Engineering Vascularized Bone Graft With Osteogenic and Angiogenic Lineage Differentiated Bone Marrow Mesenchymal Stem Cells
Bibliographic record
Abstract
Tissue-engineered bone provides a promising method for the rehabilitation of acquired bone defects and congenital deformities. However, generating a vascular supply to the engineered graft remains a major challenge. We report a novel strategy to engineer vascularized bone grafts with osteogenic and angiogenic lineage differentiated marrow mesenchymal stem cells (MSCs). MSCs were expanded to form an osteogenic cell sheet using a continuous culture method and a scraping technique under osteogenic culture conditions. Another portion of MSCs was directed to differentiate into highly proliferative endothelial progenitor cells (EPCs), which were then seeded onto the cell sheets. Cell sheet-EPC complexes were implanted subcutaneously in nude mice. Cell sheets without EPCs were also implanted as a control. The mice were sacrificed, and the samples were harvested for evaluation consisting of micro-CT scanning, histological analysis and scanning electronic microscopy 4 and 8 weeks after implantation. The results showed that cell sheets were composed of viable cells and extracellular matrix and showed apparent mineralization. The obtained EPCs could express the specific antigen marker of CD31 and form capillary-like structures in vitro. The osteogenic cell sheet-EPC complexes yielded well-vascularized bone grafts 4 and 8 weeks after implantation. Both bone density and vascular density were significantly higher in the cell sheet-EPC complex group than in the control group. The results demonstrated that the introduction of EPCs could not only generate a vascular network but also increase bone formation for cell sheet-based bone engineering. These findings suggest that the strategy of engineering bone grafts with osteogenic and angiogenic lineage differentiated MSCs has great potential for clinical applications to repair large bone defects.
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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".