Design, Rationalization, and Automation of a Catalytic Sensing Mechanism for Homogeneous SERS Biosensors.

The current pandemic has shown that we need sensitive and deployable diagnostic technologies. Surface-enhanced Raman scattering (SERS) sensors can be an ideal solution for developing such advanced point-of-need (PON) diagnostic tests. Homogeneous (reagentless) SERS sensors work by directly responding to the target without any processing step, making them capable for simple one-pot assays, but their limitation is the achievable sensitivity, insufficient compared to what is needed for sensing of viral biomarkers. Noncovalent DNA catalysis mechanisms have been recently exploited for catalytic amplification in SERS assays. These advances used catalytic hairpin assembly (CHA) and other DNA self-assembly processes to develop sensing mechanisms with improved sensitivities. However, these mechanisms have not been used in OFF-to-ON homogeneous sensors, and they often target the same biomarker, likely due to the complexity of the mechanism design. There is still a strong need for a catalytic SERS sensor with a homogeneous mechanism and a rationalization of the catalytic sensing mechanism to translate this sensing strategy to different targets and applications. We developed and investigated a homogeneous SERS sensing mechanism that uses catalytic amplification based on DNA self-assembly. We systematically investigated the role of three domains in the fuel strand (internal loop, stem, and toehold), which drives the catalytic mechanism. The thermodynamic parameters determined in our studies were used to build an algorithm for automated design of catalytic sensors that we validated on target sequences associated with malaria and SARS-CoV-2 strains. With our mechanism, we were able to achieve an amplification level of 20-fold for conventional DNA and of 36-fold using locked nucleic acids (LNAs), with corresponding improvements observed in the sensor limit of detection (LOD). We also show a single-base sequence specificity for a sensor targeting a sequence associated with the omicron variant, tested against a delta variant target. This work on catalytic amplification of homogeneous SERS sensors has the potential to enable the use of this sensing modality in new applications, such as infectious disease surveillance, by improving the LOD while conserving the sensor's homogeneous character.

Near full-automation of COPMAN using a LabDroid enables high-throughput and sensitive detection of SARS-CoV-2 RNA in wastewater as a leading indicator.

Wastewater-based epidemiology (WBE) is a promising tool to efficiently monitor COVID-19 prevalence in a community. For WBE community surveillance, automation of the viral RNA detection process is ideal. In the present study, we achieved near full-automation of a previously established method, COPMAN (COagulation and Proteolysis method using MAgnetic beads for detection of Nucleic acids in wastewater), which was then applied to detect SARS-CoV-2 in wastewater for half a year. The automation line employed the Maholo LabDroid and an automated-pipetting device to achieve a high-throughput sample-processing capability of 576 samples per week. SARS-CoV-2 RNA was quantified with the automated COPMAN using samples collected from two wastewater treatment plants in the Sagami River basin in Japan between 1 November 2021 and 24 May 2022, when the numbers of daily reported COVID-19 cases ranged from 0 to 130.3 per 100,000 inhabitants. The automated COPMAN detected SARS-CoV-2 RNA from 81 out of 132 samples at concentrations of up to 2.8 × 105 copies/L. These concentrations showed direct correlations with subsequently reported clinical cases (5-13 days later), as determined by Pearson's and Spearman's cross-correlation analyses. To compare the results, we also conducted testing with the EPISENS-S (Efficient and Practical virus Identification System with ENhanced Sensitivity for Solids, Ando et al., 2022), a previously reported detection method. SARS-CoV-2 RNA detected with EPISENS-S correlated with clinical cases only when using Spearman's method. Our automated COPMAN was shown to be an efficient method for timely and large-scale monitoring of viral RNA, making WBE more feasible for community surveillance.

Gyrolab Immunoassays: Miniaturization, Automation, and Integration into a Rapid Workflow.

