Antibacterial Properties of hLf1–11 Peptide onto Titanium Surfaces: A Comparison Study Between Silanization and Surface Initiated Polymerization
Bibliographic record
Abstract
Dental implant failure can be associated with infections that develop into peri-implantitis. In order to reduce biofilm formation, several strategies focusing on the use of antimicrobial peptides (AMPs) have been studied. To covalently immobilize these molecules onto metallic substrates, several techniques have been developed, including silanization and polymer brush prepared by surface-initiated atom transfer radical polymerization (ATRP), with varied peptide binding yield and antibacterial performance. The aim of the present study was to compare the efficiency of these methods to immobilize the lactoferrin-derived hLf1-11 antibacterial peptide onto titanium, and evaluate their antibacterial activity in vitro. Smooth titanium samples were coated with hLf1-11 peptide under three different conditions: silanization with 3-aminopropyltriethoxysilane (APTES), and polymer brush based coatings with two different silanes. Peptide presence was determined by X-ray photoelectron spectroscopy, and the mechanical stability of the coatings was studied under ultrasonication. The LDH assays confirmed that HFFs viability and proliferation were no affected by the treatments. The in vitro antibacterial properties of the modified surfaces were tested with two oral strains (Streptococcus sanguinis and Lactobacillus salivarius) showing an outstanding reduction. A higher decrease in bacterial attachment was noticed when samples were modified by ATRP methods compared to silanization. This effect is likely due to the capacity to immobilize more peptide on the surfaces using polymer brushes and the nonfouling nature of polymer PDMA segment.
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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".