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Sensors and Materials, Vol. 35, No. 10 (2023) 4731–4750
MYU Tokyo
S & M 3426

Clinical Performance of Reverse Transcription
Loop-mediated Isothermal Amplification COVID-19 Assay
on Gold-nanoparticle-modified Screen-printed Carbon Electrode
Using Differential Pulse Voltammetry
Munirah Zulhairee,1,2 Rozainanee Mohd Zain,2 Adibah Che Mohamad Nor,2,3
Nur Fadhilah Jafar,1 Mohd Fairulnizal Md Noh,2 Mohd Shihabuddin Ahmad Noorden,3Anis Nurashikin Nordin,4 Rosminazuin Ab Rahim,4 Teddy Surya Gunawan,4
Lim Ying Chin,1 Yusairie Mohd,1 Nur Asyiqin Azman,1 and Zainiharyati Mohd Zain1*
1Electrochemical Material and Sensors (EMaS), Faculty of Applied Sciences, Universiti Teknologi MARA (UiTM),

40450 Shah Alam, Selangor, Malaysia

2Institute for Medical Research (IMR), National Institute of Health (NIH), Jalan Setia Murni U13/52,

Seksyen U13 Setia Alam, 40170 Shah Alam, Selangor, Malaysia

3Faculty of Pharmacy, Universiti Teknologi MARA (UiTM), Puncak Alam, Bandar Puncak Alam,

42300 Puncak Alam, Selangor, Malaysia

4International Islamic University Malaysia, Jalan Gombak, 53100 Kuala Lumpur,

Wilayah Persekutuan Kuala Lumpur, Malaysia(Received April 17, 2023; accepted July 24, 2023)

Keywords: COVID-19, RT-PCR, RT-LAMP, AuNP/SPCE, differential pulse voltammetry,electrochemical sensor

The World Health Organization (WHO) has recommended real-time reverse transcription
polymerase chain reaction (RT-PCR) as the gold standard for coronavirus disease detection. In
this study, we aim to validate the clinical performance of reverse transcription loop-mediatedisothermal amplification (RT-LAMP) assay on a gold-nanoparticle-modified screen-printed
carbon electrode (AuNP/SPCE) using differential pulse voltammetry (DPV) and to compare itwith real-time RT-PCR. The electrodeposited AuNP on SPCE was quasi-spherical with a size of±500 nm. The developed RT-LAMP primer was designed from the GenBank database using the
NCBI Multiple Alignment tools and Jalview software. Nasopharyngeal clinical samples wereobtained from suspected COVID-19 patients (n = 148). The RT-LAMP products were dropped
on the modified AuNP/SPCE under DPV setting, which resulted in current change (∆I)
responses. The positive and negative samples produced significantly different ∆I signals with ap-value <0.0001 at a 95% confidence interval using Student’s t-test. The RT-LAMP assay using
Au/SPCE exhibited a 30 s response time per analysis. The clinical sensitivity and specificity
obtained were 79.7 and 85.1%, respectively, with a detection limit of 0.4 copies µl−1. Hence, this
proposed method is suitable for COVID-19 RNA detection in resource-limited settings.

*Corresponding author: e-mail: zainihar@uitm.edu.myhttps://doi.org/10.18494/SAM4430

ISSN 0914-4935 © MYU K.K.
https://myukk.org/

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1.

Sensors and Materials, Vol. 35, No. 10 (2023)

Introduction

In late December 2019, the world was shocked by the emergence of a novel coronavirus(CoV) that causes coronavirus disease (known as COVID-19). In an effort to curb the spread of
the virus, the U.S Food Drug and Administration (FDA) issued the Emergency Use Authorization(EUA) to authorize unapproved drugs and medical devices, including the implementation ofreal-time RT-PCR, which is a diagnostic laboratory tool recommended by WHO and considered
the gold standard for COVID-19 detection.(1,2) It requires a series of procedures involving theamplification of nucleic acids, including reverse transcription, denaturation, primer annealing,and extension.(3,4) However, real-time RT-PCR can only be carried out in certified laboratories
with expensive equipment maintenance and trained personnel, and with a turnaround time(TAT) of more than 24 h.(3) In our context, TAT is the time taken from sample extraction up to a
result declaration. In addition, the drawback of real-time RT-PCR is that it might lead to highfalse-negative and false-positive results.(4,5) The term of analysis time here is defined as theoperational time taken by an instrument to obtain the output signal. In this study, differential
pulse voltammetry (DPV) was used for the detection of SARS-CoV-2’s RNA.
DPV can be used to detect viral DNA as it is sensitive to low capacitive current and thus
excellent for trace detection.(6) This was proven by Moço et al. for the detection of a Zika virus
antigen on graphite electrodes modified with the reduced graphene oxide and polytyramineconducting polymer immobilized with a Zika virus oligonucleotide probe.(7) Furthermore, theelectrochemical sensor as a sensing platform has been proven effective in detecting SARSCoV-2 viral infection.(8,9) The electrochemical sensor is the best alternative to biochemical
analysis and immunoassays in medical diagnostics owing to low-cost instrumentation,
portability, rapidity, precision, and sensitivity.(10–12) As such, Alafeef et al. employed theintegration of thiol-modified antisense oligonucleotide probes, which are single-stranded DNA(ssDNA) sequence probes with capped gold nanoparticles (AuNPs) that are specific and selective
for the nucleocapsid (N) gene for COVID-19 detection.(13)Hence, to mitigate the limitations of real-time RT-PCR, an electrochemical sensor for clinical
COVID-19 detection following the guidelines formulated by WHO is an ideal diagnostic tool ina resource-limited setting.(14,15) Therefore, a miniaturized electroanalytical system was
implemented with Au-nanoparticle-modified screen-printed carbon electrodes as these sensors
require only microliters of samples.(16) Malecka et al. used a screen-printed gold electrode(SPGE) for the electrochemical detection of specific DNA and RNA sequences derived from
avian influenza virus (AIV H5N1).(15) In their study, a monolayer of thiolated DNA probe(SH-NC3) and 6-mercaptohexanol (MCH) was deposited onto the SPGE surfaces incorporated
with a sensitive ion-channel mimetic miniaturized genosensor.(15) The critical challenges would
be in the mass production of SH-NC3 and MCH via deposition on the SPGE surface and direct
RNA sequencing without a reverse transcription from RNA to DNA.
The RT-LAMP assay coupled with Au/SPCE can be one of the unified platforms for theInternet of Things (IoT). The electrochemical signals can be read and recognized throughinformation-sensing devices controllable through IoT. The RT-LAMP method involves six target
sequences simultaneously recognized by separate definite primers in the same reaction; thus, theSensors and Materials, Vol. 35, No. 10 (2023)

