key: cord-0964869-t1tto9xw
authors: Rohaim, M. A.; Clayton, E.; Sahin, I.; Vilela, J.; Khalifa, M.; Al-Natour, M.; Bayoumi, M.; Poirier, A.; Branavan, M.; Tharmakulasingam, M.; Chaudhry, N. S.; Sodi, R.; Brown, A.; Burkhart, P.; Hacking, W.; Botham, J.; Boyce, J.; Wilkinson, H.; Williams, C.; Bates, M.; LaRagione, R.; Balachandran, W.; Fernando, A.; Munir, M.
title: Artificial Intelligence-Assisted Loop Mediated Isothermal Amplification (ai-LAMP) for Rapid and Reliable Detection of SARS-CoV-2
date: 2020-07-10
journal: nan
DOI: 10.1101/2020.07.08.20148999
sha: c944f7cb14fe5b4de0ac9e06f7fb2ea6276df046
doc_id: 964869
cord_uid: t1tto9xw

Until vaccines and effective therapeutics become available, the practical way to transit safely out of the current lockdown may include the implementation of an effective testing, tracing and tracking system. However, this requires a reliable and clinically validated diagnostic platform for the sensitive and specific identification of SARS-CoV-2. Here, we report on the development of a de novo, high-resolution and comparative genomics guided reverse-transcribed loop-mediated isothermal amplification (LAMP) assay. To further enhance the assay performance and to remove any subjectivity associated with operator interpretation of result, we engineered a novel hand-held smart diagnostic device. The robust diagnostic device was further furnished with automated image acquisition and processing algorithms, and the collated data was processed through artificial intelligence (AI) pipelines to further reduce the assay run time and the subjectivity of the colorimetric LAMP detection. This advanced AI algorithm-implemented LAMP (ai-LAMP) assay, targeting the RNA-dependent RNA polymerase gene, showed high analytical sensitivity and specificity for SARS-CoV-2. A total of ~200 coronavirus disease (CoVID-19)-suspected patient samples were tested using the platform and it was shown to be reliable, highly specific and significantly more sensitive than the current gold standard qRT-PCR. The system could provide an efficient and cost-effective platform to detect SARS-CoV-2 in resource-limited laboratories.

Until vaccines and effective therapeutics become available, the practical way to transit 24

safely out of the current lockdown may include the implementation of an effective 25 testing, tracing and tracking system. However, this requires a reliable and clinically 26 validated diagnostic platform for the sensitive and specific identification of SARS-CoV-27

2. Here, we report on the development of a de novo, high-resolution and comparative 28 genomics guided reverse-transcribed loop-mediated isothermal amplification (LAMP) 29 assay. To further enhance the assay performance and to remove any subjectivity 30 associated with operator interpretation of result, we engineered a novel hand-held 31 smart diagnostic device. The robust diagnostic device was further furnished with 32 automated image acquisition and processing algorithms, and the collated data was 33 processed through artificial intelligence (AI) pipelines to further reduce the assay run 34 time and the subjectivity of the colorimetric LAMP detection. This advanced AI 35 algorithm-implemented LAMP (ai-LAMP) assay, targeting the RNA-dependent RNA 36

polymerase gene, showed high analytical sensitivity and specificity for SARS-CoV-2.

A total of ~200 coronavirus disease (CoVID-19)-suspected patient samples were 38 tested using the platform and it was shown to be reliable, highly specific and 39 significantly more sensitive than the current gold standard qRT-PCR. named as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and led to 53 a global pandemic [1] [2] [3] . While the major impact of SARS-CoV-2 was attributed to frail 54 and elderly people with co-morbidities, coronavirus disease 2019 (CoVID-19) was 55 mainly spread by asymptomatic or mildly symptomatic patients [2] . Due to their high 56 mutation rates and recombination events, coronaviruses can infect a range of animal 57 species including humans, avian, rodents, carnivores, chiropters and other mammals 58

[4]. Before the emergence of SARS-CoV-2, a total of six different coronaviruses were 59

reported to infect humans, including HCoV-229E, HCoV-OC43, HCoV-NL63, HCoV-60 HKU1, MERS and SARS-CoV-1 (also known as classical SARS). The SARS-CoV-2 61

belongs to the β-coronavirus of the group 2B within the family of Coronaviridae [3] .

