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 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".