Data analysis for COVID-19 deaths using a novel statistical model: Simulation and fuzzy application.

This paper provides a novel model that is more relevant than the well-known conventional distributions, which stand for the two-parameter distribution of the lifetime modified Kies Topp-Leone (MKTL) model. Compared to the current distributions, the most recent one gives an unusually varied collection of probability functions. The density and hazard rate functions exhibit features, demonstrating that the model is flexible to several kinds of data. Multiple statistical characteristics have been obtained. To estimate the parameters of the MKTL model, we employed various estimation techniques, including maximum likelihood estimators (MLEs) and the Bayesian estimation approach. We compared the traditional reliability function model to the fuzzy reliability function model within the reliability analysis framework. A complete Monte Carlo simulation analysis is conducted to determine the precision of these estimators. The suggested model outperforms competing models in real-world applications and may be chosen as an enhanced model for building a statistical model for the COVID-19 data and other data sets with similar features.

Overview Of Proteinuria Among Hospitalized Patients With COVID-19

COVID-19 was thought to be primarily a respiratory disease, progressing in some patients to serious respiratory symptoms, pneumonia, severe respiratory distress syndrome and even death.Patients infected with SARS-CoV-2 might have a variety of clinical presentations, ranging from no symptoms to life-threatening sickness. Later analysis revealed entire systems were compromised, affecting other vital organs, including the kidneys, and a correlation was observed between chronic kidney disease (CKD) and COVID-19 severity. A large percentage of affected patients present with acute kidney injury. However, specific phenotypic aspects of acute kidney injury or other renal manifestations of COVID-19 remain sparsely characterized. Urine proteincreatinine ratio (UPCR) in single-voided urine samples correlates well with measurements of 24-hour urinary protein when properly interpreted by taking into consideration the different rates of creatinine excretion. Arguably, knowing the baseline status of proteinuria can be equally important in the assessment of AKI in a hospitalized patient. Many reports indicate that proteinuria can be detected in acute kidney injury associated with COVID-19 despite being largely described as a form of acute tubular injury. Therefore, this study aimed to review acute kidney injury among hospitalized patients with COVID 19.Copyright © 2022 Wolters Kluwer Medknow Publications. All rights reserved.

Updated Review about Liver Cirrhosis in the COVID-19 Era

Background: The clinical manifestations in patients with COVID-19 may be nonspecific, but most have fever, cough, followed by dyspnea, fatigue, or sputum production. approximately 14% to 53% of patients experience various degrees of liver damage, although most of these injuries are mild and transient, with a satisfactory prognosis in patients without prior liver disease. In contrast, COVID-19 in patients with pre-existing liver disease has been reported to result in higher hospitalization and mortality rates. Among these pre-existing liver diseases, cirrhosis is a chronic liver disease that involves the collapse of the structure of the liver and distortion of the vascular architecture. Cirrhosis is associated with inherent immune dysfunction and an altered gut-liver axis;patients with cirrhosis are particularly at elevated risk of infections and the associated complications. It remains uncertain whether immunocompromised patients with COVID-19 have a higher risk of adverse outcomes. Patients with cancer or solid organ transplant recipients may have an elevated risk of more severe COVID-19;whereas patients taking biologic therapies may not have a greater risk of developing severe COVID-19. No additional risk of death was observed in cancer patients receiving active treatment except in those undergoing chemotherapy. Whether patients with human immunodeficiency virus infection are at higher risk of mortality due to COVID-19 is unclear.Limited evidence has shown that the clinical manifestations in cirrhotic patients with COVID-19 are similar to those in the general population with COVID-19, with fever and cough remaining the most common symptoms, followed by shortness of breath and sputum production. Interestingly, whereas similar proportions of cirrhotic and noncirrhotic patients developed respiratory and cardiovascular symptoms, cirrhotic patients were less likely to develop gastrointestinal symptoms (e.g., diarrhea, nausea, vomiting). Copyright © 2022 Wolters Kluwer Medknow Publications. All rights reserved.

