Tailoring Injectable Chitosan‐Cellulose Quaternary Hydrogels Through Room‐Temperature Diels‐Alder for Accelerating Wound Healing
Bibliographic record
Abstract
Abstract The development of injectable hydrogel wound dressings with inherent antibacterial and antioxidant properties for treating full‐thickness skin injuries represents one of the most promising solutions in clinical care. In this study, a series of injectable hydrogels with tunable gelation times is designed and prepared through the Diels‐Alder (DA) reaction between diene‐functionalized quaternary cellulose and maleimide‐grafted quaternary chitosan. The high degree of substitution of diene and maleimide groups on these polysaccharides enables the formation of highly crosslinked hydrogels. By fine‐tuning the structure of the dienes, the gelation time can be reduced to 4 min at 37 ° C. Both precursors and hydrogels showed strong antibacterial activity with killing efficacies >98% against E. coli and S. aureus . Meanwhile, the encapsulation of epigallocatechin gallate (EGCG, a green tea derivative) endowed the hydrogel with strong antioxidant performance. Moreover, the hydrogel exhibited good biocompatibility with 100% cell viability for NIH3T3 cells. In vivo wound healing assessment in a mouse full‐thickness skin defect model revealed that the optimum hydrogel exhibited significant collagen deposition and vascularization, as well as remarkable regenerative wound healing performance, demonstrating its great potential as a bio‐based wound dressing for improving wound healing.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 teacher head, 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".