Getting by with a little help from my friends: The impact of the pandemic on the collaboration of small Finnish food and beverage ventures

Ventures' social ties are important enablers of value creation. Particularly during crises, the value of relationships is highlighted as scale-limited ventures can draw from their partners' resources to find opportunities for survival and renewal. Crises also shape ventures' collaboration opportunities by disrupting networks and changing ways of engagement. Yet, longitudinal research on the impact of crises on ventures' collaboration remains limited. In the current study, we combine pre and during-pandemic interviews with social media data from 14 packaged food and beverage ventures to explore the impact of the crisis on venture collaboration. The data illustrates four distinct approaches to collaboration during the pandemic, differing in terms of scope of collaborations, variety of different partner types, proportion of developmental collaborations, and engagement in collaboration due to or despite the crisis. The findings show that the crisis significantly shaped ventures' collaborations, which may shape their social capital beyond the crisis. Copyright © 2023 Inderscience Enterprises Ltd.

Examining the impact of the COVID-19 pandemic on container carrier performance

The COVID-19 pandemic has affected various industries in different ways depending on their type of business. We attempt to understand the impact of the pandemic on the performance of 26 major container carriers. To capture performance differences before and during the pandemic, we collect data the years 2019 and 2020. Further, we divide carriers' business processes into two stages, i.e., asset acquisitions and transport operations. We employ relevant data envelopment analysis (DEA) models to this effect. The mean efficiency score in 2020 was higher than in 2019. Overall, container carriers increased their performance during the COVID-19 pandemic. However, seven companies showed lower efficiency in 2020. Similarly, eight companies were efficient in their operations but not in asset acquisition decisions. Our results show that firm strategy is important in coping with external shocks. The contribution of the paper is in measuring the impact of external shocks, such as the COVID-19 pandemic, on the performance of container carriers, both in regard of their acquisitions and their operations. © 2023, This is a U.S. Government work and not under copyright protection in the US;foreign copyright protection may apply.

Chitosan Coating in the Form of Polymer and Nanowhiskers on Clothing Fabrics for Improved Particulate Matter Removal Efficiency in Face Mask Filters

The demand for face masks is increasing exponentially due to the coronavirus pandemic and the particulate matter (PM) in the atmosphere. As a result, an enormous number of disposable mask filters have been produced and discarded, contributing to plastic waste. Underprivileged people who cannot afford to purchase commercial face masks have started making fabric masks with waste clothing;however, this material does not effectively filter viruses or PM. Therefore, in this study, a chitosan coating was applied to clothing fabrics to increase their effectiveness as face masks. The improvement in the PM removal efficiency owing to the chitosan polymer was observed for stocking, innerwear, and bamboo materials, but not for cotton. Furthermore, chitosan prepared in the form of a nanowhisker (CsW) achieved a PM 2.5 removal efficiency of 96% in a five-layer cotton fabric. In addition, a commercial biodegradable poly(lactic acid) filter was coated with CsW, which increased the PM 2.5 removal efficiency from 67% to 83%. Additionally, microbial growth was significantly suppressed in the chitosan-coated fabrics, and the degree to which it was suppressed depended on the coating concentration. The study will aid in the utilization of face mask filters that are more sustainable, efficient, and widely accessible. © 2023 The Author(s). Published with license by Taylor & Francis Group, LLC.

Telecytology Makes Significant Contribution in ROSE with Great Accuracy - Experience During the COVID-19 Pandemic at Stony Brook University Hospital

Introduction: Telecytology (TC) has been used in rapid onsite evaluation (ROSE) of touch preparations (TPs) from core biopsies and smears from fine-needle aspiration (FNA) procedures. Although TC was established at our institution in 2010, utilization has fluctuated. The COVID-19 pandemic highlighted the value of TC for maintaining high-quality patient care with the advantages of minimizing contact with hospital personnel and patients, conserving personal protective equipment (PPE), and saving cytopathologists' time. However, the accuracy of TC compared to on site evaluation by cytopathologists has been a concern. This study aimed to address this question. Material(s) and Method(s): 697 ROSEs were identified from our quality assurance (QA) data base in 2021 (Q2-Q4) including 1) TC;2) onsite cytopathologist (OC);and 3) onsite cytotechnologistonly (OCT). Discrepancy rates between ROSE and final diagnoses were compared between TC and OC groups. A student T-test was performed to determine the statistical significance of the discrepancy rate between TC and OC. Result(s): Results are summarized in Table 1. On average, there were 26% FNA and 74% Cores by TP. The difference in discrepancy rates between TC and OC during the 3 quarters in 2021 is not statistically significant (p=0.5012). The type of discrepancy for TC was all minor while 17% major and 83% minor for OC. Conclusion(s): The value of TC for ROSE was highlighted during the pandemic, allowing cytopathologists to participate in ROSE while minimizing interpersonal contact at procedures, conserving PPE, and saving cytopathologists' time. TC provides as high quality as OC for ROSE service with statistically insignificant minor discrepancies with the final diagnosis. Although pandemic-related restrictions are easing, we anticipate that TC will continue to play a major role in ROSE.

