Starfish-Inspired Synergistic Reinforced Hydrogel Wound Dressing: Dual Responsiveness and Enhanced Bioactive Compound Delivery for Advanced Skin Regeneration and Management
Bibliographic record
Abstract
Effective wound management demands advanced dressings that protect while actively supporting healing. Traditional wound dressings often fall short of meeting the complex needs of skin repair. Inspired by the regenerative abilities of starfish, we developed a bionically engineered hydrogel designed to enhance wound healing. The hydrogel is synthesized through the coassembly of dopamine-modified cellulose nanofibers, chitosan, (3-aminobenzeneboronic acid)-grafted oxidized dextran, and poly(vinyl alcohol), utilizing dynamic Schiff base and boronic ester linkages. This innovative design imparts multifunctional properties, including injectability, 3D printability, antibacterial activity, self-adhesion, self-healing, antioxidant protection, and hemostasis, which emulate the defense mechanisms and regenerative processes of starfish. These characteristics work synergistically to reduce infection and oxidative stress and improve healing efficiency. Additionally, the hydrogel incorporates mangiferin and Vitamin C, which are released in a controlled manner in response to the wound’s microenvironment (pH and reactive oxygen species), promoting tissue regeneration and reducing inflammation. In vitro tests confirmed its dual responsiveness, while finite element modeling validated the controlled release of bioactive compounds. In vivo testing on a rat full-thickness wound model showed a 100% healing rate by day 13, significantly outperforming commercial alternatives. The hydrogel’s nontoxicity and advanced healing capabilities make it a promising solution for patients with critical healing needs, offering a comprehensive integration of natural biological processes and cutting-edge engineering.
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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) | 0.000 | 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) | 0.000 | 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".