Enhanced Mechanical Properties of Injectable Chitosan–Guar Gum Hydrogel Reinforced with Bacterial Cellulose Nanofibers for Tissue Engineering Applications
Bibliographic record
Abstract
Demand is increasing for devices capable of regenerating or replacing damaged tissues, highlighting the need for advanced biomaterials. Hydrogels are promising for regenerative medicine, but often lack mechanical strength. To overcome this, a novel nanocomposite hydrogel based on N ‐succinyl chitosan (NSC) and oxidized guar gum (OxGG) reinforced with bacterial cellulose nanofibers (BCFs) is developed. These hydrogels are produced through a simple and safe Schiff‐base reaction and hydrogen bonding, avoiding potentially toxic cross‐linker or external stimuli. Chemical characterization is performed using Fourier transform infrared, X‐ray photoelectron spectroscopy, and thermogravimetric analysis. Scanning electron microscopy reveals significant changes in the hydrogel's internal structure after BCF incorporation, resulting in a more compact and organized porous matrix. This modification also reduces phosphate buffer solution uptake, modifying the swelling behavior of the hydrogel, due to the formation of a rigid polymeric network. Both hydrogels exhibit fast gelation times (<30 s), ensuring injectability for minimally invasive therapy. NSC/OxGG/BCF hydrogels exhibit enhanced mechanical properties, with storage and Young's moduli of 3.97 and 197.1 kPa, respectively—more than double the values observed for NSC/OxGG hydrogels. Additionally, hydrogels are noncytotoxic to neonatal human dermal fibroblast cells (cell viability > 70%). These results suggest that NSC/OxGG/BCF hydrogels demonstrate promising potential for tissue engineering 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".