Gyrolab® is an open immunoassay platform that automates the complete immunoassay protocol in a microfluidic disc. The column profiles generated with Gyrolab immunoassays are used to gain more information about biomolecular interactions that can be useful in assay development or quantify analytes in samples. Gyrolab immunoassays can be used to cover a broad concentration range and diversity of matrices in applications ranging from biomarker monitoring, pharmacodynamics and pharmacokinetics studies, to bioprocess development in many areas, including therapeutic antibodies, vaccines, and cell and gene therapy.This chapter is an overview of Gyrolab technology, including system components and the assay development workflow, including the process of selecting affinity reagents, Gyrolab Bioaffy CDs, and assay conditions to optimize immunoassays. Two case studies are included. The first involves an assay for the humanized antibody pembrolizumab used in cancer immunotherapy that can generate data for pharmacokinetics studies. The second case study involves quantification of the biomarker and biotherapeutic interleukin-2 (IL-2) in human serum and buffer. IL-2 has been implicated in the cytokine storm associated with COVID-19, and cytokine release syndrome (CRS), which can occur during chimeric antigen receptor T cell (CART) therapy used in treating cancer. These molecules also have therapeutic relevance in combination.

Conceptualization and Validation of the Occupation Insecurity Scale (OCIS): Measuring Employees' Occupation Insecurity Due to Automation.

Increased use and implementation of automation, accelerated by the COVID-19 pandemic, gives rise to a new phenomenon: occupation insecurity. In this paper, we conceptualize and define occupation insecurity, as well as develop an Occupation Insecurity Scale (OCIS) to measure it. From focus groups, subject-matter expert interviews, and a quantitative pilot study, two dimensions emerged: global occupation insecurity, which refers to employees' fear that their occupations might disappear, and content occupation insecurity, which addresses employees' concern that (the tasks of) their occupations might significantly change due to automation. In a survey-study sampling 1373 UK employees, psychometric properties of OCIS were examined in terms of reliability, construct validity, measurement invariance (across gender, age, and occupational position), convergent and divergent validity (with job and career insecurity), external discriminant validity (with organizational future time perspective), external validity (by comparing theoretically secure vs. insecure groups), and external and incremental validity (by examining burnout and work engagement as potential outcomes of occupation insecurity). Overall, OCIS shows good results in terms of reliability and validity. Therefore, OCIS offers an avenue to measure and address occupation insecurity before it can impact employee wellbeing and organizational performance.

Re-envisioning the design of nanomedicines: harnessing automation and artificial intelligence.

INTRODUCTION: Interest in nanomedicines has surged in recent years due to the critical role they have played in the COVID-19 pandemic. Nanoformulations can turn promising therapeutic cargo into viable products through improvements in drug safety and efficacy profiles. However, the developmental pathway for such formulations is non-trivial and largely reliant on trial-and-error. Beyond the costly demands on time and resources, this traditional approach may stunt innovation. The emergence of automation, artificial intelligence (AI) and machine learning (ML) tools, which are currently underutilized in pharmaceutical formulation development, offers a promising direction for an improved path in the design of nanomedicines. AREAS COVERED: the potential of harnessing experimental automation and AI/ML to drive innovation in nanomedicine development. The discussion centers on the current challenges in drug formulation research and development, and the major advantages afforded through the application of data-driven methods. EXPERT OPINION: The development of integrated workflows based on automated experimentation and AI/ML may accelerate nanomedicine development. A crucial step in achieving this is the generation of high-quality, accessible datasets. Future efforts to make full use of these tools can ultimately contribute to the development of more innovative nanomedicines and improved clinical translation of formulations that rely on advanced drug delivery systems.

Machine learning in general practice: scoping review of administrative task support and automation.

