An Injectable Kartogenin-Incorporated Hydrogel Supports Mesenchymal Stem Cells for Cartilage Tissue Engineering
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Bibliographic record
Abstract
BACKGROUND: Cartilage defects and injuries often lead to osteoarthritis, posing significant challenges for cartilage repair. Traditional treatments have limited efficacy, necessitating innovative therapeutic strategies. This study aimed to develop an injectable hydrogel-based tissue engineering construct to enhance cartilage regeneration by combining mesenchymal stem cells (MSCs) and the small molecule drug kartogenin (KGN). METHODS: An injectable hydrogel was synthesized by crosslinking carboxymethyl chitosan (CMC) with aldehyde-modified cellulose nanocrystals (DACNCs). KGN was incorporated into the hydrogel during crosslinking to achieve sustained drug release. Three hydrogels with varying CMC/DACNC molar ratios (MR = 0.11, 0.22, and 0.33) were developed and characterized for their structural, mechanical, and biocompatible properties. The hydrogel with the optimal ratio (MR = 0.33) was further evaluated for its ability to support MSC viability and differentiation in vitro. Additionally, signaling pathways (TGF-β, FOXO, and PI3K-AKT) were investigated to elucidate the underlying mechanisms. In vivo efficacy was assessed using a rabbit femoral trochlear cartilage defect model. RESULTS: The hydrogel with a higher CMC/DACNC molar ratio (MR = 0.33) exhibited increased compressive modulus, a reduced swelling rate, and superior biocompatibility, effectively promoting MSC differentiation in vitro. Signaling pathway analysis revealed activation of the TGF-β, FOXO, and PI3K-AKT pathways, suggesting enhanced chondrogenic potential. In vivo experiments demonstrated that the KGN-MSC-encapsulated hydrogel significantly improved cartilage repair. CONCLUSIONS: The injectable CMC/DACNC hydrogel, combined with KGN and MSCs, synergistically enhanced cartilage regeneration both in vitro and in vivo. This study highlights the potential of this hydrogel as a promising scaffold for cartilage tissue engineering, offering a novel therapeutic approach for cartilage defects and injuries.
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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 it