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Background: During COVID-19 pandemic, most studies have focused on sampling technique in adults. Considering the need to be aware of the effectiveness and evaluation of sampling methods in children, we have motivated a search for introducing and performing sampling techniques, especially upper respiratory tract sampling in children. We systematically reviewed the literature to understand the performance of different sampling methods in children in COVID-19. Methods: We systematically reviewed PubMed, Google Scholar, medRxiv, and bioRxiv (last retrieval August 1st, 2021) for comparative studies of deferent sampling techniques by using the search keywords including: children, pediatric sampling, nasopharyngeal, COVID-19, oropharyngeal, swabs, SARS, CoV2. 8 relevant manuscripts were sourced from a total of 4852 search results. Results: Nasopharyngeal (NP) swabs testing significantly had higher positivity rate over oropharyngeal swab in detecting SARS-CoV-2. Nasal swab has a low sensitivity in detecting SARSCoV-2 in children when referred to the Nasopharyngeal Aspiration (NPA), whereas its specificity is high. Therefore, NPA can be as the gold standard for detection of SARS-CoV-2. Conclusion: Saliva is not a useful for diagnosing COVID-19 in children. Negative nasopharyngeal and oropharyngeal swabs do not rule out COVID-19 and in patients with strong clinical suspicion, and Bronchoalveolar lavage (BAL) can be helpful. Copyright © 2023, Journal of Iranian Medical Council. All rights reserved. This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
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A long-standing challenge of deep learning models involves how to handle noisy labels, especially in applications where human lives are at stake. Adoption of the data Shapley Value (SV), a cooperative game-theoretic approach, is an intelligent valuation solution to tackle the issue of noisy labels. Data SV can be used together with a learning model and an evaluation metric to validate each training point's contribution to the model's performance. The SV of a data point, however, is not unique and depends on the learning model, the evaluation metric, and other data points collaborating in the training game. However, effects of utilizing different evaluation metrics for computation of the SV, detecting the noisy labels, and measuring the data points' importance has not yet been thoroughly investigated. In this context, we performed a series of comparative analyses to assess SV's capabilities to detect noisy input labels when measured by different evaluation metrics. Our experiments on COVID-19-infected of CT images illustrate that although the data SV can effectively identify noisy labels, adoption of different evaluation metric can significantly influence its ability to identify noisy labels from different data classes. Specifically, we demonstrate that the SV greatly depends on the associated evaluation metric.
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Recently, as a consequence of the COVID-19 pandemic, dependence on Contact Tracing (CT) models has significantly increased to prevent spread of this highly contagious virus and be prepared for the potential future ones. Since the spreading probability of the novel coronavirus in indoor environments is much higher than that of the outdoors, there is an urgent and unmet quest to develop/design efficient, autonomous, trustworthy, and secure indoor CT solutions. Despite such an urgency, this field is still in its infancy. The paper addresses this gap and proposes the Trustworthy Blockchain-enabled system for Indoor Contact Tracing (TB-ICT) framework. The TB-ICT framework is proposed to protect privacy and integrity of the underlying CT data from unauthorized access. More specifically, it is a fully distributed and innovative blockchain platform exploiting the proposed dynamic Proof of Work (dPoW) credit-based consensus algorithm coupled with Randomized Hash Window (W-Hash) and dynamic Proof of Credit (dPoC) mechanisms to differentiate between honest and dishonest nodes. The TB-ICT not only provides a decentralization in data replication but also quantifies the node’s behavior based on its underlying credit-based mechanism. For achieving high localization performance, we capitalize on availability of Internet of Things (IoT) indoor localization infrastructures, and develop a data driven localization model based on Bluetooth Low Energy (BLE) sensor measurements. The simulation results show that the proposed TB-ICT prevents the COVID-19 from spreading by implementation of a highly accurate contact tracing model while improving the users’privacy and security. IEEE
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Introduction: Although various liquid, solid, and gaseous streams of wastewater treatment plants (WWTPs) have been analyzed in many studies for the presence of SARS-CoV-2 RNA, no study was found to sample and detect SARS-CoV-2 RNA in screenings and grit samples separated from primary treatment units of WWTP. Hence, this study aims to provide an experimental protocol for sampling and extracting SARS-CoV-2 RNA from screenings and grits separated from WWTPs. Materials and Methods: First, sampling was conducted to extract SARS-CoV-2 RNA from screenings and grit samples. After sample processing and viral RNA extraction, SARS-CoV-2 RNA detection was performed by one-step reverse transcription quantitative polymerase chain reaction (RT-qPCR). Results: Based on the results, SARS-CoV-2 RNA was successfully extracted from screenings and grit samples of the studied WWTP with concentrations of (1.54 –3.9 × 104) and (0.8 – 2.3 × 104) genomic copies/L, respectively. Conclusion: Considering the successfully isolation and detection of SARSCoV-2 viral RNA in solid phase samples of WWTP, this method can be applied for extracting SARS-CoV-2 RNA and maybe other viruses from the screenings and grit samples of WWTPs in related studies © 2022, Journal of Environmental Health and Sustainable Development.All Rights Reserved.
