TIMELESS is a key gene mediating thrombogenesis in COVID-19 and antiphospholipid syndrome.

Abnormal coagulation and increased risk of thrombosis are some of the symptoms associated with COVID-19 severity. Anti-phospholipid antibodies (aPLs) present in critically ill COVID-19 patients contribute to systemic thrombosis. The aim of this study was to identify key common genes to characterize genetic crosstalk between COVID-19 and antiphospholipid syndrome (APS) using bioinformatics analysis and explore novel mechanisms of immune-mediated thrombosis in critically ill COVID-19 patients. The transcriptome data of mononuclear cells from severe COVID-19 patients and APS patients were evaluated to obtain the common genes. The protein-protein interaction network and cytoHubba module analysis in Cytoscape software were used to find the associated hinge genes and hub genes. Among the common differentially expressed genes, TIMELESS depletion was identified only in patients with severe COVID-19 and not in patients with mild COVID-19, and it was validated with the GSE159678 dataset. Functional analyses using gene ontology terms and the Kyoto Encyclopedia of Genes and Genomes pathway suggested that TIMELESS might contribute to the production of antiphospholipid antibody and thrombosis in both COVID-19 and APS patients. The potential role of TIMELESS and autophagy genes in momonuclear cells were further investigated, and GSK3B was found to be associated with TIMELESS. Autophagy targeting agents have a therapeutic potential against COVID-19 and thrombogenesis in APS, which may be related to the role of autophagy genes in the modification of circadian clock proteins. Interference with TIMELESS and other genes associated with it to regulate autoantibody expression may be a potential strategy for immunotherapy against thrombogenesis in severe COVID-19 patients.

Long-term predictions of current confirmed and dead cases of COVID-19 in China by the non-autonomous delayed epidemic models.

In this paper, we make long-term predictions based on numbers of current confirmed cases, accumulative dead cases of COVID-19 in different regions in China by modeling approach. Firstly, we use the SIRD epidemic model (S-Susceptible, I-Infected, R-Recovered, D-Dead) which is a non-autonomous dynamic system with incubation time delay to study the evolution of the COVID-19 in Wuhan City, Hubei Province and China Mainland. According to the data in the early stage issued by the National Health Commission of China, we can accurately estimate the parameters of the model, and then accurately predict the evolution of the COVID-19 there. From the analysis of the issued data, we find that the cure rates in Wuhan City, Hubei Province and China Mainland are the approximately linear increasing functions of time t and their death rates are the piecewisely decreasing functions. These can be estimated by finite difference method. Secondly, we use the delayed SIRD epidemic model to study the evolution of the COVID-19 in the Hubei Province outside Wuhan City. We find that its cure rate is an approximately linear increasing function and its death rate is nearly a constant. Thirdly, we use the delayed SIR epidemic model (S-Susceptible, I-Infected, R-Removed) to predict those of Beijing, Shanghai, Zhejiang and Anhui Provinces. We find that their cure rates are the approximately linear increasing functions and their death rates are the small constants. The results indicate that it is possible to make accurate long-term predictions for numbers of current confirmed, accumulative dead cases of COVID-19 by modeling. In this paper the results indicate we can accurately obtain and predict the turning points, the end time and the maximum numbers of the current infected and dead cases of the COVID-19 in China. In spite of our simple method and small data, it is rather effective in the long-term prediction of the COVID-19.

Glycyrrhizic Acid Inhibits SARS-CoV-2 Infection by Blocking Spike Protein-Mediated Cell Attachment.

Glycyrrhizic acid (GA), also known as glycyrrhizin, is a triterpene glycoside isolated from plants of Glycyrrhiza species (licorice). GA possesses a wide range of pharmacological and antiviral activities against enveloped viruses including severe acute respiratory syndrome (SARS) virus. Since the S protein (S) mediates SARS coronavirus 2 (SARS-CoV-2) cell attachment and cell entry, we assayed the GA effect on SARS-CoV-2 infection using an S protein-pseudotyped lentivirus (Lenti-S). GA treatment dose-dependently blocked Lenti-S infection. We showed that incubation of Lenti-S virus, but not the host cells with GA prior to the infection, reduced Lenti-S infection, indicating that GA targeted the virus for infection. Surface plasmon resonance measurement showed that GA interacted with a recombinant S protein and blocked S protein binding to host cells. Autodocking analysis revealed that the S protein has several GA-binding pockets including one at the interaction interface to the receptor angiotensin-converting enzyme 2 (ACE2) and another at the inner side of the receptor-binding domain (RBD) which might impact the close-to-open conformation change of the S protein required for ACE2 interaction. In addition to identifying GA antiviral activity against SARS-CoV-2, the study linked GA antiviral activity to its effect on virus cell binding.

