Evidence map›Paper›PMID 41686337›Full record

ArticleBiomedical microdevices2026

In situ detection of dead cells from live cells via a DC plus low frequency AC resistive pulse sensor.

Parker Lybrook, Heyi Chen, Emma Barna, Jacob Brown, Ashley Wong, Joseph Ketchum, Ge Zhang, Jiang Zhe

Abstract read
In one paragraph

Article in Biomedical microdevices, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

8 authors.

Parker Lybrook *Department of Mechanical Engineering, University of Akron, Akron, OH, 44325, USA.
Heyi Chen *Department of Mechanical Engineering, University of Akron, Akron, OH, 44325, USA.
Emma BarnaDepartment of Biomedical Engineering, University of Akron, Akron, OH, 44325, USA.
Jacob BrownDepartment of Biomedical Engineering, University of Akron, Akron, OH, 44325, USA.
Ashley WongDepartment of Biomedical Engineering, University of Akron, Akron, OH, 44325, USA.
Joseph KetchumDepartment of Biomedical Engineering, University of Akron, Akron, OH, 44325, USA.
Ge ZhangDepartment of Biomedical Engineering, University of Akron, Akron, OH, 44325, USA.
Jiang ZheDepartment of Mechanical Engineering, University of Akron, Akron, OH, 44325, USA. jzhe@uakron.edu.

Funding

National Science Foundation ECCS-2232940
6 · The paper itself

Abstract

Differentiation and detection of live and dead cells are critical for assessing cell viability in biomedical research, evaluating drug efficacy, and monitoring cytotoxicity in therapeutic applications. We present a microfluidic sensor that consists of two successive resistive pulse sensing channels. An excitation signal composed of a low-frequency AC (75 kHz) component and a DC bias was used to measure four key parameters. Through the AC measurement, differences in cell impedance causes variations in phase angle and voltage peak. From the DC measurement, cell size can be inferred from the resistive pulse magnitude, and the cell's zeta potential is represented by the transit time difference. Human umbilical vein endothelial cells (HUVECs) and human mesenchymal stem cells (hMSCs) were used to demonstrate the device's utility. A soft margin support vector machine (SVM) was applied to define the decision boundary based on analysis of the four parameters. For both cell types, live and dead cells formed distinct clusters, achieving maximum classification accuracies of up to 100%. Additionally, HUVECs treated with either ethanol or staurosporine (STS) were classified with accuracies up to 100%. Compared to previous microfluidic resistive pulse sensor (RPS), this approach can determine cell viability without the need for complex labeling or modifications. Unlike impedance cytometry, it does not require high-frequency measurements, significantly reducing hardware requirements and data processing complexity, while still providing multiparametric measurements of cells. These measurements allow the use of soft SVM to classify cell groups with higher accuracy than single-parameter differentiation.

Indexed as

Lab-On-A-Chip DevicesCell DeathCell SurvivalElectric ImpedanceHumansHuman Umbilical Vein Endothelial CellsMesenchymal Stem CellsSupport Vector MachineCell analysisCell viabilityMicrochannelMultispectral

Identifiers

PMID41686337
PMCPMC12904940

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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.