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Gelatin methacryloyl bioinks for bioprinting nasal cartilage: Balancing mechanical integrity and extracellular matrix formation

2025· article· en· W4409839446 on OpenAlexafffund
Alexander C. Perry, Xiaoyi Lan, Zhiyao Ma, Haoyang Li, Yifu Chu, Aillette Mulet‐Sierra, Melanie Kunze, Lindsey Westover, Lingyun Chen, Khalid Ansari, Martin Osswald, Hadi Seikaly, Adetola B. Adesida

Bibliographic record

VenueInternational Journal of Biological Macromolecules · 2025
Typearticle
Languageen
FieldEngineering
Topic3D Printing in Biomedical Research
Canadian institutionsUniversity of Alberta
FundersInstitute of AgingCanadian Institutes of Health ResearchUniversity of Alberta
KeywordsGelatinExtracellular matrixCartilageChemistryMatrix (chemical analysis)BiophysicsAnatomyBiochemistryBiology

Abstract

fetched live from OpenAlex

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

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.000
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.024
GPT teacher head0.326
Teacher spread0.302 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2025
Admission routes2
Has abstractyes

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