Screening of Potential Inhibitors Against SARS-CoV-2 Based on Prescription Mining and Molecular Dynamics Simulations

Objective To detect the active ingredients in the traditional Chinese medicine prescription and its molecular mechanisms against SARS-CoV-2 by prescription mining and molecular dynamics simulations. Methods Herein, prescription mining and virtual screening of drugs were performed to screen the potential inhibitors against SARS-CoV-2. Molecular docking and molecular dynamics (MDs) simulations were further performed to explore the molecular recognition and inhibition mechanism between the potential inhibitors and SARS-CoV-2. Results The natural compounds library was constructed by 143 prescriptions of traditional Chinese medicine, which contained 640 natural compounds. Ten compounds were screened out from the natural compounds library. Among the 10 compounds, 23-trans-p-coumaryhormentic acid, the main active constituent of the Loquat leaf, showed the best binding affinity targeting the recognizing interface of SARS-CoV-2 S protein/ACE2. Upon binding 23-trans-p-coumaryhormentic acid, the key interactions between SARS-CoV-2 S protein and ACE2 were almost interrupted. Conclusion Ten compounds targeting SARS-CoV-2 S protein/ACE2 interface were screened out from natural compound library. And we inferred that 23-trans-p-coumaryhormentic acid is a potential inhibitor against SARS-CoV-2, which would contribute to the development of the antiviral drug for SARS-CoV-2. © 2022 Institute of Biophysics,Chinese Academy of Sciences. All rights reserved.

Screening of Potential Inhibitors Against SARS-CoV-2 Based on Prescription Mining and Molecular Dynamics Simulations

Objective To detect the active ingredients in the traditional Chinese medicine prescription and its molecular mechanisms against SARS-CoV-2 by prescription mining and molecular dynamics simulations. Methods Herein, prescription mining and virtual screening of drugs were performed to screen the potential inhibitors against SARS-CoV-2. Molecular docking and molecular dynamics (MDs) simulations were further performed to explore the molecular recognition and inhibition mechanism between the potential inhibitors and SARS-CoV-2. Results The natural compounds library was constructed by 143 prescriptions of traditional Chinese medicine, which contained 640 natural compounds. Ten compounds were screened out from the natural compounds library. Among the 10 compounds, 23-trans-p-coumaryhormentic acid, the main active constituent of the Loquat leaf, showed the best binding affinity targeting the recognizing interface of SARS-CoV-2 S protein/ACE2. Upon binding 23-trans-p-coumaryhormentic acid, the key interactions between SARS-CoV-2 S protein and ACE2 were almost interrupted. Conclusion Ten compounds targeting SARS-CoV-2 S protein/ACE2 interface were screened out from natural compound library. And we inferred that 23-trans-p-coumaryhormentic acid is a potential inhibitor against SARS-CoV-2, which would contribute to the development of the antiviral drug for SARS-CoV-2.

Health Journalists' Social Media Sourcing During the Early Outbreak of the Public Health Emergency

Although journalists' social media sourcing can empower non-elite sources and diversify public discussions, counterarguments maintain that social media sourcing relies on a small group of elites and reinforces social division. To contribute to that debate, we examined how health journalists from the mainstream news organizations in the U.S. used Twitter's @mention for sourcing during the first three months of the COVID-19 outbreak. Using a sample of public Twitter posts published by the journalists, we formed co-@mentioned networks (i.e., two sources were connected if @mentioned in the same post) to examine the structure of the networks and identify important sourcing informants. Among the results, elite sources (e.g., health journalists and health experts in the public sector) and influential users (i.e., verified users with a large number of followers and who post frequently) dominated the sourcing repertoire. Moreover, the networks were fragmented because the sources were clustered into several close-knit subgroups. Analyzing exponential random graph models to examine the formation mechanism of the networks revealed that, as the pandemic's severity increased, influential users played a more salient role in the sourcing repertoire, and a homogeneous cluster consisting of journalists and news organizations emerged.

Accelerated Airborne Virus Spread Simulation: Coupling Agent-based Modeling with GPU-accelerated Computational Fluid Dynamics

The Coronavirus Disease 2019 (COVID-19) has shown us the necessity to understand its transmission mechanisms in detail in order to establish practice in controlling such infectious diseases. An important instrument in doing so are mathematical models. However, they do not account for the spatiotemporal heterogeneity introduced by the movement and interaction of individuals with their surroundings. Computational fluid dynamics (CFD) simulations can be used to analyze transmission on micro- and mesostructure level, however become infeasible in larger scale scenarios. Agent-based modeling (ABM) on the other hand is missing means to simulate airborne virus transmission on a micro- and mesostructure level. Therefore, we present a system that combines CFD simulations with the dynamics given by trajectories from an ABM to form a basis for producing deeper insights. The proposed system is still work in progress;thus we focus on the system architecture and show preliminary results.

