Vaccine Literacy and Vaccination: A Systematic Review.

Objectives: Vaccine literacy (VL) is an essential component of health literacy and is regarded as the promising technique for eliminating vaccine hesitancy. This review summarizes the relationship between VL and vaccination, including vaccine hesitancy, vaccination attitude, vaccination intention, and vaccination uptake. Methods: A systematic search was conducted in the PubMed, Embase, Web of Science, CINAHL, PsycINFO, and Cochrane Library databases. Studies that explored the relationship between VL and vaccination were included, and the PRISMA recommendations were followed. Results: 1523 studies were found, and 21 articles were selected. The earliest article was published in 2015 and focused on the HPV vaccination and VL of female college students. Three studies surveyed parents' VL about childhood vaccinations, and the remaining 17 focused on COVID-19 VL in different groups. Conclusion: Although VL plays a role in determining the level of vaccine hesitancy across various populations, the association remains unclear. In the future, additional assessment methods could be developed and used to conduct prospective cohort and longitudinal studies to determine the causal relationship between VL and vaccination.

Comparison of BNT162b2-, mRNA-1273- and Ad26.COV2.S-Elicited IgG and Neutralizing Titers against SARS-CoV-2 and Its Variants.

Because the vaccine-elicited antibody and neutralizing activity against spike protein of SARS-CoV-2 are associated with protection from COVID-19, it is important to determine the levels of specific IgG and neutralization titers against SARS-CoV-2 elicited by the vaccines. While three widely used vaccine brands (Pfizer-BNT162b2, Moderna-mRNA-1273 and Johnson-Ad26.COV2.S) are effective in preventing SARS-CoV-2 infection and alleviating COVID-19 illness, they have different efficacy against COVID-19. It is unclear whether the differences are due to varying ability of the vaccines to elicit a specific IgG antibody response and neutralization activity against spike protein of the virus. In this study, we compared the plasma IgG and neutralization titers against spike proteins of wild-type SARS-CoV-2 and eight variants in healthy subjects who received the mRNA-1273, BNT162b2 or Ad26.COV2.S vaccine. We demonstrated that subjects vaccinated with Ad26.COV2.S vaccine had significantly lower levels of IgG and neutralizing titers as compared to those who received the mRNA vaccines. While the linear regression analysis showed a positive correlation between IgG levels and neutralizing activities against SARS-CoV-2 WT and the variants, there was an overall reduction in neutralizing titers against the variants in subjects across the three groups. These findings suggest that people who received one dose of Ad26.COV2.S vaccine have a more limited IgG response and lower neutralization activity against SARS-CoV-2 WT and its variants than recipients of the mRNA vaccines. Thus, monitoring the plasma or serum levels of anti-SARS-CoV-2 spike IgG titer and neutralization activity is necessary for the selection of suitable vaccines, vaccine dosage and regimens.

Augmentation and heterogeneous graph neural network for AAAI2021-COVID-19 fake news detection.

Misinformation has become a frightening specter of society, especially fake news that concerning Covid-19. It massively spreads on the Internet, and then induces misunderstandings of information to the national and global communities during the pandemic. Detecting massive misinformation on the Internet is crucial and challenging because humans have struggled against this phenomenon for a long time. Our research concerns detecting fake news related to covid-19 using augmentation [random deletion (RD), random insertion (RI), random swap (RS), synonym replacement (SR)] and several graph neural network [graph convolutional network (GCN), graph attention network (GAT), and GraphSAGE (SAmple and aggreGatE)] model. We constructed nodes and edges in the graph, word-word node, and word-document node to graph neural network. Then, we tested those models in different amounts of sample training data to obtain accuracy for each model and compared them. For our fake news detection task, we found training accuracy steadily increasing for GCN, GAT, and SAGE models from the beginning to the end of the epochs. This result proved that the performance of GNN, whether GCN, GAT, or SAGE gained an entirely insignificant difference precision result.

Feasibility analysis and mechanism exploration of Rhei Radix et Rhizome-Schisandrae Sphenantherae Fructus (RS) against COVID-19.

