Effect of cell‐based VEGF gene therapy on healing of a segmental bone defect
Bibliographic record
Abstract
Fracture healing requires coordinated coupling between osteogenesis and angiogenesis in which vascular endothelial growth factor (VEGF) plays a key role. We hypothesized that targeted over-expression of angiogenic and osteogenic factors within the fracture would promote bone healing by inducing development of new blood vessels and stimulating/affecting proliferation, survival, and activity of skeletal cells. Using a cell-based method of gene transfer, without viral vector, 5.0 x 10(6) fibroblasts transfected with VEGF were delivered to a 10-mm bone defect in rabbit tibiae (Group 1) (n = 9); control groups were treated with fibroblasts (Group 2) (n = 7), or saline (Group 3) (n = 7) only. After 12 weeks, eight tibial fractures healed in Group 1, compared to four each in Groups 2 and 3. In Group 1, ossification was seen across the entire defect; in Groups 2 and 3, the defects were fibrous and sparsely ossified. Group 1 had more positively stained (CD31) vessels than Groups 2 and 3. MicroCT 3-D showed complete bridging of the new bone for Group 1, but incomplete healing for Groups 2 and 3. MicroCT bone structural parameters showed significant differences between VEGF treatment and control groups (p < 0.05). These results indicate that the cell-based VEGF gene therapy has significant angiogenic and osteogenic effects to enhance healing of a segmental defect in the long bone of rabbits.
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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".