Endothelial progenitor cells promote fracture healing in a segmental bone defect model
Bibliographic record
Abstract
The objective of this study was to evaluate the effects of local endothelial progenitor cell (EPC) therapy on bone regeneration in a rat model. A segmental bone defect (5 mm) was created in the femur and fixed with a mini-plate. There were two groups: EPC-treated (N = 28) and control (N = 28). Seven animals were sacrificed from each group at 1, 2, 3, and 10 weeks postoperatively. Healing of the defect was evaluated with radiographic, histological, and quantitative micro-computed tomography (micro-CT) scans. Radiographically, mean scores of the EPC and control groups were, respectively, 1.16-0.61 (p < 0.05) at 1 week, 2.53-1.54 (p < 0.05) at 2 weeks, and 4.58-2.35 at 3 weeks (p < 0.05). At 10 weeks, all the animals in the EPC-treated group had complete union (7/7), but in the control group none achieved union (0/7). Histological evaluation revealed that specimens from EPC-treated animals had abundant new bone and vessel formation compared to that in controls. Micro-CT assessment of the samples from the animals sacrificed at 10 weeks (N = 14) showed significantly improved parameters of bone volume (36.58-10.57, p = 0.000), bone volume density (0.26-0.17, p = 0.000), model index -2.22-2.79, p = 0.000), trabecular number (1.28-0.91, p = 0.063), trabecular thickness (0.21-0.15, p = 0.001), trabecular spacing (0.63-1.07, p = 0.022), bone surface (353.75-152.08, p = 0.000), and bone surface to bone volume ratio (9.54-14.24, p = 0.004) for the EPC group compared to control, respectively. In conclusion, local EPC therapy significantly enhanced bone regeneration in a segmental defect model in rat femur diaphysis.
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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 | 0.001 |
| 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".