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Record W2087979050 · doi:10.1021/ac800231k

Same-Single-Cell Analysis for the Study of Drug Efflux Modulation of Multidrug Resistant Cells Using a Microfluidic Chip

2008· article· en· W2087979050 on OpenAlexafffund
Xiujun Li, Victor Ling, Paul C. H. Li

Bibliographic record

VenueAnalytical Chemistry · 2008
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Capillary Electrophoresis Applications
Canadian institutionsSimon Fraser University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsEffluxMultiple drug resistanceDaunorubicinChemistryDrug resistanceCancer cellSingle-cell analysisDrugVerapamilPharmacologyCellCancerLeukemiaBiochemistryBiologyImmunologyInternal medicineMedicineAntibioticsMicrobiology

Abstract

fetched live from OpenAlex

Since multidrug resistance (MDR) is a major cause of failure in cancer chemotherapy, we report a microfluidic approach combined with the same-single-cell analysis to investigate the modulation of MDR, manifested as the inhibition of drug efflux. A microfluidic chip that was capable of selecting and retaining a single multidrug-resistant cancer cell was used to investigate drug efflux inhibition in leukemia cell lines. Three advantages of the microfluidic-based same-single-cell analysis (dubbed as SASCA) method have been revealed. First, it readily detects the modulation of drug efflux of anticancer compounds (e.g., daunorubicin) by MDR modulators (e.g., verapamil) among cellular variations. Second, SASCA is able to compare the different cellular abilities in response to drug efflux modulation based on the drug transport kinetics of single cells. Third, SASCA requires only a small number of cells, which may be beneficial for investigating drug resistance in minor cell subpopulations (e.g., cancer "stem" cells).

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 score0.091
Threshold uncertainty score0.571

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.019
GPT teacher head0.225
Teacher spread0.206 · 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

Citations73
Published2008
Admission routes2
Has abstractyes

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