Rapid detection of SARS-CoV-2 RNA using a one-step fast multiplex RT-PCR coupled to lateral flow immunoassay

Rapid detection of SARS-CoV-2 RNA using a one-step fast multiplex RT-PCR coupled to lateral flow immunoassay | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Rapid detection of SARS-CoV-2 RNA using a one-step fast multiplex RT-PCR coupled to lateral flow immunoassay Insaf Bel Hadj Ali, Hejer Souguir, Mouna Melliti, Mohamed Vall Taleb Mohamed, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4595176/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 18 Dec, 2024 Read the published version in BMC Infectious Diseases → Version 1 posted 4 You are reading this latest preprint version Abstract Background The COVID-19 pandemics has put emphasis on pivotal needs for diagnosis and surveillance worldwide, with the subsequent shortage of diagnostic reagents and kits. Therefore, it has become strategic for the countries to be able to access diagnosis, expand it, and acquire its own capacity to deploy diagnostics and alternative rapid accurate nucleic acid tests that are at lower costs. Here, we propose a visual SARS-CoV-2 detection using a one-step fast multiplex reverse transcription-PCR (RT-PCR) amplification coupled to lateral flow immunoassay detection on a PCRD device (Abingdon Health, UK). Methods Various simplex fast-PCRs were developed for screening sets of primer pairs newly designed or selected from literature or from validated WHO tests, targeting S , N , E , RdRp or ORF1ab genes. Primers showing specific and stable amplification were retained to assess for their suitability for detection on PCRD. Thus, fast RT-PCR amplifications were performed using the retained primers. They were doubly labeled with Fam and Biotin or Dig and Biotin to allow visual detection of the labeled amplicons on the lateral flow immunoassay PCR D etection (PCRD) device, looking at lack of interaction of the labeled primers (or primer dimers) with the test lines in negative or no RNA controls. All the assays were set up using RNAs isolated from patients’ nasopharyngeal swabs. Two simplex assays, targeting two different viral genomic regions ( N and E ) and showing specific detection on PCRD, were used to set up a one-step fast multiplex RT-PCR assay (where both differently labeled primer pairs were engaged) coupled to amplicons’ detection on a PCRD device. This novel method was evaluated on 50 SARS-CoV-2 positive and 50 SARS-CoV-2 negative samples and its performance was compared to the results of the quantitative RT-PCR (RT-qPCR) tests used for diagnosing the patients, here considered as the standard methods. Results This way, the new method showed a sensitivity of 88% (44/50) and a specificity of 98% (49/50). All patients who presented Ct values lower than 33 were positive for our assay. Except for one patient, those with Ct values greater than 33 showed negative results. Conclusion Our results have brought proof of principle on the usefulness of the one-step fast multiplex RT- PCR assay coupled to PCRD as new method for specific, sensitive, and rapid detection of SARS-CoV-2 without requiring costly laboratory equipment, and thus at reduced costs, in a format prone to be deployed when resources are limited. This new method of SARS-CoV-2 detection appears to be a good alternative for COVID-19 diagnosis or screening at points of need. Molecular diagnosis SARS-CoV-2 One-step fast multiplex RT-PCR Lateral flow immunoassay on PCRD N gene E gene Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction The COVID-19 pandemic is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). It is an emerging and highly infectious disease that has rapidly spread worldwide and become a public health emergency. This pandemic has created considerable pressure on health systems and countries, and has notably put emphasis on diagnosis and concurrently, on a shortage of diagnostic reagents and kits. Thus, it is strategic for the countries to enhance access to diagnosis and acquire the capacity to face such needs now and during future threats. Early detection of the disease was a key step in controlling its spread. The molecular diagnosis of COVID-19 is based on the amplification of viral genetic material. The World Health Organization (WHO) has designated reverse transcription and real-time quantitative PCR (RT-qPCR) as the gold standard diagnosis technique using a selection of protocols aiming at the amplification of one or more viral genome targets. Different protocols have been developed by different institutions in the United States, Germany, France, China, Hong Kong, Japan and Thailand, targeting different viral genomic regions (WHO, 2020). For example, the United States Center for Disease Control (US-CDC) protocol used three nucleocapsid gene targets ( N1, N2 and N3) . The protocol developed by German Consiliary Laboratory for Coronaviruses hosted at the Charité in Berlin (Charité/Berlin) used first line screening with the envelope ( E ) gene assay followed by a confirmatory assay using the RNA-dependent RNA polymerase ( RdRp ) gene [ 1 ]. The Hong Kong University, China protocol used two simplex assays targeting the nucleoprotein ( N ) gene and the Open reading frame ORF1ab [ 1 ]. The protocols developed by the Institiut Pasteur of Paris (IP2 and IP4) targeted two RdRp regions [ 1 ]. The China CDC protocol targeted the N gene and ORF1ab [ 1 ]. The one from Thailand was developed by the Department of Medical Sciences, Ministry of Public Health, and targeted the N gene [ 1 ]. Multiplex RT-qPCR tests have also been developed, representing an interesting advancement allowing simultaneous detection of two or more targets [ 2 , 3 ]. The RT-qPCR methods have been deployed in centralized diagnosis centers, but their major drawback is the need for relatively complex and expensive equipment and highly qualified personnel. This limited their use as a diagnostic tool in settings lacking adequate infrastructure and equipped laboratories [ 4 ]. Therefore, developing simpler molecular assays will improve testing approach and allow for the development of diagnosis algorithms, thus overcoming equipment and resource challenges. In addition, given the worldwide pressure to access reagents during the pandemics, alternative solutions should be considered to address supply shortages, to lower diagnostic costs and thus to expand testing capacity, and to better prepare for future pandemics. Testing is a cornerstone to fighting pandemics, for surveillance, and during likely resurgence events. Several methods have been developed to meet this need. These methods mainly include isothermal methods like loop-mediated isothermal amplification (LAMP), a technique that allows nucleic acid amplification at a constant temperature of 60°C to 65°C [ 5 ]. It uses a DNA polymerase with high strand displacement activity and four to six pairs of primers [ 6 ]. An additional reverse transcription step was combined, in a single tube, with the LAMP amplification technique, minimizing the reaction time to approximately 30 minutes [ 7 ]. RT-LAMP is a less expensive, simpler, and faster detection method that does not require the use of a thermocycler or expensive reagents [ 7 ]. This method has been used for the direct detection of SARS-CoV-2 RNA targeting the N , ORF1ab , and RNA polymerase genes [ 7 , 8 ]. As a simple and rapid technique, RT-LAMP can be coupled with various methods for visualizing amplified products. The most commonly used methods include visualizing amplified products with the naked eye either through a color indicator change [ 7 , 8 ] or on lateral flow strips [ 9 ]. Although the performance of LAMP is comparable to that of RT-qPCR for SARS-CoV-2 detection [ 10 ], this method is limited by the complexity of primer design [ 11 ] and the generation of false-positive results [ 12 ]. Moreover, multiplexing is complex due to the number of primers needed for a single reaction. Other widely used isothermal technologies include recombinase-based amplification, that includes recombinase polymerase amplification (RPA) and the similar recombinase-aided amplification (RAA). It has also been used for SARS-CoV-2 detection. It represents a faster, simpler, and less expensive alternative to RT-qPCR. It allows amplification at 37–42°C using only a pair of primers and a mix of enzymes (including recombinase) that eliminate the need for instruments such as thermocyclers [ 13 ]. This technology simplifies the testing process and allows SARS-CoV-2 detection in resource-limited environments. RPA/RAA offers various strategies for detecting amplified products, which can be either real-time using labeled probes generating visually detectable signals in 15 minutes (owing to fluorescent label) [ 14 ] or lateral flow strips in 5 minutes (owing to 6-FITC/biotin labels) [ 15 ]. However, longer primer sequences as well as lower primer binding temperatures in recombinase-based amplification reactions result in the formation of primer-dimers that may produce false positive signals [ 16 ]. CRISPR-Cas technology has recently been employed as another highly sensitive detection method [ 17 ]. Several CRISPR-based approaches such as SHERLOCK [ 18 ], DETECTR [ 19 ], ENHANCE [ 20 ], iSCAN [ 21 ], FELUDA [ 22 ], and AIOD-CRISPR [ 23 ], have been developed for accurate and portable diagnosis of COVID-19 from various types of samples. Nevertheless, CRISPR-based approaches are always associated with a pre-amplification step, which makes them time consuming and prone to cross-contamination. Despite the important number of novel technologies developed for pathogen detection, PCR remains the gold standard molecular diagnostic technique for infectious diseases. During the last decade, it has evolved to achieve better performance in terms of sensitivity, cost, and duration [ 24 ]. The introduction of DNA polymerases with improved processivity, drastically reduced the duration of a reaction and made fast-PCR an interesting alternative for rapid pathogen detection [ 25 ]. Thermocyclers, which are necessary for PCR, are becoming increasingly affordable and are currently considered basic equipment for infectious disease diagnosis laboratories. Furthermore, portable formats are now commercially available, enabling the creation of highly useful mobile laboratories to be closer to the patients or for field investigation notably to promptly respond to health emergencies that require immediate intervention. During the COVID-19 pandemic, one of the most used technique for disease diagnosis was based on lateral flow immunoassays for the detection of SARS-CoV2 antigens or human antibodies (anti-SARS-Cov2-IgG and anti-SARS-Cov2-IgM) thanks to their accessibility, feasibility and affordability [ 26 , 27 ]. Commercially available generic lateral flow immunoassays have enabled the accurate detection of nucleic acids from a range of pathogens [ 25 , 28 , 29 ]. These accessible assays proved to be user-friendly, cost effective making them strong candidates for decentralized diagnosis of infectious diseases and control strategies [ 30 ]. In this study, we aimed to address needs for alternative, rapid and less expensive approach to Covid 19 diagnosis by developing a one-step fast multiplex reverse transcription-PCR (RT-PCR) coupled to visual detection on a lateral flow immunoassay PCR Detection (PCRD) device. This study provides a proof of principle for the potential of this new method for rapid amplification and detection of viral RNA, and supports the feasibility of adapting it into a mobile version. Material and methods 1. Ethical statement The study is approved by the Biomedical Ethics Committee of the Institut Pasteur de Tunis (Ref: 2020/21/I/LR16IPT) in accordance with the Declaration of Helsinki. 2. Human samples Human samples used in this study correspond to nasopharyngeal swabs collected from anonymous patients suspected of having COVID-19 disease and referred for routine diagnosis to the Clinical Virology Laboratory of the Institut Pasteur de Tunis. Patients were enrolled between November 2020 and January 2021. They include 50 SARS-CoV-2-positive (CoV+) and 52 SARS-CoV-2-negative (CoV-) for COVID-19, as shown by the RT-qPCR (Table 1). RNAs were extracted using QIAMp Viral RNA Mini Kit (Qiagen, USA). Thirty additional RNAs extracted from 15 CoV+ (C+) and 15 CoV- (C-) were used for primers screening. For these RNAs, we synthesized the cDNAs using M-MuLV reverse transcriptase (GeneON-Bioscience, Germany). The cDNA synthesis included first annealing step where 10 µM of OligodT(23) were incubated with the RNA in a final volume of 8µl for 10 minutes at 70°C and then for 5 minutes at 4°C. During the second step, the cDNA synthesis reaction itself was performed by mixing the OligodT-RNA complex with 1X reaction buffer, 0.5 mM of dNTPs and 200 U of M-MuLV reverse transcriptase and incubating the mix at 42°C for 1 hour and at 65°C for 10 minutes. Synthesis was then checked by amplifying the human beta-globin gene ( β-globin ) as described in [31]. 3. Targets selection and primers design We performed a bibliography search to identify the most used genes in published protocols for the diagnosis of COVID-19. The primers used in this study were selected from the bibliography, from protocols approved by the WHO, or were manually designed in our laboratory. We designed a total of 13 primer pairs targeting genes encoding the S protein (N=6 pairs), the N protein (N=2 pairs), the E protein (N=2 pairs), and the open reading frame ORF1ab (N=3 pairs). We also selected five other primer pairs published and validated by the WHO for RT-qPCR protocols. These protocols correspond to those described in the protocols of the Institut Pasteur of Paris (IP2 and IP4) targeting the RdRp gene, the protocol described by the US-CDC targeting the N gene (US-CDC-N2), the protocol described by the China-CDC targeting the ORF1ab region and finally the protocol described by China Hong Kong University (China-HKU) targeting ORF1ab . Three additional primer sets were selected from the literature, they target the E gene [32], the RdRp gene [33] and ORF1ab [34]. We selected primers that did not show secondary structures or dimers based on NetPrimer software analysis (NetPrimer :: PREMIER Biosoft) to avoid background noise during the PCRD readout. The primer screening thus included a total of 23 primer pairs, targeting 5 regions ( E, N, S, RdRp and ORF1ab ) of the viral genome (Table 2). 4. Fast simplex PCR assays for target screening Screening assays were undertaken on cDNAs synthesized from RNAs extracted from nasopharyngeal swabs of CoV+ and CoV- patients using M-MuLV reverse transcriptase (GeneON-Bioscience, Germany) as described above. Fast simplex (engaging one primer pair) PCR assays were performed using SolisFAST Master Mix (Solis Biodyne, Estonia) containing a fast hot-start and fast extension DNA polymerase. The reaction was performed in 20 µl containing a ready-to-use 1X Master mix, 0.5 µM for each forward and reverse primer and template cDNA. The reactions were run for 45 minutes using the following fast PCR program: initial denaturation at 98°C for 2 minutes followed by 35 cycles of denaturation at 98°C for 10 seconds, annealing for 30 seconds at the appropriate temperature for each primer pair tested, and extension at 72°C for 30 seconds. The amplification products were then visualized on a 2% agarose gel. The selection criteria of the primer pairs were mainly based on specificity, stability, and reproducibility of the amplification results. 5. PCRD detection assays The primer pairs that gave specific and reproducible results, when analyzed by electrophoresis on agarose gels, were selected to be labeled for the purpose of their use for lateral flow immunoassay detection using PCRD cassettes as recommended by the manufacturer (Abingdon Health, UK). PCRD lateral flow detection is a two-test-line sandwich immuno-chromatography based assay that relies on fluorescein (Fam)/Biotin and digoxigenin (Dig)/Biotin labeled primers used for the PCR assays. Six µl of the PCR products were diluted in 84 µl dilution buffer (Abingdon Health, UK), 75 µl of which were then transferred to the sample pad of the PCRD cassette as recommended by the manufacturer. During the flow migration, labeled amplicons are captured by the antibodies (anti-Biotin) immobilized at the test lines to form colored complexes and therefore become a visible line. The result was then read with the naked eye after 5, 10 and 15 minutes of flow migration, and pictures were taken for our records. 6. One-step fast multiplex RT-PCR assay and PCRD detection optimization and set up The selected primer pairs in the simplex assays were used to set up a one-step fast multiplex RT-PCR (that target in the same tube two different viral genome regions) coupled to PCRD detection. The one-step fast multiplex RT-PCR assays were performed using the ready-to use Palm PCR Express One-Step RT-PCR Kit I (Ahram Biosystems, Korea). The PCR mixture (20 µl) contained 1X Palm PCR Master Mix (0.8 U Taq polymerase, 50 U reverse transcriptase, 2.5 mM MgCl 2 and 0.2 mM each dNTPs) and two differently labeled primer pairs. Three different primers concentrations were tested (0.3 µM, 0.4 µM and 0.5 µM) to select most optimal one. The one-step fast multiplex RT-PCR tests were performed in a thermocycler using the following cycling parameters: 30 minutes of reverse transcription at 50°C, an initial denaturation at 95°C for 1 minute followed by 35 cycles consisting of 95°C for 5 seconds and an annealing/extension step for 10 seconds. Two annealing temperatures, 56°C and 57°C, were tested to set up this assay. The amplification products were then visualized on a 2% agarose gel upon electrophoresis and on a PCRD cassette, as described above in section 5. 7. Performance evaluation of the fast multiplex RT-PCR coupled to the PCRD detection assays using the optimized protocols The optimized fast multiplex RT-PCR assay was tested on 50 CoV+ and 52 CoV- RNAs as defined by the RT-qPCR standard technique (Table 1). In parallel, RT-fast PCR targeting the human β -globin gene was performed using the Palm PCR Express One-step RT PCR Kit I in order to verify RNA integrity. The RT-PCR protocol used for β -Globin is the same as that used for the fast multiplex RT-PCR described in the previous section (Section 6). We used GH20/PCO4 primers with a concentration of 0.16 µM and the annealing temperature was set to 57°C. The multiplex and β -Globin RT-PCRs were run in parallel, at the same time and in the same thermocycler. Sensitivity and specificity were computed to evaluate test performance using RT-qPCR as a gold standard technique. Results 1. The primers and targets were selected through fast simplex PCR and PCRD detection assays The already described primers and the new ones designed (Table 2) were tested in fast simplex PCR assays against the selection criteria. Twelve primer pairs showed specific and reproducible amplification in the agarose gels. These primer pairs were labeled for PCRD detection. However, save for two pairs, all the tested primer pairs showed background noise with the negative and no template controls when PCR products were visualized in the PCRD (Figure 1). The two remaining primer pairs showed specific and stable results with no artefacts when negative samples (CoV-) and no template controls (NTC) were tested (Figure 2). They correspond to primers used to amplify the N gene (US-CDC-N2) and those amplifying the E gene [32]. To allow PCRD lateral flow detection, these primers were differently labeled: N-F-Fam/R-Biotin and E-F-Dig/R-Biotin, respectively (Table 3). The amplicons were first visualized on agarose gels, followed by PCRD lateral flow detection (Figure 2). On the PCRD, the N gene amplicon is captured on test line 1, while the E gene amplicon is captured on the test line 2 (Figure 2). Table 1. Results of the evaluation of the one-step fast multiplex RT-PCR/PCRD assays on clinical samples Fast multiplex RT-PCR/PCRD Fast multiplex RT-PCR/PCRD retained result Patient code RTqPCR Ct (test1) Ct (test2) RT-Fast PCR β -globin N E (N+E) 1 - - - + - - - 2 - - - + - - - 3 - - - + - - - 4 - - - + - - - 6 + 23 23 + + + + 7 + 21 21 + + + + 8 + 30 30 + + + + 9 - - - + - - - 10 - - - + - - - 11 + 33 33 + - - - 14 - - - + - - - 18 - - - + - - - 20 - - - + - - - 21 + 28 25 + + + + 23 - - - + - - - 26 - - - + - - - 27 - - - + - - - 28 + 24 22 + + + + 29 + 23 19 + + + + 30 + 22 19 + + + + 31 + 17 15 + + + + 32 - - - + - - - 33 - - - + - - - 34 + 35 37 + - - - 35 - - - + - - - 36 + 34 34 + - - - 37 - - - + - - - 39 - - - + - - - 40 - - - + - - - 41 - - - + - - - 42 - - - + - - - 43 - - - + - - - 47 + 30 28 + - + + 48 - - - + - - - 51 + 34 34 + - - - 52* - - - - - - - 53 + 15 15 + + + + 60 - - - + - - - 83 + 18 20 + + + + 86 + 19 22 + + + + 88 - - - + - - - 91 + 29 30 + + + + 95 - - - + - - - 101 - - - + - - - 102 - - - + - - - 103 - - - + - - - 104 - - - + - - - 112 - - - + - - - 113 + 14 16 + + + + 114 - - - + - - - 115 - - - + - - - 116 - - - + - - - 117 + 25 26 + + + + 118 - - - + - - - 119 + 23 24 + + + + 122 - - - + - - - 123 + 21 20 + + + + 124 + 22 19 + + + + 125 + 35 34 + - - - 127 + 17 18 + + + + 128 + 18 19 + + + + 129 + 20 20 + + + + 131 - - - + - - - 132 + 38 36 + - - - 133 + 24 24 + + + + 135 + 25 26 + + + + 136 - - - + - - - 137 + 22 22 + + + + 138 - - - + - - - 139 - - - + + + + 140 + 25 25 + + + + 141 - - - + - - - 142 + 25 25 + + + + 143 - - - + - - - 145 - - - + - - - 147 - - - + - - - 148* - - - - - - - 149 - - - + - - - 152 - - - + - - - 155 - - - + - - - 156 + 15 17 + + + + 158 + 18 18 + + + + 160 - - - + - - - 161 - - - + - - - 264 + 25 27 + + + + 268 - + - - - 269 - + - - - 273 + 20 23 + + + + 278 + 27 30 + + - + 281 + 29 31 + + + + 285 + 37 38 + + + + 307 + 25 26 + + + + 311 + 30 31 + + - + 312 + 22 24 + + + + 314 + 21 23 + + + + 315 + 25 26 + + + + 316 + 31 33 + + + + 317 + 23 25 + + + + 55 + 21 19 + + + + 64 + 19 20 + + + + 71 + 15 14 + + + + 105 + 21 21 + + + + *: Samples excluded from the study -: Negative +: Positive Table 2. Targets and primers used for the set-up of the assays Gene Primers name Sequences 5'-->3' Length (bp) Tm (°C) Reference N CDC_N2-F* TTACAAACATTGGCCGCAAA 67 57 WHO, 2020, US-CDC N2 CDC_N2-R* GCGCGACATTCCGAAGAA PCR-CoV-Ngene-F1* GCATCATATGGGTTGCAACT 166 60 New design PCR-CoV-Ngene-R1* GAAGAGGCTTGACTGCCGCC PCR-CoV-Ngene-F2* TCATATGGGTTGCAACTGAGG 181 60 PCR-CoV-Ngene-R2* CTACGTGATGAGGAACGAGA RdRp IP2_F* ATGAGCTTAGTCCTGTTG 108 57 WHO, 2020, Pasteur IP2 IP2_R* CTCCCTTTGTTGTGTTGT IP4_F* GGTAACTGGTATGATTTCG 107 57 WHO, 2020, Pasteur IP4 IP4_R* CTGGTCAAGGTTAATATAGG RdRP 2-F* TGAAATCAATAGCCGCCACT 199 57 Park et al., 2020 RdRP 2-R* TGTTTGCGAGCAAGAACAAG ORF1ab CDC-ORF1ab.F* CCCTGTGGGTTTTACACTTAA 119 58 WHO, 2020, China-CDC CDC-ORF1ab.R* ACGATTGTGCATCAGCTGA PCR-CoV-ORF1ab-F1* CTACCGAAGTTGTAGGAG 115 60 New design PCR-CoV-ORF1ab-R1* GTAAGACTAGAATTGTCTAC PCR-CoV-ORF1ab-F2* CGAAGTTGTAGGAGACAT 98 60 PCR-CoV-ORF1ab-R2* TTGTCTACATAAGCAGCC ORF1ab-PJC-F1 TGGGGTTTTACAGGTAACCT 107 55 WHO, 2020, China-HKU ORF1ab-PJC-R1 AACACGCTTAACAAAGCACTC ORF1ab-PJC-F2 ACCTCATGGTCATGTTATGG 322 55 Shirato et al, 2020 ORF1ab-PJC-R2 GACATAGCGAGTGTATGCC ORF1ab-PJC-F3 GGTGTCCTTGTCCCTCATGT 246 55 New design ORF1ab-PJC-R2 GACATAGCGAGTGTATGCC Shirato et al, 2020 ORF1ab-PJC-F2 ACCTCATGGTCATGTTATGG 205 55 Shirato et al, 2020 ORF1ab-PJC-R3 AGTCATTTGACTTAGGCGAC New design ORF1ab-PJC-F3 GGTGTCCTTGTCCCTCATGT 129 55 New design ORF1ab-PJC-R3 AGTCATTTGACTTAGGCGAC E PCR-CoV-Egene-F1* GCACAAGCTGATGAGTACG 185 60 New design PCR-CoV-Egene-R1* GGTTTTACAAGACTCACG PCR-CoV-Egene-F2* GTACGAACTTATGTACTC 155 60 New design PCR-CoV-Egene-R2* CGTTAACAATATTGCAGCAG E-PJC-F* GGAAGAGACAGGTACGTTAA 146 55 Mollaei et al,. 