Gene-activated matrix harboring a miR20a-expressing plasmid promotes rat cranial bone augmentation
Bibliographic record
Abstract
Abstract Gene-activated matrix (GAM) has a potential usefulness in bone engineering as an alternate strategy for the lasting release of osteogenic proteins but efficient methods to generate non-viral GAM remain to be established. In this study, we investigated whether an atelocollagen-based GAM containing naked-plasmid (p) DNAs encoding microRNA (miR) 20a, which may promote osteogenesis in vivo via multiple pathways associated with the osteogenic differentiation of mesenchymal stem/progenitor cells (MSCs), facilitates rat cranial bone augmentation. First, we confirmed the osteoblastic differentiation functions of generated pDNA encoding miR20a (pmiR20a) in vitro, and its transfection regulated the expression of several of target genes, such as Bambi1 and PPARγ, in rat bone marrow MSCs and induced the increased expression of BMP4. Then, when GAMs fabricated by mixing 100 μl of 2% bovine atelocollagen, 20 mg β-TCP granules and 0.5 mg (3.3 μg/μl) AcGFP plasmid-vectors encoding miR20a were transplanted to rat cranial bone surface, the promoted vertical bone augmentation was clearly recognized up to 8 weeks after transplantation, as were upregulation of VEGFs and BMP4 expressions at the early stages of transplantation. Thus, GAM-based miR delivery may provide an alternative non-viral approach by improving transgene efficacy via a small sequence that can regulate the multiple pathways.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".