MétaCan
Menu
Back to cohort
Record W2913642780 · doi:10.1002/elps.201800451

Dielectrophoretic trapping of single leukemic cells using the conventional and compact optical measurement systems

2019· article· en· W2913642780 on OpenAlexafffund
Hamideh Sharifi Noghabi, Mandy Soo, Avid Khamenehfar, Paul C. H. Li

Bibliographic record

VenueElectrophoresis · 2019
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Bio-sensing Technologies
Canadian institutionsSimon Fraser University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSingle-cell analysisRhodamine 123Inverted microscopeMicrofluidicsFluorescenceCellEffluxFluorescence microscopeChemistryDielectrophoresisMicroscopeRhodamineBiophysicsMaterials scienceNanotechnologyMultiple drug resistanceBiologyBiochemistryPathologyOptics

Abstract

fetched live from OpenAlex

Here, we report a microfluidic same-single-cell analysis to study the inhibition of multidrug resistance due to drug efflux on single leukemic cells. Drug efflux inhibition was investigated in the microfluidic chip using two different fluorescence detection systems, namely, a compact single-cell bioanalyzer and the conventional optical detection system constructed from an inverted microscope and a microphotometer. More importantly, a compact signal generator was used to conduct dielectrophoretic cell trapping together with the compact SCB. By using the DEP force, a single acute myeloid leukemia cell was trapped in the cell retention structure of the chip. This allowed us to detect dye accumulation in the MDR leukemic cells in the presence of cyclosporine A (CsA). CsA and rhodamine 123 were used as the P-glycoprotein inhibitor and fluorescent dye, respectively. The result showed that the Rh123 fluorescence signal in a single-cell increased dramatically over its same-cell control on both fluorescence detection systems due to the inhibition by CsA.

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 score0.018
Threshold uncertainty score0.496

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 head0.018
GPT teacher head0.186
Teacher spread0.168 · 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

Citations13
Published2019
Admission routes2
Has abstractyes

Explore more

Same venueElectrophoresisSame topicMicrofluidic and Bio-sensing TechnologiesFrench-language works237,207