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As a consequence of the COVID-19 pandemic, the demand for telecommunication for remote learning/working and telemedicine has significantly increased. Mobile Edge Caching (MEC) in the 6G networks has been evolved as an efficient solution to meet the phenomenal growth of the global mobile data traffic by bringing multimedia content closer to the users. Although massive connectivity enabled by MEC networks will significantly increase the quality of communications, there are several key challenges ahead. The limited storage of edge nodes, the large size of multimedia content, and the time-variant users' preferences make it critical to efficiently and dynamically predict the popularity of content to store the most upcoming requested ones before being requested. Recent advancements in Deep Neural Networks (DNNs) have drawn much research attention to predict the content popularity in proactive caching schemes. Existing DNN models in this context, however, suffer from long-term dependencies, computational complexity, and unsuitability for parallel computing. To tackle these challenges, we propose an edge caching framework incorporated with the attention-based Vision Transformer (ViT) neural network, referred to as the Transformer-based Edge (TEDGE) caching, which to the best of our knowledge, is being studied for the first time. Moreover, the TEDGE caching framework requires no data pre-processing and additional contextual information. Simulation results corroborate the effectiveness of the proposed TEDGE caching framework in comparison to its counterparts. © 2022 IEEE.
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Aim: The late elderly, are the leading group of non-survivors infected with the coronavirus disease 2019 (COVID-19). Computed tomography (CT) imaging has been recognized as an important diagnostic method for COVID-19. This study aimed to determine the prognostic performance of CT imaging in patients above 75 years old. Material and Methods: After meeting the inclusion and exclusion criteria 56 elderly patients, 28 male, and 28 female were included in the study. Two radiologists interpreted CT imaging and a third experienced radiologist was in charge of reviewing the data and imaging findings in the controversial and disagreement cases. The lung score was determined for each patient, and radiologic signs were also examined. Results: The mean age of the patients was 81.4±5.0 years. Thirty-six patients survived, and 20 did not. 28 (50.0%) patients had central involvement, while 25 (44.6%) patients had diffuse involvement. Radiologic signs such as consolidation and air bronchogram were more common among non-survivors than survivors (both p=0.001). The mean lung score for the survivors was 8.75±6.21 and 13.45±6.41 for non-survivors, and the difference between the two groups was statistically significant (p=0.010). The area under the receiver operating characteristic curve for a cut-off score of 12 was 0.714 (95% CI, 0.577 to 0.827, p=0.003). Conclusion: It seems that using lung scores can play a very important role in predicting the condition of hospitalized patients over 75 years old.
