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Record W4407421619 · doi:10.1002/adma.202408616

Dynamic and Reversible Tuning of Hydrogel Viscoelasticity by Transient Polymer Interactions for Controlling Cell Adhesion

2025· article· en· W4407421619 on OpenAlexaff
Shane Scott, Maria Villiou, Federico Colombo, Angeles De la Cruz‐García, Leon Tydecks, Lotta Toelke, Katharina Siemsen, Christine Selhuber‐Unkel

Bibliographic record

VenueAdvanced Materials · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCellular Mechanics and Interactions
Canadian institutionsMcMaster University
FundersBundesministerium für Bildung und ForschungVolkswagen FoundationH2020 European Research CouncilDeutsche Forschungsgemeinschaft
KeywordsSelf-healing hydrogelsViscoelasticityMaterials scienceMechanobiologyAdhesionPolymerMechanotransductionEthylene glycolNanotechnologyCell adhesionPEG ratioTissue engineeringBiophysicsChemical engineeringBiomedical engineeringPolymer chemistryComposite materialCell biology

Abstract

fetched live from OpenAlex

Cells are highly responsive to changes in their mechanical environment, influencing processes such as stem cell differentiation and tumor progression. To meet the growing demand for materials used for high throughput mechanotransduction studies, simple means of dynamically adjusting the environmental viscoelasticity of cell cultures are needed. Here, a novel method is presented to dynamically and reversibly control the viscoelasticity of naturally derived polymer hydrogels through interactions with poly (ethylene glycol) (PEG). Interactions between PEG and hydrogel polymers, possibly involving hydrogen bonding, stiffen the hydrogel matrices. By dynamically changing the PEG concentration of the solution in which polymer hydrogels are incubated, their viscoelastic properties are adjusted, which in turn affects cell adhesion and cytoskeletal organization. Importantly, this effects is reversible, providing a cost-effective and simple strategy for dynamically adjusting the viscoelasticity of polymer hydrogels. This method holds promise for applications in mechanobiology, biomedicine, and the life sciences.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
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 score0.115
Threshold uncertainty score0.363

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.004
GPT teacher head0.250
Teacher spread0.246 · 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; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations18
Published2025
Admission routes1
Has abstractyes

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