Since January 2020 Elsevier has created a COVID-19 resource centre with

free information in English and Mandarin on the novel coronavirus COVID-

19. The COVID-19 resource centre is hosted on Elsevier Connect, the
company's public news and information website.

Elsevier hereby grants permission to make all its COVID-19-related

research that is available on the COVID-19 resource centre - including this
research content - immediately available in PubMed Central and other
publicly funded repositories, such as the WHO COVID database with rights
for unrestricted research re-use and analyses in any form or by any means
with acknowledgement of the original source. These permissions are
granted for free by Elsevier for as long as the COVID-19 resource centre
remains active.
 Talanta 244 (2022) 123409

Contents lists available at ScienceDirect

Talanta
journal homepage: www.elsevier.com/locate/talanta

Review of COVID-19 testing and diagnostic methods

Olena Filchakova a, Dina Dossym b, Aisha Ilyas b, Tamila Kuanysheva b, Altynay Abdizhamil b,
Rostislav Bukasov b, *
a
Biology Department, SSH, Nazarbayev University, Nur-Sultan, 010000, Kazakhstan
b
Chemistry Department, SSH, Nazarbayev University, Nur-Sultan, 010000, Kazakhstan

A R T I C L E I N F O A B S T R A C T

Keywords: More than six billion tests for COVID-19 has been already performed in the world. The testing for SARS-CoV-2
COVID-19 tests (Severe Acute Respiratory Syndrome Coronavirus-2) virus and corresponding human antibodies is essential not
SARS CoV-2 only for diagnostics and treatment of the infection by medical institutions, but also as a pre-requisite for major
Antibody to SARS-CoV-2
semi-normal economic and social activities such as international flights, off line work and study in offices, access
RT-PCR of SARS CoV-2
to malls, sport and social events. Accuracy, sensitivity, specificity, time to results and cost per test are essential
CRISPR
LAMP parameters of those tests and even minimal improvement in any of them may have noticeable impact on life in
ELISA the many countries of the world. We described, analyzed and compared methods of COVID-19 detection, while
CLIA representing their parameters in 22 tables. Also, we compared test performance of some FDA approved test kits
Lateral flow immunoassays with clinical performance of some non-FDA approved methods just described in scientific literature. RT-PCR still
Rapid detection remains a golden standard in detection of the virus, but a pressing need for alternative less expensive, more
CT scanning rapid, point of care methods is evident. Those methods that may eventually get developed to satisfy this need are
Ultrasound scanning
explained, discussed, quantitatively compared. The review has a bioanalytical chemistry prospective, but it may
Time to results
be interesting for a broader circle of readers who are interested in understanding and improvement of COVID-19
False positive results
False negative results testing, helping eventually to leave COVID-19 pandemic in the past.

1. Introduction single-stranded RNA (ssRNA), but some contain double-stranded RNA

(dsRNA). ssRNA viruses are subdivided into positive-sense, negative-
Viruses are nanometer-scale pathogens that can only replicate inside sense, and ambisense RNA viruses. Positive-sense RNA viruses contain
a host organism. Viruses infect animals, plants, bacteria, and fungi. The genetic material in the form identical to mRNA that can be translated
majority of viruses are specific to one species, but some viruses can directly by host cells. Negative-sense RNA viruses contain RNA com­
infect organisms that belong to different species, such as influenza A and plementary to mRNA, so that viral RNA serves as a template for host
C. Some viruses mutate and jump from one species to another, such as cells’ transcription via RNA-dependent RNA polymerase before trans­
coronaviruses. Viruses can not survive without a host. Often viral in­ lation is possible. Ambisense RNA viruses contain RNA that has different
fections lead to the host cell death. Some viruses do not cause obvious parts having positive-sense and negative-sense properties. RNA poly­
changes in host cells and can stay inactive for years, causing chronic merase does not have proof-reading properties; therefore, RNA viruses
viral infections. The majority of viruses do not cause death of a host are prone to a much higher rate of mutations. RNA viruses are also more
organism because it would stop viral spread. However, some viruses pathogenic than DNA viruses, for example, coronaviruses, HIV, hepatitis
result in potentially lethal diseases. viruses, Ebola, and others.
Viruses are grouped based on the nucleic acid they use to encode Methods of viral detection include detection of viral particles (vi­
genetic material: DNA or RNA. The majority of DNA viruses carry rions), viral antigen, antibodies to the virus, and viral nucleic acid. We
double-stranded DNA (dsDNA) and rarely single-stranded DNA (ssDNA). consider two categories of scientific publications: one that reports per­
These viruses use host cells’ DNA-dependent DNA polymerase for formance of commercialized and/or government-approved diagnostics
replication. As DNA polymerase has proof-reading abilities, DNA viruses methods/kits, and another that describes relatively new not-yet-
mutate to a lesser extent and are milder. RNA viruses usually carry commercialized and not-yet-approved methods. A detection method is

* Corresponding author.
E-mail address: rostislav.bukasov@nu.edu.kz (R. Bukasov).

https://doi.org/10.1016/j.talanta.2022.123409
Received 27 January 2022; Received in revised form 23 March 2022; Accepted 24 March 2022
Available online 31 March 2022
0039-9140/© 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
O. Filchakova et al. Talanta 244 (2022) 123409

accepted as a diagnostic test if it satisfies certain analytical re­ estimated to recover until 2025 [8]. Restrictions have been introduced
quirements. These requirements are a sufficiently low limit of detection, in order to prevent the spread of SARS-CoV-2, and while it is a necessary
high selectivity, sensitivity, accuracy and preferably high speed of test step, it cannot be (and is not) endorsed as the only step to control
run. Limit of detection is the lowest concentration of detection target SARS-CoV-2.
(analyte) that a test can detect. For qualitative diagnostic tests it is the The COVID-19 testing is essential not only for diagnostics and
parameter that defines whether the test can be applied to patients with containment of the virus but for resumption of flights, reopening of in­
low viral load. For quantitative diagnostic tests another important ternational travels and resumption of other normal economic activities.
parameter is – limit of quantification, which is the lowest analyte con­ The total number of COVID tests taken in the world up to March 11,
centration that can be correctly measured by the test. Limit of detection 2022 is about 6 billion while the largest number of tests have been
is especially important since the purpose of testing is to detect virus at performed in USA (960 M) India (780 M) UK (490 M)and Spain (470 M)
much lower viral load than the one seen at the time of symptom onset [4]. As for COVID-19 testing, most countries perform 2–3 tests per 1000
which for COVID-19 starts from 104 copies/mL [1]. Most of the current people every day. The majority of countries have performed 400–1000
PCR-based methods have limit of detection at 100 copies/mL or less, tests per 1000 people by now. This puts an additional constraint on
which corresponds to the viral detection at 2–3 days before the onset of economy during coronavirus crisis. RT-PCR tests that constitute the
symptoms [1]. Limit of detection defines a test’s sensitivity – the ability predominant part of these numbers cost on average $50–100 per test, in
of the test to detect viral infection when a virus is present in samples. addition to the costs of electricity, equipment maintenance, and salaries
Sensitivity is expressed in percentage: 100% - % (false negatives). for staff. For this reason, there is a considerable difference in the number
Specificity refers to the ability of a test to return a negative result in cases of tests between high- and low-income countries. High income countries
when virus is absent in samples. Specificity is also expressed in per­ such as Denmark, Austria, UAE, United Kingdom, and the USA have
centage: 100% - % (false positives). Accuracy reflects overall reliability performed 2000–15 000 tests per 1000 people, while many low income
of a method and is expressed as: 100% - % (false negative) - % (false countries such as Sudan, Haiti, DRC, Yemen, Afghanistan and Nigeria
positive). Sensitivity is the rate of true positives, and specificity is the performed less than 20 tests per 1000 people [9]. These statistics clearly
rate of true negatives, and these rates will not necessarily be the same. demonstrate the need of development of new inexpensive detection
Therefore, accuracy is another important analytical parameter that methods that can be used in developing countries. COVID-19 is the most
measures the rate of correct results for a certain test, expressed in per­ tested disease in human history, and the need for testing will be pre­
centage. Usually to calculate accuracy, sensitivity, and specificity, a served in a foreseeable future even after the time when efficient
test’s results are compared to an established “gold standard” test, usually COVID-19 vaccines would be availed to the most people on our planet.
RT-PCR. Some other tests with even more remarkable accuracy, sensi­ Vaccines became available in many countries in early 2021 and majority
tivity, and specificity parameters are being developed, such as mass of adult population got vaccinated in a few countries by end of summer
spectroscopy for detection, which can serve as a reference for other tests. 2021. Now 65% of world population or 5 billion people received a
Therefore, development of various detection methods is important. If a vaccine [10]. Nowadays Omicron spreads faster and considered to be
test is expensive, requires complicated equipment, and takes a long time more contagious than original COVID-19 variants [11]. This new trend
to results, but has a very low limit of detection and accuracy, it can be drove the demand for COVID testing in many countries, particularly
used for scientific research. developed countries even higher. For instance, 1.2–1.7 million tests per
Coronaviruses are recurrent in human population, such as MERS- day were performed in UK as 7-day average in January 2022, while it
CoV (Middle East Respiratory Syndrome Coronavirus) and SARS-CoV was only 0.5–0.6 M tests performed per day in UK in January [12].
(Severe Acute Respiratory Syndrome Coronavirus) viruses, active Detection of SARS-COV-2 is equally important for realistically
SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus-2) and assessing the epidemiological situation nationally and globally and for
other coronaviruses leading to symptoms of common cold. Coronavi­ controlled return towards pre-coronavirus life as is social distancing.
ruses are positive-sense ssRNA-containing enveloped viruses, having Today clinical detection of SARS-CoV-2 is represented mainly by RT-
been detected by multiple methods. SARS-CoV-2 is one of coronaviruses, PCR, and serological detection – by antibody-targeted ELISA. RT-PCR
closely related to bat and pangolin coronaviruses [2]. Coronaviruses are is a sensitive and specific method for checking infection status of a
known causes of global pandemics due to high transmissibility and person, but it is lengthy, requires complex equipment and a highly
mortality; for instance, SARS-CoV outbreak in 2002–2003 with 9.56% skilled operator. In order to safely return to normal life, a test needs to be
mortality rate [3]. MERS-CoV started an epidemic in Middle East in rapid, portable, and have enough analytical sensitivity to maintain a
2012 with 34.4% mortality rate [4]. 90% or higher true positive rate. In this way, a test can be applied to
Highly transmissible SARS-CoV-2 originated in Wuhan, China at the employees who want to return to offline work, in the airports, cinemas,
end of 2019 and caused viral respiratory distress and pneumonia, later sport complexes. A test that can be run outside laboratory settings is
named COVID-19. By the end of January 2022 about 350 million people called a point-of-care test. Ideally, it would be a relatively inexpensive
have been tested positively with the virus worldwide, with about 5.6 test that can be run with many samples and return results in up to 30 min
million deaths [5]. GDP (gross domestic product) dropped worldwide in with naked-eye detection. This also becomes important if the zoonotic
2020 with the exception of China and Vietnam that experienced up to origin of SARS-CoV-2 is taken into account: in case of transmission of
3.8% growth. The level of global economic decline in 2020 due to another coronavirus it will be beneficial to have established robust
COVID-19 is unprecedented since WW2 and estimated at about 4.2% by diagnostic tests that are known to detect a virus from this family. For this
World Bank [6]. The most significant decline in GDP happened in the reason, the most recent detection methods were assessed in this review,
EU, especially Spain and Italy, and India, which lost more than 10% of some of which have a potential to become novel point-of-care tests.
GDP compared to 2019. The USA and Russia each underwent less than Some key features of SARS-CoV-2 relevant to its detection include: its
5% decline in GDP. GDP is estimated to return to pre-coronavirus value genome consists of six open reading frames (ORFs): replicase (ORF1a/
by the end of 2021 and experience growth from 2022 [7]. SARS-CoV-2 ORF1b), spike (S), envelope (E), membrane (M) and nucleocapsid (N)
has had a profound effect on global economy, resulting in stock market (genes targeted in RNA-based detection). In addition, ORFs encoding
volatility, with some national stock markets not having been able to accessory proteins are present within viral genome. The virions have
recover yet, e.g. Coronavirus pandemic has caused loss of jobs and in­ spike protein on the surface that binds receptors of the cells for cell
crease in worldwide unemployment rates (related to the lack of new job infection, and nucleocapsid protein – these two proteins are targeted by
opportunities) due to lockdowns. Travel and hospitality businesses are antigen detection methods. SARS-CoV-2 shares 79% genome sequence
the most damaged part of industry, with hundred billions of dollars lost identity with SARS-CoV, and SARS-CoV-2 spike protein’s receptor-
in 2020 and 2021 due to governmental restrictions. This sector is not binding domain has 73% amino acid similarity with SARS-CoV [2].

2
O. Filchakova et al. Talanta 244 (2022) 123409

This can potentially cause cross-reactivity with SARS-CoV during RNA 2. Detection of SARS-CoV-2 viruses
and antigen detection. However, because nowadays occurrence of
SARS-CoV is relatively insignificant, such cross-reactivity should not Major methods of detection of SARS-CoV-2 virus is based on detec­
affect test results. tion of viral RNA. Those methods include RT-PCR, as so far, the most
There are a lot of novel methods for SARS-CoV-2 detection in the common method of viral RNA detection. RT-PCR is represented by
fields of antigen, antibody to the virus, RNA, and viral particles (virions) publications about non-commercially approved methods, and by reports
detection. Some conventional methods were improved and commer­ on commercialized government-approved assays. Other methods
cialized, such as antibody and antigen testing kits for point-of-care use include Droplet Digital PCR (ddPCR), multiplex PCR, Clustered Regu­
[13], developed methods of SARS-CoV-2 RNA detection, such as larly Interspaced Short Palindromic Repeats (CRISPR), Loop-mediated
RT-LAMP (Reverse Transcription Loop-Mediated Isothermal Amplifica­ Isothermal Amplification (LAMP), Recombinase polymerase amplifica­
tion) and Cas-Crispr, and testing for virus in saliva in addition to blood, tion (RPA), Recombinase Aided Amplification (RAA), and Pulse
serum, and nasopharyngeal swabs. Some conventional gold standard Controlled Amplification (PCA).
methods are also tuned for SARS-CoV-2 detection, such as lateral flow
immunoassays, sandwich ELISA, RT-PCR, making them for 2.1. Detection of RNA by RT-PCR: principles and applications
lab-on-a-chip basis and/or increasing their sensitivity. Some less con­
ventional methods arise such as field-effect transistor sensors, chro­ The golden standard of COVID-19 virus detection is considered a
noamperometry sensors, and even mass spectrometry for detection of reverse-transcription Polymerase Chain Reaction (RT-PCR). PCR is one
viral antigen. of the common techniques used to detect viral nucleic acid. PCR is used
Previous reviews on detection of SARS-CoV-2 have focused on lab­ to amplify the number of copies of DNA samples. Its creator Kary B.
oratory scientific methods [14], commercialized technologies [15], or Mullis was awarded the Nobel Prize in Chemistry in 1993 [27], while he
advances in detection [16]. A thorough meta-analysis of SARS-CoV-2 died in 2019 from complications of pneumonia [28].
detection through antibody testing has been conducted by Kontou Original PCR method can detect DNA [29], but if RNA is needed to be
et al. who evaluated performance of ELISA, CLIA, FIA, and LFIA tests detected, RNA is reverse transcribed into the complementary DNA
[17]. Another review of antibody-based detection consolidated early (cDNA) by reverse transcriptase. The PCR method used nowadays is
published studies and preprints [18]. Hereafter we present a broad scope mostly Real-Time PCR or, in other words, Quantitative PCR (qPCR).
review about COVID-19 testing and diagnostics methods and applica­ Using this method, one is able to amplify and detect the concentration
tions. In the present review we attempted to calculate average perfor­ changes in amplicon concentration in real time, while conventional PCR
mance parameters (LOD, accuracy, time to results, etc.) for major measures that at the end of the process. Common methods used in qPCR
detection methods from available literature and we compared those for the detection of PCR products are DNA binding fluorescent dyes and
average analytical parameters to the parameters of commercialized using fluorescent signals produced by DNA probes. Photodetectors are
methods. used in the qPCR to collect data by only allowing passage of the wave of
This review describes both: detection of COVID-19 virus and detec­ desired wavelengths [30].
tion of antibodies to the virus as reported in scientific peer reviewed Fig. 1 shows the process of qPCR. First, the sample is taken from a
literature, but also it quantitatively compares those methods with vali­ person and RNA is extracted. This can be done by automated equipment
dated commercial methods of virus/antibodies detection already or kits prepared for RNA extraction. Moreover, Arizti-Sanz et al. [31]
approved by FDA or other regulatory agencies. Clinical methods of and Ramachandran et al. [32] developed their own techniques for RNA
COVID-19 diagnostics, such as X-ray, CT, and lung ultrasound are also extraction, such as HUDSON (Heating Unextracted Diagnostic Samples
included in the review. to Obliterate Nucleases), and ITP (Isotachophoresis). Usually, RNA
extraction requires 5–30 min and is followed by the amplification and
1.1. COVID-19 specimen collection and sample handling reverse transcription of viral RNA to obtain cDNA.
Then, reverse transcription is performed to form complementary
Antigens and virions are detected in nasopharyngeal swab or saliva DNA. Using qPCR apparatus cDNA is amplified and its amount is
specimens [19]. Before discussing detection methods, it is necessary to analyzed.
discuss pre-analytical issues in nasopharyngeal swab collection and While reviewing analytical methods, it is important to mention the
storage. Pre-analytical handling of nasopharyngeal swabs is critical for prices of the machines. As of January 30, 2019, the price of a simple PCR
obtaining reliable results. According to Pondaven-Letourmy et al., false machine was 4912 USD (Bio-Rad T100 thermal cycler), while rtPCR
negative rate of RT-PCR is around 30%, although RT-PCR itself is a costs from $15000 (RotorGene models) to over $90000 (QuantStudio
highly sensitive detection method. One of the reasons for high rate of 12k). The price of the apparatus usually differs because of the perfor­
false negatives could be an improper nasopharyngeal sampling [20–23]. mance quality of the instrument. Therefore, careful considerations
SARS-CoV-2 uses binding of the spike protein to a cellular receptor ACE2 should be taken to choose the appropriate instrument [33].
for cell infection, which is expressed at a higher rate in distal part of a The PCR method has several advantages. This method requires a
nose in comparison to their expression in a proximal part of a nose [24]. small sample amount for analysis and has high sensitivity and accuracy
Assuming that the technique for nasopharyngeal swab collection is values. In comparison with other diagnostic methods (cultivation of
standardized; storage conditions are coming into consideration. Basso bacteria, etc.) this method is relatively fast, as it takes only several
et al. checked how different storage conditions affect sensitivity of hours. Newly developed methods based on PCR can even identify mi­
RT-PCR test. The lowest values of threshold cycles (lowest detection croorganisms in several minutes [34–36]. However, PCR still has some
limit) were observed when nasopharyngeal swabs were stored in the drawbacks. For example, the method is condition-sensitive and some­
refrigerator at +4 ◦ C in the solution of extraction buffer to preserve RNA. times can produce false-positive results. As it was mentioned before,
The researchers also concluded that it is very reliable to store swabs in a PCR machines are expensive and apart from the cost of the apparatus,
viral transport media at room temperature for up to 2 days before the transportation also contributes to the cost. Workers at the laboratory
RT-PCR test. For longer time periods refrigeration is required [25]. As should have special training on how to use the instrument. Detection of
for antigen testing, it is recommended to store nasopharyngeal swabs in each type of microorganisms’ genetic material requires the usage of
sterile, dry, sealed plastic tube. If swabs are stored in a viral transport special primers, which should be purposely manufactured. Given that,
media, its volume should not exceed 1 mL and it should not contain scientists are working on a cost-effective and accurate method of the
guanidinium. Swabs can be stored for up to 8 h at room temperature and detection of the virus.
1 day refrigerated at +2–8 ◦ C [26]. RT-PCR methods used for COVID-19 diagnostics are summarized in

3
O. Filchakova et al. Talanta 244 (2022) 123409

Fig. 1. Illustrated qPCR process from sample collection to result readout (Created in canva.com//Google Spreadsheet//"File:Baby Blue - a prototype polymerase
chain reaction (PCR), c 1986. (9663810586).jpg" by Science Museum London/Science and Society Picture Library is licensed under CC BY-SA 2.0s). (For inter­
pretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

Table 1
Analysis of RT-PCR methods from scientific literature.
Method Sensitivity Specificity Accuracy Sample size, Time (min) LOD
samples

RT-PCR [37] 100% 100% 95.45% 44 3-5/batch 1 cp/rxn

RT-PCR [40] 32.5–32.9 per 1000 copies N/A N/A 1000 N/A N/A
RT-PCR [41] 100% 100% 100% 9 N/A N/A
RT-PCR [38] 29.2% 100% 92% 2923 N/A 5 cp/rxn
RT-PCR [42] 100% 100% 100% 184 N/A N/A
RT-PCR [43] 91.7% N/A 91.7% 12 days N/A
qRT-PCR [35] 100% 100% 100% 51 70 5 cp/rxn
Automated Direct rt-qPCR 100% 100% 100% 2127 57 20 cp/rxn
[39]
qRT-PCR [44] 100% 100% 100% 26 120 15 cp/rxn
RT-PCR [45] N/A N/A 100% 297 N/A 3.6–39 cp/rxn
Average ± st.dev 83.7 ± 30.1% (29.2%; 100 ± 0% (-; 97.7 ± 3.6% (91.7%; 667 ± 1040 (9; 62.75 ± 47.7 (3–5 min; 9.2 ± 7.9 (1;
Range 100%) 100%) 100%) 2923) days) 10)

Note: Average ± standard deviation; Range (x%, y%); Accuracy= (TP + TN)/(TP + TN + FP + FN); Sensitivity = (TP)/(TP + FN); Specificity = (TN)/(TN + FP). TP –
true positive, TN – true negative, FP – false positive, FN – false negative.

Table 1. The major performance parameters summarized in this table accuracy (95.45%). The LOD of this method was equal to 1 copy per
include sensitivity, specificity, and accuracy. The sensitivity is defined reaction, which was the lowest among similar methods. Wang et al.
as the ability of a particular test to detect a virus when the virus is (2020) also reported that this method was effective among COVID-19
present in a sample, and is expressed in % (100% or all negative – % of mutations due to single nucleotide specificity [37]. The authors
false negative results). Specificity refers to the ability of a particular test acknowledge, however, that the performance of the method is
to show a negative result when the virus is absent from a sample, and is probe-sensitive. Therefore, wide application of this method requires
expressed in % (100% or all positive – % of false positive results). Ac­ thorough prior validation.
curacy denotes the percentage of times at which the performed test re­ Almost all methods reported high sensitivity values, but the method
sults are correct. Mathematically it is expressed as Accuracy = (TP + by Lu et al. (2020) showed low sensitivity values (29.22%) [38]. Lu et al.
TN)/(TP + FP + FN + TN), where TP are true positive, TN are true (2020) based their assay design on previous diagnostic assays that had
negative, FP are false positive and FN are false negative results. Among been developed for detection of MERS-CoV and SARS-CoV and targeted
detection methods that used RT-PCR to detect SARS-CoV-2 virus, Wang the N gene. Also, this study began when a limited amount of genetic
et al. (2020) was the fastest [37]. In this method nasopharyngeal or material was available and samples from different sources were used.
oropharyngeal swabs were used as samples. First, RT-PCR was per­ Each of these factors could have affected the final sensitivity value. The
formed to amplify viral DNA, in particular, the conserved regions in the main conclusion of this study is that RT-PCR assays are most efficient
genome of the virus. The uniqueness of this method is the usage of when samples are taken from the upper respiratory tract.
upgraded Pyrococcus furiosus Argonaute (PfAgo), which were added The highest (worst) LOD was observed in the work of Ji et al. (2020) -
(along with guide DNAs and molecular beacons) to the PCR products. 20 copies per reaction [39]. The developed assay was automated and
After the incubation fluorescence signal was detected. Even though the direct, so the process of detection is simple. The method had high
cost of the RT-PCR apparatus is high, the time used was shortened from sensitivity, specificity and accuracy values (100%) which were tested on
more than an hour to 3–5 min per batch. Apart from being fast, this 2127 samples. A big sample size guarantees the method’s reliability. The
method showed high value of specificity and sensitivity (100%) and whole process takes 90 min and the positive result can be detected in 57

4
O. Filchakova et al. Talanta 244 (2022) 123409

min. These values are faster than other suggested methods based on replicates, the most sensitive FDA-registered kits will detect it, as they
RT-PCR. Moreover, the apparatus can detect 16 samples simultaneously, have much lower detection limits. Average clinical accuracy of non-FDA
and other viruses can be detected as well, so it has a very broad spectrum registered tests is higher than that of FDA-registered tests, but this data is
of usage. only for 5 non-FDA registered tests with reported clinical accuracy.
Concluding, the methods mentioned above once again show that RT- Geometric mean of limit of detection of FDA-registered RT-PCR kits
PCR is a gold standard for the detection of viruses. Almost all analyzed is lower than that of non-FDA registered tests, meaning that FDA-
methods showed 100% specificity, good sensitivity and accuracy values, registered tests have higher analytical sensitivity. This means that
as well as low limit of detection. However, these methods are relatively more tests need to be approved by FDA for emergency use authorization.
slow and they were tested on small sample sizes. Another interesting It is also necessary to keep in mind that FDA-registered tests use on
thing to note is that the number of papers using RT-PCR is relatively average much more samples for validation of results, and all except 3
small, which could mean that this method has achieved its best, so now non-FDA registered tests did not report the number samples used. This
science should take another approach and come up with other methods contributes to the clinical performance results, which can change upon
to detect the microorganisms. using a bigger sample pool. Comparison between tests can be seen in the
Table 3.
2.2. Overview of the results of the commercialized RT-PCR methods
2.3. Other nucleic acid-based methods (ddPCR, multiplex PCR)
The most important commercial method for diagnostics of COVID-19
is a conventional RT-PCR, which was discussed in detail in the corre­ PCR kits are widely used in the detection of viral genome and
sponding section. This is a gold standard in SARS-CoV-2 detection, to considered to be the golden standard. However, as mentioned earlier,
which other methods are often compared in terms of specificity and PCR methods have limitations (time of detection, condition-sensitive,
sensitivity. In this section modified RT-PCR and other methods that are cost, etc.) Biosensors are believed to be better at detection. Biosensors
represented with point-of-care tests and portable kits will be discussed. showed high sensitivity, selectivity and accuracy. Moreover, biosensors
All point-of-care tests discussed in this section are listed in the table are cost-effective and have faster time of detection. Biosensors for the
below. detection of other respiratory viruses have been developed. For
Table 2 summarizes performance of government-approved RT-PCR example, Veerapandian, et al. developed dual immunosensor for the
kits. RT-PCR kits provide the most sensitive detection, with the best detection of influenza A virus [77] or optical biosensor for the detection
methods having detection limit of several copies per reaction. These of SARS-CoV by Huang et al. [78]. At the time of writing this review, two
tests can take up to 3 h to get results. Results are usually detected by a methods for the detection of SARS-CoV-2 have already been proposed.
fluorescence analyzer. The sensitivity of these tests tends to be lower Biosensors created by several groups of scientists in general were
than in RT-PCR published in the literature but higher than for antigen faster in the detection of viral genome than qPCR methods. Alafeef et al.
and antibody detection. One disadvantage for some of the RT-PCR tests were able to create a biosensor with the detection time less than 5 min
based on kits is that they are not fully automated. For example, “Real­ [79]. Moreover, this biosensor is low-cost and easy-to-implement. It has
Star® SARS-CoV-2 RT-PCR Kit” by Altona comes with software that a quantitative paper-based electrochemical sensor chip which can
must be run and controlled by the user, who also needs to prepare pu­ digitally detect the SARS-CoV-2 genetic material. It uses gold nano­
rification reagents and control centrifugation [46]. In other kits RNA particles, capped with highly specific antisense oligonucleotides
extraction must be performed manually by the user. This means that (ssDNA) targeting viral nucleocapsid phosphoprotein (N-gene). The
even medical staff may need additional training in using these devices. sensing probes are immobilized on a paper-based electrochemical plat­
Another caveat is that sometimes RT-PCR kits require materials for form to yield a nucleic-acid-testing device with a readout that can be
detection, but do not provide them. For example, “Real-Time Fluores­ recorded with a simple hand-held reader. Its detection limit is 6.9 copies
cent RT-PCR Kit” by BGI in collaboration with Pathomics Health re­ per μL, which can be low in comparison with PCR methods [79].
quires RT-PCR system with software, nucleic acid extraction kit, vortex However, this method does not require further amplification, which
mixer and other materials that are not provided [47]. helps to save time. Another advantage of this method is an ability to
Among FDA-registered RT-PCR kits the best sensitivity results are by detect mutated virus genomes, which is useful given the rise in cases
“Primerdesign Ltd COVID-19 genesig® Real-Time PCR assay” (France, with varied and more contagious types of the virus. The biosensor was
2.65 copies/reaction), “PerkinElmer® New Coronavirus Nucleic Acid tested on clinical samples, when tests swabs were collected from the
Detection Kit” (UK, 3 copies/reaction), “Allplex™ 2019-nCoV Assay” nasal region or saliva. The sample size was equal to 48, among which 22
(USA, 1.25 copies/reaction), “1copy™ COVID-19 qPCR Multi Kit” are positive samples and 26 are negative. It had 100% sensitivity, 100%
(South Korea, 4 copies/reaction), and “TaqPath™ COVID-19 Combo specificity and 100% accuracy [79]. However, the sample size is rela­
Kit” (USA, 10 copies/reaction). These kits also have high clinical accu­ tively small. Therefore, to achieve higher confidence in those impressive
racy, up to 100%. The worst sensitivity results among FDA-registered results, a bigger sample size should be tested. Qiu et al. created photo­
RT-PCR kits are by “PowerChek™ 2019-nCoV Real-time PCR Kit” thermal biosensors with LOD equal to 0.22 pM. They used
(South Korea, 560 copies/reaction), “Logix Smart™ Coronavirus Disease two-dimensional gold nanoislands (AuNI chips) with complementary
2019 (COVID-19) Kit” (China, 600 copies/reaction) and “NxTAG CoV DNA receptors to achieve nucleic acid hybridization, which allowed the
Extended Panel Assay” (Canada, 1000 copies/reaction). All 3 of those sensitive detection of the viral genome. Time required for detection is
least sensitive FDA-approved test kits are reported to have an absolute yet to be reported. Clinical sample size (82) was also small, so this
accuracy of 100%, however they report using specimens spiked with method is yet to be researched. However, low limit of detection shows
viral RNA to the concentration above detection limit. It means that it is that this method can be reliable and practical [80].
unknown if the limit of detection of these kits is enough for detection of Droplet Digital PCR (ddPCR) was first introduced by Saiki et al.
RNA in clinical specimens of patients infected with SARS-CoV-2. (1988) [81]. The principle used commonly now was introduced by Diehl
Among non-FDA registered tests, the best sensitivity results are et al. (2006) [82]. ddPCR is a relatively new technique and became
shown by “Aridia COVID-19 Real-Time PCR test” (USA, <10 copies/ commercially available in 2011 by BioRad (Hindson et al., 2011) [83].
reaction) and “Zena Max – AMD SARS-COV-2” (UK, 10 copies/reaction). ddPCR uses Taq polymerase in a standard PCR reaction for the ampli­
FDA-registered PCR tests demonstrate better sensitivity than non- fication of a target DNA fragment from a complex sample using
FDA registered tests. It can be concluded that nearly all registered pre-validated primer or primer or probe assays. During the ddPCR the
commercialized kits are suitable for detection of virus in an average reaction is partitioned into thousands of reactions prior to amplification.
clinical specimen with viral infection. If virus has small number of Also, the data in ddPCR is collected at the reaction end point. These

