Mussel-inspired cross-linking mechanisms enhance gelation and adhesion of multifunctional mucin-derived hydrogels
Bibliographic record
Abstract
Mucus supports human health by hydrating, lubricating, and preventing infection of wet epithelial surfaces. The beneficial material properties and bioactivity of mucus stem from glycoproteins called mucins, motivating the development of mucin-derived hydrogels for wound dressings and antifouling coatings. However, these applications require robust gelation and adhesion to a wide range of substrates. Inspired by the chemical cross-linking and water-tolerant adhesion of marine mussel adhesive structures, we use catechol-thiol bonding to drive gelation of native mucin proteins and synthetic mucin-inspired polymers, forming soft, adhesive hydrogels that can be coated onto diverse surfaces. The gelation dynamics and adhesive properties can be systematically tuned by varying the hydrogel composition, polymer architecture, and thiol availability, with gelation timescales adjustable from seconds to hours, and values of elastic modulus, failure stress, and debonding work spanning orders of magnitude. We demonstrate the functionality of these gels in two applications: as tissue adhesives, using porcine skin as a proxy for human skin, and as bioactive surface coatings to prevent bacterial colonization. The results highlight the potential of catechol-thiol cross-linking as a versatile platform for engineering multifunctional glycoprotein hydrogels with applications in wound repair and antimicrobial surface 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".