Evidence map›Paper›PMID 35334331›Full record

ArticleBiosensors & bioelectronics2022

Smartphone-based sensitive detection of SARS-CoV-2 from saline gargle samples via flow profile analysis on a paper microfluidic chip.

Patarajarin Akarapipad, Kattika Kaarj, Lane E Breshears, Katelyn Sosnowski, Jacob Baker, Brandon T Nguyen, Ciara Eades, Jennifer L Uhrlaub, Grace Quirk, Janko Nikolich-Žugich and 2 more

Open access · greenAbstract read
In one paragraph

Article in Biosensors & bioelectronics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
4.6field-weighted citation impact, top 4% of its field
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

14 citing papers in PubMed, 45 citations in OpenAlex.

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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

12 authors at 1 institution in 1 country.

Patarajarin AkarapipadDepartment of Biomedical Engineering, The University of Arizona, Tucson, AZ, 85721, United States.
Kattika KaarjDepartment of Biosystems Engineering, The University of Arizona, Tucson, AZ, 85721, United States.
Lane E BreshearsDepartment of Biomedical Engineering, The University of Arizona, Tucson, AZ, 85721, United States.
Katelyn SosnowskiDepartment of Biomedical Engineering, The University of Arizona, Tucson, AZ, 85721, United States.
Jacob BakerDepartment of Biomedical Engineering, The University of Arizona, Tucson, AZ, 85721, United States.
Brandon T NguyenDepartment of Biomedical Engineering, The University of Arizona, Tucson, AZ, 85721, United States.
Ciara EadesDepartment of Chemistry & Biochemistry, The University of Arizona, Tucson, AZ, 85721, United States.
Jennifer L UhrlaubDepartment of Immunobiology and Arizona Center on Aging, The University of Arizona College of Medicine, Tucson, AZ, 85724, United States.
Grace QuirkDepartment of Ecology and Evolutionary Biology, The University of Arizona, Tucson, AZ, 85721, United States.
Janko Nikolich-ŽugichDepartment of Immunobiology and Arizona Center on Aging, The University of Arizona College of Medicine, Tucson, AZ, 85724, United States.
Michael WorobeyDepartment of Ecology and Evolutionary Biology, The University of Arizona, Tucson, AZ, 85721, United States.
Jeong-Yeol YoonDepartment of Biomedical Engineering, The University of Arizona, Tucson, AZ, 85721, United States; Department of Biosystems Engineering, The University of Arizona, Tucson, AZ, 85721, United States; Department of Chemistry & Biochemistry, The University of Arizona, Tucson, AZ, 85721, United States. Electronic address: jyyoon@arizona.edu.
University of Arizona · US

Funding

Computational and Mathematical Modeling of Biomedical SystemsT32GM132008 · NIGMS · UNIVERSITY OF ARIZONA · PI Ryan Gutenkunst, JOANNA MASEL · 2019 to 2026
$2.3M
NIGMS NIH HHS T32 GM132008
6 · The paper itself

Abstract

Respiratory viruses, especially coronaviruses, have resulted in worldwide pandemics in the past couple of decades. Saliva-based paper microfluidic assays represent an opportunity for noninvasive and rapid screening, yet both the sample matrix and test method come with unique challenges. In this work, we demonstrated the rapid and sensitive detection of SARS-CoV-2 from saliva samples, which could be simpler and more comfortable for patients than existing methods. Furthermore, we systematically investigated the components of saliva samples that affected assay performance. Using only a smartphone, an antibody-conjugated particle suspension, and a paper microfluidic chip, we made the assay user-friendly with minimal processing. Unlike the previously established flow rate assays that depended solely on the flow rate or distance, this unique assay analyzes the flow profile to determine infection status. Particle-target immunoagglutination changed the surface tension and subsequently the capillary flow velocity profile. A smartphone camera automatically measured the flow profile using a Python script, which was not affected by ambient light variations. The limit of detection (LOD) was 1 fg/μL SARS-CoV-2 from 1% saliva samples and 10 fg/μL from simulated saline gargle samples (15% saliva and 0.9% saline). This method was highly specific as demonstrated using influenza A/H1N1. The sample-to-answer assay time was <15 min, including <1-min capillary flow time. The overall accuracy was 89% with relatively clean clinical saline gargle samples. Despite some limitations with turbid clinical samples, this method presents a potential solution for rapid mass testing techniques during any infectious disease outbreak as soon as the antibodies become available.

Indexed as

Biosensing TechniquesCOVID-19Influenza A Virus, H1N1 SubtypeHumansMicrofluidicsSARS-CoV-2SmartphoneCapillary actionCOVID-19Particle immunoagglutinationRespiratory virusSmartphone-based biosensor

Identifiers

PMID35334331
PMCPMC8926431
OpenAlexW4220807776

What OpenQuestion 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 OpenQuestion graph.