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Record W2901718275 · doi:10.1039/c8lc00981c

A radial microfluidic platform for higher throughput chemotaxis studies with individual gradient control

2018· article· en· W2901718275 on OpenAlexafffund
Jiandong Wu, Aditya Kumar-Kanojia, Sabine Hombach‐Klonisch, Thomas Klonisch, Francis Lin

Bibliographic record

VenueLab on a Chip · 2018
Typearticle
Languageen
FieldEngineering
Topic3D Printing in Biomedical Research
Canadian institutionsUniversity of WinnipegUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of CanadaMitacsCanadian Cancer SocietyCanadian Institutes of Health ResearchCancer Research Society
KeywordsThroughputMicrofluidicsChemotaxisControl (management)NanotechnologyComputer scienceBiologyMaterials scienceArtificial intelligenceGeneticsTelecommunications

Abstract

fetched live from OpenAlex

Chemotaxis plays a fundamental role in immune defense and cancer metastasis. Microfluidic devices are increasingly applied to studying chemotaxis, owing to their advantages of reduced reagent consumption, ability to control chemical gradients, tracking of individual cells, and quantification of chemotaxis. Many existing microfluidic chemotaxis devices suffer from limited throughput and complex operation. Here, we describe a microfluidic device with a radial channel design which allows for simultaneous chemotaxis tests of different cell types and different gradient conditions. This radial microfluidic device was capable of stand-alone stable gradient generation using passive pumping and pressure-balancing strategies. The device was validated by testing the migration of fast-migrating human neutrophils and two slower-migrating human breast cancer cell lines, MDA-MB-231 and MCF-7 cells. Furthermore, this radial microfluidic device was useful in studying the influence of the nuclear chromatin binding protein high mobility group A2 (HMGA2) on the migration of the human triple negative breast cancer cell line MDA-MB-231.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.200
Threshold uncertainty score0.598

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 head0.048
GPT teacher head0.302
Teacher spread0.253 · 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 designNot applicable
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

Citations39
Published2018
Admission routes2
Has abstractyes

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