Long-Term Predictions of COVID-19 in Some Countries by the SIRD Model

As COVID-19 in some countries has increasingly become more severe, there have been significant efforts to develop models that forecast its evolution there. These models can help to control and prevent the outbreak of these infections. In this paper, we make long-term predictions based on the number of current confirmed cases, accumulative recovered cases, and dead cases of COVID-19 in some countries by the modeling approach. We use the SIRD (S: susceptible, I: infected, R: recovered, D: dead) epidemic model which is a nonautonomous dynamic system with incubation time delay to study the evolution of COVID-19 in some countries. From the analysis of the recent data, we find that the cure and death rates may not be constant and, in some countries, they are piecewise functions. They can be estimated from the delayed SIRD model by the finite difference method. According to the recent data and its subsequent cure and death rates, we can accurately estimate the parameters of the model and then predict the evolution of COVID-19 there. Through the predicted results, we can obtain the turning points, the plateau period, and the maximum number of COVID-19 cases. The predicted results suggest that the epidemic situation in some countries is very serious. It is advisable for the governments of these countries to take more stringent and scientific containment measures. Finally, we studied the impact of the infection rate β on COVID-19. We find that when the infection rate β decreases, the cumulative number of confirmed cases and the maximum number of currently infected cases will greatly decrease. The results further affirm that the containment techniques taken by these countries to curb the spread of COVID-19 should be strengthened further.

The road to economic recovery: Pandemics and innovation

In this paper, we investigate the economic consequences of pandemics from an idea-based theory of economic growth. We assume that pandemics pose a threat to research productivity and analyse the long-term consequences of pandemic shocks to innovation output. We demonstrate that following a pandemic, innovation output is disrupted for approximately seven years. The effect of pandemic shocks on innovation output varies between countries, and sector to sector regarding economic activity. Pandemic shocks lead to a short-term drop in the number of patent applications. Crucially, the duration of a pandemic has a strong effect on innovation output. Overall, the effects of this most recent pandemic on future innovation output, and subsequently on growth, are expected to be felt long into the future. This paper supports the policies designed to reduce the effect of the “Great Lockdown” on research productivity. Policies that target the more innovative firms are moving in the right direction in terms of reducing the time it will take for innovation to recover from the effects of COVID19.

Investigating the mechanism of ShuFeng JieDu capsule for the treatment of novel Coronavirus pneumonia (COVID-19) based on network pharmacology.

ShuFeng JieDu capsule (SFJDC), a traditional Chinese medicine, has been recommended for the treatment of COVID-19 infections. However, the pharmacological mechanism of SFJDC still remains vague to date. The active ingredients and their target genes of SFJDC were collected from TCMSP. COVID-19 is a type of Novel Coronavirus Pneumonia (NCP). NCP-related target genes were collected from GeneCards database. The ingredients-targets network of SFJDC and PPI networks were constructed. The candidate genes were screened by Venn diagram package for enrichment analysis. The gene-pathway network was structured to obtain key target genes. In total, 124 active ingredients, 120 target genes of SFJDC and 251 NCP-related target genes were collected. The functional annotations cluster 1 of 23 candidate genes (CGs) were related to lung and Virus infection. RELA, MAPK1, MAPK14, CASP3, CASP8 and IL6 were the key target genes. The results suggested that SFJDC cloud be treated COVID-19 by multi-compounds and multi-pathways, and this study showed that the mechanism of traditional Chinese medicine (TCM) in the treatment of disease from the overall perspective.

Analysis of inflammatory parameters and disease severity for 88 hospitalized COVID-19 patients in Wuhan, China.

Background and aim: The outbreak of coronavirus disease 2019 (COVID-19) is quickly turning into a pandemic. We aimed to further clarify the clinical characteristics and the relationship between these features and disease severity. Methods: In this retrospective single-center study, demographic, clinical and laboratory data were collected and analyzed among moderate, severe and critically ill group patients. Results: 88 hospitalization patients confirmed COVID-19 were enrolled in this study. The average age of the patients was 57.11 years (SD, ±15.39). Of these 88 patients, the median body mass index (BMI) was 24.03 (IQR, 21.64-26.61; range 15.05-32.39), the median duration from disease onset to hospital admission were 11 days (IQR, 6.50-14.50). 46.59% patients had one or more comorbidities, with hypertension being the most common (26.14%), followed by diabetes mellitus (12.50%) and coronary atherosclerotic heart disease (CAD) (7.95%). Common symptoms at onset of disease were fever (71.59%), cough (59.09%), dyspnea (38.64%) and fatigue (29.55%). 88 patients were divided into moderate (47 [53.41%]), severe (32 [36.36%]) and critically ill (9 [10.23%]) groups. Compared with severe and moderate patients, lymphocytopenia occurred in 85.71% critically ill patients, and serum IL-2R, IL-6, IL-8, TNF-α, LDH, and cTnI were also increased in 71.42%, 83.33%, 57.14%, 71.43%, 100% and 42.86% in critically ill patients. Through our analysis, the age, comorbidities, lymphocyte count, eosinophil count, ferritin, CRP, LDH, PT and inflammatory cytokines were statistically significant along with the disease severity. Conclusion: We found some clinical characteristic and inflammatory cytokines could reveal the severity of COVID-19 during the outbreak phage. Our research could assist the clinicians recognize severe and critically ill patients timely and focus on the expectant treatment for each patient.