Biomimetic gradient hydrogels for tissue engineering
Bibliographic record
Abstract
During tissue morphogenesis and homeostasis, cells experience various signals in their environments, including gradients of physical and chemical cues. Spatial and temporal gradients regulate various cell behaviours such as proliferation, migration, and differentiation during development, inflammation, wound healing, and cancer. One of the goals of functional tissue engineering is to create microenvironments that mimic the cellular and tissue complexity found in vivo by incorporating physical, chemical, temporal, and spatial gradients within engineered three-dimensional (3D) scaffolds. Hydrogels are ideal materials for 3D tissue scaffolds that mimic the extracellular matrix (ECM). Various techniques from material science, microscale engineering, and microfluidics are used to synthesise biomimetic hydrogels with encapsulated cells and tailored microenvironments. In particular, a host of methods exist to incorporate micrometer to centimetre scale chemical and physical gradients within hydrogels to mimic the cellular cues found in vivo. In this review, we draw on specific biological examples to motivate hydrogel gradients as tools for studying cell-material interactions. We provide a brief overview of techniques to generate gradient hydrogels and showcase their use to study particular cell behaviours in two-dimensional (2D) and 3D environments. We conclude by summarizing the current and future trends in gradient hydrogels and cell-material interactions in context with the long-term goals of tissue engineering.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".