In situ forming microporous gelatin methacryloyl hydrogel scaffolds from thermostable microgels for tissue engineering
Bibliographic record
Abstract
Abstract Converting biopolymers to extracellular matrix (ECM)‐mimetic hydrogel‐based scaffolds has provided invaluable opportunities to design in vitro models of tissues/diseases and develop regenerative therapies for damaged tissues. Among biopolymers, gelatin and its crosslinkable derivatives, such as gelatin methacryloyl (GelMA), have gained significant importance for biomedical applications due to their ECM‐mimetic properties. Recently, we have developed the first class of in situ forming GelMA microporous hydrogels based on the chemical annealing of physically crosslinked GelMA microscale beads (microgels), which addressed several key shortcomings of bulk (nanoporous) GelMA scaffolds, including lack of interconnected micron‐sized pores to support on‐demand three‐dimensional‐cell seeding and cell–cell interactions. Here, we address one of the limitations of in situ forming microporous GelMA hydrogels, that is, the thermal instability (melting) of their physically crosslinked building blocks at physiological temperature, resulting in compromised microporosity. To overcome this challenge, we developed a two‐step fabrication strategy in which thermostable GelMA microbeads were produced via semi‐photocrosslinking, followed by photo‐annealing to form stable microporous scaffolds. We show that the semi‐photocrosslinking step (exposure time up to 90 s at an intensity of ~100 mW/cm2 and a wavelength of ~365 nm) increases the thermostability of GelMA microgels while decreasing their scaffold forming (annealing) capability. Hinging on the tradeoff between microgel and scaffold stabilities, we identify the optimal crosslinking condition (exposure time ~60 s) that enables the formation of stable annealed microgel scaffolds. This work is a step forward in engineering in situ forming microporous hydrogels made up from thermostable GelMA microgels for in vitro and in vivo applications at physiological temperature well above the gelatin melting point.
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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".