Title: Multiplexed, quantitative serological profiling of COVID-19 from blood by a point-of-care test
Highly sensitive, specific, and point-of-care (POC) serological assays are an essential tool to manage coronavirus disease 2019 (COVID-19). Here, we report on a microfluidic POC test that can profile the antibody response against multiple severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antigens—spike S1 (S1), nucleocapsid (N), and the receptor binding domain (RBD)—simultaneously from 60 μl of blood, plasma, or serum. We assessed the levels of antibodies in plasma samples from 31 individuals (with longitudinal sampling) with severe COVID-19, 41 healthy individuals, and 18 individuals with seasonal coronavirus infections. This POC assay achieved high sensitivity and specificity, tracked seroconversion, and showed good concordance with a live virus microneutralization assay. We can also detect a prognostic biomarker of severity, IP-10 (interferon-γ–induced protein 10), on the same chip. Because our test requires minimal user intervention and is read by a handheld detector, it can be globally deployed to combat COVID-19. more »« less
Ganguli, Anurup; Mostafa, Ariana; Berger, Jacob; Aydin, Mehmet Y.; Sun, Fu; Ramirez, Sarah A.; Valera, Enrique; Cunningham, Brian T.; King, William P.; Bashir, Rashid(
, Proceedings of the National Academy of Sciences)
null
(Ed.)
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 protocols and primer sequences become widely known, many laboratories perform diagnostic tests using methods such as RT-PCR or reverse transcription loop mediated isothermal amplification (RT-LAMP). Here, we report an RT-LAMP isothermal assay for the detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus and demonstrate the assay on clinical samples using a simple and accessible point-of-care (POC) instrument. We characterized the assay by dipping swabs into synthetic nasal fluid spiked with the virus, moving the swab to viral transport medium (VTM), and sampling a volume of the VTM to perform the RT-LAMP assay without an RNA extraction kit. The assay has a limit of detection (LOD) of 50 RNA copies per μL in the VTM solution within 30 min. We further demonstrate our assay by detecting SARS-CoV-2 viruses from 20 clinical samples. Finally, we demonstrate a portable and real-time POC device to detect SARS-CoV-2 from VTM samples using an additively manufactured three-dimensional cartridge and a smartphone-based reader. The POC system was tested using 10 clinical samples, and was able to detect SARS-CoV-2 from these clinical samples by distinguishing positive samples from negative samples after 30 min. The POC tests are in complete agreement with RT-PCR controls. This work demonstrates an alternative pathway for SARS-CoV-2 diagnostics that does not require conventional laboratory infrastructure, in settings where diagnosis is required at the point of sample collection.
Ceci, A.; Muñoz-Ballester, C.; Tegge, A. N.; Brown, K. L.; Umans, R. A.; Michel, F. M.; Patel, D.; Tewari, B.; Martin, J.; Alcoreza, O.; et al(
, Nature Communications)
Abstract Rapid and widespread testing of severe acute respiratory coronavirus 2 (SARS-CoV-2) is essential for an effective public health response aimed at containing and mitigating the coronavirus disease 2019 (COVID-19) pandemic. Successful health policy implementation relies on early identification of infected individuals and extensive contact tracing. However, rural communities, where resources for testing are sparse or simply absent, face distinctive challenges to achieving this success. Accordingly, we report the development of an academic, public land grant University laboratory-based detection assay for the identification of SARS-CoV-2 in samples from various clinical specimens that can be readily deployed in areas where access to testing is limited. The test, which is a quantitative reverse transcription polymerase chain reaction (RT-qPCR)-based procedure, was validated on samples provided by the state laboratory and submitted for FDA Emergency Use Authorization. Our test exhibits comparable sensitivity and exceeds specificity and inclusivity values compared to other molecular assays. Additionally, this test can be re-configured to meet supply chain shortages, modified for scale up demands, and is amenable to several clinical specimens. Test development also involved 3D engineering critical supplies and formulating a stable collection media that allowed samples to be transported for hours over a dispersed rural region without the need for a cold-chain. These two elements that were critical when shortages impacted testing and when personnel needed to reach areas that were geographically isolated from the testing center. Overall, using a robust, easy-to-adapt methodology, we show that an academic laboratory can supplement COVID-19 testing needs and help local health departments assess and manage outbreaks. This additional testing capacity is particularly germane for smaller cities and rural regions that would otherwise be unable to meet the testing demand.