BACKGROUND: Artificial intelligence (AI) is increasingly used to support general practice in the early detection of disease and treatment recommendations. However, AI systems aimed at alleviating time-consuming administrative tasks currently appear limited. This scoping review thus aims to summarize the research that has been carried out in methods of machine learning applied to the support and automation of administrative tasks in general practice. METHODS: Databases covering the fields of health care and engineering sciences (PubMed, Embase, CINAHL with full text, Cochrane Library, Scopus, and IEEE Xplore) were searched. Screening for eligible studies was completed using Covidence, and data was extracted along nine research-based attributes concerning general practice, administrative tasks, and machine learning. The search and screening processes were completed during the period of April to June 2022. RESULTS: 1439 records were identified and 1158 were screened for eligibility criteria. A total of 12 studies were included. The extracted attributes indicate that most studies concern various scheduling tasks using supervised machine learning methods with relatively low general practitioner (GP) involvement. Importantly, four studies employed the latest available machine learning methods and the data used frequently varied in terms of setting, type, and availability. CONCLUSION: The limited field of research developing in the application of machine learning to administrative tasks in general practice indicates that there is a great need and high potential for such methods. However, there is currently a lack of research likely due to the unavailability of open-source data and a prioritization of diagnostic-based tasks. Future research would benefit from open-source data, cutting-edge methods of machine learning, and clearly stated GP involvement, so that improved and replicable scientific research can be done.

CRISPR-Based Diagnostics: Challenges and Potential Solutions toward Point-of-Care Applications.

The COVID-19 pandemic has challenged the conventional diagnostic field and revealed the need for decentralized Point of Care (POC) solutions. Although nucleic acid testing is considered to be the most sensitive and specific disease detection method, conventional testing platforms are expensive, confined to central laboratories, and are not deployable in low-resource settings. CRISPR-based diagnostics have emerged as promising tools capable of revolutionizing the field of molecular diagnostics. These platforms are inexpensive, simple, and do not require the use of special instrumentation, suggesting they could democratize access to disease diagnostics. However, there are several obstacles to the use of the current platforms for POC applications, including difficulties in sample processing and stability. In this review, we discuss key advancements in the field, with an emphasis on the challenges of sample processing, stability, multiplexing, amplification-free detection, signal interpretation, and process automation. We also discuss potential solutions for revolutionizing CRISPR-based diagnostics toward sample-to-answer diagnostic solutions for POC and home use.

Validation of SARS-CoV-2 pooled testing for surveillance using the Panther Fusion® system: Impact of pool size, automation, and assay chemistry.

Combining diagnostic specimens into pools has been considered as a strategy to augment throughput, decrease turnaround time, and leverage resources. This study utilized a multi-parametric approach to assess optimum pool size, impact of automation, and effect of nucleic acid amplification chemistries on the detection of SARS-CoV-2 RNA in pooled samples for surveillance testing on the Hologic Panther Fusion® System. Dorfman pooled testing was conducted with previously tested SARS-CoV-2 nasopharyngeal samples using Hologic's Aptima® and Panther Fusion® SARS-CoV-2 Emergency Use Authorization assays. A manual workflow was used to generate pool sizes of 5:1 (five samples: one positive, four negative) and 10:1. An automated workflow was used to generate pool sizes of 3:1, 4:1, 5:1, 8:1 and 10:1. The impact of pool size, pooling method, and assay chemistry on sensitivity, specificity, and lower limit of detection (LLOD) was evaluated. Both the Hologic Aptima® and Panther Fusion® SARS-CoV-2 assays demonstrated >85% positive percent agreement between neat testing and pool sizes ≤5:1, satisfying FDA recommendation. Discordant results between neat and pooled testing were more frequent for positive samples with CT>35. Fusion® CT (cycle threshold) values for pooled samples increased as expected for pool sizes of 5:1 (CT increase of 1.92-2.41) and 10:1 (CT increase of 3.03-3.29). The Fusion® assay demonstrated lower LLOD than the Aptima® assay for pooled testing (956 vs 1503 cp/mL, pool size of 5:1). Lowering the cut-off threshold of the Aptima® assay from 560 kRLU (manufacturer's setting) to 350 kRLU improved the assay sensitivity to that of the Fusion® assay for pooled testing. Both Hologic's SARS-CoV-2 assays met the FDA recommended guidelines for percent positive agreement (>85%) for pool sizes ≤5:1. Automated pooling increased test throughput and enabled automated sample tracking while requiring less labor. The Fusion® SARS-CoV-2 assay, which demonstrated a lower LLOD, may be more appropriate for surveillance testing.

