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Objectives: To measure the prevalence of viral infections, length of stay (LOS), and outcome in children admitted to the pediatric intensive care unit (PICU) during the period preceding the COVID-19 pandemic in a MERS-CoV endemic country. Methods: A retrospective chart review of children 0–14 years old admitted to PICU with a viral infection. Results: Of 1736 patients, 164 patients (9.45%) had a positive viral infection. The annual prevalence trended downward over a three-year period, from 11.7% to 7.3%. The median PICU LOS was 11.6 days. Viral infections were responsible for 1904.4 (21.94%) PICU patient-days. Mechanical ventilation was used in 91.5% of patients, including noninvasive and invasive modes. Comorbidities were significantly associated with intubation (P-value = 0.025). Patients infected with multiple viruses had median pediatric index of mortality 2 (PIM 2) scores of 4, as compared to 1 for patients with single virus infections (p < 0.001), and a median PICU LOS of 12 days, compared to 4 in the single-virus group (p < 0.001). Overall, mortality associated with viral infections in PICU was 7 (4.3%). Patients with viral infections having multiple organ failure were significantly more likely to die in the PICU (p = 0.001). Conclusion: Viral infections are responsible for one-fifth of PICU patient-days, with a high demand for mechanical ventilation. Patients with multiple viral infections had longer LOS, and higher PIM 2 scores. The downward trend in the yearly rate of PICU admissions for viral infections between the end of the MERS-CoV outbreak and the start of the COVID-19 pandemic may suggest viral interference that warrants further investigations. © 2022 Japanese Society of Chemotherapy and The Japanese Association for Infectious Diseases
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Venous thromboembolism (VTE) is known to be a common respiratory and/or cardiovascular complication in hospitalized patients with viral infections. Numerous studies have proven human immunodeficiency virus infection to be a prothrombotic condition. An elevated VTE risk has been observed in critically ill H1N1 influenza patients. VTE risk is remarkably higher in patients infected with the Hepatitis C virus in contrast to uninfected subjects. The elevation of D-dimer levels supported the association between Chikungunya and the Zika virus and the rise of clinical VTE risk. Varicella-zoster virus is a risk factor for both cellulitis and the consequent invasive bacterial disease which may take part in thrombotic initiation. Eventually, hospitalized patients infected with the coronavirus disease of 2019 (COVID-19), the cause of the ongoing worldwide pandemic, could mainly suffer from an anomalous risk of coagulation activation with enhanced venous thrombosis events and poor quality clinical course. Although the risk of VTE in nonhospitalized COVID-19 patients is not known yet, there are a large number of guidelines and studies on thromboprophylaxis administration for COVID-19 cases. This study aims to take a detailed look at the effect of viral diseases on VTE, the epidemiology of VTE in viral diseases, and the diagnosis and treatment of VTE.
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The coronavirus disease (COVID-19) continued to strike as a highly infectious and fast-spreading disease in 2020 and 2021. As the research community actively responded to this pandemic, we saw the release of many COVID-19-related datasets and visualization dashboards. However, existing resources are insufficient to support multiscale and multifaceted modeling or simulation, which is suggested to be important by the computational epidemiology literature. This work presents a curated multiscale geospatial dataset with an interactive visualization dashboard under the context of COVID-19. This open dataset will allow researchers to conduct numerous projects or analyses relating to COVID-19 or simply geospatial-related scientific studies. The interactive visualization platform enables users to visualize the spread of the disease at different scales (e.g., country level to individual neighborhoods), and allows users to interact with the policies enforced at these scales (e.g., the closure of borders and lockdowns) to observe their impacts on the epidemiology.
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Purpose>Unsafe food can lead to various foodborne diseases and even death, especially among children. This paper aims to assess food safety knowledge and changes in practices and concerns among adults ≥ 18 years during the coronavirus disease 2019 (COVID-19) pandemic.Design/methodology/approach>A cross-sectional, web-based survey was conducted among 325 adults living in Northern India. Demographic data and information regarding their knowledge, practices and concerns about various food safety issues were collected to see if there were any changes due to the COVID-19 pandemic.Findings>The results showed that the participants had slightly higher than average knowledge and good food safety practices with mean scores of 9.75 ± 2.23 and 24.87 ± 2.28, respectively. Contracting COVID-19 from food and food packaging materials was of high concern for more than 70% of the participants. Majority (> 80%) of them reported an increase in the frequency of handwashing. About 16% of the participants used chemical disinfectants for washing fruits and vegetables. An increase (57.5%) in the frequency of food label reading was also noted during the pandemic. Freshness and the general quality of food items (49.5%), safety of food (30.8%) and cost (18.2%) were the top drivers that influenced the purchase decision.Originality/value>This study highlighted the need to send out clear messages on safe food handling practices and keeping the tempo up for sustaining good hygienic practices. This will help in reducing the risk of foodborne diseases.
