From 2D to 3D Co-Culture Systems: A Review of Co-Culture Models to Study the Neural Cells Interaction.

The central nervous system (CNS) controls and regulates the functional activities of the organ systems and maintains the unity between the body and the external environment. The advent of co-culture systems has made it possible to elucidate the interactions between neural cells in vitro and to reproduce complex neural circuits. Here, we classified the co-culture system as a two-dimensional (2D) co-culture system, a cell-based three-dimensional (3D) co-culture system, a tissue slice-based 3D co-culture system, an organoid-based 3D co-culture system, and a microfluidic platform-based 3D co-culture system. We provide an overview of these different co-culture models and their applications in the study of neural cell interaction. The application of co-culture systems in virus-infected CNS disease models is also discussed here. Finally, the direction of the co-culture system in future research is prospected.

Molecular Basis for Antiviral Action of EDP‐235: A Potent and Selective SARS‐CoV‐2 3CLpro Inhibitor for the Treatment of Covid 19

To date, there are no approved oral antiviral therapies that can be administered early in the course of COVID‐19 to suppress progression of the disease or for prophylaxis. EDP‐235 is a potent and selective inhibitor of SARS‐CoV‐2 3C‐like protease (3CLpro). EDP‐235 inhibits SARS‐CoV‐2 3CLpro protease activity with an IC50 of 5.8 ± 3.7 nM and retains its activity against variant 3CLpro proteins from multiple SARS‐CoV‐2 lineages (IC50range of 2.8­–5.8 nM). 3CLpro protease activity progress curves showed significant curvature in a time‐ and EDP‐235‐concentration‐dependent manner indicative of slow‐onset inhibition. Slow reversal of inhibition of SARS‐CoV‐2 3CLpro enzyme activity was observed in a jump dilution experiment. Michaelis‐Menten kinetic studies with a FRET peptide substrate in the presence of EDP‐235 indicated that EDP‐235 is a substrate‐competitive inhibitor of SARS‐CoV‐2 3CLpro with an overall dissociation constant Kiof 3.0 ± 1.6 nM. SARS‐CoV‐2 3CLpro was crystallized bound to a close analog of EDP‐235 and structure elucidation revealed that the ligand bound at the active site and interacted with side chains of conserved residues Cys‐145, His‐163, and Glu‐166. EDP‐235 also potently inhibits 3CLpro enzymes from other α‐coronaviruses (IC50range of 2–4 nM) and β‐coronaviruses (SARS‐CoV IC50 of 5.4 nM, MERS‐CoV IC50 of 70 nM) which cause disease in humans to date. EDP‐235 resistance mutations in HCoV‐229E map to the active site of 3CLpro close to the predicted binding site and offer additional support to the mechanism of inhibition. EDP‐235 also showed a favorable selectivity profile (>300 selectivity index) when tested against a panel of 30 mammalian proteases. In summary, EDP‐235 acts as a slow‐onset, slow‐reversible, substrate‐competitive inhibitor of SARS‐CoV‐2 3CLpro. The outstanding preclinical profile of EDP‐235 supports its further evaluation as an oral therapeutic for the management of COVID‐19.

Global COVID-19 Epidemic Prediction and Analysis Based on Improved Dynamic Transmission Rate Model with Neural Networks

The cross-regional spread of COVID-19 had a huge impact on the normal global social order. This paper aims to build an improved dynamic transmission rate model based on the conjugate gradient neural network predicting and analyzing the global COVID-19 epidemic. First, we conduct an exploratory analysis of the COVID-19 epidemic from Canada, Germany, France, the United States, South Korea, Iran, Spain, and Italy. Second, a two-parameter power function is used for the nonlinear fitting of the dynamic transmission rate on account of data-driven approaches. Third, we correct the residual error and construct an improved nonlinear dynamic transmission rate model utilizing the conjugate gradient neural network. Finally, the inflection points of the global COVID-19 epidemic and new outbreaks, as well as the corresponding existing cases are predicted under the optimal sliding window period. The experimental results show that the model presented in this paper has higher prediction accuracy and robustness than some other existing methods. [ FROM AUTHOR] Copyright of Mathematical Problems in Engineering is the property of Hindawi Limited and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Nonlinear Combinational Dynamic Transmission Rate Model and Its Application in Global COVID-19 Epidemic Prediction and Analysis

The outbreak of coronavirus disease 2019 (COVID-19) has caused a global disaster, seriously endangering human health and the stability of social order. The purpose of this study is to construct a nonlinear combinational dynamic transmission rate model with automatic selection based on forecasting effective measure (FEM) and support vector regression (SVR) to overcome the shortcomings of the difficulty in accurately estimating the basic infection number R0 and the low accuracy of single model predictions. We apply the model to analyze and predict the COVID-19 outbreak in different countries. First, the discrete values of the dynamic transmission rate are calculated. Second, the prediction abilities of all single models are comprehensively considered, and the best sliding window period is derived. Then, based on FEM, the optimal sub-model is selected, and the prediction results are nonlinearly combined. Finally, a nonlinear combinational dynamic transmission rate model is developed to analyze and predict the COVID-19 epidemic in the United States, Canada, Germany, Italy, France, Spain, South Korea, and Iran in the global pandemic. The experimental results show an the out-of-sample forecasting average error rate lower than 10.07% was achieved by our model, the prediction of COVID-19 epidemic inflection points in most countries shows good agreement with the real data. In addition, our model has good anti-noise ability and stability when dealing with data fluctuations.

Persistence of Antibody and Cellular Immune Responses in Coronavirus Disease 2019 Patients Over Nine Months After Infection.

