Attenuation of Pseudomonas aeruginosa virulence factors and biofilms by co-encapsulation of bismuth-ethanedithiol with tobramycin in liposomes
Bibliographic record
Abstract
OBJECTIVES: This study examined the activities of tobramycin and bismuth against quorum sensing, virulence factors and biofilms of Pseudomonas aeruginosa by co-encapsulating the agents in liposomes in order to achieve greater delivery of the agents. METHODS: The inhibitory effects of the agents, in either their conventional (free) or vesicle-entrapped (liposomal) formulations, were assessed by measuring the changes in the quorum-sensing signal molecule N-acyl homoserine lactone, pyoverdine, pyocyanin, elastase, protease, chitinase, bacterial attachment and biofilms in vitro. RESULTS: The effectiveness of tobramycin and bismuth was superior when they were co-administered as a liposomal formulation as measured by their ability to attenuate the production of N-acyl homoserine lactone, elastase (P < 0.01), protease (P < 0.05) and chitinase (P < 0.01). In the presence of non-lethal concentrations of free and liposomal tobramycin and bismuth, bacterial attachment was attenuated. Biofilm formation was also attenuated with free tobramycin and bismuth, yet, in the presence of liposomal tobramycin and bismuth, biofilm complexes could form but contained mostly dead bacteria. When established biofilms were treated with higher concentrations, free tobramycin and bismuth killed and detached bacteria, while the liposomal tobramycin and bismuth penetrated and killed bacteria in the cores of the biofilms. CONCLUSIONS: These data suggest that treatment of P. aeruginosa with tobramycin and bismuth, as measured by the changes in quorum sensing, virulence factors and biofilms, is most effective when delivered as a liposomal formulation at a lower concentration compared with the free formulation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".