Promoting oral mucosal wound healing using a DCS-RuB2A2 hydrogel based on a photoreactive antibacterial and sustained release of BMSCs
Bibliographic record
Abstract
The high occurrence rate and difficulties in symptom control are listed as the major problems of oral mucosal disease by medical professionals. Following the development of oral mucosal lesions, the oral microenvironment changes, immunity declines, and continuous bacterial stimulation causes wound infection. Traditional antibacterial drugs are ineffective for oral mucosal lesions. To overcome this problem, a light-responsive antibacterial hydrogel containing sustained-release BMSCs was inspired by the trauma environment in the oral cavity, which is different from that on the body surface since it mostly remains under dark conditions. In the absence of light, the hydrogel seals the wound to form a barrier, exerts a natural bacteriostatic effect, and prevents invasion by foreign bacteria. Simultaneously, mesenchymal stem cells are presented, and the released growth factors and other substances have excellent anti-inflammatory and angiogenic effects, which result in rapid repair of the damaged site. Under light conditions, after photo-induced shedding of the hydrogel, RuB2A exerts an antibacterial effect accompanied by degradation of the hydrogel. Results in a rat oral mucosal repair model demonstrate that DCS-RuB2A2-BMSCs could rapidly repair the oral mucosa within 4 days. Sequencing data provide ideas for further analysis of the intrinsic molecular mechanisms and signaling pathways. Taken together, our results suggest that this light-responsive antibacterial hydrogel loaded with BMSCs can be used for rapid wound repair and may advance the development of therapeutic strategies for the treatment of clinical oral mucosal defects.
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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".