The SARS-CoV-2 shares a high level of genetic similarity (up to 96%) with 64

coronaviruses originating from bats [3] . The genome of β-coronavirus encodes for the 65 replicase complex (ORF1ab), spike (S), envelope (E), membrane (M) and 66 nucleoprotein (N) genes in addition to the several non-structural and accessory 67 proteins in the order from 5'-untranslated to 3'-untranslated regions [3] . Owing to the 68 nature of viral genetics, the N gene is the most transcribed and highly conserved gene 69 within the Coronaviridae family and has been a major target for both antigen and 70 antibodies diagnostics. Across the genome, the RNA-dependent RNA polymerase 71

(RdRP), encoded by the ORF1b gene segment, presents a high level of intra-group 72 conservation and therefore is an ideal target for a diagnostic application [5, 6].

As evident by previous pandemics caused by coronaviruses, a highly specific, 75

sensitive and easily deployable diagnostic is critical for the identification, tracing, 76 rationalizing control measures and documentation of asymptomatic carriers and 77

clinically evident patients [7-9]. Additionally, due to the unavailability of the registered 78 vaccines or effective therapeutics, rapid and reliable diagnostics are of paramount 79

importance to curtail SARS-CoV-2 infection. Because of shortcomings associated with 80 the virus isolation (time consuming and required containment) and cross-reactivities 81 of antigen and antibodies assay, several real-time reverse transcription-polymerase 82 chain reactions (qRT-PCR) and reverse-transcription loop mediated isothermal 83 amplification (RT-LAMP) assays have been developed, validated and commercialized 84 as useful laboratory diagnostics for the detection of SARS-CoV-2 [10] . However, the 85 majority of these assays are time-consuming and require laboratory-intense 86

instrumentation. Furthermore, they are unable to meet the current unprecedented 87 rapid growth and demand for testing a large proportion of the population, identification 88 of asymptomatic carriers and contact tracing.

Though qRT-PCR remains the gold standard for the diagnosis of SARS-CoV-2, RT-91 LAMP assays have been demonstrated to produce diagnostic results with increased 92 sensitivity and specificity [11] . Furthermore, its ability to tolerate PCR inhibitors 93 eliminates the need for laborious RNA extraction and purification methodologies [ intelligence-based image processing algorithm and mobile app.

In this study, we developed a high-resolution comparative genomics analysis-guided 107 novel RT-LAMP assay for the specific and sensitive detection of SARS-CoV-2 in 108 comparison to WHO recommended qRT-PCR assays. In order to provide a simple 109

"sample-to-answer workflow", an ultra-low-cost and user-friendly diagnostic platform 110 was engineered and was further supplemented with a module for automated image 111 acquisition and processing. Artificial intelligence-guided assessment of the LAMP 112 assay provided faster detection of colour changes in the LAMP reaction to further 113 enhance the assay performance and to reduce the human error in results 114

interpretation. CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)

The copyright holder for this preprint this version posted July 10, 2020. CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted July 10, 2020. homogeneously and centrifuged. The LAMP was performed in a thermocycler 202

(MJResearch) at 65°C for 30 min or in the engineered device ( Figure 4A ). Colour 203 change was observed directly by the naked eye or through image processing, and 204 agarose gel electrophoresis was performed to confirm the results. The completion of 205 amplification was indicated by the colour in the tube, wherein yellow was considered 206

positive and pink was regarded as negative. CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted July 10, 2020. It is imperative to critically assess the evolving nature of viruses in identifying 263

conserved gene signatures and guiding the selection of the most appropriate primers. 264

In order to identify important genomic loci, we downloaded and aligned all the available multiple loci ( Figure 1A) . However, most of the genomes maintained high 274

conservation. The divergence at the 5' and 3' ends was primarily due to length 275 heterogeneity, which may be partly as a result of sequencing artifact or potentially 276 coronaviruses ragged termini ( Figure 1B) developed LAMP assay ( Figure 1C ).

