OMICRON SPIKE ENDOWS SARS-CoV-2 WITH ENHANCED INFECTIVITY IN NASAL EPITHELIA

Background: Omicron lineages, including BA.1 and BA.2, emerged following mass COVID-19 vaccination campaigns, displaced previous SARS-CoV-2 variants of concern worldwide, and gave rise to sublineages that continue to spread among humans. Previous research has shown that Omicron lineages exhibit a decreased propensity for lower respiratory tract (lung) infection compared to ancestral SARS-CoV-2, which may explain the decreased pathogenicity associated with Omicron infections. Nonetheless, Omicron lineages exhibit an unprecedented transmissibility in humans, which until now has been solely attributed to escape from vaccine-induced neutralizing antibodies. Method(s): We comprehensively analyzed BA1 and BA2 infection in primary human nasal epithelial cells cultured at the air-liquid interface, which recapitulates the physiological architecture of the nasal epithelium in vivo. Meanwhile we also took advantage of the VSV-based pseudovirus decorated with different Spike variants. Result(s): In primary human nasal epithelial cells cultured at the air-liquid interface, which recapitulates the physiological architecture of the nasal epithelium in vivo, BA.1 and BA.2 exhibited enhanced infectivity relative to ancestral SARS-CoV-2. Using VSV-based pseudovirus decorated with different Spike variants, we found that increased infectivity conferred by Omicron Spike is due to superior attachment and entry into nasal epithelial cells. In contrast to ancestral SARS-CoV-2, invasion of nasal epithelia by Omicron occurred via the cell surface and endosomal routes of entry and was accompanied by elevated induction of type-I interferons, indicative of a robust innate immune response. Furthermore, BA.1 was less sensitive to inhibition by the antiviral state elicited by type-I and type-III interferons, and this was recapitulated by pseudovirus bearing BA.1 and BA.2 Spike proteins. Conclusion(s): Our results suggest that the constellation of Spike mutations unique to Omicron allow for increased adherence to nasal epithelia, flexible usage of virus entry pathways, and interferon resistance. These findings inform our understanding of how Omicron evolved elevated transmissibility between humans despite a decreased propensity to infect the lower respiratory tract. Additionally, the interferon insensitivity of the Omicron Spike-mediated entry process may explain why Omicron lineages lost the capacity to antagonize interferon pathways compared to ancestral SARS-CoV-2.

DOES THE COVID-19 MEDIA COVERAGE AFFECT AH PREMIUM DISPARITY?

This article explores the dynamic causality between the COVID-19 Media Coverage Index (MCI) in China (Chinese mainland and Hong Kong) and the AH premium index (both price and volatility) by applying a novel time-varying causality technology. Our findings show that the MCIs in China do not significantly cause the log-prices of the AH premium index throughout the full sample period, whereas significantly positive and time-varying causalities from the MCIs in China to the volatilities of the AH premium index are detected. The results thus provide evidence that the change of the MCIs does not lead to a wider or narrower AH premium but unidirectionally causes the change of its volatilities. Furthermore, the effect of MCIs in Chinese mainland on the AH premium volatilities is more pronounced and stable compared to that in Hong Kong, which indicates that the AH premium disparity is more sensitive to the media coverage in the Chinese mainland than in Hong Kong. Finally, the causal relationship from the MCIs in China to the AH premium volatilities disappears after November 2021. Our results provide implications for policymakers to decrease the fluctuation of the AH premium by effectively guiding the trend of media coverage;the results also remind AH stock investors to pay more attention to the COVID-19 media coverage. © 2023, Bucharest University of Economic Studies. All rights reserved.

Classification of COVID-19 and non-COVID-19 lung computed tomography images using machine learning

