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Record W2316141537 · doi:10.1109/nano.2014.6968129

Microinstrument for optical monitoring of endothelial cell migration under controlled tension/compression via integrated magnetic composite polymer actuation

2014· article· en· W2316141537 on OpenAlexaff
Bonnie L. Gray, Mona Rahbar, Avin Babataheri, Abdul I. Barakat

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
Topic3D Printing in Biomedical Research
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsMicrochannelPolydimethylsiloxaneMicrofluidicsActuatorMaterials scienceMicrosystemCompression (physics)Composite numberTension (geology)PolymerFluidicsOptoelectronicsNanotechnologyComposite materialBiomedical engineeringElectrical engineeringEngineering

Abstract

fetched live from OpenAlex

We present a microfabricated platform that allows simultaneous application of controlled stretch/compression forces and fluid flow shear stresses during endothelial cell (EC) live-cell monitoring. Our device employs a highly flexible magnetic composite polymer (M-CP) for actuation of a flexible microchannel system. We combine our M-CP with micropatterned non-magnetic polydimethylsiloxane (PDMS), resulting in flexible microsystems with integrated actuators and microfluidic channels whereby we can optically visualize cells in order to monitor various aspects of cell behavior including migration, proliferation, and morphological changes. The M-CP can be rendered permanently magnetic, so it can be employed for both substrate tension and compression using the same electro- or permanent magnet with pole reversal. We have demonstrated proof-of-concept of an instrument designed to simultaneously stimulate ECs grown in microfluidic channels with both fluid flow and mechanical stretch/compression using the new M-CP actuators.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.011
GPT teacher head0.249
Teacher spread0.238 · 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 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

Citations3
Published2014
Admission routes1
Has abstractyes

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Same topic3D Printing in Biomedical ResearchFrench-language works237,207