Evidence map›Paper›PMID 32878225›Full record

ReviewMicromachines2020

Electrical Impedance Spectroscopy for Monitoring Chemoresistance of Cancer Cells.

Lexi L Crowell, Juan S Yakisich, Brian Aufderheide, Tayloria N G Adams

Abstract readReview
In one paragraph

Review in Micromachines, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

23 citing papers in PubMed.

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  20. Editorial: Signaling in stress sensing and resistance in parasitic protozoa.Frontiers in cellular and infection microbiology · 2022
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Lexi L CrowellDepartment of Chemical and Biomolecular Engineering, University of California-Irvine, Irvine, CA 92697, USA.
Juan S YakisichDepartment of Pharmaceutical Sciences, Hampton University, Hampton, VA 23668, USA.
Brian AufderheideDepartment of Chemical Engineering, Hampton University, Hampton, VA 23668, USA.
Tayloria N G AdamsDepartment of Chemical and Biomolecular Engineering, University of California-Irvine, Irvine, CA 92697, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Electrical impedance spectroscopy (EIS) is an electrokinetic method that allows for the characterization of intrinsic dielectric properties of cells. EIS has emerged in the last decade as a promising method for the characterization of cancerous cells, providing information on inductance, capacitance, and impedance of cells. The individual cell behavior can be quantified using its characteristic phase angle, amplitude, and frequency measurements obtained by fitting the input frequency-dependent cellular response to a resistor-capacitor circuit model. These electrical properties will provide important information about unique biomarkers related to the behavior of these cancerous cells, especially monitoring their chemoresistivity and sensitivity to chemotherapeutics. There are currently few methods to assess drug resistant cancer cells, and therefore it is difficult to identify and eliminate drug-resistant cancer cells found in static and metastatic tumors. Establishing techniques for the real-time monitoring of changes in cancer cell phenotypes is, therefore, important for understanding cancer cell dynamics and their plastic properties. EIS can be used to monitor these changes. In this review, we will cover the theory behind EIS, other impedance techniques, and how EIS can be used to monitor cell behavior and phenotype changes within cancerous cells.

Indexed as

cancer cellscapacitancecell phenotypedielectrophoresiselectrical impedance spectroscopyelectrokineticsimpedancemicrofluidics

Identifiers

PMID32878225
PMCPMC7570252

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.