MSC-seeded dense collagen scaffolds with a bolus dose of VEGF promote healing of large bone defects
Bibliographic record
Abstract
The functional repair of large skeletal defects remains a significant challenge to orthopaedic surgeons due to the lack of effective strategies to promote bone regeneration, particularly in the elderly. This study investigated the potential use of bone marrow derived mesenchymal stromal cells (MSC) in a dense collagen scaffold with a bolus dose of vascular endothelial growth factor (VEGF) to repair a defect in the femoral diaphysis of mice. MSC isolated from bone marrow of 4-month-old donor mice were seeded in type I collagen gels that were then compressed to form scaffolds with a fibrillar density similar to osteoid. The cells remained metabolically active in scaffolds incubated in vitro for up to 15 days and differentiated into osteoblasts that deposited calcium-phosphate mineral into the scaffold, which was quantified using micro-computed tomographic (micro-CT) imaging. When implanted in a 1 mm x 3 mm unicortical defect the MSC-loaded scaffolds were rapidly mineralised and integrated into host bone with administration of 10 ng of recombinant VEGF injected into the femoral canal at 4 days postoperative. Empty scaffolds and MSC-seeded scaffolds implanted in defects that did not receive a bolus dose of VEGF did not mineralise or integrate with native bone. The approach with MSC, hydrogels and a biologic factor already approved for human use warrants further pre-clinical investigation with a large animal model.
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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".