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method has high detection specificity and sensitivity.(17,18) Moreover, the advantages of the RTLAMP assay for SARS-CoV-2 nucleic acid amplification include a quick and reliable detection
as it can detect small amounts of DNA or RNA templates within an hour. Moreover, RT-LAMP
requires inexpensive instruments and environment-friendly reagents and promotes point-of-care
testing (POCT).(19,20) Ramírez-Chavarría et al. successfully developed a technique for the
genetic amplification of the COVID-19 genome by RT-LAMP integrated with electrical
impedance spectroscopy (EIS) analysis using time-constant domain spectroscopy (TCDS).(21)The TCDS served to assess the RT-LAMP products and classify the positive and negative
COVID-19 samples.(21) Interestingly, a rapid multiplexed ultrasensitive sample-to-answer loopmediated isothermal amplification chip for simple LED-driven photothermal amplification wasdeveloped for the detection of SARS-CoV-2 and an influenza virus concurrently. This chip is
highly sensitive with a limit of detection (LOD) of 0.5 copies μl−1 for SARS-CoV-2 detection andcould identify different genes within an assay in the presence of many other viruses.(22) Zhang etal. enabled the identification of purified COVID-19 viral RNA using RT-LAMP assay bycolorimetric detection without IoT embodiment.(23,24) In the scenario of the COVID-19
pandemic, IoT could store a vast amount of patient data, provide personalized medicine, andretrieve test results via smartphone applications, which can be accessed by not only healthcare
providers but also individuals.(24)Here, the clinical performance of the RT-LAMP assay using COVID-19 nasopharyngeal
swab samples on the AuNP/SPCE using DPV was evaluated. First, a bioinformatics study on the
N gene region of the SARS-CoV-2 genome structure was conducted to find a specific DNA
sequence as the RT-LAMP primer. The RT-LAMP primer was then used in the RT-LAMP mix
reagent for the amplification process. The amplification product was validated using gelelectrophoresis. AuNP/SPCE was used as a sensing platform with 5 mM K4[Fe(CN)6] as the
background electrolyte. Approximately 4 µL of the RT-LAMP products was dropped on AuNP/
SPCE and DPV was conducted, which resulted in distinct signal outputs of the blank (5 mM
K4[Fe(CN)6], positive control, negative control, and positive and negative samples. The results ofthe clinical assays using the developed electrochemical sensor were statistically compared withthose obtained by the real-time RT-PCR (gold standard) endorsed by the Institute for Medical
Research, National Institute of Health, and Ministry of Health (MOH), Malaysia.

2.

Data, Materials, and Methods

2.1

Ethical approval

Ethical approval was granted by the UiTM Research Ethics Committee (REC) with referral
number REC/03/2022 (PG/MR/51) and Medical Research Ethics Committee (MREC), MOH,
with referral number NMRR ID-22-006675-CFP; Protocol ID: 14022022.
2.2

Reagents and materials

The COVID-19 RNA extraction kit used in this research was the QIAamp Viral RNA Mini
Kit (Qiagen, German) with 99% ethanol obtained from Thermo Fisher Scientific Inc. The

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Sensors and Materials, Vol. 35, No. 10 (2023)

Real-Q 2019-nCoV detection kit purchased from BioSewoom Inc. (Seoul, Republic of Korea)
was the COVID-19 detection kit used for the real-time RT-PCR procedure. WarmStart®
Colorimetric 2X Master Mix with UDG (New England Biolabs Inc., Massachusetts, USA) wasused as the RT-LAMP reagent kit. The ISO 13485-certified SPCE modified with AuNP was
obtained from Metrohm DropSens, Spain. Potassium ferrocyanide (K4[Fe(CN)6]) and potassium
chloride (KCl) were purchased from Sigma Aldrich, USA. Sulphuric acid (H2SO4) was obtained
from R&M Chemicals, Malaysia.
2.3

Clinical COVID-19 samples

The nasopharyngeal swabs of clinical COVID-19 samples were placed in viral transport
media (VTM) upon collection (n = 148) from the COVID-19 laboratory, Institute for Medical
Research, National Institute of Health, Setia Alam, Selangor, Malaysia. The COVID-19 samples
were transported in an ice box for a maximum of 72 h at 2–8 ℃ after collection. The RNA
extraction and real-time RT-PCR procedure were conducted immediately after the collection.
2.4

Inactivation of COVID-19 samples

The COVID-19 samples were heat-inactivated at 65 ℃ for 1 h in a GFL-70 Blast Dry Oven at
the COVID-19 laboratory, Institute for Medical Research, National Institute of Health, Setia
Alam, Selangor, Malaysia.
2.5

Extraction of COVID-19 RNA

Approximately 140 µl of COVID-19 RNA from the nasopharyngeal swab samples was
extracted using the QIAamp Viral RNA Mini Kit (Qiagen). The samples were extracted
following a standard procedure guided by the manufacturer and approved by the Institute for
Medical Research, National Institute of Health, Malaysia. The extraction procedure involves aseries of incubation and filtration steps with several types of buffer such as lysis, wash, andelution buffers to obtain the extracted RNA of COVID-19.
2.6

COVID-19 RT-LAMP assay development

The WarmStart® colorimetric assay was conducted using a specifically selected sequence ofprimers targeting the N gene of SARS-CoV-2. The RT-LAMP assay was performed in 23.0 µl ofreaction mixture consisting of 12.5 µl of WarmStart® colorimetric 2× master mix with UDG(NEB, Ipswich, United Kingdom), 2.5 µl of 10X primer mix, 2.5 µl of 4 mM guanidinehydrochloride solution, and 5.5 µl of nuclease-free molecular-biology-grade water incubated at
65 ℃ in an incubator (GFL-70 Blast Dry Oven) for 35–40 min.

Sensors and Materials, Vol. 35, No. 10 (2023)

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2.6.1 RT-LAMP target selection and specific primer design
The conserved sequence of the N gene was determined on the basis of the multiple alignmentof the available completed SARS-CoV-2 sequences obtained from the GenBank database using
the NCBI Multiple Alignment tools and Jalview software. The RT-LAMP target region was
selected on the basis of the sequences that showed more than 98% nucleotide similarities. For insilico specificity analysis, the homology of the target region sequence was compared with that of
the N gene sequence of several other related coronaviruses and high-priority pathogens using
Nucleotide BLAST (BLASTn). The result for in silico specificity was obtained on the basis of
local and global pairwise alignment.
2.6.2 Colorimetric RT-LAMP productApproximately 2 µl of extracted RNA of COVID-19 samples was dropped into 23.0 µl of RTLAMP reagent. The samples were incubated at 65 ℃ in an incubator (GFL-70 Blast Dry Oven)for 35–40 min. The RT-LAMP product also included positive and negative controls. The positive
control (PC) consisted of 2 µl of a plasmid containing the target sequence of SARS-CoV-2. The
negative control (NC) consisted of only RT-LAMP reagents without any amplification templates.
The color change (pink to yellow) indicates positive amplification, whereas the unchanged color(remained pink) indicates negative amplification. The product of RT-LAMP amplification was
further analyzed by running the products by 2% agarose gel electrophoresis for 40 min (4000mA, 90 V) and visualized under blue light. The presence of a ladder pattern band on lane (L) in
the gel electrophoresis readout indicated positive amplification. The LOD of the RT-LAMP
assay was obtained by determining the lowest number of plasmid copies (1.6, 0.8, 0.4, or 0.2) per
microliter that caused the color change from pink to yellow.
2.7

