Mussel-inspired antimicrobial coating on PTFE barrier membranes for guided tissue regeneration
Bibliographic record
Abstract
Abstract Guided tissue regeneration procedures to treat periodontitis lesions making use of polytetrafluoroethylene (PTFE) membranes exhibit large variability in their surgical outcomes, due to bacterial infection following implantation. This work reports on a facile method to obtain antimicrobial coatings for such PTFE membranes, by exploiting a mussel-inspired approach and in-situ formation of silver nanoparticles (AgNPs). PTFE films were initially coated with self-polymerized 3,4-dihydroxy-DL-phenylalanine (DOPA) (PTFE-DOPA), then incubated with AgNO 3 solution. In the presence of catechol moieties, Ag + ions reduced into Ag 0 , forming AgNPs of around 68 nm in the polyDOPA coating on PTFE membranes (PTFE-DOPA-Ag). The x-ray photoelectron spectroscopy, atomic force microscopy and scanning electron microscopy analyses indicated that the AgNPs were distributed quite homogeneously in the polymeric membrane. The antimicrobial ability of PTFE-DOPA-Ag membranes against Staphylococcus aureus and Escherichia coli was assessed. In vitro cell assay using NIH 3T3 fibroblasts showed that, although cells were adhered to PTFE-DOPA-Ag membranes, their viability and proliferation were limited demonstrating again the antibacterial activities of PTFE-DOPA-Ag membranes. This work provides proof-of-concept study of a new versatile approach for AgNPs coating, which may be easily applied to many other types of polymeric or metallic implants through exploiting the adhesive behavior of mussel-inspired coatings.
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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 | 0.001 | 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".