Impact of Crosslinking Degree on Chitosan and Oxidized Guar Gum‐Based Injectable Hydrogels for Biomedical Applications
Bibliographic record
Abstract
Abstract Evaluating the biodegradability and biocompatibility of hydrogels is essential for identifying materials suitable for biomedical applications. This study describes the fabrication of hydrogels utilizing physiological‐soluble chitosan (N‐succinyl chitosan, NSC) crosslinked with dialdehyde guar gum (Oxidized Galactomannan, OxGM) via the Schiff‐base reaction. Hydrogels with varying volumetric ratios of NSC/OxGM, resulting in distinct NH 2 /CHO functional group ratios and crosslinking degrees, underwent comprehensive characterization using Fourier‐transform infrared spectroscopy (FTIR), X‐ray photoelectron spectroscopy (XPS), thermogravimetric analysis (TGA), swelling, and scanning electron microscopy (SEM). Gelation time (tgel) is assessed by rheological analysis (tgel = G′ > G″), where tgel increased with higher crosslinking density, reaching a maximum value of ≈80 s. Biodegradation analysis in phosphate‐buffered saline (PBS) with lysozyme (13 mg L −1 ) revealed that the crosslinking degree significantly influenced degradation, with lower crosslinking associated with an elevated degradation profile. Moreover, cell viability assays with fibroblastic cells demonstrated minimal cytotoxicity, but an increase in free aldehyde groups correlated with decreased cell viability. For the 75C25C hydrogel, the compressive test yielded a Young's modulus value of 67.2 kPa (±8.5). These results imply that the hydrogels developed exhibit favorable biodegradability and biocompatibility, making them promising candidates for diverse biomedical applications.
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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) | 0.001 | 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".