Cerebral venous thrombosis after ChAdOx1 nCoV-19 vaccination: a systematic review

OBJECTIVE: To perform a systematic review of case reports or case series regarding thrombosis with thrombocytopenia syndrome (TTS) and cerebral venous thrombosis (CVT) related to ChAdOx1 nCoV-19 vaccination to address the clinical features, laboratory findings, treatment modalities, and prognosis related with CVT. SUBJECTS AND METHODS: We included 64 TTS patients from 19 articles, 6 case series and 13 case reports, in which thrombosis occurred after the first dose of ChAdOx1 nCoV-19 vaccination published up to 30 June 2021 in Embase, ePubs, Medline/PubMed, Scopus, and Web of Science databases. RESULTS: Of the 64 TTS patients, 38 (59.3%) had CVT. Patients with CVT were younger (median 36.5 vs. 52.5 years, p<0.001), had lower fibrinogen levels (130 vs. 245 mg/dL, p=0.008), had more frequent history of intracerebral hemorrhage (ICH), and had higher mortality rate (48.6% vs. 19.2%, p=0.020) than that of patients without CVT. In multivariable analysis, the possibility of presence of CVT was higher in younger age groups [odd ratio (OR): 0.91, 95% confidence interval (CI): (0.86-0.97, p<0.001)] and those with accompanying intracerebral hemorrhage (ICH) (OR: 13.60, 95% CI (1.28-144.12, p=0.045). CONCLUSIONS: Our study demonstrated that CVT related to ChAdOx1 nCoV-19 vaccination was associated with younger age, low levels of fibrinogen, presence of ICH and more frequent mortality compared to those of non-CVT. If TTS occurs after ChAdOx1 nCoV-19 vaccination, the presence of CVT in patients with young age or ICH should be considered.

Power transformation in quasi-likelihood innovations for GARCH volatility

This paper is concerned with power transformations in estimating GARCH volatility. To handle a semiparametric case for which the exact likelihood is not known, quasi-likelihood (QL) rather than maximumlikelihood method is investigated to best estimate GARCH via maximizing the information criteria. A power transformation is introduced in the innovation generating QL estimating functions and then optimum power is selected by maximizing the profile information. A combination of two different power transformations is also studied in order to increase the parameter estimation efficiency. Nine domestic stock prices data are analyzed to order to illustrate the main idea of the paper. The data span includes Covid-19 pandemic period in which financial time series are really volatile.

Similarity-Principle-Based Machine Learning Method for Clinical Trials and Beyond

With recent success in supervised learning, artificial intelligence (AI) and machine learning (ML) can play a vital role in precision medicine. Deep learning neural networks have been used in drug discovery when larger data is available. However, applications of machine learning in clinical trials with small sample size (around a few hundreds) are limited. We propose a Similarity-Principle-Based Machine Learning (SBML) method, which is applicable for small and large sample size problems. In SBML, the attribute-scaling factors are introduced to objectively determine the relative importance of each attribute (predictor). The gradient method is used in learning (training), that is, updating the attribute-scaling factors. We evaluate SBML when the sample size is small and investigate the effects of tuning parameters. Simulations show that SBML achieves better predictions in terms of mean squared errors for various complicated nonlinear situations than full linear models, optimal and ridge regressions, mixed effect models, support vector machine and decision tree methods. Copyright © 2022 American Statistical Association.

Cerebral venous thrombosis after ChAdOx1 nCoV-19 vaccination: a systematic review.

