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    Topic review

    Nucleic Acid Testing of SARS-CoV-2

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    Submitted by: Seil Kim


    The coronavirus disease 2019 (COVID-19) has caused a large global outbreak. It is accordingly important to develop accurate and rapid diagnostic methods. The polymerase chain reaction (PCR)-based method including reverse transcription-polymerase chain reaction (RT-PCR) is the most widely used assay for the detection of SARS-CoV-2 RNA. Along with the RT-PCR method, digital PCR has emerged as a powerful tool to quantify nucleic acid of the virus with high accuracy and sensitivity. Non-PCR based techniques such as reverse transcription loop-mediated isothermal amplification (RT-LAMP) and reverse transcription recombinase polymerase amplification (RT-RPA) are considered to be rapid and simple nucleic acid detection methods and were reviewed in this entry. Non-conventional molecular diagnostic methods including next-generation sequencing (NGS), CRISPR-based assays and nanotechnology are improving the accuracy and sensitivity of COVID-19 diagnosis. 

    1. Background

    Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is a novel coronavirus that first appeared in Wuhan, Hubei Province, China in December 2019, connected to a seafood market [1][2]. Seven coronaviruses have been reported to infect humans; four of them, human CoV-NL63 [3], HCoV-OC43 [4][5][6], HCoV-229E [7][8], and HCoV-HKU [9][10], cause mild and seasonal respiratory tract disease, whereas SARS-CoV [11][12][13][14][15], MERS-CoV [16][17][18][19], and SARS-CoV-2 can cause severe symptoms. In particular, SARS- CoV-2 is suited to human-to-human transmission and spreads rapidly to other locations, causing lung injury, multiorgan failure, and death [20][21]. As of this date, the number of confirmed cases is still increasing, as is the number of deaths [22][23]. Therefore, an understanding of the SARS-CoV-2 host and pathogen biology is important to offer valuable insights into the diagnosis and treatment of the disease including the development of new therapies [24][25]. Here, we review the basic biology of SARS-CoV-2 including the origin, pathophysiology, and diagnosis methods.

    1.1. Nomenclature of SARS-CoV-2

    Currently, almost a million sequences of the SAR-CoV-2 genome are publicly available via the Global Initiative on Sharing All Influenza Data (GISAID) and GenBank [26][27]. Based on these genome sequences, the phylogenetic classification of SARS-CoV-2 was performed and the nomenclature of GISAID, Phylogenetic Assignment of Named Global Outbreak LINeages (PANGO lineage), and Nextstrain are widely used in scientific and clinical communities [26][28][29] The major lineages of each nomenclature are summarized in Table 1.
    Table 1. Nomenclature of SARS-CoV-2.
    GISAID Clades PANGO Lineage Nextstrain Clades Notable Variants
    S A 19B A.23.1
    L B 19A Wuhan-Hu-1
    G B.1 20A B.1.525, B.1.627
    GH B.1 20C B.1.427, B.1.429, B.1.526
      B.1.2 20G  
      B.1.351 20H/501.Y.V2 B.1.351
    GR B.1.1.1 20B  
      P.3   P.3
      C 20D  
      D.2 20F  
      P.1 20J/501.Y.V3 P.1
    GV B.1.177 20E (EU1) B.1.177
    GRY B.1.1.7 20I/501.Y.V1 B.1.1.7
    GISAID introduced the nomenclature system of SARS-CoV-2 based on marker mutations and named the clade with actual letters of marker mutations [30]. For example, the clade G has a characteristic mutation in the spike protein gene, D614G. In the nomenclature of GISAID, the initial strains of SARS-CoV-2 were grouped as S and L clades and the current strains of SARS-CoV-2 were classified as eight major clades (S, L, V, G, GH, GR, GV, and GRY) [30][31]. The L clade contains the reference strain WIV-04 and was the dominant lineage in early 2020. The L clade later diverged into clades V and G, and clade G diverged into clades GH, GR, GV, and GRY.
    The PANGO nomenclature systems focused on the active virus lineage [29]. This nomenclature is dynamic and the lineages of the PANGO nomenclature are marked as three statuses: active, unobserved, or inactive. The lineages documented within a month are marked as active. The lineages documented within three months are marked as unobserved, and the lineages that were not documented for more than three months are regarded as inactive. The lineages of PANGO nomenclature are named with a letter and numerical values. The initial lineages are denoted as lineages A and B. Although clade B includes the first genome sequenced strain, the phylogenetic analysis suggested that the most recent common ancestor of SARS-CoV-2 was close to early lineage A [29]. The descendent lineages from initial lineages were assigned with numerical labels. The descendent lineages can be designated with the phylogenetic evidence that the descendent emerged from parental lineages and the descendants showed significant transmission to geographically distinct populations. The designated descendent lineages can also be parental of new emerging lineages and these new lineages have been labeled as parental lineages with additional numerical values. For example, a new emerging lineage from lineage B1 can be labeled as B1.1. The lineages can have a maximum of three sublevels and newly designated lineages emerging from a lineage with three sublevels will be labeled with new alphabetical letters. For example, the parental lineage of lineage C.1 is the lineage B.1.1.1.
    The clades of Nextstrain nomenclature were initially named according to year–letter combinations [32]. Major clades were designated as the clade reached more than 20% of global frequency for more than two months. Based on this criteria, the initial clades were designated as 19A, 19B, 20A, 20B, and 20C. However, due to the global travel restriction, no more clades were designated according to the criteria. For this reason, Nextstrain updated their major clade designation criteria with regional frequency (>30%) and recognized variants of concern. Currently, 12 major clades (19A, 19B, 20A, 20B, 20C, 20D, 20E, 20F, 20G, 20H/501Y.V2, 20I/501Y.V1, 20J/501Y.V3) are designated in the nomenclature of Nextstrain.

    1.2. Notable Variants of SARS-CoV-2

    The first strain of SARS-CoV-2 was discovered in Wuhan, China and designated as Wuhan-Hu-1 or WIV-04 [1][33]. The comparison of whole genome sequences showed that the strain was closest to the SARS-like coronavirus RATG13 found in bats (Rhinolophus affinis) in China [2]. The overall genomic sequence similarity of RATG13 to SARS-CoV-2 was 96.1%. However, the spike protein gene of RATG13 lacked the furin cleavage site that is essential for the cell entry of SARS-CoV-2, indicating that RATG13 was not the immediate ancestor of SARS-CoV-2 [34][35]. After discovery of the first SARS-CoV-2 strains, SARS-CoV-2 like viruses were found in pangolins and bats [36][37][38]. The genome sequences of pangolin-derived CoVs also showed high similarity to those of SARS-CoV-2, but the furin cleavage site was missing in the spike gene sequences of pangolin-derived CoVs [35][37][38]. A bat-derived CoV, RmYN02, was identified and the genome of the virus showed high similarity to that of SARS-CoV-2 [36]. Although the sequence similarity of RmYN02 was slightly lower than those of RATG13 and pangolin-derived CoVs, the furin cleavage site was inserted, indicating that the addition of the cleavage site can occur naturally [35][36].
    The D614G variants had a change in spike gene and replaced the initial strains of SARS-CoV-2 [39]. The studies on the variant D614G showed that the infectivity of the variant was increased without increased severity [39][40][41]. The engineered variants containing the D614G substitution showed more efficient infection in human cells and animal models without altering antibody neutralization and pathogenicity [41]. A population genetic analysis of COVID-19 also showed that the transmissibility of the variant was increased but there was no sign of increased mortality or clinical severity of the variants [40].
    As new variants with increased pathogenicity, reduced neutralization, and/or increased transmissibility emerged, the U.S. Centers for Disease Control and Prevention (CDC) and Public Health England (PHE) classified some notable variants according to the attributes of the variants [42][43]. The CDC classified the variants according to the evidence and significance of the variants into Variant of Interest, Variant of Concern, and Variant of High Consequences. PHE classified variants as Variant Under Investigation (VUI) and Variants Of Concern (VOC). When the variants are considered to have concerning characteristics, they are designated as VUI. After a risk assessment of VUI is conducted, they can be re-designated as VOC. These notable variants are summarized in Table 2.
    Table 2. Notable variants of SARS-CoV-2.
    PANGO Lineage CDC Designation PHE Designation First Detected Spike Protein Substitutions
    B.1.1.7 VOC VOC-20DEC-01, VOC-21FEB-02 * United Kingdom 69del, 70del, 144del, (E484K), (S494P), N501Y, A570D, D614G, P681H, T716I, S982A, D1118H (K1191N)
    B.1.351 VOC VOC-20DEC-02 South Africa D80A, D215G, 241del, 242del, 243del, K417N, E484K, N501Y, D614G, A701V
    P.2 VOI VUI-21JAN-01 Brazil E484K, (F565L), D614G, V1176F
    P.1 VOC VOC-21JAN-02 Brazil L18F, T20N, P26S, D138Y, R190S, K417T, E484K, N501Y, D614G, H655Y, T1027I
    A.23.1 - VUI-21FEB-01 * Uganda F157L, V367F, (E484K), Q613H, P681R
    B.1.525 VOI VUI-21FEB-03 United Kingdom A67V, 69del, 70del, 144del, E484K, D614G, Q677H, F888L
    B.1.1.318 - VUI-21FEB-04 United Kingdom D614G, D796H, E484K, P681H, T95I, 144del
    P.3 - VUI-21MAR-02 Philippines E484K, N501Y, P681H
    B.1.617 VOI VUI-21APR-01 India L452R, E484Q, D614G
    B.1.617.2 VOI VOC-21APR-02 India T19R, (G142D), 156del, 157del, R158G, L452R, T478K, D614G, P681R, D950N
    B.1.617.3 VOI VUI-21APR-03 India T19R, G142D, L452R, E484Q, D614G, P681R, D950N
    AV.1 - VUI-21MAY-01 United Kingdom D80G, T95I, G142D, 144del, N439K, E484K, D614G, P681H, I1130V, D1139H
    B.1.617.1 VOI - India (T95I), G142D, E154K, L452R, E484Q, D614G, P681R, Q1071H
    B.1.526 VOI - United States (L5F), T95I, D253G, (S477N), (E484K), D614G, (A701V)
    B.1.526.1 VOI - United States D80G, 144del, F157S, L452R, D614G, (T791I), (T859N), D950H
    B.1.427 VOC - United States L452R, D614G
    B.1.429 VOC - United States S13I, W152C, L452R, D614G
    * with E484K; ( ) detected in some sequences but not all; VOC; Variant of Concern, VOI; Variant of Interest, VUI; Variant under Investigation.
    VOC-20DEC-01, also known as 20I/501Y.V1 or B.1.1.7, was first discovered in the United Kingdom in December 2020 [42] and is defined by 13 mutations [42]. Recent studies have estimated that the transmissibility of VOC-20DEC-01 is increased by 43–90% and a similar transmission increase was observed globally [44]. VOC-20DEC-01 was also detected in domestic cats and dogs, raising concern over human-to-animal transmission or vice versa [45]. Previously reported animal infections were asymptomatic to mild symptomatic, but VOC-20DEC-01 infection in animals showed relatively severe symptoms such as myocarditis [45]. In February 2021, the variants with a spike gene E484K mutation were reported and designated as VOC-202102/02 [42]. Another variant with N501Y mutation is VOC-20DEC-02 (20H/501Y.V2, or B.1.351), which was first discovered in South Africa. VOC-20DEC-02 is defined by 17 mutations including the E484K mutation, K417N mutation, and two deletions. The variant also showed increased transmissibility (approximately 50%) compared to previous variants [44]. The third variant with N501Y is VOC-21JAN-02 (P.1 or 20J/501Y.V3), discovered in Brazil [42][46]. The genome of VOC-21JAN-02 is defined with 17 non-synonymous mutations, four synonymous mutations, three deletions, and four insertions [42]. VOC-202101/02 almost fully replaced its parental variant within two months, indicating increased transmissibility of VOC-21JAN-02 [47][48]. Molecular clock analysis showed that the variants emerged in mid-November 2020 at which time hospitalizations rapidly increased [49].
    The characteristic mutations (N501Y, E484K, and K417N) of the variants with N501Y are mutations in binding sites to viral receptor ACE2 and were already a concern prior to the discovery of these variants [50][51][52][53]. The studies on these variants showed that they had impacts on neutralization by immunity [54][55][56][57][58][59][60]. However, recent research showed that the residual immunity still provided protection, although variants reduced the efficacy of the vaccine [61].
    There were also emerging variants without N501Y, E484K, and/or K417N. The characteristic mutations of A.23.1 are F157L, V367F, Q613H, and P681R [62]. A.23.1 with E484K was designated as VUI-21FEB-01 in the United Kingdom. These strains were first identified in Uganda and are spreading. One of the characteristic mutations, Q613H, is regarded as functionally equivalent to the D614G mutation of ‘G’ clade strains. B.1.427 and B.1.429 were first discovered and designated as Variants of Concern in the United States [43]. The characteristic mutations of both lineages are L452R and D614G; these variants showed increased transmissibility and reduced neutralization by convalescent and post-vaccination sera [63].
    B.1.617 was the emerging lineage in India and also designated as VUI-21APR-01 in the United Kingdom [42]. B.1.167 has two characteristic mutations of different lineages: L452R and E484Q [64]. The variants were neutralized with convalescent sera of COVID-19 patients and vaccine of BBV152, although the efficacy was low [64].