A novel coronavirus disease 2019 (COVID-19) was first reported in Wuhan, China in late December 2019. In March 2020, World Health Organization (WHO) declared this sudden epidemic as a global pandemic. It is highly contagious and can cause serious lung inflammation. The typical symptoms are fever, cough, shortness of breath, headache and sore throat. Till 23 August 2021, a total of more than 211 million cases of COVID-19 have been reported to WHO worldwide, with a total of more than 4.4 million of deaths. Hence early detection is crucial to control the spread. Currently, the key diagnosis method is the reverse transcription polymerase chain reaction (RT-PCR) test using swab samples. However, it is subject to certain limitations, such as low sensitivity and shortage of kits. To address these issues, lung computed tomography (CT) scan can be the alternative as it is fast, easy, and proven to be sensitive in detecting COVID-19 cases. This study presents an automated method to differentiate the COVID-19 CT images from the Non-COVID-19 images using different convolutional neural networks (CNN) through three stages procedures. In the first stage, the dataset which consists of 746 images of COVID-19 and Non-COVID-19 was split into 3 parts for training, validation, and testing, respectively. The training and validation data were then applied with different augmentation techniques to increase the dataset, while the testing data remained with no augmentation. In stage 2, 10 different pretrained CNNs were initialized to train and classify the binary class. In stage 3, gradient descent class activation mapping (GradCAM) was used for abnormality localization. The best performance was achieved by ResNet152, ResNeXt, GoogleNet, and DenseNet201 with the highest overall accuracy of 98.51%. ResNet152, GoogleNet, and DenseNet201 had achieved a sensitivity of 100%, and specificity of 97.06%, whereas ResNeXt had achieved a sensitivity of 96.97%, and specificity of 100%. © 2023 Elsevier Inc. All rights reserved.

Post-Pandemic Follow-Up Audit of Security Checkpoints

This paper provides a follow-up audit of security checkpoints (or simply checkpoints) for mass transportation hubs such as airports and seaports aiming at the post-pandemic R&D adjustments. The goal of our study is to determine biometric-enabled resources of checkpoints for a counter-epidemic response. To achieve the follow-up audit goals, we embedded the checkpoint into the Emergency Management Cycle (EMC) –the core of any doctrine that challenges disaster. This embedding helps to identify the technology-societal gaps between contemporary and post-pandemic checkpoints. Our study advocates a conceptual exploration of the problem using EMC profiling and formulates new tasks for checkpoints based on the COVID-19 pandemic lessons learned. In order to increase practical value, we chose a case study of face biometrics for an experimental post-pandemic follow-up audit. Author

The spillovers among cryptocurrency, clean energy and oil

The COVID-19 causes strong spillover effects between financial markets. This paper explores the dynamic spillover effects among cryptocurrency, clean energy and oil during the COVID-19 by employing TVP-VAR extended joint connectedness approach. The empirical results show that clean energy and oil markets appear to be the net receivers of spillovers, whereas cryptocurrency market appears to be a net transmitter of spillovers. The dynamic total connectedness experiences a rapid increase in March 2020 when the COVID-19 spreads around the world. © 2022 The Authors. Published by Elsevier B.V.

Incorporating Fuzzy Cognitive Inference for Vaccine Hesitancy Measuring

Vaccine hesitancy plays a key role in vaccine delay and refusal, but its measurement is still a challenge due to multiple intricacies and uncertainties in factors. This paper attempts to tackle this problem through fuzzy cognitive inference techniques. Firstly, we formulate a vaccine hesitancy determinants matrix containing multi-level factors. Relations between factors are formulated through group decision-making of domain experts, which results in a fuzzy cognitive map. The subjective uncertainty of linguistic variables is expressed by fuzzy numbers. A double-weighted method is designed to integrate the distinguished decisions, in which the subjective hesitancy is considered for each decision. Next, three typical scenarios are constructed to identify key and sensitive factors under different experimental conditions. The experimental results are further discussed, which enrich the approaches of vaccine hesitancy estimation for the post-pandemic global recovery.

Tele-Neuro-Ophthalmology: An Update One YearInto The COVID-19 Pandemic

Objective: This study evaluates the usage of tele-neuro-ophthalmology one year into the pandemic. Background: Tele-neuro-ophthalmology emerged as a resource early in the COVID-19 pandemic. Since then, telehealth utilization has evolved. Design/Methods: Telehealth utilization pre-COVID-19, early pandemic (March, 2020), and 1 year later (March, 2021) was surveyed among practicing neuro-ophthalmologists in and outside the United States using an online platform. Demographics, utilization, perceived benefits, barriers, and data utility opinions were collected over a two-week period in May 2021, prior to the emergence of the Delta and Omicron variants. Results: 135 practicing neuro-ophthalmologists (81.5% United States, 47.4% females) participated in the survey. One year into the pandemic, the proportion of respondents utilizing video telemedicine (50%) decreased from early-pandemic rates (65%) (p<0.0005, McNemar), but was sustained above pre-pandemic percentages (6%, p<0.0005, McNemar). 82% of current video users plan to continue video visits. Proportion of respondents using remote testing (42.2% vs 46.2%, p=0.45), virtual second opinions (14.5% vs 11.9%, p=0.38), eConsults (13.5% vs 16.2%, p=0.25), online portal communications, and remote interpretation of patient-submitted testing remained similar between March 2020 and 2021. The majority selected increased access to care, better continuity of care, and enhanced patient appointment efficiency as benefits, while reimbursement, liability, disruption of in-person clinic flow, limitations of video exams, and patient technology use were barriers. Many neuro-ophthalmic exam elements were deemed more suitable collected in a separate in-person visit rather than during a live video session, although respondents felt some exam components could be evaluated adequately via a virtual platform. Conclusions: One year into the COVID-19 pandemic yet prior to the emergence of the Delta and Omicron variants, neuro-ophthalmologists have maintained telemedicine utilization at rates higher than pre-pandemic levels. Tele-neuro-ophthalmology remains a valuable tool in augmenting patient care.

