Osseointegration of Zirconia in the Presence of Multinucleated Giant Cells
Bibliographic record
Abstract
BACKGROUND: Current strategies to reduce medical device-associated infections propose zirconia as a potential implant material which may limit bacterial adhesion. Because multinucleated giant cells (MNGCs) have been detected on these implant surfaces, concerns have been raised regarding tissue integration. PURPOSE: The present study examined the presence of MNGCs and their subsequent effect upon tissue integration. Surface-modified implants made of yttria-stabilized (TZP) and alumina-toughened zirconia (ATZ) were compared with commercially pure titanium (Ti). MATERIALS AND METHODS: Seven miniature pigs received three implants on either side of the maxilla. After healing periods of 4 and 8 weeks, the tissue response at the implant surfaces was characterized according to three specific parameters: bone-to-implant contact (BIC), MNGC-to-implant contact (MIC), and the peri-implant bone density (BD). RESULTS: Despite being present on all tested implant surfaces, MNGCs were not associated with an inflammatory cell infiltrate or with fibrous encapsulation. MNGCs were less numerous on the Ti implants (range: 3.9-5.2%) compared with the ceramic implants (range: 17.6-30.3%, p < .0001). Even though the values of newly formed bone and pristine bone in direct contact with the implant surfaces were high at 4 weeks (tBIC: Ti = 82.3%, TZP = 64.3%, ATZ = 70%), a negative correlation was observed between the presence of MNGCs and newly formed bone at the implant surface (p < .001). Interestingly, the newly formed peri-implant bone density, defined as the percentage of new bone area inside the screw threads (nBD), was not diminished by the presence of MNGCs. CONCLUSIONS: Differences in the presence of MNGCs and the BIC parameters between Ti and the ceramic implants appear to be a local cellular phenomenon which is restricted to the implant-bone marrow interface and do not affect the peri-implant bone formation. Factors triggering MNGC differentiation and their persistence in response to biomaterial surface need to be investigated in future studies.
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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".