OBJECTIVE: To perform a systematic review of case reports or case series regarding thrombosis with thrombocytopenia syndrome (TTS) and cerebral venous thrombosis (CVT) related to ChAdOx1 nCoV-19 vaccination to address the clinical features, laboratory findings, treatment modalities, and prognosis related with CVT. SUBJECTS AND METHODS: We included 64 TTS patients from 19 articles, 6 case series and 13 case reports, in which thrombosis occurred after the first dose of ChAdOx1 nCoV-19 vaccination published up to 30 June 2021 in Embase, ePubs, Medline/PubMed, Scopus, and Web of Science databases. RESULTS: Of the 64 TTS patients, 38 (59.3%) had CVT. Patients with CVT were younger (median 36.5 vs. 52.5 years, p<0.001), had lower fibrinogen levels (130 vs. 245 mg/dL, p=0.008), had more frequent history of intracerebral hemorrhage (ICH), and had higher mortality rate (48.6% vs. 19.2%, p=0.020) than that of patients without CVT. In multivariable analysis, the possibility of presence of CVT was higher in younger age groups [odd ratio (OR): 0.91, 95% confidence interval (CI): (0.86-0.97, p<0.001)] and those with accompanying intracerebral hemorrhage (ICH) (OR: 13.60, 95% CI (1.28-144.12, p=0.045). CONCLUSIONS: Our study demonstrated that CVT related to ChAdOx1 nCoV-19 vaccination was associated with younger age, low levels of fibrinogen, presence of ICH and more frequent mortality compared to those of non-CVT. If TTS occurs after ChAdOx1 nCoV-19 vaccination, the presence of CVT in patients with young age or ICH should be considered.

Effects of Online Purchase e-CRM activities and e-Customer Satisfaction on e-Loyalty during COVID-19: The Mediating Effects of e-Customer Satisfaction

Purpose: The purpose of this study is to investigate the influence between online purchase e-CRM activities and e-customer satisfaction on e-loyalty. In particular, it focuses on the mediating effect of e-customer satisfaction in the relationship between online purchase e-CRM activities and e-loyalty. Design/methodology/approach: The data was collected from customers who have purchased online products in South Korea for a week. The study used 1307 questionnaires in the quantitative analysis and the proposed research model and a set of hypotheses were tested using a structural equation model (SEM). Findings: The findings of this study indicate that online purchase e-CRM activities (including e-Marketing, e-Sales, e-Support and e-System) have a significant positive impact on e-customer satisfaction. E-Marketing and e-Sales were found to have a statistically significant positive influence on e-loyalty, but e-Support and e-System did not have a statistically significant effect on e-loyalty. This study also found that e-customer satisfaction significantly mediated the link between online purchase e-CRM activities (including e-Marketing, e-Sales, e-Support and e-System) and e-loyalty. Research limitations/implications: The limitation of this study is that since the data only targets customers in their 20s and 40s, it is difficult to say that it represents customers of all ages. Therefore, it is necessary to carry out research on e-CRM activities for customers of different ages who have experience in online purchasing in the future. Moreover, as the e-commerce market using digital technology grows, companies need to preemptively re-spond to the expansion of "untact" (non-face-to-face) services in the post-COVID-19 era. Therefore, the results of this study presented theoretical and practical implications for improving online purchase e-CRM activities, e-custom-er satisfaction, and e-loyalty to e-commerce companies. Originality/value: The recent COVID-19 pandemic has caused great losses and considerable difficulties in various industries around the world, but to overcome this, the e-commerce market has expanded and has led to the advance-ment of the digital transformation era. Therefore, the originality of this study suggested the importance of e-CRM activities reflecting the purchasing trend of the online market in line with the trend of the Fourth Industrial Revolution and IT era, and it is significant in that it provides theoretical and managerial marketing and strategic implications for better recognition and utilization of these e-CRM activities from corporate and customer perspectives. © 2022 People and Global Business Association.

Virtual Care for the Longitudinal Management of Chronic Conditions: A Systematic Review

As both the largest integrated health system and largest provider of telehealth in the country, the Veterans Health Administration (VHA) has a particular interest in understanding how best to implement and utilize virtual care. VHA has long embraced virtual care as part of its mission to "serve all who have served" regardless of their socioeconomic and geographic circumstances. Having begun conducting "virtual care" in the 1960s when doctors first communicated with patient's via TV screens,1 VHA has since provided over 2.6 million episodes of care to more than 900,000 Veterans in 20192 and has distributed over 50,000 data- and video-enabled iPads for Veterans throughout the country.3 Virtual care within VHA includes services such as MyHealtheVet secure messaging, the Home Telehealth program that combines case management principles with remote monitoring to improve access and coordinate care, and the VA Video Connect (VVC) video platform for synchronous visits within both specialty and primary care.4 Increasing Veteran access to care via virtual care has been an integral part of VHA's strategy for improving chronic disease management for a population that is on average older and sicker than their civilian counterparts.5,6 Given the importance that virtual care has for Veteran care even beyond the COVID-19 pandemic, understanding the strengths and limitations associated with synchronous virtual care will be critical in shaping how VHA utilizes virtual care going forward.