Real-time RT-PCR for COVID-19 detection

The extracted RNA of COVID-19 samples was subjected to real-time RT-PCR, which was
performed using the Real-Q 2019-nCoV detection kit (BioSewoom, Seoul, Republic of Korea)
with the CFX96 real-time PCR detection system (Bio-Rad Laboratory, Inc., California, U.S.A.)
as approved by the Institute for Medical Research, National Institute of Health, and MOH,
Malaysia. The reaction mixture or master mix is composed of 3.50 µl of RNase-free water(RFW), 12.50 µl of 2X PCR reaction mixture, 3.00 µl of nCoV primer and probe mix, and 1.00
µl of RT-PCR enzyme with 5.0 µl of template or sample. The positive control used in this study
was provided by the manufacturer, whereas for the negative control, no reagent was added to themaster mix. The real-time RT-PCR was also carried out under the following conditions: reverse
transcription at 50 ℃ for 30 min, pre-denaturation at 95 ℃ for 15 min, denaturation at 95 ℃ for
15 s, and extension at 62 ℃ for 45 s in 40 cycles. The results of the real-time RT-PCR wereanalyzed by reading the threshold value (CT), which was the replication cycle required to yield
the fluorescent signal. CT is the cycle during which the fluorescent signal exceeded thebackground level and crossed the threshold. The cut-off value for the real-time RT-PCR was4736

Sensors and Materials, Vol. 35, No. 10 (2023)

standardized following the guidelines provided by the Institute for Medical Research and MOH,
Malaysia. A CT value of less than 38 indicated that COVID-19 was detected (a positive reading),
whereas a CT value of more than 38 indicated that COVID-19 was not detected (a negative
reading).
2.8

Fabrication of gold nanoparticles on screen-printed carbon electrode

A mixture of 10 mM gold (III) chloride trihydrate (HAuCl4‧3H2O) and 1 M sulphuric acid
(H2SO4) was prepared as the deposition solution. The electrodeposition of AuNP on the SPCE
was carried out using a three-electrode system with the built-in printed electrode in the SPCE
serving as the reference electrode (Ag) and carbon (C) serving as the counter electrode. The
SPCE was submerged in 10 mM HAuCl4‧3H2O + 1 M H2SO4, and the cyclic voltammetry sweep
was performed for 50 cycles (15 min) from +0.5 to −0.6 V at room temperature. The resulting
AuNP/SPCE was then rinsed with deionized water, dried using a nitrogen stream, and stored ina desiccator for future use.
2.9

Clinical COVID-19 assay on AuNP/SPCE using DPV

A 5 mM K4[Fe(CN)6] solution containing 0.1 mM KCl and 5 mM ferrocyanide ([Fe(CN)6]4−
was used not only as an electrochemical label but also as a blank solution. Approximately 4 µl of
the COVID-19 samples (RT-LAMP product) [N = 148; (n = 74 positive samples (PS) and n = 74
negative samples (NS)] was dispensed onto the AuNP/SPCE (gold working electrode), followedby 100 µl of K4[Fe(CN)6] background electrolyte. An incubation time of 5 min (optimal) was
employed prior to DPV electroanalysis. The DPV was conducted by scanning the potential range
from −0.2 to +0.6 V, the pulse amplitude of 0.002 V, the pulse period (tpulse) of 50 ms, and thescan rate of 0.025 Vs−1. The resulting signals of the samples were analyzed using a portablecustom-built potentiostat (NACOTS,UiTM, Malaysia) connected to the sensor through a USB
port for the signal readout. The DPV response signals of the peak current (Ip) (µA) for all thesamples were obtained. The ∆I (µA) for the COVID-19 samples was obtained from thedifferences between the Ip of the blank and that of the COVID-19 samples.
		(1)=∆I I p ( BLANK ) − I p ( POSITIVE SAMPLES )		(2)=∆I I p ( BLANK ) − I p ( NEGATIVE SAMPLES )2.10

Receiver operating characteristic (ROC) curve analysis

The ROC curve analysis was applied to investigate the cut-off ∆I (µA) values for thedeveloped sensor between the positive and negative clinical COVID-19 samples. The ROC curveis useful for assessing the effectiveness of the diagnostic procedure. The curve is interpreted as
the test’s sensitivity (the y-coordinate) against the test’s 1-specificity (the x-coordinate). The
value with the highest Youden’s index and adequate sensitivity based on the ROC analysis waschosen as the ideal cut-off value for further analysis.

Sensors and Materials, Vol. 35, No. 10 (2023)

2.11

4737

Statistical analysis

The significant difference between positive and negative clinical samples was analyzed using
Student’s t-test and validated on GraphPad Prism version 9 (GraphPad Software Inc., USA). At a
95% confidence interval (CI), the results were not significant when the two-sided p-value wasmore than 0.05 (p > 0.05). The 148 nasopharyngeal swab samples [n = 74 (PS) and n = 74 (NS)]with 95% confidence or 95% reliability were tested on AuNP/SPCE and clinically comparedwith the real-time RT-PCR test. The clinical sensitivity, which is defined as the number ofabnormal conditions, and the specificity, which is defined as the number of normal conditions,were analyzed.(25)3.

Results and Discussion

3.1

Identification of COVID-19 samples by RT-LAMP assay and CFX96 real-time PCR
detection system

The extracted RNAs from COVID-19 samples (N = 148) were subjected to the RT-LAMP
assay and produced the following results: 53 samples (n = 53) showed color changes, whereas theother samples (n = 95) remained pink. The results obtained indicate that 52 samples were true
positive, 22 samples were false negative, 73 samples were true negative, and a sample was false
positive. The results of the colorimetric RT-LAMP indicated that the PS showed a change incolor from pink to yellow, whereas the NS showed no change in color (remained pink) after heatincubation at 65 ℃ for 35–40 min as shown in Fig. 1. However, some of the PS remained pink

Fig. 1. (Color online) Typical detection of COVID-19 by reverse transcription loop-mediated isothermal
amplification (RT-LAMP) with the end product of color changes based on colorimetric readout. (A) Positive control(PC) and positive samples (PS 1 to PS 5) or amplified RT-LAMP products that changed color from pink to yellow.
(B) Negative control (NC) and negative samples (NS 6 to NS 10) of RT-LAMP products that showed no change incolor or remained pink.

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Sensors and Materials, Vol. 35, No. 10 (2023)

under 30 min of incubation at 65 ℃ because the CT values of RT-PCR ranged from 30 to 38(CT > 30–38). The detected COVID-19 samples appeared orange, which was classified as anindeterminate viral load. This classification is due to false positives or false negatives fromvisual observation.(26,27) Hence, the development of a COVID-19 diagnostic sensor assay as suchis relevant for viral detection because color changes from calorimetric RT-LAMP assays mightlead to the misinterpretation of clinical results. The positive and negative controls were also run
to indicate the reliability of the test.
In principle, the color change in the RT-LAMP assay from pink to yellow is attributable tophenol red as a pH indicator, which allowed a clear visual detection to distinguish between NS
and PS. The color changes from pink to yellow at pH 6.8 because the reaction mix was boundwith phenol red. The production of protons and the decrease in pH occurred because of theextensive RNA polymerase activity in the RT-LAMP reaction. Throughout the amplification by
RT-LAMP, the protons produced caused the pH to change from neutral to acidic. Next, dNTPs
were integrated by DNA polymerase into the growing DNA chain. Pyrophosphate compounds
and hydrogen ions were emitted as by-products of this chemical reaction. The emission ofhydrogen ions was directly proportional to the quantity of newly integrated dNTPs, thus
releasing a large amount of hydrogen ions. The formation of hydrogen ions during RT-LAMP
substantially and promptly shifted the pH across the threshold of phenol red from pH 8.5 to pH
6.8. The positive amplifications can be seen clearly owing to the increase in the amount of H+
ions resulting from DNA amplification in the RT-LAMP master mix.(28)
The RT-LAMP products were further visualized by agarose gel electrophoresis to determine
the presence of amplified products for PS and to detect any contamination in the NS. Figure 2

Fig. 2. (Color online) Agarose gel electrophoresis of typical loop-mediated isothermal amplification (RT-LAMP)
products for PS from lanes 1 to 5 and NS from lanes 6 to 10.

