Biomaterials and Scaffold Design Strategies for Regenerative Endodontic Therapy
Bibliographic record
Abstract
Challenges with traditional endodontic treatment for immature permanent teeth exhibiting pulp necrosis have prompted interest in tissue engineering approaches to regenerate the pulp-dentin complex and allow root development to continue. These procedures are known as regenerative endodontic therapies. A fundamental component of the regenerative endodontic process is the presence of a scaffold for stem cells from the apical papilla to adhere to, multiply and differentiate. The aim of this review is to provide an overview of the biomaterial scaffolds that have been investigated to support stem cells from the apical papilla in regenerative endodontic therapy and to identify potential biomaterials for future research. An electronic search was conducted using Pubmed and Novanet databases for published studies on biomaterial scaffolds for regenerative endodontic therapies, as well as promising biomaterial candidates for future research. Using keywords "regenerative endodontics," "scaffold," "stem cells" and "apical papilla," 203 articles were identified after duplicate articles were removed. A second search using "dental pulp stem cells" instead of "apical papilla" yielded 244 articles. Inclusion criteria included the use of stem cells from the apical papilla or dental pulp stem cells in combination with a biomaterial scaffold; articles using other dental stem cells or no scaffolds were excluded. The investigated scaffolds were organized in host-derived, naturally-derived and synthetic material categories. It was found that the biomaterial scaffolds investigated to date possess both desirable characteristics and issues that limit their clinical applications. Future research investigating the scaffolds presented in this article may, ultimately, point to a protocol for a consistent, clinically-successful regenerative endodontic therapy.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".