    2. PCR-Based SARS-CoV-2 Detection

    2.1. Reverse Transcription Quantitative PCR (RT-qPCR) Method

    Detection of the SARS-CoV-2 viral genome, consisting of single-stranded RNA, is effectively done by reverse transcription quantitative polymerase chain reaction (RT-qPCR), which is the gold standard technique widely used in molecular diagnostics [65][66]. There are several practical considerations when performing diagnostic assays using RT-qPCR.
    (1) Sample quality: RT-qPCR tests are presently being used for the identification of SARS-CoV-2 in clinical specimens such as upper respiratory tract specimens (saliva, oropharyngeal swab-OPS, nasopharyngeal swab-NPS, nasal swabs), lower respiratory specimens (sputum, bronchoalveolar lavage-BAL, endotracheal aspirate-ET, fibrobronchoscope brush biopsy-FBB), blood (serum, plasma), urine, feces, rectal/anal swabs, stool, and corneal secretion [67][68]. To check the sample quality of clinical specimens from different origins, an RNA isolation procedure is required to obtain purified high-quality RNA from the samples, which then needs to be analyzed using chip-based capillary electrophoresis (such as the Agilent Bioanalyzer system), electrophoretic separation on a high-resolution agarose gel, and spectrophotometry [69].
    (2) Reference curve: Data processing can critically affect the analysis of RT-qPCR results [70]. PCR data processing is based on standard curves or on PCR efficiency assessments [70]. Standard curves are used to assess RT-qPCR efficiency without theoretical and practical problems [70]. The estimation of RT-qPCR efficiency using standard curves usually involves the serial dilution of a concentrated stock solution, after which standard samples are analyzed through RT-qPCR by measuring the quantification cycle (Cq) using standard procedures [70]. The most widely used Cq value is the threshold cycle (Ct), the cycle at which the expression of a target gene first exceeds a calculated fluorescence threshold level [71]. For example, to detect low amounts of SARS-CoV-2 RNA, a series of diluted RNA templates are used to determine the Ct value, which can provide a standard curve for evaluating the reaction efficiency [72]. However, the Ct value itself cannot be directly explained as viral load without a standard curve using reference materials [73]. When interpreting the results of SARS-CoV-2 RT-qPCR, the validity of the standard curve should be proved using reference materials with accurate viral copy numbers to interpret Ct values as viral loads [73].
    (3) Viral load: The success of virus isolation depends on the viral load [74]. Viral loads in sputum samples and throat swabs are high when obtained within seven days after initial symptoms are observed, ranging from 104 to 107 copies per mL. This pattern is broken as low quantity of virus are obtained from samples taken after day 8 [75]. In general, sputum samples show higher viral loads than throat swab samples, whereas low viral RNA is detected in urine or stool samples [75]. The two main factors that influence the quantitative measurement of viral roads are Cq values that are repeatable with acceptable uncertainty and a reliable means of converting from the Ct value to viral load [76][77][78]. For molecular diagnostic assays, a limit of detection (LoD) and a limit of quantification (LoQ) are also considered the lowest concentrations of target RNA that can be detected by RT-qPCR [79].
    (4) Sampling methods: RT-qPCR tests for SARS-CoV-2 have shown a high variation of false-negative rates (FNR) and false-positive rates (FPR) [80][81]. Numerous methods have been developed with the goal of improving the sensitivity, safety, and rapidity of COVID-19 tests by RT-qPCR. For example, one group tested the efficiency and sensitivity of SARS-CoV-2 detection of clinical specimens collected directly in nucleic acid stabilization and lysis buffer (NSLB), a mixture of lysis buffer and RNA preservative, instead of a viral transport medium (VTM), thus inactivating the virus immediately after sampling [82].
    (5) Sample source: To improve the expandability of SARS-CoV-2 testing, several sampling approaches have been developed including nasal, pooled nasal, and throat (oropharyngeal) swabs as well as saliva. Different clinical sampling methods affect the diagnostic performance of SARS-CoV-2 infection tests by RT-qPCR including sensitivity and specificity, and thus should be carefully considered [83][84][85][86]. The combined swab is largely recommended as a more appropriate specimen for diagnosis by RT-qPCR [87][88][89].
    (6) Sensitivity: The conserved regions, ORF 1ab (RNA-dependent RNA polymerase, RdRp), envelope (E), and nucleocapsid (N) genes of SARS-CoV-2, are usually selected as the standard target genes for primer and probe design [90][91]. However, initial reports of SARS-CoV-2 and other coronavirus sequences gave rise to an incorrect degenerate base that did not align with the SARS-CoV-2 RNA sequence found, and there were reports regarding the decreased sensitivity of using RdRp as a target gene for RT-qPCR assays [90][92]. As the pandemic continues, many laboratories around the world rely on routine diagnostic primers and probes. Thus, proper assays can increase the sensitivity of SARS-CoV-2 detection and help prevent the further spread of the virus [92][93][94][95].
    (7) Pooling technologies: The pooling of multiple swab samples before RNA isolation and RT-qPCR analysis has been proposed as a promising solution to reduce costs and time as well as elevate the throughput of SARS-CoV-2 tests for large-scale testing as in the case of schools [96][97][98][99]. For example, batch testing of over 100,000 hospital-collected nasopharyngeal swab samples from patients alleviated three quarters of testing reactions with a minor reduction in sensitivity, indicating the effectiveness of the pooling approach in the field [100][101]. Current studies suggest that the pooling of individual samples before testing should be considered to increase the reliability of SARS-CoV-2 testing throughput.
    Once all practical considerations have been evaluated, there are two ways that RT-qPCR can be performed. The two-step RT-qPCR method is required to convert RNA into complementary DNA (cDNA) [102]. On the other hand, the one-step RT-qPCR method combines reverse transcription and PCR in a single tube and uses reverse transcriptase as well as a DNA polymerase [103]. The schematic procedure of RT-qPCR is shown in Figure 1A.
    Figure 1. Overview of nucleic acid testing for SARS-CoV-2. The schematic procedure of RT-qPCR (A), and dPCR (B). Current isothermal amplification methods (C), CRISPR detection systems (D), and nanoparticles (E) are also shown.
    A critical need for rapid and accurate diagnostic methods has emerged in the clinic and public health organizations. Several PCR-based assays have been developed and are currently being used in clinical, research, and public health laboratories [104][105][106]. However, it is not clear which PCR condition they should adopt or whether the data are comparable. In response to the growing need and the lack of publicly available information, several research groups have optimized real-time PCR-based primer sets, protocols, and PCR conditions [107][108].
    Independent evaluations of the designed primer–probe sets used in SARS-CoV-2 RT–qPCR detection assays are necessary to compare and select appropriate assays [90][109]. Additionally, several studies have utilized serum and stool specimens for the RT-qPCR-based detection method [110][111][112][113].