Sensors and Materials, Vol. 35, No. 10 (2023)

4739

shows the presence of ladder pattern bands for the amplified products of PS in lanes 1 to 5,
whereas ladder pattern bands are absent for the NS in lanes 6 to 10. The absence of ladderpattern bands for samples indicates that there was no contamination or any reaction.
From the real-time RT-PCR, the results obtained were 74 PS and 74 NS. From these results,
the PS showed CT values of less than 38 (CT < 38), whereas the NS presented CT values of morethan 38 (CT > 38). The CT values for three gene must be determined to indicate that the samples
were either positive or negative. Three types of genes were considered in the procedure, namely,
RNA-dependent RNA polymerase (RdRp), envelope protein (E), and ribonuclease (RNP) genes
or internal control (IC). The first-line screening test was based on an E-gene assay followed by aconfirmatory assay using the RdRp gene, since real-time RT-PCR assays targeting the RdRp
gene had the greatest analytical sensitivity.(29) The RNP gene indicated the presence of humangenes or showed a good sampling procedure by nasopharyngeal swab and proper nucleic acidextraction.(2) Hence, the values of PS were calculated as the CT values of the E, RdRp, and
RNP genes to be less than 38 (CT < 38). The values of the NS were more than 38 (CT > 38) for the
RdRp and E genes, whereas IC presented CT values of less than 38 (CT < 38) to indicate theappropriate method of nasopharyngeal swab sampling.(30)
As mentioned, the target site for the developed RT-LAMP assay was located at the N gene.
According to CDC, it is recommended to utilize two primer-probe sets targeting the N gene (N1and N2) to achieve a more sensitive and specific detection of COVID-19.(2) Furthermore, theinfected cell was found to have many coronavirus N proteins since it has been postulated thatthese proteins play roles in virus replication, transcription, and translation.(30) Fifty-three
COVID-19 samples for RT-LAMP showed a color change (pink to yellow), whereas the othersamples showed no change in color (remained pink). The results of both methods showed
equivalent qualitative values. The results of RT-LAMP and real-time RT-PCR are shown in
Supplementary Table 1.

Table 1
In silico cross-reactivity of RT-LAMP primer with other viruses.
RT-LAMP Target Region

OrganismGene

N gene of SARS-CoV-2

Human Coronavirus 229E (NC_002645.1)
Human Coronavirus OC43 (AY391777.1)
Human Coronavirus HKU1 (NC_006577.2)
Human Coronavirus NL63 (NC_005831)
SARS-Coronavirus (NC_004718.3)
MERS-Coronavirus (KT006149.2)
Human Adenovirus (DQ315364.2)
Influenza A (NC_007369.1)
Influenza B (NC_002208.1)
Human Parainfluenza 1 (NC_003461.1)
Human Parainfluenza 2 (NC_003443.1)
Human Parainfluenza 3 (NC_001796.2)
Human Parainfluenza 4 (NC_021928.1)

Nucleocapsid
Nucleocapsid
Nucleocapsid
Nucleocapsid
Nucleocapsid
Nucleocapsid
Nucleocapsid
Nucleocapsid
Nucleocapsid
Nucleocapsid
Nucleocapsid
Nucleocapsid
Nucleocapsid

Searches
Global (%) Local (%)
15.0
17.6
13.0
12.2
14.0
13.1
16.0
16.6
14.0
94.9
14.0
16.8
13.0
13.2
12.0
13.2
11.0
12.4
12.0
10.8
11.0
11.8
12.0
11.7
11.0
10.7

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3.2

Sensors and Materials, Vol. 35, No. 10 (2023)

In silico cross-reactivity analysis of RT-LAMP assay

The cross-reactivity of the RT-LAMP target region of the N gene was further tested withother coronaviruses such as the influenza virus and adenovirus. Table 1 shows that the RTLAMP primer does not indicate a cross-reactivity with other N genes of other viruses. Thus, thedeveloped RT-LAMP primer was unique and specific to the N gene of COVID-19.
In contrast, the RT-LAMP could also be used with multiplex amplifications with thedevelopment of a high-fidelity DNA polymerase-mediated probe (HFman probe) in detecting
other target variants of COVID-19 that are aligned with the recent emerging COVID-19 mutants.
This system could simultaneously detect open reading frame (ORF) and E genes in an assay.(31)
In addition, there are also other studies with three different primer sets of RT-LAMP showingthat the multiplex isothermal amplification could detect the RdRP, E, and N genes of SARS
CoV-2 in a single tube.(32)3.3

Morphology and electrochemical performance of AuNP/SPCEFESEM analysis was conducted to study the morphology of the electrodeposited AuNP/
SPCE and unmodified SPCE. It can be observed from the FESEM images shown in Fig. 3 that
the AuNPs obtained were quasi-spherical with a faceted shape and a diameter of ±500 nm. Aftermodification, the SPCE surface is fully covered with AuNPs as shown in Figs. 3(a) and 3(b). The
agglomeration of AuNPs can be observed in Fig. 3(b) owing to the aggregated growth of twoneighboring AuNPs or amorphous atom clusters.(33)
The deposition of AuNPs onto the SPCE was performed using cyclic voltammetry with apotential range from +0.5 to 0 V for 90 cycles at a scan rate of 50 mVs−1. The deposition process
typically takes up to 30 min. As the number of cycles increased, the reduction potential shifted

(a)

(b)

Fig. 3. FESEM images of (a) unmodified SPCE and (b) AuNP/SPCE at 5 K magnification. The inset in (b) shows
10 K magnification.

Sensors and Materials, Vol. 35, No. 10 (2023)

4741

to a more positive value, indicating the successful electrodepositions of AuNPs from theprevious scans as shown in Fig. 4. This cathodic peak shifting is consistent with the finding by
Zakaria et al.(33) The performance characteristics of both the AuNP/SPCE and the unmodified
SPCE were evaluated using cyclic voltammetry in a solution containing 10 mM K₄[Fe(CN)₆] and
0.1 M KNO3. The results shown in Fig. 5 indicate that the AuNP/SPCE surface had the highest
redox current response of ferrocyanide ions when compared with the unmodified SPCE. The
observed increase in redox current response can be attributed to the enhanced electricalconductivity of the SPCE surface resulting from the good electrical contact of the deposited
AuNPs. This increased conductivity allows for a more efficient electron transfer during the

Fig. 4.

Cyclic voltammograms of electrodeposition of AuNP/SPCE in 10 mM HAuCl4 in 0.1 M H2SO4.

Fig. 5. Cyclic voltammograms of electrochemical performance characteristics of AuNP/SPCE and unmodified
SPCE in 10 mM K4Fe(CN)6 + 0.1 M KNO3.

