The COVID-19 pandemic, economic policy uncertainty and the hedging role of cryptocurrencies: a global perspective

PurposeThe COVID-19 pandemic has led to global economic policy uncertainty, which has increased the need to investigate ways to mitigate the uncertainty. This study aims to examine the potential of cryptocurrencies as a hedge and safe haven avenue against economic policy uncertainty.Design/methodology/approachThis study investigates the behavior of the five leading cryptocurrencies in relation to country-level and group-level economic policy uncertainty indices, as measured by the text-based method developed by Baker et al. (The Quarterly Journal of Economics, 2016, 131, 1593-1636). The research covers a broad range of emerging and developed economies from July 2013 to September 2020. The study employs the approach of Narayan et al. (Economic Modelling, 2016, 53, 388-397) to examine the hedging and safe-haven properties of cryptocurrencies.FindingsThis study finds that the top cryptocurrencies play a hedging role against economic policy uncertainty, with some exceptions. Additionally, there is evidence to support the idea that cryptocurrencies can serve as a safe haven during the COVID-19 pandemic. As a result, investors may benefit from using cryptocurrencies as a risk-management avenue during times of uncertainty.Originality/valueThis research contributes to the existing literature by testing the cryptocurrencies' hedging and safe haven properties in a new way, by analyzing their lead and lag behaviors using a recent and innovative approach. Additionally, it examines a wide range of emerging and advanced markets, providing insight into the potential of using cryptocurrencies as a risk mitigation avenue.

An Analysis of Dynamic Correlations among Oil, Natural Gas and Ethanol Markets: New Evidence from the Pre- and Post-COVID-19 Crisis

After the economic shock caused by COVID-19, with relevant effects on both the supply and demand for energy assets, there was greater interest in understanding the relationships between key energy prices. In order to contribute to a deeper understanding of energy price relationships, this paper analyzes the dynamics between the weekly spot prices of oil, natural gas and benchmark ethanol in the US markets. The analysis period started on 23 June 2006 and ended on 10 June 2022. This study used the DMCA cross-correlation coefficient in a dynamic way, using sliding windows. Among the main results, it was found that: (i) in the post-pandemic period, oil and natural gas were not correlated, in both short- and long-term timescales;and (ii) ethanol was negatively associated with natural gas in the most recent post-pandemic period, especially in short-term scales. The results of the present study are potentially relevant for both market and public agents regarding investment diversification strategies and can aid public policies due to the understanding of the interrelationship between energy prices. © 2023 by the authors.

Phages and SARS-CoV-2

The emerging human pathogenic viruses, including the recently emerged severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2), have markedly affected the human health and have become a challenge for researchers worldwide. Antibiotic therapy and existing vaccines have reduced the pandemic burden to some extent. However, there is still need for efficient treatment, vaccination, and antiviral agents to control the pandemic. This chapter illustrates the role of bacteriophage in bacterial infections, SARS-COV-2 infected patients, biological activities of phages, phage display method, phages as potential inducers of antiviral immunity, phage-based vaccines, CRISPR and phage-based SARS-CoV-2 vaccines, and possible advantages of phage-based vaccines. It is concluded that phages have considerable breadth in the SARS-CoV-2 pandemic and offer many substantial advantages, such as clearing respiratory bacterial infections, which significantly reduce the burden of mortalities. Phage plays a vital role in triggering antiviral immunity by inducing cytokines such as IFN-α and IL-12. It suggests the role in driving antiviral immunity, triggering TLR3-dependent pattern recognition receptors, inhibiting TNF-driving type I IFN, inducing antiviral immunity through upregulation of the expression of defensin in IL-2, and encouraging a marked upregulation of gene hBD2 that induces virucidal effects, thus playing a key role in anti-SARS-COV-2 immunity. Moreover, phages have been presented as an alternative universal adjuvant-free nano-vaccine platform in which single-phage scaffolds are used to incorporate multiple targets. © 2023 Elsevier Inc. All rights reserved.

