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Record W2055759385 · doi:10.1115/sbc2008-192940

Comparison of Three Material Models to Predict the Time-Dependent Deformation of a Single Cell Under Micropipette Aspiration

2008· article· en· W2055759385 on OpenAlexaff
Ruogang Zhao, Kristine Wyss, Craig A. Simmons

Bibliographic record

VenueASME 2008 Summer Bioengineering Conference, Parts A and B · 2008
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCellular Mechanics and Interactions
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPipetteViscoelasticityHyperelastic materialRheologyDeformation (meteorology)Materials scienceCreepMechanicsRotational symmetryFinite element methodStandard linear solid modelComposite materialBiomedical engineeringStructural engineeringPhysicsChemistryEngineering

Abstract

fetched live from OpenAlex

Micropipette aspiration is an experimental technique that is used widely to measure the mechanical properties of single cells [1]. The viscoelastic properties of the probed cell are often estimated by fitting experimental data to a three-parameter standard linear solid (SLS) half-space model (e.g., [1]). However, this analytical model does not account for the large strains that can occur with micropipette aspiration. This limitation has motivated the development of numerical methods to interpret the experimental data. For example, Zhou [2] implemented a material model combining a hyperelastic neo-Hookean material and a viscoelastic SLS material in an axisymmetric finite element (FE) model to simulate large strain micropipette aspiration of a suspended cell. The time-dependent creep deformation of cells has also been described by power-law rheology [3]; this material model has been applied to micropipette aspiration of nuclei [4], but not whole cells.

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 score0.097
Threshold uncertainty score0.344

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 head0.041
GPT teacher head0.239
Teacher spread0.198 · 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

Citations0
Published2008
Admission routes1
Has abstractyes

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