Synthesis and Characterization of Tunable Poly(Ethylene Glycol): Gelatin Methacrylate Composite Hydrogels
Bibliographic record
Abstract
Poly(ethylene glycol) (PEG) hydrogels are popular for cell culture and tissue-engineering applications because they are nontoxic and exhibit favorable hydration and nutrient transport properties. However, cells cannot adhere to, remodel, proliferate within, or degrade PEG hydrogels. Methacrylated gelatin (GelMA), derived from denatured collagen, yields an enzymatically degradable, photocrosslinkable hydrogel that cells can degrade, adhere to and spread within. To combine the desirable features of each of these materials we synthesized PEG-GelMA composite hydrogels, hypothesizing that copolymerization would enable adjustable cell binding, mechanical, and degradation properties. The addition of GelMA to PEG resulted in a composite hydrogel that exhibited tunable mechanical and biological profiles. Adding GelMA (5%-15% w/v) to PEG (5% and 10% w/v) proportionally increased fibroblast surface binding and spreading as compared to PEG hydrogels (p<0.05). Encapsulated fibroblasts were also able to form 3D cellular networks 7 days after photoencapsulation only within composite hydrogels as compared to PEG alone. Additionally, PEG-GelMA hydrogels displayed tunable enzymatic degradation and stiffness profiles. PEG-GelMA composite hydrogels show great promise as tunable, cell-responsive hydrogels for 3D cell culture and regenerative medicine applications.
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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) | 0.001 | 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".