A Novel <scp>BMP2</scp>‐Coprecipitated, Layer‐by‐Layer Assembled Biomimetic Calcium Phosphate Particle: A Biodegradable and Highly Efficient Osteoinducer
Bibliographic record
Abstract
PURPOSE: To repair large-size bone defects, most bone-defect-filling materials in clinic need to obtain osteoinductivity either by mixing them with particulate autologous bone or adsorbing bone morphogenetic protein 2 (BMP2). However, both approaches encounter various limitations. In this study, we hypothesized that our novel particles of biomimetic BMP2-coprecipitated calcium phosphate (BMP2-cop.BioCaP) could serve as an independent and biodegradable osteoinducer to induce bone formation efficiently for these bone-defect-filling materials, for example, deproteinized bovine bone (DBB). MATERIALS AND METHODS: We alternately layer-by-layer assembled amorphous and crystalline CaP triply to enable a "bamboo-like" growth of the particles. We functionalized BioCaP by coprecipitating BMP2 into the most outer layer of BioCaP. We monitored the degradation, osteoinductivity, and foreign-body reaction of either BMP2-cop.BioCaP or its combination with DBB in an ectopic site in rats. RESULTS: After 5 weeks, the BMP2-cop.BioCaP significantly induced new bone formation not only alone but also when mixed with DBB. Its osteoinductive efficiency was 10-fold higher than the adsorbed BMP2. Furthermore, BMP2-cop.BioCaP also reduced significantly the host foreign-body reaction to DBB in comparison with the adsorbed BMP2. After a 5-week implantation, more than 90% of BMP2-cop.BioCaP degraded. CONCLUSIONS: These findings indicate a promising clinical potential for BMP2-cop.BioCaP in the repair of large-size bone defects.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".