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redox process, which leads to a higher redox current response. Therefore, the AuNP/SPCE
sensor is more efficient than the unmodified SPCE sensor in detecting analytes at lower
concentrations.
3.4

Evaluation of COVID-19 samples by COVID-19 diagnostic assay

The RT-LAMP products were dropped on the electrodes and run with the sensor by the DPV
method. As shown in Fig. 6(a), there were differences in the electrochemical and morphological
profiles of the AuNP/SPCE with PS and NS. The peak height (Ip) (µA) for the blank is larger
than those for NS and PS. The blocking effect due to the presence of DNA at the electrodecaused a decrease in the oxidation peak current (Ip) (µA) of the ferrocyanide ion.(7) The value of∆I for all COVID-19 samples was calculated using Eqs. (1) and (2) and the results are shown in
Supplementary Table 1. Figure 6(b) shows the mean difference (∆I) (µA) in PS value, whichshowed a higher ∆I response than that in NS value. From the results of the statistical analysis
using Student’s t-test, at a 95% CI, a significant difference was observed between the PS and NS(p < 0.0001) responses.
3.5

Cut-off point for COVID-19 sensor

The cut-off ∆I values between normal and abnormal conditions were analyzed using the ROC
curve shown in Fig. 7. On the basis of the analysis results, we determined that the ideal cut-off
value with the highest Youden’s index is more than 0.94 µA. The area under the curve was 0.905(p < 0.0001) with the maximum Youden’s index at 0.65, corresponding to a critical cut-off valueof 0.94 µA. The clinical sensitivity of 79.7% and the specificity of 85.1% (Table 2) were obtained.

(a)

(b)

Fig. 6. (Color online) (a) DPV using 5 mM K4[Fe(CN)6] as blank, PS, and NS with amplitude modulation:
potential range from −0.2 to +0.6 V, pulse amplitude of 0.002 V, pulse period (tpulse) of 50 ms, and scan rate of 0.025
Vs−1. (b) Mean values and standard deviations of PS and NS.

Sensors and Materials, Vol. 35, No. 10 (2023)

Fig. 7.

(Color online) ROC curve of COVID-19 samples.

Table 2
Area under the curve and diagnostic performance of the test.
ROC curve area Cut-off value (µA) Youden’s index
Sensitivity (%)
0.905
>0.94
0.65
79.7

3.6

4743

Specificity (%)
85.1

p-value<0.0001

Reliability of sensor performance compared with real-time RT-PCR

Real-time RT-PCR is demanding as it is laboratory-based and requires highly trained
personnel to perform. These inconveniences cause difficulties for healthcare providers to
perform the tests. However, compared with real-time RT-PCR, the electrochemical sensor tested
in this study was found to be more reliable in detecting COVID-19. The sensor can be utilized as
a POCT instrument in different sites such as stadiums and schools or in any events. The longer
time required to run the samples using real-time RT-PCR may contribute to delays in delivering
the results, which could cause fatalities due to late treatment and increase the number of cases
due to an increased possibility that the COVID-19 virus has been more widely spread.
Furthermore, the cost of utilizing the electrochemical sensor is not as high as the cost of realtime RT-PCR. The instrumentation cost of electrochemical analysis is about US$ 1000 compared4744

Sensors and Materials, Vol. 35, No. 10 (2023)

with RT-PCR that costs US$ 30000. Hence, there are price reduction of 25% per test and 96% for
the instrument. Therefore, the price reduction can helps the government in controlling the budget
and sustain the economy. Table 3 shows the comparison between real-time RT-PCR and theelectrochemical sensor. The significant difference in performance between both instruments or
methods was evaluated using GraphPad Prism Version 9 and Student’s t-test at a 95% CI. The
mean difference in the bar graph with standard deviation showed no significant difference
between real-time RT-PCR and the sensor as the p-value is 0.7729 (p ≥ 0.05) as shown in Fig. 8.
The data from real-time RT-PCR and the COVID-19 sensor are tabulated in Supplementary
Table 1. Table 4 shows the diversities of sensing platforms for COVID-19 detection, namely,
electrochemical immunosensors, paper-based biosensors, and microfluidic multiplexed polymerbased biosensors that are in accordance with specific medical and economic situations.
Ultimately, this developed detection method is much more suitable for SARS-CoV-2 RNA
sensing in a resource-limited setting.

Table 3Comparison between real-time RT-PCR and electrochemical sensor.
Components
Real-time RT-PCR
Portability
Not portable
Analysis time
~2 hTurnaround time (TAT)~6 hCost per test
RM70.68
Limit of detection (LOD)
3.125 copies µl−1

Fig. 8.

Electrochemical Sensor
Portable~30 s~1 to 2 h
RM53.03
0.4 copies µl−1

(Color online) Mean difference with standard deviation between real-time RT-PCR and COVID-19 sensor.

Sensors and Materials, Vol. 35, No. 10 (2023)

Table 4Comparison of three sensing platforms for COVID-19 detection.
Sensing Platform
Remarks
The LOD claimed by the authors forthe DPV signal was 0.14 nM, and it was
Screen-printed carbon electrodes (SPCEs)
suggested to be applied to COVID-19
with electrodeposited gold nanostructures
patients before and after vaccination as the(AuNS) modified with SARS-CoV-2
spike proteins (rSpike) for the detection of specificity test was evaluated and found toshow nonspecificity towards anti-rSpikespecific antibodies
antibodies.
Lateral flow immunoassay (LFIA)-based
biosensor: production of antibodies and
The biosensor can detect COVID-19 withinsingle-chain variable fragment (scFv)20 min with no cross-reactivity with othercrystallizable fragment (Fc) fusion proteins
coronaviruses such as SARS-CoV and(scFv-Fcs) by phage display technology
MERS-CoV.
for the specific detection of SARS-CoV-2
nucleocapsid protein (NP).
Microfluidic multiplexed polymer-based
The LOD for the E gene was 2000 copies
biosensor in combination with CRISPR/µl−1 and that for the RdRp gene was 7520
Cas-powered assays. Omicron variants ofcopies µl−1. The time taken to process theenvelope (E) and RNA-dependent RNA
sample until the result was obtained waspolymerase (RdRp) genes were detected byapproximately 30 min.
using Leptotrichia buccalis (Lbu)Cas13a.
The LOD for the RT-LAMP assay was
Detection of nucleocapsid (N) gene using a
0.4 copies µl−1 with an analysis time of
specifically designed primer sequence for
30 s and a TAT of approximately 1 h. The
RT-LAMP and dropped on AuNP/SPCE
clinical sensitivity and specificity wereand portable potentiostat
79.7% and 85.1%, respectively.

4.

4745

References

Drobysh et al., 2022(34)Kim et al., 2021(35)Johnston et al., 2022(36)This work

Conclusions

This study revealed that the clinical results for COVID-19 samples (N = 148) using the RTLAMP assay were comparable to those obtained using real-time RT-PCR. The sensor platform
used in this study was AuNP/SPCE, and the morphological surface was proven by FESEM tohave gold nanoparticles. The RT-LAMP products were analyzed on Au/SPCE with DPV
response using a custom-built portable potentiostat. The PS showed a lower (∆I) mean than the
NS. The PPA value of the sensor was 100%. The ∆I values of the PS and NS were significantly
different with a p-value of 0.0001 (p < 0.05) at a 95% CI on Student’s t-test. The real-time RTPCR instrumentation and analysis were more tedious, laborious, and costly than the RT-LAMP
electroanalytical method, which was convenient, inexpensive, and portable. The LOD for the
RT-LAMP assay was 0.4 copies µl−1, whereas that for the Biosewoom Real-Q 2019-nCoV
commercial detection kit was 3.125 copies µl−1. However, the colorimetric result from the RTLAMP alone could lead to insensitivity in evaluating the color changes as the reading would be
affected by surrounding light due to the spectral composition of the light source. A color-blind
person might also contribute to the false positive and negative reading of the RT-LAMP product.
Hence, DPV enables the integration with the IoT system for better data collection, storage, andanalysis that enhance the decision making in outbreak management.

