Effects of magnesium‐substituted nanohydroxyapatite coating on implant osseointegration
Bibliographic record
Abstract
OBJECTIVE: The objective of this study was to compare magnesium-substituted and pure hydroxyapatite coatings on the promotion of osteogenesis in vitro and on the osseointegration in vivo. METHODS: Electrochemically deposited pure hydroxyapatite (EDHA) or electrochemically deposited magnesium-substituted hydroxyapatite (EDMHA) coatings were formed on the surface of pure titanium disks or implants. MC3T3-E1 preosteoblasts were cultured in the EDHA and EDMHA coated disks, and cell growth, alkaline phosphatase (ALP) activity, and osteocalcin secretion were measured at various time points. For studies on osseointegration, 30 roughened implants coated either with EDHA or EDMHA (n = 15 for each coating) were implanted in the femurs of 15 NZW rabbits. After 2, 4, and 8 weeks, femurs were retrieved and prepared for histomorphometric evaluation (n = 5 for each coating at each time point). RESULTS: MC3T3-E1 cells cultured on EDMHA coated disks showed increased cell number, ALP, and osteocalcin secretion compared with the EDHA coated disks at all time points (P < 0.05 for all). Histologic observation of the coated implants showed woven bone in direct contact with both implant surfaces after 2 weeks and mature bone after 8 weeks. While there were no differences in the amount of bone between the threads at any time point, the percentage of implant in direct contact with bone (bone implant contact) was slightly higher along the EDMHA coated implants at 2 weeks (P = 0.086), although this difference was no longer seen at 4 and 8 weeks. CONCLUSION: Mg-substituted HA coated surfaces promote osteogenic differentiation of preosteoblasts in vitro and may improve implant osseointegration during the early stages of bone healing compared with pure EDHA coated surfaces.
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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) | 0.000 | 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 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".