Economic Policy Uncertainty, Energy and Sustainable Cryptocurrencies: Investigating Dynamic Connectedness during the COVID-19 Pandemic

The purpose of the research is to explore the dynamic multiscale linkage between economic policy uncertainty, equity market volatility, energy and sustainable cryptocurrencies during the COVID-19 period. We use a multiscale TVP-VAR model considering level (EPUs and IDEMV) and returns series (cryptocurrencies) from 1 December 2019 to 30 September 2022. The data are then decomposed into six wavelet components, based on the wavelet MODWT method. The TVP-VAR connectedness approach is used to uncover the dynamic connectedness among EPUs, energy and sustainable cryptocurrency returns. Our findings reveal that CNEPU (USEPU) is the strongest (weakest) NET volatility transmitter. IDEMV is the most consistent volatility NET transmitter among all uncertainty indices across the original returns and wavelet scales (D1~D6). Energy cryptocurrencies, i.e., GRID, POW and SNC, are more likely to receive volatility spillovers than sustainable cryptocurrencies during a turbulent period (COVID-19). XLM (XNO) is least (most) affected by volatility spillover in system-wide connectedness, and XLM (ADA and MIOTA) showed a consistent (heterogeneous) non-recipient behavior across the six wavelet (D1~D6) scales and original return series. This study uncovers the dynamic connectedness across multiscale, which will support investors considering different investment horizons (D1~D6). © 2023 by the authors.

Natural products and SARS-CoV-2

Natural products have been proven to be the source of many antiviral drugs in the past. History has a bunch of natural products used as traditional medicine, therapies, mixtures, and oils. However, there are many bioactive natural products that need to be evaluated against severe acute respiratory syndrome (SARS-CoV-2) to curb the ongoing pandemic. Several plants and fungal-derived natural products are extensively reported with antiviral activities against SARS-CoV-2. In vitro, preliminary study assays and computational studies revealed several antiviral drugs from natural fungal compounds, including cordycepin isolated from Cordyceps militaris fungi. Polyphenolic compounds isolated from the Broussonetia papyrifera plant showed promising antiviral activity against SARS CoV-2 in in silico studies. Two alkaloid compounds, 10-hydroxyusambarensine and cryptoquindoline isolated from African medicinal plants, inhibited the main protease (Mpro) of SARS CoV-2. At the start of the COVID-19 pandemic, FDA approved the emergency use of chloroquine against SARS CoV-2;chloroquine is a derivative of alkaloid. The development of modern technologies has streamlined the discovery of new drugs from natural products. Gas chromatography–mass spectrometry, infrared radiation, nuclear magnetic resonance, high-performance thin-layer chromatography, and high-performance liquid chromatography and other high output technologies should be available for the structural interpretation and distinguishability of prudent lead molecules © 2023 Elsevier Inc. All rights reserved.

An Interprofessional Peer Teacher Training program for health professional students: ‘face to face' versus ‘online only'

In 2020, following the disruption of COVID-19, we rapidly moved the interprofessional Peer Teacher Training (PTT) program, traditionally delivered via blended learning to ‘online only' format. Consisting of seven modules, the PTT program is designed to provide health professional students with opportunities to develop skills in teaching, feedback, assessment, teamwork and communication, in preparation for peer teaching and future practice. This study sought to compare ‘blended learning' with ‘online only' delivery. ‘Blended learning' format, included a one-day face-to-face session, requiring 9 facilitators. Students participated in small group learning activities, and were formatively assessed on their teaching and feedback skills. ‘Online only' delivery occurred across three weeks, using asynchronous and synchronous activities, requiring 11 facilitators. Students completed a post-course questionnaire. Data were analysed using descriptive statistics and thematic analysis. Eighty-five students completed the program;36 in ‘blended learning' and 49 ‘online only' format, from six disciplines (health sciences, medicine, nursing, pharmacy, oral health and public health). All (100%) ‘blended learning' and 67% ‘online only' participants completed the questionnaire. Both sets valued the online reading, discussion boards, videos, with opportunities to practice teaching skills, give and receive feedback. They reported an increased understanding of the roles of other disciplines. However, the ‘face-to-face' component had some associated benefits, including a more positive attitude towards interprofessional learning and intention to teach. While ‘online only' delivery of the program provided an effective alternative to the traditional ‘blended learning' format, additional ‘real-time' sessions may improve student engagement. Practitioner Notes 1. ‘Online only' delivery of teacher training provides an effective alternative to ‘blended learning' format. 2. Opportunities for ‘real time' participation, with formative assessment and feedback increases engagement. 3. Clearly structured online modules and provision of simple teaching frameworks assist students to apply what they have learnt to different contexts. 4. Face-to-face sessions bring associated benefits, promoting a more positive attitude towards interprofessional learning and intention to teach. 5. Ensuring the provision of opportunities for Peer Teacher Training alumni is an important next step. © 2023, University of Wollongong. All rights reserved.

