Resveratrol loaded native silk fiber-sericin hydrogel double interpenetrating bioactive wound dressing facilitates full-thickness skin wound healing
Bibliographic record
Abstract
Abstract Wound repair is challenging for traditional wound dressings. New bioactive dressings need to be developed urgently. Herein, we reported a highly bioactive silk protein wound dressing (SPD) with natural silk fiber-sericin hydrogel interpenetrating double network structure, which combines the dual characteristics of natural silk and sericin hydrogel. Silk fiber scaffolds were secreted directly from silkworms bred by regulating their spinning behaviors. Sericin in SPD is obtained by dissolving silkworm cocoons at high temperature and high pressure, while it remains intact activities to self-assemble a hydrogel. To explore the effect of SPD, we first systematically evaluated its physicochemical properties and biological activities in vitro . The SPD exhibits high porosity, prominent mechanical strength, pH-responsive degradability, and excellent anti-oxidation and cell compatibility. Besides, SPD can load and maintain long-term drug release. Based on the satisfactory performance of SPD in vitro , effective in vivo treatment was achieved in a mouse full-thickness wound model, as demonstrated by a significantly accelerated wound healing process, promote the regeneration of hair follicles and sebaceous glands, increased expression of vascular endothelial growth factor, and reduced inflammation. Further, resveratrol was loaded into SPD to enhance the effects of anti-oxidation and anti-inflammation for wound healing. Our investigation shows that SPD with excellent physicochemical and biological properties applied in a murine full-thickness skin wound model resulted in remarkable and efficient acceleration of healing process, which may inspire the design of new, effective, and safer medical materials for tissue regeneration.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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; both teacher heads agree on what is shown here.
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".