How to Design Catechol-Containing Hydrogels for Cell Encapsulation Despite Catechol Toxicity
Bibliographic record
Abstract
Catechol (cat) is a highly adhesive diphenol that can be chemically grafted to polymers such as chitosan (CH) to make them adhesive as well. However, catechol-containing materials experimentally show a large variability of toxicity, especially in vitro. While it is unclear how this toxicity emerges, most concerns are directed toward the oxidation of catechol into quinone that releases reactive oxygen species (ROS) which can, in turn, cause cell apoptosis through oxidative stress. To better understand the mechanisms at play, we examined the leaching profiles, hydrogen peroxide (H 2 O 2 ) production, and in vitro cytotoxicity of several cat-chitosan (cat-CH) hydrogels that were prepared with different oxidation levels and cross-linking methods. To create cat-CH with different propensities toward oxidation, we grafted either hydrocaffeic acid (HCA, more prone to oxidation) or dihydrobenzoic acid (DHBA, less prone to oxidation) to the backbone of CH. Hydrogels were cross-linked either covalently, using sodium periodate (NaIO 4 ) to trigger oxidative cross-linking, or physically, using sodium bicarbonate (SHC). While using NaIO 4 as a cross-linker increased the oxidation levels of the hydrogels, it also significantly reduced in vitro cytotoxicity, H 2 O 2 production, and catechol and quinone leaching in the media. For all gels tested, cytotoxicity could be directly related to the release of quinones rather than H 2 O 2 production or catechol release, showing that oxidative stress may not be the main reason for catechol cytotoxicity, as other pathways of quinone toxicity come into play. Results also suggest that the indirect cytotoxicity of cat-CH hydrogels fabricated through carbodiimide chemistry can be reduced if (i) catechol groups are chemically bound to the polymer backbone to prevent leaching or (ii) the chosen cat-bearing molecule has a high resistance to oxidation. Coupled with the use of other cross-linking chemistries or more efficient purification methods, these strategies can be adopted to synthesize various types of cytocompatible cat-containing scaffolds.
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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 | 0.001 |
| 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".