Evidence map›Paper›PMID 30879117›Full record

ArticleAnalytical and bioanalytical chemistry2019

A mathematical model of dielectrophoretic data to connect measurements with cell properties.

Shannon Huey Hilton, Mark A Hayes

Abstract read
In one paragraph

Article in Analytical and bioanalytical chemistry, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

11 citing papers in PubMed.

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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

2 authors.

Shannon Huey HiltonSchool of Molecular Sciences, Arizona State University, Mail Stop 1604, Tempe, AZ, 85281, USA.
Mark A HayesSchool of Molecular Sciences, Arizona State University, Mail Stop 1604, Tempe, AZ, 85281, USA. mhayes@asu.edu.

Funding

Biophysical Isolation and Concentration of Individual Salmonella SerotypesR21AI130855 · NIAID · ARIZONA STATE UNIVERSITY-TEMPE CAMPUS · PI HAYES, MARK A · 2018 to 2019
$413k
Differentiation of Mutant Virus ParticlesR03AI133397 · NIAID · ARIZONA STATE UNIVERSITY-TEMPE CAMPUS · PI HAYES, MARK A, HOGUE, BRENDA G. · 2018 to 2019
$145k
Isolating Viral Particles from Whole BloodR03AI094193 · NIAID · ARIZONA STATE UNIVERSITY-TEMPE CAMPUS · PI HAYES, MARK A, HOGUE, BRENDA G. · 2012 to 2013
$138k
Pathogen Isolation and Concentration for Phenotypic SubtypingR03AI099740 · NIAID · ARIZONA STATE UNIVERSITY-TEMPE CAMPUS · PI HAYES, MARK A · 2012 to 2013
$138k
Isolation of Pathogenic ListeriaR03AI111361 · NIAID · ARIZONA STATE UNIVERSITY-TEMPE CAMPUS · PI HAYES, MARK A · 2015 to 2016
$136k
National Institute of Allergy and Infectious Diseases 1R21AI130855-01NIAID NIH HHS R03 AI094193NIAID NIH HHS R03 AI099740NIAID NIH HHS R03 AI111361NIAID NIH HHS R03 AI133397NIAID NIH HHS R21 AI130855
6 · The paper itself

Abstract

Dielectrophoresis (DEP) brings about the high-resolution separations of cells and other bioparticles arising from very subtle differences in their properties. However, an unanticipated limitation has arisen: difficulty in assignment of specific biological features which vary between two cell populations. This hampers the ability to interpret the significance of the variations. To realize the opportunities made possible by dielectrophoresis, the data and the diversity of structures found in cells and bioparticles must be linked. While the crossover frequency in DEP has been studied in-depth and exploited in applications using AC fields, less attention has been given when a DC field is present. Here, a new mathematical model of dielectrophoretic data is introduced which connects the physical properties of cells to specific elements of the data from potential- or time-varied DEP experiments. The slope of the data in either analysis is related to the electrokinetic mobility, while the potential at which capture initiates in potential-based analysis is related to both the electrokinetic and dielectrophoretic mobilities. These mobilities can be assigned to cellular properties for which values appear in the literature. Representative examples of high and low values of properties such as conductivity, zeta potential, and surface charge density for bacteria including Streptococcus mutans, Rhodococcus erythropolis, Pasteurella multocida, Escherichia coli, and Staphylococcus aureus are considered. While the many properties of a cell collapse into one or two features of data, for a well-vetted system the model can indicate the extent of dissimilarity. The influence of individual properties on the features of dielectrophoretic data is summarized, allowing for further interpretation of data. Graphical abstract.

Indexed as

AlgorithmsBacteriaElectric ConductivityElectroosmosisElectrophoresisKineticsModels, BiologicalModels, ChemicalStatic ElectricitySurface PropertiesBacterial variationsBiophysical propertiesData modelingDielectrophoresisElectrokinetic mobilityElectrophoresis

Identifiers

PMID30879117
PMCPMC6459731

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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.