5
O. Filchakova et al. Talanta 244 (2022) 123409

Table 2
Performance of commercialized government-approved RT-PCR kits.
Name Sample LOD Clinical Time, Ref
accuracy min

BGI Real-Time Fluorescent RT-PCR Kit for Nasopharyngeal swabs, 750 μL 75 copies/reaction Sens = 180 [46,48]
Detecting SARS-CoV-2 384 samples 88.1% US FDA
Spec =
99.6%
Acc =
87.7%
Altona RealStar® SARS-CoV-2 RT-PCR Kit Nasopharyngeal swabs, 500 μL 19 copies/reaction Sens = 95% >30 [47]
69 samples Spec = US FDA
100%
Acc = 95%
3DMed ANDiS FAST SARS-CoV-2 RT-qPCR Nasopharyngeal swabs, 200 μL 40 copies/reaction Sens = 30 [49]
Detection Kit 136 samples 97.2% US FDA
Spec =
93.1%
Acc =
90.3%
Primerdesign Ltd COVID-19 genesig® Real- Oropharyngeal swabs, 700 μL 2.65 copies/reaction Sens = 70 [50]
Time PCR assay 100 samples 94.7% US FDA
Spec =
100%
Acc =
94.7%
GeneFinder™ COVID-19 Plus RealAmp Kit Nasopharyngeal swabs, 140–250 μL 70 copies/reaction Sens = 150 [51]
120 samples 100% US FDA
Spec =
100%
Acc = 100%
Logix Smart™ Coronavirus Disease 2019 Nasopharyngeal swabs, 140 μL 600 copies/reaction Sens = 50 [52]
(COVID-19) Kit 180 samples 100% US FDA
Spec =
100%
Acc = 100%
Sansure Novel Coronavirus (2019-nCoV) Nasopharyngeal swabs, 200 μL 40 copies/reaction Sens = 30 [53]
Nucleic Acid Diagnostic Kit 246 samples 94.3% US FDA
Spec =
98.9%
Acc =
93.2%
SD Biosensor STANDARD M nCoV Real-Time Nasopharyngeal swabs, 600 μL 150 copies/reaction Sens = 30 [54]
Detection kit 60 samples 100% US FDA
Spec =
100%
Acc = 100%
Beijing Applied Biological Multiple Real- Nasopharyngeal swabs, 200 μL 40 copies/reaction Sens = 100 [55]
Time PCR Kit for Detection of 2019-nCoV 757 samples 99.1% US FDA
Spec =
94.9%
Acc = 94%
BioFire® COVID-19 Test Nasopharyngeal swabs, 300 μL 99 copies/reaction Sens = 45 [56]
536 samples 97.1% US FDA
Spec =
100%
Acc =
97.1%
PerkinElmer® New Coronavirus Nucleic Nasopharyngeal swabs, 70 μL 3 copies/reaction Sens = 110 [57]
Acid Detection Kit 384 samples 100% US FDA
Spec =
100%
Acc = 100%
BioMerieux ARGENE® SARS-COV-2 R- Nasopharyngeal swabs, 200 μL 76 copies/reaction Sens = 45 [58]
GENE® 186 samples 100% US FDA
Spec =
100%
Acc = 100%
Allplex™ 2019-nCoV Assay Nasopharyngeal swabs, 190–300 μL 1.25 copies/reaction Sens = 70 [59]
300 samples 100% US FDA
Spec =
93.1%
Acc =
93.1%
QIAstat-Dx Respiratory SARS-CoV-2 Nasopharyngeal swabs, 300 μL 150 copies/reaction Sens = 60 [60]
3801 samples 97.2% US FDA
Spec =
(continued on next page)

6
O. Filchakova et al. Talanta 244 (2022) 123409

Table 2 (continued )
Name Sample LOD Clinical Time, Ref
accuracy min

96.1%
Acc =
93.3%
NeuMoDx™ SARS-CoV-2 Assay Nasopharyngeal swabs (500 μL) or saliva 75 copies/reaction (nasopharyngeal Sens = 80 [61]
(700 μL) swabs) or 35 copies/reaction (saliva) 100% US FDA
131 samples Spec =
100%
Acc = 100%
NxTAG CoV Extended Panel Assay Nasopharyngeal swabs, 200 μL 1000 copies/reaction Sens = 165 [62]
60 samples 100% US FDA
Spec =
100%
Acc = 100%
TaqPath™ COVID-19 Combo Kit Nasopharyngeal swabs, 200 μL 10 copies/reaction Sens = 40 [63]
120 samples 100% US FDA
Spec =
100%
Acc = 100%
1copy™ COVID-19 qPCR Multi Kit Nasopharyngeal swabs, 140 μL 4 copies/reaction Sens = 95% 110 [64]
120 samples Spec = US FDA
100%
Acc = 95%
cobas® SARS-CoV-2 Test Nasopharyngeal swabs, 600 μL 51 copies/reaction Sens = 180 [65]
312 samples 100% US FDA
Spec =
95.5%
Acc =
95.5%
GenePro SARS-CoV-2 Test Nasopharyngeal swabs, 140–200 μL 77 copies/reaction Sens = 90 [66]
100 samples 100% US FDA
Spec =
100%
Acc = 100%
PowerChek™ 2019-nCoV Real-time PCR Kit Nasopharyngeal swabs, 140 μL 560 copies/reaction Sens = 90 [67]
140 samples 100% US FDA
Spec =
100%
Acc = 100%
FTD™ SARS-CoV-2 Nasopharyngeal swabs, 200 μL 108 copies/reaction Sens = >60 [68]
80 samples 100% US FDA
Spec =
100%
Acc = 100%
Biomaxima SARS-CoV-2 Real Time PCR LAB- Nasopharyngeal swabs ≥10 copies/reaction Sens = 99% 62 [69]
KITTM Spec = 99% URPL,
Acc = 98% CE-IVD
Liferiver Novel Coronavirus (2019-nCoV) Nasopharyngeal swabs, bronchoalveolar 1 × 103 copies/mL N/A N/A [70]
Real Time Multiplex lavage fluid and deep cough sputum CE-IVD
RT-PCR Kit
GenePro COVID-19 Detection Test Nasopharyngeal swabs 1 × 103 copies/mL Sens = 90 [71]
100 samples 97.9% CE-IVD
Spec =
100%
Acc =
97.9%
AssayGenie COVID-19 (SARS-CoV-2) Triplex Nasopharyngeal swabs 200 copies/mL Sens = >45 [72]
RT-qPCR Detection Kit 100% CE
Spec =
100%
Acc = 100%
CTK Aridia COVID-19 Real-Time PCR test Nasopharyngeal swabs <10 copies/reaction Sens = 90 [73]
100% CE
Spec =
100%
Acc = 100%
VIASURE SARS-CoV-2 Real Time PCR Nasopharyngeal swabs ≥10 copies/reaction N/A N/A [74]
Detection Kit CE-IVD
RADI COVID-19 Detection Kit Nasopharyngeal swabs 660 copies/mL Sens = 80 [75]
764 samples 98.9% CE-IVD
Spec =
100%
Acc =
98.9%
Zena Max – AMD SARS-COV-2 Nasopharyngeal swabs 10 copies/reaction Sens = N/A [76]
192 samples 100% CE-IVD

7
O. Filchakova et al. Talanta 244 (2022) 123409

Sensitivity - Sens = 100% - % (false negative); Specificity - Spec = 100% - % (false positive); Accuracy -Acc = 100% - % (false negative) - % (false positive).

Table 3
Comparison of performance of commercialized government-approved RT-PCR kits.
Group Geometric mean LOD Average sens Average spec Average acc Average time Average number of samples

FDA 41.8 copies/reaction 98.1% 98.7% 96.8% 82.5 min 378

Other 44.1 copies/reaction 99.2% 99.8% 98.8% 73.5 min 352

Note: 322 μL was taken as an average sample volume to estimate LOD.

differences from the standard PCR method allow one to independently and by applying nano PCR [36,95]. Regarding Wang et al. study, the
quantify DNA without standard curves, which gives more precise and method was able to detect the viral genome in 10 min with LOD of 10
reproducible data. ddPCR can be used for very low-target quantitation copies/reaction [95]. They developed a nanopore targeting sequence
from variably contaminated samples [84]. The method is also used for which allows the detection of respiratory viruses simultaneously. The
clinical purposes: the first one was CE-marked in 2017 and approved by method showed relatively low specificity value (61%), so there is an
US FDA in 2019 for diagnosing chronic myeloid leukemia [85]. Studies increased risk of false positive results, but this method has a potential, as
using ddPCR were cited 36191 times and the number of citations was it can be further extended for diagnostics of other pathogens and viruses.
increasing exponentially since 2016. In 2021 alone such papers were Cheong et al. used the classical principle of RT-PCR, but integrated it
cited 5923 times, which means that the method is becoming more into one device [36]. The main advantage of it is that this device does
popular each year [86]. In comparison, papers using RT-PCR were cited not require bulky instrumentation and specialized laboratories, it is
381972 times, but this method has been developed much earlier than portable and has small detection time (17 min). The LOD of the device is
ddPCR. Also, since 2014 the number of citations has plateaued, 3.2 copies per μL, with high accuracy (100%), so this device is efficient
increasing significantly only in 2020 (presumably due to newly devel­ and reliable.
oped SARS-CoV-2 virus) [87]. The performance parameters of other Moitra et al. integrated nanoparticles to develop colorimetric assay
methods of SARS-CoC-2 RNA detection are summarized in Table 4. [92]. This method also allows the detection of the virus without
For instance, ddPCR was used in the method proposed by Suo et al. advanced instrumentation. The assay is based on gold nanoparticles
which showed good results [90]. The LOD was equal to 2.1 copies/­ capped with thiol-modified antisense oligonucleotides, which are spe­
reaction for ORFlab primers and 1.8 copies/reaction for N primers. cific for N-gene of SARS-CoV-2. The mechanism involves selective
These numbers are much lower than that for RT-PCR (1039 and 873.2 agglomeration in the presence of target RNA leading to a change in its
copies/reaction respectively). This method showed high accuracy surface plasmon resonance. The addition of RNaseH leads to the for­
(95%), specificity (100%) and sensitivity (94%) meaning that this mation of visible precipitate. The LOD of this method was 0.18 ng/μL
method is reliable. and the time of the detection was equal to 10 min.
Multiplex PCR is the simultaneous amplification of more than one There were other methods that identified SARS-CoV-2 RNA. The
target sequence in a single reaction tube using more than one primer ATR-FTIR (Attenuated Total Reflection – Fourier Transform Infrared)
pair. Two studies used this type of PCR to detect the virus. Ishige et al. spectroscopy method used showed good results as well. Barauna et al.
(2020) were the most successful [96]. Their method showed 100% used contrived saliva samples spiked with inactivated γ-irradiated
specificity, sensitivity and accuracy, but these results were achieved on COVID-19 virus particles, which generated infrared (IR) spectra with a
tests of 24 clinical samples, therefore, are inconclusive. With only 30 good signal-to-noise ratio [93]. The method allowed the detection of the
min of detection time and LOD of 21 copies per reaction, this method can viral content in 2 min which makes it the fastest among all methods. The
be used widely for urgent testing. However, further research is needed. accuracy of the method was equal to 90% was calculated with the
Another study by Visseaux et al. using multiplex PCR showed good re­ sample size of 181 participants. Also, this method can be used to test
sults as well [34]. While the specificity and accuracy values were less people on site, as it does not require reagents or additional procedures.
than that of Ishige et al. (2020) [96], the sample size was bigger (69 This method had a relatively low LOD (1582 copies/mL). Yu et al. have
samples: 40 positive and 29 negative). The method was slower (67 min) developed a new lateral flow strip membrane assay that can detect
than the method proposed by Ishige et al. [96], but it is still faster than RdRP, ORF3a, N genes of SARS-CoV-2 simultaneously [91]. The assay
traditional RT-PCR methods. was tested on 162 clinical samples and showed 100% sensitivity, 99%
Wang et al. and Cheong et al. received good results using nanopores specificity and 99.4% accuracy. The values are good, but the sample size

Table 4
Analysis of other methods using SARS-CoV-2 RNA.
Method Sensitivity Specificity Accuracy Sample size, samples Time (min) LOD

Plasmonic Photothermal Biosensors [80] N/A N/A N/A 82 N/A 0.22 pM

DNA nanoscaffold [88] N/A N/A N/A N/A 10 0.96 pM
Electrochemical Biosensor [89] 100% 100% 100% 48 <5 6.9 cp/μL
ddPCR [90] 94% 100% 95% 63 N/A 1.8–2.1 cp/rxn
Lateral Flow Strip Membrane assay [91] 100% 99% 99.4% 162 30 10 cp/test
Nanoparticles for colorimetric assay [92] N/A N/A N/A N/A 10 0.18 ng/μL
ATR-FTIR [93] 95% 85% 90% 181 2 1582 copies/mL
RdRP [94] N/A N/A N/A N/A 30 100 aM
Nanopore Targeted Sequencing [95] 100% 61% 85% 61 10 10 cp/rxn
Multiplex PCR [34] 100% 93% 97% 69 67 1000 cp/mL
Multiplex rRT-PCR [96] 100% 100% 100% 24 30 21 cp/rxn
BD MAX [97] 100% N/A 100% 205 150 50 cp/PCR
Nano PCR [36] N/A N/A 100% 75 17 3.2 cp/μL

Note: Accuracy=(TP + TN)/(TP + TN + FP + FN); Sensitivity = (TP)/(TP + FN); Specificity = (TN)/(TN + FP). TP – true positive, TN – true negative, FP – false
positive, FN – false negative.

8
O. Filchakova et al. Talanta 244 (2022) 123409

is inconclusive, thus further research of this method would determine Comparing commercial methods within each other, the numbers did
more practical values. The method is fast (30 min) and has relatively low not differ significantly. For example, the difference in average sensitivity
LOD (10 copies per test). Both methods have a potential to be used and specificity was 1.1%, the average accuracy values differed by 2%
widely alone or together with other methods to detect the virus among (Table 5). The average sample size of FDA approved methods was
the population, especially when rapid testing is required (airports, gate higher, but looking at standard deviation it can be said that the numbers
controls or different events). were more consistent among non-FDA approved methods. The same
Concluding, methods based on RT-PCR showed high sensitivity, pattern was recorded in time to detection and the limit of detection. The
specificity and accuracy values, but in comparison to other methods they median values for sensitivity show that literature and FDA approved
required longer time to detect the virus (hours and days). When nano­ commercial methods mostly had 100% values, while non-FDA approved
pores or nanomembranes, other PCR methods were integrated, the time methods were not equal to 100% [69–75,98]. But median values for
of detection was lowered down to 17 min preserving 100% accuracy specificity were 100% [69–75,98]. Accuracy median was highest for
[36]. Another method had even lower detection time (10 min), but ac­ literature methods, however commercial methods also showed good
curacy was lower as well (85%) [95]. Some suggested methods used results. Generally, median values showed the same trend as average
portable devices with a good limit of detection values and small time of values - there is a significant difference between commercial and liter­
detection. These devices are good when rapid detection is required, ature methods, but commercial methods within each other do not differ
especially in airports or events. Biosensors also were used to detect the much. Commercial methods are more reliable as they were tested on
viral genome. In general, developed biosensors had good LOD (0.22 pM, bigger sample sizes and more practical - as they can detect the viral
0.96 pM, 6.9 cp/μL) [80,88,89], two of them had good detection time genome better.
(10 min and less than 5 min) [88,89]. They still can lose to RT-PCR
methods due to decreased accuracy values (one method showed 100% 2.4. CRISPR
value [89], but others still need to be tested on clinical samples), but
other characteristics make these methods practical. In 2002, the term CRISPR short for clustered regularly interspaced
Comparing 10 RT-PCR methods mentioned in the scientific literature short palindromic repeats was introduced by Jansen et al. [99], to the
and 30 commercial methods approved by FDA and other organizations, sequences in prokaryotes identified earlier in 1987 [100]. CRISPR were
it can be concluded that the latter is more reliable. The average speed of evolutionarily developed to protect the bacteria and archaea from vi­
detection of literature methods was 62.8 min (ranging from 3 to 5 min to ruses and plasmids analogous to the RNA interference system present in
days) [35,37–45], while the minimal time taken by commercial methods eukaryotes [101]. The defense against the foreign genome by CRISPR
was 30 min [49,53,54]. However, the average time of detection of the includes incorporation of a foreign genomic sequences into host
literature methods was calculated without one method, which took days genome, with a subsequent usage of these sequences to attack invader
to detect the virus. Considering that, the average should have been [102]. CRISPR-Cas systems consist of two classes: Class 1 includes
higher, but To et al. did not mention the exact number of days, so the multiple Cas proteins for the interference step, while Class 2 depends on
calculation was impossible. Only several commercial methods showed one multidomain protein [103]. Signature genes of Class 1 are Cas3 and
LOD less than 5 copies/reaction [50,57,59,64] (maximum 1000 Cas10 that cut DNA and RNA respectively. In Class 2, Cas 9 and Cas12
copies/reaction) [62], while methods published in literature mostly had proteins make cuts in target DNA, while Cas13 cleaves RNA [104,105].
lower LOD (maximum 20 copies/reaction) [39]. They were compatible Due to the efficient genome recognition and editing of CRISPR com­
with each other in terms of specificity, sensitivity and accuracy. Even if plexes, and the simplicity of Class 2 proteins, Cas12, Cas13, Cas9 pro­
the average sample size (667) of literature methods was higher than that teins discussed in this review along with the other CRISPR-Cas Class 2
of commercial (352 for non-FDA approved and 378 for FDA-approved), complexes are used for the detection of nucleic acids [106].
the standard deviation was higher (1040 for literature and 359, 783 for The mechanism for the detection of the genome starts with the
commercial). Thus, the results obtained from testing the commercial recognition and cleavage of the targeted nucleic acids by CRISPR-Cas
methods are considered to be more reliable. This does not mean that complexes using the guide of designed gRNA. This causes the activa­
literature methods are bad, but further research is required. Comparing tion of the non-specific collateral activities of Cas proteins, which is the
sensitivity values of literature methods (83.7%) and commercial cleavage of any ssDNA nearby for Cas12 and Cas 9, and ssRNA for Cas13
methods (98.1% for FDA approved and 99.2% for methods approved by proteins. Cleavage of reporter molecules during the collateral activity
other organizations), the latter had higher sensitivity. The standard releases a signal for the presence of targeted nucleic acid, which could be
deviation of commercial methods was also low, so all commercial SARS-COV-2 gene [107].
methods had consistently high sensitivity values, while that of literature Fig. 2 displays the application of CRISPR for the detection of SARS-
methods was relatively big. However, literature methods showed high COV-2. The analyzed methods in this review mostly require the naso­
specificity (all methods - 100%) [35,37–45], but there were values less pharyngeal swab, while for some methods saliva can also be used as the
than 100% among commercial methods [46,48,49,53,55,59,60,65,69]. sample. After obtaining the sample for analysis, the viral RNA should be
Accuracy values were compatible within each other, but the lowest ac­ extracted from the swab. RT-PCR, RT-LAMP, and RT-RPA (Reverse
curacy was recorded among FDA-approved methods. Even if the average Transcription Recombinant Polymerase Amplification) were applied for
sample size of literature methods was higher than that of commercial, amplification in the methods discussed in this review. Depending on the
the standard deviation was higher. Thus, the results obtained from amplification type, the sample to result time, reactants, equipment, and
testing the commercial methods are considered to be more reliable. reaction conditions vary significantly. RT-LAMP at RT-RPA, which are

Table 5
Comparison of RT-PCR in scientific literature and commercial methods.
Sensitivity Specificity Accuracy Sample size Time (min) LOD Ref.

RT-PCR (literature) 83.7 ± 30.1% (29.2; 100 ± 0% (-; 100%) - 97.7 ± 3.6% (91.7; 667 ± 1040 (9; 62.7 ± 47.7 (3–5 9.2 ± 7.9 (1; 10) [35,
100%) - 100% 100% 100%) - 100% 2923) - 117.5 min; days) - 63.5 -5 37–45]
PCR commercial (FDA 98.1 ± 3.0% (88.1; 98.7 ± 2.4% (93.1; 96.8 ± 3.8% (87.7; 378 ± 784 (60; 82.5 ± 49 (30; 148 ± 254 [46–68]
approved) 100%) - 100% 100%) - 100% 100%) - 98.5% 3801) - 138 180) - 70 (2.65; 1000) - 70
PCR commercial (non- 99.2 ± 1.1% (97.1; 99.8 ± 0.4% (99; 98.8 ± 1.3% (97.1; 352 ± 360 (100; 73.4 ± 19.6 (45; 120 ± 142 (10; [69–75,
FDA approved) 100%) - 99.5% 100%) - 100% 100%) - 98.9% 764) - 192 90) - 80 322) - 37.2 98]

Note: Average ± stdev; Range (x; y); Median.

9
O. Filchakova et al. Talanta 244 (2022) 123409

Fig. 2. The illustration of the CRISPR-based amplification assay process for the detection of SARS-COV-2 (created in BioRender.com).

mostly applied for CRISPR-based SARS-COV-2 detection, required the the same results as mNGS in all 114 samples, containing SARS-COV-2,
incubation for 20–30 min at 62 ◦ C and at 37 ◦ C correspondingly, while SARS-CoV-2-/hCoV+, and healthy genome. Important to note that
for RT-PCR several temperature changes are needed as it is stated in the RT-PCR could not determine the presence of the targeted virus in 5 out
protocol. After the double-stranded DNA is obtained as the result of the of 57 cases detected by mNGS and CRISPR-COVID. This means that the
preceding step, CRISPR reactions may begin. However, if Cas13 protein sensitivity of the CRISPR-COVID method is higher in comparison to
is selected for the detection, amplified DNA should be transcribed back RT-PCR. Furthermore, according to Hou et al. this method runs at less
into RNA and this is possible by application of T7 polymerase. The than $3.5 per test and estimated to cost as low as $0.7 at a production
average CRISPR master mix includes the amplification product, gRNA, scale. Since CRISPR-COVID is more reliable and cost-effective than
CRISPR-Cas protein of choice, a fluorescent probe, buffer, and the RT-PCR, it can be a good alternative to the latter.
nuclease-free water to reach the desired reaction volume. Incubation of Reverse transcription All-In-One Dual CRISPR-Cas12a (RT-AIOD-
products generally occurs at 37◦ for 5–10 min. The detection and the CRISPR) is the fast assay with a turnout time of 40 min established by
cleavage of SARS-COV-2 by the CRISPR-Cas system causes the collateral Ding et al. [110]. 2.5 μL of RNA extract from the swab is put into the tube
cleavage of the single-stranded DNA, and this results in the fluorescent containing primers targeting the N gene of SARS-COV-2, avian myelo­
signal. Next, in less than 5 min results can be obtained by lateral flow or blastosis virus (AMV) reverse transcriptase, Cas12a-crRNA mix,
fluorescence detection by naked eye or apparatus depending on the ssDNA-FQ reporters, buffers, and other components required for both
specific method. Generally, around 50 min are needed for SARS-CoV-2 RT-RPA and CRISPR reactions. Generally, the positive result can be
detection with the CRISPR method including the time for setup after obtained after 20 min of incubation at 37 ◦ C and the fluorescence can be
RNA extraction. detected with the naked eye under an LED light. This assay can detect
The lowest LOD among the CRISPR methods presented in the review down to 5 RNA copies per reaction and the clinical results of 28 samples
is 2 RNA copies per sample, obtained by Huang et al. [108]. This method were consistent with the RT-PCR test. The following assay is isothermal
involves the amplification of the target fragments by RT-PCR using 5 μL and does not require a high temperature, therefore, a low-cost hand
of isolated RNA followed by DNA amplification protocols or by RT-RPA warmer can be applied for the incubation. Since RT-AIOD-CRISPR is a
method on 5 μL of isolated RNA. Then, for the CRISPR reaction, the quick, reliable, easy, single-step, and low-cost method, it is an excellent
Cas12a protein and the fluorescent probe were applied, and the signal method that can be developed for point of care devices. All the necessary
was observed by SpectraMax i3x Multi-Mode Microplate Reader after reagents can be integrated into the chip, and the RNA extracted from the
20 min of the incubation at 37 ◦ C in the dark. The primers and gRNA swab can be added later. After 40 min of incubation with the hand
were designed to target the N and ORF1ab regions of the SARS-COV-2. warmer, the programmed smartphone can be used to take photos and
While testing the clinical swab samples, 15 out of 15 positive samples return qualitative or semi-quantitative test results. According to the
were detected, thus 100% sensitivity was achieved. However, 4 false authors, at the current time, the cost of this test is about $6, however, it
positives were obtained leading to the specificity of 71.4%. The appli­ is expected to decrease significantly at the production scale.
cation of RT-PCR for amplification of RNA extract to increase the The major part of the methods with viral RNA detection requires the
sensitivity turned out to be the double sword, which generated the low pre-extracted nucleic acid for detection. The extraction of RNA needs
specificity, because of the false positives. expensive instruments and a lot of time. Therefore, the CRISPR method
CRISPR-COVID method developed by Hou et al. is a method with proposed by Ramachandran et al. that directly uses swab samples can be
high sensitivity and quick turnaround time [109]. In 30 min, RT-RPA useful to detect SARS-CoV-2 [32]. Isotachophoresis (ITP), which is a
and T7 transcription is performed on the extracted RNA, then addi­ two-buffer system consisting of a high-mobility leading electrolyte (LE)
tional 10 min are required for the CRISPR-Cas 13a reaction till obtaining and low-mobility trailing electrolyte (TE), is practiced to extract RNA
the signal. The LOD of CRISPR-COVID is lower than 3 copies per from nasopharyngeal swab and to speed up CRISPR-Cas12 reaction. The
microliter. The clinical sensitivity of the method was tested by using purification and the acceleration reactions by ITP are possible because
RT-PCR and metagenomic next-generation sequencing (mNGS) when the electric field is applied, sample ions with effective mobilities
methods, and CRISPR-COVID demonstrated 100% accuracy by having are concentrated on the 10 μm zone at the LE-to-TE interface. By using

