Overview of Nanoparticle Coating of Dental Implants for Enhanced Osseointegration and Antimicrobial Purposes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 it