A Deep Learning Approach for Detecting Covid-19 Using the Chest X-Ray Images

Real-time detection of Covid-19 has definitely been the most widely-used world-wide classification problem since the start of the pandemic from 2020 until now. In the meantime, airspace opacities spreads related to lung have been of the most challenging problems in this area. A common approach to do on that score has been using chest X-ray images to better diagnose positive Covid-19 cases. Similar to most other classification problems, machine learning-based approaches have been the first/most-used candidates in this application. Many schemes based on machine/deep learning have been proposed in recent years though increasing the performance and accuracy of the system has still remained an open issue. In this paper, we develop a novel deep learning architecture to better classify the Covid-19 X-ray images. To do so, we first propose a novel multi-habitat migration artificial bee colony (MHMABC) algorithm to improve the exploitation/exploration of artificial bee colony (ABC) algorithm. After that, we optimally train the fully connected by using the proposed MHMABC algorithm to obtain better accuracy and convergence rate while reducing the execution cost. Our experiment results on Covid-19 X-ray image dataset show that the proposed deep architecture has a great performance in different important optimization parameters. Furthermore, it will be shown that the MHMABC algorithm outperforms the state-of-the-art algorithms by evaluating its performance using some well-known benchmark datasets. © 2023 Tech Science Press. All rights reserved.

Determination of Methanol in Commercialized Alcohol-based Hand Sanitizing and Other Similar Products using Headspace GC-MS

Background: Demand for alcohol-based products, including gel- and aqueous-type hand sanitizers, room sprays, and mouthwashes, has rapidly increased during the ongoing COVID-19 pandemic because of their microbicidal properties. However, toxic methanol can be found from the intentional addition of methanol by manufacturers and invariable production during the manufacturing of alcohol (ethanol). Although the FDA has recommended that such products should contain less than 630 ppm of methanol, it is only a temporary measure established specifically to regulate such products during the current COVID-19 pandemic and hence is not strictly regulated. Objective: This study aims to detect and quantify the level of methanol in alcohol-based products. However, some manufacturers unethically add methanol to their products and promote them as methanol-free. Besides, they do not provide proficiency and toxicity test results. Therefore, these kinds of products need to be analyzed to determine if they are acceptable to use. Methods: This study qualitatively and quantitatively investigates the amount of methanol in commercial alcohol-based products using a newly developed headspace gas chromatography/mass spectrometry method. Moreover, alcoholic beverages which contain methanol are analyzed to be compared with the levels of methanol in alcohol-based products and determine if their methanol levels are acceptable. Results: Methanol concentrations in gel-type hand sanitizers (517 ppm) and mouthwashes (202 ppm) were similar to those in white wine (429 ppm) and beer (256 ppm), respectively, while that of aqueous-type hand sanitizers (1139 ppm) was 1.5 times more than that of red wine (751 ppm). Conclusion: Methanol levels in most of the alcohol-based products did not exceed the FDA-recommended limit.

An analysis of the number of deceased donors and organ transplantations during COVID-19 pandemic in Korea

Objectives: Coronavirus disease 2019 (COVID-19) pandemic is severe enough to discourage organ donation. Since prevalence of COVID-19 in Korea was much lower than in Western countries, we decided to determine the real-world impact on organ donation and transplantation in Korea. Materials and Methods: The annual number of kidney transplantation (KT) and liver transplantation (LT) in 2020 were compared with those in 2019 using Korean Network for Organ Sharing database and Asan Medical Center (AMC) database. Results: The number of deceased donors (DD) was 450 in 2019 and 478 in 2020. Monthly DD number was 37.5 ± 5.9 in 2019 and 39.8 ± 4.4 in 2020 (p = 0.284). Annual number of DDKT was 794 in 2019 and 848 in 2020, and monthly number was 66.1 ± 10.4 in 2019 and 70.7 ± 9.8 2020 (p = 0.285). Annual number of DDLT was 391 in 2019 and 395 in 2020, and monthly number was 32.6 ± 5.7 in 2019 and 32.9 ± 4.7 in 2020 (p = 0.877). Number of living donor (LD) KT was 2,293 in 2019 and 1,432 in 2020, and monthly number was 191.1 ± 19.5 in 2019 and 119.3 ± 11.7 in 2020 (p<0.001). Number of LD LT was 1,577 in 2019 and 1,146 in 2020, and monthly number was 131.4 ± 18.1 in 2019 and 95.5 ± 8.0 in 2020 (p<0.001). In AMC, all types of KT and LT were not significantly changed. Conclusion: The results of this study indicate that the number of DD organ transplantations remained stable in Korea during 2020, but the number of LD organ transplantations significantly reduced during COVID-19 pandemic. However, organ transplantation number was not changed in AMC. (Figure Presented).

