Ag-plasma modification enhances bone apposition around titanium dental implants: an animal study in Labrador dogs
Bibliographic record
Abstract
Dental implants with proper antibacterial ability as well as ideal osseointegration are being actively pursued. The antimicrobial ability of titanium implants can be significantly enhanced via modification with silver nanoparticles (Ag NPs). However, the high mobility of Ag NPs results in their potential cytotoxicity. The silver plasma immersion ion-implantation (Ag-PIII) technique may remedy the defect. Accordingly, Ag-PIII technique was employed in this study in an attempt to reduce the mobility of Ag NPs and enhance osseointegration of sandblasted and acid-etched (SLA) dental implants. Briefly, 48 dental implants, divided equally into one control and three test groups (further treated by Ag-PIII technique with three different implantation parameters), were inserted in the mandibles of six Labrador dogs. Scanning electron microscopy, X-ray photoelectron spectroscopy, and inductively coupled plasma optical emission spectrometry were used to investigate the surface topography, chemical states, and silver release of SLA- and Ag-PIII-treated titanium dental implants. The implant stability quotient examination, Microcomputed tomography evaluation, histological observations, and histomorphometric analysis were performed to assess the osseointegration effect in vivo. The results demonstrated that normal soft tissue healing around dental implants was observed in all the groups, whereas the implant stability quotient values in Ag-PIII groups were higher than that in the SLA group. In addition, all the Ag-PIII groups, compared to the SLA-group, exhibited enhanced new bone formation, bone mineral density, and trabecular pattern. With regard to osteogenic indicators, the implants treated with Ag-PIII for 30 minutes and 60 minutes, with the diameter of the Ag NPs ranging from 5-25 nm, were better than those treated with Ag-PIII for 90 minutes, with the Ag NPs diameter out of that range. These results suggest that Ag-PIII technique can reduce the mobility of Ag NPs and enhance the osseointegration of SLA surfaces and have the potential for future use.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".