4746

Sensors and Materials, Vol. 35, No. 10 (2023)

Acknowledgments
This research project was funded by the UiTM Internal Grant (600-RMC/KEPU 5/3(157/2021) and the Ministry of Science, Technology, and Innovation (MOSTI) under the
Combating COVID-19 Fund (project number: CV 02211032) grants.

References
1 World Health Organization: https://apps.who.int/iris/handle/10665/331686%0ADescription (accessed January2023).
2 Centers for Diseases Control and Prevention (CDC): https://www.fda.gov/media/134922/download (accessed
November 2022).
3 X. Lizhou, L. Danyang, R. Sami, L. Yanbin, and K. Norbert: Biosens. Bioelectron. 170 (2020) 2. https://doi.
org/10.1016/j.bios.2020.112673
4 N. Chen, M. Zhou, X. Dong, J. Qu, F. Gong, Y. Han, Y. Qiu, J. Wang, Y. Liu, Y. Wei, J. Xia, T. Yu, X. Zhang,and L. Zhang: Lancet 395 (2020) 508. https://doi.org/10.1016/S0140-6736(20)30211-7
5 C. Huang, Y. Wang, L. Xingwang, L. Ren, J. Zhao, Y. Hu, L. Zhang, G. Fan, J. Xu, X. Gu, Z. Cheng, T. Yu,
J. Xia, Y. Wei, W. Wu, X. Xie, W. Yin, H. Li, M. Liu, Y. Xiao, H. Gao, L. Guo, J. Xie, G. Wang, R. Jiang,
Z. Gao, Q. Jin, J. Wang, and B. Cao: Lancet. 395 (2020) 500. https://doi.org/10.1016/S0140-6736(20)30183-5
6 S. Monteil, A. J. Casson and S. T. Jones: PloS One. 16 (2021) 1. http://dx.doi.org/10.1371/journal.pone.0258002
7 A. C. R. Moço, P. H. Guedes, J. M. R. Flauzino, S. Silva, J. G. Vieira, A. C.Castro, E. V. R. Gomes, F. M.
Tolentino, M. M. C. N. Soares, J. M. Madurro, and A. G. Brito-Madurro: Electroanalysis. 31 (2019) 1. https://doi.org/10.1002/elan.201900068
8 L. F. D. Lima, A. L. Ferreira, M. D. T. Torres, W. R. D. Araujo, and C. D. L Fuente-Nunez: Proc. National
Academy of Sciences of the United States of America (2021) 1–9.
9 S. Tripathy and S. G. Singh: Trans. Indian Natl. Acad. Eng. 5 (2020) 205. https://doi.org/10.1007/s41403-02000103-z
10 Y. Lee, J. Choi, H. K. Han, S. Park, S. Y. Park, C. Park, C. Baek, T. Lee, and J. Min: Sens. Actuators, B 326(2021) 2. https://doi.org/10.1016/j.snb.2020.128677
11 E. Zeynaloo, E. Zahran, Y. P. Yang, E. Dikici, T. Head, L. G. Bachas, and S. Daunert: Biosens. Bioelectron.
200 (2021) 2. https://doi.org/10.1016/j.bios.2021.113861
12 S. Jain, M. Nehra, R. Kumar, N. Dilbaghi, T. Y. Hu, S. Kumar, A. Kaushik, and C. Z. Li: Biosens. Bioelectron.
179 (2021) 4. https://doi.org/10.1016/j.bios.2021.113074
13 M. Alafeef, K. Dighe, P. Moitra, and D. Pan. Rapid: ACS Nano. 14 (2020) 17028. https://doi.org/10.1016/j.
mattod.2022.11.001
14 G. Rosati, A. Idili, C. Parolo, C. Fuentes-Chust, E. Calucho, L. Hu, C. D. C. C. E Silva, L. Rivas, E.P. Nguyen,
J. F. Bergua, R. Alvarez-Diduk, J. Munoz, C. Junot, O. Penon, D. Monferrer, E. Delamarche, and A. Merkoci:
ACS Nano. 15 (2021). https://doi.org/10.1021/acsnano.1c06839
15 K. Malecka, A. Stachyra, A. Góra-sochacka, A. Sirko, W. Zagórski-ostoja, H. Radecka, and J. Radecki: Sens.
Actuators, B 224 (2015) 290. http://dx.doi.org/10.1016/j.snb.2015.10.044
16 A. C. Power and A. Morri: Electrochemistry (IntechOpen, Rijeka, 2013) Chap. 7.
17 T. Notomi, H. Okayama, H. Masubuchi, T. Yonekawa, K. Watanabe, N. Amino, and T. Hase: Nucleic Acids
Res. Spec. Publ. 28 (2000) 63. https://doi.org/10.1093/nar/28.12.e63
18 X. Huang, G. Tang, N. Ismail, and X Wang: EBioMedicine. 75 (2022) 103736. https://doi.org/10.1016/j.
ebiom.2021.103736
19 M. Shen, Y. Zhou, Ye J, A. A. A. AL-Maskri, Y. Kang, S. Zeng, and S. Cai: J. Pharm. Anal. 10 (2020) 97.
https://doi.org/10.1016/j.jpha.2020.02.010
20 A. Basu, T. Zinger, K. Inglima, K. M. Woo, O. Atie, L. Yurasits, B. See, and M. E. Aguero-Rosenfeld: J. Clin.
Microbiol. 58 (2020) 2. https://doi.org/10.1128/jcm.01136-20
21 R. G. Ramírez-Chavarría, E. Castillo-Villanueva, B. E. Alvarez-Serna, J. Carrillo-Reyes, L. Torres,
R. M. Ramírez-Zamora, G. Buitron, and L. Alvarez-Icaza: Chemosensors. 11 (2023) 2. https://doi.org/10.3390/
chemosensors11040230
22 M. Song, S. Hong, and L. P. Lee: Adv. Mater. 35 (2023) 2. https://doi.org/10.1002/adma.202207138
23 Y. Zhang, N. Odiwuor, J. Xiong, L. Sun, R. O. Nyaruaba, H. Wei, and Na. Tanner: MedRxiv. 2 (2020) 2. https://doi.org/10.1101/2020.02.26.20028373

Sensors and Materials, Vol. 35, No. 10 (2023)

