Development and Characterization of an Injectable Alginate/Chitosan Composite Hydrogel Reinforced with Cyclic-RGD Functionalized Graphene Oxide for Potential Tissue Regeneration Applications
Bibliographic record
Abstract
Background: In tissue engineering, developing injectable hydrogels with tailored mechanical and bioactive properties remains a challenge. This study introduces an injectable hydrogel composite for soft tissue regeneration, composed of oxidized alginate (OA) and N-succinyl chitosan (NSC) cross-linked via Schiff base reaction, reinforced with graphene oxide (GOx) and cyclic arginylglycylaspartic acid (c-RGD). The objective was to create a multifunctional platform combining injectability, bioactivity, and structural stability. Methods: The OA/NSC/GOx-cRGD hydrogel was synthesized through Schiff base cross-linking (aldehyde-amine reaction). Characterization included FTIR (C=N bond at 1650 cm⁻¹), Raman spectroscopy (D/G bands at 1338/1567 cm⁻¹), SEM (porous microstructure), and rheological analysis (shear-thinning behavior). In vitro assays assessed fibroblast viability (MTT) and macrophage TNF-α secretion (ELISA), while ex-vivo injectability and retention were evaluated using chicken cardiac tissue. Results: The hydrogel exhibited shear-thinning behavior (viscosity: 10 to <1 Pa·s) and elastic-dominated mechanics (G′ > G″), ensuring injectability. SEM revealed an interconnected porous structure mimicking native extracellular matrix. Fibroblast viability remained ≥95%, and TNF-α secretion in macrophages decreased by 80% (30 vs. 150 pg/μL in controls), demonstrating biocompatibility and anti-inflammatory effects. The hydrogel adhered stably to cardiac tissue without leakage. Conclusions: The OA/NSC/GOx-cRGD composite integrates injectability, bioactivity, and structural stability, offering a promising scaffold for tissue regeneration. Its modular design allows further functionalization with peptides or growth factors. Future work will focus on translational applications, including scalability and optimization for dynamic biological environments.
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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".