Local delivery of antimicrobial peptides using self‐organized TiO<sub>2</sub> nanotube arrays for peri‐implant infections
Bibliographic record
Abstract
Peri-implant infections have been reported as one of the major complications that lead to the failure of orthopedic implants. An ideal solution to the peri-implant infection is to locally deliver antimicrobial agents through the implant surface. The rising problem of infections caused by multiple antibiotic-resistant bacteria makes traditional antibiotics less desirable for the prevention of peri-implant infections. One of the promising alternatives is the family of antimicrobial peptides (AMPs). In this study, we report the local delivery of AMPs through the nanotubular structure processed on titanium surface. Self-organized and vertically oriented TiO2 nanotubes, about 80 nm in diameter and 7 μm thick, were prepared by the anodization technique. HHC-36 (KRWWKWWRR), one of the most potent broad-spectrum AMPs, was loaded onto the TiO2 nanotubes via a simple vacuum-assisted physical adsorption method. Antimicrobial activity testing against Gram-positive bacterium, Staphylococcus aureus, demonstrated that this AMP-loaded nanotubular surface could effectively kill the bacteria (≈ 99.9% killing) and reduce the total bacterial number adhered to the surface after 4 h of culture. In vitro AMP elution from the nanotubes was investigated using liquid chromatography-mass spectrometry (LC-MS). The release profiles strongly depended on the crystallinity of the TiO2 nanotubes. Anatase TiO2 nanotubes released significantly higher amounts of AMP than amorphous nanotubes during the initial burst release stage. Both followed almost the same slow release profile from 4 h up to 7 days. Despite the differences in release kinetics, no significant difference was observed between these two groups in bactericidal efficiency.
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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) | 0.000 | 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".