Citation: Sharma, S.; Thomas, E.;
Caputi, M.; Asghar, W.
RT-LAMP-Based Molecular
Diagnostic Set-Up for Rapid
Hepatitis C Virus Testing. Biosensors
2022, 12, 298. https://doi.org/
10.3390/bios12050298
Received: 31 March 2022
Accepted: 29 April 2022
Published: 5 May 2022
Publisher’s Note: MDPI stays neutral
with regard to jurisdictional claims in
published maps and institutional affil-
iations.
Copyright: © 2022 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
Article
RT-LAMP-Based Molecular Diagnostic Set-Up for Rapid
Hepatitis C Virus Testing
Sandhya Sharma
1,2
, Emmanuel Thomas
3
, Massimo Caputi
4
and Waseem Asghar
1,2,5,
*
1
Department of Electrical Engineering and Computer Science, Florida Atlantic University,
Boca Raton, FL 33431, USA; ssharma2013@fau.edu
2
Asghar-Lab: Micro and Nanotechnology in Medicine, College of Engineering and Computer Science,
Boca Raton, FL 33431, USA
3
Department of Microbiology and Immunology and Sylvester Comprehensive Cancer Center,
University of Miami School of Medicine, Miami, FL 33136, USA; ethomas1@med.miami.edu
4
Charles E. Schmidt College of Medicine, Florida Atlantic University, Boca Raton, FL 33431, USA;
mcaputi@health.fau.edu
5
Department of Biological Sciences (Courtesy Appointment), Florida Atlantic University,
Boca Raton, FL 33431, USA
* Correspondence: wasghar@fau.edu
Abstract:
Hepatitis C virus (HCV) infections occur in approximately 3% of the world population.
The development of an enhanced and extensive-scale screening is required to accomplish the World
Health Organization’s (WHO) goal of eliminating HCV as a public health problem by 2030. However,
standard testing methods are time-consuming, expensive, and challenging to deploy in remote and
underdeveloped areas. Therefore, a cost-effective, rapid, and accurate point-of-care (POC) diagnostic
test is needed to properly manage the disease and reduce the economic burden caused by high case
numbers. Herein, we present a fully automated reverse-transcription loop-mediated isothermal
amplification (RT-LAMP)-based molecular diagnostic set-up for rapid HCV detection. The set-up
consists of an automated disposable microfluidic chip, a small surface heater, and a reusable magnetic
actuation platform. The microfluidic chip contains multiple chambers in which the plasma sample is
processed. The system utilizes SYBR green dye to detect the amplification product with the naked
eye. The efficiency of the microfluidic chip was tested with human plasma samples spiked with HCV
virions, and the limit of detection observed was 500 virions/mL within 45 min. The entire virus
detection process was executed inside a uniquely designed, inexpensive, disposable, and self-driven
microfluidic chip with high sensitivity and specificity.
Keywords:
RT-LAMP; point-of-care diagnostics; microfluidics; molecular HCV test; colorimetric
detection
1. Introduction
According to the World Health Organization (WHO), more than 354 million peo-
ple worldwide are infected with the Hepatitis C virus (HCV), of which 70 million are
chronically infected. Each year, an estimated 1 million people die from this disease [
1
,
2
].
According to the WHO, in the year 2019, 58 million people were reported with HCV infec-
tion worldwide [
1
]. During the same time, in the United States, 4136 were reported with
acute infection; however, the estimated number of cases was 57,500 [
3
]. The Centers for
Disease Control and Prevention (CDC) estimates that, in the United States, about 50% of
the infected individuals are unaware of their infection [4].
HCV is primarily transmitted by parenteral drug administration, blood/plasma trans-
fusion, the reuse of medical equipment, sexual practices resulting in blood transfer, and
sharing the same needle while injecting drugs [
4
–
6
]. Patients are diagnosed with HCV
after they exhibit symptoms arising from the chronic infection. However, patients are often
Biosensors 2022, 12, 298. https://doi.org/10.3390/bios12050298 https://www.mdpi.com/journal/biosensors