10
O. Filchakova et al. Talanta 244 (2022) 123409

ITP, RNA can be extracted from the nasopharyngeal swab in 3 min, after important to note that Cas9 was applied only in one technique presented
pre-incubating the sample for 2 min at 62 ◦ C. After this, for RT-LAMP in this review. In more than half of the cases Cas12 was used for CRISPR
reaction, 20–30 min incubation is required, and the ITP and CRISPR reaction and this protein has an accuracy of 96%. The accuracy of Cas13
detection of DNA is performed in less than 5 min. RNA extraction and is considerably high, being 97%, and since Cas 13 was used in 4
CRISPR detection are conducted on a chip, which means that this methods, it can be assumed that this protein gives the highest accuracy.
method can be developed into the point-of-care device. The volume of The sample-to-time result of both Cas 12 and Cas13 is around 50 min,
all the reactants required for the CRISPR reaction in this assay is about while for Cas 9 about 60 min are required to know the outcome. LOD of
100 times less than the average amount being only 0.2 μl, from this it Cas 12 is better than of Cas13, being 5.3 and 6.6, respectively. From the
follows that this method is cost-effective and reduces the use of chem­ data presented in Table 6 it can be assumed that there are no significant
icals. Another advantage of ITP based CRISPR method is that time differences from the protein of choice, and Cas9, Cas12, Cas13 are all
needed from the sample to the result is very rapid, being about 40 min, reliable for SARS-CoV-2 detection.
and still, the method has an average LOD for CRISPR of about 10 Table 7 presents the same features of the methods as Table 6, but the
copies/μl. The accuracy of the method with the usage of pre-extracted techniques were grouped according to their amplification types, and the
RNA from 40 clinical samples was measured to be 93.8% in compari­ average values of these characteristics were quantified. RPA was
son with RT-qPCR results. However, the results obtained from the ITP measured to be the most reliable method with an accuracy of 98%. LOD
based RNA extraction are less reliable, 3 out of 4 positive samples were of RPA is equal to 5.9 cp/μL of RNA that can be considered good in
detected, thus resulting in the sensitivity of 75% for ITP based nucleic comparison to LOD of LAMP method with 13 cp/μL. However, the
acid extraction and the CRISPR reaction method. lowest LOD of 0.4 cp/μL is obtained while amplifying the RNA with PCR.
Another CRISPR assay that does not require the extraction of RNA for Overall, RT-PCR is an expensive but highly sensitive method, because of
SARS-CoV-2 detection is SHINE, the method developed by Arizti-Sanz the great sensitivity of RT-PCR, the specificity can be decreased due to
et al. [31]. Only about 50 min are needed to achieve the result after contamination. Other types of amplification such as RPA and LAMP, do
obtaining the swab or saliva of the patient. SHINE, short for Streamlined not require the change of temperature, therefore expensive instruments
Highlighting of Infections to Navigate Epidemics, is based on SHER­ are not required. Furthermore, the incubation temperature for RPA is
LOCK (Specific High-sensitivity Enzymatic Reporter unlocking) and in­ about 37 ◦ C, since RPA has the smallest deviation from the ambient
cludes a reverse transcription step (RT) followed by isothermal temperature, it also has significance as the amplification method for
recombinase polymerase amplification (RPA), T7 transcription and Cas point of care devices.
13 cleavage of single-stranded RNA. SHINE is optimized to be conducted The average accuracy of all CRISPR methods is about 96.5%, and
on unextracted nucleic acid, within a single tube to reduces about 52 min are needed to obtain the results of the test. CRISPR-
cross-contamination. The results can be visualized by the lateral flow or coupled RT-RPA and RT-LAMP are good alternatives to RT-PCR since
in-tube fluorescence. The fluorescence visualization was applied for the these methods are more cost-effective and faster. The majority of the
clinical testing, since it requires less incubation time, and allows the methods relied on RNA extraction by kit, while Ramachandran et al.
testing of a large number of samples simultaneously, and reduces the extracted the RNA by using ITP as described before [32], and heat lysis
risk for cross-contamination. Portable transilluminators (<$500) or was performed by Arizti-Sanz et al. to obtain the viral RNA [31]. The
small, blue LED lights (~$15) can be used as a required blue average accuracy of these two methods is 95%, while other methods
light-emitting device, then smartphone applications can analyze the have an accuracy of 97%. The slight differences between these per­
captured photos to provide the result of the testing. 50 clinical nasal centages suggest that for CRISPR-based detection, SARS-CoV-2 RNA can
swabs tested with SHINE demonstrated 90% sensitivity and 100% be obtained by heat lysis or by the usage of Isotachophoresis (ITP) as
specificity in comparison to the conventional RT-qPCR method. well, and still fairly accurate results can be achieved.
Table 6 contains the information about the specificity, sensitivity,
and accuracy of the CRISPR-based methods grouped by their Cas protein 2.5. LAMP, RPA, RAA, PCA
of choice. The average sample to result time, the number of clinical
samples tested, LOD of the method are also presented in Table 6. For Loop-mediated isothermal amplification, also known as LAMP, is one
some methods, LOD was displayed in copies per reaction and was con­ of the popular amplification methods, and, according to Scopus, the
verted into copies per μl. The average accuracy for CRISPR-based SARS- number of citations with this keyword is 96532. LAMP was established
CoV-2 detection was calculated to be 96.5%, while the specificity is 98% by Notomi et al., in 2000 and employs a DNA polymerase and 4 (or 6)
and the sensitivity is 96%. The specificity is higher than the sensitivity, DNA primers to recognize the 6 (or 8) sequences in the target DNA. The
which means that the possibility of differentiating between different LAMP reaction is initiated by the inner primer, and then the strand
viruses is higher than detecting the SARS-CoV-2. Between different types displacement DNA synthesis by an outer primer produces single-
of Cas proteins, Cas9 showed the best accuracy of 98%, although it is stranded DNA. After this, the second inner and outer primer generate

Table 6
CRISPR-based SARS-COV-2 detection methods.
Cas Specificity Sensitivity Accuracy N of samples N of positive Time LOD (cp/μL Log addition Ref.
samples (min) of RNA) of LOD

Cas 9 100% 97.1% 98.4% 64 35 60 4 4 [111]

Cas 12 95.9 ± 10.8% 96.8 ± 4.5% 95.9 ± 5.5% 82; 10; 28; 29; 40; 7; 19; 14; 51 ± 11 9.6 ± 9.8 5.3 ± 4.1 [108,110,
(71%; 100%) (88%; 100%) (86%; 100%) 100; 22; 31 50; 11; 26 (40; 66) (0.4; 30) (0.4; 30) 112–116]
100% 100% 97.6% 50 10 10
Cas 13 100% 95.0 ± 4.2% 97.2 ± 2.5% 50; 114; 64; 154 30; 52; 32; 81 50 ± 14 7.4 ± 3.4 6.6 ± 1.8 (3; [31,32,109,
(90%; 100%) (94%; 100%) (40; 70) (3; 10) 10) 117]
95.0% 97.5% 45 8.3 8.0
Average 97.6 ± 8.3% 96.2 ± 4.1% 96.5 ± 4.3% 51 ± 11 8.4 ± 7.7 5.2 ± 2.9
(71%; 100%) (88%; 100%) (86%; 100%) (40; 70) (0.4; 30) (0.4; 30)
100% 96.7% 97.8% 50 8.2 8.0

Note: Average ± standard deviation; Range (x%; y%); Median are shown. LOD conversion from cp/rxn to cp/μL was done by dividing the cp/rxn by the amount of RNA
used in μL. Accuracy=(TP + TN)/(TP + TN + FP + FN); Sensitivity = (TP)/(TP + FN); Specificity = (TN)/(TN + FP). TP – true positive, TN – true negative, FP – false
positive, FN – false negative.

11
O. Filchakova et al. Talanta 244 (2022) 123409

Table 7
CRISPR with different amplification methods.
Specificity Sensitivity Accuracy N of samples N of positive Time (min) LOD (cp/μL) Ref.
samples

RT-PCR 71.4% 100% 86.2% 29 14 50 0.4 [108]

RT- 100% 94.3 ± 4.1% (88%; 96.2 ± 3.9% (89%; 82; 10; 100; 40; 7;50; 26; 32 46 ± 11 (40; 13.0 ± 9.8 (5; [32,112–114,116]
LAMP 100%) 100%) 29; 64 65) 30)
94.0% 97.0% 40 10
Best 100% 95% 97.6% 82 40 45 10 [112]
RT-RPA 100% 97.2 ± 3.9% (90%; 98.4 ± 2.3% (94%; 64; 28; 22; 50; 114; 35; 19; 11; 30; 52; 56 ± 11 (40; 5.9 ± 3.5 (2; [31,109–111,115,
100%) 100%) 154 81 70) 10) 117]
98.6% 99.2% 55 5.2
Best 100% 100% 100% 114 52 40 3 [109]

Note: Average ± standard deviation; Range (x%; y%) and Median are shown; LOD conversion from cp/rxn to cp/μL was done by dividing the cp/rxn by the amount of
RNA used in μL. Accuracy=(TP + TN)/(TP + TN + FP + FN); Sensitivity = (TP)/(TP + FN); Specificity = (TN)/(TN + FP). TP – true positive, TN – true negative, FP –
false positive, FN – false negative.

the stem-loop DNA structure by hybridizing the ends of the target. before nucleic acid amplification. One of the widely used options is the
Further by cycling and elongation steps, original stem-loop DNA and one RNA extraction by kit, which might take 5–30 min. Furthermore, heat­
newly synthesized stem-loop DNA with a stem twice as long is produced. ing the sample or magnetic beads can also be used to obtain the RNA.
In less than an hour, 10⁹ of DNA copies are generated through LAMP There are also some cases where the clinical specimens were directly
reaction [118]. Since the nucleic acid in the SARS-CoV-2 virus is RNA, subjected to amplification without any pre-treatment. In the majority of
reverse transcription is performed to derive cDNA. Because the the cases the SARS-CoV-2 positive sample that is being amplified can be
isothermal amplification happens at around 62–65 ◦ C, RT-LAMP does detected in less than 20 min with a fluorescence reader or a naked eye. If
not require expensive instruments for temperature changes, and LAMP the corresponding color change is not observed after the amplification,
has the potential to be adapted into portable devices. For SARS-CoV-2 then the test result is SARS-CoV-2 negative. In the case of the lateral flow
detection with RT-LAMP method, incubation for 30–60 min is test, the result can be determined in less than 5 min after incubation.
required, and by the addition of DNA intercalating object - SYBR Green The amplification method with LOD of 80 cp/mL is the SARS-CoV-2
dye - colorimetric visualization is possible. detection by RT-LAMP proposed by Huang et al. [119]. 4 sets of primers
Another isothermal amplification technique widely applied for each consisting of 6 primers were designed to target N, S, and Orf1ab
nucleic acid detection is Recombinase polymerase amplification (RPA). regions of the viral RNA. After the nucleic acid extraction from the 16
The main difference of RPA from other amplification methods is it’s use clinical samples using an RNA extraction kit, the RT-LAMP reaction was
of Recombinase-primer complexes to scan double-stranded DNA, and to performed in triplets. In the first and the second tubes, the primers for
facilitate strand exchange at cognate sites. Then, the single-stranded the N and Orf1ab gene were placed, while human β-actin primers pre­
DNA binding proteins stabilize the displaced DNA by binding to it and sent in the third tube served as a negative control for the experiment.
thus preventing the binding of primers to the displaced DNA strands. Reactants were incubated for 30 min at 65 ◦ C after which the observa­
Finally, the DNA synthesis at places where the primer is bound to the tion of yellow color with a naked eye was considered as a positive result,
DNA is initiated by the strand displacing polymerase. The continuous while the pink or orange color of the sample similar to the color of
repetition of these steps increases the number or amplification product negative control implies negative test result. The following RT-LAMP
exponentially to the detectable level. For the RT-RPA process for SARS- test was 100% in accordance with the result of the RT-PCR test. Thus,
CoV-2 detection sample is incubated at around 42 ◦ C and about 15–35 this method is accurate, simple, and does not require any expensive
min are required to obtain the result. Due to the lower incubation instruments for both amplification and visualization of the product.
temperature and short time, RT-RPA is preferable as the point-of-care Moreover, RT-LAMP with a sample to the result time less than 70 min is
method to RT-PCR and RT-LAMP. considerably more rapid than the conventional RT-PCR that requires
A brief flowchart of the detection of SARS-CoV-2 from the clinical more than 2 h.
sample via amplification is presented in Fig. 3. After collecting the Earlier the importance of a portable detecting device was mentioned,
sample from the patient, different types of treatments can be applied and Rodriguez-Manzano et al. already adapted RT-LAMP into the point-

Fig. 3. Schematic Illustration of the detection of SARS-COV-2 by Amplification methods as RT-LAMP, RT-RPA, and RT-RAA (Reverse Transcription Recombinase-
Aided Amplification) (Created with BioRender.com).

12
O. Filchakova et al. Talanta 244 (2022) 123409

of-care method [120]. The method relies on detection of changes in pH were successfully detected by PCA, and no false negatives were
following nucleotide incorporation during amplification and uses observed. Despite the good clinical performance and the LOD of 4.9
custom lab-on-chip platform with 4368 ion-sensitive field-effect tran­ cp/μL, PCA requires expensive equipment, therefore it might be difficult
sistors serving as sensors. The reaction on-chip is performed at 63 ◦ C for to adapt this method to conventional use.
30 min. The data is transmitted to the smartphone app via Bluetooth and Table 8 presents the information about the clinical performance,
analyzed in MATLAB. The LOD of this method is 10 copies per reaction, time to result, LOD of various virus detection methods that involves
while the sensitivity and specificity for 183 samples are 91% and 100% amplification, such as previously described LAMP, PCA, RPA, and its
respectively. The method by Rodriguez-Manzano et al. demonstrates alternative RAA. Sample-to-result time was divided into two parts that
that RT-LAMP can be successfully used in a lab-on-chip platform. include time passed after the start of amplification till obtaining the
Thi et al. performed the RT-LAMP on different samples as extracted result, and the overall time from swab to result. Some research papers
RNA, clinical swab, and heated clinical swab, and the results indicate have not included the time needed for RNA extraction with the kit,
that the extracted RNA provides the best result after amplification for 1 h therefore, these measures were applied. LOD of methods deviated from
with the accuracy for 768 samples being 95% [121]. The test results of 0.08 to 62 cp/μL and in order to minimize the effect of the outliers,
343 hot swabs were in 80% accordance with RT-PCR, while this per­ logarithmic addition was applied. RAA detection provided good results
centage was decreased to 72% for directly used 235 clinical specimens. for all criteria with an accuracy of 97%, LOD of 0.7 cp/μL, and overall
Important to note that despite the type of used sample specificity time of 40 min, however, it is significant to note that only two studies
remained high by being 99.7% and 99.5% for extracted RNA and hot applied this type of amplification. RPA that was used in five experiments
swab, the specificity of the not heated sample did not decrease consid­ showed a high clinical accuracy of 96% and LOD of 7 cp/μL with an
erably being 94%. The research conducted by Thi et al. suggests that to average time to result of 45 min. As it was mentioned before, LAMP is
derive the most reliable result the viral RNA should be extracted from widely used for virus detection and 18 different clinical studies dis­
the swab using the kit, or the clinical sample should be at least heated for cussed in the research were performed by using LAMP, which conse­
5 min at 95 ◦ C to obtain more accurate results than from the directly quently resulted in the outliers.
used swab. As displayed in Fig. 3, the different types of treatments can be per­
The difference between using saliva and nasopharyngeal swab (NPS) formed on the clinical sample before exposing it to amplification.
as the sample for SARS-CoV-2 detection with RT-LAMP was studied by Table 9 presents the analysis of methods depending on their RNA
Kitajima et al. and the study identified that the LOD for NPS is twice as extraction method. RNA extraction with a kit had the best clinical per­
lower than the LOD for saliva, being 1.0 and 2.3 cp/μL correspondingly formance with the average accuracy, specificity, and sensitivity of
[122]. The sensitivity, specificity, and overall accuracy of NPS for 151 96.5%, 98%, and 93.5%, respectively. RNA extraction by other treat­
samples are equal to 89%, 99%, 93%, while for 88 saliva samples tested ments had a lower sensitivity of 85%, an accuracy of 93%, and speci­
by this method the percentages were equal to 83%, 98%, and 93%. ficity being 99.9%. When the clinical swab or saliva was directly
These results indicate that using NPS results in slightly higher sensitivity amplified, even though the specificity was not affected and remained
than the usage of saliva samples for SARS-CoV-2 detection. 98%, the sensitivity suffered significantly and was equal to 68%, and
The impact of targeting different regions of SARS-COV-2 was thus resulting in an accuracy of 84.5%. The specificity is not consider­
investigated by El Wahed et al. by applying the primers for RdRp, N, and able affected by the existence or absence of any treatment for the sam­
E gene of the virus; it was found that targeting RdRp provides the most ple; however, only after the extraction with the kit SARS-CoV-2 positive
reliable outcome, this claim is supported by LOD and the clinical per­ samples can be precisely detected. This can be supported by the LOD of
formance of the different genes. LOD of RdRp was 2 cp/rxn, while for the the three methods as well, as shown in Table 9. In conclusion, the data in
other two genes it was 15 cp/rxn, when 1 μL of extracted RNA was used Table 9 supports the findings by Thi et al. that RNA extracted by the kit
for amplification [123]. To identify the clinical accuracy of RT-RPA on generates the most accurate results [121].
RdRp, N, and E genes 36 swab samples were tested, and the following The performance of different amplification methods discussed pre­
results were obtained: the specificity and sensitivity for RdRp in com­ viously categorized according to the target region in SARS-CoV-2 RNA is
parison to RT-PCR was 100% and 94%, while for N gene 94% and 83%, summarized in the Table 10. Data analysis revealed that the best accu­
and for E gene 77% and 65%. From the study of El Wahed it can be racy and LOD are obtained when multiple genes are targeted, 98% and
concluded that targeting the RdRp gene of SARS-CoV-2 will result in 3.8 cp/μl, correspondingly. Among the single genes, RdRp gives the
better correlation with the RT-PCR, than targeting other genes. most reliable results with an accuracy of 96% and LOD of 2.6 cp/μl. In
The study conducted by Nawattanapaiboon et al. in Thailand the study of El Wahed et al. RdRp was preferable to other genes as well in
employed RT-LAMP assay targeting RdRp gene on a large number of terms of clinical performance and LOD [123].
clinical samples (2120). The study demonstrated high sensitivity and The comparison between the SARS-CoV-2 detection by using RT-
specificity of the test at 95.74% and 99.95%, respectively, values similar LAMP, RT- RPA and RT-RAA, RT-PCR in literature, RT-PCR approved
to the conventional RT-PCR [124]. In the RT-LAMP reaction that lasted by FDA and other authorization can be viewed in Table 11. The main
for 60 min at 65 ◦ C, LOD was estimated to be 25 RNA copies per reac­ advantage of using RT-RPA and RT-RAA is short time of the assay which
tion, where the 5 μL of extracted RNA was used for the reaction. Only is 20–40 min faster than the other methods. RT-LAMP showed the lowest
one false positive and two false negatives were detected from 47 clinical reliability among all methods of 93.3%. Generally, RT-PCR tests
SARS-CoV-2 positives and 2073 negative nasopharyngeal swabs. (commercialized and literature) provide more accurate results than the
Zwirglmaier et al. proposed the novel nucleic acid-based SARS-CoV- rest. Despite the fact that the specificity of RT-PCR in literature is 100%,
2 detection method named Pulse Controlled Amplification (PCA) [125]. and the overall accuracy is 97.7%, the sensitivity of this method is
The clinical swabs in universal transport medium were treated with 4 83.7%, which is the lowest percentage between all detection methods.
vol of AVL buffer and 4 vol of ethanol, or samples were heated at 80 ◦ C Even though there is not a significant difference in the specificity and
for 10 min before performing the RT-PCA. The treated sample and the accuracy of the different methods, the stronger deviation in sensitivity
hybridization buffer were incubated at room temperature for 5 min to can be observed, which can lead to the conclusion that commercialized
allow the hybridization of RNA to the reverse primers. Following this, RT-PCR tests, especially the ones approved by authorizations other than
Pharos Micro was set to 55 ◦ C for 5 min for reverse transcription reac­ FDA, provide the most sensitive and reliable test results. However, it
tion, then to 67 ◦ C for 60 s for thermalizing step. Then, for the dena­ should be noted that the data about the clinical performance of only five
turation 250 μsec pulses were applied to the wires suspended into the non-FDA registered RT-PCR methods were available.
reaction solution every 3 s for 800 cycles. The clinical accuracy of the
following method was 94% for 154 swabs, 74 out of 83 positive swabs

13
O. Filchakova et al. Talanta 244 (2022) 123409

Table 8
SARS-COV-2 detection by LAMP, RPA, RAA and PCA.
Specificity Sensitivity Accuracy Time started from Overall time, LOD (cp/μL) Log LOD Ref.
reaction, min min

LAMP 98 ± 4.9% (81%; 86 ± 19% (32%; 93.3 ± 7.6% (72%; 43 ± 14 (30; 60) 61 ± 28 (30; 15.3 ± 22.2 4.3 ± 7.1 [119–122,124,
100%) 100%); 100%) 37.5 130) (0.1; 62) (0.1; 62) 126–135]
100% 91.7% 93.4% 60 4.9 4.9
RPA 98.9 ± 2.5% (94%; 92.6 ± 7.4% (83%; 95.5 ± 4.8% (89%; 22 ± 8 (15; 35) 45 6.9 ± 5.3 (2; 5.4 ± 2.3 (2; [123,136–138]
100%) 100%) 100%) 20 15) 15)
100% 94.4% 97.2% 7.7 7.7
RAA 98.9 ± 1.5% (98%; 98.8 ± 1.7% (98%; 97.3 ± 2.9% (98%; 28 ± 11 (20; 35) 40 0.7 ± 0.4 (0.4; 0.6 ± 1.9 [139,140]
100%) 100%) 100%) 28 1) (0.4; 1)
98.9% 98.8% 97.3% 0.7 0.6
PCA 100% 89.2% 94.2% 51 61 4.9 [125]

Note: Average ± standard deviation; Range (x%; y%) are shown; LOD conversion from cp/rxn to cp/μL was done by dividing the cp/rxn by the amount of RNA used in
μL. Accuracy=(TP + TN)/(TP + TN + FP + FN); Sensitivity = (TP)/(TP + FN); Specificity = (TN)/(TN + FP). TP – true positive, TN – true negative, FP – false positive,
FN – false negative.

Table 9
SARS-COV-2 detection by amplification with different pre-treatments to the sample.
Specificity Sensitivity Accuracy Time after RNA Overall Time LOD (cp/ LOD by Log Ref.
extraction (min) (min) μL) (cp/μL)

Extraction 98 ± 4.9% (81%; 93.5 ± 8.2% 96.5 ± 4% 37 ± 16 (15; 60) 58 ± 12 (40; 7.9 ± 15 2.6 ± 5.4 [119–124,127–130,
with kit 100%) 100% (71%; 100%) (89%; 100%) 35 70) (0.1; 62) (0.08; 62) 135–137,139,140]
95.7% 97.8% 60 3.6 3.4
Extraction w/ 99.9 ± 0.2% (99%; 84.6 ± 19.6% 92.7 ± 7.5% 39 ± 11 (30; 60) 64 ± 34 (35; 18 ± 22 9.8 ± 3.7 (3; [121,125,131,132,
o kit 100%) (47%; 100%) (78%; 100%) 38 130) (3; 50) 50) 134,138]
100% 87.9% 93.2% 55 9.8 8.5
Directly swab 98 ± 3.4% (94%; 67.9 ± 31.4% 84.5 ± 10.5% 40 ± 17 (30; 60) 42 ± 16 (30; 28 ± 36 11.6 ± 8.7 [121,126,133]
100%) (32%; 87%) (72%; 91%) 30 60) (3; 54) (3; 54)
100% 85.0% 90.0% 35 28.3 11.6

Note: Average ± standard deviation; Range (x%; y%); Median are shown; LOD conversion from cp/rxn to cp/μL was done by dividing the cp/rxn by the amount of RNA
used in μL. Accuracy=(TP + TN)/(TP + TN + FP + FN); Sensitivity = (TP)/(TP + FN); Specificity = (TN)/(TN + FP). TP – true positive, TN – true negative, FP – false
positive, FN – false negative.

Table 10
SARS-CoV-2 detection by targeting different genes.
Specificity Sensitivity Accuracy Time started from Overall LOD (cp/ Log LOD Ref.
the reaction, min time, min μL)
N gene 97.4 ± 5.2% 85.7 ± 20% 92.9 ± 7.9% 38 ± 15 (15; 60) 61 ± 31 (30; 18.9 ± 22.6 8.5 ± 5.3 [120,121,123,125–127,
(81%; 100%) (32%; 100%) (72%; 100%) 33 130) (0.2; 62) (0.2; 62) 130–132,134–137,140]
100% 91.7% 93.8% 60 7.7 10.0
RdRp 99.2 ± 0.9% 90.4 ± 6.0% 95.9 ± 3.2% 36 ± 18 (15; 60) N/A 2.6 ± 1.7 2.6 ± 1.7 [122–124]
(98%; 100%) (83%; 96%) (93%; 100%) 35 (1; 5) (1; 5)
99.3% 91.5% 95.3% 2.2 2.2
Orf1ab 98.9 ± 1.5% 91.3 ± 8.9% 93.9 ± 5.5% 25 ± 7 (20; 30) 38 ± 4 (35; 1.5 ± 1.5 1.5 ± 1.5 [133,139]
(98%; 100%) (85%; 98%) (90%; 98%) 25 40) (0.4; 2.5) (0.4; 2.5)
98.9% 91.3% 93.9% 38 1.5 1.0
Multiple 100% 96.7 ± 6.7% 98 ± 3.9% 42 ± 13 (30; 60) 58 ± 13 (45; 3.8 ± 4.3 1.5 ± 7.8 [119,128,129,138]
targets (87%; 100%) (92%; 100%) 39 70) (0.1; 10) (0.1; 10)
100% 100% 60 2.5 2.5

Note: Average ± standard deviation; Range (x%, y%); Median are shown; LOD conversion from cp/rxn to cp/μL was done by dividing the cp/rxn by the amount of RNA
used in μL. Accuracy=(TP + TN)/(TP + TN + FP + FN); Sensitivity = (TP)/(TP + FN); Specificity = (TN)/(TN + FP). TP – true positive, TN – true negative, FP – false
positive, FN – false negative.

3. Detection of antigens and viral particles methods.

In the first method presented on the top of the Fig. 4, the nucleo­
Detection of viral particles and antigen is a viable alternative to RT- capsid, spike protein, or any other structural protein of SARS-CoV-2 is
PCR. These methods are potentially inexpensive, portable, rapid, and detected by the fluorescence generated by the antigen-antibody special
can be used to diagnose patients at the early stage of viral infection. interaction. On the right side of the figure, the fluorescence signal vs
Some methods have already been commercialized and are available as concentration graph is presented, which is in practice rarely linear and
point-of-care tests. They are not required to be performed by a skilled mainly tends to flatten with the rise of concentration. At the bottom, the
operator and can be run by patients themselves. Structural SARS-CoV-2 human clinal sample is digested into peptides and further subjected to
proteins such as nucleocapsid protein (nucleoprotein), spike protein, mass spectroscopy to find out whether the sample is infected or not by
membrane protein, and envelope protein can be detected. Usually, determining the peaks that correspond to SARS-CoV-2 proteins.
nucleoprotein and spike protein are used as analytes. Detection can be
instrumental-based (e.g. mass spectrometry) or chemical reaction-based
(e.g. antigen-antibody binding). Fig. 4 provides an overview of detection

14
O. Filchakova et al. Talanta 244 (2022) 123409

Table 11 clinical diagnostics, as it is sensitive (LOD = 0.8 pg/mL) and analysis

The comparison of different SARS-COV-2 RNA detection methods. time is 20 min [141]. Another electrochemical biosensor that does not
RT- RT-RPA RT- RT-PCR RT-PCR require sample pretreatment is a field-effect transistor-based biosensor
LAMP and RT- PCR commerical commercial produced by Seo et al. This device uses carbon (graphene) and detects
RAA (FDA (other SARS-CoV-2 spike protein through its binding to anti-spike antibodies.
approved) authorization)
This method is also very fast (analysis in 10 min) and sensitive with a
Time 61 min 43 min 63 83 min 74 min limit of detection of 242 copies/mL in clinical samples [142]. All ap­
min proaches discussed so far involve anti-SARS-CoV-2 antibodies for spe­
LOD 15.3 cp/ 5.2 cp/ 9.2 41.8 cp/rxn 44.1 cp/rxn
μL -mean μL -mean cp/
cific binding. Some researchers developed an electrochemical biosensor
4.3 cp/ 2.9 cp/ rxn that uses membrane-modified green monkey kidney cells bearing
μL - log μL - log transmembrane anti-spike antibodies for detection of coronavirus spike
addition addition protein. The biosensor produces very rapid results (in 3 min) with high
4.9 cp/ 2.5 cp/
sensitivity (LOD = 1 fg/mL) [143]. However, this biosensor is difficult to
μL - μL -
median median construct because it requires cell engineering and culturing and its
Specificity 97.9% 98.9% 100% 98.7% 99.8% performance depends on cell viability. For this reason, this approach has
Sensitivity 86.2% 94.4% 83.7% 98.1% 99.2% much less potential in clinical diagnostics.
Accuracy 93.3% 96.5% 97.7% 96.8% 98.8%

Note: Table 11 is a compound table with the data from Table 1, Table 3, Table 8,
Table 10. Full clinical data is reported only for 5 non-FDA registered (spec, sens, 3.2. Immunoassay-based methods
acc); Average ± standard deviation; Range (x%; y%) are shown; LOD conversion
from cp/rxn to cp/μL was done by dividing the cp/rxn by the amount of RNA Immunoassay-based methods are old and widespread. These
used in μL. Accuracy=(TP + TN)/(TP + TN + FP + FN); Sensitivity = (TP)/(TP methods are relatively inexpensive, simple, and have a great potential
+ FN); Specificity = (TN)/(TN + FP). TP – true positive, TN – true negative, FP – for being point-of-care tests. Some of these methods have been suc­
false positive, FN – false negative. cessfully commercialized for rapid point-of-care testing. For example,
some methods measure fluorescence signal to quantitatively detect
3.1. Electrochemical detection SARS-CoV-2 nucleoprotein in a clinical sample. Two such methods
include “STANDARD F COVID-19 Ag” and “Sofia SARS Antigen FIA.
Electrochemical detection can be realized using biosensors that Fluorescence immunoassay consists of a single step of a nasopharyngeal
measure electrical signals from antigen-antibody binding. Electro­ swab loading, and results are returned in 30 min. The methods rely on a
chemical biosensors have important advantages over other methods, fluorescence analyzer. Limit of detection of these methods is much
such as low cost of analysis, quantitative detection, high sensitivity and higher than for electrochemical methods discussed above - 2.5 × 105
selectivity, and the potential for portability. Eissa and Zourob developed RNA copies/mL [13]. Other portable fluorescence immunoassays were
an electrochemical immunosensor without the need for sample pre­ developed to simultaneously detect viral antigen with IgG and IgM an­
treatment. In this device carbon nanofiber serves as an electrode func­ tibodies [144].
tionalized with diazonium salt as a linker that binds coronavirus The other commercialized point-of-care test based on immunoassay
nucleocapsid protein. The electrode is covered with cotton fiber used to is even simpler – a lateral flow immunoassay where a nasopharyngeal
collect nasopharyngeal swabs. Detection is achieved by competitive swab sample reacts on a test card called “BinaxNOW COVID-19 Ag
assay foolowing exposure of the immunosensor to N protein antibody Card”. It is the first FDA-approved rapid antigen test that does not
solution. This device is portable and requires a potentiostat connected to require an instrument. This test provides qualitative results in 15 min,
a smartphone for measurements. This immunosensor can be used for and has a detection limit equal to 4.04 × 104 copies/swab [145].

Fig. 4. Detection of viral antigen and particles in nasopharyngeal swabs. Top – fluorescence-based sensor; bottom – mass spectrometry method. (created with
MS Publisher).

15
O. Filchakova et al. Talanta 244 (2022) 123409

Overall, sensitivity of rapid antigen testing is much lower than RT-PCR

and electrochemical detection. Nevertheless, lower detection limit of
0.65 ng/mL for a portable lateral flow immunoassay was reported [146].
There is another immunoassay-based method described in the liter­
ature which utilizes gold nanoparticles functionalized with antibodies
and colorimetric readout [147]. This method is depicted on Fig. 5.

