Evidence map›Paper›PMID 35927498›Full record

ArticleMikrochimica acta2022

Non-invasive detection of COVID-19 using a microfluidic-based colorimetric sensor array sensitive to urinary metabolites.

Mohammad Mahdi Bordbar, Hosein Samadinia, Azarmidokht Sheini, Jasem Aboonajmi, Mohammad Javid, Hashem Sharghi, Mostafa Ghanei, Hasan Bagheri

Open access · bronzeAbstract read
In one paragraph

Article in Mikrochimica acta, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
2.0field-weighted citation impact, top 14% 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

7 citing papers in PubMed, 27 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

8 authors at 3 institutions in 1 country.

Mohammad Mahdi BordbarChemical Injuries Research Center, Systems Biology and Poisonings Institute, Baqiyatallah University of Medical Sciences, Tehran, Iran.
Hosein SamadiniaChemical Injuries Research Center, Systems Biology and Poisonings Institute, Baqiyatallah University of Medical Sciences, Tehran, Iran.
Azarmidokht SheiniDepartment of Mechanical Engineering, Shohadaye Hoveizeh Campus of Technology, Shahid Chamran University of Ahvaz, Dashte Azadegan, Khuzestan, Iran.
Jasem AboonajmiDepartment of Chemistry, College of Sciences, Shiraz University, Shiraz, Iran.
Mohammad JavidChemical Injuries Research Center, Systems Biology and Poisonings Institute, Baqiyatallah University of Medical Sciences, Tehran, Iran.
Hashem SharghiDepartment of Chemistry, College of Sciences, Shiraz University, Shiraz, Iran.
Mostafa GhaneiChemical Injuries Research Center, Systems Biology and Poisonings Institute, Baqiyatallah University of Medical Sciences, Tehran, Iran.
Hasan BagheriChemical Injuries Research Center, Systems Biology and Poisonings Institute, Baqiyatallah University of Medical Sciences, Tehran, Iran. h.bagheri@bmsu.ac.ir.ORCID http://orcid.org/0000-0003-2895-6189
Baqiyatallah University of Medical Sciences · IRShiraz University · IRShahid Chamran University of Ahvaz · IR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A colorimetric sensor array designed on a paper substrate with a microfluidic structure has been developed. This array is capable of detecting COVID-19 disease by tracking metabolites of urine samples. In order to determine minor metabolic changes, various colorimetric receptors consisting of gold and silver nanoparticles, metalloporphyrins, metal ion complexes, and pH-sensitive indicators are used in the array structure. By injecting a small volume of the urine sample, the color pattern of the sensor changes after 7 min, which can be observed visually. The color changes of the receptors (recorded by a scanner) are subsequently calculated by image analysis software and displayed as a color difference map. This study has been performed on 130 volunteers, including 60 patients infected by COVID-19, 55 healthy controls, and 15 cured individuals. The resulting array provides a fingerprint response for each category due to the differences in the metabolic profile of the urine sample. The principal component analysis-discriminant analysis confirms that the assay sensitivity to the correctly detected patient, healthy, and cured participants is equal to 73.3%, 74.5%, and 66.6%, respectively. Apart from COVID-19, other diseases such as chronic kidney disease, liver disorder, and diabetes may be detectable by the proposed sensor. However, this performance of the sensor must be tested in the studies with a larger sample size. These results show the possible feasibility of the sensor as a suitable alternative to costly and time-consuming standard methods for rapid detection and control of viral and bacterial infectious diseases and metabolic disorders.

Indexed as

COVID-19Metal NanoparticlesColorimetryHumansMicrofluidicsSilverSilverColorimetric detectionDigital color imagingNanoparticle receptorsPaper-based devicePattern recognition analysisSensor arrayViral infection

Identifiers

PMID35927498
PMCPMC9361914
OpenAlexW4289832095

What OpenQuestion holds

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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.