2020 E-PJC-R* AAGGTTTTACAAGACTCACG S HS1.Sgene.F AGGTTGATCACAGGCAGACT 185 60 New design HS1.Sgene.R ATGTCCTTCCCTCAGTCAGC HS2.Sgene.F TCCATCAAAACCAAGCAAGAGG 173 61 HS2.Sgene.R TGTTTTGCCACCTTTGCTCA HS3.Sgene.F AGACTGTGCACTTGACCCTC 211 61 HS3.Sgene.R TGGAACAGGAAGAGAATCAGC PCR-CoV-Sgene-F1 CATCACTAGGTTTCAAAC 174 60 PCR-CoV-Sgene-R1 GCACAGTCTACAGCATCT PCR-CoV-Sgene-F2 GGTTTCAAACTTTACTTGC 112 60 PCR-CoV-Sgene-R2 GGGTTATCTTCAACCTAGGA PCR-CoV-Sgene-F3 GCCAATAGGTATTAACATCA 120 60 PCR-CoV-Sgene-R3 GCAGCTTATTATGTGGGTTA *: Labeled primers 2. A one-step fast multiplex RT-PCR and a PCRD detection were set using the primer pairs selected The 2 selected primer pairs were engaged in a one-step fast multiplex RT-PCR where they were added to the same reaction mixture. The reaction conditions were established by varying primers concentrations (Figure 3a) and melting temperatures (Figure 3b) in an attempt to balance band intensities as band of the E amplicon was more intense than the N one in both agarose gel and PCRD. In the retained protocol, the primers final concentrations were set to 0.5 µM each and the Tm at 57°C. The one-step fast multiplex RT-PCR requires 1 hour 15 minutes including 30 minutes for reverse transcription (RT) and 45 minutes for PCR. The amplification products were visualized on agarose gel and PCRD lateral flow. The PCRD lateral flow detection showed that there was no differences between 5, 10, and 15 minutes of flow migrations (Figure 3c). Therefore, the results were read, and the corresponding pictures were taken after 5 minutes of flow migration. 3. The performance of the established one-step fast multiplex RT-PCR coupled to the PCRD detection was evaluated using nasopharyngeal samples The retained amplification and detection protocols were applied to a collection of 102 RNAs extracted from patient’s nasopharyngeal swabs to evaluate the performance of the one-step fast multiplex RT-PCR and the PCRD detection assays. The status of the collected RNAs was confirmed by RT-qPCR at time of sampling: 50 were CoV+ and 52 were CoV2-. The RNA integrity, at time of testing the novel assay, was verified by amplification the human β-globin gene by one-step fast RT-PCR. In case of two samples (52 and 148), the fast RT-PCR for β-globin was negative (Table 1). Therefore, these two samples were excluded from the study. We tested the remaining 100 RNAs and considered a test positive if, at least one of the test lines showed a positive result. If both test lines showed negative results, the test was considered negative (Table 1). This way, our one-step fast multiplex RT-PCR assay coupled to PCRD detection showed a sensitivity of 88% (44/50) and a specificity of 98% (49/50). For some samples, the N target showed ambiguous results on the agarose gel, as the amplicons were easily mistaken for primers dimers and/or primers excess because of their small size (67 bp). This ambiguity is resolved by the PCRD detection. Only positive samples showed positive results on the PCRD (Figure 4). Additionally, we noticed that this one-step fast multiplex RT-PCR/PCRD method is able to detect all positive samples having Ct values lower than 33. Samples with Ct values greater than 33 gave negative results, except for one sample (285), which had Ct values of 37/38, but gave positive results for both targets tested. Discussion In the context of strengthening local and international diagnostic facilities and technical capacity for pathogen detection during pandemics, epidemics or outbreaks, our study aims to contribute to the development of a simple and rapid method that does not require the use of sophisticated equipment and can be implemented in minimally equipped healthcare centers. Indeed, the emergence of infectious diseases, such as COVID-19, has stressed the importance and necessity of rapid and effective diagnosis for detecting pathogens and controlling their spread. COVID-19 represented an international public health emergency due to its pathogenesis and rapid spread. Simple, rapid, sensitive, specific, and equipment-free diagnostic techniques were essential to meet the needs of epidemic control strategies and thus limit the spread of the disease. While different approaches have been used for disease diagnosis based on serology and computing tomography imaging, the WHO recommended real-time reverse transcription polymerase chain reaction (RT-qPCR) as the reference method [1]. The classical RT-qPCR is a sensitive and accurate technique but has several disadvantages, including the high cost of kits, the prolonged time to result delivery, the need for trained personnel, and the requirement for a suitable and costly piece of equipment that is usually missing in low-resource setting areas. [35]. The development of a simple, rapid, accurate, and field-applicable methods for detecting SARS-CoV-2 and future emerging pathogens remains a pressing need. Various molecular diagnostic alternatives, such as RT-LAMP [9], RT-RPA [15], and tests based on the CRISPR-Cas systems [19, 21, 22], have been developed for detecting the SARS-CoV-2 virus. These tests allow for rapid, specific, and effective detection under isothermal conditions, therefore, eliminating the need for sophisticated equipment. However, despite their advantages, these techniques have drawbacks, namely complex primer design and optimization of reaction conditions in case of LAMP [4], the risk of false positives with RPA [36], and multiple workflow steps for the CRISPR Cas systems [37]. As PCR is still one of the most valuable methods used in different fields, including food security, forensics, research and biomedicine, in this study, we chose to develop PCR assays using newly designed primers or re-adapt the WHO and published real time PCRs into simpler PCR-based tests. The test here reported is based on a one-step fast multiplex RT-PCR using a ready-to-use master mix and two pairs of already described primers that is coupled to lateral flow PCRD immunoassay detection. It is faster than the classical real time PCR. It is also easy to perform and read, and it obviates need for skilled operators and costly equipment mobilization that makes RT-qPCR only available in central laboratories and the ensuing transfer of samples to these central laboratories, which results in delays in result delivery. Indeed, for example, in Tunisia during the pandemics, real time PCR facilities and expert technicians were missing in different regions of the country. Therefore, in the beginning of the COVID-19 pandemics, all the samples from most regions of the country were transferred to the Institut Pasteur de Tunis. Personnel, including MDs, researchers and lab technicians from different departments and labs of the institute, were called to join the COVID-19 testing team and were organized into three daily shifts, which led to postponing all other research and/or diagnostic activities. Therefore, one of the strategic actions for preparedness for future pandemics, epidemics or outbreaks must be the investment in laboratory infrastructure and diagnostic capabilities [38]. Here, we bring a proof of concept that the one-step fast multiplex RT-PCR coupled to PCRD detection could be a good alternative when there is a shortage in reagents or at points of need where real time PCR facilities are missing. In addition, this technology could be easily applied for the diagnosis of other pathogens [24]. Furthermore, lateral flow immuno-chromatographic assay devices are commercially available. They represent a simple method for the generic detection of nucleic acids without the need for specialized and costly equipment. Their use relies on the detection of dual-labeled amplicons. Primers’ labels are chosen according to the device used; like in this study, we used different labels associations, Fam/biotin and Dig/biotin for each primer pair, which target two different genomic viral regions, to be able to detect the amplicons on their respective test line on the PCRD cassette. Compared to agarose gel electrophoresis, the PCRD assay is rapid (5 min), easy to use and read and does not require extensive molecular biology expertise. Our study showed that the results observed in the agarose gel upon electrophoresis are concordant with those obtained with the PCRD analysis of the E target amplification. However, for the N target, where the amplicon is smaller in length (67 bp), the PCRD assay was able to resolve interpretation ambiguity of the read outs on agarose gels, as the amplicons were not distinguishable from excess primers and/or primer dimers upon electrophoresis. The most commonly used genomic regions of SARS-CoV-2 for RT-qPCR diagnosis are highly conserved and/or highly expressed genes [39]. Notably, they include the ORF1ab regions, the genes encoding for nucleocapsid ( N ), envelope ( E ), spike protein ( S ), membrane protein ( M ), RNA-dependent RNA polymerase ( RdRp ), hemagglutinin-esterase ( HE ), and helicase genes. In this study, we considered most of the mentioned genes to develop the assay aimed for. We selected and designed our primers in the N , S , E , RdRp and ORF1ab viral genomic sequences. Our selection criteria for the primers were based, namely, on the stability and reproducibility of the fast-PCR as analyzed by the agarose gel electrophoresis, and then on the absence of background noise on the PCRD. The two targets that meet these criteria were the N (US-CDC-N2) and E genes [32]. The multiplexing of the amplification of these two targets within a same reaction was also successful, but we noticed that there was an imbalance in terms of band intensity on both the agarose gel and PCRD. The E amplicon band was more intense compared to the N one, despite our attempts to improve the reaction conditions. This could be due to a sort of competition between primers for the same mix of reagents [25, 40]. The GC content of the target (40,4% for E and 52,2% for N ) could also lead to preferential denaturation, resulting in preferential amplification [40], or secondary structures within genomes could induce differential accessibility of targets [40, 41]. Nevertheless, PCRD results were easily read by the naked eye for both the E and N targets. The two targets showed the same sensitivity and specificity when tested in the multiplexed assay but gave discordant results for two patients: patient 47 was positive for the E target but negative for the N target, and patient 311 was negative for the E target but positive for the N target. For these patients, mutations in the priming sites could have occurred, leading to negative results. Or, SARS-CoV-2 gene dynamic lead to differential genes expression depending on the patient status and disease stage [42, 43]. This is the interest in using multiple targets to define the infected patient’s status and minimize false negative results [1, 42]. However, our results showed that six patients who were positive according to RT-qPCR were negative with our assay. These patients had Ct values greater than 33, except for patient 285, who had Ct values of 37/38 for the two targets tested in RT-qPCR but showed positive results for the two targets tested in our one-step fast multiplex RT-PCR/PCRD. In this case, we could come up with two hypotheses. First, our assay is not sensitive enough and is able to detect only patients with a high viral load and a Ct below 33. The second hypothesis, given the fact that the RNAs in this study were extracted in 2021 (and our assays were performed in 2024), long-term stability of the RNAs constitutes a factor influencing the performance of the DNA assays, especially in case of samples with a low viral load. Indeed, it was demonstrated that RNA with a low viral load is more prone to a reduction in its RNA content than RNA with a high viral load [44]. Therefore, our one-step fast multiplex RT-PCR assay should be evaluated with freshly extracted RNAs and, at the same time when RT-qPCR is performed, to accurately determine their performances. The main limitation of the study is that the internal control (human β-globin ) was not included in our multiplex assay. We have attempted to minimize this drawback by setting in parallel this internal control on the same day and run. The one-step fast simplex PCR targeting the human β-globin was performed in a separate reaction as one cannot visualize more than 2 amplicons on the PCRD lateral flow device. Indeed, to our knowledge, immunoassay devices with 3 test lines are not yet commercially available. For future test developments, it would be more interesting to have all the targets (viral targets and internal control) in the same reaction. Conclusion The COVID-19 pandemics proved the relevance and importance of molecular diagnostics for disease control and transmission monitoring. It stressed the need for simple, fast and equipment- free assays as alternative solutions to bring testing closer to the patients. In this study, we brought the proof of concept that the one-step fast multiplex RT-PCR coupled to PCRD detection, here developed, is a good alternative for SARS-CoV-2 detection. It requires a conventional PCR thermocycler and PCRD devices, with result delivery within only 1 hour and 20 minutes. Importantly, the assay developed using the ready to use kit for a portable RT-PCR machine could be easily adapted to point of care settings by using a commercially available portable thermocyclers. Declarations Ethics approval The study is approved by the Biomedical Ethics Committee of the Institut Pasteur de Tunis under the reference Ref: 2020/21/I/LR16IPT in accordance with the Declaration of Helsinki. Consent for publication Not Applicable Availability of data and materials Nucleotide sequences of the SARS-CoV-2 used for primers design are available in NCBI public databases and are available at the following URL: txid2697049[organism:exp] - Nucleotide - NCBI (nih.gov) All other data supporting the findings of this study are available within the paper. Primer sequences are provided in Table 2. Competing interests The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results. Funding This research was funded by the Ministry of Higher Education and Research Tunisia through the programme “Projet de Recherche Fédéré” PRF-Lutte COVID, and the Research laboratory contract program LR16IPT04, and the Institut Pasteur de Tunis through the intramural program ”Projet Collaboratif Interne-PCI39”. Authors' contributions IBA: Conceptualization and study design, writing of the original draft, data analysis, supervision, funding acquisition, project administration; HSO: Investigation, data analysis, project administration ; MM: Investigation, data analysis; MVT: Investigation; MA: Investigation; YSBA: Study design; KA: Investigation; SHB: Investigation, resources; HT: Funding Acquisition, resources; IG: Conceptualization, study design, funding acquisition, project administration, writing, review and editing. All authors read and approved the final manuscript. Acknowledgments The authors acknowledge all the Virology Lab staff who contributed to the process of the COVID-19 diagnosis including sampling, RNA extraction and RT-qPCR performing. We would like to thank Ahmed Sahbi Chakroun for his technical support and his advices on kits and reagent purchasing. References WHO. 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Shirato K, Nao N, Katano H, Takayama I, Saito S, Kato F, et al. Development of Genetic Diagnostic Methods for Detection for Novel Coronavirus 2019(nCoV-2019) in Japan. Jpn J Infect Dis. 2020;73:304–7. El-Daly MM. Advances and Challenges in SARS-CoV-2 Detection: A Review of Molecular and Serological Technologies. Diagnostics. 2024;14:519. Munawar MA. Critical insight into recombinase polymerase amplification technology. Expert Rev Mol Diagn. 2022;22:725–37. Zhou Y, Zhang L, Xie Y-H, Wu J. Advancements in detection of SARS-CoV-2 infection for confronting COVID-19 pandemics. Lab Invest. 2022;102:4–13. Williams BA, Jones CH, Welch V, True JM. Outlook of pandemic preparedness in a post-COVID-19 world. Npj Vaccines. 2023;8:1–12. Ahirwar R, Gandhi S, Komal K, Dhaniya G, Tripathi PP, Shingatgeri VM, et al. Biochemical composition, transmission and diagnosis of SARS-CoV-2. Biosci Rep. 2021;41:BSR20211238. Elnifro EM, Ashshi AM, Cooper RJ, Klapper PE. Multiplex PCR: Optimization and Application in Diagnostic Virology. Clin Microbiol Rev. 2000;13:559–70. Rangan R, Zheludev IN, Hagey RJ, Pham EA, Wayment-Steele HK, Glenn JS, et al. RNA genome conservation and secondary structure in SARS-CoV-2 and SARS-related viruses: a first look. RNA. 2020;26:937–59. Valadan R, Golchin S, Alizadeh-Navaei R, Haghshenas M, Zargari M, Mousavi T, et al. Differential gene expression analysis of common target genes for the detection of SARS-CoV-2 using real time-PCR. AMB Express. 2022;12:112. Benrahma H, Idrissa D, Imane S, Jalila R, Nida M, Rachid B, et al. Epidemiological description and analysis of RdRp, E and N genes dynamic by RT-PCR of SARS-CoV-2 in Moroccan population: Experience of the National Reference Laboratory (LNR)-UM6SS. 2020;:2020.06.18.20135137. Bai H, Zhao J, Ma C, Wei H, Li X, Fang Q, et al. Impact of RNA degradation on influenza diagnosis in the surveillance system. Diagn Microbiol Infect Dis. 2021;100:115388. Additional Declarations No competing interests reported. Supplementary Files SupplementaryfileUncroppedgels.pptx Cite Share Download PDF Status: Published Journal Publication published 18 Dec, 2024 Read the published version in BMC Infectious Diseases → Version 1 posted Editorial decision: Revision requested 20 Jun, 2024 Editor assigned by journal 19 Jun, 2024 Submission checks completed at journal 19 Jun, 2024 First submitted to journal 17 Jun, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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due to background noise (red arrows) in the negative (C-) and No template control (NTC) samples. C+: Positive control\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4595176/v1/1d6d467fd3ba6a69d764e650.png"},{"id":60601149,"identity":"b74b0770-b864-4f90-b83a-2ef0dbfe2f4d","added_by":"auto","created_at":"2024-07-18 16:04:18","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":359731,"visible":true,"origin":"","legend":"\u003cp\u003eSimplex fast RT-PCR assays targeting the N and E genes visualized on (a) 2% agarose gels and (b) PCRD. Test line 1 detects the Dig/biotin-labeled E target amplicon. Test line 2 detects the Fam/biotin-labeled N target amplicon. C: Control line for flow migration. MW: 100 bp Molecular weight. CoV+: SARS-CoV-2 positive samples. CoV-: SARS-CoV-2 negative samples. NTC: No Template Control. The black arrows show positive results.