The nucleocapsid (N) protein is an important antigen for coronavirus, which participate in RNA package and virus particle release. In this study, we expressed the N protein of SARS-CoV-2 and characterized its biochemical properties. Static light scattering, size exclusive chromatography, and small-angle X-ray scattering (SAXS) showed that the purified N protein is largely a dimer in solution. CD spectra showed that it has a high percentage of disordered region at room temperature while it was best structured at 55 C, suggesting its structural dynamics. Fluorescence polarization assay showed it has non-specific nucleic acid binding capability, which raised a concern in using it as a diagnostic marker. Immunoblot assays confirmed the presence of IgA, IgM and IgG antibodies against N antigen in COVID-19 infection patients' sera, proving the importance of this antigen in host immunity and diagnostics.

Coronavirus disease , caused by a novel betacoronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has rapidly developed into a pandemic since it was first reported in December 2019. Nucleic acid testing is the standard method for the diagnosis of viral infections. However, this method reportedly has a low positivity rate. To increase the sensitivity of COVID-19 diagnoses, we developed an IgM-IgG combined assay and tested it in patients with suspected SARS-CoV-2 infection. In total, 56 patients were enrolled in this study and SARS-CoV-2 was detected by using both IgM-IgG antibody and nucleic acid tests. Clinical and laboratory data were collected and analyzed. Our findings suggest that patients who develop severe illness might experience longer virus exposure times and develop a more severe inflammatory response. The IgM-IgG test is an accurate and sensitive diagnostic method. A combination of nucleic acid and IgM-IgG testing is a more sensitive and accurate approach for diagnosis and early treatment of COVID-19.

Significance This paper compares the probabilistic accuracy of short-term forecasts of reported deaths due to COVID-19 during the first year and a half of the pandemic in the United States. Results show high variation in accuracy between and within stand-alone models and more consistent accuracy from an ensemble model that combined forecasts from all eligible models. This demonstrates that an ensemble model provided a reliable and comparatively accurate means of forecasting deaths during the COVID-19 pandemic that exceeded the performance of all of the models that contributed to it. This work strengthens the evidence base for synthesizing multiple models to support public-health action.

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Interferon (IFN)-mediated pathways are a crucial part of the cellular response against viral infection. Type III IFNs, which include IFN-λ1, 2 and 3, mediate antiviral responses similar to Type I IFNs via a distinct receptor complex. IFN-λ1 is more effective than the other two members. Transcription of IFN-λ1 requires activation of IRF3/7 and nuclear factor-kappa B (NF-κB), similar to the transcriptional mechanism of Type I IFNs. Using reporter assays, we discovered that viral infection induced both IFN-λ1 promoter activity and that of the 3'-untranslated region (UTR), indicating that IFN-λ1 expression is also regulated at the post-transcriptional level. After analysis with microRNA (miRNA) prediction programs and 3'UTR targeting site assays, the miRNA-548 family, including miR-548b-5p, miR-548c-5p, miR-548i, miR-548j, and miR-548n, was identified to target the 3'UTR of IFN-λ1. Further study demonstrated that miRNA-548 mimics down-regulated the expression of IFN-λ1. In contrast, their inhibitors, the complementary RNAs, enhanced the expression of IFN-λ1 and IFN-stimulated genes. Furthermore, miRNA-548 mimics promoted infection by enterovirus-71 (EV71) and vesicular stomatitis virus (VSV), whereas their inhibitors significantly suppressed the replication of EV71 and VSV. Endogenous miRNA-548 levels were suppressed during viral infection. In conclusion, our results suggest that miRNA-548 regulates host antiviral response via direct targeting of IFN-λ1, which may offer a potential candidate for antiviral therapy.

How do we forecast an emerging pandemic in real time in a purely data-driven manner? How to leverage rich heterogeneous data based on various signals such as mobility, testing, and/or disease exposure for forecasting? How to handle noisy data and generate uncertainties in the forecast? In this paper, we present DeepCOVID, an operational deep learning framework designed for real-time COVID-19 forecasting. DeepCOVID works well with sparse data and can handle noisy heterogeneous data signals by propagating the uncertainty from the data in a principled manner resulting in meaningful uncertainties in the forecast. The framework also consists of modules for both real-time and retrospective exploratory analysis to enable interpretation of the forecasts. Results from real-time predictions (featured on the CDC website and FiveThirtyEight.com) since April 2020 indicates that our approach is competitive among the methods in the COVID-19 Forecast Hub, especially for short-term predictions.

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multi-model ensemble forecast that combined predictions from dozens of different research groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-week horizon 3-5 times larger than when predicting at a 1-week horizon. This project underscores the role that collaboration and active coordination between governmental public health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks. Significance Statement This paper compares the probabilistic accuracy of short-term forecasts of reported deaths due to COVID-19 during the first year and a half of the pandemic in the US. Results show high variation in accuracy between and within stand-alone models, and more consistent accuracy from an ensemble model that combined forecasts from all eligible models. This demonstrates that an ensemble model provided a reliable and comparatively accurate means of forecasting deaths during the COVID-19 pandemic that exceeded the performance of all of the models that contributed to it. This work strengthens the evidence base for synthesizing multiple models to support public health action.

Background:Interleukin-32 participates in responses to viral infection, but how virus induces its expression and the mechanisms of its antiviral activities remain unclear. Results: Interleukin-32 selectively stimulates IFN-1 via activating distinct regulatory elements in the promoter. Conclusion: Interleukin-32 promotes IFN-1-mediated antiviral response. Significance: Interleukin-32 is one immune response factor and plays an important role in the control of viral infection.