Thiol-Reactive Polymers for Titanium Interfaces: Fabrication of Antimicrobial Coatings
Bibliographic record
Abstract
Infection associated with surgical implants is a major cause of their failure. Often in such cases, the implant has to be removed and replaced, which causes undesirable patient discomfort and complications. Bacterial adhesion and growth on implant surface is the primary reason for such infections. Among the approaches to prevent implant associated infections, the conjugation of antimicrobial peptide (AMP) onto the surface of the implant is a very promising approach. In this study, we describe a facile method for the surface modification of titanium (Ti), a widely used material in dental and orthopedic implants, to prevent bacterial adhesion and growth. Thin polymeric films were synthesized on the Ti surface by using a copolymer containing the maleimide group as a thiol-reactive handle to enable the conjugation of AMPs. Robust attachment of the polymeric coating on Ti surfaces was ensured through installation of catechol moieties on the polymer as surface anchoring groups and the variation of the amount of thiol-reactive maleimide group on titanium surfaces. As a proof of concept, to demonstrate a viable application of such thiol reactive surfaces, the antimicrobial peptide E6 (RRWRIVVIRVRRC) was immobilized onto these well-characterized thin polymeric layers through Michael addition. The antimicrobial activity of peptide-modified surfaces was screened against both Gram-positive and Gram-negative bacteria. The hydrophilic polymer coatings decreased the bacterial adhesion, and the immobilized peptide killed >80% of the adhered bacteria. The developed surface modification method has broad applicability in terms of the choice of substrates and peptides in the design of bioactive surfaces.
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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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".