Endothelial Progenitor Cell Therapy for Fracture Healing: A Dose-Response Study in a Rat Femoral Defect Model
Bibliographic record
Abstract
Endothelial progenitor cell (EPC) therapy has been successfully used in orthopaedic preclinical models to heal bone defects. However, no previous studies have investigated the dose-response relationship between EPC therapy and bone healing. This study aimed to assess the effect of different EPC doses on bone healing in a rat model to define an optimal dose. Five-millimeter segmental defects were created in the right femora of Fischer 344 rats, followed by stabilization with a miniplate and screws. Rats were assigned to one of six groups (control, 0.1 M, 0.5 M, 1.0 M, 2.0 M, and 4.0 M; n = 6), receiving 0, 1 × 105, 5 × 105, 1 × 106, 2 × 106, and 4 × 106 EPCs, respectively, delivered into the defect on a gelatin scaffold. Radiographs were taken every two weeks until the animals were euthanized 10 weeks after surgery. The operated femora were then evaluated using micro-computed tomography and biomechanical testing. Overall, the groups that received higher doses of EPCs (0.5 M, 1.0 M, 2.0 M, and 4.0 M) reached better outcomes. At 10 weeks, full radiographic union was observed in 67% of animals in the 0.5 M group, 83% of animals in the 1.0 M group, and 100% of the animals in the 2.0 M and 4.0 M groups, but none in the control and 0.1 M groups. The 2.0 M group also displayed the strongest biomechanical properties, which significantly improved relative to the control and 0.1 M groups. In summary, this study defined a dose-response relationship between EPC therapy and bone healing, with 2 × 106 EPCs being the optimal dose in this model. Our findings emphasize the importance of dosing considerations in the application of cell therapies aimed at tissue regeneration and will help guide future investigations and clinical translation of EPC therapy.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".