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The internet of things (IoT) is quickly evolving, allowing for the connecting of a wide range of smart devices in a variety of applications including industry, military, education, and health. Coronavirus has recently expanded fast across the world, and there are no particular therapies available at this moment. As a result, it is critical to avoid infection and watch signs like fever and shortness of breath. This research work proposes a smart and robust system that assists patients with influenza symptoms in determining whether or not they are infected with the coronavirus disease (COVID-19). In addition to the diagnostic capabilities of the system, the system aids these patients in obtaining medical care quickly by informing medical authorities via Blynk IoT. Moreover, the global positioning system (GPS) module is used to track patient mobility in order to locate contaminated regions and analyze suspected patient behaviors. Finally, this idea might be useful in medical institutions, quarantine units, airports, and other relevant fields.
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Mitigation of the activity of the main protease (Mpro) and papain-like protease (PLpro) of SARS CoV-2 has direct implications in combating the ongoing deadly COVID-19 pandemic. The active site of these proteases contains cysteine thiols which are covalently modified by the sulfur drugs such as ebselen and disulfiram. The natural product of Allium contains several reactive sulfur compounds that may covalently modify the active site cysteine thiols of coronavirus proteases. The report has assessed the binding affinity of the 52 different sulfur compounds of Allium against both Mpro and PLpro of coronavirus by conventional docking methods. Three of the top six compounds have demonstrated high affinity for both the proteases, namely, E-ajoene (S3), S-(3-pentanyl)-L-cysteine-sulfoxide (S49), and 1-propenyl allyl thiosulfinate (S14). The reactive sulfur compounds E-ajoene and 1-propenyl allyl thiosulfinate were subjected to the calculation of energetics of the putative reactions and covalent docking studies. The results indicate they covalently modify the active site cysteine thiols of the proteases through S-thioallylation, S-thioallyl sulfinyl propenylation, and S-thiopropenylation. The diversity of covalent modifications, high affinity for both the proteases and sulfur-mediated hydrogen bonds at the active site indicate that E-ajoene is a potential dual protease targeting covalent inhibitor of SARS CoV-2.
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Solid waste management is one of the critical challenges seen everywhere, and the coronavirus disease (COVID-19) pandemic has only worsened the problems in the safe disposal of infectious waste. This paper outlines a design for a mobile robot that will intelligently identify, grasp, and collect a group of medical waste items using a six-degree of freedom (DoF) arm, You Only Look Once (YOLO) neural network, and a grasping algorithm. Various designs are generated before running simulations on the selected virtual model using Robot Operating System (ROS) and Gazebo simulator. A lidar sensor is also used to map the robot's surroundings and navigate autonomously. The robot has good scope for waste collection in medical facilities, where it can help create a safer environment.
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The adsorption of viruses from aqueous solution is frequently performed to detect viruses. Charged filtration materials capture viruses via electrostatic interactions, but lack the specificity of biological virus‐binding substances like heparin. Herein, we present three methods to immobilize heparin‐mimicking, virus‐binding polymers to a filter material. Two mussel‐inspired approaches are used, based on dopamine or mussel‐inspired dendritic polyglycerol, and post‐functionalized with a block‐copolymer consisting of linear polyglycerol sulfate and amino groups as anchor (lPGS‐b‐NH2). As third method, a polymer coating based on lPGS with benzophenone anchor groups is tested (lPGS‐b‐BPh). All three methods yield dense and stable coatings. A positively charged dye serves as a tool to quantitatively analyze the sulfate content on coated fleece. Especially lPGS‐b‐BPh is shown to be a dense polymer brush coating with about 0.1 polymer chains per nm2. Proteins adsorb to the lPGS coated materials depending on their charge, as shown for lysozyme and human serum albumin. Finally, herpes simplex virus type 1 (HSV‐1) and severe acute respiratory syndrome coronavirus type 2 (SARS‐CoV‐2) can be removed from solution upon incubation with coated fleece materials by about 90% and 45%, respectively. In summary, the presented techniques may be a useful tool to collect viruses from aqueous environments.
