Towards tunable mechanical properties of in situ gelling chitosan hydrogels: impact of macromolecular structure including pattern of acetylation
Bibliographic record
Abstract
Injectable in situ gelling physically crosslinked chitosan (CH) hydrogels allowing cell encapsulation are appealing for biomedical applications. However, the variability of the CH source is one of the major difficulties in ensuring their reproducibility. We investigated here the effect of the CH degree and pattern of acetylation (DA and PA) on its final physicochemical properties. Hydrogels made from re-acetylated CH presenting statistical repartition of repeat units along the chains, with DA of 35, 10 and 1 %, were compared to a commercial CH with DA of 10 % differing by their PA. WAXS analysis showed different crystalline signatures depending on the PA of CH samples. Rheometry revealed faster gelation kinetics, but lower final modulus for hydrogels made of commercial versus statistical CH of same DA. Both also differed in terms of porosity and stability in solution. Hydrogel stiffness from 60 to 0.3 kPa were obtained by varying DA and PA. Hydrogels with the lowest DA were the most stable in solutions. Encapsulated L929 fibroblasts presented similar increasing metabolic activity over 7 days of culture within all hydrogels. This work demonstrates the relevance of controlling chitosan DA and PA for the generation of reproducible hydrogels with tunable final mechanical properties for targeted bio-applications. Statement hypothesis Not only the degree of acetylation (DA, i.e. , the molar fraction of N -acetyl D-glucosamine units), but also the pattern of acetylation (PA; the repartition of the acetylated/deacetylated residue sequences along the chain), can influence the mechanical properties, the porosity, and the stability of physical in situ gelling CH hydrogels, therefore the behavior of encapsulated cells. Indeed, when the gel is formed using weak bases (here a combination of β-glycerophosphate and sodium hydrogen carbonate), the physical crosslinking density between CH chains can be influenced by the DA and the PA due to the resulting variation of NH 2 moieties repartition. We expect that for CH presenting a higher DA, weak gels will be generated due to reduced physical interactions established between protonated NH3+ groups and the weak base. Also, we expect to generate more stable constructs with CH presenting lower DA. Furthermore, for the same DA, the chemical process used for obtaining CH ( i.e. , from the reacetylation of low DA CH or deacetylation of chitin under heterogeneous or homogenous conditions) yields CHs with different PA, which we presume to significantly affect final structural and mechanical properties of the obtained hydrogels. Understanding the impact of the CH macromolecular structure on its injectable in situ gelling hydrogel form is fundamental for the generation of suitable and reproducible CH-based hydrogel materials in biomedical applications.
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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 | 0.000 | 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 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".