Overview of Nanoparticle Coating of Dental Implants for Enhanced Osseointegration and Antimicrobial Purposes
Bibliographic record
Abstract
PURPOSE: Nanomaterials are suitable candidates for coating of titanium based (Ti-based) dental implants due to their unique properties. The objective of this article is to summarize the application of nanoparticles as Ti-based implant coating materials in order to control and improve the implant success rate with focus on enhanced osseointegration and antimicrobial purposes. METHOD: This review was conducted using electronic databases and MeSH keywords to detect associated scientific literature published in English. RESULTS: The reviewed articles exhibited that a significant progress in research has occurred in the case of nanomaterial-based coatings for dental implants. Coating of Ti surfaces with nanoparticles can improve soft tissue integration and osteogeneration that leads to improved fixation of implants. Furthermore, osteoconductive nanoparticles induce a chemical bond with bone to attain good biological fixation for implants. Surface modification of implants using antibacterial properties can also decrease the potential for infection, and certainly, present improve clinical outcomes. CONCLUSIONS: Considering the reported success, more clinically and in vivo information on the nanoparticle-based implant coatings will add to the successful application of the device in the clinic. This article is open to POST-PUBLICATION REVIEW. Registered readers (see "For Readers") may comment by clicking on ABSTRACT on the issue's contents page.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".