Targeting biofilms of multidrug-resistant bacteria with silver oxynitrate
Bibliographic record
Abstract
A topical antimicrobial, silver oxynitrate (Ag 7 NO 11 ), has recently become available that exploits the antimicrobial activity of ionic silver but has enhanced activity because highly oxidised silver atoms are stabilised with oxygen in a unique chemical formulation. The objective of this study was to use a multifaceted approach to characterise the spectrum of antimicrobial and antibiofilm activity of a wound dressing coated with Ag 7 NO 11 at a concentration of 0.4 mg Ag/cm 2 . Physiochemical properties that influence efficacy were also evaluated, and Ag 7 NO 11 was found to release a high level of Ag ions, including Ag 2+ and Ag 3+ , without influencing the pH of the medium. Time–kill analysis demonstrated that a panel of multidrug-resistant pathogens isolated from wound specimens remained susceptible to Ag 7 NO 11 over a period of 7 days, even with repeated inoculations of 1 × 10 6 CFU/mL to the dressing. Furthermore, established 72-h-old biofilms of Pseudomonas aeruginosa , Staphylococcus aureus and two carbapenem-resistant Gram-negative bacteria ( bla NDM-1 -positive Klebsiella pneumoniae and bla VIM-2 -positive P. aeruginosa ) were disrupted and eradicated by Ag 7 NO 11 in vitro. Ag 7 NO 11 is a proprietary compound that exploits novel Ag chemistry and can be considered a new class of topical antimicrobial agent. Biocompatibility testing has concluded Ag 7 NO 11 to be non-toxic for cytotoxicity, acute systemic toxicity, irritation and sensitisation.
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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) | 0.000 | 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 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".