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With the global coronavirus pandemic still persisting, the repeated disinfection of large spaces and small rooms has become a priority and matter of focus for researchers and developers. The use of ultraviolet light (UV) for disinfection is not new;however, there are new efforts to make the methods safer, more thorough, and automated. Indeed, continuous very low dose-rate far-UVC light in indoor public locations is a promising, safe and inexpensive tool to reduce the spread of airborne-mediated microbial diseases. This paper investigates the problem of disinfecting surfaces using autonomous mobile robots equipped with UV light towers. In order to demonstrate the feasibility of our autonomous disinfection framework, we also present a teleoperated robotic prototype. It consists of a robotic rover unit base, on which two separate UV light towers carrying 254 nm UVC and 222 nm far-UVC lights are mounted. It also includes a live-feed camera for remote operation, as well as power and communication electronics for the remote operation of the UV lamps. The 222 nm far-UVC light has been recently shown to be non-inammatory and non-photo carcinogenic when radiated on mammalian skin, while still sterilizing the coronavirus on irradiated surfaces. With far-UVC light, disinfection robots may no longer require the evacuation of spaces to be disinfected. The robot demonstrates promising disinfection performance and potential for future autonomous applications. © 2022 SPIE. All rights reserved.
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This study was conducted to evaluate the effect of HYSSOP (composed of Hyssopus officinalis L., Echium amoenum Fisch & C. A. Mey and Glycyrrhiza glabra L.) and POLIUM (contained Teucrium polium L., Cuscuta epithymum Murr and Cichorium intybus L.) combined distilled herbal medicines compared to placebo in the prevention of COVID-19. This is a double-blind parallel placebo-controlled field trial conducted on 751 asymptomatic individuals whose one of the family members recently had a positive RT-PCR test for COVID-19. They were divided into three groups including POLIUM, HYSSOP and placebo using random blocks with a 1:1:1 allocation ratio. Participants received daily 5 cc (under 12 years) or 10 cc (over 12 years) of allocated oral medications for 20 days. The primary outcome was the frequency of positive RT-PCR test among participants who became symptomatic. The mean age of participants was 36.6. Nineteen participants get infected by COVID-19 during the intervention;fifteen of them belonged to the placebo and four to the POLIUM group. Fisher's exact test indicated significant differences between HYSSOP and placebo (p<0.001) as well as POLIUM and placebo (p=0.009) groups in terms of COVID-19 confirmed by PCR tests. Cox regression model adjusted for confounders illustrated that the hazard of getting infection by COVID-19 in POLIUM and HYSSOP groups decreased by 66% (OR (95% CI): 0.34 (0.12 to 0.94);p=0.038) and 93% (OR (95% CI): 0.07 (0.01to 0.56);p=0.012) respectively, compared to placebo. Oral administration of HYSSOP and POLIUM with the other supportive health care could decrease the risk of getting COVID-19.
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Due to the COVID-19 pandemic, the need for remote education is felt more than ever. New technologies such as Augmented Reality (AR) can improve students' training experiences and directly affect the learning process, especially in remote education. By using AR in medical education, we no longer need to worry about patient safety during the education process because AR helps students see inside the human body without needing to cut human flesh in the real world. In this paper, we present an augmented reality framework that has the ability to add a virtual eye muscle to a person's face in a single photo or a video. We go one step further to not just show the muscle of the eye but also customize it for each person by modeling the person's face with a 3D morphable model (3DMM). © 2022 IEEE.
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Background and purpose: The actual prevalence of Coronavirus Disease 2019 (COVID-19) can only be estimated by population-based serological examinations and individuals without clinical symptoms may not be identified or reported. In this sero-epidemiological study we aimed at exploring the serum prevalence of COVID-19 in highly exposed occupational groups in western Iran. Materials and methods: A total of 1106 people with jobs with a high potential for exposure to COVID-19 (excluding doctors) were selected in Sanandaj, Kermanshah, and Hamedan. Demographic information of all participants were recorded and venous blood samples (3 ml) were taken. IgG levels were measured to determine the serum prevalence of immunoglobulin using EUROIMMUN kit.