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The world's agricultural needs are growing with the pace of increase in its population. Agricultural farmers play a vital role in our society by helping us in fulfilling our basic food needs. So, we need to support farmers to keep up their great work, even in difficult times such as the coronavirus disease (COVID-19) outbreak, which causes hard regulations like lockdowns, curfews, and social distancing procedures. In this article, we propose the development of a recommender system that assists in giving advice, support, and solutions for the farmers' agricultural related complaints (or queries). The proposed system is based on the latent semantic analysis (LSA) approach to find the key semantic features of words used in agricultural complaints and their solutions. Further, it proposes to use the support vector machine (SVM) algorithm with Hadoop to classify the large agriculture dataset over Map/Reduce framework. The results show that a semantic-based classification system and filtering methods can improve the recommender system. Our proposed system outperformed the existing interest recommendation models with an accuracy of 87%.
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PurposeSocial distancing. Travel bans. Confinement. The purpose of this paper is to document that more than 50% of the world population is affected by World Health Organization (WHO) recommendations for the 2020 coronavirus crisis. The WHO admits that the evidence quality for the effectiveness of these recommendations is low or very low.Design/methodology/approachThis self-contradiction is confirmed by a WHO document published in October 2019 as well as supporting documentation from the European Centre for Disease Prevention and Control.FindingsThis viewpoint concludes that an obvious resolution of this self-contradiction would be to limit restrictions and interventions to those for whose effectiveness the WHO's document reported that there was at least moderate evidence.Originality/valueA shift of focus is suggested from discussions on the commensurability and social costs of anti-COVID-19 interventions to their actual effectiveness.
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Chronic obstructive pulmonary disease (COPD) is a general clinical issue in numerous countries considered the fifth reason for inability and the third reason for mortality on a global scale within 2021. From recent reviews, a deep convolutional neural network (CNN) is used in the primary analysis of the deadly COPD, which uses the computed tomography (CT) images procured from the deep learning tools. Detection and analysis of COPD using several image processing techniques, deep learning models, and machine learning models are notable contributions to this review. This research aims to cover the detailed findings on pulmonary diseases or lung diseases, their causes, and symptoms, which will help treat infections with high performance and a swift response. The articles selected have more than 80% accuracy and are tabulated and analyzed for sensitivity, specificity, and area under the curve (AUC) using different methodologies. This research focuses on the various tools and techniques used in COPD analysis and eventually provides an overview of COPD with coronavirus disease 2019 (COVID-19) symptoms.
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Food industry is highly affected by COVID‐19 pandemic;therefore, the preventive measures and precautions to be taken to reduce the risk of transmission of the SARS‐CoV‐2 virus and incidence of infection are necessary. This review integrate and analyze the available information and data on precautions of corona virus in food industry in pursuance of informing the public and the scientific community, as well as creating collective knowledge among food industry establishment and raising awareness of preventive measures during COVID‐19 pandemic. In this study, the information and data have been reviewed about the transmitting routs of the SARS‐CoV‐2 virus and the preventive measures and precautions to be taken in food industry during COVID‐19 pandemic in order to reduce the risk of transmission of the virus and incidence of the infection. The restriction of spreading corona virus through food industry in procedures, such as manufacturing, processing, packing, transporting. The transmitting routs of the SARS‐CoV‐2 virus are studied adequately in favor of suggesting strategies from the present scenarios. Limited number of publications has identified the risk factors for COVID‐19 and preventive measures, especially in food industry. Findings from this review may contribute to promoting research and spreading knowledge among food industry establishment and raise awareness of preventive measures in food industry during COVID‐19 pandemic.
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Even though coronavirus disease 2019 (COVID-19) vaccination has been done, preparedness for the possibility of the next outbreak wave is still needed with new mutations and virus variants. A near real-time surveillance system is required to provide the stakeholders, especially the public, to act in a timely response. Due to the hierarchical structure, epidemic reporting is usually slow particularly when passing jurisdictional borders. This condition could lead to time gaps for public awareness of new and emerging events of infectious diseases. Online news is a potential source for COVID-19 monitoring because it reports almost every infectious disease incident globally. However, the news does not report only about COVID-19 events, but also various information related to COVID-19 topics such as the economic impact, health tips, and others. We developed a framework for online news monitoring and applied sentence classification for news titles using deep learning to distinguish between COVID-19 events and non-event news. The classification results showed that the fine-tuned bidirectional encoder representations from transformers (BERT) trained with Bahasa Indonesia achieved the highest performance (accuracy: 95.16%, precision: 94.71%, recall: 94.32%, F1-score: 94.51%). Interestingly, our framework was able to identify news that reports the new COVID strain from the United Kingdom (UK) as an event news, 13 days before the Indonesian officials closed the border for foreigners.
