Identification of Darunavir Derivatives for Inhibition of SARS-CoV-2 3CLpro.

The effective antiviral agents that treat severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are urgently needed around the world. The 3C-like protease (3CLpro) of SARS-CoV-2 plays a pivotal role in virus replication; it also has become an important therapeutic target for the infection of SARS-CoV-2. In this work, we have identified Darunavir derivatives that inhibit the 3CLpro through a high-throughput screening method based on a fluorescence resonance energy transfer (FRET) assay in vitro. We found that the compounds 29# and 50# containing polyphenol and caffeine derivatives as the P2 ligand, respectively, exhibited favorable anti-3CLpro potency with EC50 values of 6.3 µM and 3.5 µM and were shown to bind to SARS-CoV-2 3CLpro in vitro. Moreover, we analyzed the binding mode of the DRV in the 3CLpro through molecular docking. Importantly, 29# and 50# exhibited a similar activity against the protease in Omicron variants. The inhibitory effect of compounds 29# and 50# on the SARS-CoV-2 3CLpro warrants that they are worth being the template to design functionally improved inhibitors for the treatment of COVID-19.

Analyzing the impact of COVID-19 vaccination requirements on travelers' selection of hotels using a fuzzy multi-criteria decision-making approach.

In the later stages of the COVID-19 pandemic, hotels are taking various measures to balance pandemic prevention and business operations. Some hotels require travelers to be fully vaccinated prior to check-in, while others do not. In the latter type of hotels, fully vaccinated travelers may encounter others who are not vaccinated. All of these have created constraints for travelers to choose suitable hotel accommodation during this time. To address this issue, a fuzzy multi-criteria decision-making approach is proposed in this study to help traveler choose suitable hotel accommodation. In the proposed methodology, firstly, hotels are divided into two types considering their requirements for COVID-19 vaccination. Travelers are then asked to list the key factors to consider when choosing between these two types of hotels. To derive the priorities of these key factors, the proportionally calibrated fuzzy geometric mean (pcFGM) method is proposed. Subsequently, the fuzzy VIsekriterijumskoKOmpromisnoRangiranje (fuzzy VIKOR) method is applied to evaluate and compare the overall performances of different types of hotels for recommendations to travelers. The applicability of the proposed methodology is illustrated by a real case study. According to the experimental results, most hotels did not request travelers to be full vaccinated. Nevertheless, the hotels recommended to travelers covered both hotel types.

Travel satisfaction and travel well-being: Which is more related to travel choice behaviour in the post COVID-19 pandemic? Evidence from public transport travellers in Xi'an, China.

The spread of Coronavirus Disease 2019 (COVID-19) has decreased the willingness to choose public transport where travellers are more likely to be infected due to intensive passenger flow, in which case it is hard to attract passenger volume if the subjective well-being of travellers is not improved. However, the traditional measurement of travel evaluation may be not applicable to the context of the pandemic and it is necessary to analyse the changes in the internal mechanisms of travel well-being to avoid the loss of passengers. Based on structural equation modelling, this paper explored the internal relationship between the constructs of travel well-being and emphasised the significance of taking psychological factors into consideration in the post COVID-19 pandemic. The results show that travel satisfaction with the anti-pandemic related service quality of public transport is related to overall travel well-being, which can be used as a key part of well-being measurement scale design in the future. The results also indicate that, due to negative mood on the affective level induced by COVID-19, travel satisfaction on the cognitive level is not directly but indirectly related to travel choice behaviour through overall travel well-being. Compared to travel satisfaction, travel well-being is more extensive and covers travel satisfaction to some extent. Therefore, instead of studying travel satisfaction simply, taking travel well-being as the dependent variable to identify shortages existing in public transport will provide a more accurate perspective for policymakers in the post COVID-19 pandemic.

Analyzing the impact of COVID-19 vaccination requirements on travelers’ selection of hotels using a fuzzy multi-criteria decision-making approach

In the later stages of the COVID-19 pandemic, hotels are taking various measures to balance pandemic prevention and business operations. Some hotels require travelers to be fully vaccinated prior to check-in, while others do not. In the latter type of hotels, fully vaccinated travelers may encounter others who are not vaccinated. All of these have created constraints for travelers to choose suitable hotel accommodation during this time. To address this issue, a fuzzy multi-criteria decision-making approach is proposed in this study to help traveler choose suitable hotel accommodation. In the proposed methodology, firstly, hotels are divided into two types considering their requirements for COVID-19 vaccination. Travelers are then asked to list the key factors to consider when choosing between these two types of hotels. To derive the priorities of these key factors, the proportionally calibrated fuzzy geometric mean (pcFGM) method is proposed. Subsequently, the fuzzy VIšekriterijumskoKOmpromisnoRangiranje (fuzzy VIKOR) method is applied to evaluate and compare the overall performances of different types of hotels for recommendations to travelers. The applicability of the proposed methodology is illustrated by a real case study. According to the experimental results, most hotels did not request travelers to be full vaccinated. Nevertheless, the hotels recommended to travelers covered both hotel types.