Polyketides and SARS-CoV-2

Historically, antiviral drugs have been mined from natural products, including polyketides. Polyketides are produced by various plants, microorganisms, and marine organisms as secondary metabolites. They are considered potential therapeutic antiviral compounds to treat the ongoing COVID-19 pandemic. Preciously, polyketides showed significant antiviral activity in vitro against A59 coronavirus: Herpes Simplex Virus 1 (HSV1) and Poliovirus 1. Several polyketide compounds such as adipostatin, bilobol, onnamide, dihydro-onnamide, and pseudo-onnamide showed promising anti-SARS-CoV-2 by binding with main protease (Mpro) that play a key role in the SARS-CoV-2 replication and transcription. Interestingly, according to the molecular dynamic simulation studies, all of them were stable at the Mpro binding site. The preclinical and clinical studies of those compounds/congeners or structurally related modified members are attributed to their flexibility in chemical synthesis. The diverse structural modifications of SARS-CoV-2 can be correlated using the structure–activity relationship (RAS) that will pave the way to develop promising antiviral drugs to reduce the burden of the ongoing COVID-19 pandemic. © 2023 Elsevier Inc. All rights reserved.

COVID-19-related orbital abscesses: a case report

Orbital cellulitis is a condition with a high risk of morbidity, including visual loss. It commonly originates from the paranasal sinuses. We present a case of multifocal intraorbital abscesses secondary to viral sinusitis in an adolescent with SARS-CoV-2 infection. This patient presented with classic symptoms of orbital cellulitis, but did not display classical symptoms of COVID-19. The patient initially underwent endoscopic drainage, followed by a combined approach which yielded no pus. He recovered without complication. This is the second report of its type showing a causative link between SARS-CoV-2 and orbital cellulitis.

A New Mathematical Model of COVID-19 with Quarantine and Vaccination

A mathematical model revealing the transmission mechanism of COVID-19 is produced and theoretically examined, which has helped us address the disease dynamics and treatment measures, such as vaccination for susceptible patients. The mathematical model containing the whole population was partitioned into six different compartments, represented by the SVEIQR model. Important properties of the model, such as the nonnegativity of solutions and their boundedness, are established. Furthermore, we calculated the basic reproduction number, which is an important parameter in infection models. The disease-free equilibrium solution of the model was determined to be locally and globally asymptotically stable. When the basic reproduction number (Formula presented.) is less than one, the disease-free equilibrium point is locally asymptotically stable. To discover the approximative solution to the model, a general numerical approach based on the Haar collocation technique was developed. Using some real data, the sensitivity analysis of (Formula presented.) was shown. We simulated the approximate results for various values of the quarantine and vaccination populations using Matlab to show the transmission dynamics of the Coronavirus-19 disease through graphs. The validation of the results by the Simulink software and numerical methods shows that our model and adopted methodology are appropriate and accurate and could be used for further predictions for COVID-19. © 2022 by the authors.

A fractional mathematical model for COVID-19 outbreak transmission dynamics with the impact of isolation and social distancing

The Covid illness (COVID-19), which has emerged, is a highly infectious viral disease. This disease led to thousands of infected cases worldwide . Several mathematical compartmental models have been examined recently in order to better understand the Covid disease. The majority of these models rely on integer-order derivatives, which are incapable of capturing the fading memory and crossover behaviour observed in many biological phenomena. Similarly, the Covid disease is investigated in this paper by exploring the elements of COVID-19 pathogens using the non-integer Atangana-Baleanu-Caputo derivative. Using fixed point theory, we demonstrate the existence and uniqueness of the model's solution. All basic properties for the given model are investigated in addition to Ulam-Hyers stability analysis. The numerical scheme is based on Lagrange's interpolation polynomial developed to estimate the model's approximate solution. Using real-world data, we simulate the outcomes for different fractional orders in Matlab to illustrate the transmission patterns of the present Coronavirus-19 epidemic through graphs.