Factors Affecting City Image During the COVID-19 Era1

This study examined the perceived contingent factors that affect South Korean citizens’ image of the city they reside in. The respondents in this study perceived the image of their city through two dimensions––leading and safe––during the COVID-19 era. When respondents perceived the openness and expertise of the local government, the transformational and transactional leadership of the government leader, liberal political orientation of the leader, lower degree of law compliance of the mayor, high degree of citizenship, and high level of living infrastructure and competitiveness as attributes of the city, they were more likely to perceive the city as having a “leading” image. The perceived cultural characteristics of the local government, specifically the factor of hierarchy and regulation, the perceptions of citizenship, and all three variables regarding the perceptions related to city attributes (i.e., environmental, cultural, and living infrastructures and competitiveness) positively influenced the perception of a “safe” city image. Based on the results, various theoretical and practical implications were discussed in this study. © 2022, Center for Asian Public Opinion Research and Collaboration Initiative. All rights reserved.

A Comprehensive Analysis of Privacy Protection Techniques Developed for COVID-19 Pandemic

Since the emergence of coronavirus disease–2019 (COVID-19) outbreak, every country has implemented digital solutions in the form of mobile applications, web-based frameworks, and/or integrated platforms in which huge amounts of personal data are collected for various purposes (e.g., contact tracing, suspect search, and quarantine monitoring). These systems not only collect basic data about individuals but, in most cases, very sensitive data like their movements, spatio-temporal activities, travel history, visits to churches/clubs, purchases, and social interactions. While collection and utilization of person-specific data in different contexts is essential to limiting the spread of COVID-19, it increases the chances of privacy breaches and personal data misuse. Recently, many privacy protection techniques (PPTs) have been proposed based on the person-specific data included in different data types (e.g., tables, graphs, matrixes, barcodes, and geospatial data), and epidemic containment strategies (ECSs) (contact tracing, quarantine monitoring, symptom reports, etc.) in order to minimize privacy breaches and to permit only the intended uses of such personal data. In this paper, we present an extensive review of the PPTs that have been recently proposed to address the diverse privacy requirements/concerns stemming from the COVID-19 pandemic. We describe the heterogeneous types of data collected to control this pandemic, and the corresponding PPTs, as well as the paradigm shifts in personal data handling brought on by this pandemic. We systemically map the recently proposed PPTs into various ECSs and data lifecycle phases, and present an in-depth review of existing PPTs and evaluation metrics employed for analysis of their suitability. We describe various PPTs developed during the COVID-19 period that leverage emerging technologies, such as federated learning, blockchain, privacy by design, and swarm learning, to name a few. Furthermore, we discuss the challenges of preserving individual privacy during a pandemic, the role of privacy regulations/laws, and promising future research directions. With this article, our aim is to highlight the recent PPTs that have been specifically proposed for the COVID-19 arena, and point out research gaps for future developments in this regard. Author