Association of city-level walkability, accessibility to biking and public transportation and socio-economic features with COVID-19 infection in Massachusetts, USA: An ecological study.

With people restricted to their residences, neighbourhood characteristics may affect behaviour and risk of coronavirus disease 2019 (COVID-19) infection. We aimed to analyse whether neighbourhoods with higher walkability, public transit, biking services and higher socio-economic status were associated with lower COVID-19 infection during the peak of the COVID-19 pandemic in Massachusetts. We used Walk Score®, Bike Score®, and Transit Score® indices to assess the walkability and transportation of 72 cities in Massachusetts, USA based on availability of data and collected the total COVID-19 case numbers of each city up to 10 April 2021. We used univariate and multivariate linear models to analyse the effects of these scores on COVID-19 cases per 100,000 in each city, adjusting for demographic covariates and all covariates, respectively. In the 72 cities studied, the average Walk Score, Transit Score and Bike Score was 48.7, 36.5 and 44.1, respectively, with a total of 426,182 COVID-19 cases. Higher Walk Score, Transit Score, and Bike Score rankings were negatively associated with COVID-19 cases per 100,000 persons (<0.05). Cities with a higher proportion of Hispanic population and a lower median household income were associated with more COVID-19 cases per 100,000 (P<0.05). Higher Walk Score, Transit Score and Bike Score were shown to be protective against COVID-19 transmission, while socio-demographic factors were associated with COVID-19 infection. Understanding the complex relationship of how the structure of the urban environment may constrain commuting patterns for residents and essential workers during COVID-19 would offer potential insights on future pandemic preparedness and response.

Enhancement of Binding Kinetics on Affinity Substrates Using Asymmetric Electroosmotic Flow on a Sinusoidal Bipolar Electrode.

In the context of the COVID-19 epidemic, enhancing the transport of analyte to a sensor surface is crucial for rapid detection of biomolecules since common conditions, including low diffusion coefficients, cause inordinately long detection times. Integrated microfluidic immunoassay chips are receiving increasing attention for their low sample volume and fast response time. We herein take advantage of asymmetric ICEO flow at a bipolar sinusoidal electrode to improve the rate of antibody binding to the reaction surface based on finite element modeling. Three different microfluidic cavities are proposed by changing the positions of the surface reaction area. We further investigate the relationship between binding enhancement and reaction surface positions, Damkohler number, and the voltage and frequency of the AC signal applied to the driving electrodes. Furthermore, the influence of the AC signal applied to the sinusoidal bipolar electrode on antigen-antibody-binding performance is studied in detail. Above all, the simulation results demonstrate that the microfluidic immune-sensor with a sinusoidal bipolar electrode could not only significantly improve the heterogeneous immunoassays but also enable efficient enhancement of assays in a selected reaction region within the micro-cavity, providing a promising approach to a variety of immunoassay applications, such as medical diagnostics and environmental and food monitoring.

2-((1H-indol-3-yl)thio)-N-phenyl-acetamides: SARS-CoV-2 RNA-dependent RNA polymerase inhibitors.

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is the causative agent of Coronavirus Disease 2019 (COVID-19) pandemic. Despite intensive and global efforts to discover and develop novel antiviral therapies, only Remdesivir has been approved as a treatment for COVID-19. Therefore, effective antiviral therapeutics are still urgently needed to combat and halt the pandemic. Viral RNA-dependent RNA polymerase (RdRp) of SARS-CoV-2 demonstrates high potential as a reliable target for the development of antivirals. We previously developed a cell-based assay to assess the efficiency of compounds that target SARS-CoV-2 RdRp, as well as their tolerance to viral exoribonuclease-mediated proof-reading. In our previous study, we discovered that 2-((1H-indol-3-yl)thio)-N-phenyl-acetamides specifically targets the RdRp of both respiratory syncytial virus (RSV) and influenza A virus. Thus, we hypothesize that 2-((1H-indol-3-yl)thio)-N-phenyl-acetamides may also have the ability to inhibit SARS-CoV-2 replication by targeting its RdRp activity. In this research, we test a compound library containing 103 of 2-((1H-indol-3-yl)thio)-N-phenyl-acetamides against SARS-CoV-2 RdRp, using our cell-based assay. Among these compounds, the top five candidates strongly inhibit SARS-CoV-2 RdRp activity while exhibiting low cytotoxicity and resistance to viral exoribonuclease. Compound 6-72-2a is the most promising candidate with the lowest EC50 value of 1.41 µM and highest selectivity index (CC50/EC50) (above 70.92). Furthermore, our data suggests that 4-46b and 6-72-2a also inhibit the replication of HCoV-OC43 and HCoV-NL63 virus in a dose-dependent manner. Compounds 4-46b and 6-72-2a exhibit EC50 values of 1.13 µM and 0.94 µM, respectively, on HCoV-OC43 viral replication. However, higher concentrations of these compounds are needed to effectively block HCoV-NL63 replication. Together, our findings successfully identified 4-46b and 6-72-2a as promising inhibitors against SARS-CoV-2 RdRp.

