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PubMed; 2022.
Preprint in English | PubMed | ID: ppcovidwho-337234


In the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic1, considerable focus has been placed on a model of viral entry into host epithelial populations, with a separate focus upon the responding immune system dysfunction that exacerbates or causes disease. We developed a precision-cut lung slice model2,3 to investigate very early host-viral pathogenesis and found that SARS-CoV-2 had a rapid and specific tropism for myeloid populations in the human lung. Infection of alveolar macrophages was partially dependent upon their expression of ACE2, and the infections were productive for amplifying virus, both findings which were in contrast with their neutralization of another pandemic virus, Influenza A virus (IAV). Compared to IAV, SARS-CoV-2 was extremely poor at inducing interferon-stimulated genes in infected myeloid cells, providing a window of opportunity for modest titers to amplify within these cells. Endotracheal aspirate samples from humans with the acute respiratory distress syndrome (ARDS) from COVID-19 confirmed the lung slice findings, revealing a persistent myeloid depot. In the early phase of SARS-CoV-2 infection, myeloid cells may provide a safe harbor for the virus with minimal immune stimulatory cues being generated, resulting in effective viral colonization and quenching of the immune system.

PubMed; 2020.
Preprint in English | PubMed | ID: ppcovidwho-333583


While SARS-CoV-2 infection has pleiotropic and systemic effects in some patients, many others experience milder symptoms. We sought a holistic understanding of the severe/mild distinction in COVID-19 pathology, and its origins. We performed a whole-blood preserving single-cell analysis protocol to integrate contributions from all major cell types including neutrophils, monocytes, platelets, lymphocytes and the contents of serum. Patients with mild COVID-19 disease display a coordinated pattern of interferon-stimulated gene (ISG) expression across every cell population and these cells are systemically absent in patients with severe disease. Severe COVID-19 patients also paradoxically produce very high anti-SARS-CoV-2 antibody titers and have lower viral load as compared to mild disease. Examination of the serum from severe patients demonstrates that they uniquely produce antibodies with multiple patterns of specificity against interferon-stimulated cells and that those antibodies functionally block the production of the mild disease-associated ISG-expressing cells. Overzealous and auto-directed antibody responses pit the immune system against itself in many COVID-19 patients and this defines targets for immunotherapies to allow immune systems to provide viral defense. ONE SENTENCE SUMMARY: In severe COVID-19 patients, the immune system fails to generate cells that define mild disease;antibodies in their serum actively prevents the successful production of those cells.

6th IEEE International Conference on Computer and Communication Systems, ICCCS 2021 ; : 753-758, 2021.
Article in English | Scopus | ID: covidwho-1379524


Demand for remote work has surged as the COVID-19 epidemic has spread around the world. As one of the main implementations of desktop virtualization, Virtual Desktop Infrastructure (VDI) is popular and widely used in corporate remote work. A VDI user can connect to and use a virtual machine in a remote data center by logging in with a username and password using any device anywhere with Internet access. VDI has mobile convenience but is at risk of password leakage and insider threat. Traditional authentication methods, such as password and PIN, cannot withstand these threats. This work presents a keystroke-based continuous user authentication based on the Bidirectional Long Short-Term Memory (Bi-LSTM) network and embedding mechanism in deep learning to defend against such risks. It verifies the current user's identity based on the user's typing behavior continuously and non-invasively. We implement it on SPICE VDI and evaluate its performance and deployment feasibility on a public keystroke dataset - the Clarkson II dataset, which collected in uncontrolled and natural settings. The results show that it achieves state-of-art performance. It detects intruders with 8.28% of EER when only using 30 keystrokes and 0.85% of EER when using 990 keystrokes. © 2021 IEEE.

Annals Academy of Medicine Singapore ; 49(10):829-830, 2020.
Article in English | Web of Science | ID: covidwho-955162