Effects of Critical Incident Stress Management on Nurses During COVID-19 Pandemic

Objective: To analyze the effect of Critical Incident Stress Management on the mental health of nurses during COVID-19. Study Design: Quasi-experimental study. Place and Duration of Study: Combined Military Hospital, Jhelum Pakistan from Mar to Jun 2020. Methodology: Forty-six nurses were consecutively recruited and evaluated regarding Knowledge about COVID-19, thoughts regarding its origin, emotional reactions, and coping mechanisms. The evaluation was followed by the provision of Critical Incident Stress Management sessions in March. Finally, in the second phase conducted in June, the participants were evaluated again to assess the effect of intervention regarding the above parameters. Results: Results indicated a statistically significant shift in Knowledge from Social Media to Academic Resources (p<0.001). Thoughts regarding the origin of COVID-19 showed that ‘Religious Causes' and belief in ‘Religious and Biological Causes both” significantly reduced (p=0.001 and p=0.003, respectively), while opinions regarding ‘Biological Causes' increased in frequency (p< 0.001). Emotionally a significant reduction was seen in Confusion (p<0.001). A significant reduction was observed in Religious Rituals (p=0.002) for Coping Mechanisms. However, observing Safety Precautions and Distraction Strategies were insignificantly affected (p=0.668 and p=1, respectively). Conclusion: Critical Incident Stress Management helped healthcare workers sublimate their emotional reactions and helped them cope with a productive mindset for better management of the pandemic.

Efficiency, market concentration and bank performance during the COVID-19 outbreak: Evidence from the MENA region.

This study aims to contribute to the existing literature that explores the impact of market concentration on bank efficiency in emerging economies. Using a sample of 225 banks in 18 countries in the Middle East and North Africa (MENA) region over the period 2006-2020, we empirically investigate the significance of this relationship. Since the evidence of concentration effect on efficiency during the COVID-19 outbreak is ambiguous, we test the hypothesis that the efficiency is positively affected by the level of banking market concentration in the MENA region. We adopt fixed effect model specifications and test the robustness of our results with the two-step Generalized Method of Moments (GMM) estimation technique. Our analysis finds a strong positive association between market concentration and bank efficiency. The analysis of different types of banking systems that co-existing in the MENA region (Islamic and conventional) indicates the market concentration effect is more pronounced when the banking institution is Islamic and during the COVID-19 outbreak. Moreover, the better economic performance of Islamic banks during the initial stage of pandemic further increases their efficiency. Our analysis indicated that the impact of market competitive conditions on bank efficiency varies significantly across banks with different ownership structures and is more pronounced for government-owned banks. The results are robust using different model specifications and alternative estimation techniques.

Analysis of the ARTIC V4 and V4.1 SARS-CoV-2 primers and their impact on the detection of Omicron BA.1 and BA.2 lineage-defining mutations.

The ARTIC protocol uses a multiplexed PCR approach with two primer pools tiling the entire SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) genome. Primer pool updates are necessary for accurate amplicon sequencing of evolving SARS-CoV-2 variants with novel mutations. The suitability of the ARTIC V4 and updated V4.1 primer scheme was assessed using whole genome sequencing of Omicron from clinical samples using Oxford Nanopore Technology. Analysis of Omicron BA.1 genomes revealed that 93.22 % of clinical samples generated improved genome coverage at 50× read depth with V4.1 primers when compared to V4 primers. Additionally, the V4.1 primers improved coverage of BA.1 across amplicons 76 and 88, which resulted in the detection of the variant-defining mutations G22898A, A26530G and C26577G. The Omicron BA.2 sub-variant (VUI-22JAN-01) replaced BA.1 as the dominant variant by March 2022, and analysis of 168 clinical samples showed reduced coverage across amplicons 15 and 75. Upon further interrogation of primer binding sites, a mutation at C4321T [present in 163/168 (97 %) of samples] was identified as a possible cause of complete dropout of amplicon 15. Furthermore, two mutations were identified within the primer binding regions for amplicon 75: A22786C (present in 90 % of samples) and C22792T (present in 12.5 % of samples). Together, these mutations may result in reduced coverage of amplicon 75, and further primer updates would allow the identification of the two BA.2-defining mutations present in amplicon 75: A22688G and T22679C. This work highlights the need for ongoing surveillance of primer matches as circulating variants evolve and change.