Recommending Suitable Smart Technology Applications to Support Mobile Healthcare after the COVID-19 Pandemic Using a Fuzzy Approach.

The COVID-19 pandemic seems to be entering its final stage. However, to restore normal life, the applications of smart technologies are still necessary. Therefore, this research is dedicated to exploring the applications of smart technologies that can support mobile healthcare after the COVID-19 pandemic. To this end, this study compares smart technology applications to support mobile healthcare within the COVID-19 pandemic with those before the pandemic, so as to estimate possible developments in this field. In addition, to quantitatively assess and compare smart technology applications that may support mobile healthcare after the COVID-19 pandemic, the calibrated fuzzy geometric mean (CFGM)-fuzzy technique for order preference by similarity to ideal solution (FTOPSIS) approach is applied. The proposed methodology has been applied to evaluate and compare nine potential smart technology applications for supporting mobile healthcare after the COVID-19 pandemic. According to the experimental results, "vaccine passport and related applications" and "smart watches" were the most suitable smart technology applications for supporting mobile healthcare after the COVID-19 pandemic.

A Bi-objective AHP-MINLP-GA Approach for Flexible Alternative Supplier Selection amid the COVID-19 Pandemic

A decision maker may hold multiple viewpoints regarding the relative priorities of criteria simultaneously, but this has rarely been considered in past studies. Therefore, this study proposes a bi-objective analytic hierarchy process (AHP)–mixed integer nonlinear programming (MINLP)–genetic algorithm (GA) approach. First, AHP is applied to decompose the decision maker's judgment matrix into several sub-judgment matrices. Each sub-judgment matrix represents a single viewpoint and generates a priority set. To generate diversified priority sets, a bi-objective MINLP problem is solved using a GA, and multiple alternatives can be selected based on these priority sets. The proposed approach has been applied to the real case of choosing diversified alternative suppliers amid the COVID-19 pandemic to assess its effectiveness. Several existing methods were also applied to this case for comparison. Experimental results showed that only the proposed approach was able to diversify the recommended alternative suppliers that were simultaneously optimal, thereby enhancing decision-making flexibility. In addition, the application of GA increased the solution efficiency by up to 75%.

Discovery and optimization of 2-((1H-indol-3-yl)thio)-N-benzyl-acetamides as novel SARS-CoV-2 RdRp inhibitors.

The emerging severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is responsible for the global pandemic coronavirus disease (COVID-19), but no specific antiviral drug has been proven effective for controlling this pandemic to date. In this study, several 2-((indol-3-yl)thio)-N-benzyl-acetamides were identified as SARS-CoV-2 RNA-dependent RNA polymerase (RdRp) inhibitors. After a two-round optimization, a new series of 2-((indol-3-yl)thio)-N-benzyl-acetamides was designed, synthesized, and evaluated for SARS-CoV-2 RdRp inhibitory effect. Compounds 6b2, 6b5, 6c9, 6d2, and 6d5 were identified as potent inhibitors with IC50 values of 3.35 ± 0.21 µM, 4.55 ± 0.2 µM, 1.65 ± 0.05 µM, 3.76 ± 0.79 µM, and 1.11 ± 0.05 µM, respectively; the IC50 of remdesivir (control) was measured as 1.19 ± 0.36 µM. All of the compounds inhibited RNA synthesis by SARS-CoV-2 RdRp. The most potent compound 6d5, which showed a stronger inhibitory activity against the human coronavirus HCoV-OC43 than remdesivir, is a promising candidate for further investigation.

A calibrated piecewise-linear FGM approach for travel destination recommendation during the COVID-19 pandemic.

After months of lockdown due to the COVID-19 pandemic, more people are planning regional trips because overseas travel is still not feasible. However, choosing a suitable travel destination during the COVID-19 pandemic is challenging because the factors critical to the selection process are very different from those usually considered. Furthermore, without sufficient literature or data for reference, existing methods based on psychological analyses or mining past experiences may not be applicable. Consequently, a fuzzy multi-criteria decision-making method - the calibrated piecewise-linear fuzzy geometric mean (FGM) approach - is proposed in this study for travel destination recommendation during the COVID-19 pandemic. The contribution of this research is twofold. First, the critical factors that affect the selection of a suitable travel destination during the COVID-19 pandemic are discussed. Second, the accuracy and efficiency using existing fuzzy analytic hierarchy process (FAHP) methods have been enhanced. The calibrated piecewise-linear FGM approach has been successfully applied to recommend suitable travel destinations to fifteen travelers for regional trips in Taiwan during the COVID-19 pandemic.

