Bone response to the multilayer <scp>BMP</scp>‐2 gene coated porous titanium implant surface
Bibliographic record
Abstract
OBJECTIVES: Evaluate hBMP-2 expression following gene delivery from plasmid multilayers formed on sandblasted titanium in vitro and bone formation around similarly prepared implant surfaces in vivo. MATERIALS AND METHODS: Multilayers of cationic lipid/rhBMP-2 plasmid DNA complex (LDc) and anionic hyaluronic acid (HA) was assembled on sandblasted-dual acid etched pure titanium disks or implant surfaces using layer-by-layer (LBL) assembly. Gene delivery and hBMP-2 expression in cells exposed to the LDc multilayers was measured in vitro. To determine the effect of BMP delivery from such multilyaers in vivo, roughened implants coated with BMP-2 LDc multilayers or uncoated control implants (n = 15 for both) were implanted in the femurs of NZW rabbits. After 2, 4, 8 weeks, femurs were retrieved and prepared for histomorphometric evaluation (n = 5 rabbits per time point). RESULTS: MC3T3-E1 cells cultured directly on the BMP-2 LDc coated titanium disks showed EGFP and hBMP-2 expression after 48 h in culture. Increased gene delivery occurred by increasing the number of assembly layers when cells were cultured for 48 h. Cells cultured on LDc coated surfaces had significantly higher cell viability than control cells cultured on uncoated porous titanium surfaces. Histologic observation of the implants showed that after 4 weeks healing, the bone to implant contact (BIC) on the LDc coated surface was much lower than that on the control surface, but didn't reach significant. In contrast, the percentage of bone within the implant's threads was significantly higher than the control group (P = 0.047). CONCLUSION: The BMP-2 gene coated sandblasted dual acid etched titanium implants slightly accelerated early bone formation around 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".