A robust tracking system for COVID-19 like pandemic using advanced hybrid technologies

In order to track patients in coronavirus (COVID-19) like pandemic, this paper proposes a novel model based on hybrid advance technologies, which is capable to trace and track COVID-19 affectees with high accuracy. The hybrid technologies include, cellular, cyber and low range wireless technologies. This technique is capable to trace patients through call data record using cellular technology, voice over Internet protocol calls using cyber technology and physical contact without having a call history using low range wireless technologies. The proposed model is also capable to trace COVID-19 suspects. In addition to tracking, the proposed model is capable to provide surveillance capability as well by geo tagging the patients. In case of any violation by the patients an alert is sent to the concerned department. The proposed model is cost effective and privacy preserved as the entire process is carried out under the umbrella of a concerned government department. The potential outcomes of the proposed model are tracking of COVID-19 patients, monitoring of isolated patients, tracking of suspected ones and inform the mass about the safest path to use. [ABSTRACT FROM AUTHOR] Copyright of Computing is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Minimizing Viral Transmission in COVID-19 Like Pandemics: Technologies, Challenges, and Opportunities.

Coronavirus (COVID-19) pandemic has incurred huge loss to human lives throughout the world. Scientists, researchers, and doctors are trying their best to develop and distribute the COVID-19 vaccine throughout the world at the earliest. In current circumstances, different tracking systems are utilized to control or stop the spread of the virus till the whole population of the world gets vaccinated. To track and trace patients in COVID-19 like pandemics, various tracking systems based on different technologies are discussed and compared in this paper. These technologies include, cellular, cyber, satellite-based radio navigation and low range wireless technologies. The main aim of this paper is to conduct a comprehensive survey that can overview all such tracking systems, which are used in minimizing the spread of COVID-19 like pandemics. This paper also highlights the shortcoming of each tracking systems and suggests new mechanisms to overcome such limitations. In addition, the authors propose some futuristic approaches to track patients in prospective pandemics, based on artificial intelligence and big data analysis. Potential research directions, challenges, and the introduction of next-generation tracking systems for minimizing the spread of prospective pandemics, are also discussed at the end.

Cardiac sarcoma: A rare case of primary cardiac sarcoma.

INTRODUCTION AND IMPORTANCE: Primary cardiac sarcoma is a rare malignancy with a poor prognosis because of diagnostic delay, therapeutic difficulties, and high metastatic potential. The therapeutic approach includes surgery, chemotherapy, and radiation therapy, alone or in combination. However, there is a lack of evidence to guide the treatment. CASE PRESENTATION: We present a case of primary cardiac sarcoma. Our patient was presented in the department of emergency medicine (ED) in our institute with shortness of breath on exertion associated with orthopnea. Based on the history and cardiovascular examination, he underwent an echocardiogram, which revealed a sizeable echogenic density in the right ventricular outflow tract. He underwent surgical resection of the cardiac mass via median sternotomy and total cardiopulmonary bypass approach. The patient was eventually diagnosed with primary cardiac sarcoma, confirmed by tissue biopsy after surgical intervention. CLINICAL DISCUSSION: Through this report, we highlight the rarity of primary cardiac sarcomas, the importance of multidisciplinary tumor board (MDT) discussion and provide evidence of surgical excision being the treatment of choice, followed by systemic chemotherapy in selected cases. CONCLUSION: Cardiac sarcoma is a rare but highly malignant tumor with a poor prognosis. However, early diagnosis and surgical resection of a primary cardiac sarcoma can significantly increase the patient's survival and quality of life. Therefore, physicians should keep a high suspicion of a patient with clinical features suggestive of cardiac sarcoma, and echocardiography should be the diagnostic modality of choice in such patients.

The impact of COVID-19 safety interventions on creating a controlled environment on campus.

Objectives: During COVID-19 the re-opening of educational institutes was frequently debated, however with the decline in the number of COVID-19 cases, The Aga Khan University (AKU) in Karachi, Pakistan opened its campus for medical and nursing students after more than 6 months of closure. To ensure gradual resumption of activities on-campus, a combination of interventions was diligently deployed to minimize student infection rates. Scarce literature exists on students' perceptions regarding decisions implemented by university leadership. The aim of the study was to determine the efficacy of these interventions. Methods: We conducted a convergent, parallel, mixed-methods observational study targeting medical and nursing students. An online questionnaire was disseminated to elicit students' degree of (dis)agreement on a four-point Likert scale. Focused group discussions (FGDs) were conducted to comprehend reasons for (dis)agreement. Results: Total of 183 students responded to questionnaire (59.0% nursing, 67.8% female), 11 FGDs were conducted with 85 students. Interventions with highest agreement were mandatory face masks policy (94.54%), weekly mandated COVID-testing (92.35%) and students' Academic Bubble (91.26%); highest disagreement was for Sehat Check application (41.53%); and stay strong campaign (40.44%). Four themes emerged from FGDs: Effective safety interventions, Safety interventions with limited effectiveness, Utility of Sehat Check Application and Future recommendations for informing policy. Conclusion: It is paramount to seek student-feedback at forefront of university re-opening strategy. Clear communication channels are as important as an administrative response system's robustness. Bidirectional communication channels are fundamental and requisite during ever-changing policies and regulations. Engaging student representatives in decision making or implementation processes (such as "pilot" before "roll-out") would allow any potential issues to be managed early on. Gather real-time anonymous feedback and identify key areas that need further promulgation and those that need to be replaced with more effective ones.

