A catechol-chitosan-based adhesive and injectable hydrogel resistant to oxidation and compatible with cell therapy
Bibliographic record
Abstract
Injectable hydrogels designed for cell therapy need to be adhesive to the surrounding tissues to maximize their retention and the communication between the host and the encapsulated cells. Catechol grafting is an efficient and well-known strategy to improve the adhesive properties of various polymers, including chitosan. However, catechol groups are also known to be cytotoxic as they oxidize into quinones in alkaline environments. Usually, hydrogels made from catechol-grafted chitosan (cat-CH) oxidize quickly, which tends to limit adhesion and prevent cell encapsulation. In this work, we limited oxidation and improved the cytocompatibility of cat-CH hydrogels by grafting chitosan with dihydroxybenzoic acid (DHBA), a small cat-bearing molecule known to have a high resistance to oxidation. We show that DHBA-grafted CH (dhba-CH) oxidized significantly slower and to a lesser extent that cat-CH made with hydrocaffeic acid (hca-CH). By combining dhba-CH with sodium bicarbonate and phosphate buffer, we fabricated thermosensitive injectable hydrogels with higher mechanical properties, quicker gelation and significantly lower oxidation than previously designed cat-CH systems. The resulting gels are highly adhesive on inorganic substrates and support L929 fibroblast encapsulation with high viability (≥90% after 24 hours), something that was not possible in any previously designed cat-CH gel system. These properties make the dhba-CH hydrogels excellent candidates for minimally invasive and targeted cell therapy in applications that require high adhesive strength.
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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) | 0.001 | 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".