Antimicrobial Materials Used in Coating Dental Implant Surfaces: State of Art and Future Prospectives
Bibliographic record
Abstract
This review presents a comprehensive overview of dental materials that support tissue healing while exhibiting antimicrobial properties. Emphasis is placed on materials that are biocompatible, bioactive, and non-toxic to host cells, with demonstrated bacteriostatic and bactericidal activity. The review summarizes current research on natural bactericides, antimicrobial polymers, and bioactive glass/polymer composites, along with various techniques employed for surface coating of dental implants. Three principal categories of antimicrobial coatings have been identified: antibacterial phytochemicals, synthetic antimicrobial agents (including polymers and antibiotics), and metallic nanoparticles. Among these, antibacterial peptide-based coatings have been the most extensively studied and have shown the greatest effectiveness in reducing bacterial colonization, especially during extended incubation periods. These coatings offer high antimicrobial potency, durability, and excellent biocompatibility, positioning them as promising candidates for long-term protection against microbial contamination. However, additional in vitro and pre-clinical studies are warranted to thoroughly evaluate their therapeutic potential and to establish their efficacy and safety for clinical applications in the prevention of peri-implant infections.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".