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Novel coronavirus or severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes severe respiratory infectious disease, known as coronavirus disease-19 (COVID-19). Over the past few months, a considerable rise in the incidence rate and prevalence of COVID-19 infection have been witnessed. Considering the high disease burden and rapid spread of the COVID-19 and no effective treatment is currently existing, stem cells, engineered nanobiomaterials, natural killer cells based therapy, RNA metabolites and extracellular vesicles are promising alternatives to tackle devastating epidemic. This review spotlights the applications and potential of above-mentioned methods in the treatment of COVID-19.
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The substantial ascendant trend within the number of daily infected new cases with coronavirus around the world is a warning, and several other researchers are utilizing various mathematical and machine learning-based prediction models to forecast the long-term trend of the COVID-19 pandemic. During this research, the Autoregressive Integrated Moving Average or ARIMA model was implemented to forecast the COVID-19 expected daily number of cases in Ukraine. We implemented Autoregressive Integrated Moving Average for this research. The forecasting results showed that the trend in Ukraine will continue ascending and should reach up to more than 1.8 million total cases if stringent precautionary and control measures don’t get implemented to prohibit the spread of COVID-19. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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SARS-CoV-2 that causes Coronavirus disease 2019 (COVID-19) was first known in Wuhan, China, in December 2019. The aim of this study was to evaluate the level of common hepatic, renal, and cardiac diagnostic markers in hospitals in patients with severe COVID 19. In this study, 259 patients with symptoms of severe COVID-19 and a positive RT-PCR assay of nasopharyngeal samples were enrolled. Inclusion criteria are positive for COVID-19 patients at the diagnosis of an infectious disease physician. Diagnostic markers of liver, kidney, and heart were evaluated by age and gender. In this study, 48.3% of patients severe with COVID-19 were male, and 51.7% were female. The mean of markers such as LDH, Direct Bilirubin, SGOT, SGPT, D-dimer was higher than normal, which was observed in men more than women. The mean of CK-MB also was higher than normal, which was observed in women more than men. The highest mean of markers was seen in the older ages. The mean of BUN was observed in the age range of 55-64 years and above 65 years above normal. But the mean of CPK, creatinine, potassium and alkaline phosphatase were normal. The results of the present study showed an increase in the level of some of the most important diagnostic markers of hepatic, renal, and cardiac in patients with COVID 19. This increase was greater in some markers, including SGOT, SGPT, Direct bilirubin, LDH, D-dimer, in men than in women, and more in older patients.
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Coronavirus or COVID-19 is a widespread pandemic that has affected almost all countries around the globe. The quantity of infected cases and deceased patients has been increasing at a fast pace globally. This virus not only is in charge of infecting billions of people but also affecting the economy of almost the whole world drastically. Thus, detailed studies are required to illustrating the following trend of the COVID-19 to develop proper short-term prediction models for forecasting the number of future cases. Generally, forecasting techniques are be inculcated in order to assist in designing better strategies and as well as making productive decisions. The forecasting techniques assess the situations of the past thereby enabling predictions about the situation in the future would be possible. Moreover, these predictions hopefully lead to preparation against potentially possible consequences and threats. It’s crucial to point out that Forecasting techniques play a vital role in drawing accurate predictions. In this research, we categorize forecasting techniques into different types, including stochastic theory mathematical models and data science/machine learning techniques. In this perspective, it is feasible to generate and develop strategic planning in the public health system to prohibit more deceased cases and managing infected cases. Here, some forecast models based on machine learning are introduced and comprising the Linear Regression model which is assessed for time series prediction of confirmed, deaths, and recovered cases in Ukraine and the globe. It turned out that the Linear Regression model is feasible to implement and reliable in illustrating the trend of COVID-19. © 2021 IEEE.