Updated Review about Liver Cirrhosis in the COVID-19 Era

Background: The clinical manifestations in patients with COVID-19 may be nonspecific, but most have fever, cough, followed by dyspnea, fatigue, or sputum production. approximately 14% to 53% of patients experience various degrees of liver damage, although most of these injuries are mild and transient, with a satisfactory prognosis in patients without prior liver disease. In contrast, COVID-19 in patients with pre-existing liver disease has been reported to result in higher hospitalization and mortality rates. Among these pre-existing liver diseases, cirrhosis is a chronic liver disease that involves the collapse of the structure of the liver and distortion of the vascular architecture. Cirrhosis is associated with inherent immune dysfunction and an altered gut-liver axis;patients with cirrhosis are particularly at elevated risk of infections and the associated complications. It remains uncertain whether immunocompromised patients with COVID-19 have a higher risk of adverse outcomes. Patients with cancer or solid organ transplant recipients may have an elevated risk of more severe COVID-19;whereas patients taking biologic therapies may not have a greater risk of developing severe COVID-19. No additional risk of death was observed in cancer patients receiving active treatment except in those undergoing chemotherapy. Whether patients with human immunodeficiency virus infection are at higher risk of mortality due to COVID-19 is unclear.Limited evidence has shown that the clinical manifestations in cirrhotic patients with COVID-19 are similar to those in the general population with COVID-19, with fever and cough remaining the most common symptoms, followed by shortness of breath and sputum production. Interestingly, whereas similar proportions of cirrhotic and noncirrhotic patients developed respiratory and cardiovascular symptoms, cirrhotic patients were less likely to develop gastrointestinal symptoms (e.g., diarrhea, nausea, vomiting). Copyright © 2022 Wolters Kluwer Medknow Publications. All rights reserved.

Energy Efficiency Indicators and the First Design Stages for Commercial Centers after the Coronavirus Pandemic

The COVID-19 pandemic, which emerged in the last quarter of 2019, has seriously affected the global economy, including sectors such as the energy and building industries. Studies of COVID-19 transmission indicate a direct relationship between the number of occupants in a building and the risk of infection. The aims of this study were to focus on workplace density strategies as a primary, overlooked factor that can affect energy consumption and the risk of transmission of viruses within buildings and to determine optimal workplace density strategies to reduce energy consumption, especially in commercial buildings. To this end, the practical approach was used by applying COVE.TOOL technology and data from COVID-19 tracking projects to the proposed occupant density after new design considerations for the food court of the Mall of Arabia – the most famous shopping mall in Egypt. This approach was also used to evaluate customer visits to reduce the spread of disease and improve their energy efficiency. © 2022. Scientific Journal of King Faisal University Basic and Applied Sciences. All Rights Reserved.

The Increasing Prevalence of DM in COVID-19 Patients in North Sudan: Is it a Matter of Diabetogenicity?

Background: COVID-19 had made more than 197 million infections and 4 million death. Therefore, we need to assess the prevalence of DM among COVID-19 patients and its effect on the outcome. Objectives: We aim to assess the prevalence, risk factors and outcome of diabetes among COVID-19 patients. Methods: This was a prospective, cross-sectional, hospital-based study enrolled 400 COVID-19 patients and was conducted in COVID-19 isolation centres in North Sudan. Results: Males constituted 275 (68.9%) of the study participants, and the majority of participants were aged between 40 and 60 years 150(37.4%). The prevalence of DM was found to be 49.25% in the study participants. Diabetics were significantly more likely to have a respiratory rate higher than 30 (P=0.012), and oxygen saturation less than 93% (P<0.001), to develop shock (P=0.004), to require oxygen therapy (P<0.001), to be intubated (P<0.001), to develop respiratory failure and organ failure (P<0.001), and to have a poorer outcome (P<0.001). New-onset diabetes occurred in 20 (5%) participants and their mortality was higher compared to non-patients with diabetes (P=0.04). The total mortality of participants was 15.8%. Factors associated with poorer outcome were older age (P<0.001), and having type 1 diabetes (P=0.025).Conclusion: The prevalence of diabetes is very high among COVID-19 patients, and is associated with a more severe disease and a poorer outcome. New onset diabetes was associated with poorer outcome compared to non-diabetics.

RESCOVIDTCNnet: A residual neural network-based framework for COVID-19 detection using TCN and EWT with chest X-ray images.

Since the advent of COVID-19, the number of deaths has increased exponentially, boosting the requirement for various research studies that may correctly diagnose the illness at an early stage. Using chest X-rays, this study presents deep learning-based algorithms for classifying patients with COVID illness, healthy controls, and pneumonia classes. Data gathering, pre-processing, feature extraction, and classification are the four primary aspects of the approach. The pictures of chest X-rays utilized in this investigation came from various publicly available databases. The pictures were filtered to increase image quality in the pre-processing stage, and the chest X-ray images were de-noised using the empirical wavelet transform (EWT). Following that, four deep learning models were used to extract features. The first two models, Inception-V3 and Resnet-50, are based on transfer learning models. The Resnet-50 is combined with a temporal convolutional neural network (TCN) to create the third model. The fourth model is our suggested RESCOVIDTCNNet model, which integrates EWT, Resnet-50, and TCN. Finally, an artificial neural network (ANN) and a support vector machine were used to classify the data (SVM). Using five-fold cross-validation for 3-class classification, our suggested RESCOVIDTCNNet achieved a 99.5 percent accuracy. Our prototype can be utilized in developing nations where radiologists are in low supply to acquire a diagnosis quickly.