    2.2. Reverse Transcription Digital PCR (RT-dPCR) Method

    In recent years, we have seen the advance of digital PCR (dPCR) as a complementary approach for measuring nucleic acids, a technique that is highly accurate and reproducible when targeting the viral genes of SARS-CoV-2 [114][115][116][117]. The advantages of digital PCR compared to quantitative PCR include quantification without the need for calibration curves, higher accuracy, and sensitivity that may arise from sub-optimal amplification efficacy because dPCR can detect low amounts of nucleic acid [118][119]. The schematic procedure of dPCR is shown in Figure 1B.
    Reverse transcriptase quantitative PCR (RT-qPCR) and digital PCR (dPCR) have been widely used for quantitative analyses of clinical samples. Recently, many groups have developed a reverse transcription droplet digital PCR (RT-ddPCR) assay for sensitive detection of the SARS-CoV-2 virus [120][121][122][123]. Optimization of the primer. and probe assays is necessary to remove false negatives or positives for clinical diagnosis of viral infection [72][124]. Multiple molecular diagnostic kits have been developed and validated for use nationwide [125]. However, the analytical sensitivity and the relative sensitivity of different kits to detect low copy number of SARS-CoV-2 viral RNA are variable [126][127].

    3. Isothermal Nucleic Acid Amplification Methods

    Although the RT-qPCR method is considered the ‘gold standard’ for SARS-CoV-2 detection [128], its limitations have stimulated the development of simple, rapid yet sensitive nucleic acid detection methods [129]. As a result, isothermal nucleic acid amplification has emerged as an alternative detection method for SARS-CoV-2 viral RNA from clinical samples [130]. In general, isothermal amplification techniques increase the analytical signal by increasing the target nucleic acid concentration through enzymatic activities at a fixed temperature, and simultaneously detecting the signal with colorimetric or fluorescence indicators [131]. Changes in color, fluorescence level, or turbidity indicate the presence of SARS-CoV-2 RNA or DNA [131]. Therefore, unlike RT-qPCR, isothermal amplification methods do not require thermal cycling instruments or specialized technicians for disease diagnosis [132]. Current isothermal nucleic acid amplification methods used for SARS-CoV-2 detection include, but are not limited to, loop-mediated isothermal amplification (LAMP), recombinase polymerase amplification (RPA), nucleic acid sequence-based amplification (NASBA), strand-displacement amplification (SDA), and rolling circle amplification (RCA) [133][134][135][136][137] (Figure 1C). Herein, we describe the general procedures and components of isothermal amplification methods commonly used for diagnosis of SARS-CoV-2.

    3.1. Loop-Mediated Isothermal Amplification (LAMP)

    The loop-mediated isothermal amplification method, coupled with reverse transcription (RT-LAMP), is the most widely used isothermal amplification technique for SARS-CoV-2 nucleic acid detection. First described by Notomi et al. [138], this method uses strand displacement activity of DNA polymerase and a set of inner and outer primers (four or six specific primer sequences) to amplify the target nucleic acids. LAMP is carried out at a single temperature between 60 and 65 °C, and generates up to 109 copies of DNA in less than an hour [133][137][139]. The LAMP procedure is initiated by hybridization of the forward inner primer (FIP) toward the target DNA template, which synthesizes the complementary strand. Then, the outer primer hybridizes to the target DNA, which initiates DNA synthesis by strand displacement. Subsequently, a FIP-hybridized complementary strand is released and forms a loop structure at one end of the sequence. The corresponding sequence becomes the template for the backward inner primer (BIP), which initiates another DNA synthesis by strand displacement, and then produces a ‘dumb-bell’ like DNA structure. Self-primed DNA synthesis of the corresponding sequence then converts the ‘dumb-bell’ like structure into a ‘stem-loop’ like DNA structure. Corresponding stem-loop DNA then becomes the template for LAMP cycling, and the target DNA sequence exponentially amplifies until the reaction is completed [138]. Amplified products are detected by changes of color as the accumulation of DNA changes, pH levels, or by changes in turbidity as magnesium pyrophosphate level increases [140][141][142]. Amplified products are also detected by Calcein fluorescent dye or fluorescent intercalating dye [129][139]. The schematic procedure of RT-LAMP is shown as Figure 2.
    Figure 2. Schematic procedure of reverse transcription loop-mediated isothermal amplification (RT-LAMP) and reverse transcription recombinase polymerase amplification (RT-RPA). FIP = Forward Inner Primer, FOP = Forward Outer Primer, BIP = Backward Inner Primer, BOP = Backward Outer Primer.
    Researchers have made efforts to optimize RT-LAMP for the development of rapid and sensitive detection of SARS-CoV-2. Several studies have evaluated the experimental parameters for RT-LAMP such as incubation temperature, incubation time, LoD, target genes, and primer sequences [143][144][145]. Aside from optimizing the experimental parameters, researchers have developed modified RT-LAMP procedures including methods without prior RNA extraction steps and high-throughput colorimetric assay methods using a 96-well plate format [146][147]. Modified RT-LAMP procedures also include methods coupled with Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) technology, a nanoparticle-based biosensor, and artificial intelligence [148][149][150][151].

    3.2. Recombinase Polymerase Amplification (RPA)

    Recombinase polymerase amplification is another isothermal amplification method that is widely used for SARS-CoV-2 detection. First described by Piepenburg et al. [152], RPA uses a complex of recombinase and two target specific primers (forward and reverse primers) to amplify the target nucleic acids [152]. Once the target nucleic acids are identified, recombinase-primer complex unwinds the target DNA and allows forward and reverse primers to hybridize [153]. The displaced DNA strand is amplified in the presence of DNA polymerase as primers elongate, and the template DNA is exponentially amplified until the reaction is completed [153]. RPA reaction is carried out at a single temperature between 37 and 42 °C, and the reaction is completed when ATPs are depleted, typically in less than an hour [154]. Amplified products are detected by gel electrophoresis, antigenic tags on primers and tag-specific antibodies, or fluorescent signals produced by a conjugated fluorophore and quencher on primers [152][153][154]. The schematic procedure of reverse transcription RPA (RT-RPA) is shown as Figure 2.
    For SARS-CoV-2 detection, researchers have optimized RPA procedures by testing various experimental parameters that can now detect less than five viral copies from patient samples within 45 min from the sample collection [134]. RPA methods have also been optimized for SARS-CoV-2 detection by coupling RPA-based amplification with various CRISPR-based detection methods [155][156][157].

    3.3. Other Isothermal Nuleic Acid Amplification Methods

    Other than LAMP and RPA, isothermal amplification methods such as NASBA, SDA, and RCA have been used for the detection of SARS-CoV-2 [136][137][158]. Although we will not describe each technique in detail here, Table 3 presents the general features and components of each isothermal amplification method.
    Table 3. General features of the isothermal amplification techniques for SARS-CoV-2 detection.
    Method Components Temperature Time Detection Method Advantages * Disadvantages *
    Loop-mediated isothermal amplification (LAMP) DNA polymerase, forward inner primer, backward inner primer, forward outer primer, backward outer primer 60–65 °C >1 h Colorimetric, turbimetric, fluorescence probe, intercalating dye High specificity. Less sensitive to inhibitors in biological samples False positive in negative control
    Recombinase polymerase amplification (RPA) Recombinase, single stranded binding protein, DNA polymerase, forward primer, reverse primer 37–42 °C >1 h Fluorescence, antigenic-tag (antibody) Performed in the presence of PCR inhibitors. Fast and sensitive Inhibited by detergents (SDS and CTAB). Non-specific/high background signal
    Nucleic acid sequence-based amplification (NASBA) RNase H, reverse transcriptase, T7 DNA-dependent RNA polymerase, forward primer with T7 promoter sequence, reverse primer 41 °C >2 h Fluorescence More sensitive and less time-consuming Non-specific reactions/false positives
    Strand-displacement amplification (SDA) DNA polymerase, restriction endonuclease, primers, dCTP, dTTP, dGTP, dATPα 37–49 °C >2 h Fluorescence High specificity. Detection of large RNA molecules Non-specific reaction/high background signal
    Rolling circle amplification (RCA) DNA ligase, DNA polymerase, primer, padlock probe 30–37 °C >1.5 h Fluorescence High specificity False negatives and false positives
    * Advantages and disadvantages in comparison with RT-qPCR methods.

