A ROS-responsive hydrogel incorporated with dental follicle stem cell-derived small extracellular vesicles promotes dental pulp repair by ameliorating oxidative stress
Bibliographic record
Abstract
Pulpitis, an inflammatory disease of dental pulp tissues, ultimately results in the loss of pulp defense properties. Existing clinical modalities cannot effectively promote inflamed pulp repair. Oxidative stress is a major obstacle inhibiting pulp repair. Due to their powerful antioxidative capacity, mesenchymal stem cell-derived small extracellular vesicles (MSC-sEVs) exhibit potential for treating oxidative stress-related disorders. However, whether MSC-sEVs shield dental pulp tissues from oxidative damage is largely unknown. Here, we showed that dental follicle stem cell-derived sEVs (DFSC-sEVs) have antioxidative and prohealing effects on a rat LPS-induced pulpitis model by enhancing the survival, proliferation and odontogenesis of H2O2-injured dental pulp stem cells (DPSCs). Additionally, DFSC-sEVs restored the oxidative/antioxidative balance in DPSC mitochondria and had comparable effects on ameliorating mitochondrial dysfunction with the mitochondrion-targeted antioxidant Mito-Tempo. To improve the efficacy of DFSC-sEVs, we fabricated an intelligent and injectable hydrogel to release DFSC-sEVs by combining sodium alginate (SA) and the ROS sensor RhB-AC. The newly formed SA-RhB hydrogel efficiently encapsulates DFSC-sEVs and exhibits controlled release of DFSC-sEVs in a HClO/ClO− concentration-dependent manner, providing a synergistic antioxidant effect with DFSC-sEVs. These results suggest that DFSC-sEVs-loaded SA-RhB is a promising minimally invasive treatment for pulpitis by enhancing tissue repair in the pulp wound microenvironment.
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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) | 0.001 | 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".