3.3. Mass spectrometry

Mass spectrometry for SARS-CoV-2 antigen detection was reported

twice. In both studies scientists used targeted mass spectrometry.
Cazares et al. reported limit of detection equal to 200 amol, meaning
that quantitation and identification of proteins was performed at a single
amino acid resolution. However, the analysis takes around 1.5 h to get
results. Parallel reaction monitoring was used, and detection proceeded
in artificial mucus samples, reflecting how this method can be used in
biological matrices [148]. Bezstarosti et al. also received good results
using the same method, with a detection limit of 0.9 pg of nucleocapsid
protein in 1.5 h [149]. Sensitivity of targeted mass spectrometry is
comparable to and can exceed RT-PCR. Parallel reaction monitoring is a
very specific method of detection where preselected peptides (digestion
products of proteins of interest) are measured, and their fragmentation
pattern is identified. Then digested target proteins are analyzed, and
Fig. 6. Reagent-free chronoamperometry sensor. Binding of virions or spike
results of preselected peptides serve as a control for identification. This
protein to the spike-specific antibody anchored to the electrode through DNA
way only target proteins from the matrix will be correctly detected.
aptamer causes changes in current, which are detected by chronoamperometry.
However, mass spectrometry is lengthy, difficult and requires highly
Reproduced with permission granted by American Chemical Society from the
trained personnel. article by Yousefi et al. [150].

3.4. Detection of viral antigen and particles in saliva commercial methods is difficult because only a small number of com­
mercial tests report detection limit, and those that do, report TCID50/mL
Another interesting approach uses chronoamperometry to detect or pfu/mL units. To make a comparison, TCID50/mL was converted to
SARS-CoV-2 virions or spike protein using a first electrochemical copies/mL. TCID50/mL ≈ 4000 copies/mL using conversion from Liotti
reagent-free sensor. In this approach the authors used changes in current et al. [13], but conversion of copies of RNA further into mass is not
caused by binding of viral particles or spike protein to the spike-specific performed here because antigen was detected in commercial tests, not
antibody anchored to the electrode through DNA aptamer. The sche­ RNA, so this conversion would be misleading. Thus, commercial tests
matic representation of this process can be seen on Fig. 6. Results are that reported detection limit were used to estimate their LOD in
obtained in 5 min with a detection limit of 4000 copies/mL – much copies/mL: “Quidel Sofia SARS Antigen FIA” (4.52 × 105 copies/mL),
lower than for reagent-free lateral flow immunoassays. Another “SD Biosensor STANDARD F COVID-19 Ag Fluorescent immunoassay
important feature of this sensor is its ability to detect coronavirus (FIA)” (2.5 × 105 copies/mL), “BinaxNOW™ COVID-19 Ag CARD”
infection in patients’ saliva without pretreatment. The sensor can be put (4.04 × 104 copies), and “SD Biosensor STANDARD Q COVID-19 Ag
in a person’s mouth and give results without even the need for sample Test” (1.98 × 106 copies/mL). All of them have LOD worse than LOD of
collection [150]. scientific methods that reported LOD in copies/mL: 2.42 × 102
Making comparison between scientific laboratory methods with copies/mL [142] and 4 × 103 copies/mL [150]. It can be concluded that
scientific methods of detection of antigen have better analytical sensi­
tivity than commercial methods.

4. Detection of antibodies to SARS-CoV-2

4.1. Comparison of ELISA, CLIA, and LFIA

SARS-CoV-2 specific IgG, IgM, and IgA antibodies most often become
objects of detection using different methods. IgM antibodies appear in
the acute phase of infection, and after reaching the maximum, they
decrease to diagnostically insignificant levels. IgG antibodies build up
more slowly than IgM antibodies, but they remain high in the patient’s
blood longer. After recovery, IgG antibodies can remain at a low level
indefinitely as evidence of a previous illness.
Coronavirus antibody tests are important, as they provide informa­
Fig. 5. SARS-CoV-2 viral particles bind to gold nanoparticles functionalized tion about whether a person has had a coronavirus infection in the past,
with antibodies targeting three SARS-CoV-2 surface proteins (spike, envelope, i.e. whether he is a potential carrier of the disease and whether he has
and membrane), and color of the solution changes. The binding of the viral developed immunity [151–154]. While there is an estimate that anti­
antigen to functionalized nanoparticles red-shifts extinction spectrum of the body testing prevented about 12% of COVID-19 related deaths within
solution. The extent of such shift depends on viral load. Reproduced under the first year of pandemic [155]. Also, the measured level of antibodies
Creative Commons License from an open access article by Ventura et al. [147].
provides feedback about vaccination efficiency, which may vary
(For interpretation of the references to color in this figure legend, the reader is
significantly [156]. It may help to predict when second or even third
referred to the Web version of this article.)

16
O. Filchakova et al. Talanta 244 (2022) 123409

vaccination is recommended since decrease in antibodies level with time chemiluminescence analyzer. LFIA, the technical basis of which was
after vaccination is subject to individual variability. This kind of invented in 1956 by Plotz and Singer [176], like other methods, includes
personalized vaccination scheduling may optimize the application of the reaction between an antigen and its corresponding antibody in
vaccines, which still remains a scarce resource in many countries of the biological materials, but it is less popular than above mentioned
world, and it may decrease some unnecessary side effects of premature methods: it is cited in Scopus for only 468 times. However, in 2006 the
vaccination of individuals who still have sufficient antibody titer after production of various LFIA formats for a total amount of about $ 2.1
natural exposure to virus or after the first/previous vaccination [157]. billion was carried by more than 200 companies around the world
Since COVID-19 is sometimes asymptomatic, antibodies can be found [176]. LFIA is an immunochemical method of analysis based on the
even in a person who does not feel sick and is sure that he was not sick. principle of thin layer chromatography, carried out using special test
Tests for antibodies allow to determine the stage of infection [153,158]. strips, panels or test cassettes. The principle of operation is that when the
Overall, antibody testing is a complementary method for COVID-19 di­ test strip is immersed in a biological fluid (or other liquid samples), it
agnostics. Besides, those who have antibodies to the coronavirus can begins to migrate along the strip according to the principle of thin-layer
donate blood, which will help treat others who are infected [154,159, chromatography. Together with it, labelled specific antibodies applied
160]. Plasma transfusion procedure helps people with severe courses of to the lower part of the test strip move.
COVID-19. Also, considering the ongoing large-scale vaccination, anti­ Fig. 7 illustrates the differences in working principles of the three
body tests are becoming even more important: those who have recently most widely used serological assays: The enzyme-linked immunosorbent
had COVID-19 can delay vaccinations because they already have natural assay (ELISA), chemiluminescence immunoassay (CLIA), and lateral
immunity. And since there are not enough vaccines in many countries, flow immunoassay (LFIA). The ELISA and CLIA experimental plates
those who have been infected with SARS-CoV-2 are asked to wait three already contain SARS-CoV-2 antigen molecules attached to it. Then,
to six months to enable people without natural immunity to be vacci­ when the serum from the patient’s sample is introduced, antibodies
nated first [161]. Moreover, mass testing for the presence of antibodies against SARS-CoV-2 attach to their antigens. At this point, the main
is of great importance for the state, as it shows a real epidemic picture. differences between these assays become distinct. In ELISA, secondary
The more people are tested, the more accurate the data on morbidity, antibodies linked with the enzyme bind to the patient antibodies, while
mortality, severity, and characteristics of the clinical course of a new in CLIA some enzymes bind directly to the patient’s antibodies. The
infection will be. Various nations, including the UK, Germany, and Italy, function of these enzymes is to catalyze the reactions of specific sub­
considered using antibody testing for ‘immunity passports and this strates (chemiluminescent substrates in CLIA). In ELISA this reaction
procedure can be critical to re-opening the economy [162]. leads to the measurable color change, while in CLIA detectable amounts
Nowadays, methods for detecting COVID-19, taking a short time of light are produced. These observations help to identify the amount of
between collecting a sample from a patient and obtaining test results, antibodies present in the patient’s organism. The mechanism of LFIA is
are becoming more and more critical. Methods that take no more than 3 not much similar to the mechanism of the rest of the serological assays,
h are in demand, so researchers are working in this direction. During but it is somewhat similar to one step pregnancy tests, which were
antibody testing, patients almost always donate either a blood sample or introduced by Unilever in 1988 [177]. The sample, containing the
a saliva sample. However, although a saliva sample is less complicated SARS-CoV-2 antibodies, is placed on the special strip. Then, the sample
and more comfortable to obtain, most testing methods work with a flows laterally across the pad, and at the test line COVID-19 antibodies
patient’s blood sample. attach to their specific antibodies, and nanoparticle-linked antibodies
Most often, blood for the sample is obtained by venipuncture, but bind to the virus antibodies. Due to these nanoparticles, the test line
blood obtained by a finger stick is also common. Collected blood samples becomes visible, indicating the presence of SARS-CoV-2 antibodies in
are centrifuged; different researchers use different centrifuge conditions, the sample. A control line is needed to verify that the assay actually
for example at 800 g for 5 min [158], at 1000 rpm for 15 min [153], at works: nanoparticle-linked antibodies bind to specific antibodies on the
1740 g for 10 min [163], at 2200–2500 rpm for 15 min [164]. After that, control line, visualizing this line. The negative result is obtained when
the supernatant (serum) is removed and sent to storage. In the case of only the control line is visible; the positive result is obtained when both
short-term storage, the serum is stored at − 20 ◦ C [163,165–169], and in lines are visible. If the test line is visible while the control line is not, the
the case of long-term storage the serum is stored at − 80 ◦ C [164,165, strip must be defective.
170,171]. Sometimes researchers do not specify the storage temperature
of the serum, but simply write “frozen” [172]. Before analysis, the serum 4.2. Validation of CLIA, LFIA, ELISA serological tests by comparison with
is thawed [163,166] or heat-inactivated at 56 ◦ C [173,174]. RT-PCR results
ELISA (enzyme-linked immunosorbent assay), CLIA (chem­
iluminescence immunoassay) and LFIA (lateral flow immunoassay) are The review paper written by Ejazi, Ghosh, & Ali provides informa­
among the popular serological assays utilized to detect the antibodies of tion on the sensitivity and specificity values of serological assays CLIA,
a virus. All these methods begin with the biofluids collection (most often LFIA and ELISA [18], which use serum from RT-PCR confirmed patients.
blood) and sample preparation. ELISA, CLIA and LFIA searches in the First, most of the research based on serological tests was performed in
Scopus database gave 223488, 1830 and 468 citations for each of those China. According to this review, generally, the sensitivity of IgG anti­
methods respectively. Apparently, ELISA is by far the most commonly body detection by CLIA after two weeks of disease is more than 90%. The
used method among them. The ELISA method is based on the reaction sensitivity during detection by LFIA in the one-week interval from
between specific antigens and antibodies, and the result becomes visible becoming ill was really low - almost always it did not exceed 30%. For
and possible for quantitative analysis due to the enzymatic reaction. ELISA, sensitivity gets better as the time after infection onset increases;
CLIA is a laboratory test that was developed in 1983 by Anthony for instance, sensitivity increases from 50 to 81% for IgM and from 81 to
Campbell when he replaced the radioactive iodine used in immunoas­ 100% for IgG when time increases from 0 to 5 days. In another assay
says with an acridinium ester that emits its own light [175], cited in made in China (The rS-based ELISA kit made in Hotgen, Beijing, China)
Scopus for 1742 times (range 1990–2020). CLIA combines chem­ the sensitivity improved from 46% to 91% between 0 and 5 days after
iluminescence (electromagnetic radiation caused by a chemical reaction the visible start of an infection and 11–15 days after the onset of the
to produce light) with an antigen-antibody immune complex. Both infection [178]. A similar trend of a drastic decrease in rates of
ELISA and CLIA are conducted in microplate wells, but if reaction in false-negative results was observed in Germany when the time of the
ELISA is most often confirmed by the change in color even with the ELISA test shifted from 5-9 days to 10–18 days after onset [179]. So the
naked eye (microplate reader is needed for quantitative analysis of optimum time for ELISA testing may be within 10–15 days after onset of
wells’ content), CLIA is verified by the production of light in disease or about 14–21 days after the patient may contract the virus,

17
O. Filchakova et al. Talanta 244 (2022) 123409

Fig. 7. Schemes of serological assays ELISA, CLIA, and LFIA for detection of SARS-CoV-2 antibodies. CIA is KPS-QQ80 Chemiluminescence Immunoassay Analyzer is
from instrumentstrade.com; MR is NS-100 Nano Scan Microplate Reader from hercuvan.com. The figure is created with miro.com.

since the average incubation period is about 4–6 days. Serological tests Most of the research on the detection of COVID-19 antibodies by LFIA
conducted during this time frame would show the most sensitive results. reported common sensitivity values for all antibodies, not differentiating
The tests are not convenient ways to diagnose COVID-19 before the first between IgM, IgG, and IgA. The average sensitivity of separate IgG an­
symptoms appear. tibodies (where it was given) was not high - only about 68%. So, LFIA is
After the incubation time, IgM antibodies that constitute the initial worse than CLIA in the detection of particular types of antibodies; it can
immune response are produced. IgG antibodies are produced next, be used only in complex antibodies detection. The specificity values for
developing a more specific immune response. However, IgG antibodies’ all methods were almost always higher than sensitivity values and
concentration may be several times higher than the concentration of IgM overall high. For CLIA, specificity values were more than 90%, no matter
antibodies. The average sensitivity of detection of IgM antibodies by which antibodies were analyzed. The CLIA method, therefore, should be
CLIA is lower than that of IgG antibodies (83.6% vs. 94.5%). Based on specific for all antibodies even if the sensitivity values for different an­
the analysis of 14 representative ELISA tests, sensitivity for IgG is better tibodies, for example, IgM and IgG, differ. From Table 2 in the review
than for IgM. For instance, IgG vs IgM was 81% vs 50% after 0 days and paper of Ejazi we calculated the average reported sensitivity of the
100% vs 81% after 5 days in the relatively representative assay of 178 ELISA method for all antibodies - about 81% - but the average reported
COVID-19-infected people [180]. As a result, IgG detection is more false-positives rate that we calculated from the same source was only
reliable than detection of other antibodies (IgM and IgA) [181]. Kontou 3%. The standard error for this specificity was calculated as 1.02 that is
et al. reached the same conclusion in their review [17]. lower than the standard error of sensitivity - 3.85. The low standard
Most of the results of the action of CLIA were able to detect the IgG error for specificity indicates that this specificity value well represents
antibodies separately from the rest of the antibodies, showing that the the whole ELISA test. For LFIA, average specificity values are about
CLIA method is useful in determining the specific type of antibodies in 92%, which is much higher than average sensitivity values of 63%.
the analyzed sample; its sensitivity for IgG antibodies constituted 94%. Another trend in ELISA testing is that S-protein based ELISA is

18
O. Filchakova et al. Talanta 244 (2022) 123409

preferable to N-protein based ELISA in terms of sensitivity and earlier same kit was 100% and it was observed when 22–30 days from onset of
term of antibodies detection [178]. ELISA test in the USA achieved 97% the disease have passed [170]. For CLIA the lowest sensitivity value was
sensitivity and 100% specificity when performed with the S1 domain the 0% for IgG detection by SARS-CoV-2 IgG assay from Abbott Labora­
spike protein of the virus [182]. It is much more likely to get tories, USA, when less than 3 days from the onset of symptoms have
false-negative results than false-positive results with ELISA serological passed [185] - it was the worst sensitivity value for IgG detection among
testing and overall false-negative results are far more likely in serolog­ all CLIA assays. For total antibodies detection, the lowest sensitivity of
ical tests for antibodies in comparison to PCR tests for viral RNA. 40% was shown by Elecsys Anti-SARS-CoV-2 assay from Roche Diag­
nostic GmbH, Germany, and SARS-CoV-2 Total COV2T assay made by
4.3. Other assays used to detect SARS-CoV-2 antibodies Siemens Healthineers, Germany [186] when 0–7 days passed from dis­
ease onset. But if the CLIA tests were conducted when at least more than
Single-step, wash-free digital immunoassay has also demonstrated 14 days have passed from the onset of symptoms, sensitivity values were
that it is possible to detect human IgG antibodies to COVID-19 in 15 min much higher, even maximum: 100% for the detection of total antibodies
[160]. This method is an adaptation of the AC + DC assay method. In by Elecsys immunoassay (Roche Diagnostics, Germany) [185] and
general, during IgG antibodies incubation with a test sample and gold SARS-CoV-2 Total COV2T (Siemens Healthineers, Germany) [186]; and
nanoparticles (2oAb-AuNPs), the recombinant PC biosensor serves as a 100% for the detection of IgG antibodies by SARS-CoV-2 IgG (Abbott
surface to form a sandwich immunocomplex, which provides antibody Laboratories, USA) [185]. The same trend can be seen in LFIA assays: the
detection. This method showed high accuracy: using 4 μL of serum, 26.7 lowest sensitivity of 0% for total antibody detection was observed in
± 7.7 pg/mL of antibodies were detected. The limit of quantification Genrui Biotech Inc. (Shenzhen, China) and CTK Biotech Inc. (Poway, CA,
(LOQ) was 32.0 ± 8.9 pg/mL. USA) when less than 7 days from the disease onset have passed, but
Cady and others’ team has described another method for the rapid when the time period was more than 7 days, the sensitivity values for
detection of antibodies against COVID-19 [153]. With multiplexed both assays exceeded 80% [187]. The maximum sensitivity for total
grating-coupled fluorescent plasmonics (GC-FP) biosensor platform, the antibody was detected when more than 14 days from symptoms onset
entire biosensing procedure took less than 30 min. They used human have passed and constituted 97.6% [186]. Overall, more favorable
blood serum and dried blood spot samples as a sample to detect IgM, IgG values of sensitivity (>90) are obtained after at least 2 weeks from
and IgA antibodies against COVID-19. In terms of sensitivity and spec­ disease onset.
ificity, this method showed 100% sensitivity and 100% specificity for Also, it was found in ELISA that average sensitivity and specificity
serum IgG. The test results almost always coincided with two commer­ values for IgA antibodies are very similar to that for IgG antibodies: 70%
cial COVID-19 antibody tests based on enzyme-linked immunosorbent sensitivity for IgA and 72% sensitivity for IgG and 97% for specificity for
assay (ELISA) and a Luminex-based microsphere immunoassay that both antibodies. From the same data, it can be clearly seen that the
shows the viability of multiplexed grating-coupled fluorescent plas­ average sensitivity values are lower than the average specificity values.
monics (GC-FP) biosensor as a method of detection of antibodies against Situation is similar with the CLIA assays: average sensitivity for IgG is
the coronavirus SARS-CoV-2. lower than the average specificity for IgG (77% vs 98%). In CLIA and
A lateral flow immunoassay is another method to test for antibodies LFIA average sensitivity values for IgG detection are lower than that for
in a blood sample quickly. For example, Cavalera et al. reported that total antibody detection, being 77% and 83% for CLIA and 42% and 56%
multi-target lateral flow immunoassay allows the evaluation of test re­ for LFIA respectively, while average specificity values are close to each
sults for all immunoglobulin classes (IgG, IgM, IgA) with the naked eye other: 98% and 99% for CLIA and 99% and 100% for LFIA, respectively.
20 min after the addition of a blood sample [183]. This study reported From the same data it can be derived that the average sensitivity values
100% diagnostic specificity and 94.6% sensitivity. Such high sensitivity for LFIA are much lower than average sensitivities of ELISA and CLIA.
values were obtained due to the double-line LFIA which increased the Kontou et al. also reported this conclusion in their review [17]. Another
likelihood of detecting SARS-CoV-2 antibodies when they are present in difference between LFIA and other methods is that almost all of its im­
the sample. munoassays detect total antibodies with only a small part of the im­
Another report improved a lateral flow immunoassay immunosensor munoassays detecting IgG. Another point to mention is that the standard
to obtain dual lateral flow optical/chemiluminescence immunosensors error for specificity of IgG antibodies is much lower than the standard
to detect salivary and serum IgA [184]. As with the previous method, error for sensitivity of IgG antibodies in both ELISA and CLIA: 1.0 vs. 5.7
only 80 μL of diluted sample were required, and results that were visible in ELISA and 0.6 vs. 5.6 in CLIA. Low standard errors for specificity
to the naked eye were obtained in a short time of 15 min. No false indicate that these specificity values well represent the whole ELISA and
positives were obtained in this study. Antibodies from saliva samples CLIA tests. But, since the standard error for specificity in CLIA is slightly
were also detected in a study conducted by MacMullan et al. [164]. In lower than that in ELISA, whole CLIA tests are better represented by
this study, a commercially available, serum-based enzyme-linked their specificity values compared to ELISA tests.
immunosorbent assay (ELISA) was adapted to work with a saliva sample. According to the Table 12, if we take the average sensitivity and
The specificity of this method was 100%, while the sensitivity only specificity of assays, regardless of antibodies detected, the sensitivity of
reached 84.2% for a set of 149 clinical samples. This indicates that the ELISA is higher than the sensitivity of both CLIA and LFIA, as when less
immunoassay in the saliva sample requires further research and than 7 days and more than 7 days have passed after the onset of the
refinement to improve sensitivity. Considering that collection of saliva disease. The specificity values at <7 days after the onset of the disease
samples is much more convenient, faster and safer than the collection of were absent for ELISA and LFIA but were very high for CLIA: 99.9%.
blood, saliva-based sample methods will be in wide demand after Among all assays, the highest specificity at >7 days after the onset of the
improvement of sensitivity values. disease was recorded in CLIA and was equal to 98.4%.
The statistical analysis could be applied to ELISA, CLIA, and LFIA Comparison of the mean values of the sensitivity and specificity of
assays reported in the literature. In the majority of the reports, the ELISA and CLIA kits taken from research articles with FDA approved and
sensitivity and specificity of the immunoassays currently used in the not FDA approved automated immunoassay tests shows the superiority
world are compared. It was observed that for all ELISA, CLIA, and LFIA of the sensitivity and specificity of both FDA approved methods (96.6%
assays, like in Ejazi review, the fewer days have passed since the onset of and 99.5%, respectively) and not FDA approved methods (86% and
the disease, the less sensitive the method was to antibodies (both IgG 99.5%, respectively). The situation is similar with LFIA kits. When
and total antibodies). For example, the lowest sensitivity of Euroimmun comparing LFIA immunoassays from research articles with FDA-
IgG ELISA kit was 20% and it was observed when less than 3 days from approved and not FDA-approved strip immunoassay tests, it is imme­
onset of disease have passed, while the maximum sensitivity for the diately noticeable that LFIA kits are lower in sensitivity and specificity

19
O. Filchakova et al. Talanta 244 (2022) 123409

than FDA-approved tests (average sensitivity 97.5%, average specificity

Note: Average ± standard deviation; Range (x%; y%) are shown. Accuracy=(TP + TN)/(TP + TN + FP + FN); Sensitivity = (TP)/(TP + FN); Specificity = (TN)/(TN + FP). TP – true positive, TN – true negative, FP – false
98.1%) and not FDA-approved tests (94.9% and 98.2%, respectively).

[167,183,186,
Most likely, this indicates that the ELISA, CLIA, and LFIA antibody

[186,187]
detection kits show lower sensitivity and specificity values than those

187]
Ref.
stated by the manufacturers. Among the FDA-approved automated
immunoassay tests for detection of antibodies the greatest clinical per­
formance is shown by “OmniPATH COVID-19 Total Antibody ELISA

98 ± 4% (90%;
Test” (USA) with 100% accuracy and “ZEUS ELISA SARS-CoV-2 IgG Test
System” (USA) with 99.1% accuracy. Nevertheless, some of the new

100%)
Spec. assays from research papers approached the level of the FDA approved
N/A methods. For example, the In-house ELISA showed 100% sensitivity to
IgG antibodies after 22–30 days from the onset of the disease [170], and
71.8 ± 19% (40%;
human anti-IgGAM SARS-CoV-2 ELISA showed 94.7% sensitivity and
26.6 ± 18% (0%;

98.4% specificity for total antibodies detection when more than 2 weeks
have passed since the onset of the disease [166]. The sensitivity of these
LFIA kits

assays is already higher than the average sensitivity of the CE-IVD

97.6%)
54%)
Sens.

approved methods (86%), therefore, it is possible that in the future

they will be used widely.
[167–169,171,185,186,188,

4.4. Overview of the results of the commercialized antibodies detection

methods

Rapid antigen testing is currently represented by immunostrip or

fluorescence-based detection in nasopharyngeal swab samples,
[185,186]

190,191]

including two FDA-approved point-of-care tests, which were discussed

in a corresponding section: “Sofia SARS Antigen FIA” and “BinaxNOW
Ref.

COVID-19 Ag Card”. Other tests have been developed and registered in

other regulation centers. These tests are much less sensitive than RT-PCR
98.6 ± 2%; (90.5%;
99.9 ± 0.2% (99.6;

kits, but provide results in 30 min or less and are much easier to use.
They either require adding several drops of extraction buffer to a nasal
swab as a single manual step (“BinaxNOW COVID-19 Ag Card”) or are
fully automated (“Sofia SARS Antigen FIA”). The tests are lateral flow-
100%)

100%)
Spec.

based and fluorescence-based, respectively.

Antibody testing is achieved by using serum, plasma, or blood of a
patient using immunostrip or ELISA-based methods. “Euroimmun Anti-
83 ± 18% (32.9%;
45.1 ± 34% (0%;

SARS-CoV-2 ELISA” is one of the first FDA-registered automated ELISA

kits. These methods can give results in as short as 10 min, but detection
limit for the majority of them is not established and antibody testing is
CLIA kits

100%)

100%)

meaningful only at least 2 weeks after the onset of symptoms. Therefore,

Sens.

antibody testing cannot be used to diagnose a current infection. Another

disadvantage is the need to draw blood from a patient. This procedure
needs to be done under sterile conditions, preferably by a health care
[158,164,167,170,173,185,

provider. For this reason, these tests have a limited scope of application
compared to rapid antigen testing. The performance of government-
Average sensitivity and specificity values for ELISA, CLIA, and LFIA kits.

approved immunoassay tests is summarized in Table 13.

Among the FDA-approved automated immunoassay tests for detec­
186,188–190]

tion of antibodies the greatest clinical performance is by “OmniPATH

[170,186]

COVID-19 Total Antibody ELISA Test” (USA, 100% accuracy) and “ZEUS
ELISA SARS-CoV-2 IgG Test System” (USA, 99.1% accuracy). Among
Ref.

non-FDA registered immunoassays, the highest clinical performance is

achieved by EDI™ Novel Coronavirus COVID-19 IgG ELISA Kit (100%
95.5 ± 7% (69.5%;

accuracy). Clinical performance of FDA-approved tests is higher than

that of non-FDA approved tests, and sensitivity is considerably higher.
This is in addition to a higher number of samples used to validate the
100%)

clinical results. Statistical comparison of various types of immunoassay

Spec.

N/A

tests is shown in Tables 14–18.

