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1. Use of compressed sensing to expedite high-throughput diagnostic testing for COVID-19 and beyond

   https://doi.org/10.1371/journal.pcbi.1010629  

   Waldstein, Kody A. ; Yi, Jirong ; Cho, Myung ; Mudumbai, Raghu ; Wu, Xiaodong ; Varga, Steven M. ; Xu, Weiyu ( October 2022 , PLOS Computational Biology)

   Faeder, James R. (Ed.)

   The rapid spread of SARS-CoV-2 has placed a significant burden on public health systems to provide swift and accurate diagnostic testing highlighting the critical need for innovative testing approaches for future pandemics. In this study, we present a novel sample pooling procedure based on compressed sensing theory to accurately identify virally infected patients at high prevalence rates utilizing an innovative viral RNA extraction process to minimize sample dilution. At prevalence rates ranging from 0–14.3%, the number of tests required to identify the infection status of all patients was reduced by 69.26% as compared to conventional testing in primary human SARS-CoV-2 nasopharyngeal swabs and a coronavirus model system. Our method provided quantification of individual sample viral load within a pool as well as a binary positive-negative result. Additionally, our modified pooling and RNA extraction process minimized sample dilution which remained constant as pool sizes increased. Compressed sensing can be adapted to a wide variety of diagnostic testing applications to increase throughput for routine laboratory testing as well as a means to increase testing capacity to combat future pandemics. 

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2. Spatially distributed infection increases viral load in a computational model of SARS-CoV-2 lung infection

   https://doi.org/10.1371/journal.pcbi.1009735  

   Moses, Melanie E. ; Hofmeyr, Steven ; Cannon, Judy L. ; Andrews, Akil ; Gridley, Rebekah ; Hinga, Monica ; Leyba, Kirtus ; Pribisova, Abigail ; Surjadidjaja, Vanessa ; Tasnim, Humayra ; et al ( December 2021 , PLOS Computational Biology)

   Smith, Amber M (Ed.)

   A key question in SARS-CoV-2 infection is why viral loads and patient outcomes vary dramatically across individuals. Because spatial-temporal dynamics of viral spread and immune response are challenging to study in vivo, we developed Spatial Immune Model of Coronavirus (SIMCoV), a scalable computational model that simulates hundreds of millions of lung cells, including respiratory epithelial cells and T cells. SIMCoV replicates viral growth dynamics observed in patients and shows how spatially dispersed infections can lead to increased viral loads. The model also shows how the timing and strength of the T cell response can affect viral persistence, oscillations, and control. By incorporating spatial interactions, SIMCoV provides a parsimonious explanation for the dramatically different viral load trajectories among patients by varying only the number of initial sites of infection and the magnitude and timing of the T cell immune response. When the branching airway structure of the lung is explicitly represented, we find that virus spreads faster than in a 2D layer of epithelial cells, but much more slowly than in an undifferentiated 3D grid or in a well-mixed differential equation model. These results illustrate how realistic, spatially explicit computational models can improve understanding of within-host dynamics of SARS-CoV-2 infection. 

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3. Computational characterization of inhaled droplet transport to the nasopharynx

   https://doi.org/10.1038/s41598-021-85765-7  

   Basu, Saikat ( March 2021 , Scientific Reports)

   How human respiratory physiology and the transport phenomena associated with inhaled airflow in the upper airway proceed to impact transmission of SARS-CoV-2, leading to the initial infection, stays an open question. An answer can help determine the susceptibility of an individual on exposure to a COVID-2019 carrier and can also provide a preliminary projection of the still-unknown infectious dose for the disease. Computational fluid mechanics enabled tracking of respiratory transport in medical imaging-based anatomic domains shows that the regional deposition of virus-laden inhaled droplets at the initial nasopharyngeal infection site peaks for the droplet size range of approximately 2.5–19$$\upmu $$$\mu$. Through integrating the numerical findings on inhaled transmission with sputum assessment data from hospitalized COVID-19 patients and earlier measurements of ejecta size distribution generated during regular speech, this study further reveals that the number of virions that may go on to establish the SARS-CoV-2 infection in a subject could merely be in the order of hundreds.