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Home-based workspaces have considerably increased all over the world. Besides, the recent outbreak of the COVID-19 disease forced many people to work from their homes. However, existing residential apartment buildings (ERABs) had been designed for accommodation but not for office works. Low-quality visual environments in ERABs, which have no shading controls on their windows, are evident in tropical climates with extremely high solar radiation. Thus, interior retrofit is significant to provide visual comfort for users in ERABs with low flexibility for modification of their facades. Different interior design variables were simulated by the Radiance-based program to analyse daylighting in a closed-plan room. Before the simulation experiments, field measurement of daylight was performed under a tropical sky to validate the results, and the findings revealed significant Pearson correlations. This paper showed that ERABs are confronting extremely high indoor daylight quantity, up to 10,228 lx, and low quality with intolerable glare. An adjustable model of internal shading, including an integrated Venetian blind with a horizontal light shelf and the window films, was proposed to improve quantitative and qualitative performances of daylighting in tropical regions. This dynamic model could be adjusted to various positions based on daylighting conditions in the buildings. By comparing the simulation results of this model with the base model, indoor illuminance levels could successfully reduce from 32% to 86%;Illuminance Uniformity Ratio (IUR) and Target Daylight Illuminance (TDI) significantly improved up to 180% and 300%, respectively;Daylight Glare Probability (DGP) and CIE Glare Index (CGI) changed from intolerable to imperceptible status. Accordingly, the proposed model can considerably improve daylight quantity and quality in the test room during different times. This study concludes that the dynamic model of internal shadings could provide efficient daylighting, by decreasing the extremely high indoor illuminance and glare in the ERABs in tropical climates. © 2021 WITPress. All rights reserved.
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The article presents an approach to modeling epidemic processes based on machine learning. A model is built based on the polynomial regression method. The simulation results allow us to calculate the predicted incidence of coronavirus infection in a certain area. The model has been shown to be accurate enough for use in public health policy-making settings. The disadvantage of using machine learning methods is the impossibility of identifying factors affecting the dynamics of the epidemic process. But, due to their high accuracy, such models can be used in an ensemble with agent-based and compartment models. © 2021 CEUR-WS. All rights reserved.
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Home office workspaces have significantly grown in residential sectors throughout the world. Nowadays, many people worldwide are forced to work from their housing units due to the outbreak of the COVID-19 pandemic. However, the existing residential buildings were only designed for living activities, not for desk-related tasks. This is more critical in tropical regions with the overabundance of indoor daylight and lack of external shadings on existing buildings. Despite the limitations for modifying the external facades, interior retrofit plays a major role in improving visual environments. Daylighting performances of various configurations, including internal shading devices, interior surfaces, and window films, were experimented with the Radiance-IES program. A field measurement of daylight was conducted in a home office room under the Malaysian tropical sky to validate the simulated results. This research proved that the existing residential buildings in the tropical climates had poor daylighting performance where the mean indoor illuminance could be over 10,000 lx. The combination of a light shelf, a partial blind, and the tinted window film could effectively 85% alleviate the excessive indoor daylight level. This configuration recorded a significant improvement in Useful Daylight Zone (around 300%), and Daylight Glare Probability was considerably reduced from 0.46 to 0.34. © 2021 Penerbit UTM Press. All rights reserved.