    The entry is from 10.3390/ijms22116150


    1. Wu, F.; Zhao, S.; Yu, B.; Chen, Y.M.; Wang, W.; Song, Z.G.; Hu, Y.; Tao, Z.W.; Tian, J.H.; Pei, Y.Y.; et al. A New Coronavirus Associated with Human Respiratory Disease in China. Nature 2020, 579, 265–269.
    2. Zhou, P.; Yang, X.L.; Wang, X.G.; Hu, B.; Zhang, L.; Zhang, W.; Si, H.R.; Zhu, Y.; Li, B.; Huang, C.L.; et al. A Pneumonia Outbreak Associated with a New Coronavirus of Probable Bat Origin. Nature 2020, 579, 270–273.
    3. Van Der Hoek, L.; Pyrc, K.; Jebbink, M.F.; Vermeulen-Oost, W.; Berkhout, R.J.M.; Wolthers, K.C.; Wertheim-Van Dillen, P.M.E.; Kaandorp, J.; Spaargaren, J.; Berkhout, B. Identification of a New Human Coronavirus. Nat. Med. 2004, 10, 368–373.
    4. Vabret, A.; Mourez, T.; Gouarin, S.; Petitjean, J.; Freymuth, F. An Outbreak of Coronavirus OC43 Respiratory Infection in Normandy, France. Clin. Infect. Dis. 2003, 36, 985–989.
    5. Vijgen, L.; Keyaerts, E.; Moës, E.; Thoelen, I.; Wollants, E.; Lemey, P.; Vandamme, A.-M.; Van Ranst, M. Complete Genomic Sequence of Human Coronavirus OC43: Molecular Clock Analysis Suggests a Relatively Recent Zoonotic Coronavirus Transmission Event. J. Virol. 2005, 79, 1595–1604.
    6. Owczarek, K.; Szczepanski, A.; Milewska, A.; Baster, Z.; Rajfur, Z.; Sarna, M.; Pyrc, K. Early Events during Human Coronavirus OC43 Entry to the Cell. Sci. Rep. 2018, 8, 7124.
    7. Pene, F.; Merlat, A.; Vabret, A.; Rozenberg, F.; Buzyn, A.; Dreyfus, F.; Cariou, A.; Freymuth, F.; Lebon, P. Coronavirus 229E-Related Pneumonia in Immunocompromised Patients. Clin. Infect. Dis. 2003, 37, 929–932.
    8. Hendley, J.O.; Fishburne, H.B.; Gwaltney, J.M. Coronavirus Infections in Working Adults. Eight-Year Study with 229 E and OC 43. Am. Rev. Respir. Dis. 1972, 105, 805–811.
    9. Woo, P.C.Y.; Lau, S.K.P.; Chu, C.; Chan, K.; Tsoi, H.; Huang, Y.; Wong, B.H.L.; Poon, R.W.S.; Cai, J.J.; Luk, W.; et al. Characterization and Complete Genome Sequence of a Novel Coronavirus, Coronavirus HKU1, from Patients with Pneumonia. J. Virol. 2005, 79, 884–895.
    10. Lau, S.K.P.; Woo, P.C.Y.; Yip, C.C.Y.; Tse, H.; Tsoi, H.W.; Cheng, V.C.C.; Lee, P.; Tang, B.S.F.; Cheung, C.H.Y.; Lee, R.A.; et al. Coronavirus HKU1 and Other Coronavirus Infections in Hong Kong. J. Clin. Microbiol. 2006, 44, 2063–2071.
    11. Nitsche, A.; Schweiger, B.; Ellerbrok, H.; Niedrig, M.; Pauli, G. SARS Coronavirus Detection. Emerg. Infect. Dis. 2004, 10, 1300–1303.
    12. Drosten, C.; Günther, S.; Preiser, W.; van der Werf, S.; Brodt, H.-R.; Becker, S.; Rabenau, H.; Panning, M.; Kolesnikova, L.; Fouchier, R.A.M.; et al. Identification of a Novel Coronavirus in Patients with Severe Acute Respiratory Syndrome. N. Engl. J. Med. 2003, 348, 1967–1976.
    13. Poutanen, S.M.; Low, D.E.; Henry, B.; Finkelstein, S.; Rose, D.; Green, K.; Tellier, R.; Draker, R.; Adachi, D.; Ayers, M.; et al. Identification of Severe Acute Respiratory Syndrome in Canada. N. Engl. J. Med. 2003, 348, 1995–2005.
    14. Ksiazek, T.G.; Erdman, D.; Goldsmith, C.S.; Zaki, S.R.; Peret, T.; Emery, S.; Tong, S.; Urbani, C.; Comer, J.A.; Lim, W.; et al. A Novel Coronavirus Associated with Severe Acute Respiratory Syndrome. N. Engl. J. Med. 2003, 348, 1953–1966.
    15. Peiris, J.S.M.; Lai, S.T.; Poon, L.L.M.; Guan, Y.; Yam, L.Y.C.; Lim, W.; Nicholls, J.; Yee, W.K.S.; Yan, W.W.; Cheung, M.T.; et al. Coronavirus as a Possible Cause of Severe Acute Respiratory Syndrome. Lancet 2003, 361, 1319–1325.
    16. Killerby, M.E.; Biggs, H.M.; Midgley, C.M.; Gerber, S.I.; Watson, J.T. Middle East Respiratory Syndrome Coronavirus Transmission. Emerg. Infect. Dis. 2020, 26, 191–198.
    17. Zaki, A.M.; van Boheemen, S.; Bestebroer, T.M.; Osterhaus, A.D.M.E.; Fouchier, R.A.M. Isolation of a Novel Coronavirus from a Man with Pneumonia in Saudi Arabia. N. Engl. J. Med. 2012, 367, 1814–1820.
    18. Al Johani, S.; Hajeer, A.H. MERS-CoV Diagnosis: An Update. J. Infect. Public Health 2016, 9, 216–219.
    19. Seddiq, N.; Al-Qahtani, M.; Al-Tawfiq, J.A.; Bukamal, N. First Confirmed Case of Middle East Respiratory Syndrome Coronavirus Infection in the Kingdom of Bahrain: In a Saudi Gentleman after Cardiac Bypass Surgery. Case Rep. Infect. Dis. 2017, 2017, 1–4.
    20. Zheng, F.; Liao, C.; Fan, Q.H.; Chen, H.B.; Zhao, X.G.; Xie, Z.G.; Li, X.L.; Chen, C.X.; Lu, X.X.; Liu, Z.S.; et al. Clinical Characteristics of Children with Coronavirus Disease 2019 in Hubei, China. Curr. Med. Sci. 2020, 40, 275–280.
    21. Tay, M.Z.; Poh, C.M.; Rénia, L.; MacAry, P.A.; Ng, L.F.P. The Trinity of COVID-19: Immunity, Inflammation and Intervention. Nat. Rev. Immunol. 2020, 20, 363–374.
    22. COVID-19 Map - Johns Hopkins Coronavirus Resource Center. Available online: (accessed on 31 May 2021).
    23. Toyoshima, Y.; Nemoto, K.; Matsumoto, S.; Nakamura, Y.; Kiyotani, K. SARS-CoV-2 Genomic Variations Associated with Mortality Rate of COVID-19. J. Hum. Genet. 2020, 65, 1075–1082.
    24. Vandenberg, O.; Martiny, D.; Rochas, O.; van Belkum, A.; Kozlakidis, Z. Considerations for Diagnostic COVID-19 Tests. Nat. Rev. Microbiol. 2020, 19, 171–183.
    25. Dong, Y.; Dai, T.; Wei, Y.; Zhang, L.; Zheng, M.; Zhou, F. A Systematic Review of SARS-CoV-2 Vaccine Candidates. Signal Transduct. Target. Ther. 2020, 5, 1–14.
    26. Elbe, S.; Buckland-Merrett, G. Data, Disease and Diplomacy: GISAID’s Innovative Contribution to Global Health. Glob. Challenges 2017, 1, 33–46.
    27. Agarwala, R.; Barrett, T.; Beck, J.; Benson, D.A.; Bollin, C.; Bolton, E.; Bourexis, D.; Brister, J.R.; Bryant, S.H.; Canese, K.; et al. Database Resources of the National Center for Biotechnology Information. Nucleic Acids Res. 2018, 46, D8–D13.
    28. Hadfield, J.; Megill, C.; Bell, S.M.; Huddleston, J.; Potter, B.; Callender, C.; Sagulenko, P.; Bedford, T.; Neher, R.A. NextStrain: Real-Time Tracking of Pathogen Evolution. Bioinformatics 2018, 34, 4121–4123.
    29. Rambaut, A.; Holmes, E.C.; O’Toole, Á.; Hill, V.; McCrone, J.T.; Ruis, C.; du Plessis, L.; Pybus, O.G. A Dynamic Nomenclature Proposal for SARS-CoV-2 Lineages to Assist Genomic Epidemiology. Nat. Microbiol. 2020, 5, 1403–1407.
    30. GISAID Clade and Lineage Nomenclature Aids in Genomic Epidemiology Studies of Active HCoV-19 Viruses. Available online: (accessed on 31 May 2021).
    31. Tang, X.; Wu, C.; Li, X.; Song, Y.; Yao, X.; Wu, X.; Duan, Y.; Zhang, H.; Wang, Y.; Qian, Z.; et al. On the Origin and Continuing Evolution of SARS-CoV-2. Natl. Sci. Rev. 2020, 7, 1012–1023.
    32. Hodcroft, E.B.; Hadfield, J.; Neher, R.A.; Bedford, T. Year-Letter Genetic Clade Naming for SARS-CoV-2 on Nextstain.Org. Available online: (accessed on 31 May 2021).
    33. Zhu, N.; Zhang, D.; Wang, W.; Li, X.; Yang, B.; Song, J.; Zhao, X.; Huang, B.; Shi, W.; Lu, R.; et al. A Novel Coronavirus from Patients with Pneumonia in China, 2019. N. Engl. J. Med. 2020, 382, 727–733.
    34. Xia, S.; Lan, Q.; Su, S.; Wang, X.; Xu, W.; Liu, Z.; Zhu, Y.; Wang, Q.; Lu, L.; Jiang, S. The Role of Furin Cleavage Site in SARS-CoV-2 Spike Protein-Mediated Membrane Fusion in the Presence or Absence of Trypsin. Signal Transduct. Target. Ther. 2020, 5, 92.
