ABSTRACT
Expanding our global testing capacity is critical to preventing and containing pandemics1-9. Accordingly, accessible and adaptable automated platforms that in decentralized settings perform nucleic acid amplification tests resource-efficiently are required10-14. Pooled testing can be extremely efficient if the pooling strategy is based on local viral prevalence15-20; however, it requires automation, small sample volume handling and feedback not available in current bulky, capital-intensive liquid handling technologies21-29. 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.
Subject(s)
Automation , COVID-19 Testing , Magnets , Molecular Diagnostic Techniques , Nucleic Acid Amplification Techniques , Robotics , SARS-CoV-2 , Humans , COVID-19/diagnosis , COVID-19/virology , COVID-19 Testing/methods , Molecular Diagnostic Techniques/economics , Molecular Diagnostic Techniques/methods , Nucleic Acid Amplification Techniques/economics , Nucleic Acid Amplification Techniques/methods , Pandemics/prevention & control , RNA, Viral/analysis , RNA, Viral/genetics , SARS-CoV-2/genetics , SARS-CoV-2/isolation & purification , Sensitivity and Specificity , Algorithms , Automation/economics , Automation/methods , Robotics/methods , Indicators and Reagents/economicsABSTRACT
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.
Subject(s)
Automation/methods , Autonomous Vehicles/statistics & numerical data , Mechanical Tests/methods , Accidents, Traffic/prevention & control , Accidents, Traffic/trends , Automation/economics , Automobile Driving/statistics & numerical data , COVID-19/economics , California , Humans , Mechanical Tests/economics , User-Centered DesignABSTRACT
OBJECTIVE: We investigated systematic review automation tool use by systematic reviewers, health technology assessors and clinical guideline developerst. STUDY DESIGN AND SETTING: An online, 16-question survey was distributed across several evidence synthesis, health technology assessment and guideline development organizations. We asked the respondents what tools they use and abandon, how often and when do they use the tools, their perceived time savings and accuracy, and desired new tools. Descriptive statistics were used to report the results. RESULTS: A total of 253 respondents completed the survey; 89% have used systematic review automation tools - most frequently whilst screening (79%). Respondents' "top 3" tools included: Covidence (45%), RevMan (35%), Rayyan and GRADEPro (both 22%); most commonly abandoned were Rayyan (19%), Covidence (15%), DistillerSR (14%) and RevMan (13%). Tools saved time (80%) and increased accuracy (54%). Respondents taught themselves to how to use the tools (72%); lack of knowledge was the most frequent barrier to tool adoption (51%). New tool development was suggested for the searching and data extraction stages. CONCLUSION: Automation tools will likely have an increasingly important role in high-quality and timely reviews. Further work is required in training and dissemination of automation tools and ensuring they meet the desirable features of those conducting systematic reviews.
