Injectable Dual‐Crosslinked Poly(oligo(Ethylene Glycol) Methacrylate) Hydrogels Inspired by Mussel Adhesion for Cutaneous Wound Healing and Functional Tissue Regeneration
Bibliographic record
Abstract
Traditional dermal wound closure methods, such as sutures and staples, are invasive, causing soft tissue trauma, increasing the likelihood of inflammation and infections. Alternatively, while existing tissue adhesives can seal and adhere to wounds, they may cause immunogenic responses, tissue necrosis, restricted movement, and wound disruption upon removal, leading to secondary injuries and scarring. Herein, injectable poly(oligo(ethylene glycol) methyl ether methacrylate) (POEGMA)-dopamine (DA) hydrogels co-crosslinked via hydrazone linkages and dopamine self-polymerization are fabricated that promote high water retention, effective tissue adhesion, re-epithelialization, and functional skin regeneration. The dual crosslinking mechanism allows for gelation as fast as 24 s (enabling injection and rapid filling of irregularly-shaped wounds) while achieving compressive moduli of up to 37 kPa and skin adhesion strengths of up to 3.3 kPa. In a 14-day stented mouse skin wound model, the POEGMA-DA hydrogels induce no significant inflammation, effective tissue adhesion, and promote tissue regeneration, including enhanced collagen remodelling, 3-5× higher hair follicle, ≈5-7× higher sebaceous gland and 1.5-1.7× higher blood vessel regeneration at the excision site compared to untreated wounds. These hydrogels represent an alternative nontoxic wound closure system that mimics the soft skin tissue environment to promote regeneration after acute superficial dermal wounds.
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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".