The conserved region of the RdRP gene with the lowest mutation frequencies was 288 used as a template to manually design three sets of basic LAMP primers and selected 289

with PrimerExplorer V5 for appropriate primer lengths, loop selection and melting 290 temperature optimization ( Figure 1C) . In order to preclude the non-specific 291

amplification of common coronaviruses, efforts were made to design primers in the 292 regions where there is a high level of divergence among more than 3 of the 6 total 293 primers in a specific set. Amongst the most suitable targets, the primers with high 294 scores were aligned with MERS-CoV, hCoV-229E, hCoV-OC43, hCoV-NL63, hCoV-295 . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted July 10, 2020. . https://doi.org/10.1101/2020.07.08.20148999 doi: medRxiv preprint HKU1 and SARS-CoV-1 ( Figure 1D) . These selected primers were used for further 296 validation and screening. 297 298 2.2. Determination of limit of detection using biochemically synthesis RNA 299

In order to assess the robustness of the primers, we used a fully identical in vitro 300

transcribed target RNA unanimously spanning the length of the RdRP-gene based 301

LAMP and qRT-PCR target regions. The pre-determined copy numbers of the 302 biochemically synthesised RNA were 10-fold serially diluted from 10 7 copies to 0 303 copies of the target gene per reaction. To determine the analytical sensitivity of the 304 assays, we first evaluated their limits of detection (LoD) for both qRT-PCR and LAMP 305

assays. The LoD of the qRT-PCR was 10 copies as evident from the relative 306 fluorescence units (Figure 2A) and electrophoreses of the amplified products ( Figure  307 2B). The standard curve generated by the RdRP-based qRT-PCR was linear and 308 generated a coefficient of correlation (R 2 ) = 0.9481 and a slope of -2.6509 ( Figure  309 2C). Melting curve analysis revealed the specificity of primers for the target gene 310

sequence, as all the amplified products showed a uniform melting temperature (Tm) 311 of ~75.10°C and specific amplification patterns ( Figure 2B and data not shown).

Compared to the qRT-PCR assay, the LoD for the LAMP which targeted the same 313

RdRP gene was 1 log unit higher (10 2 copies/reaction) ( Figure 2D The SARS-CoV-2 embraces genetic and phenotypic features of several common cold 323

coronaviruses and other viruses of the respiratory tract. Owing to high genetic 324 similarity (up to 96% at nucleotide levels) and common respiratory specimen for 325 clinical identification of CoVID-19 patients, we aimed to investigate any non-specific 326 amplification in the LAMP assay. In order to demonstrate the specificity of the LAMP 327 assay, we used pathogens belonging to 5 families of the most important medical and 328 respiratory viruses. As shown in the Figure 3A , the qRT-PCR specifically detected 329

only the SARS-CoV-2 and this specificity was noticed on Gel-red staining of amplified 330 products ( Figure 3B) . Consistently, no cross-reactivities were noticed with the LAMP 331 in both colorimetric detection (Figure 3C , upper panel) or electrophoreses (Figure 3C , 332 lower panel). Collectively, a high level of specificity was observed in primers set using 333 either of the assays. 334 335

One of the major advantages of LAMP is its robustness. In order to determine the time 337

optimal for sufficient amplification of targeted genes, in vitro transcribed RNA was used 338 as a template for 30 minutes and assessed after every 5 minutes post-start of the 339 reaction. The change in colour was monitored visually by the naked eye. As shown in 340 Table 1 , under equivalent conditions similar results were obtained from 20-30 minutes 341 of amplification. Therefore, 30 min was selected as the optimal visual interpretation 342 time for the results. 343 344 . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted July 10, 2020. Bluetooth and selecting the required diagnostic assay. 376 377 2.6. Automated image acquisition and processing through template matching-378 based algorithm 379

The LAMP assay in 8 separate tubes was remotely started to initiate heating to 65 o C.