Quinoline and Quinazoline Derivatives Inhibit Viral RNA Synthesis by SARS-CoV-2 RdRp.

Coronavirus disease 2019 (COVID-19) is a fatal respiratory illness caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The identification of potential drugs is urgently needed to control the pandemic. RNA dependent RNA polymerase (RdRp) is a conserved protein within RNA viruses and plays a crucial role in the viral life cycle, thus making it an attractive target for development of antiviral drugs. In this study, 101 quinoline and quinazoline derivatives were screened against SARS-CoV-2 RdRp using a cell-based assay. Three compounds I-13e, I-13h, and I-13i exhibit remarkable potency in inhibiting RNA synthesis driven by SARS-CoV-2 RdRp and relatively low cytotoxicity. Among these three compounds, I-13e showed the strongest inhibition upon RNA synthesis driven by SARS-CoV-2 RdRp, the resistance to viral exoribonuclease activity and the inhibitory effect on the replication of CoV, thus holding potential of being drug candidate for treatment of SARS-CoV-2.

Analyzing the Impact of Vaccine Availability on Alternative Supplier Selection Amid the COVID-19 Pandemic: A cFGM-FTOPSIS-FWI Approach.

The supply chain disruption caused by the coronavirus disease 2019 (COVID-19) pandemic has forced many manufacturers to look for alternative suppliers. How to choose a suitable alternative supplier in the COVID-19 pandemic has become an important task. To fulfill this task, this research proposes a calibrated fuzzy geometric mean (cFGM)-fuzzy technique for order preference by similarity to ideal solution (FTOPSIS)-fuzzy weighted intersection (FWI) approach. In the proposed methodology, first, the cFGM method is proposed to accurately derive the priorities of criteria. Subsequently, each expert applies the FTOPSIS method to compare the overall performances of alternative suppliers in the COVID-19 pandemic. The sensitivity of an expert to any change in the overall performance of the alternative supplier is also considered. Finally, the FWI operator is used to aggregate the comparison results by all experts, for which an expert's authority level is set to a value proportional to the consistency of his/her pairwise comparison results. The cFGM-FTOPSIS-FWI approach has been applied to select suitable alternative suppliers for a Taiwanese foundry in the COVID-19 pandemic.

Assessing the Robustness of a Factory Amid the COVID-19 Pandemic: A Fuzzy Collaborative Intelligence Approach.

The COVID-19 pandemic has affected the operations of factories worldwide. However, the impact of the COVID-19 pandemic on different factories is not the same. In other words, the robustness of factories to the COVID-19 pandemic varies. To explore this topic, this study proposes a fuzzy collaborative intelligence approach to assess the robustness of a factory to the COVID-19 pandemic. In the proposed methodology, first, a number of experts apply a fuzzy collaborative intelligence approach to jointly evaluate the relative priorities of factors that affect the robustness of a factory to the COVID-19 pandemic. Subsequently, based on the evaluated relative priorities, a fuzzy weighted average method is applied to assess the robustness of a factory to the COVID-19 pandemic. The assessment result can be compared with that of another factory using a fuzzy technique for order preference by similarity to ideal solution. The proposed methodology has been applied to assess the robustness of a wafer fabrication factory in Taiwan to the COVID-19 pandemic.

Assessing and Comparing COVID-19 Intervention Strategies Using a Varying Partial Consensus Fuzzy Collaborative Intelligence Approach

The COVID-19 pandemic has severely impacted our daily lives. For tackling the COVID-19 pandemic, various intervention strategies have been adopted by country (or city) governments around the world. However, whether an intervention strategy will be successful, acceptable, and cost-effective or not is still questionable. To address this issue, a varying partial consensus fuzzy collaborative intelligence approach is proposed in this study to assess an intervention strategy. In the varying partial consensus fuzzy collaborative intelligence approach, multiple decision makers express their judgments on the relative priorities of factors critical to an intervention strategy. If decision makers lack an overall consensus, the layered partial consensus approach is applied to aggregate their judgments for each critical factor. The number of decision makers that reach a partial consensus varies from a critical factor to another. Subsequently, the generalized fuzzy weighted assessment approach is proposed to evaluate the overall performance of an intervention strategy for tackling the COVID-19 pandemic. The proposed methodology has been applied to compare 15 existing intervention strategies for tackling the COVID-19 pandemic.