Evidence map›Paper›PMID 40096419›Full record

ArticleSensors (Basel, Switzerland)2025

Automated Electrical Detection of Proteins for Oral Squamous Cell Carcinoma in an Integrated Microfluidic Chip Using Multi-Frequency Impedance Cytometry and Machine Learning.

Muhammad Tayyab, Zhongtian Lin, Seyed Reza Mahmoodi, Mehdi Javanmard

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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.

Muhammad TayyabDepartment of Electrical and Computer Engineering, Rutgers University, Piscataway, NJ 08854, USA.ORCID 0000-0002-4372-504X
Zhongtian LinDepartment of Electrical and Computer Engineering, Rutgers University, Piscataway, NJ 08854, USA.
Seyed Reza MahmoodiDepartment of Electrical and Computer Engineering, University of Denver, Denver, CO 80210, USA.ORCID 0000-0002-8920-9227
Mehdi JavanmardDepartment of Electrical and Computer Engineering, Rutgers University, Piscataway, NJ 08854, USA.

Funding

Anonymous corporation 000
6 · The paper itself

Abstract

Proteins can act as suitable biomarkers for the prognosis and diagnosis of certain conditions and can help us gain an understanding of the fundamental processes that occur inside an organism. In this work, we present a fully automated machine learning-assisted label-free method for the electrical detection of proteins in an integrated microfluidic chip using multi-frequency impedance cytometry and off-the-shelf components for realizing an automated and programmable fluid control system. We verify the robustness of our mixing method on our custom microfluidic mixer composed of polydimethylsiloxane (PDMS) serpentine channels optically using a fluorescent sandwich immunoassay and comparing the results with a commercial benchtop mixer. Salivary IL-6 is a biomarker for oral squamous cell carcinoma (OSCC), and we have demonstrated that our system can be used for the detection of quantification of Interleukin-6 (IL-6) levels in a solution using the impedance response of beads conjugated with the protein of interest, which passes through the microfluidic chip with reasonable accuracy (96%). Although we have demonstrated the detection and quantification of IL-6, our system can be adapted to any protein of interest with slight modification in the reagents and bead-binding protocols.

Indexed as

Carcinoma, Squamous CellMachine LearningMouth NeoplasmsBiomarkers, TumorBiosensing TechniquesElectric ImpedanceHumansInterleukin-6Lab-On-A-Chip DevicesMicrofluidic Analytical TechniquesMicrofluidicsSalivaBiomarkers, TumorInterleukin-6biosensorsmicrofluidics

Identifiers

PMID40096419
PMCPMC11902334

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

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.