ABSTRACT
Researchers are constantly searching for drugs to combat the coronavirus pandemic caused by SARS-CoV-2, which has lasted for over two years. Natural compounds such as phenolic acids are being tested against Mpro and AAK1, which are key players in the SARS-CoV-2 life cycle. This research work aims to study the ability of a panel of natural phenolic acids to inhibit the virus's multiplication directly through Mpro and indirectly by affecting the adaptor-associated protein kinase-1 (AAK1). Pharmacophore mapping, molecular docking, and dynamic studies were conducted over 50 ns and 100 ns on a panel of 39 natural phenolic acids. Rosmarinic acid (16) on the Mpro receptor (- 16.33 kcal/mol) and tannic acid (17) on the AAK1 receptor (- 17.15 kcal/mol) exhibited the best docking energy against both receptors. These favourable docking score values were found to be superior to those of the co-crystallized ligands. Preclinical and clinical research is required before using them simultaneously to halt the COVID-19 life cycle in a synergistic manner.
Subject(s)
COVID-19 , Coronavirus 3C Proteases , Protease Inhibitors , Humans , Adaptor Proteins, Signal Transducing , Molecular Docking Simulation , Molecular Dynamics Simulation , Oligonucleotides , SARS-CoV-2ABSTRACT
Bipolar soft rough set represents an important mathematical model to deal with uncertainty. This theory represents a link between bipolar soft set and rough set theories. This study introduced the concept of topological bipolar soft set by combining a bipolar soft set with topologies. Also, the topological structure of bipolar soft rough set has been discussed by defining the bipolar soft rough topology. The main objective of this paper is to present some solutions to develop and modify the approach of the bipolar soft rough sets. Two kinds of bipolar soft ideal approximation operators which represent extensions of bipolar soft rough approximation operator have been presented. Moreover, a new kind of bipolar approximation space via two ideals, called bipolar soft biideal approximation space, was introduced and studied by two different methods. Their properties are discussed and the relationships between these methods and the previous ones are proposed. The importance of these methods is reducing the vagueness of uncertainty areas by increasing the bipolar lower approximations and decreasing the bipolar upper approximations. Also, the bipolar soft biideal rough sets represent two opinions instead of one opinion. Finally, an application in multicriteria group decision making (MCGDM) in COVID-19 by using bipolar soft ideal rough sets is suggested by using two methods. [ FROM AUTHOR]
ABSTRACT
This research project predicts and infers real-time insights on public mental health relevant to education during and after the COVID-19 pandemic by modeling, deploying, and testing an end-to-end spatiotemporal sentiment analysis framework. Moreover, the project aims to analyze the sentiments and emotions of the public;from Twitter, toward the current context of the e-learning process factored by aspects and emotions. The framework consists of four predictive models based on statistical analysis and machine learning to analyze the UAE education-related Twitter dataset. The first analytics is spatiotemporal analytics, which describes an event at a specific time and specific location. Spatiotemporal analytics is used as the base for the remaining three analytics: Aspect-based Sentiment Analysis, sentiment analysis, and emotion analysis. Aspectbased Sentiment Analysis considers the words/terms related to relevant aspects and then identify the sentiment associated with them. Sentiment Analysis is used to extract the sentiment in a specific text. Emotion Analysis identifies the type of emotion felt by users in their tweets. All the analytics will be visualized into a responsive website that provides a prompt understanding of the public opinions and their feedback towards the e-learning process. As a result, a group of recommendations is generated based on the analytics' resulting emotion to enhance the mental health. © 2021 Association for Computing Machinery.