The COVID-19 pandemic provides an urgent example where a gap exists between availability of state-of-the-art diagnostics and current needs. As assay details and primer sequences become widely known, many laboratories could perform diagnostic tests using methods such as RT-PCR or isothermal RT-LAMP amplification. A key advantage of RT-LAMP based approaches compared to RT-PCR is that RT-LAMP is known to be robust in detecting targets from unprocessed samples. In addition, RT-LAMP assays are performed at a constant temperature enabling speed, simplicity, and point-of-use testing. Here, we provide the details of an RT-LAMP isothermal assay for the detection of SARS-CoV-2 virus with performance comparable to currently approved tests using RT-PCR. We characterize the assay by introducing swabs in virus spiked synthetic nasal fluids, moving the swab to viral transport medium (VTM), and using a volume of that VTM for performing the amplification without an RNA extraction kit. The assay has a Limit-of-Detection (LOD) of 50 RNA copies/μL in the VTM solution within 20 minutes, and LOD of 5000 RNA copies/μL in the nasal solution. Additionally, we show the utility of this assay for real-time point-of-use testing by demonstrating detection of SARS-CoV-2 virus in less than 40 minutes using an additively manufactured cartridge and a smartphone-based reader. Finally, we explore the speed and cost advantages by comparing the required resources and workflows with RT-PCR. This work could accelerate the development and availability of SARS-CoV-2 diagnostics by proving alternatives to conventional laboratory benchtop tests.Significance StatementAn important limitation of the current assays for the detection of SARS-CoV-2 stem from their reliance on time- and labor-intensive and laboratory-based protocols for viral isolation, lysis, and removal of inhibiting materials. While RT-PCR remains the gold standard for performing clinical diagnostics to amplify the RNA sequences, there is an urgent need for alternative portable platforms that can provide rapid and accurate diagnosis, potentially at the point-of-use. Here, we present the details of an isothermal amplification-based detection of SARS-CoV-2, including the demonstration of a smartphone-based point-of-care device that can be used at the point of sample collection.

The COVID-19 pandemic has underscored the shortcomings in the deployment of state-of-the-art diagnostic platforms. Although several PCR-based techniques have been rapidly developed to meet the growing testing needs, such techniques often need samples collected through a swab, the use of RNA extraction kits, and expensive thermocyclers in order to successfully perform the test. Isothermal amplification-based approaches have also been recently demonstrated for rapid SARS-CoV-2 detection by minimizing sample preparation while also reducing the instrumentation and reaction complexity. There are limited reports of saliva as the sample source and some of these indicate inferior sensitivity when comparing RT-LAMP with PCR-based techniques. In this paper, we demonstrate an improved sensitivity assay to test saliva using a 2-step RT-LAMP assay, where a short 10-minute RT step is performed with only B3 and BIP primers before the final reaction. We show that while the 1-step RT-LAMP demonstrate satisfactory results, the optimized 2-step approach allows for single molecule sensitivity per reaction and performs significantly better than the 1-step RT-LAMP and conventional 2-step RT-LAMP approaches with all primers included in the RT Step. Importantly, we demonstrate RNA extraction-free RT-LAMP based assays for detection of SARS-CoV-2 from VTM and saliva clinical samples.

Sepsis is a major cause of mortality among hospitalized patients worldwide. Shorter time to administration of broad-spectrum antibiotics is associated with improved outcomes, but early recognition of sepsis remains a major challenge. In a two-center cohort study with prospective sample collection from 1400 adult patients in emergency departments suspected of sepsis, we sought to determine the diagnostic and prognostic capabilities of a machine-learning algorithm based on clinical data and a set of uncommonly measured biomarkers. Specifically, we demonstrate that a machine-learning model developed using this dataset outputs a score with not only diagnostic capability but also prognostic power with respect to hospital length of stay (LOS), thirty-day mortality, and thirty-day inpatient readmission both in our entire testing cohort and various subpopulations. The area under the Receiver Operating Curve (AUROC) for diagnosis of sepsis was 0.83. Predicted risk scores for patients with septic shock were higher compared to patients with sepsis but without shock (p < 0.0001). Scores for patients with infection and organ dysfunction were higher compared to those without either condition (p < 0.0001). Stratification based on predicted scores of the patients into low, medium and high-risk groups showed significant differences in length of stay (p < 0.0001), thirty-day mortality (p < 0.0001), and thirty-day inpatient readmission (p < 0.0001). In conclusion, a machine-learning algorithm based on EMR data and three non-routinely measured biomarkers demonstrated good diagnostic and prognostic capability at the time of initial blood culture.

In the Fall of 2020, many universities saw extensive transmission of SARS-CoV-2 among their populations, threatening the health of students, faculty and staff, the viability of in-person instruction, and the health of surrounding communities.1, 2 Here we report that a multimodal “SHIELD: Target, Test, and Tell” program mitigated the spread of SARS-CoV-2 at a large public university, prevented community transmission, and allowed continuation of in-person classes amidst the pandemic. The program combines epidemiological modelling and surveillance (Target); fast and frequent testing using a novel and FDA Emergency Use Authorized low-cost and scalable saliva-based RT-qPCR assay for SARS-CoV-2 that bypasses RNA extraction, called covidSHIELD (Test); and digital tools that communicate test results, notify of potential exposures, and promote compliance with public health mandates (Tell). These elements were combined with masks, social distancing, and robust education efforts. In Fall 2020, we performed more than 1,000,000 covidSHIELD tests while keeping classrooms, laboratories, and many other university activities open. Generally, our case positivity rates remained less than 0.5%, we prevented transmission from our students to our faculty and staff, and data indicate that we had no spread in our classrooms or research laboratories. During this fall semester, we had zero COVID-19-related hospitalizations or deaths amongst our university community. We also prevented transmission from our university community to the surrounding Champaign County community. Our experience demonstrates that multimodal transmission mitigation programs can enable university communities to achieve such outcomes until widespread vaccination against COVID-19 is achieved, and provides a roadmap for how future pandemics can be addressed.

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