Ultrashort self‐assembling Fmoc‐peptide gelators for anti‐infective biomaterial applications
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 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 it