Modulation of gene expression and bone formation by expanded and dense polytetrafluoroethylene membranes during guided bone regeneration: An experimental study
Bibliographic record
Abstract
BACKGROUND: Nonresorbable membranes promote bone formation during guided bone regeneration (GBR), yet the relationships between membrane properties and molecular changes in the surrounding tissue are largely unknown. AIM: To compare the molecular events in the overlying soft tissue, the membrane, and the underlying bone defect during GBR using dual-layered expanded membranes versus dense polytetrafluoroethylene (PTFE) membranes. MATERIALS AND METHODS: Rat femur defects were treated with either dense PTFE (d-PTFE) or dual-layered expanded PTFE (dual e-PTFE) or left untreated as a sham. Samples were collected after 6 and 28 days for gene expression, histology, and histomorphometry analyses. RESULTS: The two membranes promoted the overall bone formation compared to sham. Defects treated with dual e-PTFE exhibited a significantly higher proportion of new bone in the top central region after 28 days. Compared to that in the sham, the soft tissue in the dual e-PTFE group showed 2-fold higher expression of genes related to regeneration (FGF-2 and FOXO1) and vascularization (VEGF). Furthermore, compared to cells in the d-PTFE group, cells in the dual e-PTFE showed 2.5-fold higher expression of genes related to osteogenic differentiation (BMP-2), regeneration (FGF-2 and COL1A1), and vascularization (VEGF), in parallel with lower expression of proinflammatory cytokines (IL-6 and TNF-α). Multiple correlations were found between the molecular activities in membrane-adherent cells and those in the soft tissue. CONCLUSION: Selective surface modification of the two sides of the e-PTFE membrane constitutes a novel means of modulating the tissue response and promoting bone regeneration.
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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 | 0.001 | 0.001 |
| 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".