Lentivirus-mediated gene transfer induces long-term transgene expression of BMP-2 in vitro and new bone formation in vivo
Bibliographic record
Abstract
We examined the potential of ex vivo gene therapy to enhance bone repair using lentiviral vectors encoding either enhanced green fluorescent protein (EGFP) as a reporter gene or bone morphogenetic protein-2 (BMP-2) downstream of either the cytomegalovirus immediate early (CMV) promoter or the murine leukemia virus long terminal repeat (RhMLV) promoter derived from a murine retrovirus adapted to replicate in a rhesus macaque. In vitro, rat bone marrow stromal cells (BMSCs) transduced with Lenti-CMV-EGFP or Lenti-RhMLV-EGFP demonstrated over 90% transduction efficiency at 1 week and continued to demonstrate stable expression for 8 weeks. ELISA results demonstrated that lentivirus-mediated gene transfer into BMSCs induced stable BMP-2 production in vitro for 8 weeks. Increased EGFP and BMP-2 production was noted with the RhMLV promoter. In addition, we implanted BMSCs transduced with Lenti-RhMLV-BMP-2 into a muscle pouch in the hind limbs of severe combined immune deficient mice. Robust bone formation was noted in animals that received Lenti-RhMLV-BMP-2 cells at 3 weeks. These results demonstrate that lentiviral vectors expressing BMP-2 can induce long-term gene expression in vitro and new bone formation in vivo under the control of the RhMLV promoter. Prolonged gene expression may be advantageous when developing tissue engineering strategies 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".