The influence of the national lockdown during the COVID-19 pandemiconSurgical presentations and admissions in a University hospital

Background: The COVID-19 pandemic has significantly revolutionised medical practice around The world. Healthcare resources had to be prioritise for The management of patients infected with The virus whilst routine and elective surgeries were postponed in order to Free up hospital beds in anticipation for The influx of patients with COVID-19 requiring hospital admission. With The government announcing a national lockdown in March 2020, a striking reduction in The number of patients presenting to hospital was noted. Aim(s): Our aim was to establish how The COVID-19 pandemic has impacted on The number and presentations of General Surgical patients in a University hospital. Method(s): We conducted a retrospective study on General Surgical patients who were reviewed by The Surgical team at The University teaching hospital. Data was collected over two 7-weeks periods;between The 23rd March and 10th May 2020 (The first lockdown duration in The UK) and The corresponding 7-weeks in 2019 (that is between 25th March and 12th May 2019). Result(s): The analysis of our data showed that there was a significant reduction in The number of Surgical patients being admitted. 399 patients were admitted between 25th March and 12th May 2019 compared to only 245 patients during The lockdown period. The average admissions per day over The 7-weeks period dropped from 9 admissions per day in 2019 to 5 admission per day during national lockdown. Small bowel obstruction and pancreatitis remained common diagnoses in both 7-weeks periods, however diverticulitis was less common during lockdown.

Comparison of the Performance of Machine Learning-based Algorithms for Predicting Depression and Anxiety among University Students in Bangladesh: A Result of the First Wave of the COVID-19 Pandemic

Introduction: The purpose of this research was to predict mental illness among university students using various machine learning (ML) algorithms. Methods: A structured questionnaire-based online survey was conducted on 2121 university students (private and public) living in Bangladesh. After obtaining informed consent, the participants completed a web-based survey examining sociodemographic variables and behavioral tests (including the Patient Health Questionnaire (PHQ-9) scale and the Generalized Anxiety Disorder Assessment-7 scale). This study applied six well-known ML algorithms, namely logistic regression, random forest (RF), support vector machine (SVM), linear discriminate analysis, K-nearest neighbors, Naive Bayes, and which were used to predict mental illness among university students from Dhaka city in Bangladesh. Results: Of the 2121 eligible respondents, 45% were male and 55% were female, and approximately 76.9% were 21-25 years old. The prevalence of severe depression and severe anxiety was higher for women than for men. Based on various performance parameters, the results of the accuracy assessment showed that RF outperformed other models for the prediction of depression (89% accuracy), while SVM provided the best result than other models for the prediction of anxiety (91.49% accuracy). Conclusion: Based on these findings, we recommend that the RF algorithm and the SVM algorithm were more moderate than any other ML algorithm used in this study to predict the mental health status of university students in Bangladesh (depression and anxiety, respectively). Finally, this study proposes to apply RF and SVM classification when the prediction of mental illness status is the core interest.

Measuring Determinants of Environmental Performance during COVID-19: Evidence from the Healthcare Sector

PURPOSE: The current study aims to investigate the role of green human resource management (GHRM) (i.e., green competence building, green motivation enhancement, and green employee involvement) practices in enhancing employee environmental performance in the health care sector during the COVID-19 pandemic. DESIGN/METHODOLOGY/APPROACH: Data were collected from hospital staff (N=350) working in different private and public hospitals of China. Structural equational modeling-partial least squares (PLS-SEM) modelling was used to test the proposed model based on the respondents’ data. FINDINGS: The study results support the relationship between GHRM practices and environmental performance directly, and through employees’ green attitudes during COVID-19. The study results also extend this support to the negative moderating role of perceived personal inconvenience on the relationship between GHRM practices and environmental performance. © 2022, World Association for Sustainable Development. All rights reserved.