An Arabic Egyptian Dialect COVID-19 Twitter Dataset (ArECTD)

Citizens are increasingly expressing their ideas and feelings on social media platforms such as Twitter. During the coronavirus crisis, numerous emotions are exposed, including sadness, anger, fear, sympathy, surprise, etc. The Arabic Egyptian Dialect COVID-19 Twitter Dataset (ArECTD), comprised of 78K tweets, was collected in the period from the 1st of January 2020 till the 30th of May 2021 focusing on the Egyptian dialect. It was annotated using a combination of manual and a semi-supervised self-learning technique. The tweets of ArECTD were categorized into 10 emotions (sarcasm, sadness, anger, fear, sympathy, joy, hope, surprise, love, and none). Emotion analysis of this dataset could help decision makers understand and respond to the public reactions during the pandemic. © 2021 IEEE.

Repurposing of available antiviral drugs against SARS-CoV-2 by targeting crucial replication machinery proteins: a molecular docking study

Background and objectives The newly emerged severe acute respiratory syndrome coronavirus is spreading worldwide rapidly with increasing incidence rates. Due to lack of effective treatments and vaccines, various drug repurposing studies are being developed. Searching for available antiviral drug libraries is the best and fast option to advance to clinical trials and spread their application among infected patients. Materials and methods Molecular docking study was performed utilizing AutoDock 4.2 system and Discovery Studio 4.5, which were utilized to predict the activity pocket of the target proteins. Results and conclusion The results found that the interacting affinities resulted from the molecular simulation of 3CL protease with ligands Ledipasvir, Sofosbuvir, Ribavirin, Galidesivir, Tenofovir, and Remdesivir were -7.2, -7.4, -7.2, -6.3, -6.1 and -6.6 kcal/mol, respectively. Similarly, the interacting energies obtained from the docking of RNA helicase with ligands were -7.9, -7.4, -6.4, -7.9, -6.2, and -6.9 kcal/mol. Also, the binding energies obtained from the docking of 3′-5′ exoribonuclease with ligands were -10.6, -10.1, -6.5, -7.1, -6.1, and -9.3 kcal/mol. Finally, the binding energies score from the docking of the RNA-dependent RNA polymerase with ligands was -9.6, -6.9, -6.2, -6.6, -6.7, and -6.4 kcal/mol. Based on the binding energy score and docking result, Ledipasvir and Sofosbuvir have a higher affinity of the drug molecule such as against RNA-dependent RNA polymerase, exonuclease, and 3CL protease. Besides, Ledipasvir and Galidesivir show prominent binding interaction with severe acute respiratory syndrome coronavirus RNA helicase. The results are promising for evaluated drugs especially Ledipasvir and Sofosbuvir and could be useful in emergency treatment of coronavirus disease 2019 patients. © COLING 2018.All right reserved.

The Impact of Pandemic on the Survival of Construction Industry: A Case of COVID-19 in Bosnia and Herzegovina

COVID-19 has changed the world overnight and the question arises as to how much the course of the construction industry has changed since this industry largely depends on the dynamics of work, teamwork, but also the daily meetings of different teams. The objectives of this study are to learn about the impact of the pandemic on the construction industry in Bosnia and Herzegovina, but also to compare Bosnian-Herzegovinian trends with growing or declining trends in the construction industry and the workforce in Europe and other regions. Qualitative research methods have been employed to study this topic, meaning that after identifying the scope, problems, objectives of the study, and research goals, in-depth interviews have been conducted with leading experts from the construction industry in Bosnia and Herzegovina. Furthermore, experiences and statistics from other target countries are reviewed in this paper. The aforementioned interviews provided information on experts’ experiences in the time of the pandemic as well as their analysis of the impact of COVID-19 on the construction industry in Bosnia of Hercegovina. The findings of this research will provide an objective picture of trends in the construction industry in Bosnia and Herzegovina during the caused by COVID-19 pandemic. Moreover, the paper includes expert recommendations on how to deal with such issues as pandemics in the future and how to become more resilient. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Development of fog computing based patient behavior monitoring system

In response to the Covid19 pandemic, the Smart Wi-Fi service is offering users access to fast and free Wi-Fi in selected public areas nationwide. A healthcare application is improved to reduce the effort of people and to ensure the status checking of patients. This is performed all the time and in real-time by detecting and monitoring the patient's heartbeat by the doctor and the patient himself. The healthcare web application consists of the client-side, which provides the login, registration, and all information required. The client-side sends information using WIFI technology to the fog server-side, which works on managing and saving these data. In fog computing multi of heterogeneous devices are connected at the end of the network, these devices are costly and energy inefficient. We proposed PBM techniques by using BSN, to detect the Covid19 patient's behaviors. And use Time Slotted Channel Hopping (TSCH) in IEEE802.15.4e.Our proposed Algorithm improve Throughput, reduce cost, Bandwidth efficiency and minimizing the communication time needed by the sensor by using WIFI- 6 technology. The experimental results are evaluated in terms of Throughput, Packet delivery ratio. © 2021 IEEE.