Ferrobotic swarms enable accessible and adaptable automated viral testing.

Expanding our global testing capacity is critical to preventing and containing pandemics^1-9. Accordingly, accessible and adaptable automated platforms that in decentralized settings perform nucleic acid amplification tests resource-efficiently are required^10-14. Pooled testing can be extremely efficient if the pooling strategy is based on local viral prevalence^15-20; however, it requires automation, small sample volume handling and feedback not available in current bulky, capital-intensive liquid handling technologies^21-29. Here we use a swarm of millimetre-sized magnets as mobile robotic agents ('ferrobots') for precise and robust handling of magnetized sample droplets and high-fidelity delivery of flexible workflows based on nucleic acid amplification tests to overcome these limitations. Within a palm-sized printed circuit board-based programmable platform, we demonstrated the myriad of laboratory-equivalent operations involved in pooled testing. These operations were guided by an introduced square matrix pooled testing algorithm to identify the samples from infected patients, while maximizing the testing efficiency. We applied this automated technology for the loop-mediated isothermal amplification and detection of the SARS-CoV-2 virus in clinical samples, in which the test results completely matched those obtained off-chip. This technology is easily manufacturable and distributable, and its adoption for viral testing could lead to a 10-300-fold reduction in reagent costs (depending on the viral prevalence) and three orders of magnitude reduction in instrumentation cost. Therefore, it is a promising solution to expand our testing capacity for pandemic preparedness and to reimagine the automated clinical laboratory of the future.

A film-lever actuated switch technology for multifunctional, on-demand, and robust manipulation of liquids.

A lab-on-a-chip system with Point-of-Care testing capability offers rapid and accurate diagnostic potential and is useful in resource-limited settings where biomedical equipment and skilled professionals are not readily available. However, a Point-of-Care testing system that simultaneously possesses all required features of multifunctional dispensing, on-demand release, robust operations, and capability for long-term reagent storage is still a major challenge. Here, we describe a film-lever actuated switch technology that can manipulate liquids in any direction, provide accurate and proportional release response to the applied pneumatic pressure, as well as sustain robustness during abrupt movements and vibrations. Based on the technology, we also describe development of a polymerase chain reaction system that integrates reagent introduction, mixing and reaction functions all in one process, which accomplishes "sample-in-answer-out" performance for all clinical nasal samples from 18 patients with Influenza and 18 individual controls, in good concordance of fluorescence intensity with standard polymerase chain reaction (Pearson coefficients > 0.9). The proposed platform promises robust automation of biomedical analysis, and thus can accelerate the commercialization of a range of Point-of-Care testing devices.

Pandemic effects to autonomous vehicles test operations in California.

Companies developing automated driving system (ADS) technologies have spent heavily in recent years to conduct live testing of autonomous vehicles operating in real world environments to ensure their reliable and safe operations. However, the unexpected onset and ongoing resurgent effects of the Covid-19 pandemic starting in March 2020 has serve to halt, change, or delay the achievement of these new product development test objectives. This study draws on data obtained from the California automated vehicle test program to determine the extent that testing trends, test resumptions, and test environments have been affected by the pandemic. The importance of government policies to support and enable autonomous vehicles development during pandemic conditions is highlighted.

Using Automation, Prioritization, and Collaboration to Manage a COVID-19 Case Surge in Maricopa County, Arizona, 2020.