    35. Hakim, M.S. SARS-CoV-2, Covid-19, and the Debunking of Conspiracy Theories. Rev. Med. Virol. 2021, e2222.
    36. Zhou, H.; Chen, X.; Hu, T.; Li, J.; Song, H.; Liu, Y.; Wang, P.; Liu, D.; Yang, J.; Holmes, E.C.; et al. A Novel Bat Coronavirus Closely Related to SARS-CoV-2 Contains Natural Insertions at the S1/S2 Cleavage Site of the Spike Protein. Curr. Biol. 2020, 30, 2196–2203.e3.
    37. Lam, T.T.Y.; Jia, N.; Zhang, Y.W.; Shum, M.H.H.; Jiang, J.F.; Zhu, H.C.; Tong, Y.G.; Shi, Y.X.; Ni, X.B.; Liao, Y.S.; et al. Identifying SARS-CoV-2-Related Coronaviruses in Malayan Pangolins. Nature 2020, 583, 282–285.
    38. Zhang, T.; Wu, Q.; Zhang, Z. Probable Pangolin Origin of SARS-CoV-2 Associated with the COVID-19 Outbreak. Curr. Biol. 2020, 30, 1346–1351.e2.
    39. Korber, B.; Fischer, W.M.; Gnanakaran, S.; Yoon, H.; Theiler, J.; Abfalterer, W.; Hengartner, N.; Giorgi, E.E.; Bhattacharya, T.; Foley, B.; et al. Tracking Changes in SARS-CoV-2 Spike: Evidence That D614G Increases Infectivity of the COVID-19 Virus. Cell 2020, 182, 812–827.e19.
    40. Volz, E.; Hill, V.; McCrone, J.T.; Price, A.; Jorgensen, D.; O’Toole, Á.; Southgate, J.; Johnson, R.; Jackson, B.; Nascimento, F.F.; et al. Evaluating the Effects of SARS-CoV-2 Spike Mutation D614G on Transmissibility and Pathogenicity. Cell 2021, 184, 64–75.e11.
    41. Hou, Y.J.; Chiba, S.; Halfmann, P.; Ehre, C.; Kuroda, M.; Dinnon, K.H.; Leist, S.R.; Schäfer, A.; Nakajima, N.; Takahashi, K.; et al. SARS-CoV-2 D614G Variant Exhibits Efficient Replication Ex Vivo and Transmission in Vivo. Science 2021, 370, 1464–1468.
    42. PHE SARS-CoV-2 Variants of Concern and Variants under Investigation in England. Available online: (accessed on 31 May 2021).
    43. CDC SARS-CoV-2 Variant Classifications and Definitions. Available online: (accessed on 31 May 2021).
    44. Davies, N.G.; Abbott, S.; Barnard, R.C.; Jarvis, C.I.; Kucharski, A.J.; Munday, J.D.; Pearson, C.A.B.; Russell, T.W.; Tully, D.C.; Washburne, A.D.; et al. Estimated Transmissibility and Impact of SARS-CoV-2 Lineage B.1.1.7 in England. Science 2021, 372, eabg3055.
    45. Ferasin, L.; Fritz, M.; Ferasin, H.; Legros, V.; Leroy, E.M. Myocarditis in Naturally Infected Pets with the British Variant of COVID-19. bioRxiv 2021.
    46. Fujino, T.; Nomoto, H.; Kutsuna, S.; Ujiie, M.; Suzuki, T.; Sato, R.; Fujimoto, T.; Kuroda, M.; Wakita, T.; Ohmagari, N. Novel SARS-CoV-2 Variant Identified in Travelers from Brazil to Japan. Emerg. Infect. Dis. 2021, 27, 1243–1245.
    47. Sabino, E.C.; Buss, L.F.; Carvalho, M.P.S.; Prete, C.A.; Crispim, M.A.E.; Fraiji, N.A.; Pereira, R.H.M.; Parag, K.V.; da Silva Peixoto, P.; Kraemer, M.U.G.; et al. Resurgence of COVID-19 in Manaus, Brazil, despite High Seroprevalence. Lancet 2021, 397, 452–455.
    48. Naveca, F.; Souza, V.; Corado, A.; Nascimento, F.; Silva, G.; Costa, Á.; Duarte, D.; Pessoa, K.; Mejía, M.; Brandão, M.; et al. COVID-19 Epidemic in the Brazilian State of Amazonas Was Driven by Long-Term Persistence of Endemic SARS-CoV-2 Lineages and the Recent Emergence of the New Variant of Concern P.1. Res. Sq. 2021.
    49. Faria, N.R.; Mellan, T.A.; Whittaker, C.; Claro, I.M.; Candido, D.d.S.; Mishra, S.; Crispim, M.A.E.; Sales, F.C.S.; Hawryluk, I.; McCrone, J.T.; et al. Genomics and Epidemiology of the P.1 SARS-CoV-2 Lineage in Manaus, Brazil. Science 2021, 372, 815–821.
    50. Lan, J.; Ge, J.; Yu, J.; Shan, S.; Zhou, H.; Fan, S.; Zhang, Q.; Shi, X.; Wang, Q.; Zhang, L.; et al. Structure of the SARS-CoV-2 Spike Receptor-Binding Domain Bound to the ACE2 Receptor. Nature 2020, 581, 215–220.
    51. Shang, J.; Ye, G.; Shi, K.; Wan, Y.; Luo, C.; Aihara, H.; Geng, Q.; Auerbach, A.; Li, F. Structural Basis of Receptor Recognition by SARS-CoV-2. Nature 2020, 581, 221–224.
    52. Starr, T.N.; Greaney, A.J.; Hilton, S.K.; Ellis, D.; Crawford, K.H.D.; Dingens, A.S.; Navarro, M.J.; Bowen, J.E.; Tortorici, M.A.; Walls, A.C.; et al. Deep Mutational Scanning of SARS-CoV-2 Receptor Binding Domain Reveals Constraints on Folding and ACE2 Binding. Cell 2020, 182, 1295–1310.e20.
    53. Tian, F.; Tong, B.; Sun, L.; Shi, S.; Zheng, B.; Wang, Z.; Dong, X.; Zheng, P. Mutation N501Y in RBD of Spike Protein Strengthens the Inter-Action between COVID-19 and Its Receptor ACE2. bioRxiv 2021.
    54. FDA Fact Sheet for Health Care Providers Emergency Use Authorization (EUA) of Bamlanivimab and Etesevimab. Available online: (accessed on 31 May 2021).
    55. Wang, P.; Nair, M.S.; Liu, L.; Iketani, S.; Luo, Y.; Guo, Y.; Wang, M.; Yu, J.; Zhang, B.; Kwong, P.D.; et al. Antibody Resistance of SARS-CoV-2 Variants B.1.351 and B.1.1.7. Nature 2021, 593, 130–135.
    56. FDA Fact Sheet for Health Care Providers Emergency Use Authorization (EUA) of Regen-Covtm (Casirivimab with Imdevimab). Available online: (accessed on 31 May 2021).
    57. Wang, P.; Casner, R.G.; Nair, M.S.; Wang, M.; Yu, J.; Cerutti, G.; Liu, L.; Kwong, P.D.; Huang, Y.; Shapiro, L.; et al. Increased Resistance of SARS-CoV-2 Variant P.1 to Antibody Neutralization. Cell Host Microbe 2021, 29, 747–751.e4.
    58. Edara, V.V.; Floyd, K.; Lai, L.; Gardner, M.; Hudson, W.; Piantadosi, A.; Waggoner, J.J.; Babiker, A.; Ahmed, R.; Xie, X.; et al. Infection and MRNA-1273 Vaccine Antibodies Neutralize SARS-CoV-2 UK Variant. medRxiv 2021.
    59. Wu, K.; Werner, A.P.; Moliva, J.I.; Koch, M.; Choi, A.; Stewart-Jones, G.B.E.; Bennett, H.; Boyoglu-Barnum, S.; Shi, W.; Graham, B.S.; et al. mRNA-1273 Vaccine Induces Neutralizing Antibodies against Spike Mutants from Global SARS-CoV-2 Variants. bioRxiv 2021.
    60. Collier, D.A.; De Marco, A.; Ferreira, I.A.T.M.; Meng, B.; Datir, R.; Walls, A.C.; Kemp S, S.A.; Bassi, J.; Pinto, D.; Fregni, C.S.; et al. SARS-CoV-2 B.1.1.7 Sensitivity to MRNA Vaccine-Elicited, Convalescent and Monoclonal Antibodies. medRxiv 2021.
    61. Altmann, D.M.; Boyton, R.J.; Beale, R. Immunity to SARS-CoV-2 Variants of Concern. Science 2021, 371, 1103–1104.
    62. Lule Bugembe, D.; VTPhan, M.; Ssewanyana, I.; Semanda, P.; Dhaala, B.; Nabadda, S.; Niamh, Á.O.; Rambaut, A.; Kaleebu, P.; Cotten, M. A SARS-CoV-2 Lineage A Variant (A.23.1) with Altered Spike Has Emerged and Is 1 Dominating the Current Uganda Epidemic 2. medRxiv 2021.
    63. Deng, X.; Garcia-knight, M.A.; Khalid, M.M.; Servellita, V.; Morris, M.K.; Sotomayor-gonzález, A.; Glasner, D.R.; Reyes, R.; Gliwa, A.S.; Reddy, N.P.; et al. Transmission, Infectivity, and Neutralization of a Spike L452R SARS-CoV-2 Variant. Cell 2021, in press.
    64. Yadav, P.D.; Sapkal, G.N.; Abraham, P.; Ella, R.; Deshpande, G.; Patil, D.Y.; Nyayanit, D.A.; Gupta, N.; Sahay, R.R.; Shete, A.M.; et al. Neutralization of Variant under Investigation B.1.617 with Sera of BBV152 Vaccinees. Clin. Infect. Dis. 2021, in press.
    65. Rahbari, R.; Moradi, N.; Abdi, M. RRT-PCR for SARS-CoV-2: Analytical Considerations. Clin. Chim. Acta 2021, 516, 1–7.
