Emotion Analysis of COVID-19 Vaccines Based on a Fuzzy Convolutional Neural Network.

COVID-19 created immense global challenges in 2020, and the world will live under its threat indefinitely. Much of the information on social media supported the government in addressing this major public health event. On January 9, to control the virus, the Chinese government announced universal vaccinations. However, due to a range of varied interpretations, people held different attitudes towards vaccination. Therefore, the success of the mass immunization strategy greatly depended on the public perception of the COVID-19 vaccine. This article explores the changes in people's emotional attitudes towards vaccines and the reasons behind them in the context of the global pandemic in an effort to help mankind overcome this ongoing crisis. For this article, microblogs from January to September containing Chinese people's responses to the COVID-19 vaccines were collected. Based on fuzzy logic and deep learning, we advance the hypothesis that fuzzy vector adaptive improvements will make it possible to better express language emotion and that fuzzy emotion vectors can be integrated into deep learning models, thus making these models more interpretable. Based on this assumption, we design a deep learning model with a fuzzy emotion vector. The experimental results show the positive effect of this model. By applying the model in analyses of people's attitudes towards vaccines, we can obtain people's attitudes towards vaccines in different time periods. We discovered that the most negative emotions about the vaccine appeared in April and that the most positive emotions about the vaccine appeared in February. Combined with word cloud technology and the LDA model, we can effectively explore the reasons for the changes in vaccine attitudes. Our findings show that people's negative emotions about the vaccine are always higher than their positive emotions about the vaccine and that people's attitudes towards the vaccine are closely related to the progress of the epidemic. There is also a certain relationship between people's attitudes towards the vaccine and those towards the vaccination.

Bioinformatics approach reveals the critical role of the NOD-like receptor signaling pathway in COVID-19-associated multiple sclerosis syndrome.

Multiple sclerosis (MS) is a kind of central nervous system (CNS) autoimmune disease, which mainly damages nerves, the brain, and the spinal cord. Recently, several clinical cases reported the relativity between Coronavirus Disease 2019 (COVID-19) and the development of MS, but the mechanism of how COVID-19 affects the occurrence of MS was still not clear. It is bioinformatics technology that we use to explore the potential association at the gene level. The genetic information related to the two diseases was collected from the DisGNET platform for functional protein network analysis and used STRING to identify the complete gene set. The protein-protein interaction (PPI) network was analyzed by STRING. Finally, in the GEO database, we selected peripheral blood mononuclear cell (PBMC) RNA sequencing data (GSE164805, GSE21942) from COVID-19 patients and MS patients to verify the potential cross mechanism between the two diseases. The similar gene set of immune or inflammation existed between the patients with COVID-19 and ones with MS, including L2RA, IFNG, IL1B, NLRP3, and TNF. Interaction network analysis among proteins revealed that IL1B, P2RX7, IFNB1, IFNB1, TNF, and CASP1 enhanced the network connectivity between the combined gene set of COVID-19 and MS associated with NOD-like receptor (NLR) signaling. The involvement of NLR signaling in both diseases was further confirmed by comparing peripheral blood monocyte samples from COVID-19 and MS patients. Activation of NLR signaling was found in both COVID-19 and MS. The PBMC samples analyses also indicated the involvement of the NLR signaling pathway. Taken together, our data analyses revealed that the NLR signaling pathway might play a critical role in the COVID-19-related MS.

Recombinant SARS-CoV-2 RBD with a built in T helper epitope induces strong neutralization antibody response.

Without approved vaccines and specific treatments, COVID-19 is spreading around the world with above 26 million cases and approximately 864 thousand deaths until now. An efficacious and affordable vaccine is urgently needed. The Val308 - Gly548 of spike protein of SARS-CoV-2 linked with Gln830 - Glu843 of Tetanus toxoid (TT peptide) (designated as S1-4) and without TT peptide (designated as S1-5) were expressed and renatured. The antigenicity and immunogenicity of S1-4 were evaluated by Western Blotting (WB) in vitro and immune responses in mice, respectively. The protective efficiency was measured preliminarily by microneutralization assay (MN50). The soluble S1-4 and S1-5 protein was prepared to high homogeneity and purity. Adjuvanted with Alum, S1-4 protein stimulated a strong antibody response in immunized mice and caused a major Th2-type cellular immunity supplemented with Th1-type immunity. Furthermore, the immunized sera could protect the Vero E6 cells from SARS-CoV-2 infection with neutralizing antibody titer 256. Recombinant SARS-CoV-2 RBD with a built in T helper epitope could stimulate both strong humoral immunity supplemented with cellular immunity in mice, demonstrating that it could be a promising subunit vaccine candidate.

The biological characteristics of SARS-CoV-2 spike protein Pro330-Leu650.

SARS-CoV-2 is the cause of the worldwide outbreak of COVID-19 that has been characterized as a pandemic by the WHO. Since the first report of COVID-19 on December 31, 2019, 179,111 cases were confirmed in 160 countries/regions with 7426 deaths as of March 17, 2020. However, there have been no vaccines approved in the world to date. In this study, we analyzed the biological characteristics of the SARS-CoV-2 Spike protein, Pro330-Leu650 (SARS-CoV-2-SPL), using biostatistical methods. SARS-CoV-2-SPL possesses a receptor-binding region (RBD) and important B (Ser438-Gln506, Thr553-Glu583, Gly404-Aps427, Thr345-Ala352, and Lys529-Lys535) and T (9 CD4 and 11 CD8 T cell antigenic determinants) cell epitopes. High homology in this region between SARS-CoV-2 and SARS-CoV amounted to 87.7%, after taking the biological similarity of the amino acids into account and eliminating the receptor-binding motif (RBM). The overall topology indicated that the complete structure of SARS-CoV-2-SPL was with RBM as the head, and RBD as the trunk and the tail region. SARS-CoV-2-SPL was found to have the potential to elicit effective B and T cell responses. Our findings may provide meaningful guidance for SARS-CoV-2 vaccine design.