Moghadas, Seyed M; Vilches, Thomas N; Zhang, Kevin; Wells, Chad R; Shoukat, Affan; Singer, Burton H; Meyers, Lauren Ancel; Neuzil, Kathleen M; Langley, Joanne M; Fitzpatrick, Meagan C; et al(
, Clinical Infectious Diseases)
null
(Ed.)
Abstract Background Global vaccine development efforts have been accelerated in response to the devastating coronavirus disease 2019 (COVID-19) pandemic. We evaluated the impact of a 2-dose COVID-19 vaccination campaign on reducing incidence, hospitalizations, and deaths in the United States. Methods We developed an agent-based model of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission and parameterized it with US demographics and age-specific COVID-19 outcomes. Healthcare workers and high-risk individuals were prioritized for vaccination, whereas children under 18 years of age were not vaccinated. We considered a vaccine efficacy of 95% against disease following 2 doses administered 21 days apart achieving 40% vaccine coverage of the overall population within 284 days. We varied vaccine efficacy against infection and specified 10% preexisting population immunity for the base-case scenario. The model was calibrated to an effective reproduction number of 1.2, accounting for current nonpharmaceutical interventions in the United States. Results Vaccination reduced the overall attack rate to 4.6% (95% credible interval [CrI]: 4.3%–5.0%) from 9.0% (95% CrI: 8.4%–9.4%) without vaccination, over 300 days. The highest relative reduction (54%–62%) was observed among individuals aged 65 and older. Vaccination markedly reduced adverse outcomes, with non-intensive care unit (ICU) hospitalizations, ICU hospitalizations, and deaths decreasing by 63.5% (95% CrI: 60.3%–66.7%), 65.6% (95% CrI: 62.2%–68.6%), and 69.3% (95% CrI: 65.5%–73.1%), respectively, across the same period. Conclusions Our results indicate that vaccination can have a substantial impact on mitigating COVID-19 outbreaks, even with limited protection against infection. However, continued compliance with nonpharmaceutical interventions is essential to achieve this impact.
Abstract
The coronavirus disease 2019 (COVID-19) is a highly contagious and fatal disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In general, the diagnostic tests for COVID-19 are based on the detection of nucleic acid, antibodies, and protein. Among different analytes, the gold standard of the COVID-19 test is the viral nucleic acid detection performed by the quantitative reverse transcription polymerase chain reaction (qRT-PCR) method. However, the gold standard test is time-consuming and requires expensive instrumentation, as well as trained personnel. Herein, we report an ultrasensitive electrochemical biosensor based on zinc sulfide/graphene (ZnS/graphene) nanocomposite for rapid and direct nucleic acid detection of SARS-CoV-2. We demonstrated a simple one-step route for manufacturing ZnS/graphene by employing an ultrafast (90 s) microwave-based non-equilibrium heating approach. The biosensor assay involves the hybridization of target DNA or RNA samples with probes that are immersed into a redox active electrolyte, which are detectable by electrochemical measurements. In this study, we have performed the tests for synthetic DNA samples and, SARS-CoV-2 standard samples. Experimental results revealed that the proposed biosensor could detect low concentrations of all different SARS-CoV-2 samples, using such as S, ORF 1a, and ORF 1b gene sequences as targets. This microwave-synthesized ZnS/graphene-based biosensor could be reliably used as an on-site, real-time, and rapid diagnostic test for COVID-19.
