Influence of multilayer rhBMP‐2 DNA coating on the proliferation and differentiation of MC3T3‐E1 cells seeded on roughed titanium surface
Bibliographic record
Abstract
For bone morphogenetic protein (BMP) gene therapy to be a viable approach for enhancing implant osseointegration clinically, requires the development of efficient nonviral delivery vectors that can coat the implant. This study evaluated a multilayer cationic liposome-DNA complex (LDc) coating as a delivery vehicle for recombinant human BMP-2 (rhBMP-2). Multilayered coatings, comprising hyaluronic acid (HA) and LDc, were fabricated onto titanium using a layer-by-layer (LBL) assembly technique. Preosteoblastic MC3T3-E1 cells were cultured on the roughened titanium surfaces coated with multilayers of HA/LDc, or on uncoated or HA/liposome only surfaces as controls. The amount of rhBMP-2 secreted by the MC3T3-E1 cells and the effect of the various surfaces on cell viability, proliferation, alkaline phosphatase (ALP) activity, osteocalcin (OC) secretion, and calcium deposition were evaluated. Messenger RNA levels of OC, ALP, Runx2, and Osx were also investigated. The results demonstrated that rhBMP-2 protein secreted into culture medium at 3 days was significantly higher than control groups. MC3T3-E1 cells cultured on the HA/LDc coating displayed significantly higher ALP activity and OC secretion at 7 days and 14 days culture, respectively. MC3T3-E1 cells cultured on HA/LDc upregulated expression of the osteoblast differentiation markers, especially on days 12 for OC and on days 6 and 12 for ALP and Osx. In conclusion, MC3T3-E1 cell cultured on the multilayer HA/LDc coating surface can secret rhBMP-2 protein and the protein levels were effective in inducing early osteogenic differentiation.
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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) | 0.000 | 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".