BACKGROUND: The duration of humoral and T and B cell response after the infection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) remains unclear. METHODS: We performed a cross-sectional study to assess the virus-specific antibody and memory T and B cell responses in coronavirus disease 2019 (COVID-19) patients up to 343 days after infection. Neutralizing antibodies and antibodies against the receptor-binding domain, spike, and nucleoprotein of SARS-CoV-2 were measured. Virus-specific memory T and B cell responses were analyzed. RESULTS: We enrolled 59 patients with COVID-19, including 38 moderate, 16 mild, and 5 asymptomatic patients; 31 (52.5%) were men and 28 (47.5%) were women. The median age was 41 years (interquartile range, 30-55). The median day from symptom onset to enrollment was 317 days (range 257 to 343 days). We found that approximately 90% of patients still have detectable immunoglobulin (Ig)G antibodies against spike and nucleocapsid proteins and neutralizing antibodies against pseudovirus, whereas ~60% of patients had detectable IgG antibodies against receptor-binding domain and surrogate virus-neutralizing antibodies. The SARS-CoV-2-specific IgG+ memory B cell and interferon-γ-secreting T cell responses were detectable in more than 70% of patients. CONCLUSIONS: Severe acute respiratory syndrome coronavirus 2-specific immune memory response persists in most patients approximately 1 year after infection, which provides a promising sign for prevention from reinfection and vaccination strategy.

Low Innate Immunity and Lagged Adaptive Immune Response in the Re-Tested Viral RNA Positivity of a COVID-19 Patient.

Recent studies have highlighted observations regarding re-tested positivity (RP) of SARS-CoV-2 RNA in discharged COVID-19 patients, however, the immune mechanisms underlying SARS-CoV-2 RNA RP in immunocompetent patients remain elusive. Herein, we describe the case of an immunocompetent COVID-19 patient with moderate symptoms who was twice re-tested as positive for SARS-CoV-2 RNA, and the period between first and third viral RNA positivity was 95 days, longer than previously reported (18-25 days). The chest computed tomography findings, plasma anti-SARS-CoV-2 antibody, neutralizing antibodies (NAbs) titer, and whole blood transcriptic characteristics in the viral RNA RP patient and other COVID-19 patients were analyzed. During the SARS-CoV-2 RNA RP period, new lung lesions were observed. The COVID-19 patient with viral RNA RP had delayed seroconversion of anti-spike/receptor-binding domain (RBD) IgA antibody and NAbs and were accompanied with disappearance of the lung lesions. Further experimental data validated that NAbs titer was significantly associated with anti-RBD IgA and IgG, and anti-spike IgG. The RP patient had lower interferon-, T cells- and B cell-related genes expression than non-RP patients with mild-to-moderate symptoms, and displayed lower cytokines and chemokines gene expression than severe patients. Interestingly, the RP patient had low expression of antigen presentation-related genes and low B cell counts which might have contributed to the delayed anti-RBD specific antibody and low CD8+ cell response. Collectively, delayed antigen presentation-related gene expression was found related to delayed adaptive immune response and contributed to the SARS-CoV-2 RNA RP in this described immunocompetent patient.

Analysis of the age-structured epidemiological characteristics of SARS-COV-2 transmission in mainland China: An aggregated approach

The novel coronavirus (SARS-Cov-2) has raged in mainland China for nearly three months resulting in a huge threat to people's health and economic development. According to the cumulative numbers of confirmed cases and deathes of SARS-COV-2 infection announced by the National Health Commission of China, we divided the human population into four subgroups including the adolescents group (0-19 yr old), the youth group (20-49 yr old), the middle-aged group (50-74 yr old) and the elderly group (over 75 yr old), and proposed a discrete age-structured SEIHRQ SARS-COV-2 transmission model. We utilized contact matrixes to describe the contact heterogeneities and correlations among different age groups. Adopting the Markov chain Monte Carlo (MCMC) algorithm, we identified the parameters of the model and fitted the confirmed cases from January 24th to March 31st. Through a more in-depth study, we showed that before January 28th (95% CI [Feb. 25th, Feb. 31st]), the effective reproduction number was greater than 1 and after that day its value was less than 1. Moreover, we estimated that the peak values of infection were 66 (95% CI [65,67]) for the adolescents, 3996 (95% CI [3957,4036]) for the young group, 14714 (95% CI [14692,14735]) for middle-aged group and 297 (95% CI [295,300]) for elderly people, respectively;the proportions of the final sizes of SARS-COV-2 infection accounted for less than 90% for each group. We found that under the current restricted control strategies, the most severe and high-risk group was middle-aged people aged between 50-74 yr old;without any prevention, the most severe and high-risk group had become the young adults aged 20-49 yr old.

Detection of COVID-19: A review of the current literature and future perspectives.

The rapid spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has led to the coronavirus disease 2019 (COVID-19) worldwide pandemic. This unprecedented situation has garnered worldwide attention. An effective strategy for controlling the COVID-19 pandemic is to develop highly accurate methods for the rapid identification and isolation of SARS-CoV-2 infected patients. Many companies and institutes are therefore striving to develop effective methods for the rapid detection of SARS-CoV-2 ribonucleic acid (RNA), antibodies, antigens, and the virus. In this review, we summarize the structure of the SARS-CoV-2 virus, its genome and gene expression characteristics, and the current progression of SARS-CoV-2 RNA, antibodies, antigens, and virus detection. Further, we discuss the reasons for the observed false-negative and false-positive RNA and antibody detection results in practical clinical applications. Finally, we provide a review of the biosensors which hold promising potential for point-of-care detection of COVID-19 patients. This review thereby provides general guidelines for both scientists in the biosensing research community and for those in the biosensor industry to develop a highly sensitive and accurate point-of-care COVID-19 detection system, which would be of enormous benefit for controlling the current COVID-19 pandemic.