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

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 a...

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Detalles Bibliográficos
Autores principales: Bordbar, Mohammad Mahdi, Samadinia, Hosein, Sheini, Azarmidokht, Aboonajmi, Jasem, Javid, Mohammad, Sharghi, Hashem, Ghanei, Mostafa, Bagheri, Hasan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Vienna 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9361914/
https://www.ncbi.nlm.nih.gov/pubmed/35927498
http://dx.doi.org/10.1007/s00604-022-05423-1
Descripción
Sumario: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. GRAPHICAL ABSTRACT: [Image: see text] SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00604-022-05423-1.