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Micropipet Aspiration of the Human Neutrophil

2003· book-chapter· en· W126322434 on OpenAlexfundno aff
Jeanie L. Drury, Micah Dembo

Bibliographic record

VenueBirkhäuser Basel eBooks · 2003
Typebook-chapter
Languageen
FieldMedicine
TopicCell Adhesion Molecules Research
Canadian institutionsnot available
FundersUniversity of British ColumbiaBoston Scientific Corporation
KeywordsSurface tensionShear thinningViscosityDimensionless quantityMechanicsNewtonian fluidRADIUSRheologyReynolds numberShear (geology)Non-Newtonian fluidChemistryThermodynamicsMaterials scienceComposite materialPhysicsTurbulence

Abstract

fetched live from OpenAlex

Micropipet aspiration has been widely employed to study the mechanical behavior of passive neutrophils. The observed dynamics have been rationalized in terms of mechanical models that describe these cell as “slippery droplets” of viscous fluid enclosed by a surface or cortical tension. Here, a low Reynolds number hydrodynamics code based on the finite element method was utilized to analyze several such fluid models (e.g., Newtonian and shear thinning). We investigated the behavior of these models as a function of surface tension, droplet radius, viscosity, aspiration pressure, and pipet radius. In addition, for the simplest case of a Newtonian fluid droplet, we tabulated a dimensionless factor, M, which was utilized to approximate the apparent viscosity of the neutrophil. This analysis resulted in a useful method by which rheological parameters could be determined and subsequent models tested. Overall, of the models tested, the mechanical behavior of the passive neutrophil during micropipet aspiration can be best represented by a fluid droplet consisting of a shear thinning bulk viscosity, a shear thinning dilatational surface viscosity, and an area-dependent surface tension.

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 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: Other · Consensus signal: Other
Teacher disagreement score0.837
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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.280
Teacher spread0.240 · 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.

Study designNot applicable
Domainnot available
GenreOther

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
Published2003
Admission routes1
Has abstractyes

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