The Role of Epigenetic in Dental and Oral Regenerative Medicine by Different Types of Dental Stem Cells: A Comprehensive Overview
Bibliographic record
Abstract
Postnatal teeth, wisdom teeth, and exfoliated deciduous teeth can be harvested for dental stem cell (DSC) researches. These mesenchymal stem cells (MSCs) can differentiate and also consider as promising candidates for dental and oral regeneration. Thus, the development of DSC therapies can be considered a suitable but challenging target for tissue regeneration. Epigenetics describes changes in gene expression rather than changes in DNA and broadly happens in bone homeostasis, embryogenesis, stem cell fate, and disease development. The epigenetic regulation of gene expression and the regulation of cell fate is mainly governed by deoxyribonucleic acid (DNA) methylation, histone modification, and noncoding RNAs (ncRNAs). Tissue engineering utilizes DSCs as a target. Tissue engineering therapies are based on the multipotent regenerative potential of DSCs. It is believed that epigenetic factors are essential for maintaining the multipotency of DSCs. A wide range of host and environmental factors influence stem cell differentiation and differentiation commitment, of which epigenetic regulation is critical. Several lines of evidence have shown that epigenetic modification of DNA and DNA-correlated histones are necessary for determining cells' phenotypes and regulating stem cells' pluripotency and renewal capacity. It is increasingly recognized that nuclear enzyme activities, such as histone deacetylases, can be used pharmacologically to induce stem cell differentiation and dedifferentiation. In this review, the role of epigenetic in dental and oral regenerative medicine by different types of dental stem cells is discussed in two new and promising areas of medical and biological researches in recent studies (2010-2022).
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".