Can MSMEs in tourism survive the COVID-19 storm? A MSME view of their stakeholders

Purpose - This paper explores the challenges faced by the micro, small and medium enterprises (MSMEs) in the tourism industry in building capabilities toward being resilient in the wake of crises through a stakeholder perspective. Design/methodology/approach - This study identifies the barriers to building resilience through detailed literature review and expert interviews. A total of 13 barriers were identified and were classified into into three main categories, namely economic barriers, organizational barriers, and stakeholder barriers. Subsequently, primary data were collected to emperically validate the nature and strength of interactions between these barriers and to quantitatively identify their impact. Findings - The study identifies that in long run, stakeholder barriers are the most significant in building capabilities toward being resilient in the wake of crisis. However, for initial recovery, economic barriers need to be focused. Subsequently, organizational capabilities needs to be developed through stakeholder support. Practical implications - The study provides actionable insights to help MSMEs in the tourism industry to recover economically and to help them build lasting capabilities through organizational capability development and stakeholder support. Originality/value - This study is novel on two aspects. First, the study investigates role of MSMEs in the tourism industry and how MSMEs are pivotal in helping the industry recover from a crisis by being resilient. Second, the role of stakeholders in the MSMEs sector in tourism is underexplored area and this study adds value to this nascent literature.

Chemoinformatics and Machine Learning Approaches for Identifying Antiviral Compounds.

Current pandemics propelled research efforts in unprecedented fashion, primarily triggering computational efforts towards new vaccine and drug development as well as drug repurposing. There is an urgent need to design novel drugs with targeted biological activity and minimum adverse reactions that may be useful to manage viral outbreaks. Hence an attempt has been made to develop Machine Learning based predictive models that can be used to assess whether a compound has the potency to be antiviral or not. To this end, a set of 2358 antiviral compounds were compiled from the CAS COVID-19 antiviral SAR dataset whose activity was reported based on IC50 value. A total 1157 two-dimensional molecular descriptors were computed among which, the most highly correlated descriptors were selected using Tree-based, Correlation-based and Mutual information-based feature selection methods. Seven Machine Learning algorithms i. e., Random Forest, XGBoost, Support Vector Machine, KNN, Decision Tree, MLP Classifier and Logistic Regression were benchmarked. The best performance was achieved by the models developed using Random Forest and XGBoost algorithms in all the feature selection methods. The maximum predictive accuracy of both these models was 88 % with internal validation. Whereas, with an external dataset, a maximum accuracy of 93.10 % for XGBoost and 100 % for Random Forest based model was achievable. Furthermore, the study demonstrated scaffold analysis of the molecules as a pragmatic approach to explore the importance of structurally diverse compounds in data driven studies.

A super-potent tetramerized ACE2 protein displays enhanced neutralization of SARS-CoV-2 virus infection.

Approaches are needed for therapy of the severe acute respiratory syndrome from SARS-CoV-2 coronavirus (COVID-19). Interfering with the interaction of viral antigens with the angiotensin converting enzyme 2 (ACE-2) receptor is a promising strategy by blocking the infection of the coronaviruses into human cells. We have implemented a novel protein engineering technology to produce a super-potent tetravalent form of ACE2, coupled to the human immunoglobulin γ1 Fc region, using a self-assembling, tetramerization domain from p53 protein. This high molecular weight Quad protein (ACE2-Fc-TD) retains binding to the SARS-CoV-2 receptor binding spike protein and can form a complex with the spike protein plus anti-viral antibodies. The ACE2-Fc-TD acts as a powerful decoy protein that out-performs soluble monomeric and dimeric ACE2 proteins and blocks both SARS-CoV-2 pseudovirus and SARS-CoV-2 virus infection with greatly enhanced efficacy. The ACE2 tetrameric protein complex promise to be important for development as decoy therapeutic proteins against COVID-19. In contrast to monoclonal antibodies, ACE2 decoy is unlikely to be affected by mutations in SARS-CoV-2 that are beginning to appear in variant forms. In addition, ACE2 multimeric proteins will be available as therapeutic proteins should new coronaviruses appear in the future because these are likely to interact with ACE2 receptor.

Human species D adenovirus hexon capsid protein mediates cell entry through a direct interaction with CD46.

Human adenovirus species D (HAdV-D) types are currently being explored as vaccine vectors for coronavirus disease 2019 (COVID-19) and other severe infectious diseases. The efficacy of such vector-based vaccines depends on functional interactions with receptors on host cells. Adenoviruses of different species are assumed to enter host cells mainly by interactions between the knob domain of the protruding fiber capsid protein and cellular receptors. Using a cell-based receptor-screening assay, we identified CD46 as a receptor for HAdV-D56. The function of CD46 was validated in infection experiments using cells lacking and overexpressing CD46, and by competition infection experiments using soluble CD46. Remarkably, unlike HAdV-B types that engage CD46 through interactions with the knob domain of the fiber protein, HAdV-D types infect host cells through a direct interaction between CD46 and the hexon protein. Soluble hexon proteins (but not fiber knob) inhibited HAdV-D56 infection, and surface plasmon analyses demonstrated that CD46 binds to HAdV-D hexon (but not fiber knob) proteins. Cryoelectron microscopy analysis of the HAdV-D56 virion-CD46 complex confirmed the interaction and showed that CD46 binds to the central cavity of hexon trimers. Finally, soluble CD46 inhibited infection by 16 out of 17 investigated HAdV-D types, suggesting that CD46 is an important receptor for a large group of adenoviruses. In conclusion, this study identifies a noncanonical entry mechanism used by human adenoviruses, which adds to the knowledge of adenovirus biology and can also be useful for development of adenovirus-based vaccine vectors.