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PurposeThe novel Coronavirus (COVID-19) pandemic, which started in late December 2019, has spread to more than 200 countries. As no vaccine is yet available for this pandemic, government and health agencies are taking draconian steps to contain it. This pandemic is also trending on social media, particularly on Twitter. The purpose of this study is to explore and analyze the general public reactions to the COVID-19 outbreak on Twitter.Design/methodology/approachThis study conducts a thematic analysis of COVID-19 tweets through VOSviewer to examine people's reactions related to the COVID-19 outbreak in the world. Moreover, sequential pattern mining (SPM) techniques are used to find frequent words/patterns and their relationship in tweets.FindingsSeven clusters (themes) were found through VOSviewer: Cluster 1 (green): public sentiments about COVID-19 in the USA. Cluster 2 (red): public sentiments about COVID-19 in Italy and Iran and a vaccine, Cluster 3 (purple): public sentiments about doomsday and science credibility. Cluster 4 (blue): public sentiments about COVID-19 in India. Cluster 5 (yellow): public sentiments about COVID-19's emergence. Cluster 6 (light blue): public sentiments about COVID-19 in the Philippines. Cluster 7 (orange): Public sentiments about COVID-19 US Intelligence Report. The most frequent words/patterns discovered with SPM were "COVID-19,” "Coronavirus,” "Chinese virus” and the most frequent and high confidence sequential rules were related to "Coronavirus, testing, lockdown, China and Wuhan.”Research limitations/implicationsThe methodology can be used to analyze the opinions/thoughts of the general public on Twitter and to categorize them accordingly. Moreover, the categories (generated by VOSviewer) can be correlated with the results obtained with pattern mining techniques.Social implicationsThis study has a significant socio-economic impact as Twitter offers content posting and sharing to billions of users worldwide.Originality/valueAccording to the authors' best knowledge, this may be the first study to carry out a thematic analysis of COVID-19 tweets at a glance and mining the tweets with SPM to investigate how people reacted to the COVID-19 outbreak on Twitter.
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PurposeDuring the COVID-19 pandemic people worldwide in the same household spent more time together and school children engaged in remote learning throughout extended lockdowns and restrictions. The present study aimed to explore parents' perceptions of their involvement and enjoyment in food-related interactions with their children during coronavirus disease 2019 (COVID-19)-associated lockdowns/restrictions and changes in their children's food intake, especially children's lunches during the remote learning period.Design/methodology/approachData from parents (n = 136) were collected via an online survey in 2020. Parents' responses to closed-ended questions were analysed via descriptive statistics and open-ended responses were analysed thematically.FindingsMost parents (62%) reported that they interacted more with their school-aged (5–17 years) children about food during COVID-19 compared to pre-pandemic times. These interactions included cooking, menu planning, eating, conversations around food, and gardening. Most parents (74%) prepared meals with their children during the pandemic and most of them (89%) reported that they enjoyed it. Most parents (n = 91 out of 121) perceived that their children's lunches during remote learning were different to when attending school in person and these changes included eating hot and home-cooked food and more elaborate meals.Originality/valueThis study sheds important insights into a sample of Australian parents' food-related interactions with their school-aged children during the COVID-19 pandemic and associated lockdowns and parents' observations and perceptions of changes in the children's food intake during the remote learning period.
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An increase in number of Coronavirus disease 2019 (COVID-19) cases will lead to more cluster discovery in Malaysia. Furthermore, with the increasing population, city growth, workplace income needs, high-risk groups, and other relevant factors can contribute to the formation of the new clusters. The cluster distribution of the disease could be seen by mapping and spatial analysis to understand their spatial phenomena of the disease dynamics. The purpose of the study is to analyse the spatial distribution of COVID-19 cluster cases in Selangor for year 2020. Two objectives of the study are i) to determine the hotspot location of the COVID- 19 cluster, and ii)to examine the spatial distribution of the factors affecting the COVID-19 cluster. The data processing was conducted using hotspot analysis and ordinary least squares (OLS) in ArcGIS Pro and Microsoft Excel to explore the local disease phenomena. TheCOVID-19 cases was most prevalent in the Petaling district, followed by Hulu Langat and Klang. The virus had the least impact in Sabak Bernam, Hulu Selangor, Kuala Selangor, Sepang, Kuala Langat, and Gombak. Three environmental factors of population density, the effects of urbanisation, and workplace cases were influential variables at the local clusters. These findings could help the local agencies to facilitate and control the spread mode of the virus in a spatial human environment.
