Digital light processing 3D printing of dual crosslinked meniscal scaffolds with enhanced physical and biological properties
Bibliographic record
Abstract
Abstract Regenerating damaged meniscal tissue remains a significant challenge due to the meniscus’ limited capacity for self-repair. Photocrosslinkable hydrogels, like gelatin methacryloyl (GelMA), offer a promising solution for meniscal regeneration by providing structural flexibility to accommodate the meniscus’ complex geometry while enabling the incorporation of bioactive molecules and cells. However, GelMA alone often lacks the mechanical robustness required for load-bearing applications. In this study, we introduce a dual-crosslinked GelMA scaffold, enhanced with tannic acid (TA), designed to replicate the mechanical properties of the native meniscus. By adjusting TA concentrations, we successfully fine-tuned the scaffold’s compressive modulus to match that of human meniscal tissue. This dual crosslinking not only improved mechanical strength but also resulted in a denser matrix with smaller pore sizes and reduced degradation and swelling rates. The optimized GelMA-TA formulation was 3D-printed into complex shapes, demonstrating its potential for producing patient-specific scaffolds. Beyond its mechanical benefits, the GelMA-TA scaffold exhibited excellent antioxidant and antibacterial properties. Human mesenchymal stem cells seeded onto the scaffold showed high viability, increased proliferation, and successful chondrogenic differentiation. Additionally, the GelMA-TA scaffold acted as an immunomodulatory biomaterial, suppressing pro-inflammatory responses in monocytes while promoting an anti-inflammatory, pro-regenerative M2a macrophage phenotype. These findings suggest that the GelMA-TA scaffold holds strong potential as a viable solution for meniscal tissue repair, offering both structural integrity and enhanced biological functionality. Graphical abstract
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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) | 0.002 | 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".