Your browser doesn't support javascript.
Non-invasive detection of COVID-19 using a microfluidic-based colorimetric sensor array sensitive to urinary metabolites.
Bordbar, Mohammad Mahdi; Samadinia, Hosein; Sheini, Azarmidokht; Aboonajmi, Jasem; Javid, Mohammad; Sharghi, Hashem; Ghanei, Mostafa; Bagheri, Hasan.
  • Bordbar MM; Chemical Injuries Research Center, Systems Biology and Poisonings Institute, Baqiyatallah University of Medical Sciences, Tehran, Iran.
  • Samadinia H; Chemical Injuries Research Center, Systems Biology and Poisonings Institute, Baqiyatallah University of Medical Sciences, Tehran, Iran.
  • Sheini A; Department of Mechanical Engineering, Shohadaye Hoveizeh Campus of Technology, Shahid Chamran University of Ahvaz, Dashte Azadegan, Khuzestan, Iran.
  • Aboonajmi J; Department of Chemistry, College of Sciences, Shiraz University, Shiraz, Iran.
  • Javid M; Chemical Injuries Research Center, Systems Biology and Poisonings Institute, Baqiyatallah University of Medical Sciences, Tehran, Iran.
  • Sharghi H; Department of Chemistry, College of Sciences, Shiraz University, Shiraz, Iran.
  • Ghanei M; Chemical Injuries Research Center, Systems Biology and Poisonings Institute, Baqiyatallah University of Medical Sciences, Tehran, Iran.
  • Bagheri H; Chemical Injuries Research Center, Systems Biology and Poisonings Institute, Baqiyatallah University of Medical Sciences, Tehran, Iran. h.bagheri@bmsu.ac.ir.
Mikrochim Acta ; 189(9): 316, 2022 08 05.
Article in English | MEDLINE | ID: covidwho-1971724
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.
Subject(s)
Keywords

Full text: Available Collection: International databases Database: MEDLINE Main subject: Metal Nanoparticles / COVID-19 Type of study: Diagnostic study Limits: Humans Language: English Journal: Mikrochim Acta Year: 2022 Document Type: Article Affiliation country: S00604-022-05423-1

Similar

MEDLINE

...
LILACS

LIS


Full text: Available Collection: International databases Database: MEDLINE Main subject: Metal Nanoparticles / COVID-19 Type of study: Diagnostic study Limits: Humans Language: English Journal: Mikrochim Acta Year: 2022 Document Type: Article Affiliation country: S00604-022-05423-1