Among FDA-approved strip immunoassay tests for detection of an­
tibodies, the best clinical performance is displayed by “Assure COVID-19
positive, FN – false negative.
33.8 ± 20% (20%;

81.3 ± 20% (15%;

IgG/IgM Rapid Test Device” (100% accuracy for IgM and IgG) and
“BIOTIME SARS-CoV-2 IgG/IgM Rapid Qualitative Test” (100% overall
ELISA kits

accuracy), which give results in 15–20 min. Both tests use lateral flow
56.3%)

100%)
Sens.

immunoassay principle.
Among non-FDA approved tests, “BioMedomics COVID-19 IgM-IgG
Combined Antibody Rapid Test” (98.8% overall accuracy) and “BELT­
Table 12

days

days

EST-IT COV-2 Rapid Test” (97.9% IgG accuracy and 97.6% IgG accu­
<7

>7

racy) show the highest clinical performance. Manufacturers of both tests

20
O. Filchakova et al. Talanta 244 (2022) 123409

Table 13 Table 14
Performance of automated commercialized government-approved immunoassay Comparison of performance of automated commercialized government-
tests for detection of antibodies. approved immunoassay tests for detection of antibodies.
Name Sample LOD Accuracy Time, Ref Group Average Average Average Average Average
min sensitivity specificity accuracy time number of
samples
Euroimmun Anti- Blood N/A Sens = 120 [192]
SARS-CoV-2 (IgG), 10 μL 519 90% US FDA 96.6% 99.5% 96.1% 100 min 418
ELISA samples Spec = FDA Other 86% 99.5% 85.5% 92 min 210
100%
Acc =
90% used over 100 samples (106 and 1809, respectively) to validate clinical
WANTAI SARS- Serum, N/A Sens = 85 [193]
accuracy, which is a sufficient sample pool for reliable statistical
CoV-2 Ab ELISA plasma 736 98.7% US
(IgG, IgM), samples Spec = FDA
analysis.
100 μL 98.6% FDA-registered tests have higher sensitivity and accuracy than non-
Acc = FDA registered tests, while tests from other authorization centers use
97.3% more samples for validation, both requiring nearly the same time to get
Mount Sinai Serum, OD = Sens = N/A [194]
results. Overall, accuracy of IgG detection is better than that of IgM
COVID-19 ELISA plasma 0.15 92% US
IgG Antibody test (IgG) 114 Spec = FDA detection for both FDA and non-FDA registered tests.
samples 100% It can be seen that testing for antibodies and viral RNA is more
Acc = developed than testing for viral antigen, although clinical performance
92% of rapid strip antigen tests is comparable to that of rapid strip antibody
OmniPATH COVID- Serum (IgG, OD = Sens = 80 [195]
19 Total IgM, IgA), 0.2 100% US
tests. Commercialized antigen point-of-care tests need to be developed
Antibody ELISA 50 μL 299 Spec = FDA as they provide information on active infection status in 15–30 min, with
Test samples 100% immunostrip tests being easy to use for general public.
Acc =
100%
ZEUS ELISA SARS- Serum, OD = Sens = 85 [196]
5. Diagnostics of COVID-19 by clinical imaging techniques (X-
CoV-2 IgG Test plasma 0.198 100% US ray, CT, ultrasound)
System (IgG), 100 249 Spec = FDA
μL samples 99.1% It is possible to determine whether a person is infected with SARS-
Acc =
CoV-2 not only using the PCR test, but also by applying clinical imag­
99.1%
University of Serum (IgG, OD = Sens = N/A [197] ing techniques, the most common which are X-ray imaging, computed
Arizona COVID- IgM, IgA) 0.389 97.5% US tomography (CT) scan, and ultrasound. A simplified process for the
19 ELISA pan-Ig 360 Spec = FDA usage of these techniques to detect COVID-19 in a patient is presented in
Antibody Test samples 99.1%
Fig. 8.
Acc =
96.6%
Platelia SARS-CoV- Serum, N/A Sens = 90 [198]
5.1. X-ray diagnostics
2 Total Ab plasma 650 98% US
(IgG, IgM, samples Spec = FDA
IgA), 15 μL 99.6% Medical X-ray imaging is the diagnostic method which uses X-rays
Acc = and relies on differences in absorption of these rays by different tissues
97.6% [230]. X-rays were discovered on November 8, 1895 by Wilhelm Konrad
EDI™ Novel Serum OD = Sens = 80 [199]
Coronavirus (IgM), 10 0.0669 73.1% CE-IVD
Röntgen. When the scientist exposed his hand to the mysterious rays, he
COVID-19 IgM μL 274 Spec = saw a clear image of it on the screen, and the bones were visible much
ELISA Kit samples 100% more clearly than soft tissues. After this discovery, the first X-ray ma­
Acc = chines and films were made in 1896 [231].
73.1%
Fig. 8 demonstrates that initially the patient is irradiated with X-rays,
EDI™ Novel Serum 5 U/mL Sens = 80 [200]
Coronavirus (IgG), 10 μL 84 100% CE-IVD and then the radiologist or artificial intelligence analyzes the image for
COVID-19 IgG samples Spec = the presence or absence of COVID-19 in the patient. Currently, a lot of
ELISA Kit 100% research is aimed at developing and debugging, using deep learning and
Acc = other machine learning methods, artificial neural networks that can
100%
MIKROGEN Serum, N/A IgG: 120 [201]
determine from an X-ray image whether a person is infected with SARS-
recomWell SARS- plasma 241 Sens = CE-IVD CoV-2 or not. Statistical analysis of these research articles shows how
CoV-2 IgG and (IgG, IgA), samples 98% sensitive, specific, and accurate created neural networks are. Neural
IgA 10 μL Spec = networks are aimed at dividing X-ray images into 2 classes (covid and
98.7%
non-covid), 3 classes (covid, other infections - most often pneumonia,
Acc =
96.7% and healthy patient), and 5 classes (covid, tuberculosis, bacterial
IgA: pneumonia, viral pneumonia, and healthy). According to Table 19, it
Sens = can be seen that the distribution by 2 classes exceeds the distribution by
73% 3 classes in sensitivity, specificity, and accuracy. In other words, it is
Spec =
99.3%
more difficult for artificial intelligence to simultaneously detect not only
Acc = COVID-19, but also some other infections. In general, the sensitivity of
72.3% all types of distributions is lower than the specificity and accuracy, while
Note: OD = optical density.
the latter are quite close to each other (98% vs 97% for the 2-class
distribution, 87% vs 88% for the 3-class distribution, 96% vs 97% for
all distributions). There was no data on sensitivity and specificity for 5-
class distribution, but its average accuracy of 96% is only slightly lower
than the 97% average accuracy of 2-class distribution, showing that it is

21
O. Filchakova et al. Talanta 244 (2022) 123409

Table 15
Performance of automated commercialized government-approved immunoassay tests for detection of antigen.
Name Sample LOD Accuracy Time, Ref
min

Quidel Sofia SARS Antigen FIA Nasal swabs (nucleocapsid protein), 1.13 × 102 TCID50/mL Sens = 30 [202]
120 μL 209 samples 99.4% US FDA
Spec =
100%
Acc =
99.4%
SD Biosensor STANDARD F COVID-19 Ag Fluorescent Nasopharyngeal swabs 62.5 TCID50/mL (2.5 × 105 RNA Sens = 30 [13]
immunoassay (FIA) (nucleocapsid protein) copies/mL) 47.1% CE, KOREA
359 samples Spec = MFDS
98.4%
Acc =
45.5%

possible for neural networks to accurately classify COVID-19 among a illness is observed after 6–11 days of the onset of the symptoms [248,
few other diseases. The minimum accuracy in this type of distribution 249]. In addition, Wang et al. found that 66 of 70 discharged patients
was shown by VGG-16 and was 93.9% [232], while the maximum ac­ from the hospital after obtaining negative RT-PCR results still had re­
curacy of 99.9% was achieved by Modified MobileNet [233]. sidual disease patterns on their final CT scans, which means that CT scan
Considering all distributions, the DenseNet201 neural network might be more effective than RT-PCR in observing the progression,
showed the minimum value of specificity and accuracy (69.3 and 38.2%, regression of the infection, and full recovery from SARS-CoV-2 [249].
respectively) [234], and the minimum sensitivity of 30% was demon­ Another benefit of using CT for SARS-CoV-2 detection is the fast speed of
strated by ANOGAN when the specificity parameter was adjusted to 90% the procedure, as presented in Fig. 8. Only after a few minutes CT chest
[235]. At the same time, the LBP Bag of Tree, HOG K-ELM, and LBP scan of the patient can be available, and if Deep learning systems classify
K-ELM neural networks achieved 100% specificity [236], the Respire., the result as SARS-CoV-2 positive or negative, the results can be ob­
Emerg., And Rad-5th neural networks reached 100% sensitivity [237], tained in less than a minute. Radiologists with experience can also
and the neural network Modified MobileNet achieved a maximum ac­ quickly detect the infected lung, however, a qualified expert is required
curacy of 99.9% [233]. to distinguish SARS-CoV-2 from other diseases and the healthy scan.
In order to assess the sensitivity of CT scanning in detecting SARS-
5.2. CT diagnostics CoV-2, a total of 17 methods were studied, where in 5 of the methods
radiologists assigned the scans as healthy and infected, while in other 12
Computed Tomography is a widely used diagnostic tool that is based research papers artificial intelligence was applied to categorize the scans
on the reconstruction of the image data acquired by multiple X-ray correspondingly. Deep learning technologies were created by teaching
techniques with a help of a computer [243]. The first commercially AI and showing the infected and healthy CT scans, and then the tech­
available CT scanner was created by Sir Godfrey Hounsfield of EMI nology was tested. Table 20 displays the clinical performance of the CT
Laboratories in 1972, and later in 1979 for this invention, Hounsfield scan-based methods, where in some studies RT-PCR results were used as
was awarded the Nobel Prize in Physiology and Medicine along with the a reference, while in other studies patients were diagnosed as SARS-
co-inventor of the CT technology, Dr. Allan Cormack [244]. COo-2 positive by physicians according to the symptoms, and expo­
Chest CT scan can be used to detect different infections, and COVID- sure history, which made possible the evaluation of RT-PCR accuracy. It
19 with the associated pneumonia is not the exception. In order to should be noted that five sources [250–254] had not used any healthy
determine the hallmarks of SARS-CoV-2 infection, Bernheim et al. samples, so the results lack specificity, and the accuracy is equal to the
studied the CT scans of 121 symptomatic patients and found out that specificity in those methods. As it can be seen from Table 20, CT scan can
bilateral and peripheral ground-glass opacities and consolidative pul­ precisely find the infected patients, with the sensitivity of 94.5%,
monary opacities are the most common patterns [245]. Apart from these however, unlike other detection methods discussed in this review, the
features, vascular enlargement in the lesion and traction bronchiectasis average specificity is quite low, being around 84%. This could due to the
are other characteristics of SARS-CoV-2 CT scans according to Zhao et al. fact that CT-scan is more sensitive than RT-PCR, and some samples
[246]. Furthermore, Wang et al. studied the difference of CT scan for the assigned to be negative by RT-PCR, are actually SARS-CoV-2 positive.
patients infected with SARS-CoV-2 and influenza, to increase the spec­ This assumption can be supported by four studies [250–252,255] that
ificity of the CT scan, and even though both infections had ground-glass assessed the sensitivity of both RT-PCR and CT-scan in detecting the
opacities with consolidations, these viruses can be differentiated by the SARS-CoV-2 and used the symptoms of patients as a reference point; the
fact that 92.3% scans of SARS-CoV-2 infected patients had peripheral average sensitivity of RT-PCR and CT-scan for these papers were 85%
and non-specific distributions, while this percentage was as low as 3.3% and 96%, respectively.
for people with influenza in the following research. Moreover, The lowest specificity among the discussed papers is 25%. The study
SARS-CoV-2 infection presented the balanced lobe localization, shrink­ by Ai et al. (2020) [256] investigated 1014 patients, and employed
ing contour, and clear lesion margin in comparison to influenza [247]. RT-PCR as a reference material. As a result, RT-PCR had 601, CT scan
The progression of SARS-CoV-2 in 26 patients was studied by Pan et al. had 888 positive results. According to the authors, using RT-PCR with
and depending on the CT scans of the patients, 4 stages of the infection low positivity rate as a reference caused the underestimation of speci­
were defined, in Stage 1 (0–4 days), mainly the ground-glass opacities ficity. This is because 81% of people with negative RT-PCR but positive
were detected, and CT infection score of patients continuously raised till CT scan results are considered highly likely cases of SARS-CoV-2 by the
peaking in Stage 3 (9–13 days) and consolidation was observed in 91% symptoms and exposure history of patients [256].
of the CT scan at Stage 3, finally, the decrease in the infection CT score Following analysis of Table 20, it can be concluded that there is not
and in consolidation was noticed in stage 4 (>14 days) [248]. From the any significant difference between the assessment of scans by radiolo­
study of Pan et al. it can be concluded that the infection peaks after 9–13 gists or by AI technology, which means that deep learning technologies
days once the symptoms are noticed, and this statement can be sup­ can be used to analyze the chest CT scans and provide accurate results,
ported by the findings of Wang et al. who found out that the peak of the thus reducing the work load of the radiologists.

22
O. Filchakova et al. Talanta 244 (2022) 123409

Table 16 Table 16 (continued )

Performance of commercialized government-approved strip immunoassays for Name Sample LOD Accuracy Time, Ref
detection of antibodies. min
Name Sample LOD Accuracy Time, Ref WANTAI SARS- Serum, N/A Sens = 15 [211]
min CoV-2 Ab Rapid plasma, 403 94.7% US
Assure COVID-19 Blood, N/A IgG: 15 [203] Test blood (IgG, samples Spec = FDA
IgG/IgM Rapid serum, 110 Sens = US IgM), 10 μL 98.9%
Test Device plasma samples 100% FDA Acc =
(IgG, IgM), Spec = 93.6%
50 μL 100% Livzon The Serum, N/A Sens = 15 [212]
Acc = Diagnostic Kit for plasma 644 90.6% CE
100% IgM/IgG (IgG, IgM), samples Spec =
IgM: Antibody to 10 μL 99.2%
Sens = Coronavirus Blood (IgG, Acc =
100% IgM), 20 μL 89.8%
Spec = AIDIAN 2019-nCoV Blood (IgG, N/A IgG: 10 [213]
100% IgG/IgM Rapid IgM), 20 μL 16 Sens = CE-IVD
Acc = Test Cassette samples 100%
100% Spec =
Cellex qSARS-CoV- Blood, N/A Sens = 20 [204] 98%
2 IgG/IgM Rapid serum, 378 93.8% US Acc =
Test plasma samples Spec = FDA 98%
(IgG, IgM), 96% IgM:
10 μL Acc = Sens =
89.8% 85%
Nirmidas COVID-19 Serum, N/A IgG: 15 [205] Spec =
(SARS-CoV-2) plasma 568 Sens = US 96%
IgM/IgG (IgG, IgM), samples 100% FDA Acc =
Antibody 10 μL Spec = 81%
Detection Kit 94.8% Wondfo SARS-CoV- Serum, N/A Sens = 25 [214]
Acc = 2 Antibody Test plasma, 596 86.4% CE,
94.8% blood (IgG, samples Spec = CFDA
IgM: IgM), 10 μL 99.6%
Sens = Acc =
97.1% 86%
Spec = CTK OnSite™ Serum, N/A IgG: 15 [215]
94.8% COVID-19 IgG/ plasma, 126 Sens = CE-IVD
Acc = IgM Rapid Test blood (IgG, samples 98.8%
91.9% IgM), 30 μL Spec =
SGTi-flex COVID-19 Blood, N/A Sens = 10 [206] 100%
IgG serum, 419 96.7% US Acc =
plasma samples Spec = FDA 98.8%
(IgG), 10 μL 100% IgM:
Acc = Sens =
96.7% 88.2%
CareStart™ COVID- Blood, N/A IgG: 10 [207] Spec =
19 IgM/IgG serum, 246 Sens = US 100%
plasma samples 100% FDA Acc =
(IgG, IgM), Spec = 88.2%
10 μL 98.8% Virusee COVID-19 Serum, N/A Sens = 10 [216]
Acc = IgG/IgM Lateral plasma, 97.3% CE,
98.8% Flow blood (IgG, Spec = CFDA
IgM: IgM), 10 μL 99.2%
Sens = Acc =
90% 96.5%
Spec = UNscience COVID- Serum, N/A Sens = 10 [217]
98.8% 19 IgG/IgM plasma, 421 98.8% CE
Acc = Rapid Test Kit blood (IgG, samples Spec =
88.8% IgM), 20 μL 98%
Tell Me Fast Novel Serum, N/A Sens = 15 [208] Acc =
Coronavirus plasma 233 100% US 94.8%
(COVID-19) IgG/ (IgG, IgM), samples Spec = FDA BioMedomics Serum, N/A Sens = 10 [218]
IgM Antibody 10 μL 99.4% COVID-19 IgM- plasma 106 100% CE-IVD
Test Blood (IgG, Acc = IgG Combined (IgG, IgM), samples Spec =
IgM), 20 μL 99.4% Antibody Rapid 10 μL 98.8%
BIOTIME SARS- Serum, N/A Sens = 20 [209] Test Blood (IgG, Acc =
CoV-2 IgG/IgM plasma, 380 100% US IgM), 15 μL 98.8%
Rapid Qualitative blood (IgG, samples Spec = FDA BELTEST-IT COV-2 Serum, N/A IgG: 20 [219]
Test IgM), 100% Rapid Test blood (IgG, 1809 Sens = CE
10–15 μL Acc = IgM), 50 μL samples 98.2%
100% Spec =
Innovita 2019- Serum, N/A Sens = 15 [210] 99.7%
nCoV Ab Test plasma 468 100% US Acc =
(Colloidal Gold) (IgG, IgM), samples Spec = FDA 97.9%
10 μL 98% IgM:
Blood (IgG, Acc = Sens =
IgM), 20 μL 98% 98.1%
Spec =
(continued on next page)

23
O. Filchakova et al. Talanta 244 (2022) 123409

Table 16 (continued ) 5.3. Ultrasound diagnostics

Name Sample LOD Accuracy Time, Ref
min Lung ultrasound is also considered a potential method for detecting
99.5%
COVID-19 in patients. Due to the fact that it does not carry ionizing
Acc = radiation [271–278], this method is beneficial for those patients who
97.6% should not be exposed to radiation, such as pregnant women. As shown n
Hightop SARS-CoV- Serum, N/A Sens = 20 [220] Fig. 8, a special device sends an ultrasound signal to the organ being
2 IgM/IgG plasma 550 94.2% CE
examined. The signal is reflected from the organ and picked up by the
Antibody Rapid (IgG, IgM), samples Spec =
Test 10 μL 93.9% sensor. The information received is processed by a computer, and the
Blood (IgG, Acc = image of a cross-section of an organ appears on the screen. Sound waves
IgM), 20 μL 88.1% were first discovered by the Italian L. Spallanzani in 1794, who proved
SD Biosensor Serum, 0.02 Sens = 15 [221] that a bat with plugged ears ceases to navigate in space. Experiments
STANDARD Q plasma, mg/mL 99.1% CE-IVD
COVID-19 IgM/ blood (IgG, 504 Spec =
aimed at using ultrasound waves as a tool for diagnosing neoplasms
IgG Duo Test IgM), 10 μL samples 95.1% began in the 40s of the 20th century. In 1947, the Austrian physician
Acc = Karl Dussik and his brother physicist Friedrich introduced the hyper­
94.2% sonography method, which was able to detect a brain tumor by
measuring the intensity, with which an ultrasound wave passed through
the patient’s skull [279].
Table 17 Currently, studies are underway on the effectiveness of the use of
Comparison of performance of commercialized government-approved strip im­ lung ultrasound examination for the detection of COVID-19. These ex­
munoassays for detection of antibodies. aminations fall into two categories: examinations conducted in emer­
Group Average Average Average Average Average number gency departments using point-of-care lung ultrasound (POCUS) and
sens spec acc time of samples conventional lung ultrasound (LUS) examinations. According to
FDA 97.5% 98.1% 95.6% 14.5 min 356 Table 21, although POCUS is worse than conventional LUS in specificity
Other 94.9% 98.2% 93.1% 15 min 530 (58% versus 84%), it is better than it in sensitivity (95% versus 64%).
Also, POCUS outperforms conventional LUS in the AUC (area under the
curve) parameter: 0.94 and 0.83, respectively. When all lung ultrasound
Table 18 examinations are considered, the greatest sensitivity of 100% was
Performance of strip immunoassays for detection of antigen. demonstrated by Walsh (2020), who worked with POCUS [275]. How­
Name Sample LOD Accuracy Time, Ref
ever, in this study, CT was used as the gold standard and not all patients
min took an RT-PCR test for comparison, so the results may be slightly
inaccurate. Bosso (2020) achieved the highest specificity of 89% when
BinaxNOW™ Nasopharyngeal 4.04 × Sens = 15 [145,
COVID-19 swabs 104 84.6% 222] working with conventional LUS [271].
Ag CARD (nucleocapsid copies/ Spec = US Overall, lung ultrasound is a relatively non-invasive and harmless
protein), 50 μL swab 98.5% FDA method, but its specificity and sensitivity still need to be improved
460 Acc = before it may approach the test accuracy of CT, and so far, this technique
samples 83.1%
SD Biosensor Nasopharyngeal 3.12 × Sens = 15 [223]
may only be preferable in scanning of pregnant women and other ra­
STANDARD swabs (antigens), 102.2 84.9% CE-IVD diation vulnerable patients.
Q COVID-19 350 μL TCID50/ Spec = A receiver operating characteristic (ROC) curve, also known as an
Ag Test mL 98.9% error curve, is used in addition to sensitivity and specificity to analyze
1659 Acc =
and compare the methods of medical diagnostics that involve artificial
samples 83.8%
ACRO Biotech Nasopharyngeal N/A Sens = 15 [224] intelligence used to detect COVID-19. This curve shows the relationship
Inc. COVID- swabs (antigens), 145 97.7% CE between true positive rate and false positive rate demonstrated by some
19 Antigen 100 μL samples Spec = method. The area bounded by the ROC curve and the false positive rate
Rapid Test 99% axis is a quantitative interpretation of the ROC curve and known as the
Acc =
96.7%
area under the curve (AUC) value. The higher the AUC indicator, the
COVID-19 Ag Nasopharyngeal 5 × 103 Sens = 30 [225] better the method in terms of classification (the maximum AUC value is
Respi-Strip swabs pfu/mL 91.2% CE-IVD 1), while the value of 0.5 demonstrates the unsuitability of the selected
(nucleocapsid 189 Spec = classification method (corresponds to random fortune-telling). A value
protein), 100 μL samples 99.4%
less than 0.5 indicates that the classifier acts exactly the opposite: if
Acc =
90.6% positive are called negative and vice versa, the classifier will perform
better.
The brief summary of X-ray, CT-scan and lung ultrasound-based
Despite the high sensitivity of CT scanning in detecting SARS-COV-2, detection of SARS-CoV-2 can be found in Table 22. According to
it is significant to note the patients are exposed to ionizing radiation, and Table 22, despite the fact that CT scan sensitivity is superior to X-ray
the amount of radiation received from chest CT scan is 400 times more sensitivity, being 94.5% and 80.4% correspondingly, the specificity and
than that from chest X-ray, and the similar amount of background ra­ accuracy of X-ray detection is slightly higher than that of the counter­
diation is received in more than two years [267]. Therefore, Tofighi part. At the same time, lung ultrasound performs worse than the other
et al. (2020) proposed to apply Low-Dose and Ultra Low-Dose CT scans two methods in both parameters, showing the specificity of 68.4% and
that cause 5–8 times and 20–25 times less radiation and still retain the the sensitivity of 79.7%. Regarding AUC, X-rays and CT scans show a
significant accuracy [268]. The expensiveness of CT scan machines can fairly high value of 0.95, while its value for lung ultrasound is only 0.87.
be a possible obstacle for the popularization of the method, since the Overall, lung ultrasound appears to be the most unreliable method for
machine cost ranges between $80,000-$300,000 [269], while the price detecting COVID-19; its only advantages are that the patient is not
for taking the chest CT scan is around $675 – $8600 in the US [270]. exposed to ionizing radiation during the examination and that the cost of
the ultrasound machine is relatively low (starts from $10,000).
Regarding cost, the CT scan machine is almost twice as expensive as the

24
O. Filchakova et al. Talanta 244 (2022) 123409

Fig. 8. SARS-CoV-2 Detection Process by X-ray and CT scan. Positive and Negative X-ray and CT scans are retrieved with permission from an open-access article by
Saha et al. (2021) [226]. Lung ultrasound images are retrieved with permission from an open-access article by Smith et al. (2020) [227]. X-ray machine icon is
obtained from Alibaba (2021) [228]. Lung ultrasound machine icon is obtained from Flaticon (2021) [229]. CT scan machine icon from CleanPNG (2021), and
Computer icon from PinClipart (2021). Note: Figure was made with miro.com.

Table 19
Statistical results of detection of COVID-19 from X-ray images.
Specificity Sensitivity Accuracy N of samples N of ill patients Ref

2-class distribution (COVID and non-COVID) 98 ± 3.4% (88.7%; 89 ± 10% (56.8%; 97 ± 2.0% 727 ± 379 118 ± 52 (62; [236–239]
100%) 100%) (91.4%; 99.7%) (162; 1332) 276)
99.35% 92.4% 98.2% 625 125
3-class distribution (COVID, other infection/s, 87 ± 9.5% (69.3%; 67 ± 24% (30%; 88 ± 17% (38.2%; 988 ± 651 272 ± 189 [233–235,
healthy) 98.6%) 98.6%) 99.7%) (336; 2276) (112; 573) 239–242]
88% 59.3% 96% 632 160.5
5-class distribution (COVID, tuberculosis, bacterial N/A N/A 96 ± 1.6% 804 ± 125 119 ± 58 (51; [232,233]
pneumonia, viral pneumonia, healthy) (93.9%; 99.9%) (680; 1000) 200)
95.9% 680 51
All distributions: Average 96 ± 5.3% (69.3%; 83 ± 19% (30%; 97 ± 3.5% 896 ± 500 186 ± 153 (51; [232–242]
100%) 100%) (38.2%; 99.9%) (162; 2276) 573)
98% 91.2% 96.7% 632 125

Note: Average ± standard deviation; Range (x%; y%); Median are shown. Accuracy=(TP + TN)/(TP + TN + FP + FN); Sensitivity = (TP)/(TP + FN); Specificity =
(TN)/(TN + FP). TP – true positive, TN – true negative, FP – false positive, FN – false negative.

Table 20
Accuracy, Sensitivity, and Specificity of CT scan-based SARS-CoV-2 Detection.
Specificity Sensitivity Accuracy N of samples/scans N of ill patients/scans Ref

Assessed by 25% (only from 95.9 ± 4.4%; (89%; 90.1 ± 13.1%; 43; 51; 167; 43; 51; 167; 601; 295 [250–253,256]
Radiologists Ref. [256]) 100%) (68%; 100%) 1014; 295
97.0% 95.8%
Assessed by AI 90.8 ± 11.1%; (66%; 94.0 ± 5.1% (82%; 92.5 ± 7.3%; (77%; 434; 189; 400; 127; 83; 200; 34; 275; 250; 98; 95; [254,255,
99.6%) 100%) 99.4%) 73; 510; 496; 445; 244; 105; N/A 257–266]
94.9% 94.7% 94.5% 203; 167; 668;
495; 105; 374
Average 84.2 ± 23.3%; (25%; 94.5 ± 4.8%; (82%; 91.8 ± 9.0%; (68%; 43; 51; 167; 1014; 434; 43; 51; 167; 601; 127; 83; 200; [250–266]
Range 99.6%) 100%) 100%) 189; 400; 295; 34; 275;
Median 94.7% 95.8% 94.5% 295; 73; 510; 496; 203; 250; 98; 95; 445; 244; 105; N/A
167; 668;
495; 105; 374

Note: Average ± standard deviation; Range (x%; y%); Median are shown. Accuracy = (TP + TN)/(TP + TN + FP + FN); Sensitivity = (TP)/(TP + FN); Specificity =
(TN)/(TN + FP). TP – true positive, TN – true negative, FP – false positive, FN – false negative.

25
O. Filchakova et al. Talanta 244 (2022) 123409

Table 21
Statistical results of detection of COVID-19 from lung ultrasound images.
Specificity Sensitivity Accuracy AUC N of samples N of ill patients Ref

POCUS 58 ± 33% (21.3%; 95 ± 4.3% (92%; 33.3 (only one 0.94 (only one ref) 184 ± 174 (77; 102 ± 160 (5; [273, 275,
88%); 100%); ref) 384); 287); 280]
64.9% 93.3% 90 15
Conventional 84 ± 7.1% (79%; 64 ± 11% (52%; N/A 0.83 ± 0.12 (0.75; 66 ± 15 (53; 83); 42 ± 35 (19; 82); [271, 272,
LUS 89%); 73%); 0.92); 63 26 274]
84% 68% 0.83
All LUS 68 ± 28% (21.3%; 80 ± 18% (52%; 33.3 (only one 0.87 ± 0.11 (0.75; 125 ± 128 (53; 72 ± 109 (5; [271–275,
89%); 100%); ref) 0.94); 384); 287); 280]
79% 82.5% 0.92 79.5 22.5

Note: Average ± standard deviation; Range (x%; y%); Median are shown. POCUS - point-of-care lung ultrasound, LUS - lung ultrasound. Accuracy=(TP + TN)/(TP +
TN + FP + FN); Sensitivity = (TP)/(TP + FN); Specificity = (TN)/(TN + FP). TP – true positive, TN – true negative, FP – false positive, FN – false negative.

Table 22
Comparison of the Clinical Performance in Detecting SARS-CoV-2, Cost and Radiation Dose of CT scan, X-ray, and lung ultrasound.
CT scan Ref. X-ray scan Ref. Lung ultrasound Ref.
image

Specificity 84.2 ± 23.3% (25%; [255–257,259–263, 94.1 ± 7.9% (69.3%; 100%) [234–238,242] 68.4 ± 28.1% [271–273, 275,
99.6%) 265,266] 98% (21.3%; 89%) 280]
94.7% 79%
Sensitivity 94.5 ± 4.8% (82%; [250–266] 80.4 ± 19.7% (30%; 100%) [234–237] 79.7 ± 18.4% (52%; [271–275, 280]
100%) 91.2% 100%)
95.8% 82.5%
Accuracy 91.8 ± 9.0% (68%; [250–266] 93.2 ± 12.0% (38.2%; 99.9%) [232–234,236, 33.3% (only one ref) [273]
100%) 96.7% 238–242]
94.5%
AUC 0.95 ± 0.04 (0.89; [254,257,260, 0.95 ± 0.06 (0.77; 0.9997) [235,237,238,241, 0.87 ± 0.11 (0.75; [271, 272, 275]
0.996) 262–266] 0.9799 242] 0.94)
0.953 0.92
Cost Machine: $80,000- [269] X-ray room: $40,000-$175,000 [281] Machine: $10,000- [282]
$300,000 $200,000
Radiation Average dose: 7–8 [268] Approximate effective radiation dose for [283] No ionizing radiation [271–278]
dose mSv chest x-ray: 0.1 mSv
Low-dose: 1–1.5 mSv
Ultra low-dose: 0.3
mSv

Note: Average ± standard deviation; Range (x%; y%); Median are shown. AUC - Area under the curve. Accuracy=(TP + TN)/(TP + TN + FP + FN); Sensitivity = (TP)/
(TP + FN); Specificity = (TN)/(TN + FP). TP – true positive, TN – true negative, FP – false positive, FN – false negative.

