<i>Osteopontin</i>‐deficiency disturbs direct osteogenesis in the process of achieving osseointegration following immediate placement of endosseous implants
Bibliographic record
Abstract
BACKGROUND: The role of osteopontin (OPN) in the process of achieving osseointegration following implantation remains to be clarified. PURPOSE: This study aimed to analyze the healing patterns of the bone-implant interface after immediate placement of implants in the maxillae of 4-week-old Opn-knockout (KO) and wild-type (WT) mice. MATERIALS AND METHODS: After maxillary first molars were extracted, cavities were prepared with a drill and titanium implants blasted with ceramic abrasives containing hydroxyapatite/β-tricalcium phosphate were placed. Following fixation at 3, 5, 7, and 28 days after implantation, the samples were analyzed using immunohistochemistry, in situ hybridization, and an electron probe micro analyzer. RESULTS: Two types of bone healing were observed in the process of achieving osseointegration: "direct osteogenesis," where bone formation occurs at the implant surface, and "indirect osteogenesis," where it does at the pre-existing damaged bone surface in the WT mice. Direct osteogenesis occurred after the recruitment of tartrate resistant acid phosphatase-positive cells and the deposition of OPN on the implant surface. In contrast, the rate of osseointegration or direct osteogenesis was significantly low, and cell proliferation was disturbed in the Opn-KO mice. CONCLUSIONS: These results suggest that Opn-deficiency disturbs direct osteogenesis to lead the delayed osseointegration after immediate placement of endosseous implants.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".