BMP-2 mRNA-transfected BMSCs promote superior calvarial bone regeneration
Bibliographic record
Abstract
Large critical-size bone defects in the oral and craniofacial region are difficult to regenerate. We evaluated the effectiveness of mRNA encoding bone morphogenic protein-2 (BMP-2) in enhancing bone regeneration using a rat calvarial defect model. Two delivery approaches were investigated: (1) in vivo application of BMP-2 mRNA encapsulated in lipid nanoparticles incorporated in a scaffold, and (2) application of ex vivo BMP-2 mRNA-transfected rat bone marrow mesenchymal stem cells (rBMSCs), loaded on a scaffold and implanted into calvarial defects. The direct application of BMP-2 mRNA encapsulated in lipid nanoparticles improved bone regeneration as indicated by micro-computed tomography analysis. The enhancement was even more pronounced with ex vivo transfected rBMSCs. rBMSCs transfected with FGF-2 mRNA did not improve bone regeneration, either alone or combined with BMP-2 mRNA-transfected rBMSCs. Similarly, PDGF-BB mRNA-transfected rBMSCs failed to enhance bone regeneration alone and notably suppressed BMP-2 mRNA-transfected rBMSCs' effects. Interestingly, BMP-2 mRNA-transfected rat fibroblasts showed comparable bone regeneration to transfected rBMSCs. Osteogenic differentiation was absent in BMP-2 mRNA-transfected rBMSCs, implying that they may primarily serve as a source of translated BMP-2 for bone regeneration rather than undergoing osteogenic differentiation. These findings highlight the translational potential of BMP-2 mRNA for bone regeneration, particularly in oral and craniofacial applications.
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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) | 0.002 | 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".