Introduction. As a novel global epidemic, corona virus disease 2019 (COVID-19) caused by SARS-CoV-2 brought great suffering and disaster to mankind. Recently, although significant progress has been made in vaccines against SARS-CoV-2, there are still no drugs for treating COVID-19. It is well known that traditional Chinese medicine (TCM) has achieved excellent efficacy in the treatment of COVID-19 in China. As a treasure-house of natural drugs, Chinese herbs offer a promising prospect for discovering anti-COVID-19 drugs.Hypothesis/Gap Statement. We proposed that Rhei Radix et Rhizome-Schisandrae Sphenantherae Fructus (RS) may have potential value in the treatment of COVID-19 patients by regulating immune response, protecting the cardiovascular system, inhibiting the production of inflammatory factors, and blocking virus invasion and replication processes.Aim. We aimed to explore the feasibility and molecular mechanisms of RS against COVID-19, to provide a reference for basic research and clinical applications.Methodology. Through literature mining, it is found that a Chinese herbal pair, RS, has potential anti-COVID-19 activity. In this study, we analysed the feasibility of RS against COVID-19 by high-throughput molecular docking and molecular dynamics simulations. Furthermore, we predicted the molecular mechanisms of RS against COVID-19 based on network pharmacology.Results. We proved the feasibility of RS anti-COVID-19 by literature mining, virtual docking and molecular dynamics simulations, and found that angiotensin converting enzyme 2 (ACE2) and 3C-like protease (3 CL pro) were also two critical targets for RS against COVID-19. In addition, we predicted the molecular mechanisms of RS in the treatment of COVID-19, and identified 29 main ingredients, 21 potential targets and 16 signalling pathways. Rhein, eupatin, (-)-catechin, aloe-emodin may be important active ingredients in RS. ALB, ESR1, EGFR, HMOX1, CTSL, and RHOA may be important targets against COVID-19. Platelet activation, renin secretion, ras signalling pathway, chemokine signalling pathway, and human cytomegalovirus infection may be important signalling pathways against COVID-19.Conclusion. RS plays a key role in the treatment of COVID-19, which may be closely related to immune regulation, cardiovascular protection, anti-inflammation, virus invasion and replication processes.

Virome in the cloaca of wild and breeding birds revealed a diversity of significant viruses.

BACKGROUND: Wild birds may harbor and transmit viruses that are potentially pathogenic to humans, domestic animals, and other wildlife. RESULTS: Using the viral metagenomic approach, we investigated the virome of cloacal swab specimens collected from 3182 birds (the majority of them wild species) consisting of > 87 different species in 10 different orders within the Aves classes. The virus diversity in wild birds was higher than that in breeding birds. We acquired 707 viral genomes from 18 defined families and 4 unclassified virus groups, with 265 virus genomes sharing < 60% protein sequence identities with their best matches in GenBank comprising new virus families, genera, or species. RNA viruses containing the conserved RdRp domain with no phylogenetic affinity to currently defined virus families existed in different bird species. Genomes of the astrovirus, picornavirus, coronavirus, calicivirus, parvovirus, circovirus, retrovirus, and adenovirus families which include known avian pathogens were fully characterized. Putative cross-species transmissions were observed with viruses in wild birds showing > 95% amino acid sequence identity to previously reported viruses in domestic poultry. Genomic recombination was observed for some genomes showing discordant phylogenies based on structural and non-structural regions. Mapping the next-generation sequencing (NGS) data respectively against the 707 genomes revealed that these viruses showed distribution pattern differences among birds with different habitats (breeding or wild), orders, and sampling sites but no significant differences between birds with different behavioral features (migratory and resident). CONCLUSIONS: The existence of a highly diverse virome highlights the challenges in elucidating the evolution, etiology, and ecology of viruses in wild birds. Video Abstract.

TB-BCG: Topic-Based BART Counterfeit Generator for Fake News Detection

Fake news has been spreading intentionally and misleading society to believe unconfirmed information;this phenomenon makes it challenging to identify fake news based on shared content. Fake news circulation is not only a current issue, but it has been disseminated for centuries. Dealing with fake news is a challenging task because it spreads massively. Therefore, automatic fake news detection is urgently needed. We introduced TB-BCG, Topic-Based BART Counterfeit Generator, to increase detection accuracy using deep learning. This approach plays an essential role in selecting impacted data rows and adding more training data. Our research implemented Latent Dirichlet Allocation (Topic-based), Bidirectional and Auto-Regressive Transformers (BART), and Cosine Document Similarity as the main tools involved in Constraint @ AAAI2021-COVID19 Fake News Detection dataset shared task. This paper sets forth this simple yet powerful idea by selecting a dataset based on topic and sorting based on distinctive data, generating counterfeit training data using BART, and comparing counterfeit-generated text toward source text using cosine similarity. If the comparison value between counterfeit-generated text and source text is more than 95%, then add that counterfeit-generated text into the dataset. In order to prove the resistance of precision and the robustness in various numbers of data training, we used 30%, 50%, 80%, and 100% from the total dataset and trained it using simple Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN). Compared to baseline, our method improved the testing performance for both LSTM and CNN, and yields are only slightly different.

