Effect of sterilization treatment on mechanical properties, biodegradation, bioactivity and printability of GelMA hydrogels
Bibliographic record
Abstract
Gelatin methacryloyl (GelMA) hydrogel scaffolds and GelMA-based bioinks are widely used in tissue engineering and bioprinting due to their ability to support cellular functions and new tissue development. Unfortunately, while terminal sterilization of the GelMA is a critical step for translational tissue engineering applications, it can potentially cause thermal or chemical modifications of GelMA. Thus, understanding the effect of terminal sterilization on GelMA properties is an important, though often overlooked, aspect of material design for translational tissue engineering applications. To this end, we characterized the effects of FDA-approved terminal sterilization methods (autoclaving, ethylene oxide treatment, and gamma (γ)-irradiation) on GelMA prepolymer (bioink) and GelMA hydrogels in terms of the relevant properties for biomedical applications, including mechanical strength, biodegradation rate, cell culture in 2D and 3D, and printability. Autoclaving and ethylene oxide treatment of the GelMA decreased the stiffness of the hydrogel, but the treatments did not modify the biodegradation rate of the hydrogel; meanwhile, γ-irradiation increased the stiffness, reduced the pore size and significantly slowed the biodegradation rate. None of the terminal sterilization methods changed the 2D fibroblast or endothelial cell adhesion and spreading. However, ethylene oxide treatment significantly lowered the fibroblast viability in 3D cell culture. Strikingly, γ-irradiation led to significantly reduced ability of the GelMA prepolymer to undergo sol-gel transition. Furthermore, printability studies showed that the bioinks prepared from γ-irradiated GelMA had significantly reduced printability as compared to the GelMA bioinks prepared from autoclaved or ethylene oxide treated GelMA. These results reveal that the choice of the terminal sterilization method can strongly influence important properties of GelMA bioink and hydrogel. Overall, this study provides further insight into GelMA-based material design with consideration of the effect of terminal sterilization.
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 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) | 0.001 | 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) | 0.001 | 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".