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Record W2169992315 · doi:10.1098/rsfs.2013.0038

Introduction to cell–hydrogel mechanosensing

2014· review· en· W2169992315 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInterface Focus · 2014
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCellular Mechanics and Interactions
Canadian institutionsTrinity College
Fundersnot available
KeywordsComputer scienceData scienceBioinformaticsComputational biologyBiology

Abstract

fetched live from OpenAlex

The development of hydrogel-based biomaterials represents a promising approach to generating new strategies for tissue engineering and regenerative medicine. In order to develop more sophisticated cell-seeded hydrogel constructs, it is important to understand how cells mechanically interact with hydrogels. In this paper, we review the mechanisms by which cells remodel hydrogels, the influence that the hydrogel mechanical and structural properties have on cell behaviour and the role of mechanical stimulation in cell-seeded hydrogels. Cell-mediated remodelling of hydrogels is directed by several cellular processes, including adhesion, migration, contraction, degradation and extracellular matrix deposition. Variations in hydrogel stiffness, density, composition, orientation and viscoelastic characteristics all affect cell activity and phenotype. The application of mechanical force on cells encapsulated in hydrogels can also instigate changes in cell behaviour. By improving our understanding of cell-material mechano-interactions in hydrogels, this should enable a new generation of regenerative medical therapies to be developed.

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.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.811
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0000.001

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.

Opus teacher head0.014
GPT teacher head0.297
Teacher spread0.284 · 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