During summer 2020, the Maricopa County Department of Public Health (MCDPH) responded to a surge in COVID-19 cases. We used internet-based platforms to automate case notifications, prioritized investigation of cases more likely to have onward transmission or severe COVID-19 based on available preinvestigation information, and partnered with Arizona State University (ASU) to scale investigation capacity. We assessed the speed of automated case notifications and accuracy of our investigation prioritization criteria. Timeliness of case notification-the median time between receipt of a case report at MCDPH and first case contact-improved from 11 days to <1 day after implementation of automated case notification. We calculated the sensitivity and positive predictive value (PPV) of the investigation prioritization system by applying our high-risk prioritization criteria separately to data available pre- and postinvestigation to determine whether a case met these criteria preinvestigation, postinvestigation, or both. We calculated the sensitivity as the percentage of cases classified postinvestigation as high risk that had also been classified as high risk preinvestigation. We calculated PPV as the percentage of all cases deemed high risk preinvestigation that remained so postinvestigation. During June 30 to July 31, 2020, a total of 55 056 COVID-19 cases with an associated telephone number (94% of 58 570 total cases) were reported. Preinvestigation, 8799 (16%) cases met high-risk criteria. Postinvestigation, 17 037 (31%) cases met high-risk criteria. Sensitivity was 52% and PPV was 98%. Automating case notifications, prioritizing investigations, and collaborating with ASU improved the timeliness of case contact, focused public health resources toward high-priority cases, and increased investigation capacity. Establishing partnerships between health departments and academia might be a helpful strategy for future surge capacity planning.

Novel TB smear microscopy automation system in detecting acid-fast bacilli for tuberculosis - A multi-center double blind study.

Due to COVID-19 pandemic, there is a large global drop in the number of newly diagnosed cases with tuberculosis (TB) worldwide. Actions to mitigate and reverse the impact of the COVID-19 pandemic on TB are urgently needed. Recent development of TB smear microscopy automation systems using artificial intelligence may increase the sensitivity of TB smear microscopy. The objective is to evaluate the performance of an automation system (µ-Scan 2.0, Wellgen Medical) over manual smear microscopy in a multi-center, double-blind trial. Total of 1726 smears were enrolled. Referee medical technician and culture served as primary and secondary gold standards for result discrepancy. Results showed that, compared to manual microscopy, the µ-Scan 2.0's performance of accuracy, sensitivity and specificity were 95.7% (1651/1726), 87.7% (57/65), and 96.0% (1594/1661), respectively. The negative predictive value was 97.8% at prevalence of 8.2%. Manual smear microscopy remains the primary diagnosis of pulmonary tuberculosis (TB). Use of automation system could achieve higher TB smear sensitivity and laboratory efficiency. It can also serve as a screening tool that complements molecular methods to reduce the total cost for TB diagnosis and control. Furthermore, such automation system is capable of remote access by internet connection and can be deployed in area with limited medical resources.

Development of Advanced Artificial Intelligence and IoT Automation in the Crisis of COVID-19 Detection.

Internet of Things (IoT) is a successful area for many industries and academia domains, particularly healthcare is one of the application areas that uses IoT sensors and devices for monitoring. IoT transition replaces contemporary health services with scientific and socioeconomic viewpoints. Since the epidemic began, diverse scientific organizations have been making accelerated efforts to use a wide range of tools to tackle this global challenge and the founders of IoT analytics. Artificial intelligence (AI) plays a key role in measuring, assessing, and diagnosing the risk. It could be used to predict the number of alternate incidents, recovered instances, and casualties, also used for forecasting cases. Within the COVID-19 background, IoT technologies are used to minimize COVID-19 exposure to others by prenatal screening, patient monitoring, and postpatient incident response in specified procedures. In this study, the importance of IoT technology and artificial intelligence in COVID-19 is explored, and the 3 important steps discussed such as the evaluation of networks, implementations, and IoT industries to battle COVID-19, including early detection, quarantine times, and postrecovery activities, are reviewed. In this study, how IoT handles the COVID-19 pandemic at a new level of healthcare is investigated. In this research, the long short-term memory (LSTM) with recurrent neural network (RNN) is used for diagnosis purpose and in particular, its important architecture for the analysis of cough and breathing acoustic characteristics. In comparison with both coughing and respiratory samples, our findings indicate poor accuracy of the voice test.

