Injectable Piezoelectric Hydrogel for Vital Pulp Therapy
Bibliographic record
Abstract
Vital pulp therapy (VPT) seeks to preserve pulp vitality by using biocompatible with regenerative potential. This study tested the hypothesis that an injectable gelatin methacryloyl (GelMA) hydrogel containing piezoelectric barium titanate promotes odontogenic differentiation of dental pulp stem cells (DPSC) significantly better than a commercially available tricalcium silicate material used for vital pulp therapy. First, the light-curable, injectable piezoelectric hydrogel was engineered and characterized for its physicomechanical, piezoelectric properties and biocompatibility to DPSCs. The effect of this gel on the odontogenic differentiation of DPSCs was determined by measuring the expression level of key genes, compared to Biodentine XP. The hydrogel exhibited excellent injectability (<1 kgf of force), mechanical stability, and generated physiologically relevant voltages under cyclic loading mimicking mastication. MTT and ROS assays show no cytotoxic or damaging oxidative stress effects. When DPSCs were cultured over the materials under cyclic loading, the piezoelectric hydrogel significantly enhanced cell viability and upregulated COL1A1, DSPP, and DMP1 expression compared to Biodentine XP and non-piezoelectric hydrogel controls. These findings establish piezoelectric hydrogel as a self-powered, bioactive platform that converts physiological forces into regenerative bioelectric cues, offering a promising next-generation material for vital pulp 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 | 0.000 | 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".