Characterization and Application of Carboxymethyl Chitosan-Based Bioink in Cartilage Tissue Engineering
Bibliographic record
Abstract
Chitosan is a promising natural biomaterial for biological application; however, the weak mechanical performance of pristine chitosan limits its further utilization in hard tissue (such as cartilage) engineering. In this study, a chitosan-based 3D printing bioink with suitable mechanical properties was developed as 3D bioprinting ink for chondrocyte support. Chitosan was first modified by ethylenediaminetetraacetic acid (EDTA) to provide more carboxyl groups followed by physical crosslinking with calcium to increase the hydrogel strength. Dynamic mechanical analysis was carried out to evaluate viscoelastic properties with the addition of modified chitosan. A bioink with a combination of modified and pristine chitosan was formulated for scaffold fabrication via 3D bioprinting technique. Furthermore, cell viability, cell proliferation, and expression of chondrogenic markers were evaluated in vitro in chondrocytes loaded on the bioink. The novel bioink exhibited a favorable mechanical property and promoted cell attachment and chondrogenic gene expression in chondrocytes. Based on these results, we can conclude that the presented bioink could qualify for use in 3D bioprinting in cartilage tissue engineering.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".