    66. Eftekhari, A.; Alipour, M.; Chodari, L.; Dizaj, S.M.; Ardalan, M.R.; Samiei, M.; Sharifi, S.; Vahed, S.Z.; Huseynova, I.; Khalilov, R.; et al. A Comprehensive Review of Detection Methods for SARS-CoV-2. Microorganisms 2021, 9, 232.
    67. Sharma, K.; Aggarwala, P.; Gandhi, D.; Mathias, A.; Singh, P.; Sharma, S.; Negi, S.S.; Bhargava, A.; Das, P.; Gaikwad, U.; et al. Comparative Analysis of Various Clinical Specimens in Detection of SARS-CoV-2 Using rRT-PCR in New and Follow up Cases of COVID-19 Infection: Quest for the Best Choice. PLoS ONE 2021, 16, e0249408.
    68. Tong, Y.; Bao, A.; Chen, H.; Huang, J.; Lv, Z.; Feng, L.; Cheng, Y.; Wang, Y.; Bai, L.; Rao, W.; et al. Necessity for Detection of SARS-CoV-2 RNA in Multiple Types of Specimens for the Discharge of the Patients with COVID-19. J. Transl. Med. 2020, 18, 1–8.
    69. Udvardi, M.K.; Czechowski, T.; Scheible, W.R. Eleven Golden Rules of Quantitative RT-PCR. Plant Cell 2008, 20, 1736–1737.
    70. Larionov, A.; Krause, A.; Miller, W.R. A Standard Curve Based Method for Relative Real Time PCR Data Processing. BMC Bioinform. 2005, 6, 1–16.
    71. Sherina, V.; McMurray, H.R.; Powers, W.; Land, H.; Love, T.M.T.; McCall, M.N. Statistical Approaches to Decreasing the Discrepancy of Non-detects in qPCR Data. bioRxiv 2017.
    72. Park, C.; Lee, J.; Hassan, Z.U.; Ku, K.B.; Kim, S.J.; Kim, H.G.; Park, E.C.; Park, G.S.; Park, D.; Baek, S.H.; et al. Comparison of Digital PCR and Quantitative PCR with Various SARS-CoV-2 Primer-Probe Sets. J. Microbiol. Biotechnol. 2021, 31, 358–367.
    73. Han, M.S.; Byun, J.H.; Cho, Y.; Rim, J.H. RT-PCR for SARS-CoV-2: Quantitative versus Qualitative. Lancet Infect. Dis. 2021, 21, 165.
    74. Wölfel, R.; Corman, V.M.; Guggemos, W.; Seilmaier, M.; Zange, S.; Müller, M.A.; Niemeyer, D.; Jones, T.C.; Vollmar, P.; Rothe, C.; et al. Virological Assessment of Hospitalized Patients with COVID-2019. Nature 2020, 581, 465–469.
    75. Pan, Y.; Zhang, D.; Yang, P.; Poon, L.L.M.; Wang, Q. Viral Load of SARS-CoV-2 in Clinical Samples. Lancet Infect. Dis. 2020, 20, 411–412.
    76. Arnaout, R.; Lee, R.A.; Lee, G.R.; Callahan, C.; Cheng, A.; Yen, C.F.; Smith, K.P.; Arora, R.; Kirby, J.E. The Limit of Detection Matters: The Case for Benchmarking Severe Acute Respiratory Syndrome Coronavirus 2 Testing. Clin. Infect. Dis. 2021, in press.
    77. Platts, A.E.; Johnson, G.D.; Linnemann, A.K.; Krawetz, S.A. Real-Time PCR Quantification Using a Variable Reaction Efficiency Model. Anal. Biochem. 2008, 380, 315–322.
    78. Lievens, A.; Van Aelst, S.; Van Den Bulcke, M.; Goetghebeur, E. Enhanced Analysis of Real-Time PCR Data by Using a Variable Efficiency Model: FPK-PCR. Nucleic Acids Res. 2012, 40, e10.
    79. Forootan, A.; Sjöback, R.; Björkman, J.; Sjögreen, B.; Linz, L.; Kubista, M. Methods to Determine Limit of Detection and Limit of Quantification in Quantitative Real-Time PCR (QPCR). Biomol. Detect. Quantif. 2017, 12, 1–6.
    80. Wikramaratna, P.S.; Paton, R.S.; Ghafari, M.; Lourenço, J. Estimating the False-Negative Test Probability of SARSCoV- 2 by RT-PCR. Eurosurveillance 2020, 25, 2000568.
    81. Xiao, A.T.; Tong, Y.X.; Zhang, S. False Negative of RT-PCR and Prolonged Nucleic Acid Conversion in COVID-19: Rather than Recurrence. J. Med. Virol. 2020, 92, 1755–1756.
    82. Erster, O.; Shkedi, O.; Benedek, G.; Zilber, E.; Varkovitzky, I.; Shirazi, R.; Shorka, D.O.; Cohen, Y.; Bar, T.; Yechieli, R.; et al. Improved Sensitivity, Safety, and Rapidity of COVID-19 Tests by Replacing Viral Storage Solution with Lysis Buffer. PLoS ONE 2021, 16, e0249149.
    83. Tsang, N.N.Y.; So, H.C.; Ng, K.Y.; Cowling, B.J.; Leung, G.M.; Ip, D.K.M. Diagnostic Performance of Different Sampling Approaches for SARS-CoV-2 RT-PCR Testing: A Systematic Review and Meta-Analysis. Lancet Infect. Dis. 2021, in press.
    84. Karligkiotis, A.; Arosio, A.; Castelnuovo, P. How to Obtain a Nasopharyngeal Swab Specimen. N. Engl. J. Med. 2020, 383, e14.
    85. Pinninti, S.; Trieu, C.; Pati, S.K.; Latting, M.; Cooper, J.; Seleme, M.C.; Boppana, S.; Arora, N.; Britt, W.J.; Boppana, S.B. Comparing Nasopharyngeal and Midturbinate Nasal Swab Testing for the Identification of Severe Acute Respiratory Syndrome Coronavirus 2. Clin. Infect. Dis. 2021, 72, 1253–1255.
    86. Mohammadi, A.; Esmaeilzadeh, E.; Li, Y.; Bosch, R.J.; Li, J.Z. SARS-CoV-2 Detection in Different Respiratory Sites: A Systematic Review and Meta-Analysis. EBioMedicine 2020, 59, 102903.
    87. Fani, M.; Teimoori, A.; Ghafari, S. Comparison of the COVID-2019 (SARS-CoV-2) Pathogenesis with SARS-CoV and MERS-CoV Infections. Future Virol. 2020, 15, 317–323.
    88. Kim, J.M.; Kim, H.M.; Lee, E.J.; Jo, H.J.; Yoon, Y.; Lee, N.J.; Son, J.; Lee, Y.J.; Kim, M.S.; Lee, Y.P.; et al. Detection and Isolation of SARS-CoV-2 in Serum, Urine, and Stool Specimens of COVID-19 Patients from the Republic of Korea. Osong Public Heal. Res. Perspect. 2020, 11, 112–117.
    89. Wang, W.; Xu, Y.; Gao, R.; Al, E. Detection of SARS-CoV-2 in Different Types of Clinical Specimens. JAMA 2020, 323, 1843–1844.
    90. Vogels, C.B.F.; Brito, A.F.; Wyllie, A.L.; Fauver, J.R.; Ott, I.M.; Kalinich, C.C.; Petrone, M.E.; Casanovas-Massana, A.; Catherine Muenker, M.; Moore, A.J.; et al. Analytical Sensitivity and Efficiency Comparisons of SARS-CoV-2 RT–QPCR Primer–Probe Sets. Nat. Microbiol. 2020, 5, 1299–1305.
    91. Jung, Y.; Park, G.S.; Moon, J.H.; Ku, K.; Beak, S.H.; Lee, C.S.; Kim, S.; Park, E.C.; Park, D.; Lee, J.H.; et al. Comparative Analysis of Primer-Probe Sets for RT-qPCR of COVID-19 Causative Virus (SARS-CoV-2). ACS Infect. Dis. 2020, 6, 2513–2523.
    92. Pillonel, T.; Scherz, V.; Jaton, K.; Greub, G.; Bertelli, C. Letter to the Editor: SARS-CoV-2 Detection by Real-Time RT-PCR. Eurosurveillance 2020, 25, 1–2.
    93. Klungthong, C.; Chinnawirotpisan, P.; Hussem, K.; Phonpakobsin, T.; Manasatienkij, W.; Ajariyakhajorn, C.; Rungrojcharoenkit, K.; Gibbons, R.V.; Jarman, R.G. The Impact of Primer and Probe-Template Mismatches on the Sensitivity of Pandemic Influenza A/H1N1/2009 Virus Detection by Real-Time RT-PCR. J. Clin. Virol. 2010, 48, 91–95.
    94. Nayar, G.; Seabolt, E.E.; Kunitomi, M.; Agarwal, A.; Beck, K.L.; Mukherjee, V.; Kaufman, J.H. Analysis and Forecasting of Global Real Time RT-PCR Primers and Probes for SARS-CoV-2. Sci. Rep. 2021, 11, 8988.
    95. van Kasteren, P.B.; van der Veer, B.; van den Brink, S.; Wijsman, L.; de Jonge, J.; van den Brandt, A.; Molenkamp, R.; Reusken, C.B.E.M.; Meijer, A. Comparison of Seven Commercial RT-PCR Diagnostic Kits for COVID-19. J. Clin. Virol. 2020, 128, 104412.
    96. Simas, A.M.; Crott, J.W.; Sedore, C.; Rohrbach, A.; Monaco, A.P.; Gabriel, S.B.; Lennon, N.; Blumenstiel, B.; Genco, C.A. Pooling for SARS-CoV2 Surveillance: Validation and Strategy for Implementation in K-12 Schools. medRxiv 2020.
    97. Sun, Q.; Li, J.; Ren, H.; Pastor, L.; Loginova, Y.; Madej, R.; Taylor, K.; Wong, J.K.; Zhang, Z.; Zhang, A.; et al. Saliva as a Testing Specimen with or without Pooling for SARS-CoV-2 Detection by Multiplex RT-PCR Test. PLoS ONE 2021, 16, e0243183.