Homebrew: Protocol for glassmilk-based nucleic-acid extraction for SARS-CoV-2 diagnostics.

The gold standard protocol for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection detection remains reverse transcription quantitative polymerase chain reaction (qRT-PCR), which detects viral RNA more sensitively than any other approach. Here, we present Homebrew, a low-cost protocol to extract RNA using widely available reagents. Homebrew is as sensitive as commercially available RNA extraction kits. Homebrew allows for sample pooling and can be adapted for automation in high-throughput settings. For complete details on the use and execution of this protocol, please refer to Page et al. (2022).

Biological evaluation of a mechanical ventilator that operates by controlling an automated manual resuscitator. A descriptive study in swine.

The Covid-19 outbreak challenged health systems around the world to design and implement cost-effective devices produced locally to meet the increased demand of mechanical ventilators worldwide. This study evaluates the physiological responses of healthy swine maintained under volume- or pressure-controlled mechanical ventilation by a mechanical ventilator implemented to bring life-support by automating a resuscitation bag and closely controlling ventilatory parameters. Physiological parameters were monitored in eight sedated animals (t0) prior to inducing deep anaesthesia, and during the next six hours of mechanical ventilation (t1-7). Hemodynamic conditions were monitored periodically using a portable gas analyser machine (i.e. BEecf, carbonate, SaO2, lactate, pH, PaO2, PaCO2) and a capnometer (i.e. ETCO2). Electrocardiogram, echocardiography and lung ultrasonography were performed to detect in vivo alterations in these vital organs and pathological findings from necropsy were reported. The mechanical ventilator properly controlled physiological levels of blood biochemistry such as oxygenation parameters (PaO2, PaCO2, SaO2, ETCO2), acid-base equilibrium (pH, carbonate, BEecf), and perfusion of tissues (lactate levels). In addition, histopathological analysis showed no evidence of acute tissue damage in lung, heart, liver, kidney, or brain. All animals were able to breathe spontaneously after undergoing mechanical ventilation. These preclinical data, supports the biological safety of the medical device to move forward to further evaluation in clinical studies.

Using automation to manage donor engagement and fine-tune supply and demand during the first year of the COVID-19 pandemic.

BACKGROUND: COVID-19 disrupted blood center operations starting March 2020 and continues to affect donor presentation and blood availability today. The industry mobilized significant resources to collect COVID-19 convalescent plasma (CCP) to treat COVID-19 patients. At the same time, blood centers continued to collect platelets, plasma, and red blood cells (RBCs) to meet the needs of non-COVID-19 patients. The purpose of this study was to quantify how automation was used to fine-tune supply and demand and increase donor engagement during the first year of the pandemic. METHODS: This was a single-center retrospective study of blood collection and donor presentation at a mid-sized US blood center. Data was evaluated from January 1, 2020 through March 31, 2021. Parameters evaluated included donor presentation, platelets per procedure, concurrent RBC and plasma collections per procedure, operator compliance, total donor appointment count, and donor frequency. RESULTS: With the cancelation of mobile blood drives, fixed sites increased total apheresis procedures by 37% and increased turns per bed by 46% whereas less products were collected per donor. By collecting only what was needed, platelet expiration rate decreased from 6.8% (pre-pandemic) to less than 4%. Donor engagement as measured by donor frequency increased from 1.6 in January 2020 to 1.8 in March 2021. CONCLUSIONS: Using technological advances such as automated blood collection and information systems, the blood center improved donor engagement and avoided collecting a surplus of any one type of blood product over the course of the pandemic.

Clinical Utility of Elecsys Anti-SARS-CoV-2 S Assay in COVID-19 Vaccination: An Exploratory Analysis of the mRNA-1273 Phase 1 Trial.

