Extracellular Matrix Membrane Induces Cementoblastic/Osteogenic Properties of Human Periodontal Ligament Stem Cells
Bibliographic record
Abstract
Objective: Periodontitis affects nearly 90% of adults over the age of 70, resulting to periodontal tissue infection, destruction and ultimately tooth loss. Guided tissue regeneration (GTR) is a method widely used to treat severe periodontal disease, and involves placement of an occlusive barrier to facilitate regeneration of the damaged area by periodontal ligament stem cells (PDLSCs). In this study, we evaluate natural extracellular matrix (ECM) as a scaffold material to provide a suitable microenvironment to support the proliferation, differentiation, and tissue-regenerating properties of PDLSCs. Design: The viability, proliferation, apoptosis, and migration of PDLSCs cultured on ECM membrane, that was isolated from porcine urinary bladders, were compared with those cultured on type I collagen membrane, a commonly used scaffold in GTR. To evaluate the effects of ECM vs. type I collagen on the tissue-regenerating properties of PDLSCs, the bio-attachment and cementoblastic/osteogenic differentiation of PDLSCs were evaluated. Results: Incubation of PDLSCs with ECM resulted in increased viability, proliferation, and reduced apoptosis, compared with type I collagen treated PDLSCs. Co-culture with ECM membrane also increased the migration and bio-attachment of PDLSCs. Incubation of PDLSCs with ECM membrane increased expression of the cementoblastic/osteogenic differentiation markers BSP, RUNX2, ALP, OPN, OCN, and periostin. Conclusions: ECM membrane enhances the proliferation and regenerative properties of PDLSCs, indicating that ECM membrane can serve as a suitable scaffold in the application of GTR to treat periodontal disease.
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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) | 0.002 | 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".