Potential COVID-19 Therapies from Computational Repurposing of Drugs and Natural Products against the SARS-CoV-2 Helicase

Repurposing of existing drugs is a rapid way to find potential new treatments for SARS-CoV-2. Here, we applied a virtual screening approach using Autodock Vina and molecular dynamic simulation in tandem to screen and calculate binding energies of repurposed drugs against the SARS-CoV-2 helicase protein (non-structural protein nsp13). Amongst the top hits from our study were antivirals, antihistamines, and antipsychotics, plus a range of other drugs. Approximately 30% of our top 87 hits had published evidence indicating in vivo or in vitro SARS-CoV-2 activity. Top hits not previously reported to have SARS-CoV-2 activity included the antiviral agents, cabotegravir and RSV-604; the NK1 antagonist, aprepitant; the trypanocidal drug, aminoquinuride; the analgesic, antrafenine; the anticancer intercalator, epirubicin; the antihistamine, fexofenadine; and the anticoagulant, dicoumarol. These hits from our in silico SARS-CoV-2 helicase screen warrant further testing as potential COVID-19 treatments.     

Mean opinion score (MOS) revisited: methods and applications,limitations and alternatives

Mean opinion score (MOS) has become a very popular indicator of perceived media quality. While there is a clear benefit to such a “reference quality indicator” and its widespread acceptance, MOS is often applied without sufficient consideration of its scope or limitations. In this paper, we critically examine MOS and the various ways it is being used today. We highlight common issues with both subjective and objective MOS and discuss a variety of alternative approaches that have been proposed for media quality measurement.     

Fast projected gradient method for support vector machines

We present an algorithm for training dual soft margin support vector machines (SVMs) based on an augmented Lagrangian (AL) that uses a modification of the fast projected gradient method (FPGM) with a projection on a box set. The FPGM requires only first derivatives, which for the dual soft margin SVM means computing mainly a matrix-vector product. Therefore, AL-FPGM being computationally inexpensive may complement existing quadratic programming solvers for training large SVMs. We report numerical results for training the SVM with the AL-FPGM on data up to tens of thousands of data points from the UC Irvine Machine Learning Repository.     

Significance of Nanopatterned and Clustered DLL1 for Hematopoietic Stem Cell Proliferation

Hematopoietic stem cells are the stem cells of the blood that are applied to treat hematological disorders by transplanting donor cells to a patient. Rarity of donors and low cell counts in alternative hematopoietic stem cell sources such as cord blood limit the clinical use of hematopoietic stem cells. Here, it is shown that bifunctional surfaces containing the adhesive RGD peptide together with the Notch‐activating Delta‐like 1 (DLL1)—provided in a nanopatterned or unpatterned manner in different densities—are able to enhance hematopoietic stem and progenitor cell proliferation. Nanopatterning allows determining the maximal distance between DLL1 molecules that results in efficient cell stimulation (40 nm). Applying unpatterned substrates with statistically distributed DLL1 shows that the elicited effects depend on ligand density and clustering (minimum 2 molecules/cluster). Thereby, the present study contributes to the development of cost‐efficient bioreactors for hematopoietic stem cell expansion and to deciphering how cells gain control over Notch signaling by DLL1 clustering.