Images of those test tubes were captured using the inbuilt RPi Camera for every 20 381 seconds and were saved in the RPi in the RGB format. Each individual image 382 consisted of 8 frames around each tube with a black background. As the tube area 383 exposing colour changes was fractionally small compared to its background, we first 384 extracted each targeted tube frame from the image before applying an image 385 processing algorithm. In order to process these extracted frames, a reference tube 386 was selected as a template, and a template matching algorithm [21] was applied to 387 extract all tubes from the first image. The rationale for the template matching was to 388

search and find the location of a template image in a larger image. It simply slides the 389 template image over the input image to perform the 2-dimensional convolution and 390 compared values to get the maximum overlap to decide the exact similar areas.

Assuming that positions of the test-tube do not change over the time of an experiment, 392

images were cropped in an experiment to obtain the tube frames from the entire 393

image. These crops are then saved into a 2-dimensional array for RGB colour space 394 . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted July 10, 2020. In YUV colour space, the Y channel represented the luminance of the colour, while 401 the U and V channels represented the chrominance (Figure 4B) in Figure 4C . It consisted of four convolutional layers followed by 2 dense and an 425 output layer. To compile the model, binary cross-entropy was used as a loss of 426 function and used to optimize.

For the training of the network, the dataset was shuffled and then split into 9:1 429 proportion ( Figure 4D) . 90% of the data was used to train the network and the 430 remaining 10% was exploited to check how the network behaved on seeing a new 431

image. Training a dataset requires loading numerous images into the memory in a 432 single operation which is an expensive process. Therefore, a data generator was 433

implemented that read the data in batches from the dataset directory and fed it to the 434 model. After multiple experiments, it was observed that the network converged after 6 435 cycles (epochs) through the dataset. Therefore, we ran an experiment for only 6 436 epochs to decrease the probability of overfitting. In addition, an additional set of 108 437

test-tube crops was used to validate the network. The best performing network 438 resulted in an accuracy of 98% in classifying tubes based on their colours (images 439 with better light). 440 441 . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted July 10, 2020. . https://doi.org/10.1101/2020.07.08.20148999 doi: medRxiv preprint

In order to assess the temporal impact of the AI-assisted detection of colour changes 442 (indicative of amplification), the RT-LAMP reaction was run with 3 previously 443 confirmed positive and negative patient samples as well as positive and negative 444 controls. Colour changes were assessed every 5 minutes until the complete stoppage 445 of the LAMP reaction at 30 minutes. Gradual colour changes were detectable with the 446 naked eye as early as 20 minutes post-start of the reaction (Figure 5A) .

Corresponding samples were run on the newly developed device and temporal and 448

real-time colour changes were monitored as described earlier. Depending upon the 449 viral load in the sample, a clear colour change was calibrated as early as 20 minutes 450

using device operated processing of the data (Figure 5B) . Once the positive test 451

control is identified as positive, the test will be stopped, and results will be returned to 452 reduce the waiting time and power consumption on heating.

As shown in Fig 5B, be stopped, and results will be returned. This approach will reduce the waiting time for 460 the results and power consumption due to heating in the experiment. 461 462

In order to assess the field application of the optimized assay, we applied the ai-LAMP 464

to purified RNA spiked with miR-cel-miR-39-3p from CoVID-19 patients. A total of 199 465 swab samples were collected from CoVID-19 clinically suspected patients during 466

routine screening at the Royal Lancaster Infirmary (RLI), University Hospitals of 467

Morecambe Bay NHS Foundation Trust UK. The extracted RNA from swab samples 468

were run in parallel for ai-LAMP and two WHO/PHE recommended qRT-PCR targeting 469

the RdRP and N genes of the SARS-CoV-2. This parallel assessment allowed us to 470 assess the comparative performance of the ai-LAMP.

