Multifunctional Double‐Network Hydrogel with Porous, Adhesive, and Immunomodulatory Properties for Minimally Invasive Soft Tissue Repair
Bibliographic record
Abstract
The minimally invasive repair of soft tissue defects remains a major clinical challenge due to the lack of biomaterials that simultaneously fulfill key requirements, including extrudability, strong adhesion, seamless integration, bioactivity, and appropriate mechanical properties. Here, a multifunctional double-network composite hydrogel is presented that is synthesized from modified hyaluronic acid (HA) and silk fibroin (SF) through a stepwise gelation process. The incorporation of ferric ions enables dynamic crosslinking of dopamine-grafted HA, resulting in the rapid formation of adhesive hydrogels with microporous structures. Sonication-induced β-sheets in SF form a secondary network, enhancing mechanical strength with reduced swelling and degradation. The inclusion of curcumin-loaded particles within the hydrogel promotes anti-inflammatory and antifibrotic activity by promoting macrophage polarization toward the reparative M2 phenotype and reducing TGF-β-induced fibroblast differentiation and collagen deposition. In situ injectability and printability of the hydrogel are demonstrated in ex vivo porcine vocal fold models. In vitro and in vivo biological evaluations in rat models confirm the cytocompatibility of the hydrogel and its ability to support cell penetration. Mechanical, structural, and biological results collectively support the applicability of this hydrogel as a minimally invasive solution for soft tissue defect repair, particularly in mechanically dynamic tissues such as the human vocal folds.
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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".