Enhancing Osseointegration With <scp>LNP</scp> ‐Delivered <scp>mRNA</scp> –Encoded <scp>BMP</scp> ‐2: An Experimental In Vivo Study
Bibliographic record
Abstract
OBJECTIVE: To evaluate the effect of lipid nanoparticle (LNP)-encapsulated N1-methylpseudouridine-modified mRNA encoding BMP-2 (BMP-2 mRNA-LNP) on enhancing osseointegration and bone regeneration around titanium implants in rat femur defects. METHODS: A total of 48 rat femurs were examined in this study. BMP-2 mRNA-LNP (5 μg and 15 μg), recombinant human BMP-2 protein (4 μg), or dPBS (control) were randomly injected in a single dose into distal rat femurs (n = 6). Titanium wires were implanted, and bone formation was evaluated at 3 and 6 weeks post treatment using micro-computed tomography, histology, and immunohistochemistry analysis. Data were analyzed using the Kruskal-Wallis test, followed by the Dunn-Bonferroni test and the Wilcoxon signed-rank test with 95% confidence intervals (CIs). p-values < 0.05 were considered statistically significant. RESULTS: Micro-computed tomography analysis of bone volume, bone volume fraction, trabecular number, trabecular thickness, and bone-to-implant contact at both time points indicated a trend toward greater bone formation in the mRNA groups compared to the other groups. Significant differences were observed between the 15 μg BMP-2 mRNA-LNP group and the dPBS group at 6 weeks (p < 0.05). The 15 μg BMP-2 mRNA-LNP group also exhibited the most intense positive bone sialoprotein and osteocalcin staining compared to the other groups at 3 weeks and 6 weeks, respectively. Interestingly, histomorphometry at 6 weeks revealed a significantly higher bone area around the implants in both 5 μg and 15 μg BMP-2 mRNA-LNP groups compared to the rhBMP-2 and dPBS groups. CONCLUSION: This preclinical study highlights the potential of BMP-2 mRNA-LNP for promoting bone regeneration around dental implants.
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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".