Effects of micro/nano strontium‐loaded surface implants on osseointegration in ovariectomized sheep
Bibliographic record
Abstract
BACKGROUND: Poor osseointegration of dental implants often occurs in osteoporotic patients and processed implant surfaces could help to improve the dilemma. PURPOSE: This study aimed to compare the effects of different titanium (Ti) surfaces on bone-implant osseointegration in ovariectomized (OVX) sheep. MATERIALS AND METHODS: Four groups were included: smooth titanium (ST) was merely polished Ti; micro titanium (MT) was treated with hydrofluoric acid (HF) for 30 minutes; strontium-loaded nano titanium (NT-Sr) was formed by magnetron sputtering; strontium-loaded micro/nano titanium (MNT-Sr) was fabricated by HF etching combined with magnetron sputtering. The biological responses were evaluated by human bone marrow-derived mesenchymal stem cells (hBMMSCs) experiments in vitro. Osseointegration was evaluated in vivo after each surface implant was inserted into OVX sheep' mandibles. RESULTS: The numbers of adhered and mineralized hBMMSCs increased significantly in the MNT-Sr group. The bone-implant contact and the maximal pull-out force increased significantly with MNT-Sr surface. The bone volume ratio and trabecular number of the MNT-Sr group were significantly higher than others, whereas trabecular separation decreased. CONCLUSIONS: These results indicated that an MNT-Sr surface promotes the differentiation of hBMMSCs in vitro and enhances bone-implant osseointegration in vivo, which may be a promising option for clinical implants in osteoporotic patients.
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How this classification was reachedexpand
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 | 0.002 | 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 | 0.001 |
| 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 teacher head, 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".