    

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4. A quantitative model used to compare within-host SARS-CoV-2, MERS-CoV, and SARS-CoV dynamics provides insights into the pathogenesis and treatment of SARS-CoV-2

   https://doi.org/10.1371/journal.pbio.3001128  

   Kim, Kwang Su ; Ejima, Keisuke ; Iwanami, Shoya ; Fujita, Yasuhisa ; Ohashi, Hirofumi ; Koizumi, Yoshiki ; Asai, Yusuke ; Nakaoka, Shinji ; Watashi, Koichi ; Aihara, Kazuyuki ; et al ( March 2021 , PLOS Biology)

   Sugden, Bill (Ed.)

   The scientific community is focused on developing antiviral therapies to mitigate the impacts of the ongoing novel coronavirus disease 2019 (COVID-19) outbreak. This will be facilitated by improved understanding of viral dynamics within infected hosts. Here, using a mathematical model in combination with published viral load data, we compare within-host viral dynamics of SARS-CoV-2 with analogous dynamics of MERS-CoV and SARS-CoV. Our quantitative analyses using a mathematical model revealed that the within-host reproduction number at symptom onset of SARS-CoV-2 was statistically significantly larger than that of MERS-CoV and similar to that of SARS-CoV. In addition, the time from symptom onset to the viral load peak for SARS-CoV-2 infection was shorter than those of MERS-CoV and SARS-CoV. These findings suggest the difficulty of controlling SARS-CoV-2 infection by antivirals. We further used the viral dynamics model to predict the efficacy of potential antiviral drugs that have different modes of action. The efficacy was measured by the reduction in the viral load area under the curve (AUC). Our results indicate that therapies that block de novo infection or virus production are likely to be effective if and only if initiated before the viral load peak (which appears 2–3 days after symptom onset), but therapies that promote cytotoxicity of infected cells are likely to have effects with less sensitivity to the timing of treatment initiation. Furthermore, combining a therapy that promotes cytotoxicity and one that blocks de novo infection or virus production synergistically reduces the AUC with early treatment. Our unique modeling approach provides insights into the pathogenesis of SARS-CoV-2 and may be useful for development of antiviral therapies. 

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5. Age-Related Changes in the Nasopharyngeal Microbiome Are Associated With Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Infection and Symptoms Among Children, Adolescents, and Young Adults

   https://doi.org/10.1093/cid/ciac184  

   Hurst, Jillian H. ; McCumber, Alexander W. ; Aquino, Jhoanna N. ; Rodriguez, Javier ; Heston, Sarah M. ; Lugo, Debra J. ; Rotta, Alexandre T. ; Turner, Nicholas A. ; Pfeiffer, Trevor S. ; Gurley, Thaddeus C. ; et al ( March 2022 , Clinical Infectious Diseases)

   Children are less susceptible to SARS-CoV-2 infection and typically have milder illness courses than adults, but the factors underlying these age-associated differences are not well understood. The upper respiratory microbiome undergoes substantial shifts during childhood and is increasingly recognized to influence host defense against respiratory pathogens. Thus, we sought to identify upper respiratory microbiome features associated with SARS-CoV-2 infection susceptibility and illness severity.

   We collected clinical data and nasopharyngeal swabs from 285 children, adolescents, and young adults (<21 years) with documented SARS-CoV-2 exposure. We used 16S ribosomal RNA gene sequencing to characterize the nasopharyngeal microbiome and evaluated for age-adjusted associations between microbiome characteristics and SARS-CoV-2 infection status and respiratory symptoms.

   Nasopharyngeal microbiome composition varied with age (PERMANOVA, P < .001; R2 = 0.06) and between SARS-CoV-2–infected individuals with and without respiratory symptoms (PERMANOVA, P  = .002; R2 = 0.009). SARS-CoV-2–infected participants with Corynebacterium/Dolosigranulum-dominant microbiome profiles were less likely to have respiratory symptoms than infected participants with other nasopharyngeal microbiome profiles (OR: .38; 95% CI: .18–.81). Using generalized joint attributed modeling, we identified 9 bacterial taxa associated with SARS-CoV-2 infection and 6 taxa differentially abundant among SARS-CoV-2–infected participants with respiratory symptoms; the magnitude of these associations was strongly influenced by age.

   We identified interactive relationships between age and specific nasopharyngeal microbiome features that are associated with SARS-CoV-2 infection susceptibility and symptoms in children, adolescents, and young adults. Our data suggest that the upper respiratory microbiome may be a mechanism by which age influences SARS-CoV-2 susceptibility and illness severity.

    

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