Villanustre, Flavio; Chala, Arjuna; Dev, Roger; Xu, Lili; LexisNexis, Jesse Shaw; Furht, Borko; Khoshgoftaar, Taghi(
, Journal of Big Data)
Abstract This project is funded by the US National Science Foundation (NSF) through their NSF RAPID program under the title “Modeling Corona Spread Using Big Data Analytics.” The project is a joint effort between the Department of Computer & Electrical Engineering and Computer Science at FAU and a research group from LexisNexis Risk Solutions. The novel coronavirus Covid-19 originated in China in early December 2019 and has rapidly spread to many countries around the globe, with the number of confirmed cases increasing every day. Covid-19 is officially a pandemic. It is a novel infection with serious clinical manifestations, including death, and it has reached at least 124 countries and territories. Although the ultimate course and impact of Covid-19 are uncertain, it is not merely possible but likely that the disease will produce enough severe illness to overwhelm the worldwide health care infrastructure. Emerging viral pandemics can place extraordinary and sustained demands on public health and health systems and on providers of essential community services. Modeling the Covid-19 pandemic spread is challenging. But there are data that can be used to project resource demands. Estimates of the reproductive number (R) of SARS-CoV-2 show that at the beginning of the epidemic, each infected person spreads the virus to at least two others, on average (Emanuel et al. in N Engl J Med. 2020, Livingston and Bucher in JAMA 323(14):1335, 2020). A conservatively low estimate is that 5 % of the population could become infected within 3 months. Preliminary data from China and Italy regarding the distribution of case severity and fatality vary widely (Wu and McGoogan in JAMA 323(13):1239–42, 2020). A recent large-scale analysis from China suggests that 80 % of those infected either are asymptomatic or have mild symptoms; a finding that implies that demand for advanced medical services might apply to only 20 % of the total infected. Of patients infected with Covid-19, about 15 % have severe illness and 5 % have critical illness (Emanuel et al. in N Engl J Med. 2020). Overall, mortality ranges from 0.25 % to as high as 3.0 % (Emanuel et al. in N Engl J Med. 2020, Wilson et al. in Emerg Infect Dis 26(6):1339, 2020). Case fatality rates are much higher for vulnerable populations, such as persons over the age of 80 years (> 14 %) and those with coexisting conditions (10 % for those with cardiovascular disease and 7 % for those with diabetes) (Emanuel et al. in N Engl J Med. 2020). Overall, Covid-19 is substantially deadlier than seasonal influenza, which has a mortality of roughly 0.1 %. Public health efforts depend heavily on predicting how diseases such as those caused by Covid-19 spread across the globe. During the early days of a new outbreak, when reliable data are still scarce, researchers turn to mathematical models that can predict where people who could be infected are going and how likely they are to bring the disease with them. These computational methods use known statistical equations that calculate the probability of individuals transmitting the illness. Modern computational power allows these models to quickly incorporate multiple inputs, such as a given disease’s ability to pass from person to person and the movement patterns of potentially infected people traveling by air and land. This process sometimes involves making assumptions about unknown factors, such as an individual’s exact travel pattern. By plugging in different possible versions of each input, however, researchers can update the models as new information becomes available and compare their results to observed patterns for the illness. In this paper we describe the development a model of Corona spread by using innovative big data analytics techniques and tools. We leveraged our experience from research in modeling Ebola spread (Shaw et al. Modeling Ebola Spread and Using HPCC/KEL System. In: Big Data Technologies and Applications 2016 (pp. 347-385). Springer, Cham) to successfully model Corona spread, we will obtain new results, and help in reducing the number of Corona patients. We closely collaborated with LexisNexis, which is a leading US data analytics company and a member of our NSF I/UCRC for Advanced Knowledge Enablement. The lack of a comprehensive view and informative analysis of the status of the pandemic can also cause panic and instability within society. Our work proposes the HPCC Systems Covid-19 tracker, which provides a multi-level view of the pandemic with the informative virus spreading indicators in a timely manner. The system embeds a classical epidemiological model known as SIR and spreading indicators based on causal model. The data solution of the tracker is built on top of the Big Data processing platform HPCC Systems, from ingesting and tracking of various data sources to fast delivery of the data to the public. The HPCC Systems Covid-19 tracker presents the Covid-19 data on a daily, weekly, and cumulative basis up to global-level and down to the county-level. It also provides statistical analysis for each level such as new cases per 100,000 population. The primary analysis such as Contagion Risk and Infection State is based on causal model with a seven-day sliding window. Our work has been released as a publicly available website to the world and attracted a great volume of traffic. The project is open-sourced and available on GitHub. The system was developed on the LexisNexis HPCC Systems, which is briefly described in the paper.