A New Computational Method Based on Probabilistic Linguistic Z-Number With Unbalanced Semantics and Its Application to Multi-Criteria Group Decision Making

Z-number that proposed by Zadeh is an effective tool to describe the information with uncertainty in decision-making problems. However, most of the researches on Z-numbers employed linguistic cardinalities with uniformly distributed scales. In fact, unbalance situation is much common in terms of the psychology of experts. In this paper, we propose a new computational method based on Probabilistic Linguistic Z-number with Unbalanced semantics(UPLZ), which can represent the linguistic evaluations of experts precisely combined with individual risk appetite. A new score function of UPLZs is provided based on hesitant degree and linguistic scale function to reduce the computational complexity. Afterward, a linear programming is constructed to determine weights of criteria by considering cross entropy maximization. The robust decision result can be obtained by applying MULTIMOORA method since it is specific with peculiarities of three subordinate models. Finally, a case study concerning medicine selection for the patients with mild symptoms of the COVID-19 is provided to illustrate the feasibility and effectiveness of the proposed method. The advantages of it are highlighted by sensitivity analysis and comparative analysis with two outstanding multi-criteria decision-making methods.

The effects of psychological intervention on anxiety symptoms of COVID19-positive patients isolated in hospital wards.

OBJECTIVE: The study aimed to explore the effects of psychological intervention on alleviating anxiety in patients in novel coronavirus (2019-nCoV) isolation wards. PATIENTS AND METHODS: Between January 24th, 2020 and March 5th, 2020, 103 patients were studied. Among these, 32 were patients in the isolation ward of the Infectious Disease Department in Baoding Second Hospital with suspected 2019-nCoV, and 71 patients diagnosed with 2019-nCoV were in the Tangshan Infectious Disease Hospital. Of the 103 patients included, 97 cases were observed in isolation. Using a self-control study design, each patient's anxiety was scored on a self-rating anxiety scale before receiving the psychological intervention (on the 7th day of isolation) and after receiving the intervention (on the 14th day of isolation). The severity of anxiety was evaluated based on the anxiety score before receiving the intervention. The anxiety scores before and after receiving the intervention were then compared using the paired t-test, and p<0.05 was considered statistically significant. RESULTS: After receiving the psychological intervention once or twice a week, the anxiety of the patients improved significantly after one week. CONCLUSIONS: The anxiety of patients with 2019-nCoV in isolation wards can be alleviated through psychological intervention. By alleviating patient anxiety, this intervention also helps patients maintain their psychological wellbeing, which promotes rehabilitation and helps with the control of 2019-nCoV.

Investigate mechanism of Jinzhen Oral Liquid for prevention COVID-19 based on network pharmacology and molecular docking technology

Objective: To investigate the mechanism of Jinzhen Oral Liquid (JOL) for prevention COVID-19 through network pharmacology and molecular docking technology. Methods: The protein targets related to COVID-19 were searched by literature mining and retrieving in DisGeNET, OMIM, KEGG and UniProt databases. With the aid of Traditional Chinese Medicine Network Pharmacology Intelligent Information Platform (TCMN) searching JOL chemical components and targets, the "herb-compound-target network" was constructed using Cytoscape-3.2.1 software to predict the main active ingredients and action targets of JOL in the treatment of COVID-19. The crystal structure of novel coronavirus (SARS-CoV-2) 3CL hydrolase (3CLpro) and angiotensin converting enzyme II (ACE2) was retrieved from the RCSB PDB database, and the active compounds were docked with the two proteins by using AutoDock Vina software. Results: The herb-compound-target network contained 75 compounds including isoglabrolide, peimisine, and sennoside B, etc., which are from the three medicinal materials of Glycyrrhiza uralensis, Rheum officinale, and Fritillaria ussuriensis, and 28 targets including mammalian target of rapamycin (mTOR), Janus kinase 3 (JAK3) and mitogen-activated protein kinase 1 (MEK1). Furthermore, nine key compounds (isoglabrolide, glabrolide, ebeiedinone, desoxo- glabrolid-acetate, peimisine, verticinone, imperialine, ussuriedinone and euchrenone A5) and 10 potential targets (mTOR, JAK3, ACE2, TNFA, AKT2, PIK3CA, MEK1, BRD2, ACE and ANPEP) of JOL were predicted for treating COVID-19 by network characteristic analysis. The molecular docking results showed that some core compounds of JOL had a certain degree of affinity for 3CLpro and ACE2. Conclusion: JOL may inhibit the occurrence and development of cytokine storm in COVID-19 by regulating the expression of Brd2, CD13, and ACE2 and interfering with the PI3K/Akt, Jak-STAT, TNF and MAPK signaling pathways, and inhibit virus replication by binding with 3CLpro, thus exerting a preventive or therapeutic effect on COVID-19.

Network pharmacology study of Yinqiao Jiedu Soft Capsules in treatment of COVID-19

Objective: To explore the mechanism of Yinqiao Jiedu Soft Capsules in the treatment of coronavirus disease 2019 (COVID-19). Methods: The interactions between 1 418 compounds of Yinqiao Jiedu Soft Capsules and 48 inflammatory target proteins related to COVID-19 were analyzed by molecule docking. The drug-target network was established to clarify the active compounds and potential targets. Results: The network analysis suggested 50 active compounds of Yinqiao Jiedu Soft Capsules, which were mainly flavonoids and triterpenoids, and 37 potential targets, mainly including MTOR, JAK3, ACE, ACE2, PIK3CA, TNF, AKT2, and MAP2K1. The results of molecular docking exhibited that forsythiaside and vitexin 2″-O-rhamnoside had good affinity with SARS-CoV-2 3CL hydrolase, and glycyrrhizic acid had good affinity with ACE2. Conclusion: The molecular mechanism of Yinqiao Jiedu Soft Capsules for COVID-19 may be involved in interfering SARS-CoV-2 replication and regulating the expression of inflammatory signaling pathway and the secretion of inflammatory cytokines.