Viscoelastic Supramolecular Hyaluronan-Peptide Cross-Linked Hydrogels
Bibliographic record
Abstract
Viscoelasticity plays a key role in hydrogel design. We designed a physically cross-linked hydrogel with tunable viscoelasticity, comprising supramolecular-assembled peptides coupled to hyaluronan (HA), a native extracellular matrix component. We then explored the structural and molecular mechanisms underlying the mechanical properties of a series of these HA-peptide hydrogels. By modifying the peptide sequence, we modulated both long- and short-time stress relaxation rates as a way to target viscoelasticity with limited impact on stiffness, leading to gels that relax up to 60% of stress in 10 min. Gels with the highest viscoelasticity exhibited large mesh sizes and β-sheet secondary structures. The stiffness of the gel correlated with hydrogen bonding between the peptide chains. These gels are cytocompatible: highly viscoelastic gels that mimic the native skin microenvironment promote dermal fibroblast cell spreading. Moreover, HA-peptide gels enabled cell encapsulation, as shown with primary human T cells. Overall, these physically-cross-linked hydrogels enable tunable viscoelasticity that can be used to modulate cell morphology.
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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".