The RdRP gene-based qRT-PCR detected a total of 67 positives and 132 negatives 473 in a cohort of 199 patients ( Figure 6A) . In contrast, a higher number of positive (n=88) 474

and lower numbers of negative (n=111) were detected by the qPCR which targeted 475 the N gene ( Figure 6B) . Interestingly, the ai-LAMP detected a total of 126 positive 476 samples which constituted several times higher than RdRP and N gene-based qPCR, 477

respectively. Comparative analysis of these three molecular detection assays 478 revealed 58 total true positives (TP), 09 false negatives (FN), 64 true negatives (TN), 479

and 68 false positives (FP) in RdRP-based qRT-PCR compared to RdRP-based 480 LAMP (Figure 6A) . Similarly, upon comparative analysis of the N gene-based qPCR 481

and RdRP-based LAMP, we observed a total of 74 TP, 14 FN, 59 TN, and 52 FP 482 (Figure 6B ).

In the current clinical settings, a qRT_PCR targeting two genes (N and RdRP) was 485 conducted to conclusively identify CoVID-19 positive cases and this assay is referred 486 as cumulative (CUM) qRT-PCR. In this scenario, a sample would be considered as 487

positive only if a Ct value of =/> 35 was detected in both N and RdRP-gene based 488

qRT-PCR. Using this approach, we noticed a total of a 70 positive and 129 negative 489 samples and an improved true positive (n=61), false negatives (n=09), true negatives 490 (n=64), and false positives (n=65) limits ( Figure 6C) . Taken together, the cumulative 491 . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted July 10, 2020. . https://doi.org/10.1101/2020.07.08.20148999 doi: medRxiv preprint comparative picture of the qPCR and ai-LAMP has identified a superior detection of 492 positive cases (Figure 6D) . In order to confirm this detection performance, all ai-LAMP 493 amplification products were visualised by electrophoresis (data not shown), further 494

confirming the aided-detection and improved implication of ai-LAMP in the field 495 condition.

We Collectively, these data highlight the improved specificity and sensitivity of the AI-513 assisted LAMP assay compared to the naked-eye interpretation of the LAMP-514 positivity, thus enhancing the timely and automated detection and interpretation of the 515 assay results. 516 517

The SARS-CoV-2 is now a global pandemic, over 216 countries are currently reporting 520 active infections around the globe and the number of daily infections and deaths is 521

continuing to increase, especially in the Americas RdRP, and ORF1a/b genes mainly due to their high level of transcription and 540 abundance in expression compared to other genes of the SARS-CoV-2 [5, 6]. For the 541 . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted July 10, 2020. of sensitivities of 2x10 1 copies per reaction. The majority of these diagnostic assays 545 carry a high level of sensitivity, specificity and repeatability; however, these primarily 546 lack the clinical validation and/or optimization on the synthetic targets. 547 548

In this study, we have developed and evaluated a novel RT-LAMP in one of the most 549

conserved genes (i.e. RdRP) within the SARS-CoV-2 genome. The RT-LAMP was 550

then directly compared with the currently applied routine diagnostic assays to assess 551 the comparative performance. The RT-LAMP assay developed in this study, could 552 detect as low as 100 copies with an in vitro RNA transcript. Importantly, the RT-LAMP 553

has detected the SARS-CoV-2 RNA in 68/199 (34%) and 52/199 (26%) additional 554 specimens that were tested negative by the RdRP-based qRT-PCR and N-based 555

qRT-PCR, respectively. These findings are interesting, both clinically and 556 epidemiologically due to the high proportion of asymptomatic and mildly symptomatic 557

cases of CoVID-19. These apparently healthy people have been suggested as major 558

sources of virus propagation and basis of epidemics within the community [34, 35, 36].