4747

24 K. K Patel and S. M Patel: IJESC. 6 (2016) 1. https://doi.org/10.4010/2016.1482
25 GraphPad Software: https://www.graphpad.com/guides/prism/latest/statistics/sensitivity_and_specificity.htm(accessed Feb 2023).
26 V. L. D. Thi, K. Herbst, K. Boerner, M. Meurer, L. P. M. Kremer, D. Kirrmaier, A. Freistaedter,
D. Papagiannidis, C. Galmossi, M. L. Stanifer, S. Boulant, S. Klein, P. Chlanda, D. Khalid, I. B.
Miranda, P. Schnitzler, H. G. Krausslich, M. Knop, and S. Anders: Sci. Transl. Med. 12 (2020) 4. https://doi.
org/10.1126/scitranslmed.abc7075
27 M. N. Aoki, B. D. O. Coelho, L. G. B. Góes, P. Minoprio, E. L. Durigon, L. G. Morello, F. K. Marchini,
I. N. Riediger, M. D. C. Debur, H. I. Nakaya, and L. Blanes: Sci. Rep. 11 (2021) 1. https://doi.org/10.1038/
s41598-021-88506-y
28 L. Yu, S. Wu, X. Hao, X. Dong, L. Mao, V. Pelechano, W. H. Chen, and X. Yin: Clin. Chem. 66 (2020) 975.
https://doi.org/10.1093/clinchem/hvaa102
29 V. M. Corman, O. Landt, M. Kaiser, R. Molenkamp, A. Meijer, D. K. W. Chu, T. Bleicker, S. Brunink,
J. Schneider, M. L. Schmidt, D. G. J. C. Mulders, B. L. Haagmans, B. V. D. Veer, S. V. D. Brink, L. Wijsman,
G. Goderski, J. L. Romette, J. Ellis, M. Zambon, M. Peiris, H. Goosens, C. Reusken, M. P. G. Koopmans, and
C. Drosten: Euro Surveill. 25 (2020) 1. https://doi.org/10.2807%2F1560-7917.ES.2020.25.3.2000045
30 H. Benrahma, I. Diawara, I. Smyej, J. Rahoui, N. Meskaouni, R. Benmessaoud, A. Khadija, J. Khadija, M. F.
Zahra, A. Zahra, N. Salma, E. Hajar, J. Leila, O. Fadoua, E. L. B. Jalila, and N. Chakib: medRxiv. 2 (2020) 10.
https://www.medrxiv.org/content/10.1101/2020.06.18.20135137v1
31 Y. Dong, Y. Zhao, S. Li, Z. Wan, R. Lu, X. Yang, G. Yu, J. Reboud, J. M. Cooper, Z. Tian, and C. Zhang: ACS
Sens. 7 (2022) 730. https://doi.org/10.1021/acssensors.1c02079
32 W. S. Jang, D. H. Lim, J. Yoon, A. Kim, M. Lim, J. Nam, R. Yanagihara, S. W. Ryu, B. K. Jung, N. H. Ryoo,
and C. S. Lim: PloS One. 16 (2021) 1. http://dx.doi.org/10.1371/journal.pone.0248042
33 N. D. Zakaria, M. H. Omar, N. N. Ahmad Kamal, K. Abdul Razak, T. Sönmez, V. Balakrishnan, and H. H.
Hamzah: ACS Omega. 6 (2021) 24419. https://doi.org/10.1021/acsomega.1c02670
34 M. Drobysh, V. Liustrovaite, A. Baradoke, R. Viter, C. F. Chen, A. Ramanavicius, and A. Ramanaviciene:
Biosens. 12 (2022) 3. https://doi.org/10.3390/bios12080593
35 H. Y. Kim, J. H. Lee, M. J. Kim, S. C. Park, M. Choi, W. Lee, K. B Ku, B. T. kim, E. C. Park, H. G. Kim, and S.
I. Kim: Biosens. 175 (2021) 1. https://doi.org/10.1016/j.bios.2020.112868
36 M. Johnston, H. C. Ates, R. T. Glatz, H. Mohsenin, R.Schmachtenberg, N. Göppert, D. Huzly, G. A. Urban, W.
Waber, and C. Dincer: Mater. Today. 61 (2022) 129. https://doi.org/10.1016/j.mattod.2022.11.001

Supplementary Table 1
No.
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19

Samplelabels
1
PS1
PS2
PS3
PS4
PS5
PS6
PS7
2
NS1
NS2
NS3
NS4
NS5
NS6
NS7
PS8
PS9
PS10
PS11
PS12

ID sample
215196215197215199215200215201215203215204216139216150216140216136216141216113216118215169215193215195215206215216

IC
27.89
30.1
28.21
27.63
28.74
28.99
29.24
30.16
28.08
31.25
33.21
28.27
31.1
31.94
24.23
28.42
30.51
25.59
23.31

Ct value
RdRp gene
20.51
38.23
25.51
36.86
24.41
31.36
27.51
3
N/A
N/A
N/A
N/A
N/A
N/A
N/A
22.35
31.61
27.87
24.15
21.11

E gene
20.56
36.63
25.84
32.1
24.44
31.41
28.07
N/A
N/A
N/A
N/A
N/A
N/A
N/A
22.27
31.58
27.6
23.95
20.65

RT-LAMP
assay
4
1
1
1
5
0
1
1
1
0
0
1
0
0
0
0
1
0
0
1
1

6

Ip (µA)
2.63
2.97
3.26
3.31
2.61
2.75
2.44
3.27
3.7
3.89
3.93
3.57
3.39
3.35
3.15
3.09
3.09
3.5
3.08

7

∆I (µA)
1.62
1.28
0.99
0.94
1.64
1.5
1.81
0.98
0.55
0.36
0.32
0.68
0.86
0.9
1.37
1.43
1.43
1.02
1.44

4748

Sensors and Materials, Vol. 35, No. 10 (2023)

Supplementary Table 1 (Continued)
No.
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69

Samplelabels
NS8
NS9
NS10
NS11
NS12
PS13
PS14
PS15
PS16
PS17
NS13
NS14
NS15
NS16
NS17
PS18
PS19
PS20
PS21
PS22
PS23
NS18
NS19
NS20
NS21
NS22
NS23
PS24
PS25
PS26
PS27
PS28
NS24
NS25
NS26
NS27
NS28
PS29
PS30
PS31
PS32
PS33
NS29
NS30
NS31
NS32
NS33
PS34
PS35
PS36

ID sample
213579213547216149216119216414214354214361214394214395214397213581213564213578213562213568213582214328214337214340214400214436213571213575213554213531213536213563215297215301215305215308215312216068216067216100216102216063215318215328215714215717215754216098216075216074216033216071215764215766215769

IC
26.02
27.66
30.2
31.06
26.09
27.47
28.75
29.3
27.97
28.68
30.74
31.25
28.26
28.66
29.46
25.35
28.21
28.7
27.18
28.28
31.15
29.22
29.2
29.51
29.48
30.16
26.71
28.47
29.04
27.97
24.78
28.65
28.72
30.22
33.63
26.23
30.71
28.29
30.04
26.37
28.44
31.65
28.53
27.4
29.16
26.68
28.53
30.34
27.47
28.93

Ct value
RdRp gene
N/A
N/A
N/A
N/A
N/A
23.33
30.72
20.8
27.07
25.38
N/A
N/A
N/A
N/A
N/A
21.67
24.13
24.55
24.03
24.97
30.53
N/A
N/A
N/A
N/A
N/A
N/A
23.58
31.99
20.69
19.76
29.33
N/A
N/A
N/A
N/A
N/A
24.23
27.15
20.98
23.85
32.86
N/A
N/A
N/A
N/A
N/A
34.78
35.74
34.27

E gene
N/A
N/A
N/A
N/A
N/A
23.33
30.71
20.89
27.11
25.49
N/A
N/A
N/A
N/A
N/A
21.17
23.85
24.21
23.59
24.61
30.22
N/A
N/A
N/A
N/A
N/A
N/A
23.29
31.55
20.59
19.49
29.31
N/A
N/A
N/A
N/A
N/A
24.42
27.52
21.27
24.15
33.61
N/A
N/A
N/A
N/A
N/A
34.6
34.11
34.03

RT-LAMP
assay
0
0
0
0
0
1
0
1
0
1
0
0
0
0
0
1
0
1
0
1
1
0
0
0
0
0
0
1
0
1
1
1
0
0
0
0
0
1
1
1
1
0
0
0
0
0
0
0
0
1