Subject(s)
Attitude to Computers , Automation/methods , Research Personnel/psychology , Systematic Reviews as Topic/methods , Technology Assessment, Biomedical/statistics & numerical data , Technology Assessment, Biomedical/standards , Adult , Female , Humans , Male , Middle AgedABSTRACT
A versatile portfolio of diagnostic tests is essential for the containment of the severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) pandemic. Besides nucleic acid-based test systems and point-of-care (POCT) antigen (Ag) tests, quantitative, laboratory-based nucleocapsid Ag tests for SARS-CoV-2 have recently been launched. Here, we evaluated four commercial Ag tests on automated platforms and one POCT to detect SARS-CoV-2. We evaluated PCR-positive (n = 107) and PCR-negative (n = 303) respiratory swabs from asymptomatic and symptomatic patients at the end of the second pandemic wave in Germany (February-March 2021) as well as clinical isolates EU1 (B.1.117), variant of concern (VOC) Alpha (B.1.1.7) or Beta (B.1.351), which had been expanded in a biosafety level 3 laboratory. The specificities of automated SARS-CoV-2 Ag tests ranged between 97.0 and 99.7% (Lumipulse G SARS-CoV-2 Ag (Fujirebio): 97.03%, Elecsys SARS-CoV-2 Ag (Roche Diagnostics): 97.69%; LIAISON® SARS-CoV-2 Ag (Diasorin) and SARS-CoV-2 Ag ELISA (Euroimmun): 99.67%). In this study cohort of hospitalized patients, the clinical sensitivities of tests were low, ranging from 17.76 to 52.34%, and analytical sensitivities ranged from 420,000 to 25,000,000 Geq/ml. In comparison, the detection limit of the Roche Rapid Ag Test (RAT) was 9,300,000 Geq/ml, detecting 23.58% of respiratory samples. Receiver-operating-characteristics (ROCs) and Youden's index analyses were performed to further characterize the assays' overall performance and determine optimal assay cutoffs for sensitivity and specificity. VOCs carrying up to four amino acid mutations in nucleocapsid were detected by all five assays with characteristics comparable to non-VOCs. In summary, automated, quantitative SARS-CoV-2 Ag tests show variable performance and are not necessarily superior to a standard POCT. The efficacy of any alternative testing strategies to complement nucleic acid-based assays must be carefully evaluated by independent laboratories prior to widespread implementation.
Subject(s)
Antigens, Viral/analysis , COVID-19 Serological Testing/methods , COVID-19/virology , SARS-CoV-2/isolation & purification , Antigens, Viral/immunology , Automation/economics , Automation/methods , COVID-19/diagnosis , COVID-19 Serological Testing/economics , Cohort Studies , False Negative Reactions , Germany , Humans , SARS-CoV-2/genetics , SARS-CoV-2/immunology , Sensitivity and SpecificitySubject(s)
COVID-19/epidemiology , COVID-19/virology , Evolution, Molecular , Genomics/methods , Genomics/trends , Mutation , SARS-CoV-2/genetics , Animals , Automation/methods , Basic Reproduction Number , COVID-19/immunology , COVID-19/transmission , COVID-19 Vaccines/immunology , Genome, Viral/genetics , Humans , Mink/virology , Pandemics/statistics & numerical data , Phylogeny , Public Health/methods , Public Health/trends , SARS-CoV-2/immunology , SARS-CoV-2/isolation & purification , SARS-CoV-2/pathogenicity , Social Media , UncertaintyABSTRACT
Evidence for the use of automated or partly automated contact-tracing tools to contain severe acute respiratory syndrome coronavirus 2 is scarce. We did a systematic review of automated or partly automated contact tracing. We searched PubMed, EMBASE, OVID Global Health, EBSCO Medical COVID Information Portal, Cochrane Library, medRxiv, bioRxiv, arXiv, and Google Advanced for articles relevant to COVID-19, severe acute respiratory syndrome, Middle East respiratory syndrome, influenza, or Ebola virus, published from Jan 1, 2000, to April 14, 2020. We also included studies identified through professional networks up to April 30, 2020. We reviewed all full-text manuscripts. Primary outcomes were the number or proportion of contacts (or subsequent cases) identified. Secondary outcomes were indicators of outbreak control, uptake, resource use, cost-effectiveness, and lessons learnt. This study is registered with PROSPERO (CRD42020179822). Of the 4036 studies identified, 110 full-text studies were reviewed and 15 studies were included in the final analysis and quality assessment. No empirical evidence of the effectiveness of automated contact tracing (regarding contacts identified or transmission reduction) was identified. Four of seven included modelling studies that suggested that controlling COVID-19 requires a high population uptake of automated contact-tracing apps (estimates from 56% to 95%), typically alongside other control measures. Studies of partly automated contact tracing generally reported more complete contact identification and follow-up compared with manual systems. Automated contact tracing could potentially reduce transmission with sufficient population uptake. However, concerns regarding privacy and equity should be considered. Well designed prospective studies are needed given gaps in evidence of effectiveness, and to investigate the integration and relative effects of manual and automated systems. Large-scale manual contact tracing is therefore still key in most contexts.