Determinants of regional prevalence and variations in handwashing practices among households in Bangladesh: An application of generalized mixed-effects model

Introduction: Handwashing practice is an effective way to minimize severe infectious diseases such as COVID-19, diarrhea, and pneumonia. The study aimed to explore the prevalence of handwashing behavior and associated determinants in rural and urban areas of Bangladesh. Methods: The research was performed using cross-sectional survey data from the 2019 Bangladesh Multiple Indicator Cluster Survey, and 61,242 household members were the sample for this study. The Chi-square test was applied for the bivariate analysis. A generalized linear mixed-effects model was used to identify the risk factors of practicing handwashing in both urban and rural areas of Bangladesh. Results: Only 65% of the country's households had access to handwashing facilities (place, water, and materials). While urban dwellers were more likely to wash their hands, rural dwellers were only 63% likely to do so. The level of education of household heads, wealth status, division, number of family members, sanitation facilities, and water source were the key factors associated with handwashing behavior. This study revealed that the odds were significantly lower among illiterate respondents compared to those with secondary and above-secondary education in both areas of Bangladesh, and a positive association was found between wealth status and handwashing behavior practiced in both urban and rural areas. In this study, the size of the family was statistically significant for both rural and urban areas of Bangladesh. Conclusion: Handwashing is the most prominent consideration for controlling COVID-19. Policymakers are striving to improve handwashing facilities by increasing awareness-raising programs, especially among rural residents of Bangladesh. © 2022 Asian Journal of Social Health and Behavior ;Published by Wolters Kluwer - Medknow.

Impacts of COVID-19 on the construction sector in the least developed countries

Purpose: Global construction has been affected by COVID-19 unprecedently. The construction sectors in the least developed countries are considered as vulnerable, but the covid made the countries experience the worst situation ever. To minimize the losses by effective measures, there needs to assess the COVID-19 impacts on the construction sector. So, the aim of this study is to investigate the most critical impacts of COVID-19 on construction in the least developed countries by considering the case study of Bangladesh. Design/methodology/approach: The authors adopted multistep research methods, including (1) literature analysis and discussion with experts to establish a comprehensive list of COVID-19 impacts;(2) through a questionnaire survey, data were collected from 217 construction professionals by email, Google Form and Skype for quantifying the significance of covid impacts;(3) reliability of the survey checked by the Cronbach Alpha test;(4) Relative Importance Index (RII) to determine the ranks of the impacts based on their significance;(5) Interpretive Structural Model (ISM) to explore the corelations and the hierarchical structure;and (6) cross-impact matrix multiplication applied to classification (MICMAC) analysis to classify the COVID-19 impacts. Findings: The study identified a total of 18 COVID-19 impacts on the construction sector. Among them, the job cuts, schedule delays, project suspension, cost overrun and effects on mental health are more influential and significant than others. Further, this study found that unpaid leave and job cuts are the two most fundamental impacts which influence other succeeding significant impacts. And ultimately all the impacts lead to hampering the national economy and development. Finally, MICMAC analysis suggested that unpaid leave and job cuts should be addressed first to resolve and effects on the national economy and development should be later. Research limitations/implications: This study does not consider all the COVID-19 impacts due to the relevant context and simplicity of the ISM method. Also, the respondent's attitude might be slightly different during the post-mass vaccination period. Practical implications: This study will help the company's management, employees and government to develop effective strategies to understand the insight of their interrelations and ultimately overcome the identified covid effects. This will must contribute to the industry, its employees, the government and society by ensuring the national economy and development, construction operations, investment, employment and social security. Originality/value: This study will contribute to the knowledge body (practitioners and researchers) by providing the list of significant covid impacts and insight into their interrelations for further deep analysis of the pandemic effects. This will also help the authorities and stakeholders in developing policies and strategies to minimize or avoid these effects and avoid future consequences due to any pandemic like covid. © 2022, Emerald Publishing Limited.

On the fractional-order mathematical model of COVID-19 with the effects of multiple non-pharmaceutical interventions

In this article, the Caputo fractional derivative operator of different orders 0 < α ≤ 1 is applied to formulate the fractional-order model of the COVID-19 pandemic. The existence and boundedness of the solutions of the model are investigated by using the Gronwall-Bellman inequality. Further, the uniqueness of the model solutions is established by using the fixed-point theory. The Laplace Adomian decomposition method is used to obtain an approximate solution of the nonlinear system of fractional-order differential equations of the model with a different fractional-order α for every compartment in the model. Finally, graphical presentations are presented to show the effects of other fractional parameters α on the obtained approximate solutions. © 2022 the Author(s), licensee AIMS Press.

