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Targeting biofilms of multidrug-resistant bacteria with silver oxynitrate

2017· article· en· W2604646007 on OpenAlexaff
Lindsay Kalan, Deanna M. Pepin, Imran Ul-Haq, Steve Miller, Michelle Hay, Roderick J. Precht

Bibliographic record

VenueInternational Journal of Antimicrobial Agents · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBacterial biofilms and quorum sensing
Canadian institutionsExciton Technologies (Canada)University of Alberta
Fundersnot available
KeywordsMultiple drug resistanceBiofilmMicrobiologyBacteriaBiologyDrug resistanceGenetics

Abstract

fetched live from OpenAlex

A topical antimicrobial, silver oxynitrate (Ag7NO11), 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 Ag7NO11 at a concentration of 0.4 mg Ag/cm2. Physiochemical properties that influence efficacy were also evaluated, and Ag7NO11 was found to release a high level of Ag ions, including Ag2+ and Ag3+, 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 Ag7NO11 over a period of 7 days, even with repeated inoculations of 1 × 106 CFU/mL to the dressing. Furthermore, established 72-h-old biofilms of Pseudomonas aeruginosa, Staphylococcus aureus and two carbapenem-resistant Gram-negative bacteria (blaNDM-1-positive Klebsiella pneumoniae and blaVIM-2-positive P. aeruginosa) were disrupted and eradicated by Ag7NO11 in vitro. Ag7NO11 is a proprietary compound that exploits novel Ag chemistry and can be considered a new class of topical antimicrobial agent. Biocompatibility testing has concluded Ag7NO11 to be non-toxic for cytotoxicity, acute systemic toxicity, irritation and sensitisation.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.414

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.012
GPT teacher head0.258
Teacher spread0.246 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations42
Published2017
Admission routes1
Has abstractyes

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