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4595176/v1/f8c57b99264f9f5bcb0709aa.png"},{"id":60601150,"identity":"f660c586-b60b-4cb7-9aab-5c50620dcce6","added_by":"auto","created_at":"2024-07-18 16:04:18","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":481375,"visible":true,"origin":"","legend":"\u003cp\u003eOptimization of the fast multiplex RT-PCR targeting N and E genes. The tested conditions are (a) Primers concentrations, (b) Tm and (c) Time for PCRD detection. [E] and [N] are the concentrations tested for each primer pair. C+: [2642, 2911, 2866]. NTC: No template control\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4595176/v1/8853b94d4196c7449b53f4e1.png"},{"id":60601175,"identity":"73a46c61-e134-4979-8104-4ba33bb2d9ee","added_by":"auto","created_at":"2024-07-18 16:04:19","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":661777,"visible":true,"origin":"","legend":"\u003cp\u003eEvaluation of the one-step fast multiplex RT-PCR on clinical samples.\u003c/p\u003e\n\u003cp\u003eThe \u003cem\u003eN\u003c/em\u003e target produced ambiguous results in agarose gel, which were resolved by PCRD detection. CoV-: Negative samples, CoV+: Positive samples, MW: 100 bp Molecular weight, NTC: No template control, Black arrow: Positive test.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4595176/v1/bd7242fcc2c3a252efadb29b.png"},{"id":72202055,"identity":"b7c46ac3-80af-4038-9127-d27b71ceaa73","added_by":"auto","created_at":"2024-12-23 16:14:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3377529,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4595176/v1/b49d9699-28c1-4146-9ce8-93dfee48e3e1.pdf"},{"id":60601173,"identity":"740be6b4-3034-4114-9566-3fd3c6cb2276","added_by":"auto","created_at":"2024-07-18 16:04:19","extension":"pptx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":6580528,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryfileUncroppedgels.pptx","url":"https://assets-eu.researchsquare.com/files/rs-4595176/v1/75e674f537ceec6e0e474acb.pptx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Rapid detection of SARS-CoV-2 RNA using a one-step fast multiplex RT-PCR coupled to lateral flow immunoassay","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe COVID-19 pandemic is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). It is an emerging and highly infectious disease that has rapidly spread worldwide and become a public health emergency. This pandemic has created considerable pressure on health systems and countries, and has notably put emphasis on diagnosis and concurrently, on a shortage of diagnostic reagents and kits. Thus, it is strategic for the countries to enhance access to diagnosis and acquire the capacity to face such needs now and during future threats. Early detection of the disease was a key step in controlling its spread. The molecular diagnosis of COVID-19 is based on the amplification of viral genetic material. The World Health Organization (WHO) has designated reverse transcription and real-time quantitative PCR (RT-qPCR) as the gold standard diagnosis technique using a selection of protocols aiming at the amplification of one or more viral genome targets. Different protocols have been developed by different institutions in the United States, Germany, France, China, Hong Kong, Japan and Thailand, targeting different viral genomic regions (WHO, 2020). For example, the United States Center for Disease Control (US-CDC) protocol used three nucleocapsid gene targets (\u003cem\u003eN1, N2\u003c/em\u003e and \u003cem\u003eN3)\u003c/em\u003e. The protocol developed by German Consiliary Laboratory for Coronaviruses hosted at the Charit\u0026eacute; in Berlin (Charit\u0026eacute;/Berlin) used first line screening with the envelope (\u003cem\u003eE\u003c/em\u003e) gene assay followed by a confirmatory assay using the RNA-dependent RNA polymerase (\u003cem\u003eRdRp\u003c/em\u003e) gene [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The Hong Kong University, China protocol used two simplex assays targeting the nucleoprotein (\u003cem\u003eN\u003c/em\u003e) gene and the Open reading frame \u003cem\u003eORF1ab\u003c/em\u003e [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The protocols developed by the Institiut Pasteur of Paris (IP2 and IP4) targeted two \u003cem\u003eRdRp\u003c/em\u003e regions [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The China CDC protocol targeted the \u003cem\u003eN\u003c/em\u003e gene and \u003cem\u003eORF1ab\u003c/em\u003e [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The one from Thailand was developed by the Department of Medical Sciences, Ministry of Public Health, and targeted the \u003cem\u003eN\u003c/em\u003e gene [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMultiplex RT-qPCR tests have also been developed, representing an interesting advancement allowing simultaneous detection of two or more targets [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The RT-qPCR methods have been deployed in centralized diagnosis centers, but their major drawback is the need for relatively complex and expensive equipment and highly qualified personnel. This limited their use as a diagnostic tool in settings lacking adequate infrastructure and equipped laboratories [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Therefore, developing simpler molecular assays will improve testing approach and allow for the development of diagnosis algorithms, thus overcoming equipment and resource challenges. In addition, given the worldwide pressure to access reagents during the pandemics, alternative solutions should be considered to address supply shortages, to lower diagnostic costs and thus to expand testing capacity, and to better prepare for future pandemics. Testing is a cornerstone to fighting pandemics, for surveillance, and during likely resurgence events. Several methods have been developed to meet this need. These methods mainly include isothermal methods like loop-mediated isothermal amplification (LAMP), a technique that allows nucleic acid amplification at a constant temperature of 60\u0026deg;C to 65\u0026deg;C [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. It uses a DNA polymerase with high strand displacement activity and four to six pairs of primers [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. An additional reverse transcription step was combined, in a single tube, with the LAMP amplification technique, minimizing the reaction time to approximately 30 minutes [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. RT-LAMP is a less expensive, simpler, and faster detection method that does not require the use of a thermocycler or expensive reagents [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. This method has been used for the direct detection of SARS-CoV-2 RNA targeting the \u003cem\u003eN\u003c/em\u003e, \u003cem\u003eORF1ab\u003c/em\u003e, and \u003cem\u003eRNA polymerase\u003c/em\u003e genes [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. As a simple and rapid technique, RT-LAMP can be coupled with various methods for visualizing amplified products. The most commonly used methods include visualizing amplified products with the naked eye either through a color indicator change [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] or on lateral flow strips [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Although the performance of LAMP is comparable to that of RT-qPCR for SARS-CoV-2 detection [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], this method is limited by the complexity of primer design [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] and the generation of false-positive results [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Moreover, multiplexing is complex due to the number of primers needed for a single reaction.\u003c/p\u003e \u003cp\u003eOther widely used isothermal technologies include recombinase-based amplification, that includes recombinase polymerase amplification (RPA) and the similar recombinase-aided amplification (RAA). It has also been used for SARS-CoV-2 detection. It represents a faster, simpler, and less expensive alternative to RT-qPCR. It allows amplification at 37\u0026ndash;42\u0026deg;C using only a pair of primers and a mix of enzymes (including recombinase) that eliminate the need for instruments such as thermocyclers [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. This technology simplifies the testing process and allows SARS-CoV-2 detection in resource-limited environments. RPA/RAA offers various strategies for detecting amplified products, which can be either real-time using labeled probes generating visually detectable signals in 15 minutes (owing to fluorescent label) [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] or lateral flow strips in 5 minutes (owing to 6-FITC/biotin labels) [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. However, longer primer sequences as well as lower primer binding temperatures in recombinase-based amplification reactions result in the formation of primer-dimers that may produce false positive signals [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCRISPR-Cas technology has recently been employed as another highly sensitive detection method [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Several CRISPR-based approaches such as SHERLOCK [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], DETECTR [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], ENHANCE [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], iSCAN [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], FELUDA [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], and AIOD-CRISPR [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], have been developed for accurate and portable diagnosis of COVID-19 from various types of samples. Nevertheless, CRISPR-based approaches are always associated with a pre-amplification step, which makes them time consuming and prone to cross-contamination.\u003c/p\u003e \u003cp\u003eDespite the important number of novel technologies developed for pathogen detection, PCR remains the gold standard molecular diagnostic technique for infectious diseases. During the last decade, it has evolved to achieve better performance in terms of sensitivity, cost, and duration [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The introduction of DNA polymerases with improved processivity, drastically reduced the duration of a reaction and made fast-PCR an interesting alternative for rapid pathogen detection [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Thermocyclers, which are necessary for PCR, are becoming increasingly affordable and are currently considered basic equipment for infectious disease diagnosis laboratories. Furthermore, portable formats are now commercially available, enabling the creation of highly useful mobile laboratories to be closer to the patients or for field investigation notably to promptly respond to health emergencies that require immediate intervention.\u003c/p\u003e \u003cp\u003eDuring the COVID-19 pandemic, one of the most used technique for disease diagnosis was based on lateral flow immunoassays for the detection of SARS-CoV2 antigens or human antibodies (anti-SARS-Cov2-IgG and anti-SARS-Cov2-IgM) thanks to their accessibility, feasibility and affordability [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Commercially available generic lateral flow immunoassays have enabled the accurate detection of nucleic acids from a range of pathogens [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. These accessible assays proved to be user-friendly, cost effective making them strong candidates for decentralized diagnosis of infectious diseases and control strategies [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this study, we aimed to address needs for alternative, rapid and less expensive approach to Covid 19 diagnosis by developing a one-step fast multiplex reverse transcription-PCR (RT-PCR) coupled to visual detection on a lateral flow immunoassay PCR Detection (PCRD) device. This study provides a proof of principle for the potential of this new method for rapid amplification and detection of viral RNA, and supports the feasibility of adapting it into a mobile version.\u003c/p\u003e"},{"header":"Material and methods","content":"\u003cp\u003e\u003cstrong\u003e1. Ethical statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study is approved by the Biomedical Ethics Committee of the Institut Pasteur de Tunis (Ref: 2020/21/I/LR16IPT) in accordance with the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2. Human samples\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHuman samples used in this study correspond to nasopharyngeal swabs collected from anonymous patients suspected of having COVID-19 disease and\u0026nbsp;referred for routine diagnosis to the Clinical Virology Laboratory of the Institut Pasteur de Tunis. Patients were enrolled between November 2020 and January 2021. They include 50 SARS-CoV-2-positive (CoV+) and 52 SARS-CoV-2-negative (CoV-) for COVID-19, as shown by the RT-qPCR (Table 1). RNAs were extracted using QIAMp Viral RNA Mini Kit (Qiagen, USA). Thirty additional RNAs extracted from 15 CoV+ (C+) and 15 CoV- (C-) were used for primers screening. For these RNAs, we synthesized the cDNAs using M-MuLV reverse transcriptase (GeneON-Bioscience, Germany). The cDNA synthesis included first annealing step where 10 \u0026micro;M of OligodT(23) were incubated with the RNA in a final volume of 8\u0026micro;l for 10 minutes at 70\u0026deg;C and then for 5 minutes at 4\u0026deg;C. During the second step, the cDNA synthesis reaction itself was performed by mixing the OligodT-RNA complex with 1X reaction buffer, 0.5 mM of dNTPs and 200 U of M-MuLV reverse transcriptase and incubating the mix at 42\u0026deg;C for 1 hour and at 65\u0026deg;C for 10 minutes. Synthesis was then checked by amplifying the human \u003cem\u003ebeta-globin\u003c/em\u003e gene (\u003cem\u003e\u0026beta;-globin\u003c/em\u003e) as described in\u0026nbsp;[31].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3. Targets selection and primers design\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe performed a bibliography search to identify the most used genes in published protocols for the diagnosis of COVID-19. The primers used in this study were selected from the bibliography, from protocols approved by the WHO, or were manually designed in our laboratory. We designed a total of 13 primer pairs targeting genes encoding the \u003cem\u003eS\u003c/em\u003e protein (N=6 pairs), the \u003cem\u003eN\u0026nbsp;\u003c/em\u003eprotein (N=2 pairs), the \u003cem\u003eE\u003c/em\u003e protein (N=2 pairs), and the open reading frame \u003cem\u003eORF1ab\u003c/em\u003e (N=3 pairs). We also selected five other primer pairs published and validated by the WHO for RT-qPCR protocols. These protocols correspond to those described in the protocols of the Institut Pasteur of Paris (IP2 and IP4) targeting the \u003cem\u003eRdRp\u0026nbsp;\u003c/em\u003egene, the protocol described by the US-CDC targeting the \u003cem\u003eN\u003c/em\u003e gene (US-CDC-N2), the protocol described by the China-CDC targeting the \u003cem\u003eORF1ab\u003c/em\u003e region and finally the protocol described by China Hong Kong University (China-HKU) targeting \u003cem\u003eORF1ab\u003c/em\u003e. Three additional primer sets were selected from the literature, they target the \u003cem\u003eE\u003c/em\u003e gene\u0026nbsp;[32], the \u003cem\u003eRdRp\u003c/em\u003e gene\u0026nbsp;[33]\u0026nbsp;and \u003cem\u003eORF1ab\u003c/em\u003e [34]. We selected primers that did not show secondary structures or dimers based on NetPrimer software analysis (NetPrimer :: PREMIER Biosoft) to avoid background noise during the PCRD readout. The primer screening thus included a total of 23 primer pairs, targeting 5 regions (\u003cem\u003eE, N, S, RdRp\u0026nbsp;\u003c/em\u003eand \u003cem\u003eORF1ab\u003c/em\u003e) of the viral genome (Table 2).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4. Fast simplex PCR assays for target screening\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eScreening assays were undertaken on cDNAs synthesized from RNAs extracted from nasopharyngeal swabs of CoV+ and CoV- patients using M-MuLV reverse transcriptase (GeneON-Bioscience, Germany) as described above. Fast simplex (engaging one primer pair) PCR assays were performed using\u0026nbsp;SolisFAST Master Mix (Solis Biodyne, Estonia) containing a fast hot-start and fast extension DNA polymerase. The reaction was performed in 20 \u0026micro;l containing a ready-to-use 1X Master mix, 0.5 \u0026micro;M for each forward and reverse primer and template cDNA. The reactions were\u0026nbsp;run\u0026nbsp;for 45 minutes using the following fast PCR program: initial denaturation at 98\u0026deg;C for 2 minutes followed by 35 cycles of denaturation at 98\u0026deg;C for 10 seconds, annealing for 30 seconds at the appropriate temperature for each primer pair tested, and extension at 72\u0026deg;C for 30 seconds.\u0026nbsp;The amplification products were then visualized on a 2% agarose gel. The selection criteria of the primer pairs were mainly based on specificity, stability, and reproducibility of the amplification results.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5. PCRD detection assays\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe primer pairs that gave specific and reproducible results, when analyzed by electrophoresis on agarose gels, were selected to be labeled for the purpose of their use for lateral flow immunoassay detection using PCRD cassettes as recommended by the manufacturer\u0026nbsp;(Abingdon Health, UK). PCRD\u0026nbsp;lateral flow detection is a two-test-line sandwich immuno-chromatography based assay that relies on fluorescein (Fam)/Biotin and digoxigenin (Dig)/Biotin labeled primers used for the PCR assays. Six \u0026micro;l of the PCR products were diluted in 84 \u0026micro;l dilution buffer (Abingdon Health, UK), 75 \u0026micro;l of which were then transferred to the sample pad of the PCRD cassette as recommended by the manufacturer. During the flow migration, labeled amplicons are captured by the antibodies (anti-Biotin) immobilized at the test lines to form colored complexes and therefore become a visible line.\u0026nbsp;The result was then read with the naked eye after 5, 10 and 15 minutes of flow migration, and pictures were taken for our records.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e6. One-step fast multiplex RT-PCR assay and PCRD detection optimization and set up\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe selected primer pairs in the simplex assays were used to set up a one-step fast multiplex RT-PCR (that target in the same tube two different viral genome regions) coupled to PCRD detection. The one-step fast multiplex RT-PCR assays were performed using the ready-to use Palm PCR Express One-Step RT-PCR Kit I (Ahram Biosystems, Korea). The PCR mixture (20 \u0026micro;l) contained 1X Palm PCR Master Mix (0.8 U Taq polymerase, 50 U reverse transcriptase, 2.5 mM MgCl\u003csub\u003e2\u003c/sub\u003e and 0.2 mM each dNTPs) and two differently labeled primer pairs. Three different primers concentrations were tested (0.3 \u0026micro;M, 0.4 \u0026micro;M and 0.5 \u0026micro;M) to select most optimal one. The one-step fast multiplex RT-PCR tests were performed in a thermocycler using the following cycling parameters: 30 minutes of reverse transcription at 50\u0026deg;C, an initial denaturation at 95\u0026deg;C for 1 minute followed by 35 cycles consisting of 95\u0026deg;C for 5 seconds and an annealing/extension step for 10 seconds. Two annealing temperatures, 56\u0026deg;C and 57\u0026deg;C, were tested to set up this assay. The amplification products were then visualized on a 2% agarose gel upon electrophoresis and on a PCRD cassette, as described above in section 5.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e7. Performance evaluation of the fast multiplex RT-PCR coupled to the PCRD detection assays using the optimized protocols\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe optimized fast multiplex RT-PCR assay was tested on 50 CoV+ and 52 CoV- RNAs as defined by the RT-qPCR standard technique (Table 1). \u0026nbsp;In parallel, RT-fast PCR targeting the human\u0026nbsp;\u003cem\u003e\u0026beta;\u003c/em\u003e\u003cem\u003e-globin\u003c/em\u003e gene was performed using the Palm PCR Express One-step RT PCR Kit I in order to verify RNA integrity. The RT-PCR protocol used for\u0026nbsp;\u003cem\u003e\u0026beta;\u003c/em\u003e\u003cem\u003e-Globin\u003c/em\u003e is the same as that used for the fast multiplex RT-PCR described in the previous section (Section 6). We used GH20/PCO4 primers with a concentration of 0.16 \u0026micro;M and the annealing temperature was set to 57\u0026deg;C. \u0026nbsp;The multiplex and\u0026nbsp;\u003cem\u003e\u0026beta;\u003c/em\u003e\u003cem\u003e-Globin\u0026nbsp;\u003c/em\u003eRT-PCRs were run in parallel, at the same time and in the same thermocycler.