Predictors of fatal neurological complications among admitted COVID-19 patients with their implication in outcome: A Case Control study.

BACKGROUND: COVID-19 is known to be associated to potentially fatal neurological complications; therefore, it is essential to understand the risk factors for its development and the impact they have on the outcome of COVID-19 patients. AIMS: To determine the risk factors for developing fatal neurological complications and their outcome in hospitalized COVID-19 patients. MATERIAL AND METHODS: Case control study based on hospitalized patients was conducted from July 15th 2021 to December 15th 2021. Cases and controls were COVID-19 confirmed patients with and without severe neurological manifestations. Age, comorbid conditions, vaccination status, Blood Sugar Random (BSR), D-dimers levels, anticoagulation type and dosage were taken as predictors (exposure variables) for developing neurological complications. In the case-only (subgroup) analysis, 28-day mortality were analyzed using the same predictors including admission hypoxemia. Chi square test and regression model were built to calculate OR with 95%CI. RESULTS: Among 383 patients (median age, 56 years [IQR, 24-110]; 49.9% men); 95 had neurological complications (cases) and 288 did not (controls). Development of neurological complications among COVID-19 related hospitalizations was significantly associated with old age >71 yrs. (cases, 23.2%; controls, 13.5%; OR, 3.31; 95% CI, 1.28-8.55), presence of diabetes mellitus (37.9% vs. 24%; OR, 1.9; 95% CI, 1.2-3.1), admission hyperglycemia (BSR 351-600 mg/dl), (29.5% vs. 7.6%; OR, 3.11; 95%CI, 1.54-6.33), raised D-dimer levels 5000-10,000 ng/ml (41% vs. 11.8%; OR, 5.2; 95% CI, 3.02-8.9), prophylactic dose anticoagulation (43.2% vs. 28.1%; OR, 1.9; 95%CI, 1.2-3.1), and unvaccinated status of COVID-19 patients (90.5% vs. 75.6%; OR, 3.01; 95% CI, 1.44-6.25). Neurological complications with COVID-19 were associated with increased likelihood of death or invasive mechanical ventilation by day 28 (86.3% vs. 45.1%; OR, 7.66; 95% CI, 4.08-14.4). In case-only analysis (median age, 56 years [IQR, 27,110]; 50.5% women), 67 (70.5%) had CVE, 21 (22.1%) had Encephalitis, and 7 (7.4%) had GBS as neurological manifestations. 28-day mortality among these patients was strongly associated with a lower likelihood of vaccination. (6.1% cases vs. 30.8% controls; OR, .146; 95%CI, .033- .64), being younger 17-45 yrs. (12.2% vs. 46.2%; OR, .162; 95%CI, .045-.58), having no comorbid condition (19.5% vs. 61.5%; OR, .151; 95%CI, .044- .525), having cerebrovascular events and GBS as type of neurological manifestation (76.8% vs.30.8%; OR, 7.46; 95%CI, 2.06-26.96), (2.4% vs. 38.4%; OR, .04; 95%CI, .007- 0.24) respectively, and presence of hypoxemia at admission (91.5% vs. 15.4%; OR, 58.92; 95%CI, 10.83-320.67). CONCLUSION: Old age, presence of Diabetes Mellitus, unvaccinated status of patients, high BSR at admission, high D-dimers, and prophylactic dose anticoagulation were identifies as increased risk factors for developing serious neurological complications among COVID-19 patients. Neurological problems in COVID-19 patients raised death risk 7.6-fold. The most common neurological complication was cerebrovascular events, followed by encephalitis and GBS. Unvaccinated status, cerebrovascular events, and admission hypoxemia are associated with an increased likelihood of 28-day mortality among these patients.

