Gelatin methacryloyl bioinks for bioprinting nasal cartilage: Balancing mechanical integrity and extracellular matrix formation
Bibliographic record
Abstract
Gelatin methacryloyl (GelMA) is widely recognized as a versatile hydrogel, though few studies have examined its role in nasal cartilage engineering. In this study, we investigated how variations in GelMA concentration and lysyl oxidase-like 2 (LOXL2) supplementation would affect mechanical properties, extracellular matrix (ECM) deposition, and human nasoseptal chondrocyte remodeling in 3D bioprinted constructs. Using human serum-supplemented media to enhance clinical feasibility, we evaluated a Good Manufacturing Practices (GMP)-grade GelMA at 5, 10, and 15 % w / v . To improve mechanical properties, we investigated LOXL2's potential to enhance crosslinking of newly synthesized collagen, an approach not previously evaluated in gelatin biomaterials. After six weeks, higher GelMA concentrations increased stiffness, as demonstrated by suture pull-out, three-point bending, and compressive equilibrium moduli. However, improved mechanical performance accompanied a reduction in ECM deposition and elevated catabolic gene expression. These findings suggest that cells encapsulated in stiffer, more densely crosslinked constructs exhibited an altered anabolic-catabolic balance and chondrocyte behavior These results underscore the need to balance mechanical integrity with a microenvironment conducive to collagen synthesis and remodeling. By examining how GelMA concentration, crosslinking, and human serum-based conditions influence ECM deposition, this work advances macromolecular interactions in GelMA-based constructs in the development of clinically translatable cartilage grafts. • Balancing stiffness and remodeling is key for nasal cartilage scaffold translation • High gelatin methacryloyl enhances stiffness but lowers matrix formation • Catabolic gene expression is upregulated at high stiffness • Lysyl oxidase-like 2 does not improve mechanical performance under these conditions • Increased crosslinking density hinders remodeling and lysyl oxidase-like 2 function
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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) | 0.000 | 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 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".