An Injectable Kartogenin-Incorporated Hydrogel Supports Mesenchymal Stem Cells for Cartilage Tissue Engineering
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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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".