < 0.05), and was increasingly evident with concurrent DNase and AlgL administration. Addition of mucin inhibited liposomal aminoglycosidic activity (up to 32-fold) evidently more than the free aminoglycosides (up to 8-fold). The addition of NAC did not improve activity significantly (P > 0.05). Tobramycin was the most effective aminoglycoside to reduce biofilms and sputum. CONCLUSIONS: Liposomal aminoglycosides do not fare better than conventional forms. The co-administration of DNase and AlgL is essential for enhanced activity in reducing biofilm growth and sputum bacterial counts. While mucin retards bactericidal activity, NAC does not improve aminoglycosidic activity.
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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".