Remote Programming for Deep Brain Stimulation (DBS) therapy management in Parkinson's disease and Essential Tremor

Objective: To assess the utility of the recently FDA approved Virtual Clinic platform that enables clinicians to remotely make programming changes in DBS patients. Background: DBS in Parkinson's disease and Essential tremor requires multiple in-person programming sessions for therapy optimization and management. Until recently, this was only possible through inperson clinic sessions which place time, travel and fiscal burdens on patients and caregivers limiting their access to care. This has an even greater bearing on patients that are dependent on caregivers, need to take time off from work or are concerned about exposure during COVID-19. The recent FDA approved NeuroSphere™ Virtual Clinic enables clinicians for the first time to remotely make programming changes over the internet to help DBS patients. Methods: 34 patients provided signed written consent to Abbott or verbal consent to the Clinician to be enrolled. The enrolled Abbott Infinity™ IPGs were securely mapped to authorized Clinicians. Software on programming devices was upgraded without requiring any hardware or firmware changes. Patients initiated remote sessions and Clinicians securely connected to the IPGs using unique logins and multi-factor authentication. Stimulation changes were synchronized with integrated video and a failsafe mechanism ensured continuity of therapy in case of network failure. Results: Of the 34 patients programmed remotely, 74% benefited from stimulation changes, 26% from side effects resolution, 9% from battery status check, 6% were out of the country when they needed intervention, 29% completed routine follow-up. The use of this platform has allowed seamless virtual patient visits reducing the patient burdens related to access to care as well as increasing throughput in our clinic. We have also billed for these sessions for reimbursement in the US using the Telemedicine modifier code (95). The average time to establish a session has been less than 1 minute with no perceptible difference in system checks and stimulation changes. Remote programming is both clinically viable and valuable to patients, caregivers and clinicians. Conclusion: Remote programming is both clinically viable and valuable to patients, caregivers and clinicians.

Success of Home-to-Home Tele-neuropsychology (TeleNP) in Deep Brain Stimulation (DBS) Candidacy Assessments of Multicultural PD Patients: State of Practice for COVID-19 and Beyond

Objective: The COVID-19 pandemic created unique challenges for neuropsychological assessment of DBS candidates due to physical distancing and safety requirements. Existing literature has demonstrated guidelines and feasibility of neuropsychological measures administered via teleconference, but none to our knowledge have assessed implementation, feasibility, clinical utility, and acceptance of home-to-home TeleNP in determining candidacy for DBS in Parkinson's disease (PD) patients. Background: PD is the second most common neurodegenerative disorder and DBS surgery offers significant improvement in motor symptoms and quality of life for medication refractory patients. Since neuropsychologists play an essential role in evaluating cognitive and behavioral changes that impact decisions on DBS surgical candidacy, adapting current practices is critical for continuity of care. Design/Methods: PD patients [n=72, M =63.3(8.76), 65.3% male, M =14.7(2.93), 50.7% Hispanic, 58.3% tested in English/41.7% in Spanish) underwent neuropsychological (global cognition, language, memory, attention, processing speed, visuospatial ability) and emotional assessments via home-to-home TeleNP between May 2020 and August 2021. Independent t-tests were conducted to assess TeleNP discriminability of cognitive and emotional function in surgical candidates. Results: To date, 66 candidates were approved for DBS (44 completed surgery, 17 are pending, 5 opted not to proceed), and 6 were not surgical candidates due to neurocognitive and/or severe psychiatric disorders. Surgical candidate mean neuropsychological scores were average across all domains, with normal to mild mood symptoms. The non-selected group differed by education but not age, and was significantly different (p≤.006) in overall cognition [MoCA t(70)=3.62], age edu working memory [WAIS-IV DS t(70)=3.15], phonemic fluency [COWAT FAS t(70)=2.85], verbal reasoning [WAIS-IV SI t(67)=3.38], perceptual reasoning [WAIS-IV MR t(70)=3.02], depression [BDI-2 t(68)=3.47], and anxiety [BAI t(70)=2.88]. Conclusions: TeleNP is a successful platform for assessing DBS surgical candidacy safely in English and Spanish speaking PD patients and offers potential for providing high quality screening for those with physical limitations, reduced access to transportation, or are living in remote areas.