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The coronavirus disease 2019 (COVID-19) epidemic that began in early 2020 quickly formed a global trend, bringing unprecedented shocks to many countries' and even the global trade economy. Big data is the main feature of the Internet era, which has transformed the industrial development pattern of modern society and has now flourished in the field of trade economy;therefore, it is of great significance to apply the big data analysis technology to study the impact of the COVID-19 epidemic on the global trade economy. On the basis of summarizing and analyzing previous research works, this paper, expounded the research status and significance of the impact of the COVID-19 epidemic on the global trade economy, elaborated the development background, The study results of this paper provide a reference for further researches on the impact of the impact of the COVID-19 epidemic on the global trade economy based on big data analysis.
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The coronavirus (COVID-19) has had severe global impacts in many aspects of education. Asian countries and regions have been the first responders to move entirely online since the epidemic started. The aim of this paper is two-folded. First, this study investigates the correlations in order to understand the compounded effects on presences in the participating synchronous learning environments. Second, this paper provide empirical evidence and insights for educators on the future trends of learning and instructional strategy in online teaching. This study investigated students' perception of synchronous e-learning during the COVID-19 pandemic for the better design of the e-learning teaching pedagogy and determines how the key factors of e-learning perception are inter-correlated enabling educators to focus on. The study has important implications that student readiness in educational technology is critical to assist the recent practice in implementing online learning.
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Face masks have an effect of preventing the spread of infectious diseases such as coronavirus disease 2019 (COVID-19). With these masks, it is primarily aimed to prevent the environment from being contaminated by the user. However, in the COVID-19 outbreak, many countries made it mandatory to use masks in areas with high human circulation such as marketplaces, shopping malls and hospitals, and then in all areas outside the home. Some tests such as filtration efficiency, microbial load, resistance to body fluids, flammability and breathability are performed to determine the protection potential and wearing comfort of face masks. In this study, we investigated the bacterial filtration efficiency (%), microbial load (cfu/g), breathability (Pa/cm2) and air permeability values of five different face masks obtained by combining polypropylene (PP) nonwoven layers in different weights (accordance with EN 14683:2019 + AC:2019, EN ISO 11737-1:2018 and TS 391 EN ISO 9237 Standards). The surface morphologies of the nonwoven fabrics were characterized by scanning electron microscope (SEM). It was observed that the weight change in spunbond masks (1–4) was directly proportional to bacterial filtration efficiency and differential pressure, and inversely proportional to air permeability. In addition, SEM analysis showed that the average fiber diameter of the meltblown layer was at least 5.80 times smaller than the spunbond layers, and as a result, dramatic differences were also observed in the air permeability and differential pressure values of the Spunbond-Meltblown-Spunbond (SMS) mask (5) compared to spunbond masks.
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Covid-19 is a disease of the respiratory tract caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) virus. One way to diagnose Covid-19 can be done by examining lung abnormalities on the results of a Computed Tomography Scan (CT-Scan) of the lungs. However, the determination of the diagnostic results obtained requires high accuracy and quite a long time. For this reason, an automatic system is needed to make it easier for medical personnel to diagnose Covid-19 disease quickly and accurately. One way to do this with the help of a computer is pattern recognition. In this study, pattern recognition techniques were used which were divided into three stages, namely pre-processing, feature extraction and classification. The methods used in the pre-processing stage are grayscale and Contrast Limited Adaptive Histogram Equalization (CLAHE) to improve image quality and contrast. The extraction stage uses the Principal Component Analysis (PCA) method, because it can reduce data dimensions without eliminating important features in the data. For the classification stage, a deep learning-based method is used, namely the Convolutional Neural Network (CNN). The CNN architecture used in this study is Resnet-50. The method proposed in this research is evaluated by measuring the performance values of accuracy, recall, precision, F1-score, and Cohen Kappa. The results of the study indicate that the PCA method has worked optimally in dimension reduction, without losing important features on CT-scan images of the lungs. Besides that, the proposed method has succeeded in classifying Covid-19 very well, as seen from the accuracy, Recall, Precision, F1-Score and Cohen Kappa values above 90%.