Effect of Enamel Matrix Derivative Liquid on Osteoblast and Periodontal Ligament Cell Proliferation and Differentiation
Bibliographic record
Abstract
BACKGROUND: Enamel matrix derivatives (EMDs) have been used clinically for more than a decade for the regeneration of periodontal tissues. The aim of the present study is to analyze the effect on cell growth of EMDs in a gel carrier in comparison to EMDs in a liquid carrier. EMDs in a liquid carrier have been shown to adsorb better to bone graft materials. METHODS: Primary human osteoblasts and periodontal ligament (PDL) cells were exposed to EMDs in both gel and liquid carriers and compared for their ability to induce cell proliferation and differentiation. Alizarin red staining and real-time polymerase chain reaction for expression of genes encoding collagen 1, osteocalcin, and runt-related transcription factor 2, as well as bone morphogenetic protein 2 (BMP2), transforming growth factor (TGF)-β1, and interleukin (IL)-1β, were assessed. RESULTS: EMDs in both carriers significantly increased cell proliferation of both osteoblasts and PDL cells in a similar manner. Both formulations also significantly upregulated the expression of genes encoding BMP2 and TGF-β1 as well as decreased the expression of IL-1β. EMDs in the liquid carrier further retained similar differentiation potential of both osteoblasts and PDL cells by demonstrating increased collagen and osteocalcin gene expression and significantly higher alizarin red staining. CONCLUSIONS: The results from the present study indicate that the new formulation of EMDs in a liquid carrier is equally as potent as EMDs in a gel carrier in inducing osteoblast and PDL activity. Future study combining EMDs in a liquid carrier with bone grafting materials is required to further evaluate its potential for combination therapies.
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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".