Mollusk Glue Inspired Mucoadhesives for Biomedical Applications
Bibliographic record
Abstract
Chitosan (CH), partially N-deacetylated chitin, is a biodegradable and biocompatible polymer that has shown great potential in drug delivery and tissue engineering applications. Although bioadhesive, CH has limited mucoadhesion in wet conditions due to weak interactions with biological surfaces. DOPA (3,4-dihydroxy-L-phenylalanine), a catechol-containing molecule naturally present in marine mussel foot proteins, has been shown to increase the mucoadhesion of several polymers. We report here a simple and bioinspired approach to enhance CH mucoadhesion in wet conditions by preparing mixed hydrogels including CH and different catechol-containing compounds, namely DOPA, hydrocaffeic acid (HCA), and dopamine (DA). We characterized the hydrogels for their swelling, release kinetics of the catechol compounds, and mucoadhesive strength to rabbit small intestine. The swelling of the hydrogels was pH dependent with maximum swelling at pH 1. The hydrogel swelling was higher in the presence of the DOPA and DA but lower in the presence of HCA. HCA/CH hydrogel also showed the slowest catechol release, most likely due to electrostatic interactions between CH and HCA. Lower hydrogel swelling and slower HCA release resulted in increased mucoadhesion: HCA/CH showed more than 2-fold enhancement of mucoadhesion to rabbit small intestine compared to CH alone. Since it is known that catechol compounds can be oxidized, we analyzed the oxidation of DOPA, HCA, and DA at different pH values and its effect on mucoadhesion. We found that oxidation occurring before contact with the intestinal mucosa did not improve mucoadhesion, while oxidation occurring during the contact further increased the mucoadhesion of HCA/CH hydrogels. These results show that mucoadhesion of CH hydrogels can be increased with a simple bioinspired approach, which has the potential to be applied to other polymers since it does not require any chemical modification.
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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) | 0.000 | 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 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".