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Record W4399939433 · doi:10.1021/acs.biomac.4c00095

Viscoelastic Supramolecular Hyaluronan-Peptide Cross-Linked Hydrogels

2024· article· en· W4399939433 on OpenAlexafffund
Spencer Zhao, Chang Xue, Darcy C. Burns, Molly S. Shoichet

Bibliographic record

VenueBiomacromolecules · 2024
Typearticle
Languageen
FieldMaterials Science
TopicSupramolecular Self-Assembly in Materials
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaGovernment of Ontario
KeywordsSelf-healing hydrogelsViscoelasticityPeptideBiophysicsExtracellular matrixSupramolecular chemistryChemistryStress relaxationHyaluronic acidFibroblastMaterials sciencePolymer chemistryMoleculeBiochemistryIn vitroComposite materialAnatomy

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.010

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.

Opus teacher head0.012
GPT teacher head0.288
Teacher spread0.277 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations8
Published2024
Admission routes2
Has abstractyes

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