Silver-Doped Bioactive Glass/Chitosan Hydrogel with Potential Application in Dental Pulp Repair
Bibliographic record
Abstract
The major indications of a successful inflamed-pulp-capping procedure are the formation of a dense calcified dentin barrier and the preservation of healthy pulp tissue concomitant with elimination of inflammation. Our aim is to evaluate the effects of an injectable silver-doped bioactive glass/chitosan hydrogel (Ag-BG/CS), as a pulp-capping material, and explore the molecular mechanisms of Ag-BG/CS in regards to its bioactive and anti-inflammatory properties. First, the structure and component of the material were analyzed by scanning electron microscopy. Then, the downstream molecular mechanisms and anti-inflammatory effects were characterized by quantitative polymerase chain reaction (qPCR) and Western blot. Finally, a preclinical model of rat pulpitis was used to explore the potential of Ag-BG/CS in controlling pulp inflammation in vivo . The results showed that Ag-BG/CS induced stronger reparative dentin formation and enhanced preservation of vital pulp tissue when compared to the mineral trioxide aggregate (MTA) which is the currently used clinical standard. Ag-BG/CS also significantly increased the phosphorylation of p38 and ERK1/2(p42/44) of dental pulp cells, indicating that Ag-BG/CS enhanced pulpal repair through the mitogen-activated protein kinase (MAPK) pathway. This novel material may represent a superior solution for dental pulp-capping clinical scenarios with specific advantages for cases of early diffuse pulpitis in immature permanent teeth.
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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) | 0.000 | 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 | 0.001 | 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".