Bone Regeneration Mediated by BMP4-Expressing Muscle-Derived Stem Cells Is Affected by Delivery System
Bibliographic record
Abstract
This study investigated the delivery of bone morphogenetic protein (BMP)4-secreting muscle-derived stem cells (MDSC-B4) capable of inducing bone formation in mice using collagen gel (CG), fibrin sealant (FS), and gelatin sponge carriers. After implanting these various cell-loaded scaffolds intramuscularly or into critical-size skull defects, we measured the extent of heterotopic ossification and calvarial defect healing over a 6-week period via radiographic, radiomorphometric, histological, and micro-computed tomography analyses. As expected, in the absence of MDSC-B4, there was no ectopic ossification and only minimal calvarial regeneration using each type of scaffold. Although CG and gelatin sponges loaded with BMP4-secreting cells produced the most ectopic bone, FS constructs produced bone with comparably less mineralization. In the mouse calvaria, we observed MDSC-B4-loaded scaffolds able to promote bone defect healing to a variable degree, but there were differences between these implants in the volume, shape, and morphology of regenerated bone. MDSC-B4 delivery in a gelatin sponge produced hypertrophic bone, whereas delivery in a CG and FS healed the defect with bone that closely resembled the quantity and configuration of native calvarium. In summary, hydrogels are suitable carriers for osteocompetent MDSCs in promoting bone regeneration, especially at craniofacial injury sites.
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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".