A Computational Approach for the Characterization of Airborne Pathogen Transmission in Turbulent Molecular Communication Channels

Airborne pathogen transmission mechanisms play a key role in the spread of infectious diseases such as COVID-19. In this work, we propose a computational fluid dynamics (CFD) approach to model and statistically characterize airborne pathogen transmission via pathogen-laden particles in turbulent channels from a molecular communication viewpoint. To this end, turbulent flows induced by coughing and the turbulent dispersion of droplets and aerosols are modeled by using the Reynolds-averaged Navier-Stokes equations coupled with the realizable k-model and the discrete random walk model, respectively. Via simulations realized by a CFD simulator, statistical data for the number of received particles are obtained. These data are post-processed to obtain the statistical characterization of the turbulent effect in the reception and to derive the probability of infection. Our results reveal that the turbulence has an irregular effect on the probability of infection, which shows itself by the multi-modal distribution as a weighted sum of normal and Weibull distributions. Furthermore, it is shown that the turbulent MC channel is characterized via multi-modal, i.e., sum of weighted normal distributions, or stable distributions, depending on the air velocity. Crown

Computational Docking Study of the Phytochemical Constituent, Silybin (Silybum marianum) against SARS-CoV-2 Omicron Variant Spike Glycoprotein: An In-silico Approach

SARS-CoV-2 is continually evolving with the emergence of new variants with increased viral pathogenicity. The emergence of heavily mutated Omicron (B.1.1.529) with spike protein mutations are known to mediate its higher transmissibility and immune escape that has brought newer challenges for global public health to contain SARS-CoV-2 infection. One has to come up with a therapeutic strategy against the virus so as to effectively contain the infection and spread. Natural phytochemicals are being considered a significant source of bioactive compounds possessing an antiviral therapeutic potential. Being a promising anticancer and chemo-preventive agent, Silybin holds a significant potential to be used as a therapeutic. In the present study, molecular docking of Silybin with Omicron spike protein (7QNW) was carried out. Molecular docking results showed greater stability of Silybin in the active site of the Omicron spike protein with suitable binding mode of interactions. The study reveals that Silybin has the potential to block the host ACE2 receptor-viral spike protein binding;thereby inhibiting the viral entry to human cells. Therefore, Silybin may be further developed as a medication with the ability to effectively combat SARS-CoV-2 Omicron.Copyright © The Author(s) 2023.

A Computational Approach for Predicting the Termination of COVID-19

In 2019, there was an epidemic to the human society, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The virus causes coronavirus disease 2019 (COVID-19). It is an uncertain disease encountered in society for which the technology and human society had not prepared before. COVID-19 first spread over the Wuhan city of China. Since, the past two years of time-span, it has affected the citizen's life culture and expectancy. Now, most of the population are concern about when will be COVID-19 terminate. Basically, this paper aims to analyze the COVID-19 data with features as total confirmed cases, death rate, and vaccination rate around the world-wide region. On analyzing the data, with the help of Machine Learning (ML) algorithms, we estimate the termination of COVID-19. The rapid expansion of the COVID-19 epidemic has compelled the need for technology in this field. © 2022 IEEE.

REDIRECTION: Generating drug repurposing hypotheses using link prediction with DISNET data

In recent years and due to COVID-19 pandemic, drug repurposing or repositioning has been placed in the spotlight. Giving new therapeutic uses to already existing drugs, this discipline allows to streamline the drug discovery process, reducing the costs and risks inherent to de novo development. Computational approaches have gained momentum, and emerging techniques from the machine learning domain have proved themselves as highly exploitable means for repurposing prediction. Against this backdrop, one can find that biomedical data can be represented in terms of graphs, which allow depicting in a very expressive manner the underlying structure of the information. Combining these graph data structures with deep learning models enhances the prediction of new links, such as potential disease-drug connections. In this paper, we present a new model named REDIRECTION, which aims to predict new disease-drug links in the context of drug repurposing. It has been trained with a part of the DISNET biomedical graph, formed by diseases, symptoms, drugs, and their relationships. The reserved testing graph for the evaluation has yielded to an AUROC of 0.93 and an AUPRC of 0.90. We have performed a secondary validation of REDIRECTION using RepoDB data as the testing set, which has led to an AUROC of 0.87 and a AUPRC of 0.83. In the light of these results, we believe that REDIRECTION can be a meaningful and promising tool to generate drug repurposing hypotheses. © 2022 IEEE.

Computational analysis of user experience and customer satisfaction with mobile food delivery services: Evidence from big data approaches.

Because of the COVID-19 global pandemic, mobile food delivery services have gained new prominence in our society. With this trend, the understanding of user experience in improving mobile food delivery services has gained increasing importance. To this end, we explore how user experience factors extracted by two natural language processing methods from comments of user reviews of mobile food delivery services significantly improve user satisfaction with the services. The results of two multiple regression analyses show that sentiment dimension factors, as well as usability, usefulness, and affection, have notable effects on satisfaction with the applications. Based on several findings of this study, we examine the significant implications and present the limitations of the study.

Antiviral Effects of Artemisinin and Its Derivatives against SARS-CoV-2 Main Protease: Computational Evidences and Interactions with ACE2 Allelic Variants.

Fighting against the emergent coronavirus disease (COVID-19) remains a big challenge at the front of the world communities. Recent research has outlined the potential of various medicinal herbs to counteract the infection. This study aimed to evaluate the interaction of artemisinin, a sesquiterpene lactone extracted from the Artemisia genus, and its derivatives with the SARS-CoV-2 main protease. To assess their potential use against COVID-19, the interactions of the main active principle of Artemisia with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) main protease (Mpro) was investigated through in silico probing. Our results showed that artemesinin and its derivatives manifested good oral absorption and bioavailability scores (0.55). They potently bound to the Mpro site of action-specifically, to its Cys145 residue. The selected compounds established two to three conventional hydrogen bonds with binding affinities ranging between -5.2 and -8.1 kcal/mol. Furthermore, artemisinin interactions with angiotensin converting enzyme 2 (ACE2) were dependent on the ACE2 allelic variants. The best score was recorded with rs961360700. A molecular dynamic simulation showed sufficient stability of the artemisinin-Mpro complex on the trajectory of 100 ns simulation frame. These binding interactions, together with drug-likeness and pharmacokinetic findings, confirmed that artemisinin might inhibit Mpro activity and explain the ethnopharmacological use of the herb and its possible antiviral activity against SARS-CoV-2 infection inducing COVID-19. Nevertheless, it interacted differently with the various ACE2 allelic variants reported to bind with the SARS-CoV-2 spike protein.