Perspectives from remote sensing to investigate the COVID-19 pandemic: A future-oriented approach.

As scientific technology and space science progress, remote sensing has emerged as an innovative solution to ease the challenges of the COVID-19 pandemic. To examine the research characteristics and growth trends in using remote sensing for monitoring and managing the COVID-19 research, a bibliometric analysis was conducted on the scientific documents appearing in the Scopus database. A total of 1,509 documents on this study topic were indexed between 2020 and 2022, covering 165 countries, 577 journals, 5239 institutions, and 8,616 authors. The studies related to remote sensing and COVID-19 have a significant increase of 30% with 464 articles. The United States (429 articles, 28.42% of the global output), China (295 articles, 19.54% of the global output), and the United Kingdom (174 articles, 11.53%) appeared as the top three most contributions to the literature related to remote sensing and COVID-19 research. Sustainability, Science of the Total Environment, and International Journal of Environmental Research and Public Health were the three most productive journals in this research field. The utmost predominant themes were COVID-19, remote sensing, spatial analysis, coronavirus, lockdown, and air pollution. The expansion of these topics appears to be associated with cross-sectional research on remote sensing, evidence-based tools, satellite mapping, and geographic information systems (GIS). Global pandemic risks will be monitored and managed much more effectively in the coming years with the use of remote sensing technology.

Distributed model for customer churn prediction using convolutional neural network

Purpose>The purpose of the proposed model is to assist the e-business to predict the churned users using machine learning. This paper aims to monitor the customer behavior and to perform decision-making accordingly.Design/methodology/approach>The proposed model uses the 2-D convolutional neural network (CNN;a technique of deep learning). The proposed model is a layered architecture that comprises two different phases that are data load and preprocessing layer and 2-D CNN layer. In addition, the Apache Spark parallel and distributed framework is used to process the data in a parallel environment. Training data is captured from Kaggle by using Telco Customer Churn.Findings>The proposed model is accurate and has an accuracy score of 0.963 out of 1. In addition, the training and validation loss is extremely less, which is 0.004. The confusion matric results show the true-positive values are 95% and the true-negative values are 94%. However, the false-negative is only 5% and the false-positive is only 6%, which is effective.Originality/value>This paper highlights an inclusive description of preprocessing required for the CNN model. The data set is addressed more carefully for the successful customer churn prediction.

Pathophysiology of Methicillin-Resistant Staphylococcus aureus Superinfection in COVID-19 Patients.

The global spread of the coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), has infected humans in all age groups, deteriorated host immune responses, and caused millions of deaths. The reasons for individuals succumbing to COVID-19 were not only the SARS-CoV-2 infection but also associated bacterial infections. Antibiotics were largely used to prevent bacterial infections during COVID-19 illness, and many bacteria became resistant to conventional antibiotics. Although COVID-19 was considered the main culprit behind the millions of deaths, bacterial coinfections and superinfections were the major factors that increased the mortality rate in hospitalized patients. In the present study, we assessed the pathophysiology of methicillin-resistant Staphylococcus aureus (MRSA) superinfection in COVID-19 patients in Pakistan. A total of 3492 COVID-19 hospitalized patients were screened among which 224 strain were resistant to methicillin; 110 strains were tazobactam-resistant; 53 strains were ciprofloxacin-resistant; 23 strains were gentamicin-resistant; 11 strains were azithromycin-resistant; 3 strains were vancomycin-resistant. A high frequency of MRSA was detected in patients aged ≥50 with a prevalence of 7.33%, followed by patients aged >65 with a prevalence of 5.48% and a 5.10% prevalence in patients aged <50. In addition, pneumonia was detected in the COVID-19-associated MRSA (COVID-MRSA) that showed decreased levels of lymphocytes and albumin and increased the mortality rate from 2.3% to 25.23%. Collectively, an MRSA superinfection was associated with increased mortality in COVID-19 after 12 to 18 days of hospitalization. The study assessed the prevalence of MRSA, mortality rate, pneumonia, and the emergence of antibiotic resistance as the main outcomes. The study summarized that COVID-MRSA aggravated the treatment and recovery of patients and suggested testing MRSA as critical for hospitalized patients.

Re-examining the nexuses of communicable diseases, environmental performance, and dynamics of sustainable Development in OECD countries.

