Digital light processing 3D printing of dual crosslinked meniscal scaffolds with enhanced physical and biological properties
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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