\u003c/p\u003e\n\u003cp\u003eSensitivity and specificity were computed to evaluate test performance using RT-qPCR as a gold standard technique.\u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003e1. The primers and targets were selected through fast simplex PCR and PCRD detection assays\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe already described primers and the new ones designed (Table 2) were tested in fast simplex PCR assays against the selection criteria. Twelve primer pairs showed specific and reproducible amplification in the agarose gels. These primer pairs were labeled for PCRD detection. However, save for two pairs, all the tested primer pairs showed background noise with the negative and no template controls when PCR products were visualized in the PCRD (Figure 1).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe two remaining primer pairs showed specific and stable results with no artefacts when negative samples (CoV-) and no template controls (NTC) were tested (Figure 2). They correspond to primers used to amplify the \u003cem\u003eN\u003c/em\u003e gene (US-CDC-N2) and those amplifying the \u003cem\u003eE\u0026nbsp;\u003c/em\u003egene [32].\u003c/p\u003e\n\u003cp\u003eTo allow PCRD lateral flow detection, these primers were differently labeled: N-F-Fam/R-Biotin and E-F-Dig/R-Biotin, respectively (Table 3). The amplicons were first visualized on agarose gels, followed by PCRD lateral flow detection (Figure 2). On the PCRD, the \u003cem\u003eN\u003c/em\u003e gene amplicon is captured on test line 1, while the \u003cem\u003eE\u003c/em\u003e gene amplicon is captured on the test line 2 (Figure 2).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1.\u0026nbsp;\u003c/strong\u003eResults of the evaluation of the one-step fast multiplex RT-PCR/PCRD assays on clinical samples\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"576\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.805555555555555%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.67361111111111%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.104166666666666%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.277777777777779%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.32638888888889%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.79861111111111%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eFast multiplex RT-PCR/PCRD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.01388888888889%\"\u003e\n \u003cp\u003e\u003cstrong\u003eFast multiplex \u0026nbsp;RT-PCR/PCRD retained result\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.785095320623917%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePatient code\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.651646447140381%\"\u003e\n \u003cp\u003e\u003cstrong\u003eRTqPCR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.077989601386482%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCt (test1)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.251299826689774%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCt (test2)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.305025996533795%\"\u003e\n \u003cp\u003e\u003cstrong\u003eRT-Fast PCR\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u0026beta;\u003c/strong\u003e\u003cstrong\u003e-globin\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.105719237435009%\"\u003e\n \u003cp\u003e\u003cstrong\u003eN\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.838821490467938%\"\u003e\n \u003cp\u003e\u003cstrong\u003eE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.984402079722702%\"\u003e\n \u003cp\u003e\u003cstrong\u003e(N+E)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.785095320623917%\" valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.651646447140381%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.077989601386482%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.251299826689774%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.305025996533795%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.105719237435009%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.838821490467938%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.984402079722702%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.785095320623917%\" valign=\"bottom\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.651646447140381%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.077989601386482%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.251299826689774%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.305025996533795%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.105719237435009%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.838821490467938%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.984402079722702%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.785095320623917%\" valign=\"bottom\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.651646447140381%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.077989601386482%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.251299826689774%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.305025996533795%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.105719237435009%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.838821490467938%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.984402079722702%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.785095320623917%\" valign=\"bottom\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.651646447140381%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.077989601386482%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.251299826689774%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.305025996533795%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.105719237435009%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.838821490467938%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.984402079722702%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.785095320623917%\" valign=\"bottom\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.651646447140381%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.077989601386482%\" valign=\"bottom\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.251299826689774%\" valign=\"bottom\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.305025996533795%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.105719237435009%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.838821490467938%\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.984402079722702%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.785095320623917%\" valign=\"bottom\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.651646447140381%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.077989601386482%\" valign=\"bottom\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.251299826689774%\" valign=\"bottom\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.305025996533795%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.105719237435009%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.838821490467938%\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.984402079722702%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.785095320623917%\" valign=\"bottom\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.651646447140381%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.077989601386482%\" valign=\"bottom\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.251299826689774%\" valign=\"bottom\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.305025996533795%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.105719237435009%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.838821490467938%\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.984402079722702%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.785095320623917%\" valign=\"bottom\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.651646447140381%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.077989601386482%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.251299826689774%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.305025996533795%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.105719237435009%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.838821490467938%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.984402079722702%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.785095320623917%\" valign=\"bottom\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.651646447140381%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.077989601386482%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.251299826689774%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.305025996533795%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.105719237435009%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.838821490467938%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.984402079722702%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.785095320623917%\" valign=\"bottom\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.651646447140381%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.077989601386482%\" valign=\"bottom\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.251299826689774%\" valign=\"bottom\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.305025996533795%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.105719237435009%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.838821490467938%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.984402079722702%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.785095320623917%\" valign=\"bottom\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.651646447140381%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.077989601386482%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.251299826689774%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.305025996533795%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.105719237435009%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.838821490467938%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.984402079722702%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.785095320623917%\" valign=\"bottom\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.651646447140381%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.077989601386482%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.251299826689774%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.305025996533795%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.105719237435009%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.838821490467938%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.984402079722702%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.785095320623917%\" valign=\"bottom\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.651646447140381%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.077989601386482%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.251299826689774%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.305025996533795%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.105719237435009%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.838821490467938%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.984402079722702%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.785095320623917%\" valign=\"bottom\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.651646447140381%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.077989601386482%\" valign=\"bottom\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.251299826689774%\" valign=\"bottom\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.305025996533795%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.105719237435009%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.838821490467938%\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.984402079722702%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.785095320623917%\" valign=\"bottom\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.651646447140381%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.077989601386482%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.251299826689774%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.305025996533795%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.105719237435009%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.838821490467938%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.984402079722702%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.785095320623917%\" valign=\"bottom\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.651646447140381%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.077989601386482%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.251299826689774%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.305025996533795%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.105719237435009%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.838821490467938%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.984402079722702%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.785095320623917%\" valign=\"bottom\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.651646447140381%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.077989601386482%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.251299826689774%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.305025996533795%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.105719237435009%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.838821490467938%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.984402079722702%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.785095320623917%\" valign=\"bottom\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.651646447140381%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.077989601386482%\" valign=\"bottom\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.251299826689774%\" valign=\"bottom\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.305025996533795%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.105719237435009%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.838821490467938%\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.984402079722702%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.785095320623917%\" valign=\"bottom\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.651646447140381%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.077989601386482%\" valign=\"bottom\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.251299826689774%\" valign=\"bottom\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.305025996533795%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.105719237435009%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.838821490467938%\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.984402079722702%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.785095320623917%\" valign=\"bottom\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.651646447140381%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.077989601386482%\" valign=\"bottom\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.251299826689774%\" valign=\"bottom\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.305025996533795%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.105719237435009%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.838821490467938%\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.984402079722702%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.785095320623917%\" valign=\"bottom\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.651646447140381%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.077989601386482%\" valign=\"bottom\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.251299826689774%\" valign=\"bottom\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.305025996533795%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.105719237435009%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.838821490467938%\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.984402079722702%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.785095320623917%\" valign=\"bottom\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.651646447140381%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.077989601386482%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.251299826689774%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.305025996533795%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.105719237435009%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.838821490467938%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.984402079722702%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.785095320623917%\" valign=\"bottom\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.651646447140381%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.077989601386482%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.251299826689774%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.305025996533795%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.105719237435009%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.838821490467938%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.984402079722702%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.785095320623917%\" valign=\"bottom\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.651646447140381%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.077989601386482%\" valign=\"bottom\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.251299826689774%\" valign=\"bottom\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.305025996533795%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.105719237435009%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.838821490467938%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.984402079722702%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.785095320623917%\" valign=\"bottom\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.651646447140381%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.077989601386482%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.251299826689774%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.305025996533795%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.105719237435009%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.838821490467938%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.984402079722702%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.785095320623917%\" valign=\"bottom\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.651646447140381%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.077989601386482%\" valign=\"bottom\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.251299826689774%\" valign=\"bottom\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.305025996533795%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.105719237435009%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.838821490467938%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.984402079722702%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.785095320623917%\" valign=\"bottom\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.651646447140381%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.077989601386482%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.251299826689774%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.305025996533795%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.105719237435009%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.838821490467938%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.984402079722702%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.785095320623917%\" valign=\"bottom\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.651646447140381%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.077989601386482%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.251299826689774%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.305025996533795%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.105719237435009%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.838821490467938%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.984402079722702%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.785095320623917%\" valign=\"bottom\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.651646447140381%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.077989601386482%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.251299826689774%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.305025996533795%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.105719237435009%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.838821490467938%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.984402079722702%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.785095320623917%\" valign=\"bottom\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.651646447140381%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.077989601386482%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.251299826689774%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.305025996533795%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.105719237435009%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.838821490467938%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.984402079722702%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.785095320623917%\" valign=\"bottom\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.651646447140381%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.077989601386482%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.251299826689774%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.305025996533795%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.105719237435009%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.838821490467938%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.984402079722702%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.785095320623917%\" valign=\"bottom\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.651646447140381%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.077989601386482%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.251299826689774%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.305025996533795%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.105719237435009%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.838821490467938%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.984402079722702%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.785095320623917%\" valign=\"bottom\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.651646447140381%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.077989601386482%\" valign=\"bottom\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.251299826689774%\" valign=\"bottom\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.305025996533795%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.105719237435009%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.838821490467938%\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.984402079722702%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.785095320623917%\" valign=\"bottom\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.651646447140381%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.077989601386482%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.251299826689774%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.305025996533795%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.105719237435009%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.838821490467938%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.984402079722702%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.785095320623917%\" valign=\"bottom\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.651646447140381%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.077989601386482%\" valign=\"bottom\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.251299826689774%\" valign=\"bottom\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.305025996533795%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.105719237435009%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.838821490467938%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.984402079722702%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.785095320623917%\" valign=\"bottom\"\u003e\n \u003cp\u003e52*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.651646447140381%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.077989601386482%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.251299826689774%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.305025996533795%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.105719237435009%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.838821490467938%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.984402079722702%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.785095320623917%\" valign=\"bottom\"\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.651646447140381%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.077989601386482%\" valign=\"bottom\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.251299826689774%\" valign=\"bottom\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.305025996533795%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.105719237435009%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.838821490467938%\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.984402079722702%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.785095320623917%\" valign=\"bottom\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.651646447140381%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.077989601386482%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.251299826689774%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.305025996533795%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.105719237435009%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.838821490467938%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.984402079722702%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.785095320623917%\" valign=\"bottom\"\u003e\n \u003cp\u003e83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.651646447140381%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.077989601386482%\" valign=\"bottom\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.251299826689774%\" valign=\"bottom\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.305025996533795%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.105719237435009%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.838821490467938%\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.984402079722702%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.785095320623917%\" valign=\"bottom\"\u003e\n \u003cp\u003e86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.651646447140381%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.077989601386482%\" valign=\"bottom\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.251299826689774%\" valign=\"bottom\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.305025996533795%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.105719237435009%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.838821490467938%\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.984402079722702%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.785095320623917%\" valign=\"bottom\"\u003e\n \u003cp\u003e88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.651646447140381%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.077989601386482%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.251299826689774%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.305025996533795%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.105719237435009%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.838821490467938%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.984402079722702%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.785095320623917%\" valign=\"bottom\"\u003e\n \u003cp\u003e91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.651646447140381%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.077989601386482%\" valign=\"bottom\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.251299826689774%\" valign=\"bottom\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.305025996533795%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.105719237435009%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.838821490467938%\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.984402079722702%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.785095320623917%\" valign=\"bottom\"\u003e\n \u003cp\u003e95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.651646447140381%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.077989601386482%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.251299826689774%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.305025996533795%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.105719237435009%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.838821490467938%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.984402079722702%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.785095320623917%\" valign=\"bottom\"\u003e\n \u003cp\u003e101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.651646447140381%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.077989601386482%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.251299826689774%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.305025996533795%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.105719237435009%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.838821490467938%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.984402079722702%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.785095320623917%\" valign=\"bottom\"\u003e\n \u003cp\u003e102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.651646447140381%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.077989601386482%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.251299826689774%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.305025996533795%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.105719237435009%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.838821490467938%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.984402079722702%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.785095320623917%\" valign=\"bottom\"\u003e\n \u003cp\u003e103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.651646447140381%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.077989601386482%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.251299826689774%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.305025996533795%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.105719237435009%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.838821490467938%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.984402079722702%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.785095320623917%\" valign=\"bottom\"\u003e\n \u003cp\u003e104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.651646447140381%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.077989601386482%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.251299826689774%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.305025996533795%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.105719237435009%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.838821490467938%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.984402079722702%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.785095320623917%\" valign=\"bottom\"\u003e\n \u003cp\u003e112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.651646447140381%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.077989601386482%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.251299826689774%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.305025996533795%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.105719237435009%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.838821490467938%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.984402079722702%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.785095320623917%\" valign=\"bottom\"\u003e\n \u003cp\u003e113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.651646447140381%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.077989601386482%\" valign=\"bottom\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.251299826689774%\" valign=\"bottom\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.305025996533795%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.105719237435009%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.838821490467938%\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.984402079722702%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.785095320623917%\" valign=\"bottom\"\u003e\n \u003cp\u003e114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.651646447140381%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.077989601386482%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.251299826689774%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.305025996533795%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.105719237435009%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.838821490467938%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.984402079722702%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.785095320623917%\" valign=\"bottom\"\u003e\n \u003cp\u003e115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.651646447140381%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.077989601386482%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.251299826689774%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.305025996533795%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.105719237435009%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.838821490467938%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.984402079722702%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.785095320623917%\" valign=\"bottom\"\u003e\n \u003cp\u003e116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.651646447140381%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.077989601386482%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.251299826689774%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.305025996533795%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.105719237435009%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.838821490467938%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.984402079722702%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.785095320623917%\" valign=\"bottom\"\u003e\n \u003cp\u003e117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.651646447140381%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.077989601386482%\" valign=\"bottom\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.251299826689774%\" valign=\"bottom\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.305025996533795%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.105719237435009%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.838821490467938%\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.984402079722702%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.785095320623917%\" valign=\"bottom\"\u003e\n \u003cp\u003e118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.651646447140381%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.077989601386482%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.251299826689774%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.305025996533795%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.105719237435009%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.838821490467938%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.984402079722702%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.785095320623917%\" valign=\"bottom\"\u003e\n \u003cp\u003e119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.651646447140381%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.077989601386482%\" valign=\"bottom\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.251299826689774%\" valign=\"bottom\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.305025996533795%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.105719237435009%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.838821490467938%\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.984402079722702%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.785095320623917%\" valign=\"bottom\"\u003e\n \u003cp\u003e122\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.651646447140381%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.077989601386482%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.251299826689774%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.305025996533795%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.105719237435009%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.838821490467938%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.984402079722702%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.785095320623917%\" valign=\"bottom\"\u003e\n \u003cp\u003e123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.651646447140381%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.077989601386482%\" valign=\"bottom\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.251299826689774%\" valign=\"bottom\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.305025996533795%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.105719237435009%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.838821490467938%\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.984402079722702%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.785095320623917%\" valign=\"bottom\"\u003e\n \u003cp\u003e124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.651646447140381%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.077989601386482%\" valign=\"bottom\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.251299826689774%\" valign=\"bottom\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.305025996533795%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.105719237435009%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.838821490467938%\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.984402079722702%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.785095320623917%\" valign=\"bottom\"\u003e\n \u003cp\u003e125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.651646447140381%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.077989601386482%\" valign=\"bottom\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.251299826689774%\" valign=\"bottom\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.305025996533795%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.105719237435009%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.838821490467938%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.984402079722702%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.785095320623917%\" valign=\"bottom\"\u003e\n \u003cp\u003e127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.651646447140381%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.077989601386482%\" valign=\"bottom\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.251299826689774%\" valign=\"bottom\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.305025996533795%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.105719237435009%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.838821490467938%\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.984402079722702%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.785095320623917%\" valign=\"bottom\"\u003e\n \u003cp\u003e128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.651646447140381%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.077989601386482%\" valign=\"bottom\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.251299826689774%\" valign=\"bottom\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.305025996533795%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.105719237435009%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.838821490467938%\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.984402079722702%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.785095320623917%\" valign=\"bottom\"\u003e\n \u003cp\u003e129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.651646447140381%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.077989601386482%\" valign=\"bottom\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.251299826689774%\" valign=\"bottom\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.305025996533795%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.105719237435009%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.838821490467938%\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.984402079722702%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.785095320623917%\" valign=\"bottom\"\u003e\n \u003cp\u003e131\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.651646447140381%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.077989601386482%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.251299826689774%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.305025996533795%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.105719237435009%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.838821490467938%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.984402079722702%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.785095320623917%\" valign=\"bottom\"\u003e\n \u003cp\u003e132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.651646447140381%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.077989601386482%\" valign=\"bottom\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.251299826689774%\" valign=\"bottom\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.305025996533795%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.105719237435009%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.838821490467938%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.984402079722702%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.785095320623917%\" valign=\"bottom\"\u003e\n \u003cp\u003e133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.651646447140381%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.077989601386482%\" valign=\"bottom\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.251299826689774%\" valign=\"bottom\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.305025996533795%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.105719237435009%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.838821490467938%\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.984402079722702%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.785095320623917%\" valign=\"bottom\"\u003e\n \u003cp\u003e135\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.651646447140381%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.077989601386482%\" valign=\"bottom\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.251299826689774%\" valign=\"bottom\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.305025996533795%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.105719237435009%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.838821490467938%\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.984402079722702%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.785095320623917%\" valign=\"bottom\"\u003e\n \u003cp\u003e136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.651646447140381%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.077989601386482%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.251299826689774%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.305025996533795%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.105719237435009%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.838821490467938%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.984402079722702%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.785095320623917%\" valign=\"bottom\"\u003e\n \u003cp\u003e137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.651646447140381%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.077989601386482%\" valign=\"bottom\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.251299826689774%\" valign=\"bottom\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.305025996533795%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.105719237435009%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.838821490467938%\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.984402079722702%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.785095320623917%\" valign=\"bottom\"\u003e\n \u003cp\u003e138\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.651646447140381%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.077989601386482%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.251299826689774%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.305025996533795%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.105719237435009%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.838821490467938%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.984402079722702%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.785095320623917%\" valign=\"bottom\"\u003e\n \u003cp\u003e139\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.651646447140381%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.077989601386482%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.251299826689774%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.305025996533795%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.105719237435009%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.838821490467938%\"\u003e\n 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width=\"16.984402079722702%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.785095320623917%\" valign=\"bottom\"\u003e\n \u003cp\u003e142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.651646447140381%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.077989601386482%\" valign=\"bottom\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.251299826689774%\" valign=\"bottom\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.305025996533795%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.105719237435009%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.838821490467938%\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.984402079722702%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.785095320623917%\" valign=\"bottom\"\u003e\n \u003cp\u003e143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.651646447140381%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.077989601386482%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.251299826689774%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.305025996533795%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.105719237435009%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.838821490467938%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.984402079722702%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.785095320623917%\" valign=\"bottom\"\u003e\n \u003cp\u003e145\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.651646447140381%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.077989601386482%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.251299826689774%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.305025996533795%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.105719237435009%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.838821490467938%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.984402079722702%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.785095320623917%\" valign=\"bottom\"\u003e\n \u003cp\u003e147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.651646447140381%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.077989601386482%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.251299826689774%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.305025996533795%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.105719237435009%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.838821490467938%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.984402079722702%\" valign=\"bottom\"\u003e\n 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width=\"11.785095320623917%\" valign=\"bottom\"\u003e\n \u003cp\u003e160\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.651646447140381%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.077989601386482%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.251299826689774%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.305025996533795%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.105719237435009%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.838821490467938%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.984402079722702%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.785095320623917%\" valign=\"bottom\"\u003e\n \u003cp\u003e161\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.651646447140381%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.077989601386482%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.251299826689774%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.305025996533795%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.105719237435009%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.838821490467938%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.984402079722702%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.785095320623917%\" valign=\"bottom\"\u003e\n 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valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.077989601386482%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.251299826689774%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.305025996533795%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.105719237435009%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.838821490467938%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.984402079722702%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.785095320623917%\" valign=\"bottom\"\u003e\n \u003cp\u003e273\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.651646447140381%\" valign=\"bottom\"\u003e\n 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\u003cp\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.077989601386482%\" valign=\"bottom\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.251299826689774%\" valign=\"bottom\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.305025996533795%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.105719237435009%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.838821490467938%\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.984402079722702%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.785095320623917%\" valign=\"bottom\"\u003e\n \u003cp\u003e285\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.651646447140381%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.077989601386482%\" valign=\"bottom\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.251299826689774%\" valign=\"bottom\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.305025996533795%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.105719237435009%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.838821490467938%\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.984402079722702%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.785095320623917%\" valign=\"bottom\"\u003e\n \u003cp\u003e307\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.651646447140381%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.077989601386482%\" valign=\"bottom\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.251299826689774%\" valign=\"bottom\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.305025996533795%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.105719237435009%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.838821490467938%\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.984402079722702%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.785095320623917%\" valign=\"bottom\"\u003e\n \u003cp\u003e311\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.651646447140381%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.077989601386482%\" valign=\"bottom\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.251299826689774%\" valign=\"bottom\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.305025996533795%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.105719237435009%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.838821490467938%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.984402079722702%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.785095320623917%\" valign=\"bottom\"\u003e\n \u003cp\u003e312\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.651646447140381%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.077989601386482%\" valign=\"bottom\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.251299826689774%\" valign=\"bottom\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.305025996533795%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.105719237435009%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.838821490467938%\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.984402079722702%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.785095320623917%\" valign=\"bottom\"\u003e\n \u003cp\u003e314\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.651646447140381%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.077989601386482%\" valign=\"bottom\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.251299826689774%\" valign=\"bottom\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.305025996533795%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.105719237435009%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.838821490467938%\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.984402079722702%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.785095320623917%\" valign=\"bottom\"\u003e\n \u003cp\u003e315\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.651646447140381%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.077989601386482%\" valign=\"bottom\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.251299826689774%\" valign=\"bottom\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.305025996533795%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.105719237435009%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.838821490467938%\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.984402079722702%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.785095320623917%\" valign=\"bottom\"\u003e\n \u003cp\u003e316\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.651646447140381%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.077989601386482%\" valign=\"bottom\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.251299826689774%\" valign=\"bottom\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.305025996533795%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.105719237435009%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.838821490467938%\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.984402079722702%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.785095320623917%\" valign=\"bottom\"\u003e\n \u003cp\u003e317\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.651646447140381%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.077989601386482%\" valign=\"bottom\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.251299826689774%\" valign=\"bottom\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.305025996533795%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.105719237435009%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.838821490467938%\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.984402079722702%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.785095320623917%\" valign=\"bottom\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.651646447140381%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.077989601386482%\" valign=\"bottom\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.251299826689774%\" valign=\"bottom\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.305025996533795%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.105719237435009%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.838821490467938%\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.984402079722702%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.785095320623917%\" valign=\"bottom\"\u003e\n \u003cp\u003e64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.651646447140381%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.077989601386482%\" valign=\"bottom\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.251299826689774%\" valign=\"bottom\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.305025996533795%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.105719237435009%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.838821490467938%\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.984402079722702%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.785095320623917%\" valign=\"bottom\"\u003e\n \u003cp\u003e71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.651646447140381%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.077989601386482%\" valign=\"bottom\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.251299826689774%\" valign=\"bottom\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.305025996533795%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.105719237435009%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.838821490467938%\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.984402079722702%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.785095320623917%\" valign=\"bottom\"\u003e\n \u003cp\u003e105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.651646447140381%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.077989601386482%\" valign=\"bottom\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.251299826689774%\" valign=\"bottom\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.305025996533795%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.105719237435009%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.838821490467938%\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.984402079722702%\" valign=\"bottom\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*: Samples excluded from the study\u003c/p\u003e\n\u003cp\u003e-: Negative\u003c/p\u003e\n\u003cp\u003e+: Positive\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u003c/strong\u003e Targets and primers used for the set-up of the assays \u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"564\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.347517730496454%\"\u003e\n \u003cp\u003e\u003cstrong\u003eGene\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.645390070921987%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePrimers name\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.04255319148936%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSequences 5\u0026apos;--\u0026gt;3\u0026apos;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.042553191489361%\"\u003e\n \u003cp\u003e\u003cstrong\u003eLength (bp)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.851063829787234%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTm (\u0026deg;C)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.070921985815604%\"\u003e\n \u003cp\u003e\u003cstrong\u003eReference\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.347517730496454%\" rowspan=\"6\"\u003e\n \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.645390070921987%\" valign=\"bottom\"\u003e\n \u003cp\u003eCDC_N2-F*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.04255319148936%\"\u003e\n \u003cp\u003eTTACAAACATTGGCCGCAAA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.042553191489361%\" rowspan=\"2\"\u003e\n \u003cp\u003e67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.851063829787234%\" rowspan=\"2\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.070921985815604%\" rowspan=\"2\"\u003e\n \u003cp\u003eWHO, 2020, US-CDC N2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.99395770392749%\" valign=\"bottom\"\u003e\n \u003cp\u003eCDC_N2-R*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.00604229607251%\"\u003e\n \u003cp\u003eGCGCGACATTCCGAAGAA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.8%\" valign=\"bottom\"\u003e\n \u003cp\u003ePCR-CoV-Ngene-F1*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.4%\"\u003e\n \u003cp\u003eGCATCATATGGGTTGCAACT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.2%\" rowspan=\"2\"\u003e\n \u003cp\u003e166\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.6%\" rowspan=\"2\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17%\" rowspan=\"4\"\u003e\n \u003cp\u003eNew design\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.99395770392749%\" valign=\"bottom\"\u003e\n \u003cp\u003ePCR-CoV-Ngene-R1*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.00604229607251%\"\u003e\n \u003cp\u003eGAAGAGGCTTGACTGCCGCC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.493975903614455%\" valign=\"bottom\"\u003e\n \u003cp\u003ePCR-CoV-Ngene-F2*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"46.265060240963855%\"\u003e\n \u003cp\u003eTCATATGGGTTGCAACTGAGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.289156626506024%\" rowspan=\"2\"\u003e\n \u003cp\u003e181\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.951807228915663%\" rowspan=\"2\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.99395770392749%\" valign=\"bottom\"\u003e\n \u003cp\u003ePCR-CoV-Ngene-R2*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.00604229607251%\"\u003e\n \u003cp\u003eCTACGTGATGAGGAACGAGA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"6\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.347517730496454%\" rowspan=\"6\"\u003e\n \u003cp\u003e\u003cem\u003eRdRp\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.645390070921987%\" valign=\"bottom\"\u003e\n \u003cp\u003eIP2_F*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.04255319148936%\"\u003e\n \u003cp\u003eATGAGCTTAGTCCTGTTG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.042553191489361%\" rowspan=\"2\"\u003e\n \u003cp\u003e108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.851063829787234%\" rowspan=\"2\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.070921985815604%\" rowspan=\"2\"\u003e\n \u003cp\u003eWHO, 2020, Pasteur IP2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.99395770392749%\" valign=\"bottom\"\u003e\n \u003cp\u003eIP2_R*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.00604229607251%\"\u003e\n \u003cp\u003eCTCCCTTTGTTGTGTTGT\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.8%\"\u003e\n \u003cp\u003eIP4_F*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.4%\"\u003e\n \u003cp\u003eGGTAACTGGTATGATTTCG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.2%\" rowspan=\"2\"\u003e\n \u003cp\u003e107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.6%\" rowspan=\"2\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17%\" rowspan=\"2\"\u003e\n \u003cp\u003eWHO, 2020, Pasteur IP4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.99395770392749%\"\u003e\n \u003cp\u003eIP4_R*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.00604229607251%\"\u003e\n \u003cp\u003eCTGGTCAAGGTTAATATAGG\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.8%\"\u003e\n \u003cp\u003eRdRP 2-F*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.4%\"\u003e\n \u003cp\u003eTGAAATCAATAGCCGCCACT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.2%\" rowspan=\"2\"\u003e\n \u003cp\u003e199\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.6%\" rowspan=\"2\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17%\" rowspan=\"2\"\u003e\n \u003cp\u003ePark et al., 2020\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.99395770392749%\" valign=\"bottom\"\u003e\n \u003cp\u003eRdRP 2-R*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.00604229607251%\"\u003e\n \u003cp\u003eTGTTTGCGAGCAAGAACAAG\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.347517730496454%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"24.645390070921987%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"34.04255319148936%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.042553191489361%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.851063829787234%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.070921985815604%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.347517730496454%\" rowspan=\"16\"\u003e\n \u003cp\u003e\u003cem\u003eORF1ab\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.645390070921987%\"\u003e\n \u003cp\u003eCDC-ORF1ab.F*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.04255319148936%\"\u003e\n \u003cp\u003eCCCTGTGGGTTTTACACTTAA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.042553191489361%\" rowspan=\"2\"\u003e\n \u003cp\u003e119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.851063829787234%\" rowspan=\"2\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.070921985815604%\" rowspan=\"2\"\u003e\n \u003cp\u003eWHO, 2020, China-CDC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.99395770392749%\"\u003e\n \u003cp\u003eCDC-ORF1ab.R*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.00604229607251%\"\u003e\n \u003cp\u003eACGATTGTGCATCAGCTGA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.8%\" valign=\"bottom\"\u003e\n \u003cp\u003ePCR-CoV-ORF1ab-F1*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.4%\"\u003e\n \u003cp\u003eCTACCGAAGTTGTAGGAG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.2%\" rowspan=\"2\"\u003e\n \u003cp\u003e115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.6%\" rowspan=\"2\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17%\" rowspan=\"4\"\u003e\n \u003cp\u003eNew design\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.99395770392749%\" valign=\"bottom\"\u003e\n \u003cp\u003ePCR-CoV-ORF1ab-R1*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.00604229607251%\"\u003e\n \u003cp\u003eGTAAGACTAGAATTGTCTAC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.493975903614455%\" valign=\"bottom\"\u003e\n \u003cp\u003ePCR-CoV-ORF1ab-F2*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"46.265060240963855%\"\u003e\n \u003cp\u003eCGAAGTTGTAGGAGACAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.289156626506024%\" rowspan=\"2\"\u003e\n \u003cp\u003e98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.951807228915663%\" rowspan=\"2\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.99395770392749%\" valign=\"bottom\"\u003e\n \u003cp\u003ePCR-CoV-ORF1ab-R2*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.00604229607251%\"\u003e\n \u003cp\u003eTTGTCTACATAAGCAGCC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.8%\"\u003e\n \u003cp\u003eORF1ab-PJC-F1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.4%\"\u003e\n \u003cp\u003eTGGGGTTTTACAGGTAACCT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.2%\" rowspan=\"2\"\u003e\n \u003cp\u003e107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.6%\" rowspan=\"2\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17%\" rowspan=\"2\"\u003e\n \u003cp\u003eWHO, 2020, China-HKU\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.99395770392749%\"\u003e\n \u003cp\u003eORF1ab-PJC-R1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.00604229607251%\"\u003e\n \u003cp\u003eAACACGCTTAACAAAGCACTC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.8%\"\u003e\n \u003cp\u003eORF1ab-PJC-F2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.4%\"\u003e\n \u003cp\u003eACCTCATGGTCATGTTATGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.2%\" rowspan=\"2\"\u003e\n \u003cp\u003e322\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.6%\" rowspan=\"2\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17%\" rowspan=\"2\"\u003e\n \u003cp\u003eShirato et al, 2020\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.99395770392749%\"\u003e\n \u003cp\u003eORF1ab-PJC-R2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.00604229607251%\"\u003e\n \u003cp\u003eGACATAGCGAGTGTATGCC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.8%\"\u003e\n \u003cp\u003eORF1ab-PJC-F3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.4%\"\u003e\n \u003cp\u003eGGTGTCCTTGTCCCTCATGT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.2%\" rowspan=\"2\"\u003e\n \u003cp\u003e246\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.6%\" rowspan=\"2\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17%\"\u003e\n \u003cp\u003eNew design\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.41346153846154%\"\u003e\n \u003cp\u003eORF1ab-PJC-R2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"46.15384615384615%\"\u003e\n \u003cp\u003eGACATAGCGAGTGTATGCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.432692307692307%\"\u003e\n \u003cp\u003eShirato et al, 2020\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.8%\"\u003e\n \u003cp\u003eORF1ab-PJC-F2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.4%\"\u003e\n \u003cp\u003eACCTCATGGTCATGTTATGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.2%\" rowspan=\"2\"\u003e\n \u003cp\u003e205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.6%\" rowspan=\"2\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17%\"\u003e\n \u003cp\u003eShirato et al, 2020\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.41346153846154%\"\u003e\n \u003cp\u003eORF1ab-PJC-R3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"46.15384615384615%\"\u003e\n \u003cp\u003eAGTCATTTGACTTAGGCGAC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.432692307692307%\"\u003e\n \u003cp\u003eNew design\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.8%\"\u003e\n \u003cp\u003eORF1ab-PJC-F3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.4%\"\u003e\n \u003cp\u003eGGTGTCCTTGTCCCTCATGT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.2%\" rowspan=\"2\"\u003e\n \u003cp\u003e129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.6%\" rowspan=\"2\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17%\" rowspan=\"2\"\u003e\n \u003cp\u003eNew design\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.99395770392749%\"\u003e\n \u003cp\u003eORF1ab-PJC-R3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.00604229607251%\"\u003e\n \u003cp\u003eAGTCATTTGACTTAGGCGAC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.347517730496454%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"24.645390070921987%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"34.04255319148936%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.042553191489361%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.851063829787234%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.070921985815604%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.347517730496454%\" rowspan=\"6\"\u003e\n \u003cp\u003e\u003cem\u003eE\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.645390070921987%\" valign=\"bottom\"\u003e\n \u003cp\u003ePCR-CoV-Egene-F1*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.04255319148936%\"\u003e\n \u003cp\u003eGCACAAGCTGATGAGTACG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.042553191489361%\" rowspan=\"2\"\u003e\n \u003cp\u003e185\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.851063829787234%\" rowspan=\"2\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.070921985815604%\" rowspan=\"2\"\u003e\n \u003cp\u003eNew design\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.99395770392749%\" valign=\"bottom\"\u003e\n \u003cp\u003ePCR-CoV-Egene-R1*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.00604229607251%\"\u003e\n \u003cp\u003eGGTTTTACAAGACTCACG\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.8%\" valign=\"bottom\"\u003e\n \u003cp\u003ePCR-CoV-Egene-F2*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.4%\"\u003e\n \u003cp\u003eGTACGAACTTATGTACTC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.2%\" rowspan=\"2\"\u003e\n \u003cp\u003e155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.6%\" rowspan=\"2\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17%\" rowspan=\"2\"\u003e\n \u003cp\u003eNew design\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.99395770392749%\" valign=\"bottom\"\u003e\n \u003cp\u003ePCR-CoV-Egene-R2*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.00604229607251%\"\u003e\n \u003cp\u003eCGTTAACAATATTGCAGCAG\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.8%\"\u003e\n \u003cp\u003eE-PJC-F*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.4%\"\u003e\n \u003cp\u003eGGAAGAGACAGGTACGTTAA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.2%\" rowspan=\"2\"\u003e\n \u003cp\u003e146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.6%\" rowspan=\"2\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17%\" rowspan=\"2\"\u003e\n \u003cp\u003eMollaei et al,. 2020\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.99395770392749%\"\u003e\n \u003cp\u003eE-PJC-R*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.00604229607251%\"\u003e\n \u003cp\u003eAAGGTTTTACAAGACTCACG\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.347517730496454%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"24.645390070921987%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"34.04255319148936%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.042553191489361%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.851063829787234%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.070921985815604%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.347517730496454%\" rowspan=\"12\"\u003e\n \u003cp\u003e\u003cem\u003eS\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.645390070921987%\"\u003e\n \u003cp\u003eHS1.Sgene.F\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.04255319148936%\"\u003e\n \u003cp\u003eAGGTTGATCACAGGCAGACT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.042553191489361%\" rowspan=\"2\"\u003e\n \u003cp\u003e185\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.851063829787234%\" rowspan=\"2\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.070921985815604%\" rowspan=\"12\"\u003e\n \u003cp\u003eNew design\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.99395770392749%\"\u003e\n \u003cp\u003eHS1.Sgene.R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.00604229607251%\"\u003e\n \u003cp\u003eATGTCCTTCCCTCAGTCAGC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.493975903614455%\"\u003e\n \u003cp\u003eHS2.Sgene.F\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"46.265060240963855%\"\u003e\n \u003cp\u003eTCCATCAAAACCAAGCAAGAGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.289156626506024%\" rowspan=\"2\"\u003e\n \u003cp\u003e173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.951807228915663%\" rowspan=\"2\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.99395770392749%\"\u003e\n \u003cp\u003eHS2.Sgene.R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.00604229607251%\"\u003e\n \u003cp\u003eTGTTTTGCCACCTTTGCTCA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.493975903614455%\"\u003e\n \u003cp\u003eHS3.Sgene.F\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"46.265060240963855%\"\u003e\n \u003cp\u003eAGACTGTGCACTTGACCCTC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.289156626506024%\" rowspan=\"2\"\u003e\n \u003cp\u003e211\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.951807228915663%\" rowspan=\"2\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.99395770392749%\"\u003e\n \u003cp\u003eHS3.Sgene.R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.00604229607251%\"\u003e\n \u003cp\u003eTGGAACAGGAAGAGAATCAGC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.493975903614455%\" valign=\"bottom\"\u003e\n \u003cp\u003ePCR-CoV-Sgene-F1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"46.265060240963855%\"\u003e\n \u003cp\u003eCATCACTAGGTTTCAAAC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.289156626506024%\" rowspan=\"2\"\u003e\n \u003cp\u003e174\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.951807228915663%\" rowspan=\"2\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.99395770392749%\" valign=\"bottom\"\u003e\n \u003cp\u003ePCR-CoV-Sgene-R1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.00604229607251%\"\u003e\n \u003cp\u003eGCACAGTCTACAGCATCT\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.493975903614455%\"\u003e\n \u003cp\u003ePCR-CoV-Sgene-F2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"46.265060240963855%\"\u003e\n \u003cp\u003eGGTTTCAAACTTTACTTGC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.289156626506024%\" rowspan=\"2\"\u003e\n \u003cp\u003e112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.951807228915663%\" rowspan=\"2\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.99395770392749%\"\u003e\n \u003cp\u003ePCR-CoV-Sgene-R2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.00604229607251%\"\u003e\n \u003cp\u003eGGGTTATCTTCAACCTAGGA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.493975903614455%\"\u003e\n \u003cp\u003ePCR-CoV-Sgene-F3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"46.265060240963855%\"\u003e\n \u003cp\u003eGCCAATAGGTATTAACATCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.289156626506024%\" rowspan=\"2\"\u003e\n \u003cp\u003e120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.951807228915663%\" rowspan=\"2\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.99395770392749%\"\u003e\n \u003cp\u003ePCR-CoV-Sgene-R3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.00604229607251%\"\u003e\n \u003cp\u003eGCAGCTTATTATGTGGGTTA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*:\u0026nbsp;Labeled primers\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"https://myfiles.space/user_files/122228_c8a1650c59388082/122228_custom_files/img1720517478.png\"\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2. A one-step fast multiplex RT-PCR and a PCRD detection were set using the primer pairs selected\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe 2 selected primer pairs were engaged in a one-step fast multiplex RT-PCR where they were added to the same reaction mixture. The reaction conditions were established by varying primers concentrations (Figure 3a) and melting temperatures (Figure 3b) in an attempt to \u0026nbsp;balance band intensities as band of the \u003cem\u003eE\u003c/em\u003e amplicon was more intense than the \u003cem\u003eN\u003c/em\u003e one in both agarose gel and PCRD. In the retained protocol, the primers final concentrations were set to 0.5 \u0026micro;M each and the Tm at 57\u0026deg;C. The one-step fast multiplex RT-PCR requires 1 hour 15 minutes including 30 minutes for reverse transcription (RT) and 45 minutes for PCR. The amplification products were visualized on agarose gel and PCRD lateral flow. The PCRD lateral flow detection showed that there was no differences between 5, 10, and 15 minutes of flow migrations (Figure 3c). Therefore, the results were read, and the corresponding pictures were taken after 5 minutes of flow migration. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3. The performance of the established one-step fast multiplex RT-PCR coupled to the PCRD detection was evaluated using nasopharyngeal samples\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe retained amplification and detection protocols were applied to a collection of 102 RNAs extracted from patient\u0026rsquo;s nasopharyngeal swabs to evaluate the performance of the one-step fast multiplex RT-PCR and the PCRD detection assays. The status of the collected RNAs was confirmed by RT-qPCR at time of sampling: \u0026nbsp;50 were CoV+ and 52 were CoV2-. The RNA integrity, at time of testing the novel assay, was verified by amplification the human \u0026beta;-globin gene by one-step fast RT-PCR. In case of two samples (52 and 148), the fast RT-PCR for \u0026beta;-globin was negative (Table 1). Therefore, these two samples were excluded from the study. We tested the remaining 100 RNAs and considered a test positive if, at least one of the test lines showed a positive result. If both test lines showed negative results, the test was considered negative (Table 1). This way, our one-step fast multiplex RT-PCR assay coupled to PCRD detection showed a sensitivity of 88% (44/50) and a specificity of 98% (49/50).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor some samples, the \u003cem\u003eN\u003c/em\u003e target showed ambiguous results on the agarose gel, as the amplicons were easily mistaken for primers dimers and/or primers excess because of their small size (67 bp). This ambiguity is resolved by the PCRD detection. \u0026nbsp;Only positive samples showed positive results on the PCRD (Figure 4).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAdditionally, we noticed that this one-step fast multiplex RT-PCR/PCRD method is able to detect all positive samples having Ct values lower than 33. Samples with Ct values greater than 33 gave negative results, except for one sample (285), which had Ct values of 37/38, but gave positive results for both targets tested.\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn the context of strengthening local and international diagnostic facilities and technical capacity for pathogen detection during pandemics, epidemics or outbreaks, our study aims to contribute to the development of a simple and rapid method that does not require the use of sophisticated equipment and can be implemented in minimally equipped healthcare centers. Indeed, the emergence of infectious diseases, such as COVID-19, has stressed the importance and necessity of rapid and effective diagnosis for detecting pathogens and controlling their spread. COVID-19 represented an international public health emergency due to its pathogenesis and rapid spread. Simple, rapid, sensitive, specific, and equipment-free diagnostic techniques were essential to meet the needs of epidemic control strategies and thus limit the spread of the disease.\u0026nbsp;While different approaches have been used for disease diagnosis based on serology and computing tomography imaging, the WHO recommended real-time reverse transcription polymerase chain reaction (RT-qPCR) as the reference method\u0026nbsp;[1]. The classical RT-qPCR is a sensitive and accurate technique but has several disadvantages, including the high cost of kits, the prolonged time to result delivery, the need for trained personnel, and the requirement for a suitable and costly piece of equipment that is usually missing in low-resource setting areas.