    98. de Salazar, A.; Aguilera, A.; Trastoy, R.; Fuentes, A.; Alados, J.C.; Causse, M.; Galán, J.C.; Moreno, A.; Trigo, M.; Pérez-Ruiz, M.; et al. Sample Pooling for SARS-CoV-2 RT-PCR Screening. Clin. Microbiol. Infect. 2020, 26, 1687.e1–1687.e5.
    99. Barak, N.; Ben-Ami, R.; Sido, T.; Perri, A.; Shtoyer, A.; Rivkin, M.; Licht, T.; Peretz, A.; Magenheim, J.; Fogel, I.; et al. Lessons from Applied Large-Scale Pooling of 133,816 SARS-CoV-2 RT-PCR Tests. Sci. Transl. Med. 2021, 13, eabf2823.
    100. Cleary, B.; Hay, J.A.; Blumenstiel, B.; Harden, M.; Cipicchio, M.; Bezney, J.; Simonton, B.; Hong, D.; Senghore, M.; Sesay, A.K.; et al. Using Viral Load and Epidemic Dynamics to Optimize Pooled Testing in Resource-Constrained Settings. Sci. Transl. Med. 2021, 13, eabf1568.
    101. Kim, S.Y.; Lee, J.; Sung, H.; Lee, H.; Han, M.G.; Yoo, C.K.; Lee, S.W.; Hong, K.H. Pooling Upper Respiratory Specimens for Rapid Mass Screening of COVID-19 by Real-Time RT-PCR. Emerg. Infect. Dis. 2020, 26, 2469–2472.
    102. Wacker, M.J.; Godard, M.P. Analysis of One-Step and Two- Step Real-Time RT-PCR Using Superscript III. J. Biomol. Tech. 2005, 16, 266–271.
    103. Dharavath, B.; Yadav, N.; Desai, S.; Sunder, R.; Mishra, R.; Ketkar, M.; Bhanshe, P.; Gupta, A.; Redhu, A.K.; Patkar, N.; et al. A One-Step, One-Tube Real-Time RT-PCR Based Assay with an Automated Analysis for Detection of SARS-CoV-2. Heliyon 2020, 6, e04405.
    104. Yu, C.Y.; Chan, K.G.; Yean, C.Y.; Ang, G.Y. Nucleic Acid-Based Diagnostic Tests for the Detection SARS-CoV-2: An Update. Diagnostics 2021, 11, 53.
    105. Miranda, J.P.; Osorio, J.; Videla, M.; Angel, G.; Camponovo, R.; Henríquez-Henríquez, M. Analytical and Clinical Validation for RT-qPCR Detection of SARS-CoV-2 Without RNA Extraction. Front. Med. 2020, 7, 567572.
    106. Das Mukhopadhyay, C.; Sharma, P.; Sinha, K.; Rajarshi, K. Recent Trends in Analytical and Digital Techniques for the Detection of the SARS-Cov-2. Biophys. Chem. 2021, 270, 106538.
    107. Park, M.; Won, J.; Choi, B.Y.; Lee, C.J. Optimization of Primer Sets and Detection Protocols for SARS-CoV-2 of Coronavirus Disease 2019 (COVID-19) Using PCR and Real-Time PCR. Exp. Mol. Med. 2020, 52, 963–977.
    108. Chen, C.J.; Hsieh, L.L.; Lin, S.K.; Wang, C.F.; Huang, Y.H.; Lin, S.Y.; Lu, P.L. Optimization of the CDC Protocol of Molecular Diagnosis of COVID-19 for Timely Diagnosis. Diagnostics 2020, 10, 333.
    109. Bustin, S.A.; Nolan, T. RT-qPCR Testing of SARS-COV-2: A Primer. Int. J. Mol. Sci. 2020, 21, 3004.
    110. Wyllie, A.L.; Fournier, J.; Casanovas-Massana, A.; Campbell, M.; Tokuyama, M.; Vijayakumar, P.; Warren, J.L.; Geng, B.; Muenker, M.C.; Moore, A.J.; et al. Saliva or Nasopharyngeal Swab Specimens for Detection of SARS-CoV-2. N. Engl. J. Med. 2020, 383, 1283–1286.
    111. Takeuchi, Y.; Furuchi, M.; Kamimoto, A.; Honda, K.; Matsumura, H.; Kobayashi, R. Saliva-Based PCR Tests for SARS-CoV-2 Detection. J. Oral Sci. 2020, 62, 350–351.
    112. Gallo, G.; La Torre, M.; Pietroletti, R.; Bianco, F.; Altomare, D.F.; Pucciarelli, S.; Gagliardi, G.; Perinotti, R. Italian Society of Colorectal Surgery Recommendations for Good Clinical Practice in Colorectal Surgery during the Novel Coronavirus Pandemic. Tech. Coloproctol. 2020, 24, 501–505.
    113. Mizumoto, K.; Kagaya, K.; Zarebski, A.; Chowell, G. Estimating the Asymptomatic Proportion of Coronavirus Disease 2019 (COVID-19) Cases on Board the Diamond Princess Cruise Ship, Yokohama, Japan, 2020. Eurosurveillance 2020, 25, 2000180.
    114. Zhu, H.; Zhang, H.; Xu, Y.; Laššáková, S.; Korabečná, M.; Neužil, P. PCR Past, Present and Future. Biotechniques 2020, 69, 317–325.
    115. Kojabad, A.A.; Farzanehpour, M.; Galeh, H.E.G.; Dorostkar, R.; Jafarpour, A.; Bolandian, M.; Nodooshan, M.M. Droplet Digital PCR of Viral DNA/RNA, Current Progress, Challenges, and Future Perspectives. J. Med. Virol. 2021, 93, 4182–4197.
    116. Sedlak, R.H.; Jerome, K.R. Viral Diagnostics in the Era of Digital Polymerase Chain Reaction. Diagn. Microbiol. Infect. Dis. 2013, 75, 1–4.
    117. Salipante, S.J.; Jerome, K.R. Digital PCR—An Emerging Technology with Broad Applications in Microbiology. Clin. Chem. 2020, 66, 117–123.
    118. Quan, P.L.; Sauzade, M.; Brouzes, E. DPCR: A Technology Review. Sensors 2018, 18, 1271.
    119. Kuypers, J.; Jerome, K.R. Applications of Digital PCR for Clinical Microbiology. J. Clin. Microbiol. 2017, 55, 1621–1628.
    120. Alteri, C.; Cento, V.; Antonello, M.; Colagrossi, L.; Merli, M.; Ughi, N.; Renica, S.; Matarazzo, E.; Ruscio, F.D.; Tartaglione, L.; et al. Detection and Quantification of SARS-CoV-2 by Droplet Digital PCR in Real-Time PCR Negative Nasopharyngeal Swabs from Suspected COVID-19 Patients. PLoS ONE 2020, 15.
    121. Falzone, L.; Musso, N.; Gattuso, G.; Bongiorno, D.; Palermo, C.I.; Scalia, G.; Libra, M.; Stefani, S. Sensitivity Assessment of Droplet Digital PCR for SARS-CoV-2 Detection. Int. J. Mol. Med. 2020, 46, 957–964.
    122. Suo, T.; Liu, X.; Feng, J.; Guo, M.; Hu, W.; Guo, D.; Ullah, H.; Yang, Y.; Zhang, Q.; Wang, X.; et al. DdPCR: A More Accurate Tool for SARS-CoV-2 Detection in Low Viral Load Specimens. Emerg. Microbes Infect. 2020, 9, 1259–1268.
    123. Dang, Y.; Liu, N.; Tan, C.; Feng, Y.; Yuan, X.; Fan, D.; Peng, Y.; Jin, R.; Guo, Y.; Lou, J. Comparison of Qualitative and Quantitative Analyses of COVID-19 Clinical Samples. Clin. Chim. Acta 2020, 510, 613–616.
    124. Liu, X.; Feng, J.; Zhang, Q.; Guo, D.; Zhang, L.; Suo, T.; Hu, W.; Guo, M.; Wang, X.; Huang, Z.; et al. Analytical Comparisons of SARS-COV-2 Detection by qRT-PCR and ddPCR with Multiple Primer/Probe Sets. Emerg. Microbes Infect. 2020, 9, 1175–1179.
    125. Kevadiya, B.D.; Machhi, J.; Herskovitz, J.; Oleynikov, M.D.; Blomberg, W.R.; Bajwa, N.; Soni, D.; Das, S.; Hasan, M.; Patel, M.; et al. Diagnostics for SARS-CoV-2 Infections. Nat. Mater. 2021, 20, 593–605.
    126. Gatti, M.; Calandri, M.; Biondo, A.; Geninatti, C.; Piatti, C.; Ruggirello, I.; Santonocito, A.; Varello, S.; Bergamasco, L.; Bironzo, P.; et al. Emergency Room Comprehensive Assessment of Demographic, Radiological, Laboratory and Clinical Data of Patients with COVID-19: Determination of Its Prognostic Value for in-Hospital Mortality. Intern. Emerg. Med. 2021, in press.
    127. Mostafa, H.H.; Hardick, J.; Morehead, E.; Miller, J.A.; Gaydos, C.A.; Manabe, Y.C. Comparison of the Analytical Sensitivity of Seven Commonly Used Commercial SARS-CoV-2 Automated Molecular Assays. J. Clin. Virol. 2020, 130, 104578.
    128. D’Cruz, R.J.; Currier, A.W.; Sampson, V.B. Laboratory Testing Methods for Novel Severe Acute Respiratory Syndrome-Coronavirus-2 (SARS-CoV-2). Front. Cell Dev. Biol. 2020, 8, 1–11.
    129. Ganguli, A.; Mostafa, A.; Berger, J.; Aydin, M.Y.; Sun, F.; Stewart de Ramirez, S.A.; Valera, E.; Cunningham, B.T.; King, W.P.; Bashir, R. Rapid Isothermal Amplification and Portable Detection System for SARS-CoV-2. Proc. Natl. Acad. Sci. USA 2020, 117, 22727–22735.
