Targeted Infection Control and Tissue Integration via pH-Sensitive Smart Coatings on Implant Surfaces
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide A stimuli-responsive drug delivery coating is proposed for titanium (Ti) implants to locally treat infectious and peri-implant inflammatory diseases. The system integrates a pH-responsive film based on poly(methacrylic acid) (PMAA) film over a layer-by-layer (LbL) drug delivery platform containing tetracycline (TC) complexed with anionic beta cyclodextrin (βCD). This smart coating was designed to control drug release, maintain antimicrobial activity against biofilm-forming pathogens, and enhance soft tissue sealing at the implant interface. The coating effectively regulated drug release, exhibited favorable hydrophilicity, and reduced surface roughness compared with untreated Ti surfaces. Cytocompatibility was confirmed in both monolayer cell cultures and collagen matrix environments, with no cytotoxic effects observed up to 6 days. Atomic force microscopy (AFM) revealed enhanced interactions between the PMAA film and cellular components, as evidenced by filopodial projections at the cell margins. The coating’s antibacterial properties were validated using human saliva-derived biofilms, demonstrating broad-spectrum antimicrobial activity against pathogens typically involved in dental implant infections. In vivo, a rat subcutaneous tissue model was used to evaluate the immune response. The LbL/TCβCD/PMAA coating significantly reduced inflammation, increased collagen deposition, and elevated CD206 expression, indicating a shift toward an anti-inflammatory and tissue-repair phenotype. This stimuli-responsive coating represents a promising strategy for localized infection control, drug delivery, and soft tissue integration on implant 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) | 0.000 | 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".