Next-Generation Biomaterials for Vital Pulp Therapy: Exploring Biological Properties and Dentin Regeneration Mechanisms
Bibliographic record
Abstract
The advancement of Vital Pulp Therapy (VPT) in dentistry has shown remarkable progress, with a focus on innovative materials and scaffolds to facilitate reparative dentin formation and tissue regeneration. A comprehensive search strategy was performed across PubMed, Scopus, and Web of Science using keywords such as "vital pulp therapy", "biomaterials", "dentin regeneration", and "growth factors", with filters for English language studies published in the last 10 years. The inclusion criteria focused on in vitro, in vivo, and clinical studies evaluating traditional and next-generation biomaterials for pulp capping and tissue regeneration. Due to the limitations of calcium-based cements in tissue regeneration, next-generation biomaterials like gelatin, chitosan, alginate, platelet-rich fibrins (PRF), demineralized dentin matrix (DDM), self-assembling peptides, and DNA-based nanomaterials were explored for their enhanced biocompatibility, antibacterial properties, and regenerative potential. These biomaterials hold great potential in enhancing VPT outcomes, but further research is required to understand their efficacy and impact on dentin reparative properties. This review explores the mechanisms and properties of biomaterials in dentin tissue regeneration, emphasizing key features that enhance tissue regeneration. These features include biomaterial sources, physicochemical properties, and biological characteristics that support cells and functions. The discussion also covers the biomaterials' capability to encapsulate growth factors for dentin repair. The development of innovative biomaterials and next-generation scaffold materials presents exciting opportunities for advancing VPT in dentistry, with the potential to improve clinical outcomes and promote tissue regeneration in a safe and effective manner.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.016 | 0.015 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".