Evidence map›Paper›PMID 35597231›Full record

ArticleTalanta2022

A colorimetric electronic tongue for point-of-care detection of COVID-19 using salivary metabolites.

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

Open access · greenAbstract read
In one paragraph

Article in Talanta, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed, 1 pooled it
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

13 citing papers in PubMed, 1 synthesis or guideline pooled it, 25 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

9 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.
Hashem SharghiDepartment of Chemistry, College of Sciences, Shiraz University, Shiraz, Iran.
Pegah HashemiResearch and Development Department, Farin Behbood Tashkhis LTD, Tehran, Iran.
Hosein KhoshsafarChemical Injuries Research Center, Systems Biology and Poisonings Institute, Baqiyatallah University of Medical Sciences, Tehran, 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. Electronic address: h.bagheri@bmsu.ac.ir.
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

The monitoring of profile concentrations of chemical markers in saliva samples can be used to diagnose COVID-19 patients, and differentiate them from healthy individuals. Here, this purpose is achieved by designing a paper-based colorimetric sensor with an origami structure, containing general receptors such as pH-sensitive organic dyes, Lewis donors or acceptors, functionalized nanoparticles, and ion metal complexes. The color changes taking place in the receptors in the presence of chemical markers are visually observed and recorded with a digital instrument. Different types and amounts of the chemical markers provide the sensor with a unique response for patients (60 samples) or healthy (55 samples) individuals. These two categories can be discriminated with 84.3% accuracy. This study evidences that the saliva composition of cured and healthy participants is different from each other with accuracy of 85.7%. Moreover, viral load values obtained from the rRT-PCR method can be estimated by the designed sensor. Besides COVID-19, it may possible to simultaneously identify smokers and people with kidney disease and diabetes using the specified electronic tongue. Due to its high efficiency, the prepared paper device can be employed as a rapid detection kit to detect COVID-19.

Indexed as

COVID-19Metal NanoparticlesColorimetryElectronic NoseHumansPoint-of-Care SystemsColorimetric detectionCOVID-19MetabolomicsPaper-based deviceSalivaSensor array

Identifiers

PMID35597231
PMCPMC9107099
OpenAlexW4280596156

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.