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Effect Of Shear Stress On Microgilia (BV2) Single Cell In A Microfluidic Platform-3D Modeling Under Fluid Flow Stimulation

2019· article· en· W2999889130 on OpenAlexaff
Ehsan Yazdanpanah Moghadam, Subhathirai Subramaniyan Parimalam, Muthukumaran Packirisamy

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCellular Mechanics and Interactions
Canadian institutionsConcordia University
Fundersnot available
KeywordsShear stressMicrofluidicsMaterials scienceShear flowStress (linguistics)Deformation (meteorology)Shear (geology)MechanicsFlow (mathematics)Composite materialNanotechnologyPhysics

Abstract

fetched live from OpenAlex

A 3D numerical model is developed to analyze the force distribution over a single BV2 cell, when the cell is under biomechanical stress within a microfluidic platform. The simulation can predict the shear stress and deformation, respectively, over the cell surface and nucleus under hydrodynamic forces. The two main factors for cellular deformation-pressure and viscous forces were studies. It is found that, in the front-half of the cell facing the flow, pressure forces are greater than the viscous forces. Finally, the cellular deformation and shear stress changes are studied at two conditions; when the BV2 cell is completely- and partly- attached to the substrate. It is observed that the shear stress over the cell increases, when the contact of the cell with the substrate decreases.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

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 communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.010
GPT teacher head0.238
Teacher spread0.228 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations1
Published2019
Admission routes1
Has abstractyes

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