Preclinical Evaluation of BMP-9-Treated Human Bone-like Substitutes for Alveolar Ridge Preservation following Tooth Extraction
Bibliographic record
Abstract
The success of dental implant treatment after tooth extraction is generally maximized by preserving the alveolar ridge using cell-free biomaterials. However, these treatments can be associated with inflammatory reactions, leading to additional bone volume loss hampering dental implant positioning. Our group developed a self-assembled bone-like substitute constituted of osteogenically induced human adipose-derived stromal/stem cells (hASCs). We hypothesized that a bone morphogenetic protein (BMP) supplementation could improve the in vitro osteogenic potential of the bone-like substitute, which would subsequently translate into enhanced alveolar bone healing after tooth extraction. ASCs displayed a better osteogenic response to BMP-9 than to BMP-2 in monolayer cell culture, as shown by higher transcript levels of the osteogenic markers RUNX2, osterix (OSX/SP7), and alkaline phosphatase after three and six days of treatment. Interestingly, BMP-9 treatment significantly increased OSX transcripts and alkaline phosphatase activity, as well as pro-angiogenic angiopoietin-1 gene expression, in engineered bone-like substitutes after 21 days of culture. Alveolar bone healing was investigated after molar extraction in nude rats. Microcomputed tomography and histological evaluations revealed similar, or even superior, global alveolar bone preservation when defects were filled with BMP-9-treated bone-like substitutes for ten weeks compared to a clinical-grade biomaterial, with adequate gingival re-epithelialization in the absence of resorption.
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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 | 0.001 | 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 | 0.001 |
| 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".