\u0026nbsp;[35]. The development of a simple, rapid, accurate, and field-applicable methods for detecting SARS-CoV-2 and future emerging pathogens remains a pressing need. Various molecular diagnostic alternatives, such as RT-LAMP\u0026nbsp;[9], RT-RPA\u0026nbsp;[15], and tests based on the CRISPR-Cas systems\u0026nbsp;[19, 21, 22], have been developed for detecting the SARS-CoV-2 virus. These tests allow for rapid, specific, and effective detection under isothermal conditions, therefore, eliminating the need for sophisticated equipment. However, despite their advantages, these techniques have drawbacks, namely complex primer design and optimization of reaction conditions in case of LAMP\u0026nbsp;[4], the risk of false positives with RPA\u0026nbsp;[36], and multiple workflow steps for the CRISPR Cas systems\u0026nbsp;[37]. As PCR is still one of the most valuable methods used in different fields, including food security, forensics, research and biomedicine, in this study, we chose to develop PCR assays using newly designed primers or re-adapt the WHO and published real time PCRs into simpler PCR-based tests. \u0026nbsp; \u0026nbsp;The test here reported is based on a one-step fast multiplex RT-PCR using a ready-to-use master mix and two pairs of already described primers that is coupled to lateral flow PCRD immunoassay detection. It is faster than the classical real time PCR. It is also easy to perform and read, and it obviates need for skilled operators and costly equipment mobilization that makes RT-qPCR only available in central laboratories and the ensuing transfer of samples to these central laboratories, which results in delays in result delivery. Indeed, for example, in Tunisia during the pandemics, real time PCR facilities and expert technicians were missing in different regions of the country. Therefore, in the beginning of the COVID-19 pandemics, all the samples from most regions of the country were transferred to the Institut Pasteur de Tunis. Personnel, including MDs, researchers and lab technicians from different departments and labs of the institute, were called to join the COVID-19 testing team and were organized into three daily shifts, which led to postponing all other research and/or diagnostic activities. Therefore, one of the strategic actions for preparedness for future pandemics, epidemics or outbreaks must be the investment in laboratory infrastructure and diagnostic capabilities\u0026nbsp;[38]. Here, we bring a proof of concept that the one-step fast multiplex RT-PCR coupled to PCRD detection could be a good alternative when there is a shortage in reagents or at points of need where real time PCR facilities are missing. In addition, this technology could be easily applied \u0026nbsp;for the diagnosis of other pathogens\u0026nbsp;[24]. Furthermore, lateral flow immuno-chromatographic assay devices are commercially available. They represent a simple method for the generic detection of nucleic acids without the need for specialized and costly equipment. Their use relies on the detection of dual-labeled amplicons. Primers\u0026rsquo; labels are chosen according to the device used; like in this study, we used different labels associations, Fam/biotin and Dig/biotin for each primer pair, which target two different genomic viral regions, to be able to detect the amplicons on their respective test line on the PCRD cassette. Compared to agarose gel electrophoresis, the PCRD assay is rapid (5 min), easy to use and read and does not require extensive molecular biology expertise. Our study showed that the results observed in the agarose gel upon electrophoresis are concordant with those obtained with the PCRD analysis of the \u003cem\u003eE\u003c/em\u003e target amplification. However, for the \u003cem\u003eN\u003c/em\u003e target, where the amplicon is smaller in length (67 bp), the PCRD assay was able to resolve interpretation ambiguity of the read outs on agarose gels, as the amplicons were not distinguishable from excess primers and/or primer dimers upon electrophoresis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe most commonly used genomic regions of SARS-CoV-2 for RT-qPCR diagnosis are highly conserved and/or highly expressed genes\u0026nbsp;[39]. Notably, they include the \u003cem\u003eORF1ab\u003c/em\u003e regions, the genes encoding for nucleocapsid (\u003cem\u003eN\u003c/em\u003e), envelope (\u003cem\u003eE\u003c/em\u003e), spike protein (\u003cem\u003eS\u003c/em\u003e), membrane protein (\u003cem\u003eM\u003c/em\u003e), RNA-dependent RNA polymerase (\u003cem\u003eRdRp\u003c/em\u003e), hemagglutinin-esterase (\u003cem\u003eHE\u003c/em\u003e), and helicase genes. In this study, we considered most of the mentioned genes to develop the assay aimed for. We selected and designed our primers in the \u003cem\u003eN\u003c/em\u003e, \u003cem\u003eS\u003c/em\u003e, \u003cem\u003eE\u003c/em\u003e, \u003cem\u003eRdRp\u003c/em\u003e and \u003cem\u003eORF1ab\u003c/em\u003e viral genomic sequences. Our selection criteria for the primers were based, namely, on the stability and reproducibility of the fast-PCR as analyzed by the agarose gel electrophoresis, and then on the absence of background noise on the PCRD. The two targets that meet these criteria were the \u003cem\u003eN\u003c/em\u003e (US-CDC-N2) and \u003cem\u003eE\u003c/em\u003e genes\u0026nbsp;[32]. The multiplexing of the amplification of these two targets within a same reaction was also successful, but we noticed that there was an imbalance in terms of band intensity on both the agarose gel and PCRD. The \u003cem\u003eE\u003c/em\u003e amplicon band was more intense compared to the \u003cem\u003eN\u003c/em\u003e one, despite our attempts to improve the reaction conditions. This could be due to a sort of competition between primers for the same mix of reagents\u0026nbsp;[25, 40]. The GC content of the target (40,4% for \u003cem\u003eE\u0026nbsp;\u003c/em\u003eand 52,2% for \u003cem\u003eN\u003c/em\u003e) could also lead to preferential denaturation, resulting in preferential amplification\u0026nbsp;[40], or secondary structures within genomes could induce differential accessibility of targets\u0026nbsp;[40, 41]. Nevertheless, PCRD results were easily read by the naked eye for both the \u003cem\u003eE\u003c/em\u003e and \u003cem\u003eN\u003c/em\u003e targets. The two targets showed the same sensitivity and specificity when tested in the multiplexed assay but gave discordant results for two patients: patient 47 was positive for the \u003cem\u003eE\u003c/em\u003e target but negative for the \u003cem\u003eN\u003c/em\u003e target, and patient 311 was negative for the \u003cem\u003eE\u003c/em\u003e target but positive for the \u003cem\u003eN\u003c/em\u003e target. For these patients, mutations in the priming sites could have occurred, leading to negative results. \u0026nbsp;Or, SARS-CoV-2 gene dynamic lead to differential genes expression depending on the patient status and disease stage\u0026nbsp;[42, 43]. This is the interest in using multiple targets to define the infected patient\u0026rsquo;s status and minimize false negative results\u0026nbsp;[1, 42]. However, our results showed that six patients who were positive according to RT-qPCR were negative with our assay. These patients had Ct values greater than 33, except for patient 285, who had Ct values of 37/38 for the two targets tested in RT-qPCR but showed positive results for the two targets tested in our one-step fast multiplex RT-PCR/PCRD. In this case, we could come up with two hypotheses. First, our assay is not sensitive enough and is able to detect only patients with a high viral load and a Ct below 33. The second hypothesis, given the fact that the RNAs in this study were extracted in 2021 (and our assays were performed in 2024), long-term stability of the RNAs constitutes a factor influencing the performance of the DNA assays, especially in case of samples with a low viral load. Indeed, it was demonstrated that RNA with a low viral load is more prone to a reduction in its RNA content than RNA with a high viral load\u0026nbsp;[44]. Therefore, our one-step fast multiplex RT-PCR assay should be evaluated with freshly extracted RNAs and, at the same time when RT-qPCR is performed, to accurately determine their performances.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe main limitation of the study is that the internal control (human \u003cem\u003e\u0026beta;-globin\u003c/em\u003e) was not included in our multiplex assay. We have attempted to minimize this drawback by setting in parallel this internal control on the same day and run. The one-step fast simplex PCR targeting the human \u003cem\u003e\u0026beta;-globin\u003c/em\u003e was performed in a separate reaction as one cannot visualize more than 2 amplicons on the PCRD lateral flow device. Indeed, to our knowledge, immunoassay devices with 3 test lines are not yet commercially available. For future test developments, it would be more interesting to have all the targets (viral targets and internal control) in the same reaction.\u0026nbsp;\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe COVID-19 pandemics proved the relevance and importance of molecular diagnostics for disease control and transmission monitoring. It stressed the need for simple, fast and equipment- free assays as alternative solutions to bring testing closer to the patients. In this study, we brought the proof of concept that the one-step fast multiplex RT-PCR coupled to PCRD detection, here developed, is a good alternative for SARS-CoV-2 detection. It requires a conventional PCR thermocycler and PCRD devices, with result delivery within only 1 hour and 20 minutes. Importantly, the assay developed using the ready to use kit for a portable RT-PCR machine could be easily adapted to point of care settings by using a commercially available portable thermocyclers.\u0026nbsp;\u003c/p\u003e\n"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study is approved by the Biomedical Ethics Committee of the Institut Pasteur de Tunis under the reference Ref: 2020/21/I/LR16IPT in accordance with the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNucleotide sequences of the SARS-CoV-2 used for primers design are available in NCBI public databases and are available at the following URL: txid2697049[organism:exp] - Nucleotide - NCBI (nih.gov)\u003c/p\u003e\n\u003cp\u003eAll other data supporting the findings of this study are available within the paper. Primer sequences are provided in Table 2.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was funded by the Ministry of Higher Education and Research Tunisia through the programme \u0026ldquo;Projet de Recherche F\u0026eacute;d\u0026eacute;r\u0026eacute;\u0026rdquo; PRF-Lutte COVID, and \u0026nbsp;the Research laboratory contract program LR16IPT04, and the Institut Pasteur de Tunis through the intramural program \u0026rdquo;Projet Collaboratif Interne-PCI39\u0026rdquo;.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIBA:\u003c/strong\u003e Conceptualization \u003cstrong\u003eand\u003c/strong\u003e study design, writing of the original draft, data analysis, supervision, funding acquisition, project administration; \u003cstrong\u003eHSO: Investigation,\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003edata analysis, project administration\u003cstrong\u003e; MM: Investigation, data analysis; MVT: Investigation; MA: Investigation; YSBA: Study design; KA: Investigation; SHB: Investigation, resources; HT: Funding Acquisition, resources;\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eIG:\u003c/strong\u003e Conceptualization, study design, funding acquisition, project administration, writing, review and editing. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors acknowledge all the Virology Lab staff who contributed to the process of the COVID-19 diagnosis including sampling, RNA extraction and RT-qPCR performing. We would like to thank Ahmed Sahbi Chakroun for his technical support and his advices on kits and reagent purchasing.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWHO. Diagnostic testing for SARS-CoV-2. 2020. https://www.who.int/publications-detail-redirect/diagnostic-testing-for-sars-cov-2. Accessed 23 May 2024.\u003c/li\u003e\n\u003cli\u003eCastellar-Mendoza C, Calder\u0026oacute;n-Pel\u0026aacute;ez M-A, Castellanos JE, Velandia-Romero ML, Coronel-Ruiz C, Camacho-Ortega S, et al. Development and Optimization of a Multiplex Real-Time RT-PCR to Detect SARS-CoV-2 in Human Samples. 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N Engl J Med. 2020;383:1492\u0026ndash;4.\u003c/li\u003e\n\u003cli\u003eBroughton JP, Deng X, Yu G, Fasching CL, Servellita V, Singh J, et al. CRISPR\u0026ndash;Cas12-based detection of SARS-CoV-2. Nat Biotechnol. 2020;38:870\u0026ndash;4.\u003c/li\u003e\n\u003cli\u003eNguyen LT, Rananaware SR, Pizzano BLM, Stone BT, Jain PK. Clinical validation of engineered CRISPR/Cas12a for rapid SARS-CoV-2 detection. Commun Med. 2022;2:1\u0026ndash;11.\u003c/li\u003e\n\u003cli\u003eAli Z, Aman R, Mahas A, Rao GS, Tehseen M, Marsic T, et al. iSCAN: An RT-LAMP-coupled CRISPR-Cas12 module for rapid, sensitive detection of SARS-CoV-2. Virus Res. 2020;288:198129.\u003c/li\u003e\n\u003cli\u003eAzhar Mohd, Phutela R, Kumar M, Ansari AH, Rauthan R, Gulati S, et al. Rapid and accurate nucleobase detection using FnCas9 and its application in COVID-19 diagnosis. Biosens Bioelectron. 2021;183:113207.\u003c/li\u003e\n\u003cli\u003eDing X, Yin K, Li Z, Lalla RV, Ballesteros E, Sfeir MM, et al. Ultrasensitive and visual detection of SARS-CoV-2 using all-in-one dual CRISPR-Cas12a assay. Nat Commun. 2020;11:4711.\u003c/li\u003e\n\u003cli\u003eZhu H, Zhang H, Xu Y, La\u0026scaron;\u0026scaron;\u0026aacute;kov\u0026aacute; S, Korabečn\u0026aacute; M, Neužil P. PCR past, present and future. Biotechniques. 2020;:10.2144/btn-2020\u0026ndash;0057.\u003c/li\u003e\n\u003cli\u003eBel Hadj Ali I, Saadi-Ben Aoun Y, Hammami Z, Rhouma O, Chakroun AS, Guizani I. Handheld Ultra-Fast Duplex Polymerase Chain Reaction Assays and Lateral Flow Detection and Identification of Leishmania Parasites for Cutaneous Leishmaniases Diagnosis. Pathogens. 2023;12:1292.\u003c/li\u003e\n\u003cli\u003eLino A, Cardoso MA, Gon\u0026ccedil;alves HMR, Martins-Lopes P. SARS-CoV-2 Detection Methods. Chemosensors. 2022;10:221.\u003c/li\u003e\n\u003cli\u003eAlhamid G, Tombuloglu H, Rabaan AA, Al-Suhaimi E. SARS-CoV-2 detection methods: A comprehensive review. Saudi J Biol Sci. 2022;29:103465.\u003c/li\u003e\n\u003cli\u003eChen X, Zhou Q, Yuan W, Shi Y, Dong S, Luo X. Visual and rapid identification of Chlamydia trachomatis and Neisseria gonorrhoeae using multiplex loop-mediated isothermal amplification and a gold nanoparticle-based lateral flow biosensor. Front Cell Infect Microbiol. 2023;13:1067554.\u003c/li\u003e\n\u003cli\u003evan Dijk NJ, Menting S, Wentink-Bonnema EMS, Broekhuizen-van Haaften PE, Withycombe E, Schallig HDFH, et al. Laboratory evaluation of the miniature direct-on-blood PCR nucleic acid lateral flow immunoassay (mini-dbPCR-NALFIA), a simplified molecular diagnostic test for Plasmodium. Malar J. 2023;22:98.\u003c/li\u003e\n\u003cli\u003eBudd J, Miller BS, Weckman NE, Cherkaoui D, Huang D, Decruz AT, et al. Lateral flow test engineering and lessons learned from COVID-19. Nat Rev Bioeng. 2023;1:13\u0026ndash;31.\u003c/li\u003e\n\u003cli\u003eBauer HM, Ting Y, Greer CE, Chambers JC, Tashiro CJ, Chimera J, et al. 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Advances and Challenges in SARS-CoV-2 Detection: A Review of Molecular and Serological Technologies. Diagnostics. 2024;14:519.\u003c/li\u003e\n\u003cli\u003eMunawar MA. Critical insight into recombinase polymerase amplification technology. Expert Rev Mol Diagn. 2022;22:725\u0026ndash;37.\u003c/li\u003e\n\u003cli\u003eZhou Y, Zhang L, Xie Y-H, Wu J. Advancements in detection of SARS-CoV-2 infection for confronting COVID-19 pandemics. Lab Invest. 2022;102:4\u0026ndash;13.\u003c/li\u003e\n\u003cli\u003eWilliams BA, Jones CH, Welch V, True JM. Outlook of pandemic preparedness in a post-COVID-19 world. Npj Vaccines. 2023;8:1\u0026ndash;12.\u003c/li\u003e\n\u003cli\u003eAhirwar R, Gandhi S, Komal K, Dhaniya G, Tripathi PP, Shingatgeri VM, et al. Biochemical composition, transmission and diagnosis of SARS-CoV-2. Biosci Rep. 2021;41:BSR20211238.\u003c/li\u003e\n\u003cli\u003eElnifro EM, Ashshi AM, Cooper RJ, Klapper PE. Multiplex PCR: Optimization and Application in Diagnostic Virology. Clin Microbiol Rev. 2000;13:559\u0026ndash;70.\u003c/li\u003e\n\u003cli\u003eRangan R, Zheludev IN, Hagey RJ, Pham EA, Wayment-Steele HK, Glenn JS, et al. RNA genome conservation and secondary structure in SARS-CoV-2 and SARS-related viruses: a first look. RNA. 2020;26:937\u0026ndash;59.\u003c/li\u003e\n\u003cli\u003eValadan R, Golchin S, Alizadeh-Navaei R, Haghshenas M, Zargari M, Mousavi T, et al. Differential gene expression analysis of common target genes for the detection of SARS-CoV-2 using real time-PCR. AMB Express. 2022;12:112.\u003c/li\u003e\n\u003cli\u003eBenrahma H, Idrissa D, Imane S, Jalila R, Nida M, Rachid B, et al. Epidemiological description and analysis of RdRp, E and N genes dynamic by RT-PCR of SARS-CoV-2 in Moroccan population: Experience of the National Reference Laboratory (LNR)-UM6SS. 2020;:2020.06.18.20135137.\u003c/li\u003e\n\u003cli\u003eBai H, Zhao J, Ma C, Wei H, Li X, Fang Q, et al. Impact of RNA degradation on influenza diagnosis in the surveillance system. Diagn Microbiol Infect Dis. 2021;100:115388.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-infectious-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"infd","sideBox":"Learn more about [BMC Infectious Diseases](http://bmcinfectdis.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/infd","title":"BMC Infectious Diseases","twitterHandle":"#bmcinfectdis","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Molecular diagnosis, SARS-CoV-2, One-step fast multiplex RT-PCR, Lateral flow immunoassay on PCRD, N gene, E gene","lastPublishedDoi":"10.21203/rs.3.rs-4595176/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4595176/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe COVID-19 pandemics has put emphasis on pivotal needs for diagnosis and surveillance worldwide, with the subsequent shortage of diagnostic reagents and kits. Therefore, it has become strategic for the countries to be able to access diagnosis, expand it, and acquire its own capacity to deploy diagnostics and alternative rapid accurate nucleic acid tests that are at lower costs. Here, we propose a visual SARS-CoV-2 detection using a one-step fast multiplex reverse transcription-PCR (RT-PCR) amplification coupled to lateral flow immunoassay detection on a PCRD device (Abingdon Health, UK).\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eVarious simplex fast-PCRs were developed for screening sets of primer pairs newly designed or selected from literature or from validated WHO tests, targeting \u003cem\u003eS\u003c/em\u003e, \u003cem\u003eN\u003c/em\u003e, \u003cem\u003eE\u003c/em\u003e, \u003cem\u003eRdRp\u003c/em\u003e or \u003cem\u003eORF1ab\u003c/em\u003e genes. Primers showing specific and stable amplification were retained to assess for their suitability for detection on PCRD. Thus, fast RT-PCR amplifications were performed using the retained primers. They were doubly labeled with Fam and Biotin or Dig and Biotin to allow visual detection of the labeled amplicons on the lateral flow immunoassay \u003cb\u003ePCR D\u003c/b\u003eetection (PCRD) device, looking at lack of interaction of the labeled primers (or primer dimers) with the test lines in negative or no RNA controls. All the assays were set up using RNAs isolated from patients\u0026rsquo; nasopharyngeal swabs. Two simplex assays, targeting two different viral genomic regions (\u003cem\u003eN\u003c/em\u003e and \u003cem\u003eE\u003c/em\u003e) and showing specific detection on PCRD, were used to set up a one-step fast multiplex RT-PCR assay (where both differently labeled primer pairs were engaged) coupled to amplicons\u0026rsquo; detection on a PCRD device. This novel method was evaluated on 50 SARS-CoV-2 positive and 50 SARS-CoV-2 negative samples and its performance was compared to the results of the quantitative RT-PCR (RT-qPCR) tests used for diagnosing the patients, here considered as the standard methods.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThis way, the new method showed a sensitivity of 88% (44/50) and a specificity of 98% (49/50). All patients who presented Ct values lower than 33 were positive for our assay. Except for one patient, those with Ct values greater than 33 showed negative results.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eOur results have brought proof of principle on the usefulness of the one-step fast multiplex RT- PCR assay coupled to PCRD as new method for specific, sensitive, and rapid detection of SARS-CoV-2 without requiring costly laboratory equipment, and thus at reduced costs, in a format prone to be deployed when resources are limited. This new method of SARS-CoV-2 detection appears to be a good alternative for COVID-19 diagnosis or screening at points of need.\u003c/p\u003e","manuscriptTitle":"Rapid detection of SARS-CoV-2 RNA using a one-step fast multiplex RT-PCR coupled to lateral flow immunoassay","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-18 16:04:13","doi":"10.21203/rs.3.rs-4595176/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-06-20T11:41:59+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-06-20T00:04:11+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-06-20T00:03:59+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Infectious Diseases","date":"2024-06-17T15:43:17+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-infectious-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"infd","sideBox":"Learn more about [BMC Infectious Diseases](http://bmcinfectdis.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/infd","title":"BMC Infectious Diseases","twitterHandle":"#bmcinfectdis","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"da333e78-fecf-4797-86b4-1795f050264c","owner":[],"postedDate":"July 18th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-12-23T16:07:06+00:00","versionOfRecord":{"articleIdentity":"rs-4595176","link":"https://doi.org/10.1186/s12879-024-10296-1","journal":{"identity":"bmc-infectious-diseases","isVorOnly":false,"title":"BMC Infectious Diseases"},"publishedOn":"2024-12-18 15:57:33","publishedOnDateReadable":"December 18th, 2024"},"versionCreatedAt":"2024-07-18 16:04:13","video":"","vorDoi":"10.1186/s12879-024-10296-1","vorDoiUrl":"https://doi.org/10.1186/s12879-024-10296-1","workflowStages":[]},"version":"v1","identity":"rs-4595176","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4595176","identity":"rs-4595176","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}