Evidence map›Paper›PMID 37524456›Full record

ArticleAnalytica chimica acta2023

A microfluidic biosensor architecture for the rapid detection of COVID-19.

Sura A Muhsin, Ying He, Muthana Al-Amidie, Karen Sergovia, Amjed Abdullah, Yang Wang, Omar Alkorjia, Robert A Hulsey, Gary L Hunter, Zeynep K Erdal and 4 more

Abstract read
In one paragraph

Article in Analytica chimica acta, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Multiplex on-chip detection ofFrontiers in bioengineering and biotechnology · 2026
    Article
  2. 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

14 authors.

Sura A MuhsinDepartment of Electrical Engineering and Computer Science, College of Engineering, University of Missouri, 411 S 6th St, Columbia, Mo, 65211, USA.
Ying HeCenter for Influenza and Emerging Infectious Diseases, Department of Molecular Microbiology and Immunology, School of Medicine, Bond Life Sciences Center, University of Missouri, 1201 Rollins St, Columbia, MO, 65211, USA; Department of Molecular Microbiology and Immunology, School of Medicine, University of Missouri, Columbia, MO, USA; Bond Life Sciences Center, University of Missouri, Columbia, MO, USA.
Muthana Al-AmidieDepartment of Electrical Engineering and Computer Science, College of Engineering, University of Missouri, 411 S 6th St, Columbia, Mo, 65211, USA.
Karen SergoviaCenter for Influenza and Emerging Infectious Diseases, Department of Molecular Microbiology and Immunology, School of Medicine, Bond Life Sciences Center, University of Missouri, 1201 Rollins St, Columbia, MO, 65211, USA; Department of Molecular Microbiology and Immunology, School of Medicine, University of Missouri, Columbia, MO, USA; Bond Life Sciences Center, University of Missouri, Columbia, MO, USA.
Amjed AbdullahDepartment of Electrical Engineering and Computer Science, College of Engineering, University of Missouri, 411 S 6th St, Columbia, Mo, 65211, USA.
Yang WangCenter for Influenza and Emerging Infectious Diseases, Department of Molecular Microbiology and Immunology, School of Medicine, Bond Life Sciences Center, University of Missouri, 1201 Rollins St, Columbia, MO, 65211, USA; Department of Molecular Microbiology and Immunology, School of Medicine, University of Missouri, Columbia, MO, USA; Bond Life Sciences Center, University of Missouri, Columbia, MO, USA.
Omar AlkorjiaDepartment of Electrical Engineering and Computer Science, College of Engineering, University of Missouri, 411 S 6th St, Columbia, Mo, 65211, USA.
Robert A HulseyBlack and Veatch, 11401 Lamar, Overland Park, KS, 66211, USA.
Gary L HunterBlack and Veatch, 201 Brookfield Parkway, Suite 150, Greenville, SC, 29607, USA.
Zeynep K ErdalBlack and Veatch, 201 Brookfield Parkway, Suite 150, Greenville, SC, 29607, USA.
Ryan J PletkaBlack and Veatch, 2999 Oak Road, Suite 490, Walnut Creek, CA, 94597, USA.
Hyleme S GeorgeBlack and Veatch, 11401 Lamar, Overland Park, KS, 66211, USA.
Xiu-Feng WanDepartment of Electrical Engineering and Computer Science, College of Engineering, University of Missouri, 411 S 6th St, Columbia, Mo, 65211, USA; Center for Influenza and Emerging Infectious Diseases, Department of Molecular Microbiology and Immunology, School of Medicine, Bond Life Sciences Center, University of Missouri, 1201 Rollins St, Columbia, MO, 65211, USA; Department of Molecular Microbiology and Immunology, School of Medicine, University of Missouri, Columbia, MO, USA; Bond Life Sciences Center, University of Missouri, Columbia, MO, USA. Electronic address: wanx@missouri.edu.
Mahmoud AlmasriDepartment of Electrical Engineering and Computer Science, College of Engineering, University of Missouri, 411 S 6th St, Columbia, Mo, 65211, USA. Electronic address: almasrim@missouri.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The lack of enough diagnostic capacity to detect severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) has been one of the major challenges in the control the 2019 COVID pandemic; this led to significant delay in prompt treatment of COVID-19 patients or accurately estimate disease situation. Current methods for the diagnosis of SARS-COV-2 infection on clinical specimens (e.g. nasal swabs) include polymerase chain reaction (PCR) based methods, such as real-time reverse transcription (rRT) PCR, real-time reverse transcription loop-mediated isothermal amplification (rRT-LAMP), and immunoassay based methods, such as rapid antigen test (RAT). These conventional PCR methods excel in sensitivity and specificity but require a laboratory setting and typically take up to 6 h to obtain the results whereas RAT has a low sensitivity (typically at least 3000 TCID50/ml) although with the results with 15 min. We have developed a robust micro-electro-mechanical system (MEMS) based impedance biosensor fit for rapid and accurate detection of SARS-COV-2 of clinical samples in the field with minimal training. The biosensor consisted of three regions that enabled concentrating, trapping, and sensing the virus present in low quantities with high selectivity and sensitivity in 40 min using an electrode coated with a specific SARS-COV-2 antibody cross-linker mixture. Changes in the impedance value due to the binding of the SARS-COV-2 antigen to the antibody will indicate positive or negative result. The testing results showed that the biosensor's limit of detection (LoD) for detection of inactivated SARS-COV-2 antigen in phosphate buffer saline (PBS) was as low as 50 TCID50/ml. The biosensor specificity was confirmed using the influenza virus while the selectivity was confirmed using influenza polyclonal sera. Overall, the results showed that the biosensor is able to detect SARS-COV-2 in clinical samples (swabs) in 40 min with a sensitivity of 26 TCID50/ml.

Indexed as

Biosensing TechniquesCOVID-19Clinical Laboratory TechniquesCOVID-19 TestingHumansMicrofluidicsNucleic Acid Amplification TechniquesSARS-CoV-2Sensitivity and SpecificityBiosensorDielectrophoresisImpedance measurementMicrofluidicPolymerase chain reaction (PCR)SARS-COV-2Trapping and focusing electrodes

Identifiers

PMID37524456
PMCPMC10251744

What Socratic holds

Textmetadata
Read underepoch 390

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 Socratic graph.