We investigate the determinants of communicable diseases (CDs) and nexus of financial development, economic development, and renewable energy consumption to address the issues of ecological footprint level, the impacts of communicable diseases (CDs), and economic growth of the OECD countries throughout 2000-2019. The results from FMOLS and DOLS reveal that the levels of financial development, energy consumption, and trade volume significantly contribute to overcoming the death toll occurring due to CDs. As regards the growth function, the level of trade in the economy is significantly associated with economic growth. The findings reveal that the improvements and developments in the financial sectors and trading activities cause a reduction in the infection cases represented by COVID-19. In contrast, economic growth does have a negative but insignificant impact upon COVID-19. We conclude that sound financial development combined with economic and environmental regulations could be strategically helpful to cope with CDs.

Deployment of Wireless Sensor Network and IoT Platform to Implement an Intelligent Animal Monitoring System

This study aimed to realize Sustainable Development Goals (SDGs), i.e., no poverty, zero hunger, and sustainable cities and communities through the implementation of an intelligent cattle-monitoring system to enhance dairy production. Livestock industries in developing countries lack the technology that can directly impact meat and dairy products, where human resources are a major factor. This study proposed a novel, cost-effective, smart dairy-monitoring system by implementing intelligent wireless sensor nodes, the Internet of Things (IoT), and a Node-Micro controller Unit (Node-MCU). The proposed system comprises three modules, including an intelligent environmental parameter regularization system, a cow collar (equipped with a temperature sensor, a GPS module to locate the animal, and a stethoscope to update the heart rate), and an automatic water-filling unit for drinking water. Furthermore, a novel IoT-based front end has been developed to take data from prescribed modules and maintain a separate database for further analysis. The presented Wireless Sensor Nodes (WSNs) can intelligently determine the case of any instability in environmental parameters. Moreover, the cow collar is designed to obtain precise values of the temperature, heart rate, and accurate location of the animal. Additionally, auto-notification to the concerned party is a valuable addition developed in the cow collar design. It employed a plug-and-play design to provide ease in implementation. Moreover, automation reduces human intervention, hence labor costs are decreased when a farm has hundreds of animals. The proposed system also increases the production of dairy and meat products by improving animal health via the regularization of the environment and automated food and watering. The current study represents a comprehensive comparative analysis of the proposed implementation with the existing systems that validate the novelty of this work. This implementation can be further stretched for other applications, i.e., smart monitoring of zoo animals and poultry.

Digital technologies and circular economy in supply chain management: in the era of COVID-19 pandemic

This empirical study aims to identify the importance of Digital Technologies (DT) as an enabler in the Circular Economy (C.E.) based business model, especially during Covid-19. The concept of 'circular economy' has now been advocated as a methodology to stimulate economic growth in line with the environmental sustainability. Hence, the practices of recycling, reduction, reuse/re-manufacture, and repairing (4R's) are deemed to be the core of a circular economy. Recently, the advent of the pandemic Covid-19 has forced the nations of the world to resort to alternate resource use in their manufacturing and trading of goods and services as the supply chains have almost remained disrupted since Covid-19 appeared. We investigate the impacts of Covid-19 upon the use of technological innovation (T.I.), circular economy practices (CEP), and organizational performance (ORP) incorporating the Structural Equation Modeling (SEM). Our results show that Covid-19 significantly impacted the adoption of technological innovation, circular economy, which leads toward organizational performance. Moreover, the practices and operations under the circular economy framework also appear to influence organizational performance significantly. Our study findings bring forward meaningful insights into improving CEF-cum-technology based practices in developing and emerging markets in Asia, and convey significant implications for the business community, policymakers, and researchers.

A Nationwide Virtual Research Education Program for Medical Students in Pakistan: Methodological Framework, Feasibility Testing, and Outcomes.

