Ultrashort self‐assembling Fmoc‐peptide gelators for anti‐infective biomaterial applications
Bibliographic record
Abstract
Biomaterial-related infections have a significant impact on society and are a major contributor to the growing threat of antimicrobial resistance. Current licensed antibiotic classes struggle to breakdown or penetrate the exopolysaccharide biofilm barrier, resulting in sub-therapeutic concentrations of antibiotic at the surface of the biomaterial, treatment failure and increased spread of resistant isolates. This paper focuses for the first time on the ability of ultrashort Fmoc-peptide gelators to eradicate established bacterial biofilms implicated in a variety of medical device infections (Gram-positive: Staphylococcus aureus, Staphylococcus epidermidis and Gram-negative Escherichia coli, Pseudomonas aeruginosa). The effect of increasing the cationicity of FmocFF via addition of di-lysine and di-orntithine was also studied with regard to antibacterial activity. Our studies demonstrated that Fmoc-peptides (FmocFF, FmocFFKK, FmocFFFKK, FmocFFOO) formed surfactant-like soft gels at concentrations of 1% w/v and above using a method of glucono-δ-Lactone pH induction. The majority of Fmoc-peptides (0.5-2% w/v) demonstrated selective action against established (grown for 24 h) biofilms of Gram-positive and Gram-negative pathogens. These results are likely to increase the clinical translation of short-peptide gelator platforms within the area of anti-infective biomaterials including as wound dressings and coatings for prostheses, catheters, heart valves and surgical tubes. In the long term, this will lead to wider treatment choices for clinicians and patients involved in the management of medical device infections and reduce the burden of antimicrobial resistance. Copyright © 2017 European Peptide Society and John Wiley & Sons, Ltd.
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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) | 0.001 | 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".