6

Ip (µA)
3.35
3.9
3.92
3.61
3.49
3.09
2.96
3.2
2.82
2.87
3.32
3.89
3.11
3.49
3.36
3.36
2.51
3.51
3.34
2.75
2.91
3.55
3.79
3.84
3.86
3.8
3.5
3.51
3.32
3.19
3.85
3.64
3.63
4.08
3.79
4.21
3.73
3.75
3.82
3.72
3.65
3.34
3.83
4.04
4.32
3.86
4.07
3.32
3.54
2.94

7

∆I (µA)
1.17
0.62
0.6
0.91
1.03
1.4
1.53
1.29
1.67
1.62
1.17
0.6
1.38
1
1.13
0.67
1.52
0.52
0.69
1.28
1.12
0.48
0.24
0.19
0.17
0.23
0.53
1.16
1.35
1.48
0.82
1.03
1.04
0.59
0.88
0.46
0.94
0.78
0.71
0.81
0.88
1.19
0.7
0.49
0.21
0.67
0.46
1.38
1.16
1.76

Sensors and Materials, Vol. 35, No. 10 (2023)

4749

Supplementary Table 1 (Continued)
No.
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119

Samplelabels
PS37
PS38
NS34
NS35
NS36
NS37
NS38
PS39
PS40
PS41
PS42
NS39
NS40
NS41
NS42
PS43
PS44
PS45
PS46
PS47
PS48
PS49
NS43
NS44
NS45
NS46
NS47
NS48
NS49
PS50
PS51
PS52
PS53
PS54
PS55
NS50
NS51
NS52
NS53
NS54
NS55
PS56
PS57
PS58
PS59
PS60
PS61
NS56
NS57
NS58

ID sample
215774215784216148216105214512214891214907215795215804215807215812215729215768215788215789215814215816215823216062216064216066216070213519213520215551215718215728215738215787216072216073216078216081
216084216085215802215798213538213510213508215759216079216086216087216088216089216090215797213527213518

IC
30.44
28.52
29.1
32.11
30.68
30.98
30.12
32.07
32.8
32.59
28.95
28.14
27.16
29.13
30.56
31.15
29.57
29.99
29.78
28.79
24.6
30.39
30.71
29.74
32.74
32.17
27.94
29.05
30.21
24.1
29.91
28.6
32.1
27.92
30.81
30.52
33.47
30.07
30.83
30.72
29.36
26.08
28.53
30.13
28.94
29.31
28.34
31.41
29.51
23.81

Ct value
RdRp gene
25.53
27.06
N/A
N/A
N/A
N/A
N/A
33.48
34.25
31.25
32.07
N/A
N/A
N/A
N/A
24.87
34.31
21.77
30.14
20.21
21.49
36.13
N/A
N/A
N/A
N/A
N/A
N/A
N/A
19.18
36.38
20.82
20.1
34.73
26.17
N/A
N/A
N/A
N/A
N/A
N/A
29.18
27.05
33.54
31.01
25.56
32.19
N/A
N/A
N/A

E gene
25
26
N/A
N/A
N/A
N/A
N/A
33.31
34.17
31.18
31.89
N/A
N/A
N/A
N/A
24.73
33.9
21.75
29.95
20.12
21.5
36.82
N/A
N/A
N/A
N/A
N/A
N/A
N/A
18.67
35.12
20.51
19.89
35.03
25.93
N/A
N/A
N/A
N/A
N/A
N/A
29.6
27.83
28.94
31.22
26.18
32.9
N/A
N/A
N/A

RT-LAMP
assay
1
1
0
0
0
0
0
1
0
1
0
0
0
0
0
1
0
1
1
1
1
0
0
0
0
0
0
0
0
1
0
1
1
0
1
0
0
0
0
0
0
1
1
0
1
1
0
0
0
0

6

Ip (µA)
3.27
3.25
4.36
4.52
4.59
4.56
3.71
2.9
3.08
3.67
3.44
4.61
3.93
3.94
4.51
3.64
3.34
3.96
3.34
4.11
3.96
3.73
3.92
3.96
4.64
4.16
3.97
4.23
4.24
3.5
3.31
3.63
3.15
3.01
3.53
3.87
4.38
4.2
3.38
3.92
3.89
2.6
2.88
2.09
2.42
3.17
2.7
3.42
3.3
3.48

7

∆I (µA)
1.43
1.45
0.34
0.18
0.11
0.14
0.99
1.82
1.64
1.05
1.28
0.11
0.79
0.78
0.21
1.15
1.45
0.83
1.45
0.68
0.83
1.06
0.87
0.83
0.15
0.63
0.82
0.56
0.55
1.09
1.28
0.96
1.44
1.58
1.06
0.72
0.21
0.39
1.21
0.67
0.7
1.54
1.26
2.05
1.72
0.97
1.44
0.72
0.84
0.66

4750

Sensors and Materials, Vol. 35, No. 10 (2023)

Supplementary Table 1 (Continued)
SampleID sample
labels120
NS59
213500121
NS60215726122NS61215736123
PS62216092124
PS63216097125
PS64216099126
PS65216103127
NS62215735128NS63215745129NS64215800130NS65215776131
PS66
216095132
PS67216101
133
PS68216122134
PS69216129135NS66213514136
NS67213516137
NS68213513138NS69215799139
PS70
216130140
PS71213132141
PS72
213135142
PS73213561143
PS74213556144
NS70215744145
NS71215765146
NS72215785147
NS73215724148NS74215734*1
PS, positive sample*2
NS, negative sample*3
N/A, not available*4
1, color changes from pink to yellow*50, color remains pink*6
Ip (µA), peak height*7∆I (µA), delta I
No.

IC
29.85
29.78
27.58
31.95
30.25
25.78
29.87
29.76
26.44
27.02
24.8
30.46
25.04
30.03
27.37
30.08
27.92
30.04
28.49
28.9
31.06
27.22
25.84
30.94
29.18
30.05
30.19
28.95
30.71

Ct value
RdRp gene
N/A
N/A
N/A
30.35
34.43
21.1
18.6
N/A
N/A
N/A
N/A
25.72
21.89
22.54
27.27
N/A
N/A
N/A
N/A
28.82
19.07
18.32
27.75
32.95
N/A
N/A
N/A
N/A
N/A

E gene
N/A
N/A
N/A
30.42
35.65
21.63
19.26
N/A
N/A
N/A
N/A
25.75
21.92
22.78
27.35
N/A
N/A
N/A
N/A
27.7
17.98
17.25
26.51
32.36
N/A
N/A
N/A
N/A
N/A

RT-LAMP
assay
0
0
0
0
0
1
1
0
0
0
0
1
1
1
1
0
0
0
0
1
1
1
1
0
0
0
0
0
0

6

Ip (µA)
3.23
3.23
3.66
3.07
2.9
2.81
2.39
3.29
3.27
3.35
2.94
3.91
3.08
3.31
2.17
4.06
3.96
4.1
3.94
2.98
3.12
3.55
2.83
2.56
3.86
4.1
4.32
4.24
4.34

7

∆I (µA)
0.91
0.91
0.48
1.14
1.31
1.4
1.82
0.92
0.94
0.86
1.27
0.72
1.55
1.32
2.46
0.57
0.67
0.53
0.69
1.4
1.26
0.83
1.55
1.82
0.52
0.28
0.06
0.14
0.04