Osteoinductively Functionalized <scp>3D</scp>‐Printed Scaffold for Vertical Bone Augmentation in Beagle Dogs
Bibliographic record
Abstract
OBJECTIVE: To evaluate the efficacy of 3D-printed scaffolds that were osteoinductively functionalized with a bone morphogenetic protein 2 (BMP-2)-incorporated biomimetic calcium phosphate particles (BMP-2-inc. BpNcCaP)/hyaluronic acid (HA) composite gel in vertical bone augmentation in beagle dogs. MATERIALS AND METHODS: Four Beagle dogs were used in this study. Three months after the extraction of 1st, 2nd, 3rd, and 4th premolars at both sides of the lower jaws of Beagle dogs, one or two critical-size vertical bone defects (4 mm vertical bone defect without buccal and lingual bone) on each side were surgically created. The defects were randomly subjected to the following groups: (1) Control (without bone-defect-filling materials); (2) 3D scaffold; (3) BMP2-inc. BpNcCaP/HA-functionalized 3D scaffold. Six weeks post-surgery, samples were harvested and subjected to micro-CT and histomorphometric analyses. RESULTS: The struts of the BMP2-inc. BpNcCaP/HA-func. 3D scaffold were covered by a thick layer of cemented irregular particles with an average pore size at 327 ± 27 μm. The BpNcCaP/HA-func. 3D scaffold group bore significantly higher bone volume, bone volume fraction, trabecular number, trabecular thickness, bone mineral density, connectivity density, and bone volumes in three directions (mesiodistal, buccolingual, and apicocoronal) when compared with the groups of Control and 3D scaffold. Moreover, the BMP2-inc. BpNcCaP/HA-func. 3D scaffold group bore significantly lower trabecular separation and exhibited significantly higher bone-to-scaffold contact percentage and newly formed bone area percentage within pores in comparison with 3D scaffold. CONCLUSIONS: BMP2-inc. BpNcCaP/HA-func. 3D scaffold dramatically enhanced vertical alveolar bone augmentation, which suggests a promising application potential of BMP2-inc. BpNcCaP/HA-func. 3D scaffold in dental clinic.
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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) | 0.001 | 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) | 0.001 | 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".