X-ray room, and the average radiation dose of chest CT scanning is 84% of RT-PCR from scientific literature is comparably less than the
70–80 times higher. From Table 22 it can be concluded that X-ray average sensitivity of FDA-approved commercial PCR methods that has
detection, which is a more accurate, cost-effective and less harmful the value of 98%. Also, as the commercial kits were tested on a bigger
method, can be a more promising detection method than CT scan and number of clinical samples, they have confirmed their worth and
lung ultrasound. therefore are more reliable. Comparison between commercial kits was
different depending on the method used (RT-PCR, immunoassay tests,
6. Conclusion strip immunoassay tests, etc.), but the values did not differ significantly.
Performances of commercialized government-approved kits are shown
COVID-19 pandemic is still continuing, while new variants such as in Tables 11–18
delta-variant [284] and omicron are spreading among the population. In Although RT-PCR is considered to be the gold standard for detection
this review paper, different COVID-19 detection methods, both widely of SARS-CoV-2, many different other methods have already been
used since the beginning of the pandemic and new ones, adapted in developed, and Table 2 compares the performance of these methods. For
research laboratories, were summarized and their benefits and limita­ example, biosensors showed good results, and even though in compar­
tions were discussed. ison to RT-PCR they had lower accuracy, they are still practical and can
In this review, Figs. 1–8 illustrate the processes and visual results of be used for rapid detection of COVID-19. Also, several portable devices
detection of COVID-19 with different methods such as qPCR, CRISPR, showed promising results in detection of viral genome. The advantage of
RT-LAMP, RT-RPA, RT-RAA, mass spectrometry, fluorescence-based portable devices is that they can be used to quickly detect COVID-19
sensor, reagent-free chronoamperometry sensor, ELISA, CLIA, LFIA, X- both in the hospital and outside the hospital, for example, at the pa­
ray, CT, ultrasound. tient’s home by an ambulance team.
RT-PCR is the main method for detecting viral nucleic acids of Another alternative for RT-PCR methods are CRISPR, RT-RPA and
COVID-19 throughout the world and according to Table 1, which RT-LAMP, as they are cost effective and require less time for detection
reviewed publications about not yet commercialized RT-PCR research that can be seen from Tables 6–9 and Table 11, which compare SARS-
methods, it has an average accuracy about 97.7% CoV-2 RNA detection methods including CRISPR, RT-LAMP, RT-RPA,
Commercial kits were reviewed as well, and, in general, their per­ RT-RAA, PCA RT-PCR. However, it is important to mention that RNA
formance was better than that of the scientific methods that are not yet should preferably be extracted by kits, as other methods may negatively
implemented widely. Table 5 displays the comparison between scientific affect accuracy values.
and commercial RT-PCR, and following this table, average sensitivity of Detection of viral particles and antigens could be another alternative

26
O. Filchakova et al. Talanta 244 (2022) 123409

to RT-PCR. The main advantage of these methods is rapid and early interests or personal relationships that could have appeared to influence
detection, which can also be done by patients themselves. This is highly the work reported in this paper.
convenient for patients because the need to visit the hospital disappears,
and patients save money and time on the road to the hospital and in the References
hospital itself.
Testing the level of antibody to SARS-CoV-2 is another detection [1] K. Ejima, K.S. Kim, C. Ludema, A.I. Bento, S. Iwanami, Y. Fujita, H. Ohashi,
Y. Koizumi, K. Watashi, K. Aihara, H. Nishiura, S. Iwami, Estimation of the
method of patients infected with COVID-19. Analyzing performance of incubation period of COVID-19 using viral load data, Epidemics 35 (2021)
ELISA, CLIA and LFIA from comparative Table 10, it can be concluded 100454.
that among these methods CLIA showed the best results in terms of [2] B. Hu, H. Guo, P. Zhou, Z.-L. Shi, Characteristics of SARS-CoV-2 and COVID-19,
Nat. Rev. Microbiol. 19 (3) (2021) 141–154.
sensitivity and specificity. However, it is important to note that antibody [3] N.S. Zhong, B.J. Zheng, Y.M. Li, L.L.M. Poon, Z.H. Xie, K.H. Chan, P.H. Li, S.
testing is not 100% helpful compared to previously mentioned methods Y. Tan, Q. Chang, J.P. Xie, X.Q. Liu, J. Xu, D.X. Li, K.Y. Yuen, J.S.M. Peiris,
like RT-PCR when early detection of the virus is required. The antibody Y. Guan, Epidemiology and cause of severe acute respiratory syndrome (SARS) in
Guangdong, People’s Republic of China, in February, 2003, Lancet 362 (9393)
testing methods are useful for other practices, like producing accurate (2003) 1353–1358.
statistics on the spread of the virus, showing number of infected and [4] Worldometer Coronavirus, 2022. https://www.worldometers.info/coronavirus/.
recovered people, determination of vaccination schedules, determina­ (Accessed 15 March 2022).
[5] Eastern Mediterranean Region, 2021. http://www.emro.who.int/health-topics/
tion of whether the vaccine is working (whether antibodies appear in the
mers-cov/mers-outbreaks.html. (Accessed 10 February 2021).
body of a vaccinated person), etc. [6] World Bank Expects Global Economy to Expand by 4% in 2021, 2021. https
X-ray, ultrasound and CT scans are also used to detect the virus. ://news.un.org/en/story/2021/01/1081392.
When using these methods, the presence of a disease in a patient (some [7] Projected GDP Ranking, 2021. https://statisticstimes.com/economy/pro
jected-world-gdp-ranking.php. (Accessed 10 February 2021).
percentage of lung damage) is most often determined by a doctor, for [8] L. Jones, D. Palumbo, D. Brown, Coronavirus, How the Pandemic Has Changed
example, a radiologist. However, at present, much attention is paid to the World Economy, 2021. https://www.bbc.com/news/business-51706225.
the development of artificial intelligence to accomplish this task. (Accessed 5 February 2021). Accessed.
[9] M. Roser, H. Ritchie, E. Ortiz-Ospina, J. Hasell, Coronavirus (COVID-19) Testing -
Humans use thousands of images to train neural networks to detect Statistics and Research, 2020. https://ourworldindata.org/coronavirus-test
COVID-19 in patients from images obtained via clinical imaging tech­ ing#world-map-total-tests-performed-relative-to-the-size-of-population.
niques. The development of artificial intelligence in this direction has (Accessed 10 February 2021).
[10] Tracking Coronavirus Vaccinations Around the World, 2022. https://www.
already yielded positive results. As Table 20 shows, the sensitivity and nytimes.com/interactive/2021/world/covid-vaccinations-tracker.html.
accuracy of artificial intelligence in identifying COVID-19 patients from (Accessed 15 March 2022).
CT images is comparable to that of a radiologist (average sensitivity [11] E. Callaway, Why does the Omicron sub-variant spread faster than the original?
Nature 602 (7898) (2022) 556–557.
94–96% and average accuracy 90–93%). [12] G.O.V. UK, Coronavirus (COVID-19) in the UK, 2022. https://coronavirus.data.
If we want to compare these clinical imaging techniques, according gov.uk/details/testing. (Accessed 15 March 2022).
to Table 22, which demonstrates different characteristics of CT scan, X- [13] F.M. Liotti, G. Menchinelli, E. Lalle, I. Palucci, S. Marchetti, F. Colavita, M. La
Sorda, G. Sberna, L. Bordi, M. Sanguinetti, P. Cattani, M.R. Capobianchi,
ray scan and lung ultrasound image, among three methods X-ray and CT
B. Posteraro, Performance of a novel diagnostic assay for rapid SARS-CoV-2
scans showed similarly high clinical performance (average accuracy antigen detection in nasopharynx samples, Clin. Microbiol. Infect. 27 (3) (2021)
92–93%) relative to lung ultrasound imaging, for which accuracy of 487–488.
33.3% was known from only one research article [273]. But, of course, [14] Q. Chen, Z. He, F. Mao, H. Pei, H. Cao, X. Liu, Diagnostic technologies for COVID-
19: a review, RSC Adv. 10 (58) (2020) 35257–35264.
these two methods, due to the patient’s exposure to radiation (average [15] B. Pérez-López, M. Mir, Commercialized diagnostic technologies to combat SARS-
radiation dose for CT scan is 70–80 times higher than approximate CoV2: advantages and disadvantages, Talanta 225 (2021) 121898.
effective radiation dose for chest X-ray scan), are inferior in safety to [16] M.A.C. Huergo, N.T.K. Thanh, Current advances in the detection of COVID-19 and
evaluation of the humoral response, Analyst 146 (2) (2021) 382–402.
ultrasound that produces no ionizing radiation. In order to avoid the [17] P.I. Kontou, G.G. Braliou, N.L. Dimou, G. Nikolopoulos, P.G. Bagos, Antibody
harmful effects of these methods, it is recommended for use only for tests in detecting SARS-CoV-2 infection: a meta-analysis, Diagnostics 10 (5)
initial inspection: to identify the level of lung damage and determine the (2020).
[18] S.A. Ejazi, S. Ghosh, N. Ali, Antibody detection assays for COVID-19 diagnosis: an
next course of action, i.e. only examine those who tested positive for early overview, Immunol. Cell Biol. 99 (1) (2021) 21–33.
COVID-19 in RT-PCR or other reliable tests. [19] A.L. Wyllie, J. Fournier, A. Casanovas-Massana, M. Campbell, M. Tokuyama,
In conclusion, testing for SARS-CoV-2 of people with symptoms P. Vijayakumar, J.L. Warren, B. Geng, M.C. Muenker, A.J. Moore, C.B.F. Vogels,
M.E. Petrone, I.M. Ott, P. Lu, A. Venkataraman, A. Lu-Culligan, J. Klein,
similar to those of the COVID-19 and regular testing of people working R. Earnest, M. Simonov, R. Datta, R. Handoko, N. Naushad, L.R. Sewanan,
and visiting public places is still crucial during the pandemic. Early J. Valdez, E.B. White, S. Lapidus, C.C. Kalinich, X. Jiang, D.J. Kim, E. Kudo,
diagnosis of the disease with testing will help identify carriers of the M. Linehan, T. Mao, M. Moriyama, J.E. Oh, A. Park, J. Silva, E. Song,
T. Takahashi, M. Taura, O.E. Weizman, P. Wong, Y. Yang, S. Bermejo, C.D. Odio,
virus and limit their interaction with other people through quarantine.
S.B. Omer, C.S. Dela Cruz, S. Farhadian, R.A. Martinello, A. Iwasaki, N.
Future research in this area should focus on developing portable, D. Grubaugh, A.I. Ko, Saliva or nasopharyngeal swab specimens for detection of
harmless, fast and accurate methods of detection of COVID-19 markers, SARS-CoV-2, N. Engl. J. Med. 383 (13) (2020) 1283–1286.
which can be used by patients even at home, eliminating the need to visit [20] G. Petruzzi, A. De Virgilio, B. Pichi, F. Mazzola, J. Zocchi, G. Mercante,
G. Spriano, R. Pellini, COVID-19: nasal and oropharyngeal swab, Head Neck 42
hospitals at the times when it is unsafe to go to public places. This is also (6) (2020) 1303–1304.
important for rapid testing of a big number of people, as we want and do [21] N.N. Kinloch, G. Ritchie, C.J. Brumme, W. Dong, W. Dong, T. Lawson, R.B. Jones,
our best to return to pre-pandemic life. J.S.G. Montaner, V. Leung, M.G. Romney, A. Stefanovic, N. Matic, C.F. Lowe, Z.
L. Brumme, Suboptimal biological sampling as a probable cause of false-negative
COVID-19 diagnostic test results, J. Infect. Dis. 222 (6) (2020) 899–902.
Funding statement [22] A. Piras, D. Rizzo, S. Uzzau, G. De Riu, S. Rubino, F. Bussu, Inappropriate
nasopharyngeal sampling for SARS-CoV-2 detection is a relevant cause of false-
negative reports, Otolaryngol. Head Neck Surg. 163 (3) (2020) 459–461.
This research is supported by the Nazarbayev University Faculty [23] S. Irifune, N. Ashizawa, T. Takazono, P. Mutantu, T. Nabeshima, M.M. Ngwe Tun,
Development Competitive Research grant 090118FD5352 (Kazakhstan) K. Ota, T. Hirayama, A. Fujita, M. Tashiro, T. Tanaka, K. Yamamoto, Y. Imamura,
“Fundamental and applied SERS for detection of cancer biomarkers” T. Miyazaki, T. Sawai, K. Izumikawa, K. Yanagihara, K. Morita, H. Mukae,
Discrepancy of SARS-CoV-2 PCR results due to the sample collection sites and
(FASERSDCB) and by the Nazarbayev University Faculty Development possible improper sampling, J. Infect. Chemother. 27 (10) (2021) 1525–1528.
Competitive Research grant 11022021FD2907. [24] S. Pondaven-Letourmy, F. Alvin, Y. Boumghit, F. Simon, How to perform a
nasopharyngeal swab in adults and children in the COVID-19 era, Euro. Ann.
Otorhinol. Head Neck Dis. 137 (4) (2020) 325–327.
Declaration of competing interest [25] D. Basso, A. Aita, F. Navaglia, E. Franchin, P. Fioretto, S. Moz, D. Bozzato, C.-
F. Zambon, B. Martin, C. Dal Prà, A. Crisanti, M. Plebani, SARS-CoV-2 RNA
The authors declare that they have no known competing financial

27
O. Filchakova et al. Talanta 244 (2022) 123409

identification in nasopharyngeal swabs: issues in pre-analytics, Clin. Chem. Lab. [46] Real-Time Fluorescent RT-PCR Kit for Detecting SARS-CoV-2, US FDA, 2020.
Med. 58 (9) (2020) 1579–1586. [47] RealStar® SARS-CoV-2 RT-PCR Kit U.S. Instructions for Use, US FDA, 2020.
[26] Westburg, COVID-19 Rapid Antigen Test (Swab), 2021. Accessed 19 March [48] 2019-nCoV: Real-Time Fluorescent RT-PCR Kit, BGI Genomics Co. Ltd., China,
2021). 2020 on March 18, 2022, https://www.bgi.com/global/molecular-genetic
[27] Polymerase chain reaction (PCR) fact sheet. https://www.genome.gov/about-ge s/2019-ncov-detection-kit/.
nomics/fact-sheets/Polymerase-Chain-Reaction-Fact-Sheet, 2020. (Accessed 6 [49] ANDiS FAST SARS-CoV-2 RT-qPCR Detection Kit, 3D Biomedicine Science &
March 2021). Technology Co, China, 2020. https://www.labconsulting.at/wp-content/uploads
[28] D.L.A. McClain. https://www.nytimes.com/2019/08/15/science/kary-b-mullis- /2021/03/LabShop_Andis_FastKit_InstructionsForUse.pdf. (Accessed 17 March
dead.html, 2019, 2019. 2022).
[29] K. Mullis, F. Faloona, S. Scharf, R. Saiki, G. Horn, H. Erlich, Specific enzymatic [50] Primerdesign Ltd COVID-19 Genesig® Real-Time PCR Assay, US FDA, 2020.
amplification of DNA in vitro: the polymerase chain reaction, Cold Spring Harbor [51] GeneFinder™ COVID-19 Plus RealAmp Kit, US FDA, 2020.
Symp. Quant. Biol. 51 (Pt 1) (1986) 263–273. [52] Logix Smart™ Coronavirus Disease 2019 (COVID-19) Kit, US FDA, 2020.
[30] J. Logan, K. Edwards, N. Saunders, Real-time PCR: Current Technology and [53] Sansure Novel Coronavirus (2019-nCoV) Nucleic Acid Diagnostic Kit, US FDA,
Applications, Caister Academic Press, Norfolk, 2009. 2020.
[31] J. Arizti-Sanz, C.A. Freije, A.C. Stanton, B.A. Petros, C.K. Boehm, S. Siddiqui, B. [54] STANDARD M nCoV Real-Time Detection Kit, US FDA, 2020.
M. Shaw, G. Adams, T.-S.F. Kosoko-Thoroddsen, M.E. Kemball, J.N. Uwanibe, F. [55] Multiple Real-Time PCR Kit for Detection of 2019-CoV, WHO, 2020.
V. Ajogbasile, P.E. Eromon, R. Gross, L. Wronka, K. Caviness, L.E. Hensley, N. [56] BioFire® COVID-19 Test, US FDA, 2020.
H. Bergman, B.L. MacInnis, C.T. Happi, J.E. Lemieux, P.C. Sabeti, C. Myhrvold, [57] Instructions for PerkinElmer® New Coronavirus Nucleic Acid Detection Kit, US
Streamlined inactivation, amplification, and Cas13-based detection of SARS-CoV- FDA, 2020.
2, Nat. Commun. 11 (1) (2020) 5921. [58] BioMerieux ARGENE® SARS-COV-2 R-GENE®, US FDA, 2020.
[32] A. Ramachandran, D.A. Huyke, E. Sharma, M.K. Sahoo, C. Huang, N. Banaei, B. [59] Allplex™ 2019-nCoV Assay, US FDA, 2021.
A. Pinsky, J.G. Santiago, Electric field-driven microfluidics for rapid CRISPR- [60] QIAstat-Dx® Respiratory SARS-CoV-2 Panel Instructions for Use (Handbook),
based diagnostics and its application to detection of SARS-CoV-2, Proc. Natl. 2020.
Acad. Sci. Unit. States Am. 117 (47) (2020) 29518. [61] NeuMoDx™ SARS-CoV-2 Assay, US FDA, 2021.
[33] M. Johnson, PCR Machines, Materials and Methods, 2013. [62] NxTAG CoV Extended Panel Assay, US FDA, 2020.
[34] B. Visseaux, Q. Le Hingrat, G. Collin, D. Bouzid, S. Lebourgeois, D. Le Pluart, L. [63] TaqPath™ COVID-19 Combo Kit and TaqPath™ COVID - 19 Combo Kit
Deconinck, F.-X. Lescure, J.-C. Lucet, L. Bouadma, J.-F. Timsit, D. Descamps, Y. Advanced* INSTRUCTIONS for USE, US FDA, 2020.
Yazdanpanah, E. Casalino, N. Houhou-Fidouh, J. McAdam Alexander, Evaluation [64] 1copy™ COVID-19 qPCR Multi Kit, Instructions for Use, US FDA, 2020.
of the QIAstat-dx respiratory SARS-CoV-2 panel, the first rapid multiplex PCR [65] cobas® SARS-CoV-2, US FDA, 2021.
commercial assay for SARS-CoV-2 detection, J. Clin. Microbiol. 58(8) e00630-20. [66] GenePro SARS-CoV-2 Test, US FDA, 2020.
[35] S. Petrillo, G. Carrà, P. Bottino, E. Zanotto, M.C. De Santis, J.P. Margaria, [67] PowerChek™ 2019-nCoV Real-Time PCR Kit Instructions for Use, US FDA, 2020.
A. Giorgio, G. Mandili, M. Martini, R. Cavallo, D. Barberio, F. Altruda, A novel [68] FTD™ SARS-CoV-2, US FDA, 2021.
multiplex qRT-PCR assay to detect SARS-CoV-2 infection: high sensitivity and [69] SARS-CoV-2 Real Time PCR LAB-KIT, BIOMAXIMA, Poland, 2020. https://bi
increased testing capacity, Microorganisms 8 (7) (2020). omaxima.com/userfiles/files/SARS-CoV-2%20Real-Time%20PCR%20LAB-KIT%
[36] J. Cheong, H. Yu, C.Y. Lee, J.-u. Lee, H.-J. Choi, J.-H. Lee, H. Lee, J. Cheon, Fast 20Leaflet%20ENG%2002%202021.pdf. (Accessed 18 March 2022).
detection of SARS-CoV-2 RNA via the integration of plasmonic thermocycling and [70] Novel Coronavirus (2019-nCoV) Real Time Multiplex RT-PCR Kit (Detection for 3
fluorescence detection in a portable device, Nat. Biomed. Eng. 4 (12) (2020) Genes) Instructions for Use, BioVendor, Czech Republic, 2020. March 18, 2022),
1159–1167. https://www.biovendor.com/liferiver-novel-coronavirus-2019-ncov-real-time-m
[37] F. Wang, J. Yang, R. He, X. Yu, S. Chen, Y. Liu, L. Wang, A. Li, L. Liu, C. Zhai, ultiplex-rt-pcr-kit.
L. Ma, PfAgo-based detection of SARS-CoV-2, Biosens. Bioelectron. 177 (2021) [71] GenePro COVID-19 Detection Test, Gencurix, Korea, 2020. https://aristoscient
112932. ific.kz/CATALOGS/COVID19/GenePro%20COVID-19%20brochure-3
[38] X. Lu, L. Wang, S. Sakthivel, B. Whitaker, J. Murray, S. Kamili, B. Lynch, pg-Eng-0319.pdf.pdf. (Accessed 18 March 2022).
L. Malapati, S. Burke, J. Harcourt, A. Tamin, N. Thornburg, J. Villanueva, [72] COVID-19 (SARS-CoV-2) Triplex RT-qPCR Detection Kit, Reagent Genie Ireland
S. Lindstrom, US CDC real-time reverse transcription PCR panel for detection of Limited, Ireland, 2020. March 18, 2022), https://store-h68l9z2lnx.mybigc
severe acute respiratory syndrome coronavirus 2, Emerg. Infect. Dis. J. 26 (8) ommerce.com/content/COVID-19%20Diagnostics/COVID-19%20%28SARS-CoV-
(2020) 1654. 2%29%20Triplex%20.
[39] M. Ji, Y. Xia, J.F.-C. Loo, L. Li, H.-P. Ho, J. He, D. Gu, Automated multiplex [73] USA, CTK’s Aridia COVID-19 Real-Time PCR Test by CTK Biotech, 2020. March
nucleic acid tests for rapid detection of SARS-CoV-2, influenza A and B infection 18, 2022), https://ctkbiotech.com/product/aridia-covid-19-real-time-pcr-test/.
with direct reverse-transcription quantitative PCR (dirRT-qPCR) assay in a [74] VIASURE SARS-CoV-2 Real Time PCR Detection Kit, VIASURE, Spain, 2020. htt
centrifugal microfluidic platform, RSC Adv. 10 (56) (2020) 34088–34098. ps://www.certest.es/wp-content/uploads/2020/03/Coronavirus_Type_II_CE_EN.
[40] G. Luo, J. Zhang, S. Zhang, B. Hu, L. Hu, Z. Huang, High-quality RT-PCR with pdf. (Accessed 18 March 2022).
chemically modified RNA controls, Talanta 224 (2021) 121850. [75] RADI COVID-19 detection kit. www.khmedical.co.kr/covid01.php. (Accessed 10
[41] A. Karami, M. Hasani, F. Azizi Jalilian, R. Ezati, Conventional PCR assisted single- May 2021).
component assembly of spherical nucleic acids for simple colorimetric detection [76] Zena Max SARS-CoV-2 Direct qPCR Detection Kit CE/IVD, Zena Max SARS-CoV-2
of SARS-CoV-2, Sensor. Actuator. B Chem. 328 (2021) 128971. Direct qPCR Detection Kit CE/IVD (emelcabio.Com), 2021. (Accessed 10 May
[42] R. Ben-Ami, A. Klochendler, M. Seidel, T. Sido, O. Gurel-Gurevich, M. Yassour, 2021).
E. Meshorer, G. Benedek, I. Fogel, E. Oiknine-Djian, A. Gertler, Z. Rotstein, [77] M. Veerapandian, R. Hunter, S. Neethirajan, Dual immunosensor based on
B. Lavi, Y. Dor, D.G. Wolf, M. Salton, Y. Drier, A. Eden, A. Klar, A. Geldman, methylene blue-electroadsorbed graphene oxide for rapid detection of the
A. Arbel, A. Peretz, B. Shalom, B.L. Ochana, D. Avrahami-Tzfati, D. Neiman, influenza A virus antigen, Talanta 155 (2016) 250–257.
D. Steinberg, D. Ben Zvi, E. Shpigel, G. Atlan, H. Klein, H. Chekroun, H. Shani, [78] J.C. Huang, Y.-F. Chang, K.-H. Chen, L.-C. Su, C.-W. Lee, C.-C. Chen, Y.-M.
I. Hazan, I. Ansari, I. Magenheim, J. Moss, J. Magenheim, L. Peretz, L. Feigin, A. Chen, C. Chou, Detection of severe acute respiratory syndrome (SARS)
M. Saraby, M. Sherman, M. Bentata, M. Avital, M. Kott, M. Peyser, M. Weitz, coronavirus nucleocapsid protein in human serum using a localized surface
M. Shacham, M. Grunewald, N. Sasson, N. Wallis, N. Azazmeh, N. Tzarum, plasmon coupled fluorescence fiber-optic biosensor, Biosens. Bioelectron. 25 (2)
O. Fridlich, R. Sher, R. Condiotti, R. Refaeli, R. Ben Ami, R. Zaken-Gallili, (2009) 320–332.
R. Helman, S. Ofek, S. Tzaban, S. Piyanzin, S. Anzi, S. Dagan, S. Lilenthal, T. Licht, [79] M. Alafeef, K. Dighe, P. Moitra, D. Pan, Rapid, ultrasensitive, and quantitative
T. Friehmann, Y. Kaufman, A. Pery, A. Saada, A. Dekel, A. Yeffet, A. Shaag, detection of SARS-CoV-2 using antisense oligonucleotides directed
A. Michael-Gayego, E. Shay, E. Arbib, H. Onallah, K. Ben-Meir, L. Levinzon, electrochemical biosensor chip, ACS Nano (2020).
L. Cohen-Daniel, L. Natan, M. Hamdan, M. Rivkin, M. Shwieki, O. Vorontsov, [80] G. Qiu, Z. Gai, Y. Tao, J. Schmitt, G.A. Kullak-Ublick, J. Wang, Dual-Functional
R. Barsuk, R. Abramovitch, R. Gutorov, S. Sirhan, S. Abdeen, Y. Yachnin, plasmonic photothermal biosensors for highly accurate severe acute respiratory
Y. Daitch, Large-scale implementation of pooled RNA extraction and RT-PCR for syndrome coronavirus 2 detection, ACS Nano 14 (5) (2020) 5268–5277.
SARS-CoV-2 detection, Clin. Microbiol. Infect. 26 (9) (2020) 1248–1253. [81] R.K. Saiki, D.H. Gelfand, S. Stoffel, S.J. Scharf, R. Higuchi, G.T. Horn, K.B. Mullis,
[43] K.K.-W. To, O.T.-Y. Tsang, C.C.-Y. Yip, K.-H. Chan, T.-C. Wu, J.M.-C. Chan, W.- H.A. Erlich, Primer-directed enzymatic amplification of DNA with a thermostable
S. Leung, T.S.-H. Chik, C.Y.-C. Choi, D.H. Kandamby, D.C. Lung, A.R. Tam, R.W.- DNA polymerase, Science 239 (4839) (1988) 487.
S. Poon, A.Y.-F. Fung, I.F.-N. Hung, V.C.-C. Cheng, J.F.-W. Chan, K.-Y. Yuen, [82] F. Diehl, M. Li, Y. He, K.W. Kinzler, B. Vogelstein, D. Dressman, BEAMing: single-
Consistent detection of 2019 novel coronavirus in saliva, Clin. Infect. Dis. 71 (15) molecule PCR on microparticles in water-in-oil emulsions, Nat. Methods 3 (7)
(2020) 841–843. (2006) 551–559.
[44] B. Dharavath, N. Yadav, S. Desai, R. Sunder, R. Mishra, M. Ketkar, P. Bhanshe, [83] B.J. Hindson, K.D. Ness, D.A. Masquelier, P. Belgrader, N.J. Heredia, A.
A. Gupta, A.K. Redhu, N. Patkar, S. Dutt, S. Gupta, A. Dutt, A one-step, one-tube J. Makarewicz, I.J. Bright, M.Y. Lucero, A.L. Hiddessen, T.C. Legler, T.K. Kitano,
real-time RT-PCR based assay with an automated analysis for detection of SARS- M.R. Hodel, J.F. Petersen, P.W. Wyatt, E.R. Steenblock, P.H. Shah, L.J. Bousse, C.
CoV-2, Heliyon 6 (7) (2020). B. Troup, J.C. Mellen, D.K. Wittmann, N.G. Erndt, T.H. Cauley, R.T. Koehler, A.
[45] V.M. Corman, O. Landt, M. Kaiser, R. Molenkamp, A. Meijer, D.K. Chu, P. So, S. Dube, K.A. Rose, L. Montesclaros, S. Wang, D.P. Stumbo, S.P. Hodges,
T. Bleicker, S. Brünink, J. Schneider, M.L. Schmidt, D.G. Mulders, B.L. Haagmans, S. Romine, F.P. Milanovich, H.E. White, J.F. Regan, G.A. Karlin-Neumann, C.
B. van der Veer, S. van den Brink, L. Wijsman, G. Goderski, J.-L. Romette, J. Ellis, M. Hindson, S. Saxonov, B.W. Colston, High-Throughput droplet digital PCR
M. Zambon, M. Peiris, H. Goossens, C. Reusken, M.P. Koopmans, C. Drosten, system for absolute quantitation of DNA copy number, Anal. Chem. 83 (22)
Detection of 2019 novel coronavirus (2019-nCoV) by real-time RT-PCR, Euro (2011) 8604–8610.
Surveill. 25 (3) (2020) 2000045.

