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Record W2082148336 · doi:10.3390/s150202763

Simultaneous Characterization of Instantaneous Young’s Modulus and Specific Membrane Capacitance of Single Cells Using a Microfluidic System

2015· article· en· W2082148336 on OpenAlexaff
Yang Zhao, Deyong Chen, Yana Luo, Feng Chen, Xiaoting Zhao, Mei Jiang, Wentao Yue, Rong Long, Junbo Wang, Jian Chen

Bibliographic record

VenueSensors · 2015
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Bio-sensing Technologies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsConstrictionMicrofluidicsMembraneMaterials scienceCapacitanceElectrical impedanceBiomedical engineeringChemistryNanotechnologyElectrodeElectrical engineeringEngineeringBiology

Abstract

fetched live from OpenAlex

This paper presents a microfluidics-based approach capable of continuously characterizing instantaneous Young's modulus (E(instantaneous)) and specific membrane capacitance (C(specific membrane)) of suspended single cells. In this method, cells were aspirated through a constriction channel while the cellular entry process into the constriction channel was recorded using a high speed camera and the impedance profiles at two frequencies (1 kHz and 100 kHz) were simultaneously measured by a lock-in amplifier. Numerical simulations were conducted to model cellular entry process into the constriction channel, focusing on two key parameters: instantaneous aspiration length (L(instantaneous)) and transitional aspiration length (L(transitional)), which was further translated to E(instantaneous). An equivalent distribution circuit model for a cell travelling in the constriction channel was used to determine C(specific membrane). A non-small-cell lung cancer cell line 95C (n = 354) was used to evaluate this technique, producing E(instantaneous) of 2.96 ± 0.40 kPa and Cspecific membrane of 1.59 ± 0.28 μF/cm2. As a platform for continuous and simultaneous characterization of cellular E(instantaneous) and C(specific membrane), this approach can facilitate a more comprehensive understanding of cellular biophysical properties.

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 score0.025
Threshold uncertainty score0.731

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 head0.019
GPT teacher head0.185
Teacher spread0.166 · 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

Citations23
Published2015
Admission routes1
Has abstractyes

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