Potentiation of Tobramycin by Silver Nanoparticles against Pseudomonas aeruginosa Biofilms
Bibliographic record
Abstract
ABSTRACT Increasing antibiotic resistance among pathogenic bacterial species is a serious public health problem and has prompted research examining the antibacterial effects of alternative compounds and novel treatment strategies. Compounding this problem is the ability of many pathogenic bacteria to form biofilms during chronic infections. Importantly, these communities are often recalcitrant to antibiotic treatments that show effectiveness against acute infection. The antimicrobial properties of silver have been known for decades, but recently silver and silver-containing compounds have seen renewed interest as antimicrobial agents for treating bacterial infections. The goal of this study was to assess the ability of citrate-capped silver nanoparticles (AgNPs) of various sizes, alone and in combination with the aminoglycoside antibiotic tobramycin, to inhibit established Pseudomonas aeruginosa biofilms. Our results demonstrate that smaller 10-nm and 20-nm AgNPs were more effective at synergistically potentiating the activity of tobramycin. Visualization of biofilms treated with combinations of 10-nm AgNPs and tobramycin reveals that the synergistic bactericidal effect may be caused by disrupting cellular membranes. Minimum biofilm eradication concentration (MBEC) assays using clinical P. aeruginosa isolates shows that small AgNPs are more effective than larger AgNPs at inhibiting biofilms, but that the synergy effect is likely a strain-dependent phenomenon. These data suggest that small AgNPs synergistically potentiate the activity of tobramycin against P. aeruginosa in vitro and may reveal a potential role for AgNP/antibiotic combinations in treating patients with chronic infections in a strain-specific manner.
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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".