28
O. Filchakova et al. Talanta 244 (2022) 123409

[84] S.C. Taylor, G. Laperriere, H. Germain, Droplet Digital PCR versus qPCR for gene [108] Z. Huang, D. Tian, Y. Liu, Z. Lin, C.J. Lyon, W. Lai, D. Fusco, A. Drouin, X. Yin,
expression analysis with low abundant targets: from variable nonsense to T. Hu, B. Ning, Ultra-sensitive and high-throughput CRISPR-p owered COVID-19
publication quality data, Sci. Rep. 7 (1) (2017) 2409. diagnosis, Biosens. Bioelectron. 164 (2020) 112316.
[85] Bio-Rad Gets First CE Mark on Clinical ddPCR Test, Genomeweb, 2017. [109] T. Hou, W. Zeng, M. Yang, W. Chen, L. Ren, J. Ai, J. Wu, Y. Liao, X. Gou, Y. Li,
https://www.genomeweb. X. Wang, H. Su, B. Gu, J. Wang, T. Xu, Development and evaluation of a rapid
com/pcr/bio-rad-gets-first-ce-mark-clinical-ddpcr-test#.YKV0Dy1c5ao. CRISPR-based diagnostic for COVID-19, PLoS Pathog. 16 (8) (2020), e1008705.
(Accessed 20 May 2021). [110] X. Ding, K. Yin, Z. Li, R.V. Lalla, E. Ballesteros, M.M. Sfeir, C. Liu, Ultrasensitive
[86] Scopus search results "ddpcr". https://www.scopus.com/results/results.uri?sid and visual detection of SARS-CoV-2 using all-in-one dual CRISPR-Cas12a assay,
=8cbe6657f19544371a3c33a5520f6618&src=s&sot=b&sdt=b&origin=search Nat. Commun. 11 (1) (2020) 4711.
basic&rr=&sl=20&s=TITLE-ABS-KEY(ddpcr)&searchterm1=ddpcr&searchTer [111] E. Xiong, L. Jiang, T. Tian, M. Hu, H. Yue, M. Huang, W. Lin, Y. Jiang, D. Zhu,
ms=&connectors=&field1=TITLE_ABS_KEY&fields=, 2021. (Accessed 28 August X. Zhou, Simultaneous dual-gene diagnosis of SARS-CoV-2 based on CRISPR/
2021). Cas9-Mediated lateral flow assay, Angew. Chem. Int. Ed. 60 (10) (2021)
[87] Scopus search results "rt-pcr". https://www.scopus.com/results/results.uri?sid 5307–5315.
=1d1d02248e8c617e2d5489a2fcf77127&src=s&sot=b&sdt=b&origin=search [112] J.P. Broughton, X. Deng, G. Yu, C.L. Fasching, V. Servellita, J. Singh, X. Miao, J.
basic&rr=&sl=21&s=TITLE-ABS-KEY(RT-PCR)&searchterm1=RT-PCR&searchT A. Streithorst, A. Granados, A. Sotomayor-Gonzalez, K. Zorn, A. Gopez, E. Hsu,
erms=&connectors=&field1=TITLE_ABS_KEY&fields=, 2021. (Accessed 28 W. Gu, S. Miller, C.-Y. Pan, H. Guevara, D.A. Wadford, J.S. Chen, C.Y. Chiu,
August 2021). CRISPR–Cas12-based detection of SARS-CoV-2, Nat. Biotechnol. 38 (7) (2020)
[88] J. Jiao, C. Duan, L. Xue, Y. Liu, W. Sun, Y. Xiang, DNA nanoscaffold-based SARS- 870–874.
CoV-2 detection for COVID-19 diagnosis, Biosens. Bioelectron. 167 (2020) [113] Y. Chen, Y. Shi, Y. Chen, Z. Yang, H. Wu, Z. Zhou, J. Li, J. Ping, L. He, H. Shen,
112479. Z. Chen, J. Wu, Y. Yu, Y. Zhang, H. Chen, Contamination-free visual detection of
[89] M. Alafeef, K. Dighe, P. Moitra, D. Pan, Rapid, ultrasensitive, and quantitative SARS-CoV-2 with CRISPR/Cas12a: a promising method in the point-of-care
detection of SARS-CoV-2 using antisense oligonucleotides directed detection, Biosens. Bioelectron. 169 (2020) 112642.
electrochemical biosensor chip, ACS Nano 14 (12) (2020) 17028–17045. [114] B. Pang, J. Xu, Y. Liu, H. Peng, W. Feng, Y. Cao, J. Wu, H. Xiao, K. Pabbaraju,
[90] T. Suo, X. Liu, J. Feng, M. Guo, W. Hu, D. Guo, H. Ullah, Y. Yang, Q. Zhang, G. Tipples, M.A. Joyce, H.A. Saffran, D.L. Tyrrell, H. Zhang, X.C. Le, Isothermal
X. Wang, M. Sajid, Z. Huang, L. Deng, T. Chen, F. Liu, K. Xu, Y. Liu, Q. Zhang, amplification and ambient visualization in a single tube for the detection of SARS-
Y. Liu, Y. Xiong, G. Chen, K. Lan, Y. Chen, ddPCR: a more accurate tool for SARS- CoV-2 using loop-mediated amplification and CRISPR technology, Anal. Chem. 92
CoV-2 detection in low viral load specimens, Emerg. Microb. Infect. 9 (1) (2020) (24) (2020) 16204–16212.
1259–1268. [115] D. Xiong, W. Dai, J. Gong, G. Li, N. Liu, W. Wu, J. Pan, C. Chen, Y. Jiao, H. Deng,
[91] S. Yu, S.B. Nimse, J. Kim, K.-S. Song, T. Kim, Development of a lateral flow strip J. Ye, X. Zhang, H. Huang, Q. Li, L. Xue, X. Zhang, G. Tang, Rapid detection of
membrane assay for rapid and sensitive detection of the SARS-CoV-2, Anal. SARS-CoV-2 with CRISPR-Cas12a, PLoS Biol. 18 (12) (2020), e3000978.
Chem. 92 (20) (2020) 14139–14144. [116] Z. Ali, R. Aman, A. Mahas, G.S. Rao, M. Tehseen, T. Marsic, R. Salunke, A.
[92] P. Moitra, M. Alafeef, K. Dighe, M.B. Frieman, D. Pan, Selective naked-eye K. Subudhi, S.M. Hala, S.M. Hamdan, A. Pain, F.S. Alofi, A. Alsomali, A.
detection of SARS-CoV-2 mediated by N gene targeted antisense oligonucleotide M. Hashem, A. Khogeer, N.A.M. Almontashiri, M. Abedalthagafi, N. Hassan, M.
capped plasmonic nanoparticles, ACS Nano 14 (6) (2020) 7617–7627. M. Mahfouz, iSCAN: an RT-LAMP-coupled CRISPR-Cas12 module for rapid,
[93] V.G. Barauna, M.N. Singh, L.L. Barbosa, W.D. Marcarini, P.F. Vassallo, J.G. Mill, sensitive detection of SARS-CoV-2, Virus Res. 288 (2020) 198129.
R. Ribeiro-Rodrigues, L.C.G. Campos, P.H. Warnke, F.L. Martin, Ultrarapid on-site [117] M. Patchsung, K. Jantarug, A. Pattama, K. Aphicho, S. Suraritdechachai,
detection of SARS-CoV-2 infection using simple ATR-FTIR spectroscopy and an P. Meesawat, K. Sappakhaw, N. Leelahakorn, T. Ruenkam, T. Wongsatit,
analysis algorithm: high sensitivity and specificity, Anal. Chem. 93 (5) (2021) N. Athipanyasilp, B. Eiamthong, B. Lakkanasirorat, T. Phoodokmai,
2950–2958. N. Niljianskul, D. Pakotiprapha, S. Chanarat, A. Homchan, R. Tinikul,
[94] S. Han, O. Ko, G. Lee, S.-W. Jeong, Y.J. Choi, J.B. Lee, Rapid diagnosis of P. Kamutira, K. Phiwkaow, S. Soithongcharoen, C. Kantiwiriyawanitch,
coronavirus by RNA-directed RNA transcription using an engineered RNA-based V. Pongsupasa, D. Trisrivirat, J. Jaroensuk, T. Wongnate, S. Maenpuen,
platform, Nano Lett. 21 (1) (2021) 462–468. P. Chaiyen, S. Kamnerdnakta, J. Swangsri, S. Chuthapisith, Y. Sirivatanauksorn,
[95] M. Wang, A. Fu, B. Hu, Y. Tong, R. Liu, Z. Liu, J. Gu, B. Xiang, J. Liu, W. Jiang, C. Chaimayo, R. Sutthent, W. Kantakamalakul, J. Joung, A. Ladha, X. Jin, J.
G. Shen, W. Zhao, D. Men, Z. Deng, L. Yu, W. Wei, Y. Li, T. Liu, Nanopore targeted S. Gootenberg, O.O. Abudayyeh, F. Zhang, N. Horthongkham, C. Uttamapinant,
sequencing for the accurate and comprehensive detection of SARS-CoV-2 and Clinical validation of a Cas13-based assay for the detection of SARS-CoV-2 RNA,
other respiratory viruses, Small 16 (32) (2020) 2002169. Nat. Biomed. Eng. 4 (12) (2020) 1140–1149.
[96] T. Ishige, S. Murata, T. Taniguchi, A. Miyabe, K. Kitamura, K. Kawasaki, [118] T. Notomi, H. Okayama, H. Masubuchi, T. Yonekawa, K. Watanabe, N. Amino,
M. Nishimura, H. Igari, K. Matsushita, Highly sensitive detection of SARS-CoV-2 T. Hase, Loop-mediated isothermal amplification of DNA, Nucleic Acids Res. 28
RNA by multiplex rRT-PCR for molecular diagnosis of COVID-19 by clinical (12) (2000) e63-e63.
laboratories, Clin. Chim. Acta 507 (2020) 139–142. [119] W.E. Huang, B. Lim, C.-C. Hsu, D. Xiong, W. Wu, Y. Yu, H. Jia, Y. Wang, Y. Zeng,
[97] H.-Y. Chung, M. Jian Jr., C.-K. Chang, J.-C. Lin, K.-M. Yeh, C.-W. Chen, S.-K. Chiu, M. Ji, H. Chang, X. Zhang, H. Wang, Z. Cui, RT-LAMP for rapid diagnosis of
Y.-H. Wang, S.-J. Liao, S.-Y. Li, S.-S. Hsieh, S.-H. Tsai, C.-L. Perng, J.-R. Yang, M.- coronavirus SARS-CoV-2, Microb. Biotechnol. 13 (4) (2020) 950–961.
T. Liu, F.-Y. Chang, H.-S. Shang, Novel dual multiplex real-time RT-PCR assays for [120] J. Rodriguez-Manzano, K. Malpartida-Cardenas, N. Moser, I. Pennisi, M. Cavuto,
the rapid detection of SARS-CoV-2, influenza A/B, and respiratory syncytial virus L. Miglietta, A. Moniri, R. Penn, G. Satta, P. Randell, F. Davies, F. Bolt, W. Barclay,
using the BD MAX open system, Emerg. Microb. Infect. 10 (1) (2021) 161–166. A. Holmes, P. Georgiou, Handheld point-of-care system for rapid detection of
[98] Zena Max SARS-CoV-2 direct qPCR detection kit CE/IVD. https://emelcabio. SARS-CoV-2 extracted RNA in under 20 min, ACS Cent. Sci. 7 (2) (2021) 307–317.
com/zena-max-sars-cov-2-direct-qpcr-detection-kit-ce-ivd-kd145906d-100.html, [121] V.L. Dao Thi, K. Herbst, K. Boerner, M. Meurer, L.P.M. Kremer, D. Kirrmaier,
2021. (Accessed 10 May 2021). A. Freistaedter, D. Papagiannidis, C. Galmozzi, M.L. Stanifer, S. Boulant, S. Klein,
[99] R. Jansen, J.D.A.v. Embden, W. Gaastra, L.M. Schouls, Identification of genes that P. Chlanda, D. Khalid, I. Barreto Miranda, P. Schnitzler, H.-G. Kräusslich,
are associated with DNA repeats in prokaryotes, Mol. Microbiol. 43 (6) (2002) M. Knop, S. Anders, A colorimetric RT-LAMP assay and LAMP-sequencing for
1565–1575. detecting SARS-CoV-2 RNA in clinical samples, Sci. Transl. Med. 12 (556) (2020),
[100] Y. Ishino, H. Shinagawa, K. Makino, M. Amemura, A. Nakata, Nucleotide eabc7075.
sequence of the iap gene, responsible for alkaline phosphatase isozyme [122] H. Kitajima, Y. Tamura, H. Yoshida, H. Kinoshita, H. Katsuta, C. Matsui,
conversion in Escherichia coli, and identification of the gene product, J. Bacteriol. A. Matsushita, T. Arai, S. Hashimoto, A. Iuchi, T. Hirashima, H. Morishita,
169 (12) (1987) 5429–5433. H. Matsuoka, T. Tanaka, T. Nagai, Clinical COVID-19 diagnostic methods:
[101] K.S. Makarova, N.V. Grishin, S.A. Shabalina, Y.I. Wolf, E.V. Koonin, A putative comparison of reverse transcription loop-mediated isothermal amplification (RT-
RNA-interference-based immune system in prokaryotes: computational analysis LAMP) and quantitative RT-PCR (qRT-PCR), J. Clin. Virol. 139 (2021) 104813.
of the predicted enzymatic machinery, functional analogies with eukaryotic [123] A.A. El Wahed, P. Patel, M. Maier, C. Pietsch, D. Rüster, S. Böhlken-Fascher,
RNAi, and hypothetical mechanisms of action, Biol. Direct 1 (1) (2006) 7. J. Kissenkötter, O. Behrmann, M. Frimpong, M.M. Diagne, M. Faye, N. Dia, M.
[102] J.R. Strich, D.S. Chertow, C.S. Kraft, CRISPR-cas biology and its application to A. Shalaby, H. Amer, M. Elgamal, A. Zaki, G. Ismail, M. Kaiser, V.M. Corman,
infectious diseases, J. Clin. Microbiol. 57 (4) (2019) e01307–e01318. M. Niedrig, O. Landt, O. Faye, A.A. Sall, F.T. Hufert, U. Truyen, U.G. Liebert,
[103] E.V. Koonin, K.S. Makarova, F. Zhang, Diversity, classification and evolution of M. Weidmann, Suitcase lab for rapid detection of SARS-CoV-2 based on
CRISPR-Cas systems, Curr. Opin. Microbiol. 37 (2017) 67–78. recombinase polymerase amplification assay, Anal. Chem. 93 (4) (2021)
[104] K.S. Makarova, Y.I. Wolf, O.S. Alkhnbashi, F. Costa, S.A. Shah, S.J. Saunders, 2627–2634.
R. Barrangou, S.J.J. Brouns, E. Charpentier, D.H. Haft, P. Horvath, S. Moineau, F. [124] K. Nawattanapaiboon, E. Pasomsub, P. Prombun, A. Wongbunmak,
J.M. Mojica, R.M. Terns, M.P. Terns, M.F. White, A.F. Yakunin, R.A. Garrett, A. Jenjitwanich, P. Mahasupachai, P. Vetcho, C. Chayrach, N. Manatjaroenlap,
J. van der Oost, R. Backofen, E.V. Koonin, An updated evolutionary classification C. Samphaongern, T. Watthanachockchai, P. Leedorkmai, S. Manopwisedjaroen,
of CRISPR–Cas systems, Nat. Rev. Microbiol. 13 (11) (2015) 722–736. R. Akkarawongsapat, A. Thitithanyanont, M. Phanchana, W. Panbangred,
[105] S. Shmakov, A. Smargon, D. Scott, D. Cox, N. Pyzocha, W. Yan, O.O. Abudayyeh, S. Chauvatcharin, T. Srikhirin, Colorimetric reverse transcription loop-mediated
J.S. Gootenberg, K.S. Makarova, Y.I. Wolf, K. Severinov, F. Zhang, E.V. Koonin, isothermal amplification (RT-LAMP) as a visual diagnostic platform for the
Diversity and evolution of class 2 CRISPR–Cas systems, Nat. Rev. Microbiol. 15 detection of the emerging coronavirus SARS-CoV-2, Analyst 146 (2) (2021)
(3) (2017) 169–182. 471–477.
[106] D.G. Sashital, Pathogen detection in the CRISPR–Cas era, Genome Med. 10 (1) [125] K. Zwirglmaier, M. Weyh, C. Krüger, R. Ehmann, K. Müller, R. Wölfel, K. Stoecker,
(2018) 32. Rapid detection of SARS-CoV-2 by pulse-controlled amplification (PCA), J. Virol
[107] R. Aman, A. Mahas, M. Mahfouz, Nucleic acid detection using CRISPR/Cas Methods 290 (2021) 114083.
biosensing technologies, ACS Synth. Biol. 9 (6) (2020) 1226–1233.

29
O. Filchakova et al. Talanta 244 (2022) 123409

[126] J.Y.H. Lee, N. Best, J. McAuley, J.L. Porter, T. Seemann, M.B. Schultz, M. Sait, [145] G.A. Perchetti, M.-L. Huang, M.G. Mills, K.R. Jerome, A.L. Greninger, Analytical
N. Orlando, K. Mercoulia, S.A. Ballard, J. Druce, T. Tran, M.G. Catton, M.J. Pryor, sensitivity of the Abbott BinaxNOW COVID-19 Ag card, J. Clin. Microbiol. 59 (3)
H.L. Cui, A. Luttick, S. McDonald, A. Greenhalgh, J.C. Kwong, N.L. Sherry, (2021) e02880-20.
M. Graham, T. Hoang, M. Herisse, S.J. Pidot, D.A. Williamson, B.P. Howden, I. [146] B.D. Grant, C.E. Anderson, J.R. Williford, L.F. Alonzo, V.A. Glukhova, D.S. Boyle,
R. Monk, T.P. Stinear, Validation of a single-step, single-tube reverse transcription B.H. Weigl, K.P. Nichols, SARS-CoV-2 coronavirus nucleocapsid antigen-detecting
loop-mediated isothermal amplification assay for rapid detection of SARS-CoV-2 half-strip lateral flow assay toward the development of point of care tests using
RNA, J. Med. Microbiol. 69 (9) (2020) 1169–1178. commercially available reagents, Anal. Chem. 92 (16) (2020) 11305–11309.
[127] E. González-González, I.M. Lara-Mayorga, I.P. Rodríguez-Sánchez, Y.S. Zhang, S. [147] B.D. Ventura, M. Cennamo, A. Minopoli, R. Campanile, S.B. Censi, D. Terracciano,
O. Martínez-Chapa, G.T.-d. Santiago, M.M. Alvarez, Colorimetric loop-mediated G. Portella, R. Velotta, Colorimetric test for fast detection of SARS-CoV-2 in nasal
isothermal amplification (LAMP) for cost-effective and quantitative detection of and throat swabs, ACS Sens. 5 (10) (2020) 3043–3048.
SARS-CoV-2: the change in color in LAMP-based assays quantitatively correlates [148] L.H. Cazares, R. Chaerkady, S.H. Samuel Weng, C.C. Boo, R. Cimbro, H.-E. Hsu,
with viral copy number, Anal. Methods 13 (2) (2021) 169–178. S. Rajan, W. Dall’Acqua, L. Clarke, K. Ren, P. McTamney, N. Kallewaard-LeLay,
[128] X. Zhu, X. Wang, L. Han, T. Chen, L. Wang, H. Li, S. Li, L. He, X. Fu, S. Chen, M. Ghaedi, Y. Ikeda, S. Hess, Development of a parallel reaction monitoring mass
M. Xing, H. Chen, Y. Wang, Multiplex reverse transcription loop-mediated spectrometry assay for the detection of SARS-CoV-2 spike glycoprotein and
isothermal amplification combined with nanoparticle-based lateral flow biosensor nucleoprotein, Anal. Chem. 92 (20) (2020) 13813–13821.
for the diagnosis of COVID-19, Biosens. Bioelectron. 166 (2020) 112437. [149] K. Bezstarosti, M.M. Lamers, J.J.A. van Kampen, B.L. Haagmans, J.A.A. Demmers,
[129] C. Yan, J. Cui, L. Huang, B. Du, L. Chen, G. Xue, S. Li, W. Zhang, L. Zhao, Y. Sun, Targeted proteomics as a tool to detect SARS-CoV-2 proteins in clinical
H. Yao, N. Li, H. Zhao, Y. Feng, S. Liu, Q. Zhang, D. Liu, J. Yuan, Rapid and visual specimens, bioRxiv (2020), 2020.04.23.057810.
detection of 2019 novel coronavirus (SARS-CoV-2) by a reverse transcription [150] H. Yousefi, A. Mahmud, D. Chang, J. Das, S. Gomis, J.B. Chen, H. Wang, T. Been,
loop-mediated isothermal amplification assay, Clin. Microbiol. Infect. 26 (6) L. Yip, E. Coomes, Z. Li, S. Mubareka, A. McGeer, N. Christie, S. Gray-Owen,
(2020) 773–779. A. Cochrane, J.M. Rini, E.H. Sargent, S.O. Kelley, Detection of SARS-CoV-2 viral
[130] R. Lu, X. Wu, Z. Wan, Y. Li, X. Jin, C. Zhang, A novel reverse transcription loop- particles using direct, reagent-free electrochemical sensing, J. Am. Chem. Soc.
mediated isothermal amplification method for rapid detection of SARS-CoV-2, 143 (4) (2021) 1722–1727.
Int. J. Mol. Sci. 21 (8) (2020) 2826. [151] Q. Bayin, L. Huang, C. Ren, Y. Fu, X. Ma, J. Guo, Anti-SARS-CoV-2 IgG and IgM
[131] A. Ganguli, A. Mostafa, J. Berger, M.Y. Aydin, F. Sun, S.A.S.d. Ramirez, E. Valera, detection with a GMR based LFIA system, Talanta 227 (2021) 122207.
B.T. Cunningham, W.P. King, R. Bashir, Rapid isothermal amplification and [152] R.A. Bray, J.H. Lee, P. Brescia, D. Kumar, T. Nong, R. Shih, E.S. Woodle, J.
portable detection system for SARS-CoV-2, Proc. Natl. Acad. Sci. Unit. States Am. S. Maltzman, H.M. Gebel, Development and validation of a multiplex, bead-based
117 (37) (2020) 22727. assay to detect antibodies directed against SARS-CoV-2 proteins, Transplantation
[132] M.A. Lalli, J.S. Langmade, X. Chen, C.C. Fronick, C.S. Sawyer, L.C. Burcea, M. 105 (1) (2021) 79–89.
N. Wilkinson, R.S. Fulton, M. Heinz, W.J. Buchser, R.D. Head, R.D. Mitra, [153] N.C. Cady, N. Tokranova, A. Minor, N. Nikvand, K. Strle, W.T. Lee, W. Page,
J. Milbrandt, Rapid and extraction-free detection of SARS-CoV-2 from saliva by E. Guignon, A. Pilar, G.N. Gibson, Multiplexed detection and quantification of
colorimetric reverse-transcription loop-mediated isothermal amplification, Clin. human antibody response to COVID-19 infection using a plasmon enhanced
Chem. 67 (2) (2021) 415–424. biosensor platform, Biosens. Bioelectron. 171 (2021) 112679.
[133] S. Wei, E. Kohl, A. Djandji, S. Morgan, S. Whittier, M. Mansukhani, E. Hod, [154] K. Muller, P. Girl, H. von Buttlar, G. Dobler, R. Wolfel, Comparison of two
M. D’Alton, Y. Suh, Z. Williams, Direct diagnostic testing of SARS-CoV-2 without commercial surrogate ELISAs to detect a neutralising antibody response to SARS-
the need for prior RNA extraction, Sci. Rep. 11 (1) (2021) 2402. CoV-2, J. Virol. Methods 292 (2021) 114122.
[134] S. Klein, T.G. Müller, D. Khalid, V. Sonntag-Buck, A.-M. Heuser, B. Glass, [155] L. Guimaraes, Antibody tests: they are more important than we thought, J. Math.
M. Meurer, I. Morales, A. Schillak, A. Freistaedter, I. Ambiel, S.L. Winter, Econ. 93 (2021) 102485.
L. Zimmermann, T. Naumoska, F. Bubeck, D. Kirrmaier, S. Ullrich, I. Barreto [156] New evidence points to antibodies as A reliable indicator of vaccine protection.
Miranda, S. Anders, D. Grimm, P. Schnitzler, M. Knop, H.-G. Kräusslich, V.L. Dao https://www.npr.org/sections/health-shots/2021/08/23/1029827996/new-evid
Thi, K. Börner, P. Chlanda, SARS-CoV-2 RNA extraction using magnetic beads for ence-points-to-antibodies-as-a-reliable-indicator-of-vaccine-protection. (Accessed
rapid large-scale testing by RT-qPCR and RT-LAMP, Viruses 12 (8) (2020). 25 January 2022).
[135] Y.L. Lau, I. Ismail, N.I. Mustapa, M.Y. Lai, T.S. Tuan Soh, A. Hassan, K. [157] P.R. Krause, T.R. Fleming, R. Peto, I.M. Longini, J.P. Figueroa, J.A.C. Sterne,
M. Peariasamy, Y.L. Lee, Y.M. Chong, I.C. Sam, P.P. Goh, Real-time reverse A. Cravioto, H. Rees, J.P.T. Higgins, I. Boutron, H. Pan, M.F. Gruber, N. Arora,
transcription loop-mediated isothermal amplification for rapid detection of SARS- F. Kazi, R. Gaspar, S. Swaminathan, M.J. Ryan, A.M. Henao-Restrepo,
CoV-2, PeerJ 8 (2020) e9278. Considerations in boosting COVID-19 vaccine immune responses, Lancet 398
[136] O. Behrmann, I. Bachmann, M. Spiegel, M. Schramm, A. Abd El Wahed, G. Dobler, (10308) (2021) 1377–1380.
G. Dame, F.T. Hufert, Rapid detection of SARS-CoV-2 by low volume real-time [158] J. James, S. Rhodes, C.S. Ross, P. Skinner, S.P. Smith, R. Shipley, C.J. Warren,
single tube reverse transcription recombinase polymerase amplification using an H. Goharriz, L.M. McElhinney, N. Temperton, E. Wright, A.R. Fooks, T.W. Clark,
exo probe with an internally linked quencher (Exo-IQ), Clin. Chem. 66 (8) (2020) S.M. Brookes, I.H. Brown, A.C. Banyard, Comparison of serological assays for the
1047–1054. detection of SARS-CoV-2 antibodies, Viruses 13 (4) (2021).
[137] Y.L. Lau, I.b. Ismail, N.I.b. Mustapa, M.Y. Lai, T.S. Tuan Soh, A. Haji Hassan, K. [159] J.V. Dzimianski, N. Lorig-Roach, S.M. O’Rourke, D.L. Alexander, J.M. Kimmey, R.
M. Peariasamy, Y.L. Lee, M.K.B. Abdul Kahar, J. Chong, P.P. Goh, Development of M. DuBois, Rapid and sensitive detection of SARS-CoV-2 antibodies by biolayer
a reverse transcription recombinase polymerase amplification assay for rapid and interferometry, Sci. Rep. 10 (1) (2020) 21738.
direct visual detection of Severe Acute Respiratory Syndrome Coronavirus 2 [160] B. Zhao, C. Che, W. Wang, N. Li, B.T. Cunningham, Single-step, wash-free digital
(SARS-CoV-2), PLoS One 16 (1) (2021), e0245164. immunoassay for rapid quantitative analysis of serological antibody against
[138] J. Qian, S.A. Boswell, C. Chidley, Z.-x. Lu, M.E. Pettit, B.L. Gaudio, J. SARS-CoV-2 by photonic resonator absorption microscopy, Talanta 225 (2021)
M. Fajnzylber, R.T. Ingram, R.H. Ward, J.Z. Li, M. Springer, An enhanced 122004.
isothermal amplification assay for viral detection, Nat. Commun. 11 (1) (2020) [161] https://www.who.int/emergencies/diseases/novel-coronavirus-2019/media-reso
5920. urces/science-in-5/episode-50—do-i-still-need-the-vaccine-if-i-have-covid-19.
[139] J. Wang, K. Cai, X. He, X. Shen, J. Liu, J. Xu, F. Qiu, W. Lei, L. Cui, Y. Ge, T. Wu, [162] R. West, A. Kobokovich, N. Connell, G.K. Gronvall, COVID-19 antibody tests: a
Y. Zhang, H. Yan, Y. Chen, J. Yu, X. Ma, H. Shi, R. Zhang, X. Li, Y. Gao, P. Niu, valuable public health tool with limited relevance to individuals, Trends
W. Tan, G. Wu, Y. Jiang, W. Xu, Multiple-centre clinical evaluation of an ultrafast Microbiol. 29 (3) (2021) 214–223.
single-tube assay for SARS-CoV-2 RNA, Clin. Microbiol. Infect. 26 (8) (2020) [163] J. Favresse, J. Brauner, N. Bodart, A. Vigneron, S. Roisin, S. Melchionda,
1076–1081. J. Douxfils, A. Ocmant, An original multiplex method to assess five different
[140] Y.-Z. Zheng, J.-T. Chen, J. Li, X.-J. Wu, J.-Z. Wen, X.-Z. Liu, L.-Y. Lin, X.-Y. Liang, SARS-CoV-2 antibodies, Clin. Chem. Lab. Med. 59 (5) (2021) 971–978.
H.-Y. Huang, G.-C. Zha, P.-K. Yang, L.-J. Li, T.-Y. Zhong, L. Liu, W.-J. Cheng, X.- [164] M.A. MacMullan, A. Ibrayeva, K. Trettner, L. Deming, S. Das, F. Tran, J.
N. Song, M. Lin, Reverse transcription recombinase-aided amplification assay R. Moreno, J.G. Casian, P. Chellamuthu, J. Kraft, K. Kozak, F.E. Turner, V.
with lateral flow dipstick assay for rapid detection of 2019 novel coronavirus, I. Slepnev, L.M. Le Page, ELISA detection of SARS-CoV-2 antibodies in saliva, Sci.
Front. Cell. Infect. Microbiol. 11 (24) (2021). Rep. 10 (1) (2020) 20818.
[141] S. Eissa, M. Zourob, Development of a low-cost cotton-tipped electrochemical [165] N.C. Cady, N. Tokranova, A. Minor, N. Nikvand, K. Strle, W.T. Lee, W. Page,
immunosensor for the detection of SARS-CoV-2, Anal. Chem. 93 (3) (2021) E. Guignon, A. Pilar, G.N. Gibson, Multiplexed detection and quantification of
1826–1833. human antibody response to COVID-19 infection using a plasmon enhanced
[142] G. Seo, G. Lee, M.J. Kim, S.-H. Baek, M. Choi, K.B. Ku, C.-S. Lee, S. Jun, D. Park, biosensor platform, Biosens. Bioelectron. 171 (2021) 112679.
H.G. Kim, S.-J. Kim, J.-O. Lee, B.T. Kim, E.C. Park, S.I. Kim, Rapid detection of [166] A.M. Cook, S.E. Faustini, L.J. Williams, A.F. Cunningham, M.T. Drayson, A.
COVID-19 causative virus (SARS-CoV-2) in human nasopharyngeal swab M. Shields, D. Kay, L. Taylor, T. Plant, A. Huissoon, G. Wallis, S. Beck, S.E. Jossi,
specimens using field-effect transistor-based biosensor, ACS Nano 14 (4) (2020) M. Perez-Toledo, M.L. Newby, J.D. Allen, M. Crispin, S. Harding, A.G. Richter,
5135–5142. Validation of a combined ELISA to detect IgG, IgA and IgM antibody responses to
[143] S. Mavrikou, G. Moschopoulou, V. Tsekouras, S. Kintzios, Development of a SARS-CoV-2 in mild or moderate non-hospitalised patients, J. Immunol. Methods
portable, ultra-rapid and ultra-sensitive cell-based biosensor for the direct 494 (2021) 113046.
detection of the SARS-CoV-2 S1 spike protein antigen, Sensors 20 (11) (2020). [167] P. Ainhoa Gutiérrez Cobos Md, F. Sara Gómez de, D. Diego Domingo García
[144] Q. Lin, D. Wen, J. Wu, L. Liu, W. Wu, X. Fang, J. Kong, Microfluidic Pharm, L. Eva Navarro, C. Ayla Yarci, G.R. Leticia Fontán, T. Arturo Manuel
immunoassays for sensitive and simultaneous detection of IgG/IgM/antigen of Fraile, D. Laura Cardeñoso Domingo Pharm, Evaluation of Diagnostic Accuracy of
SARS-CoV-2 within 15 min, Anal. Chem. 92 (14) (2020) 9454–9458. 10 Serological Assays for Detection of SARS-CoV-2 Antibodies, Research Square,
2021.