Augmentation and heterogeneous graph neural network for AAAI2021-COVID-19 fake news detection

Misinformation has become a frightening specter of society, especially fake news that concerning Covid-19. It massively spreads on the Internet, and then induces misunderstandings of information to the national and global communities during the pandemic. Detecting massive misinformation on the Internet is crucial and challenging because humans have struggled against this phenomenon for a long time. Our research concerns detecting fake news related to covid-19 using augmentation [random deletion (RD), random insertion (RI), random swap (RS), synonym replacement (SR)] and several graph neural network [graph convolutional network (GCN), graph attention network (GAT), and GraphSAGE (SAmple and aggreGatE)] model. We constructed nodes and edges in the graph, word-word node, and word-document node to graph neural network. Then, we tested those models in different amounts of sample training data to obtain accuracy for each model and compared them. For our fake news detection task, we found training accuracy steadily increasing for GCN, GAT, and SAGE models from the beginning to the end of the epochs. This result proved that the performance of GNN, whether GCN, GAT, or SAGE gained an entirely insignificant difference precision result.

Statistical analysis of the community lockdown for COVID-19 pandemic.

As the global pandemic of the COVID-19 continues, the statistical modeling and analysis of the spreading process of COVID-19 have attracted widespread attention. Various propagation simulation models have been proposed to predict the spread of the epidemic and the effectiveness of related control measures. These models play an indispensable role in understanding the complex dynamic situation of the epidemic. Most existing work studies the spread of epidemic at two levels including population and agent. However, there is no comprehensive statistical analysis of community lockdown measures and corresponding control effects. This paper performs a statistical analysis of the effectiveness of community lockdown based on the Agent-Level Pandemic Simulation (ALPS) model. We propose a statistical model to analyze multiple variables affecting the COVID-19 pandemic, which include the timings of implementing and lifting lockdown, the crowd mobility, and other factors. Specifically, a motion model followed by ALPS and related basic assumptions is discussed first. Then the model has been evaluated using the real data of COVID-19. The simulation study and comparison with real data have validated the effectiveness of our model.

Identifying potential treatments of COVID-19 from Traditional Chinese Medicine (TCM) by using a data-driven approach.

ETHNOPHARMACOLOGICAL RELEVANCE: Traditional Chinese Medicine (TCM) has been widely used as an approach worldwide. Chinese Medicines (CMs) had been used to treat and prevent viral infection pneumonia diseases for thousands of years and had accumulated a large number of clinical experiences and effective prescriptions. AIM OF THE STUDY: This research aimed to systematically excavate the classical prescriptions of Chinese Medicine (CM), which have been used to prevent and treat Pestilence (Wenbing, Wenyi, Shiyi or Yibing) for long history in China, to obtain the potential prescriptions and ingredients to alternatively treat COVID-19. MATERIALS AND METHODS: We developed the screening system based on data mining, molecular docking and network pharmacology. Data mining and association network were used to mine the high-frequency herbs and formulas from ancient prescriptions. Virtual screening for the effective components of high frequency CMs and compatibility Chinese Medicine was explored by a molecular docking approach. Furthermore, network pharmacology method was used to preliminarily uncover the molecule mechanism. RESULTS: 574 prescriptions were obtained from 96,606 classical prescriptions with the key words to treat "Warm diseases (Wenbing)", "Pestilence (Wenyi or Yibing)" or "Epidemic diseases (Shiyi)". Meanwhile, 40 kinds of CMs, 36 CMs-pairs, 6 triple-CMs-groups existed with high frequency among the 574 prescriptions. Additionally, the key targets of SARS-COV-2, namely 3CL hydrolase (Mpro) and angiotensin-converting enzyme 2(ACE2), were used to dock the main ingredients from the 40 kinds by the LigandFitDock method. A total of 66 compounds components with higher frequency were docked with the COVID-19 targets, which were distributed in 26 kinds of CMs, among which Gancao (Glycyrrhizae Radix Et Rhizoma), HuangQin (Scutellariae Radix), Dahuang (Rhei Radix Et Rhizome) and Chaihu (Bupleuri Radix) contain more potential compounds. Network pharmacology results showed that Gancao (Glycyrrhizae Radix Et Rhizoma) and HuangQin (Scutellariae Radix) CMs-pairs could also interact with the targets involving in immune and inflammation diseases. CONCLUSIONS: These results we obtained probably provided potential candidate CMs formulas or active ingredients to overcome COVID-19. Prospectively, animal experiment and rigorous clinic studies are needed to confirm the potential preventive and treat effect of these CMs and compounds.