Introduction: Equipping young medical trainees with fundamental research skills can be a promising strategy to address the need for professionals who can understand and responsibly communicate evolving scientific evidence during a pandemic. Despite an ardent interest to partake in research, most educational institutions in Pakistan and other low-middle income countries have not yet adopted a comprehensive strategy for research skills education. The authors aimed to design and assess the feasibility of implementing the first nation-wide virtual research workshop for medical students in Pakistan. Methods: The course "Beginners Guide to Research," designed as a nation-wide virtual research workshop series, was conducted for medical students across Pakistan in June 2020. Four interactive live workshops took place online on alternate days from June 22nd, 2020, to June 27th, 2020, each lasting 1-2 h. Outcomes included: (i) reach, (ii) efficacy as indexed by pre-post change in score pertaining to knowledge and application of research and (iii) self-rated perceptions about understanding of research on a Likert scale. Results: 3,862 participants enrolled from 41 cities and 123 institutions. Enrolled participants belonged to the following provinces: Sindh (n = 1,852, 48.0%), Punjab (n = 1,767, 45.8%), Khyber Pakhtunkhwa (n = 109, 2.8%), Azad Jammu and Kashmir (n = 84, 2.2%) Balochistan (n = 42, 1.1%). We also saw a few registrations from international students (n = 8, 0.2%). Mean (SD) age of enrolled medical students was 21.1 (2.1) years, 2,453 (63.5%) participants were female and 2,394 (62.0%) were from private-sector medical colleges. Two thousand ninety-three participants participants filled out all four pre-test and post-test forms. The total median knowledge score improved from 39.7 to 60.3% with the highest improvements in concepts of research bioethics and literature search (p < 0.001) with greater change for females compared to males (+20.6 vs. +16.2%, p < 0.001) and private institutions compared to public ones (+16.2 vs. +22.1%, p < 0.001). Conclusion: The overwhelming enrollment and significant improvement in learning outcomes (>50% of baseline) indicate feasibility of a medical student-led research course during a pandemic, highlighting its role in catering to the research needs in the LMICs.

Discovery of Potential Antiviral Compounds against Hendra Virus by Targeting Its Receptor-Binding Protein (G) Using Computational Approaches.

Hendra virus (HeV) belongs to the paramyxoviridae family of viruses which is associated with the respiratory distress, neurological illness, and potential fatality of the affected individuals. So far, no competitive approved therapeutic substance is available for HeV. For that reason, the current research work was conducted to propose some novel compounds, by adopting a Computer Aided Drug Discovery approach, which could be used to combat HeV. The G attachment Glycoprotein (Ggp) of HeV was selected to achieve the primary objective of this study, as this protein makes the entry of HeV possible in the host cells. Briefly, a library of 6000 antiviral compounds was screened for potential drug-like properties, followed by the molecular docking of short-listed compounds with the Protein Data Bank (PDB) structure of Ggp. Docked complexes of top two hits, having maximum binding affinities with the active sites of Ggp, were further considered for molecular dynamic simulations of 200 ns to elucidate the results of molecular docking analysis. MD simulations and Molecular Mechanics Energies combined with the Generalized Born and Surface Area (MMGBSA) or Poisson-Boltzmann and Surface Area (MMPBSA) revealed that both docked complexes are stable in nature. Furthermore, the same methodology was used between lead compounds and HeV Ggp in complex with its functional receptor in human, Ephrin-B2. Surprisingly, no major differences were found in the results, which demonstrates that our identified compounds can also perform their action even when the Ggp is attached to the Ephrin-B2 ligand. Therefore, in light of all of these results, we strongly suggest that compounds (S)-5-(benzylcarbamoyl)-1-(2-(4-methyl-2-phenylpiperazin-1-yl)-2-oxoethyl)-6-oxo-3,6-dihydropyridin-1-ium-3-ide and 5-(cyclohexylcarbamoyl)-1-(2-((2-(3-fluorophenyl)-2-methylpropyl)amino)-2-oxoethyl)-6-oxo-3,6-dihydropyridin-1-ium-3-ide could be considered as potential therapeutic agents against HeV; however, further in vitro and in vivo experiments are required to validate this study.

Investigating the Relationship between Human Activity and the Urban Heat Island Effect in Melbourne and Four Other International Cities Impacted by COVID-19

Climate change is one of the biggest challenges of our times, even before the onset of the Coronavirus (COVID-19) pandemic. One of the main contributors to climate change is greenhouse gas (GHG) emissions, which are mostly caused by human activities such as the burning of fossil fuels. As the lockdown due to the pandemic has minimised human activity in major cities, GHG emissions have been reduced. This, in turn, is expected to lead to a reduction in the urban heat island (UHI) effect in the cities. The aim of this paper is to understand the relationship between human activity and the UHI intensity and to provide recommendations towards developing a sustainable approach to minimise the UHI effect and improve urban resilience. In this study, historical records of the monthly mean of daily maximum surface air temperatures collected from official weather stations in Melbourne, New York City, Tokyo, Dublin, and Oslo were used to estimate the UHI intensity in these cities. The results showed that factors such as global climate and geographic features could dominate the overall temperature. However, a direct relationship between COVID-19 lockdown timelines and the UHI intensity was observed, which suggests that a reduction in human activity can diminish the UHI intensity. As lockdowns due to COVID-19 are only temporary events, this study also provides recommendations to urban planners towards long-term measures to mitigate the UHI effect, which can be implemented when human activity returns to normal.