Heggestad, Jacob T., Kinnamon, David S., Olson, Lyra B., Liu, Jason, Kelly, Garrett, Wall, Simone A., Oshabaheebwa, Solomon, Quinn, Zachary, Fontes, Cassio M., Joh, Daniel Y., Hucknall, Angus M., Pieper, Carl, Anderson, Jack G., Naqvi, Ibtehaj A., Chen, Lingye, Que, Loretta G., Oguin, Thomas, Nair, Smita K., Sullenger, Bruce A., Woods, Christopher W., Burke, Thomas W., Sempowski, Gregory D., Kraft, Bryan D., and Chilkoti, Ashutosh. Multiplexed, quantitative serological profiling of COVID-19 from blood by a point-of-care test. Retrieved from https://par.nsf.gov/biblio/10276697. Science Advances 7.26 Web. doi:10.1126/sciadv.abg4901.
Heggestad, Jacob T., Kinnamon, David S., Olson, Lyra B., Liu, Jason, Kelly, Garrett, Wall, Simone A., Oshabaheebwa, Solomon, Quinn, Zachary, Fontes, Cassio M., Joh, Daniel Y., Hucknall, Angus M., Pieper, Carl, Anderson, Jack G., Naqvi, Ibtehaj A., Chen, Lingye, Que, Loretta G., Oguin, Thomas, Nair, Smita K., Sullenger, Bruce A., Woods, Christopher W., Burke, Thomas W., Sempowski, Gregory D., Kraft, Bryan D., & Chilkoti, Ashutosh. Multiplexed, quantitative serological profiling of COVID-19 from blood by a point-of-care test. Science Advances, 7 (26). Retrieved from https://par.nsf.gov/biblio/10276697. https://doi.org/10.1126/sciadv.abg4901
Heggestad, Jacob T., Kinnamon, David S., Olson, Lyra B., Liu, Jason, Kelly, Garrett, Wall, Simone A., Oshabaheebwa, Solomon, Quinn, Zachary, Fontes, Cassio M., Joh, Daniel Y., Hucknall, Angus M., Pieper, Carl, Anderson, Jack G., Naqvi, Ibtehaj A., Chen, Lingye, Que, Loretta G., Oguin, Thomas, Nair, Smita K., Sullenger, Bruce A., Woods, Christopher W., Burke, Thomas W., Sempowski, Gregory D., Kraft, Bryan D., and Chilkoti, Ashutosh.
"Multiplexed, quantitative serological profiling of COVID-19 from blood by a point-of-care test". Science Advances 7 (26). Country unknown/Code not available. https://doi.org/10.1126/sciadv.abg4901.https://par.nsf.gov/biblio/10276697.
@article{osti_10276697,
place = {Country unknown/Code not available},
title = {Multiplexed, quantitative serological profiling of COVID-19 from blood by a point-of-care test},
url = {https://par.nsf.gov/biblio/10276697},
DOI = {10.1126/sciadv.abg4901},
abstractNote = {Highly sensitive, specific, and point-of-care (POC) serological assays are an essential tool to manage coronavirus disease 2019 (COVID-19). Here, we report on a microfluidic POC test that can profile the antibody response against multiple severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antigens—spike S1 (S1), nucleocapsid (N), and the receptor binding domain (RBD)—simultaneously from 60 μl of blood, plasma, or serum. We assessed the levels of antibodies in plasma samples from 31 individuals (with longitudinal sampling) with severe COVID-19, 41 healthy individuals, and 18 individuals with seasonal coronavirus infections. This POC assay achieved high sensitivity and specificity, tracked seroconversion, and showed good concordance with a live virus microneutralization assay. We can also detect a prognostic biomarker of severity, IP-10 (interferon-γ–induced protein 10), on the same chip. Because our test requires minimal user intervention and is read by a handheld detector, it can be globally deployed to combat COVID-19.},
journal = {Science Advances},
volume = {7},
number = {26},
author = {Heggestad, Jacob T. and Kinnamon, David S. and Olson, Lyra B. and Liu, Jason and Kelly, Garrett and Wall, Simone A. and Oshabaheebwa, Solomon and Quinn, Zachary and Fontes, Cassio M. and Joh, Daniel Y. and Hucknall, Angus M. and Pieper, Carl and Anderson, Jack G. and Naqvi, Ibtehaj A. and Chen, Lingye and Que, Loretta G. and Oguin, Thomas and Nair, Smita K. and Sullenger, Bruce A. and Woods, Christopher W. and Burke, Thomas W. and Sempowski, Gregory D. and Kraft, Bryan D. and Chilkoti, Ashutosh},
editor = {null}
}
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