Due to the large number of cases (~5000) in the testing facility, additional positive 560 specimens could have been detected by the RT-LAMP which might have remained 561

undetected by the qRT-PCRs. Using of an internal standard that is not expressed in 562 humans such as cel-miR-39-3p can alleviate the problem of an effective RNA 563 extraction approach. In addition, we used a fixed total RNA concentration in all 564 experiments allowing for better comparisons across groups. 565 566

The main challenges of using the Colorimetric approach are background which 567 changes the colour perspective, issues in identifying small changes, bubbles in the 568 test tubes, relatively small area corresponding to colour change and pixel variation 569 due to camera flash and background reflections. The CNN based model has used 570 high-performance computer images to train using these issues and having learned the 571 patterns is able to classify colour, despite the presence of noise. The trained model 572

has successfully moved to Rpi to identify colour changes in test tubes. The study 573 produced 98% accuracy for images taken with better light (Open) and the duration of 574 testing could be dynamically controlled to reduce the length of operating time and 575

heating with a resulting reduction in energy consumption by the device. 576 577

Collectively, our data showed that the newly established ai-LAMP was highly specific 578

for the detection of SARS-CoV-2 RNA in vitro and in respiratory tract clinical 579

specimens. The usage of this novel LAMP assay might be helpful especially for 580

detecting COVID-19 cases with low viral loads and when testing upper respiratory tract 581 specimens from patients. Development of ai-LAMP into a multiplex assay which can 582 simultaneously detect other human-pathogenic coronaviruses and respiratory 583 pathogens may further increase its clinical utility in the future. 584 585

Acknowledgements 586

The authors would like to thank the Electronic Technicians William Schkzamian, 587

Gopalakirishnan Jeysundra and Michael Lateo of Brunel University London for their 588 efforts to come into the University with special permission during the early lockdown 589 period to produce eight laboratory prototypes within 5 days. We thank the Microbiology 590

Department, University Hospitals of Morecambe Bay for access to anonymised patient 591 . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted July 10, 2020. . https://doi.org/10.1101/2020.07.08.20148999 doi: medRxiv preprint samples and acknowledge the support of BLS Lancaster University Technicians 592 throughout the lockdown period. We would like to thank Dr Derek Gatherer, Lancaster 593

University, in aligning large SARS-COV-2 genome sequences. 594 595

Disclosure statement 596

No potential conflict of interest was reported by the authors. 597 598

Funding 599

The authors wish to express our sincere appreciation to the BBSRC for allowing us to 600

repurpose the LAMP prototypes produced in the grant BB/R012695/1 to be used for 601

SARS-CoV-2 laboratory testing at The University of Lancaster. We would like to thank 602 the support of BBSRC (BB/M008681/1 and BBS/E/I/00001852) and British Council 603

(172710323 and 332228521) at Division of Biomedical and Life Sciences, Lancaster 604

University, UK. We would also like to thank Brunel University London and the 605

University of Surrey for providing some financial support to rapidly produce these 606

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The copyright holder for this preprint this version posted July 10, 2020. CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)

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A) Seven different dilutions of in vitro 754 transcribed RNA were run for quantitively measurement in the qRT-PCR. Relative 755 fluorescence units show a gradient decrease in signals. (B) The corresponding PCR 756 products on the electrophoresis gel (C) The qRT-PCR standard curve based on the 757 Ct value and dilution factor. (D) The serially diluted synthetic RNAs were run for LAMP 758 assay and colour change represents positive (yellow) or negative (pink)

RNA extracted from different medically 785 or respiratory important viruses as well as two dilutions of synthetic RNA were run for 786 qPCR. (B) Corresponding PCR products were run on gel to demonstrate specificity. 787 (C) Similar to qRT-PCR, extracted RNA were run for the LAMP assay