Background: The ability to quantify an immune response after vaccination against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is essential. This study assessed the clinical utility of the quantitative Roche Elecsys® Anti-SARS-CoV-2 S assay (ACOV2S) using samples from the 2019-nCoV vaccine (mRNA-1273) phase 1 trial (NCT04283461). Methods: Samples from 30 healthy participants, aged 18-55 years, who received two injections with mRNA-1273 at a dose of 25 µg (n=15) or 100 µg (n=15), were collected at Days 1 (first vaccination), 15, 29 (second vaccination), 43 and 57. ACOV2S results (shown in U/mL - equivalent to BAU/mL per the first WHO international standard) were compared with results from ELISAs specific to antibodies against the Spike protein (S-2P) and the receptor binding domain (RBD) as well as neutralization tests including nanoluciferase (nLUC80), live-virus (PRNT80), and a pseudovirus neutralizing antibody assay (PsVNA50). Results: RBD-specific antibodies were already detectable by ACOV2S at the first time point of assessment (d15 after first vaccination), with seroconversion before in all but two participants (25 µg dose group); all had seroconverted by Day 29. Across all post-baseline visits, geometric mean concentration of antibody levels was 3.27-7.48-fold higher in the 100 µg compared with the 25 µg dose group. ACOV2S measurements were highly correlated with those from RBD ELISA (Pearson's r=0.938; p<0.0001) and S-2P ELISA (r=0.918; p<0.0001). For both ELISAs, heterogeneous baseline results and smaller increases in antibody levels following the second vs first vaccination compared with ACOV2S were observed. ACOV2S showed absence of any baseline noise indicating high specificity detecting vaccine-induced antibody response. Moderate-strong correlations were observed between ACOV2S and neutralization tests (nLUC80 r=0.933; PsVNA50, r=0.771; PRNT80, r=0.672; all p ≤ 0.0001). Conclusion: The Elecsys Anti-SARS-CoV-2 S assay (ACOV2S) can be regarded as a highly valuable method to assess and quantify the presence of RBD-directed antibodies against SARS-CoV-2 following vaccination and may indicate the presence of neutralizing antibodies. As a fully automated and standardized method, ACOV2S could qualify as the method of choice for consistent quantification of vaccine-induced humoral response.

Rapid screening and identification of viral pathogens in metagenomic data.

BACKGROUND: Virus screening and viral genome reconstruction are urgent and crucial for the rapid identification of viral pathogens, i.e., tracing the source and understanding the pathogenesis when a viral outbreak occurs. Next-generation sequencing (NGS) provides an efficient and unbiased way to identify viral pathogens in host-associated and environmental samples without prior knowledge. Despite the availability of software, data analysis still requires human operations. A mature pipeline is urgently needed when thousands of viral pathogen and viral genome reconstruction samples need to be rapidly identified. RESULTS: In this paper, we present a rapid and accurate workflow to screen metagenomics sequencing data for viral pathogens and other compositions, as well as enable a reference-based assembler to reconstruct viral genomes. Moreover, we tested our workflow on several metagenomics datasets, including a SARS-CoV-2 patient sample with NGS data, pangolins tissues with NGS data, Middle East Respiratory Syndrome (MERS)-infected cells with NGS data, etc. Our workflow demonstrated high accuracy and efficiency when identifying target viruses from large scale NGS metagenomics data. Our workflow was flexible when working with a broad range of NGS datasets from small (kb) to large (100 Gb). This took from a few minutes to a few hours to complete each task. At the same time, our workflow automatically generates reports that incorporate visualized feedback (e.g., metagenomics data quality statistics, host and viral sequence compositions, details about each of the identified viral pathogens and their coverages, and reassembled viral pathogen sequences based on their closest references). CONCLUSIONS: Overall, our system enabled the rapid screening and identification of viral pathogens from metagenomics data, providing an important piece to support viral pathogen research during a pandemic. The visualized report contains information from raw sequence quality to a reconstructed viral sequence, which allows non-professional people to screen their samples for viruses by themselves (Additional file 1).