Deep variational graph autoencoders for novel host-directed therapy options against COVID-19.

The COVID-19 pandemic has been keeping asking urgent questions with respect to therapeutic options. Existing drugs that can be repurposed promise rapid implementation in practice because of their prior approval. Conceivably, there is still room for substantial improvement, because most advanced artificial intelligence techniques for screening drug repositories have not been exploited so far. We construct a comprehensive network by combining year-long curated drug-protein/protein-protein interaction data on the one hand, and most recent SARS-CoV-2 protein interaction data on the other hand. We learn the structure of the resulting encompassing molecular interaction network and predict missing links using variational graph autoencoders (VGAEs), as a most advanced deep learning technique that has not been explored so far. We focus on hitherto unknown links between drugs and human proteins that play key roles in the replication cycle of SARS-CoV-2. Thereby, we establish novel host-directed therapy (HDT) options whose utmost plausibility is confirmed by realistic simulations. As a consequence, many of the predicted links are likely to be crucial for the virus to thrive on the one hand, and can be targeted with existing drugs on the other hand.

Psychological and behavioral responses to the declaration of COVID-19 as a pandemic: A comparative study of Hong Kong, Singapore, and the U.S.

What is the effect of declaring a pandemic? This research assesses behavioral and psychological responses to the WHO declaration of the COVID-19 pandemic, in Hong Kong, Singapore, and the U.S. We surveyed 3,032 members of the general public in these three regions about the preventative actions they were taking and their worries related to COVID-19. The WHO announcement on March 11th, 2020 created a quasi-experimental test of responses immediately before versus after the announcement. The declaration of the pandemic increased worries about the capacity of the local healthcare system in each region, as well as the proportion of people engaging in preventative actions, including actions not recommended by medical professionals. The number of actions taken correlates positively with anxiety and worries. Declaring the COVID-19 crisis as a pandemic had tangible effects-positive (increased community engagement) and negative (increased generalized anxiety)-which manifested differently across regions in line with expectancy disconfirmation theory.

Distance-based detection of out-of-distribution silent failures for Covid-19 lung lesion segmentation.

Automatic segmentation of ground glass opacities and consolidations in chest computer tomography (CT) scans can potentially ease the burden of radiologists during times of high resource utilisation. However, deep learning models are not trusted in the clinical routine due to failing silently on out-of-distribution (OOD) data. We propose a lightweight OOD detection method that leverages the Mahalanobis distance in the feature space and seamlessly integrates into state-of-the-art segmentation pipelines. The simple approach can even augment pre-trained models with clinically relevant uncertainty quantification. We validate our method across four chest CT distribution shifts and two magnetic resonance imaging applications, namely segmentation of the hippocampus and the prostate. Our results show that the proposed method effectively detects far- and near-OOD samples across all explored scenarios.

COVID-19 pandemic in the Arctic and Subarctic

The pandemic caused by the deadly Coronavirus has spread across the entire world, impacting the lives and livelihood of billions of people living in different regions. Even the Arctic and Subarctic regions are also not exempted from the spread and effect of this pandemic. In this study, we emphasize the COVID-19 pandemic situation of the Arctic and Subarctic regions. Even though the population density of these regions is significantly less, the eminent impact due to COVID-19 remains the same, perhaps more, considering the harsh weather, less communication, and health facilities. We have analyzed seasonal pandemic scenarios, risks, governance responses, and resilience of the locals as well as governments in and around the Arctic and Subarctic regions of Canada, Finland, Greenland, Iceland, Norway, Russia, Sweden, and the United States (Alaska). Despite these regions being extreme, the results reveal that the devastating effect of the pandemic remains almost the same at par with the context of the significantly lower population density. However, the governance shows a silver lining during this period, proving that humankind can win any battle for its sustenance with proper governance and management actions.

A network biology approach to identify crucial host targets for COVID-19.

The ongoing global pandemic of COVID-19, caused by SARS-CoV-2 has killed more than 5.9 million individuals out of ∼43 million confirmed infections. At present, several parts of the world are encountering the 3rd wave. Mass vaccination has been started in several countries but they are less likely to be broadly available for the current pandemic, repurposing of the existing drugs has drawn highest attention for an immediate solution. A recent publication has mapped the physical interactions of SARS-CoV-2 and human proteins by affinity-purification mass spectrometry (AP-MS) and identified 332 high-confidence SARS-CoV-2-human protein-protein interactions (PPIs). Here, we taken a network biology approach and constructed a human protein-protein interaction network (PPIN) with the above SARS-CoV-2 targeted proteins. We utilized a combination of essential network centrality measures and functional properties of the human proteins to identify the critical human targets of SARS-CoV-2. Four human proteins, namely PRKACA, RHOA, CDK5RAP2, and CEP250 have emerged as the best therapeutic targets, of which PRKACA and CEP250 were also found by another group as potential candidates for drug targets in COVID-19. We further found candidate drugs/compounds, such as guanosine triphosphate, remdesivir, adenosine monophosphate, MgATP, and H-89 dihydrochloride that bind the target human proteins. The urgency to prevent the spread of infection and the death of diseased individuals has prompted the search for agents from the pool of approved drugs to repurpose them for COVID-19. Our results indicate that host targeting therapy with the repurposed drugs may be a useful strategy for the treatment of SARS-CoV-2 infection.

Fuzzy association analysis for identifying climatic and socio-demographic factors impacting the spread of COVID-19.

Recently, the whole world witnessed the fatal outbreak of COVID-19 epidemic originating at Wuhan, Hubei province, China, during a mass gathering in a film festival. World Health Organization (WHO) has declared this COVID-19 as a pandemic due to its rapid spread across different countries within a few days. Several research works are being performed to understand the various influential factors responsible for spreading COVID. However, limited studies have been performed on how climatic and socio-demographic conditions may impact the spread of the virus. In this work, we aim to find the relationship of socio-demographic conditions, such as temperature, humidity, and population density of the regions, with the spread of COVID-19. The COVID data for different countries along with the social data are collected. For the experimental purpose, Fuzzy association rule mining is employed to infer the various relationships from the data. Moreover, to examine the seasonal effect, a streaming setting is also considered. The experimental results demonstrate various interesting insights to understand the impact of different factors on spreading COVID-19.

Machine learning techniques for sequence-based prediction of viral-host interactions between SARS-CoV-2 and human proteins.

BACKGROUND: COVID-19 (Coronavirus Disease-19), a disease caused by the SARS-CoV-2 virus, has been declared as a pandemic by the World Health Organization on March 11, 2020. Over 15 million people have already been affected worldwide by COVID-19, resulting in more than 0.6 million deaths. Protein-protein interactions (PPIs) play a key role in the cellular process of SARS-CoV-2 virus infection in the human body. Recently a study has reported some SARS-CoV-2 proteins that interact with several human proteins while many potential interactions remain to be identified. METHOD: In this article, various machine learning models are built to predict the PPIs between the virus and human proteins that are further validated using biological experiments. The classification models are prepared based on different sequence-based features of human proteins like amino acid composition, pseudo amino acid composition, and conjoint triad. RESULT: We have built an ensemble voting classifier using SVMRadial, SVMPolynomial, and Random Forest technique that gives a greater accuracy, precision, specificity, recall, and F1 score compared to all other models used in the work. A total of 1326 potential human target proteins of SARS-CoV-2 have been predicted by the proposed ensemble model and validated using gene ontology and KEGG pathway enrichment analysis. Several repurposable drugs targeting the predicted interactions are also reported. CONCLUSION: This study may encourage the identification of potential targets for more effective anti-COVID drug discovery.