Predictive value of olfactory symptoms in the diagnosis of COVID-19

Introduction: This study evaluates the diagnostic value of the various symptoms of COVID-19 in the screening of this disease. Method: Two authors (working independently) comprehensively reviewed 6 databases (PubMed, Cochrane Database, Embase, Web of Science, Scopus, and Google Scholar) from their dates of inception until November 2020. Patient-reported symptoms, including otolaryngologic and general symptoms, were evaluated for their predictive values in adults who underwent testing for COVID-19. True-positive, true-negative, false-positive, and false-negative data were extracted from each study. The methodological quality of included studies was evaluated using the Quality Assessment of Diagnostic Accuracy Studies 2 tool. Results: A total of 28 prospective and retrospective studies were included in the meta-analysis. The diagnostic odds ratio (DOR) of a change in olfaction and/or taste was 10.20 (95% CI, 8.43;12.34). The area under the summary receiveroperating characteristic curve was 0.8. Olfactory and/or taste changes had a low sensitivity (0.57;95% CI, 0.47, 0.66) but moderate negative (0.78;95% CI, 0.69, 0.85) and positive (0.78;95% CI, 0.66, 0.87) predictive values and a high specificity (0.91;95% CI, 0.83, 0.96). Olfactory and/or taste changes had a higher diagnostic value than the other otolaryngologic symptoms, a higher DOR and specificity, and a similar or higher diagnostic value than the other general symptoms. Conclusion: Among otolaryngologic symptoms, olfactory and/or taste dysfunction was the most highly associated with COVID-19 and its general symptoms and should be considered when screening for the disease.

Characterization of electroencephalographic abnormalities and their radiographic correlates associated with COVID-19

Objective: To identify the prevalence of electroencephalographic (EEG) and neuroimaging abnormalities in patients with COVID-19 and neurologic changes. Background: Neurological complications associated with COVID-19 are increasingly being recognized, though the incidence of epileptiform abnormalities and prognosis remains uncertain. Design/Methods: Retrospective case series of 192 adult COVID-19 inpatients with EEG performed between March and June 2020 in four hospitals in the Northwell Health system in the New York City area, including 159 patients undergoing continuous EEG, 24 undergoing routine EEG, and nine with reduced montage EEG. Associated patient characteristics were analyzed including EEG indication, epilepsy history, and in-hospital mortality. Cases with epileptiform abnormalities were reviewed for acute and chronic neuroimaging abnormalities. Results: EEG indications included encephalopathy (57.3%), coma (16.7%), seizure (15.6%), abnormal movements (5.2%), and focal neurological deficit (5.2%). Eight of the 30 patients with clinical seizures had known epilepsy. Generalized slowing was seen in 88.5% of patients, while epileptiform abnormalities, including periodic patterns, occurred in 38.0%. Focal intermittent epileptiform discharges were in 24.0%, lateralized periodic discharges in 5.7%, and generalized periodic discharges in 18.8%. Seizures were recorded in 3.6%, including three patients with status epilepticus. Intracranial abnormalities were seen on neuroimaging of 68.4% of patients with epileptiform abnormalities, two-thirds of which were acute. In-hospital mortality rate was 37.5% for all patients studied: 41.1% in patients with epileptiform abnormalities on EEG and 35.8% in patients without epileptiform abnormalities on EEG. Mortality rates were highest for those with periodic patterns on EEG. Conclusions: There is a high correlation between patients with COVID-19 with neurologic symptoms, and abnormalities on EEG. Many patients with EEG abnormalities, especially epileptiform abnormalities, have associated intracranial lesions on neuroimaging, most acute Mortality rates were elevated in patients with epileptiform abnormalities on EEG, particularly those with periodic patterns. These findings may guide the prognosis and management of patients with COVID-19 and neurologic changes.

Asunaprevir, a Potent Hepatitis C Virus Protease Inhibitor, Blocks SARS-CoV-2 Propagation

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has become a global health concern. Various SARS-CoV-2 vaccines have been developed and are being used for vaccination worldwide. However, no therapeutic agents against coronavirus disease 2019 (COVID-19) have been developed so far;therefore, new therapeutic agents are urgently needed. In the present study, we evaluated several hepatitis C virus direct-acting antivirals as potential candidates for drug repurposing against COVID-19. Theses include asunaprevir (a protease inhibitor), daclatasvir (an NS5A inhibitor), and sofosbuvir (an RNA polymerase inhibitor). We found that asunaprevir, but not sofosbuvir and daclatasvir, markedly inhibited SARS-CoV-2-induced cytopathic effects in Vero E6 cells. Both RNA and protein levels of SARS-CoV-2 were significantly decreased by treatment with asunaprevir. Moreover, asunaprevir profoundly decreased virion release from SARS-CoV-2-infected cells. A pseudoparticle entry assay revealed that asunaprevir blocked SARS-CoV-2 infection at the binding step of the viral life cycle. Furthermore, asunaprevir inhibited SARS-CoV-2 propagation in human lung Calu-3 cells. Collectively, we found that asunaprevir displays broad-spectrum antiviral activity and therefore might be worth developing as a new drug repurposing candidate for COVID-19.