RAPALOGS DOWNMODULATE INTRINSIC IMMUNITY and PROMOTE CELL ENTRY of SARS-CoV-2

Background: SARS-CoV-2 infection in immunocompromised individuals has been associated with prolonged virus shedding and the development of novel viral variants. Rapamycin and rapamycin analogs (rapalogs, including everolimus, temsirolimus, and ridaforolimus) are FDA-approved for use as mTOR inhibitors in multiple clinical settings, including cancer and autoimmunity, but a common side effect of these drugs is immunosuppression and increased susceptibility to infection. Immune impairment caused by rapalog use is traditionally attributed to their impacts on T cell signaling and cytokine production. Methods: We used replication-competent SARS-CoV-2 and HIV pseudotyped with betacoronavirus Spike proteins to assess how rapalog pretreatment of cells ex vivo and rodent animals in vivo impacts susceptibility to Spike-mediated infection. Results: We show that exposure to rapalogs increases cellular susceptibility to SARS-CoV-2 infection by antagonizing components of the constitutive and interferon-induced cell-intrinsic immune response. Pre-treatment of cells (including human lung epithelial cells and primary human small airway epithelial cells) with rapalogs promoted the early stages of SARS-CoV-2 infection by facilitating Spike-mediated virus entry. Rapalogs also boosted infection mediated by Spike from SARS-CoV and MERS-CoV in addition to hemagglutinin of influenza A virus and glycoprotein from vesicular stomatitis virus, suggesting that rapalogs downmodulate antiviral defenses that pose a common barrier to these viral fusion proteins. By identifying one rapalog (ridaforolimus) that lacks this function, we demonstrate that the extent to which rapalogs promote virus entry is linked to their capacity to trigger the lysosomal degradation of IFITM2 and IFITM3, intrinsic inhibitors of virus-cell membrane fusion. Mechanistically, rapalogs that promote virus entry inhibit the mTOR-mediated phosphorylation of TFEB, a transcription factor controlling lysosome biogenesis and lysosomal degradation pathways such as autophagy. In contrast, TFEB phosphorylation by mTOR was not inhibited by ridaforolimus. In the hamster model of SARS-CoV-2 infection, injection of rapamycin four hours prior to virus exposure resulted in elevated virus titers in lungs, accelerated weight loss, and decreased survival. Conclusion: Our findings indicate that preexisting use of certain rapalogs may elevate host susceptibility to SARS-CoV-2 infection and disease by activating a lysosome-mediated suppression of intrinsic immunity.

Mapping the key challenges and managing the opportunities in supply chain distribution during COVID-19: a case of Myanmar pharmaceutical company

Purpose: This study aims to present a study on the supply chain process of a Myanmar-based pharmaceutical company (named ABC Pvt. Ltd. in this study) that produces pharmaceutical products across Myanmar and aims of bringing quality medical products and best care for Myanmar people’s health. The study aims to identify the key supply chain challenges and manage the opportunities executed by this pharmaceutical company to improve the supply chain process during the COVID-19 outbreak. Design/methodology/approach: This work used a case study and conducted semistructured interviews with the manager, senior managers and senior staff of the ABC Company to improve the supply chain process and develop a comprehensive structural relationship to rank them to streamline the uncertainties, real-time information and agility in a digital supply chain using grey relational analysis (GRA) method. Findings: From the data analysis and results, “Impact of political factor,” “Delay in import process” and “Weak internet connection,” and “Weak knowledge of the use of digital platform,” “Poor information sharing in online by employees” and “Information flow from top management to operational level” have been identified as top and bottom three key challenges, respectively. “Inventory management,” “Selection of transport method” and “Operational cost”, and “Marketing and brand Innovation,” “Online delivery of products” and “E-commerce enablement (Launching applications, tracking system)” are identified as the top and bottom three managing the opportunities, respectively. Research limitations/implications: The findings of the study help to supply chain decision-makers of the company in their establishment of key challenges and opportunities during the COVID-19 era. As a leading company, it always tries to add value to its product through a supply chain system, effective management teams and working with skillful decision-making toward satisfying the demand on time and monitoring the supplier performance. Originality/value: The novelty of this study is to identify the key supply chain challenges and opportunities by the GRA method to rank them, considering the case of Myanmar pharmaceutical manufacturing company as a case-based approach to measuring its performance during the COVID-19 outbreak era. This work will assist managers and practitioners help to the company to provide optimal services to its consumers on time in this critical situation. © 2022, Emerald Publishing Limited.