30
O. Filchakova et al. Talanta 244 (2022) 123409

[168] D. Parai, G.C. Dash, H.R. Choudhary, A. Peter, U.K. Rout, R.R. Nanda, J.S. Kshatri, [190] A. Krüttgen, C.G. Cornelissen, M. Dreher, M.W. Hornef, M. Imöhl, M. Kleines,
S. Kanungo, S. Pati, D. Bhattacharya, Diagnostic accuracy comparison of three Determination of SARS-CoV-2 antibodies with assays from diasorin, Roche and
fully automated chemiluminescent immunoassay platforms for the detection of IDvet, J. Virol Methods 287 (2021) 113978.
SARS-CoV-2 antibodies, J. Virol Methods 292 (2021) 114121. [191] H. Flinck, A. Rauhio, B. Luukinen, T. Lehtimäki, A.-M. Haapala, T. Seiskari,
[169] D. Villalta, P. Martelli, A. Moratto, V. Salgarolo, E. Ligato, M. Conte, J. Aittoniemi, Comparison of 2 fully automated tests detecting antibodies against
R. Giacomello, T. Pellis, R. De Rosa, S. Venturini, M. Crapis, Diagnostic nucleocapsid N and spike S1/S2 proteins in COVID-19, Diagn. Microbiol. Infect.
performance of an automated chemiluminescence immunoassay for SARS-CoV-2 Dis. 99 (1) (2021) 115197.
IgG and IgM antibodies detection: a real life experience, Pract. Lab. Med. 25 [192] Anti-SARS-CoV-2 ELISA (IgG), Instruction for Use, US FDA, 2021.
(2021), e00227. [193] WANTAI SARS-CoV-2 Ab ELISA, US FDA, 2020.
[170] R. Kulkarni, H.P. Patil, S. Palkar, S. Lalwani, A.C. Mishra, V. Arankalle, Anti- [194] COVID-19 ELISA IgG Antibody Test (Mount Sinai Laboratories), US FDA, 2020.
SARS-CoV-2 IgG antibody response among Indian COVID-19 patients using [195] OmniPATHTM COVID-19 Total Antibody ELISA Test, US FDA, 2020.
β-propiolactone-inactivated, whole virus-based indirect ELISA, J. Virol Methods [196] ZEUS ELISA SARS-CoV-2 IgG Test System, US FDA, 2020.
287 (2021) 113996. [197] COVID-19 ELISA Pan-Ig Antibody Test (University of Arizona Genetics Core for
[171] K.Y.L. Chua, S. Vogrin, I. Bittar, J.H. Horvath, H. Wimaleswaran, J.A. Trubiano, Clinical Services), US FDA, 2020.
N.E. Holmes, Q. Lam, Clinical evaluation of four commercial immunoassays for [198] Platelia SARS-CoV-2 Total Ab, US FDA, 2020.
the detection of antibodies against established SARS-CoV-2 infection, Pathology [199] USA, EDI™ Novel Coronavirus COVID-19 IgM ELISA Kit, 2020, https://static1.
52 (7) (2020) 778–782. squarespace.com/static/52545951e4b021818110f9cf/t/5fca9ffd453475472aaf6
[172] J. Swadźba, M. Bednarczyk, T. Anyszek, D. Kozlowska, A. Panek, E. Martin, The e6d/1607114754760/KT-1033+IVD%2C+CE+V10.pdf. (Accessed 17 March
real life performance of 7 automated anti-SARS-CoV-2 IgG and IgM/IgA 2022).
immunoassays, Pract. Lab. Med. 25 (2021), e00212. [200] EDI™ Novel Coronavirus COVID-19 IgG ELISA Kit, Epitope Diagnostics, Inc.,
[173] K. Müller, P. Girl, H. von Buttlar, G. Dobler, R. Wölfel, Comparison of two USA, 2020. https://eaglebio.com/wp-content/uploads/2021/01/KT-1032-IFU-R
commercial surrogate ELISAs to detect a neutralising antibody response to SARS- UO-V7.pdf.
CoV-2, J. Virol Methods 292 (2021) 114122. [201] recomWell SARS-CoV-2 IgG recomWell SARS-CoV-2 IgA, Mikrogen Diagnostics,
[174] J.-G. Park, F.S. Oladunni, K. Chiem, C. Ye, M. Pipenbrink, T. Moran, M.R. Walter, 2020. Germany, https://www.bmgrp.eu/fileadmin/user_upload/images/news
J. Kobie, L. Martinez-Sobrido, Rapid in vitro assays for screening neutralizing /COVID_19/4_Antibody_tests_for_SARS_CoV-2/Mikrogen_recomWell_SARS-Co
antibodies and antivirals against SARS-CoV-2, J. Virol Methods 287 (2021) V2-2_IgG_IgA.pdf. (Accessed 17 March 2022).
113995. [205] Nirmidas COVID-19 (SARS-CoV-2, Nirmidas Biotech, USA, 2020, https://www.
[175] Rainbow Makers, 2003. https://www.chemistryworld.com/news/rainbow-maker fda.gov/media/142558/download. (Accessed 17 March 2022).
s/3003381.article. (Accessed 20 June 2021). Accessed. [210] Innovita 2019-nCoV Ab Test (Colloidal Gold), 2020. China, https://www.fda.
[176] B. O’Farrell, Evolution in lateral flow–based immunoassay systems, in: R. Wong, gov/media/144071/download. (Accessed 17 March 2022).
H. Tse (Eds.), Lateral Flow Immunoassay, Humana Press, Totowa, NJ, 2009, [212] The Diagnostic Kit for IgM/IgG Antibody to Coronavirus (SARS-CoV-2) (Lateral
pp. 1–33. Flow), Livzon, China, 2020. https://www.drogy-info.cz/data/obj_files/33224/1
[177] P. Kennedy, Who made that home pregnancy test?. https://www.nytimes.com 013/11_rapid-test-covid-manual.pdf.
/2012/07/29/magazine/who-made-that-home-pregnancy-test.html, 2012. [213] March 17, 2022)https://www.aidian.eu/uploads/SE-Produktmaterial/ES-Produ
(Accessed 20 June 2021). cts/ACRO/8119_02EN_Acro_2019-nCoV_IgG_IgM_Rapid_Test_Brief_Instruction
[178] W. Liu, L. Liu, G. Kou, Y. Zheng, Y. Ding, W. Ni, Q. Wang, L. Tan, W. Wu, S. Tang, s_web.pdf, AIDIAN 2019-nCoV IgG/IgM Rapid Test Cassette, Finland, 2020.
Z. Xiong, S. Zheng, Evaluation of nucleocapsid and spike protein-based enzyme- (Accessed 17 March 2022).
linked immunosorbent assays for detecting antibodies against SARS-CoV-2, [214] Wondfo SARS-CoV-2 Antibody Test, 2020. China, https://static.poder360.com.
J. Clin. Microbiol. 58 (6) (2020) e00461-20. br/2020/04/SARS-CoV-2-Antibody-Test-WondFo.pdf. (Accessed 17 March 2022).
[179] N. Kohmer, S. Westhaus, C. Rühl, S. Ciesek, H.F. Rabenau, Clinical performance of [215] OnSite™ COVID-19 IgG/IgM Rapid Test, CTK Biotech, USA, 2020. http://simoco.
SARS-CoV-2 IgG antibody tests and potential protective immunity, bioRxiv dk/files/pdf/onsite%E2%84%A2%20covid-19%20iggigm%20rapid%20test%20p
(2020) 2020, 05.08.085506. i-r0180c%20rev%20a.pdf. (Accessed 17 March 2022).
[180] W. Zhang, R.-H. Du, B. Li, X.-S. Zheng, X.-L. Yang, B. Hu, Y.-Y. Wang, G.-F. Xiao, [216] Virusee COV1D-19 IgG/IgM Lateral Flow, Genobio, China, 2020. http://www.
B. Yan, Z.-L. Shi, P. Zhou, Molecular and serological investigation of 2019-nCoV arrowdiagnostics.it/download/microbiologia/infezionirespiratorie/BROCHU
infected patients: implication of multiple shedding routes, Emerg. Microb. Infect. RE_Virusee%20COVID-19%20IgM%20IgG%20LFA%2003042020.pdf. (Accessed
9 (1) (2020) 386–389. 17 March 2022).
[181] N. Taleghani, F. Taghipour, Diagnosis of COVID-19 for controlling the pandemic: [217] UNscience COVID-19 IgG/IgM Rapid Test Kit, 2020. China, https://www.stratech
a review of the state-of-the-art, Biosens. Bioelectron. 174 (2021) 112830. .co.uk/wp-content/uploads/2020/03/Manual_UNCOV-40_IVD.pdf. (Accessed 17
[182] K.G. Beavis, S.M. Matushek, A.P.F. Abeleda, C. Bethel, C. Hunt, S. Gillen, March 2022).
A. Moran, V. Tesic, Evaluation of the EUROIMMUN anti-SARS-CoV-2 ELISA assay [218] BioMedomics COVID-19 IgM-IgG Combined Antibody Rapid Test, 2020. USA,
for detection of IgA and IgG antibodies, J. Clin. Virol. 129 (2020) 104468. https://www.accessdata.fda.gov/cdrh_docs/presentations/maf/maf3328-a001.
[183] S. Cavalera, B. Colitti, S. Rosati, G. Ferrara, L. Bertolotti, C. Nogarol, C. Guiotto, pdf. (Accessed 17 March 2022).
C. Cagnazzo, M. Denina, F. Fagioli, F. Di Nardo, M. Chiarello, C. Baggiani, [220] Hightop SARS-CoV-2 IgM/IgG Antibody Rapid Test, 2020. China, https://www.
L. Anfossi, A multi-target lateral flow immunoassay enabling the specific and medica.de/vis-content/event-medcom2020.MEDICA/exh-medcom2020.267784
sensitive detection of total antibodies to SARS COV-2, Talanta 223 (2021) 5/MEDICA-2020-QINGDAO-HIGHTOP-BIOTECH-CO.-LTD-Product-medcom202
121737. 0.2677845-2ChZsL7lSdS2p9Huhl7udg.pdf. (Accessed 17 March 2022). accessed
[184] A. Roda, S. Cavalera, F. Di Nardo, D. Calabria, S. Rosati, P. Simoni, B. Colitti, on.
C. Baggiani, M. Roda, L. Anfossi, Dual lateral flow optical/chemiluminescence [221] STANDARD Q COVID-19 IgM/IgG Duo Test, SD BIOSENSOR, Republic of Korea,
immunosensors for the rapid detection of salivary and serum IgA in patients with 2020 (accessed on March 17, 2022), http://www.aviapharm.ru/Covid19/Covid-
COVID-19 disease, Biosens. Bioelectron. 172 (2021) 112765. Test/standard-q-covid-19-igm-igg-duo.html.
[185] A. Padoan, F. Bonfante, M. Pagliari, A. Bortolami, D. Negrini, S. Zuin, D. Bozzato, [223] STANDARD Q COVID-19 Ag Test, SD BIOSENSOR, Republic of Korea, 2020
C. Cosma, L. Sciacovelli, M. Plebani, Analytical and clinical performances of five (accessed on March 17, 2022), https://366.ru/p/test-nabor-reagentov-immunokh
immunoassays for the detection of SARS-CoV-2 antibodies in comparison with rom-na-antigen-sarscov2-standart-q-covid19-ag-300001/.
neutralization activity, EBioMedicine 62 (2020). [224] COVID-19 Antigen Rapid Test, Roche Diagnosttics, USA, 2020. https://diagno
[186] F. Pérez-García, R. Pérez-Tanoira, M.E. Iglesias, J. Romanyk, T. Arroyo, P. Gómez- stics.roche.com/global/en/products/params/sars-cov-2-rapid-antigen-test.
Herruz, R. González, S. Lapeña García, J. Cuadros-González, Comparative html#productSpecs. (Accessed 17 March 2022).
evaluation of six immunoassays for the detection of antibodies against SARS-CoV- [225] COVID-19 Ag Respi-Strip, Coris Bioconcept, 2020. Belgium, https://www.cori
2, J. Virol Methods 289 (2021) 114047. sbio.com/pdf/Products/COVID-19-Respi-Strip_20201113.pdf. (Accessed 17
[187] A.M. Hashem, R.Y. Alhabbab, A. Algaissi, M.A. Alfaleh, S. Hala, T.S. Abujamel, M. March 2022).
Z. ElAssouli, A.A. Al-Somali, F.S. Alofi, A.A. Khogeer, A.A. Alkayyal, A. [226] P. Saha, D. Mukherjee, P.K. Singh, A. Ahmadian, M. Ferrara, R. Sarkar,
B. Mahmoud, N.A.M. Almontashiri, A. Pain, Performance of commercially GraphCovidNet: a graph neural network based model for detecting COVID-19
available rapid serological assays for the detection of SARS-CoV-2 antibodies, from CT scans and X-rays of chest, Sci. Rep. 11 (1) (2021) 8304.
Pathogens 9 (12) (2020). [227] M.J. Smith, S.A. Hayward, S.M. Innes, A.S.C. Miller, Point-of-care lung ultrasound
[188] H. Harritshøj Lene, M. Gybel-Brask, S. Afzal, R. Kamstrup Pia, S. Jørgensen in patients with COVID-19 – a narrative review, Anaesthesia 75 (8) (2020)
Charlotte, K. Thomsen Marianne, L. Hilsted, L. Friis-Hansen, B. Szecsi Pal, L. 1096–1104.
Pedersen, L. Nielsen, B. Hansen Cecilie, P. Garred, T.-L. Korsholm, S. Mikkelsen, [228] Orich radiology medical x ray machine. https://www.alibaba.com/product-de
O. Nielsen Kirstine, K. Møller Bjarne, T. Hansen Anne, K. Iversen Kasper, B. tail/Orich-radiology-medical-x-ray-machine_60493527889.html, 2021. (Accessed
Nielsen Pernille, B. Hasselbalch Rasmus, K. Fogh, B. Norsk Jakob, H. Kristensen 16 June 2021).
Jonas, K. Schønning, S. Kirkby Nikolai, C.Y. Nielsen Alex, H. Landsy Lone, M. [229] Sonography free icon. https://www.flaticon.com/free-icon/sonography_4080
Loftager, K. Holm Dorte, C. Nilsson Anna, G. Sækmose Susanne, B. Grum- 446?term=ultrasound%20machine&page=1&position=6&page=1&posit
Schwensen, B. Aagaard, G. Jensen Thøger, M. Nielsen Dorte, H. Ullum, B. Dessau ion=6&related_id=4080446&origin=tag, 2021. (Accessed 16 June 2021).
Ram, Y.-W. Tang, Comparison of 16 serological SARS-CoV-2 immunoassays in 16 [230] Medical X-ray imaging. https://www.fda.gov/radiation-emitting-products/me
clinical laboratories, J. Clin. Microbiol. 59(5) e02596-20. dical-imaging/medical-x-ray-imaging#description, 2020. (Accessed 10 June
[189] M. Tré-Hardy, A. Wilmet, I. Beukinga, J. Favresse, J.-M. Dogné, J. Douxfils, 2021).
L. Blairon, Analytical and clinical validation of an ELISA for specific SARS-CoV-2 [231] History of X-ray imaging. https://sunnybrook.ca/research/content/?page=sri-gr
IgG, IgA, and IgM antibodies, J. Med. Virol. 93 (2) (2021) 803–811. oups-xray-info-3, 2021. (Accessed 10 June 2021).

31
O. Filchakova et al. Talanta 244 (2022) 123409

[232] Y. Xu, H.-K. Lam, G. Jia, MANet: a two-stage deep learning method for networks (HDNNs), computed tomography, and chest X-rays for the detection of
classification of COVID-19 from Chest X-ray images, Neurocomputing 443 (2021) COVID-19, Int. J. Environ. Res. Publ. Health 18 (6) (2021) 3056.
96–105. [259] V. Shah, R. Keniya, A. Shridharani, M. Punjabi, J. Shah, N. Mehendale, Diagnosis
[233] G. Jia, H.-K. Lam, Y. Xu, Classification of COVID-19 chest X-Ray and CT images of COVID-19 using CT scan images and deep learning techniques, Emerg. Radiol.
using a type of dynamic CNN modification method, Comput. Biol. Med. 134 28 (3) (2021) 497–505.
(2021) 104425. [260] Z. Zhu, Z. Xingming, G. Tao, T. Dan, J. Li, X. Chen, Y. Li, Z. Zhou, X. Zhang,
[234] A. Makris, I. Kontopoulos, K. Tserpes, COVID-19 Detection from Chest X-Ray J. Zhou, D. Chen, H. Wen, H. Cai, Classification of COVID-19 by compressed chest
Images Using Deep Learning and Convolutional Neural Networks, 11th Hellenic CT image through deep learning on a large patients cohort, Interdiscipl. Sci.
Conference on Artificial Intelligence, Association for Computing Machinery, Comput. Life Sci. 13 (1) (2021) 73–82.
Athens, Greece, 2020, pp. 60–66. [261] H. Alshazly, C. Linse, E. Barth, T. Martinetz, Explainable COVID-19 detection
[235] S. Motamed, P. Rogalla, F. Khalvati, RANDGAN: randomized generative using chest CT scans and deep learning, Sensors 21 (2) (2021).
adversarial network for detection of COVID-19 in chest X-ray, Sci. Rep. 11 (1) [262] P. gifani, A. Shalbaf, M. Vafaeezadeh, Automated detection of COVID-19 using
(2021) 8602. ensemble of transfer learning with deep convolutional neural network based on
[236] A. Saygılı, A new approach for computer-aided detection of coronavirus (COVID- CT scans, Int. J. Comput. Assist. Radiol. Surg. 16 (1) (2021) 115–123.
19) from CT and X-ray images using machine learning methods, Appl. Soft [263] S. Ahuja, B.K. Panigrahi, N. Dey, V. Rajinikanth, T.K. Gandhi, Deep transfer
Comput. 105 (2021) 107323. learning-based automated detection of COVID-19 from lung CT scan slices, Appl.
[237] S. Liang, H. Liu, Y. Gu, X. Guo, H. Li, L. Li, Z. Wu, M. Liu, L. Tao, Fast automated Intell. 51 (1) (2021) 571–585.
detection of COVID-19 from medical images using convolutional neural networks, [264] T. Li, W. Wei, L. Cheng, S. Zhao, C. Xu, X. Zhang, Y. Zeng, J. Gu, Computer-Aided
Commun. Biol. 4 (1) (2021) 35. diagnosis of COVID-19 CT scans based on spatiotemporal information fusion,
[238] D. Sharifrazi, R. Alizadehsani, M. Roshanzamir, J.H. Joloudari, A. Shoeibi, J. Healthcare Eng. 2021 (2021) 6649591.
M. Jafari, S. Hussain, Z.A. Sani, F. Hasanzadeh, F. Khozeimeh, A. Khosravi, [265] S. Lawton, S. Viriri, Detection of COVID-19 from CT lung scans using transfer
S. Nahavandi, M. Panahiazar, A. Zare, S.M.S. Islam, U.R. Acharya, Fusion of learning, Comput. Intell. Neurosci. 2021 (2021) 5527923.
convolution neural network, support vector machine and Sobel filter for accurate [266] A. Jaiswal, N. Gianchandani, D. Singh, V. Kumar, M. Kaur, Classification of the
detection of COVID-19 patients using X-ray images, Biomed. Signal Process COVID-19 infected patients using DenseNet201 based deep transfer learning,
Control 68 (2021) 102622. J. Biomol. Struct. Dyn. (2020) 1–8.
[239] P. Kedia, Anjum, R. Katarya, CoVNet-19: a Deep Learning model for the detection [267] H.L. Fred, Drawbacks and limitations of computed tomography: views from a
and analysis of COVID-19 patients, Appl. Soft Comput. 104 (2021) 107184. medical educator, Tex. Heart Inst. J. 31 (4) (2004) 345–348.
[240] B. Nigam, A. Nigam, R. Jain, S. Dodia, N. Arora, B. Annappa, COVID-19: [268] S. Tofighi, S. Najafi, S.K. Johnston, A. Gholamrezanezhad, Low-dose CT in
automatic detection from X-ray images by utilizing deep learning methods, Expert COVID-19 outbreak: radiation safety, image wisely, and image gently pledge,
Syst. Appl. 176 (2021) 114883. Emerg. Radiol. 27 (6) (2020) 601–605.
[241] S. Das, S.D. Roy, S. Malakar, J.D. Velásquez, R. Sarkar, Bi-level prediction model [269] CT scanner price guide. https://info.blockimaging.com/how-much-does-a-ct-sca
for screening COVID-19 patients using chest X-ray images, Big Data Res. 25 nner-cost, 2021. (Accessed 9 June 2021).
(2021) 100233. [270] C. Poslusny, How much does a CT scan cost?. https://www.newchoicehealth.
[242] W. Jin, S. Dong, C. Dong, X. Ye, Hybrid ensemble model for differential diagnosis com/ct-scan/cost. (Accessed 9 June 2021).
between COVID-19 and common viral pneumonia by chest X-ray radiograph, [271] G. Bosso, E. Allegorico, A. Pagano, G. Porta, C. Serra, V. Minerva, V. Mercurio,
Comput. Biol. Med. 131 (2021) 104252. T. Russo, C. Altruda, P. Arbo, C. De Sio, F. Dello Vicario, F.G. Numis, Lung
[243] CT scan, in: https://www.mayoclinic.org/tests-procedures/ct-scan/about/pac ultrasound as diagnostic tool for SARS-CoV-2 infection, Intern. Emerg. Med. 16
-20393675, 2020. (Accessed 9 June 2021). (2) (2021) 471–476.
[244] History of the CT scan. https://catalinaimaging.com/history-ct-scan/, 2019. [272] G.B. Fonsi, P. Sapienza, G. Brachini, C. Andreoli, M.L. De Cicco, B. Cirillo,
(Accessed 10 June 2021). S. Meneghini, F. Pugliese, D. Crocetti, E. Fiori, A. Mingoli, Is lung ultrasound
[245] A. Bernheim, X. Mei, M. Huang, Y. Yang, Z.A. Fayad, N. Zhang, K. Diao, B. Lin, imaging a worthwhile procedure for severe acute respiratory syndrome
X. Zhu, K. Li, S. Li, H. Shan, A. Jacobi, M. Chung, Chest CT findings in coronavirus coronavirus 2 pneumonia detection? J. Ultrasound Med. 40 (6) (2021)
disease-19 (COVID-19): relationship to duration of infection, Radiology 295 (3) 1113–1123.
(2020) 200463. [273] N. Narinx, A. Smismans, R. Symons, J. Frans, A. Demeyere, M. Gillis, Feasibility of
[246] W. Zhao, Z. Zhong, X. Xie, Q. Yu, J. Liu, Relation between chest CT findings and using point-of-care lung ultrasound for early triage of COVID-19 patients in the
clinical conditions of coronavirus disease (COVID-19) pneumonia: a multicenter emergency room, Emerg. Radiol. 27 (6) (2020) 663–670.
study, Am. J. Roentgenol. 214 (5) (2020) 1072–1077. [274] C.M. Quarato, A. Mirijello, D. Lacedonia, R. Russo, M.M. Maggi, G. Rea,
[247] H. Wang, R. Wei, G. Rao, J. Zhu, B. Song, Characteristic CT findings A. Simeone, C. Borelli, B. Feragalli, G. Scioscia, M.P. Barbaro, V. Massa, S. De
distinguishing 2019 novel coronavirus disease (COVID-19) from influenza Cosmo, M. Sperandeo, Low sensitivity of admission lung US compared to chest CT
pneumonia, Eur. Radiol. 30 (9) (2020) 4910–4917. for diagnosis of lung involvement in a cohort of 82 patients with COVID-19
[248] F. Pan, T. Ye, P. Sun, S. Gui, B. Liang, L. Li, D. Zheng, J. Wang, R.L. Hesketh, pneumonia, Medicina 57 (3) (2021).
L. Yang, C. Zheng, Time course of lung changes at chest CT during recovery from [275] P. Walsh, A. Hankins, H. Bang, Point-of-care lung ultrasound is useful to evaluate
coronavirus disease 2019 (COVID-19), Radiology 295 (3) (2020) 715–721. emergency department patients for COVID-19, West. J. Emerg. Med. 21 (6)
[249] Y. Wang, C. Dong, Y. Hu, C. Li, Q. Ren, X. Zhang, H. Shi, M. Zhou, Temporal (2020).
changes of CT findings in 90 patients with COVID-19 pneumonia: a longitudinal [276] J. Chen, C. He, J. Yin, J. Li, X. Duan, Y. Cao, L. Sun, M. Hu, W. Li, Q. Li,
study, Radiology 296 (2) (2020) E55–E64. Quantitative analysis and automated lung ultrasound scoring for evaluating
[250] B. Sarkodie, Y. Mensah, CT scan chest findings in symptomatic COVID-19 COVID-19 pneumonia with neural networks, IEEE Trans. Ultrason. Ferroelectrics
patients: a reliable alternative for diagnosis, Ghana Med. J. 54 (4s) (2020) 97–99. Freq. Control 68 (7) (2021) 2507–2515.
[251] Y. Fang, H. Zhang, J. Xie, M. Lin, L. Ying, P. Pang, W. Ji, Sensitivity of chest CT for [277] Lung ultrasound for early diagnosis and severity assessment of pneumonia in
COVID-19: comparison to RT-PCR, Radiology 296 (2) (2020) E115–E117. patients with coronavirus disease 2019 FAU - cho, Young-Jae FAU - song,
[252] X. Xie, Z. Zhong, W. Zhao, C. Zheng, F. Wang, J. Liu, Chest CT for typical Kyoung-Ho FAU - lee, Yunghee FAU - Yoon, Joo heung FAU - park, Ji Young FAU -
coronavirus disease 2019 (COVID-19) pneumonia: relationship to negative RT- Jung, Jongtak FAU - lim, sung Yoon FAU - lee, hyunju FAU - Yoon, Ho il FAU -
PCR testing, Radiology 296 (2) (2020) E41–E45. park, Kyoung un FAU - Kim, hong bin FAU - Kim, Eu suk, Korean J Intern Med 35
[253] Z. Ling, X. Xu, Q. Gan, L. Zhang, L. Luo, X. Tang, J. Liu, Asymptomatic SARS-CoV- (4) (2020) 771–781.
2 infected patients with persistent negative CT findings, Eur. J. Radiol. 126 [278] S. Yadav, A. Singh, K. Manisha, P. Khanna, Point of care ultrasound in coronavirus
(2020). disease 2019 pandemic: one modality helping multiple specialties, J. Med.
[254] L.-s. Xiao, P. Li, F. Sun, Y. Zhang, C. Xu, H. Zhu, F.-Q. Cai, Y.-L. He, W.-F. Zhang, Ultrasound 29 (1) (2021) 9–14.
S.-C. Ma, C. Hu, M. Gong, L. Liu, W. Shi, H. Zhu, Development and validation of a [279] M.M. Nadrljanski, History of ultrasound in medicine. https://radiopaedia.org/art
deep learning-based model using computed tomography imaging for predicting icles/history-of-ultrasound-in-medicine. (Accessed 17 June 2021).
disease severity of coronavirus disease 2019, Front. Bioeng. Biotechnol. 8 (898) [280] C. Sorlini, M. Femia, G. Nattino, P. Bellone, E. Gesu, P. Francione, M. Paternò,
(2020). P. Grillo, A. Ruffino, G. Bertolini, M. Cariati, F. Cortellaro, N. the Fenice, The role
[255] N. Fink, J. Rueckel, S. Kaestle, V. Schwarze, E. Gresser, B. Hoppe, J. Rudolph, of lung ultrasound as a frontline diagnostic tool in the era of COVID-19 outbreak,
S. Goller, W.G. Kunz, J. Ricke, B.O. Sabel, Evaluation of patients with respiratory Intern. Emerg. Med. 16 (3) (2021) 749–756.
infections during the first pandemic wave in Germany: characteristics of COVID- [281] X-ray room price guide. https://info.blockimaging.com/x-ray-machine-cost-pri
19 versus non-COVID-19 patients, BMC Infect. Dis. 21 (1) (2021) 167. ce-guide, 2021. (Accessed 10 June 2021).
[256] T. Ai, Z. Yang, H. Hou, C. Zhan, C. Chen, W. Lv, Q. Tao, Z. Sun, L. Xia, Correlation [282] How much does an ultrasound machine cost?. https://www.costowl.com/healthc
of chest CT and RT-PCR testing for coronavirus disease 2019 (COVID-19) in are/healthcare-ultrasound-machine-costs.html, 2021. (Accessed 16 June 2021).
China: a report of 1014 cases, Radiology 296 (2) (2020) E32–E40. [283] Radiation dose in X-ray and CT exams. https://www.radiologyinfo.org/en/info/
[257] L. Li, L. Qin, Z. Xu, Y. Yin, X. Wang, B. Kong, J. Bai, Y. Lu, Z. Fang, Q. Song, safety-xray, 2019. (Accessed 10 June 2021).
K. Cao, D. Liu, G. Wang, Q. Xu, X. Fang, S. Zhang, J. Xia, J. Xia, Using artificial [284] S. Mishra, S. Mindermann, M. Sharma, C. Whittaker, T.A. Mellan, T. Wilton,
intelligence to detect COVID-19 and community-acquired pneumonia based on D. Klapsa, R. Mate, M. Fritzsche, M. Zambon, J. Ahuja, A. Howes, X. Miscouridou,
pulmonary CT: evaluation of the diagnostic accuracy, Radiology 296 (2) (2020) G.P. Nason, O. Ratmann, E. Semenova, G. Leech, J.F. Sandkühler, C. Rogers-
E65–E71. Smith, M. Vollmer, H.J.T. Unwin, Y. Gal, M. Chand, A. Gandy, J. Martin, E. Volz,
[258] M. Irfan, M.A. Iftikhar, S. Yasin, U. Draz, T. Ali, S. Hussain, S. Bukhari, A. N.M. Ferguson, S. Bhatt, J.M. Brauner, S. Flaxman, Changing composition of
S. Alwadie, S. Rahman, A. Glowacz, F. Althobiani, Role of hybrid deep neural SARS-CoV-2 lineages and rise of Delta variant in England, EClinicalMedicine 39
(2021).

32