Synergy of Silver Nanoparticles and Aztreonam against Pseudomonas aeruginosa PAO1 Biofilms
Bibliographic record
Abstract
Pathogenic bacterial biofilms, such as those found in the lungs of patients with cystic fibrosis (CF), exhibit increased antimicrobial resistance, due in part to the inherent architecture of the biofilm community. The protection provided by the biofilm limits antimicrobial dispersion and penetration and reduces the efficacy of antibiotics that normally inhibit planktonic cell growth. Thus, alternative antimicrobial strategies are required to combat persistent infections. The antimicrobial properties of silver have been known for decades, but silver and silver-containing compounds have recently seen renewed interest as antimicrobial agents for treating bacterial infections. The goal of this study was to assess the efficacy of citrate-capped silver nanoparticles (AgNPs) of various sizes, alone and in combination with the monobactam antibiotic aztreonam, to inhibit Pseudomonas aeruginosa PAO1 biofilms. Among the different sizes of AgNPs examined, 10-nm nanoparticles were most effective in inhibiting the recovery of P. aeruginosa biofilm cultures and showed synergy of inhibition when combined with sub-MIC levels of aztreonam. Visualization of biofilms treated with combinations of 10-nm AgNPs and aztreonam indicated that the synergistic bactericidal effects are likely caused by better penetration of the small AgNPs into the biofilm matrix, which enhances the deleterious effects of aztreonam against the cell envelope of P. aeruginosa within the biofilms. These data suggest that small AgNPs synergistically enhance the antimicrobial effects of aztreonam against P. aeruginosa in vitro, and they reveal a potential role for combinations of small AgNPs and antibiotics in treating patients with chronic infections.
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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".