Gingiva as a Source of Stem Cells with Therapeutic Potential
Bibliographic record
Abstract
Postnatal connective tissues contain phenotypically heterogeneous cells populations that include distinct fibroblast subpopulations, pericytes, myofibroblasts, fibrocytes, and tissue-specific mesenchymal stem cells (MSCs). These cells play key roles in tissue development, maintenance, and repair and contribute to various pathologies. Depending on the origin of tissue, connective tissue cells, including MSCs, have different phenotypes. Understanding the identity and specific functions of these distinct tissue-specific cell populations may allow researchers to develop better treatment modalities for tissue regeneration and find novel approaches to prevent pathological conditions. Interestingly, MSCs from adult oral mucosal gingiva possess distinct characteristics, including neural crest origin, multipotent differentiation capacity, fetal-like phenotype, and potent immunomodulatory properties. These characteristics and an easy, relatively noninvasive access to gingival tissue, and fast tissue regeneration after tissue biopsy make gingiva an attractive target for cell isolation for therapeutic purposes aiming to promote tissue regeneration and fast, scar-free wound healing. The purpose of this review is to discuss the identity, phenotypical heterogeneity, and function of gingival MSCs and summarize what is currently known about their properties, role in scar-free healing, and their future therapeutic potential.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".