Designing Hydrogels for 3D Cell Culture Using Dynamic Covalent Crosslinking
Bibliographic record
Abstract
Designing simple biomaterials to replicate the biochemical and mechanical properties of tissues is an ongoing challenge in tissue engineering. For several decades, new biomaterials have been engineered using cytocompatible chemical reactions and spontaneous ligations via click chemistries to generate scaffolds and water swollen polymer networks, known as hydrogels, with tunable properties. However, most of these materials are static in nature, providing only macroscopic tunability of the scaffold mechanics, and do not reflect the dynamic environment of natural extracellular microenvironment. For more complex applications such as organoids or co-culture systems, there remain opportunities to investigate cells that locally remodel and change the physicochemical properties within the matrices. In this review, advanced biomaterials where dynamic covalent chemistry is used to produce stable 3D cell culture models and high-resolution constructs for both in vitro and in vivo applications, are discussed. The implications of dynamic covalent chemistry on viscoelastic properties of in vitro models are summarized, case studies in 3D cell culture are critically analyzed, and opportunities to further improve the performance of biomaterials for 3D tissue engineering are discussed.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".