Detection of COVID-19 on Chest X-Ray Using Neural Networks

Coronavirus of 2019 is an ongoing pandemic that has infected millions of people and costed the life of more than three million people. It is a highly transmitting disease that has exhausted all the healthcare facilities trying to contain its spread. It has exposed the need for more health facilities and experts to cope with this pandemic without impacting on the safety of healthcare workers. The hardworking and struggling healthcare sector is in need of automated diagnostic devices that could lift the burden off the limited practitioners and also ensure their safety from coming in direct contact with the infection. This pandemic has made the world realize the need of automation for an infectious disease like COVID-19. Deep learning in radiology is an extensively researched topic over the last decade and has the potential to provide the much-needed automation required for COVID-19 diagnosis. In this paper we have fine-tuned three deep learning models—ResNet50, DenseNet121 and InceptionV3—for classification of COVID-19 CXR from regular pneumonia cases. Our models achieved an accuracy of 99.45, 99.50 and 98.55 respectively. © 2022, Springer Nature Switzerland AG.

Restoring Lesions in Low-Dose Computed Tomography Images of COVID-19 Using Deep Learning

The use of Low-dose Computed Tomography (LDCT) in clinical medicine for diagnosis and treatment planning is widespread due to the minimal exposure of patients to radiation. Also, recent studies have confirmed that LDCT is a feasible medical imaging modality for diagnosing COVID-19 cases. In general, X-ray tube current is being reduced to acquire the LDCT images. Reduction of the X-ray flux introduces the Quantum noise into the generated LDCT images and, as a result, it produces visually low-quality CT images. Therefore, it is challenging to differentiate the lesions in the diagnosis of COVID-19 patients using the LDCT images due to low contrast and failure to preserve the subtle structures. Therefore, in this study, we proposed a Deep Learning (DL) model based on the Generative Adversarial Network (GAN) for post-processing the LDCT images to enhance their visual quality. In this proposed model, the generator network is designed as a U-net to generate the restored CT images by filter out the noise. Also, the discriminator network follows a patch-GAN model to discriminate the real and generated images while preserving the texture details. The quantitative and qualitative results demonstrated the effectiveness of noise suppression and structure preservation of the proposed DL method. Hence, it provides an acceptable quality improvement for LDCT images to discriminate the lesions for diagnosing the COVID-19 positive cases. © 2022, Springer Nature Switzerland AG.

The production and distribution of face masks to other countries: a strategic approach of Taiwan during COVID-19 outbreak

Purpose COVID-19 pandemic has exposed that even the best of the developed nations have surrendered to the devastations imposed on the global supply chains. The purpose of this study is to explore how COVID-19 has exaggerated the supply chain of production and distribution of Taiwan-based face masks and also investigate the conscientious factors and subfactors for it. Design/methodology/approach In this study, an analytical hierarchy processes (AHP)-based approach has been used to assign the criterion weights and to prioritize the responsible factors. Initially, based on 26 decision-makers, successful factors were categorized into five main categories, and then main categories and their subcategories factors were prioritized through individual and group decision-maker's contexts by using the AHP approach. Findings The results of this AHP model suggest that "Safety" is the most important and top-ranked factor, followed by production, price, work environment and distribution. The key informers in this study are stakeholders which consist of managers, volunteers, associations and non-governmental organizations. The results showed that good behavior of the employees under the "Safety" category is the top positioned responsible factor for successful production and distribution of face masks to the other countries with the highest global percentage of 15.7% and using sanitizers to protect health is the second most successful factor with the global percentage of 11.7%. Research limitations/implications The limitations faced in this study were limited to only Taiwan-based mask manufacturing companies, and it was dependent on the decisions of the limited company's decision-makers. Originality/value The novelty of this study is that the empirical analysis of this study has been based on a successful Taiwan masks manufacturing company and evaluates the responsible factors for the production and distribution of Taiwan masks to other countries during COVID-19.