ArticleAnalytica chimica acta2022
Visual diagnosis of COVID-19 disease based on serum metabolites using a paper-based electronic tongue.
Article in Analytica chimica acta, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- A colorimetric sensor array based on merging silver nanozymes and gold nanoparticles for chronic kidney disease diagnosis and post-transplant monitoring.RSC advances · 2026Article
- Size-Dependent Neutralization Efficacy of Nanodecoys Against SARS-CoV-2 Mimics in Mammalian Cell Infection Models.Small (Weinheim an der Bergstrasse, Germany) · 2026Article
- Serum metabolic alterations in chickens upon infectious bursal disease virus infection.BMC veterinary research · 2024Article
- Advantages of Metabolomics-Based Multivariate Machine Learning to Predict Disease Severity: Example of COVID.International journal of molecular sciences · 2024Article
- A point of care sensor for detection of alcohols, aldehydes and esters in urinary metabolites of war veterans injured by sulfur mustard.RSC advances · 2024Article
- Electronic Tongues and Noses: A General Overview.Biosensors · 2024Review
- A review on machine learning-powered fluorescent and colorimetric sensor arrays for bacteria identification.Mikrochimica acta · 2023Review
- Monitoring saliva compositions for non-invasive detection of diabetes using a colorimetric-based multiple sensor.Scientific reports · 2023Article
- Molecular test for COVID-19 diagnosis based on a colorimetric genomagnetic assay.Analytica chimica acta · 2023Article
- Plasma-induced nanoparticle aggregation for stratifying COVID-19 patients according to disease severity.Sensors and actuators. B, Chemical · 2022Article
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Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study aims to use a paper-based sensor array for point-of-care detection of COVID-19 diseases. Various chemical compounds such as nanoparticles, organic dyes and metal ion complexes were employed as sensing elements in the array fabrication, capturing the metabolites of human serum samples. The viral infection caused the type and concentration of serum compositions to change, resulting in different color responses for the infected and control samples. For this purpose, 118 serum samples of COVID-19 patients and non-COVID controls both men and women with the age range of 14-88 years were collected. The serum samples were initially subjected to the sensor, followed by monitoring the variation in the color of sensing elements for 5 min using a scanner. By taking into consideration the statistical information, this method was capable of discriminating COVID-19 patients and control samples with 83.0% accuracy. The variation of age did not influence the colorimetric patterns. The desirable correlation was observed between the sensor responses and viral load values calculated by the PCR test, proposing a rapid and facile way to estimate the disease severity. Compared to other rapid detection methods, the developed assay is cost-effective and user-friendly, allowing for screening COVID-19 diseases reliably.
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