Accelerated craniofacial bone regeneration through dense collagen gel scaffolds seeded with dental pulp stem cells
Bibliographic record
Abstract
Therapies using mesenchymal stem cell (MSC) seeded scaffolds may be applicable to various fields of regenerative medicine, including craniomaxillofacial surgery. Plastic compression of collagen scaffolds seeded with MSC has been shown to enhance the osteogenic differentiation of MSC as it increases the collagen fibrillary density. The aim of the present study was to evaluate the osteogenic effects of dense collagen gel scaffolds seeded with mesenchymal dental pulp stem cells (DPSC) on bone regeneration in a rat critical-size calvarial defect model. Two symmetrical full-thickness defects were created (5 mm diameter) and filled with either a rat DPSC-containing dense collagen gel scaffold (n = 15), or an acellular scaffold (n = 15). Animals were imaged in vivo by microcomputer tomography (Micro-CT) once a week during 5 weeks, whereas some animals were sacrificed each week for histology and histomorphometry analysis. Bone mineral density and bone micro-architectural parameters were significantly increased when DPSC-seeded scaffolds were used. Histological and histomorphometrical data also revealed significant increases in fibrous connective and mineralized tissue volume when DPSC-seeded scaffolds were used, associated with expression of type I collagen, osteoblast-associated alkaline phosphatase and osteoclastic-related tartrate-resistant acid phosphatase. Results demonstrate the potential of DPSC-loaded-dense collagen gel scaffolds to benefit of bone healing process.
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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".