    130. Carter, L.J.; Garner, L.V.; Smoot, J.W.; Li, Y.; Zhou, Q.; Saveson, C.J.; Sasso, J.M.; Gregg, A.C.; Soares, D.J.; Beskid, T.R.; et al. Assay Techniques and Test Development for COVID-19 Diagnosis. ACS Cent. Sci. 2020, 6, 591–605.
    131. Bodulev, O.L.; Sakharov, I.Y. Isothermal Nucleic Acid Amplification Techniques and Their Use in Bioanalysis. Biochemistry 2020, 85, 147–166.
    132. Udugama, B.; Kadhiresan, P.; Kozlowski, H.N.; Malekjahani, A.; Osborne, M.; Li, V.Y.C.; Chen, H.; Mubareka, S.; Gubbay, J.B.; Chan, W.C.W. Diagnosing COVID-19: The Disease and Tools for Detection. ACS Nano 2020, 14, 3822–3835.
    133. Mautner, L.; Baillie, C.K.; Herold, H.M.; Volkwein, W.; Guertler, P.; Eberle, U.; Ackermann, N.; Sing, A.; Pavlovic, M.; Goerlich, O.; et al. Rapid Point-of-Care Detection of SARS-CoV-2 Using Reverse Transcription Loop-Mediated Isothermal Amplification (RT-LAMP). Virol. J. 2020, 17, 1–14.
    134. Qian, J.; Boswell, S.A.; Chidley, C.; Lu, Z.X.; Pettit, M.E.; Gaudio, B.L.; Fajnzylber, J.M.; Ingram, R.T.; Ward, R.H.; Li, J.Z.; et al. An Enhanced Isothermal Amplification Assay for Viral Detection. Nat. Commun. 2020, 11, 1–10.
    135. Chaibun, T.; Puenpa, J.; Ngamdee, T.; Boonapatcharoen, N.; Athamanolap, P.; O’Mullane, A.P.; Vongpunsawad, S.; Poovorawan, Y.; Lee, S.Y.; Lertanantawong, B. Rapid Electrochemical Detection of Coronavirus SARS-CoV-2. Nat. Commun. 2021, 12, 1–10.
    136. Zhang, C.; Zheng, T.; Fan, H.; Zhang, T.; Han, D. Aligner-Mediated Cleavage-Based Isothermal Amplification for SARS-CoV-2 RNA Detection. ACS Appl. Bio Mater. 2021, 4, 3805–3810.
    137. Xing, W.; Liu, Y.; Wang, H.; Li, S.; Lin, Y.; Chen, L.; Zhao, Y.; Chao, S.; Huang, X.; Ge, S.; et al. A High-Throughput, Multi-Index Isothermal Amplification Platform for Rapid Detection of 19 Types of Common Respiratory Viruses Including SARS-CoV-2. Engineering 2020, 6, 1130–1140.
    138. Notomi, T.; Okayama, H.; Masubuchi, H.; Yonekawa, T.; Watanabe, K.; Amino, N.; Hase, T. Loop-Mediated Isothermal Amplification of DNA. Nucleic Acids Res. 2000, 28, E63.
    139. Tomita, N.; Mori, Y.; Kanda, H.; Notomi, T. Loop-Mediated Isothermal Amplification (LAMP) of Gene Sequences and Simple Visual Detection of Products. Nat. Protoc. 2008, 3, 877–882.
    140. Mori, Y.; Kitao, M.; Tomita, N.; Notomi, T. Real-Time Turbidimetry of LAMP Reaction for Quantifying Template DNA. J. Biochem. Biophys. Methods 2004, 59, 145–157.
    141. Yu, L.; Wu, S.; Hao, X.; Dong, X.; Mao, L.; Pelechano, V.; Chen, W.-H.; Yin, X. Rapid Detection of COVID-19 Coronavirus Using a Reverse Transcriptional Loop-Mediated Isothermal Amplification (RT-LAMP) Diagnostic Platform. Clin. Chem. 2020, 66, 975–977.
    142. Dao Thi, V.L.; Herbst, K.; Boerner, K.; Meurer, M.; Kremer, L.P.M.; Kirrmaier, D.; Freistaedter, A.; Papagiannidis, D.; Galmozzi, C.; Stanifer, M.L.; et al. A Colorimetric RT-LAMP Assay and LAMP-Sequencing for Detecting SARS-CoV-2 RNA in Clinical Samples. Sci. Transl. Med. 2020, 12, eabc7075.
    143. Lu, R.; Wu, X.; Wan, Z.; Li, Y.; Jin, X.; Zhang, C. A Novel Reverse Transcription Loop-Mediated Isothermal Amplification Method for Rapid Detection of Sars-Cov-2. Int. J. Mol. Sci. 2020, 21, 2826.
    144. Park, G.S.; Ku, K.; Baek, S.H.; Kim, S.J.; Kim, S.I.; Kim, B.T.; Maeng, J.S. Development of Reverse Transcription Loop-Mediated Isothermal Amplification Assays Targeting Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). J. Mol. Diagnostics 2020, 22, 729–735.
    145. Baek, Y.H.; Um, J.; Antigua, K.J.C.; Park, J.H.; Kim, Y.; Oh, S.; Kim, Y.I.; Choi, W.S.; Kim, S.G.; Jeong, J.H.; et al. Development of a Reverse Transcription-Loop-Mediated Isothermal Amplification as a Rapid Early-Detection Method for Novel SARS-CoV-2. Emerg. Microbes Infect. 2020, 9, 998–1007.
    146. Wei, S.; Kohl, E.; Djandji, A.; Morgan, S.; Whittier, S.; Mansukhani, M.; Hod, E.; D’Alton, M.; Suh, Y.; Williams, Z. Direct Diagnostic Testing of SARS-CoV-2 without the Need for Prior RNA Extraction. Sci. Rep. 2021, 11, 1–6.
    147. Lalli, M.A.; Langmade, J.S.; Chen, X.; Fronick, C.C.; Sawyer, C.S.; Burcea, L.C.; Wilkinson, M.N.; Fulton, R.S.; Heinz, M.; Buchser, W.J.; et al. Rapid and Extraction-Free Detection of SARS-CoV-2 from Saliva by Colorimetric Reverse-Transcription Loop-Mediated Isothermal Amplification. Clin. Chem. 2021, 67, 415–424.
    148. Pang, B.; Xu, J.; Liu, Y.; Peng, H.; Feng, W.; Cao, Y.; Wu, J.; Xiao, H.; Pabbaraju, K.; Tipples, G.; et al. Isothermal Amplification and Ambient Visualization in a Single Tube for the Detection of SARS-CoV-2 Using Loop-Mediated Amplification and CRISPR Technology. Anal. Chem. 2020, 92, 16204–16212.
    149. Ali, Z.; Aman, R.; Mahas, A.; Rao, G.S.; Tehseen, M.; Marsic, T.; Salunke, R.; Subudhi, A.K.; Hala, S.M.; Hamdan, S.M.; et al. ISCAN: An RT-LAMP-Coupled CRISPR-Cas12 Module for Rapid, Sensitive Detection of SARS-CoV-2. Virus Res. 2020, 288, 198129.
    150. Zhu, X.; Wang, X.; Han, L.; Chen, T.; Wang, L.; Li, H.; Li, S.; He, L.; Fu, X.; Chen, S.; et al. Multiplex Reverse Transcription Loop-Mediated Isothermal Amplification Combined with Nanoparticle-Based Lateral Flow Biosensor for the Diagnosis of COVID-19. Biosens. Bioelectron. 2020, 166, 112437.
    151. Rohaim, M.A.; Clayton, E.; Sahin, I.; Vilela, J.; Khalifa, M.E.; Al-natour, M.Q.; Bayoumi, M.; Poirier, A.C.; Branavan, M.; Tharmakulasingam, M.; et al. Artificial Intelligence-Assisted Loop Mediated Isothermal Amplification (AI-LAMP) for Rapid Detection of SARS-CoV-2. Viruses 2020, 12, 972.
    152. Piepenburg, O.; Williams, C.H.; Stemple, D.L.; Armes, N.A. DNA Detection Using Recombination Proteins. PLoS Biol. 2006, 4, 1115–1121.
    153. Kersting, S.; Rausch, V.; Bier, F.F.; von Nickisch-Rosenegk, M. Rapid Detection of Plasmodium Falciparum with Isothermal Recombinase Polymerase Amplification and Lateral Flow Analysis. Malar. J. 2014, 13, 99.
    154. Lobato, I.M.; O’Sullivan, C.K. Recombinase Polymerase Amplification: Basics, Applications and Recent Advances. TrAC Trends Anal. Chem. 2018, 98, 19–35.
    155. Zhang, W.S.; Pan, J.; Li, F.; Zhu, M.; Xu, M.; Zhu, H.; Yu, Y.; Su, G. Reverse Transcription Recombinase Polymerase Amplification Coupled with CRISPR-Cas12a for Facile and Highly Sensitive Colorimetric SARS-CoV-2 Detection. Anal. Chem. 2021, 93, 4126–4133.
    156. Patchsung, M.; Jantarug, K.; Pattama, A.; Aphicho, K.; Suraritdechachai, S.; Meesawat, P.; Sappakhaw, K.; Leelahakorn, N.; Ruenkam, T.; Wongsatit, T.; et al. Clinical Validation of a Cas13-Based Assay for the Detection of SARS-CoV-2 RNA. Nat. Biomed. Eng. 2020, 4, 1140–1149.
    157. Arizti-Sanz, J.; Freije, C.A.; Stanton, A.C.; Petros, B.A.; Boehm, C.K.; Siddiqui, S.; Shaw, B.M.; Adams, G.; Kosoko-Thoroddsen, T.S.F.; Kemball, M.E.; et al. Streamlined Inactivation, Amplification, and Cas13-Based Detection of SARS-CoV-2. Nat. Commun. 2020, 11, 5921.
    158. Kim, H.S.; Abbas, N.; Shin, S. A Rapid Diagnosis of SARS-CoV-2 Using DNA Hydrogel Formation on Microfluidic Pores. Biosens. Bioelectron. 2021, 177, 113005.