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The global COVID-19 pandemic began in December 2019 and spread rapidly around the world. Worldwide, more than 230 million people fell ill, 4.75 million cases were fatal. In addition to the threat to health, the pandemic resulted in social problems, an economic crisis and the transition of an ordinary life to a "new reality". Mathematical modeling is an effective tool for controlling the epidemic process of COVID-19 in specified territories. Modeling makes it possible to predict the future dynamics of the epidemic process and to identify the factors that affect the increase in incidence in the greatest way. The simulation results enable public health professionals to take effective evidence-based responses to contain the epidemic. The study aims to develop machine learning and compartment models of COVID-19 epidemic process and to investigate experimental results of simulation. The object of research is COVID-19 epidemic process and its dynamics in territory of Ukraine. The research subjects are methods and models of epidemic process simulation, which include machine learning methods and compartment models. To achieve this aim of the research, we have used machine learning forecasting methods and have built COVID-19 epidemic process linear regression model and COVID-19 epidemic process compartment model. Because of experiments with the developed models, the predictive dynamics of the epidemic process of COVID-19 for 30 days were obtained for confirmed cases, recovered and death. For ‘Confirmed’, ‘Recovered’ and ‘Death’ cases mean errors have almost 1.15, 0.037 and 1.39 percent deviant, respectively, with a linear regression model. For ‘Confirmed’, ‘Recovered’ and ‘Death’ cases mean errors have almost 3.29, 1.08, and 0.71 percent deviant, respectively, for the SIR model. Conclusions. At this stage in the development of the epidemic process of COVID-19, it is more expedient to use a linear model to predict the incidence rate, which has shown higher accuracy and efficiency, the reason for that lies on the fact that the used linear regression model for this research was implemented on merely 30 days (from fifteen days before 2nd of March) and not the whole dataset of COVID-19. Also, it is expected that if we try to forecast in longer time ranges, the linear regression model will lose precision. Alternatively, since SIR model is more comprised in including more factors, the model is expected to perform better in fore-casting longer time ranges. © 2021. A. Mohammadi, I. Meniailov, K. Bazilevych, S. Yakovlev, D. Chumachenko, 2021
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Recently, as a consequence of the COVID-19 pandemic, dependence on telecommunication for remote learning/working and telemedicine has significantly increased. In this context, preserving high Quality of Service (QoS) and maintaining low latency communication are of paramount importance. In cellular networks, incorporation of Unmanned Aerial Vehicles (UAVs) can result in enhanced connectivity for outdoor users due to the high probability of establishing Line of Sight (LoS) links. The UAV’s limited battery life and its signal attenuation in indoor areas, however, make it inefficient to manage users’requests in indoor environments. Referred to as the Cluster-centric and Coded UAV-aided Femtocaching (CCUF) framework, the network’s coverage in both indoor and outdoor environments increases by considering a two-phase clustering framework for Femto Access Points (FAPs)’formation and UAVs’deployment. Our first objective is to increase the content diversity. In this context, we propose a coded content placement in a cluster-centric cellular network, which is integrated with the Coordinated Multi-Point (CoMP) approach to mitigate the inter-cell interference in edge areas. Then, we compute, experimentally, the number of coded contents to be stored in each caching node to increase the cache-hit-ratio, Signal-to-Interference-plus-Noise Ratio (SINR), and cache diversity and decrease the users’access delay and cache redundancy for different content popularity profiles. Capitalizing on clustering, our second objective is to assign the best caching node to indoor/outdoor users for managing their requests. In this regard, we define the movement speed of ground users as the decision metric of the transmission scheme for serving outdoor users’requests to avoid frequent handovers between FAPs and increase the battery life of UAVs. Simulation results illustrate that the proposed CCUF implementation increases the cache-hit-ratio, SINR, and cache diversity and decrease the users’access delay, cache redundancy and UAVs’energy consumption. Crown
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The global outbreak of the novel corona virus (COVID-19) disease has drastically impacted the world and led to one of the most challenging crisis across the globe since World War II. The early diagnosis and isolation of COVID-19 positive cases are considered as crucial steps towards preventing the spread of the disease and flattening the epidemic curve. Chest Computed Tomography (CT) scan is a highly sensitive, rapid, and accurate diagnostic technique that can complement Reverse Transcription Polymerase Chain Reaction (RT-PCR) test. Recently, deep learning-based models, mostly based on Convolutional Neural Networks (CNN), have shown promising diagnostic results. CNNs, however, are incapable of capturing spatial relations between image instances and require large datasets. Capsule Networks, on the other hand, can capture spatial relations, require smaller datasets, and have considerably fewer parameters. In this paper, a Capsule network framework, referred to as the "CT-CAPS", is presented to automatically extract distinctive features of chest CT scans. These features, which are extracted from the layer before the final capsule layer, are then leveraged to differentiate COVID-19 from Non-COVID cases. The experiments on our in-house dataset of 307 patients show the state-of-the-art performance with the accuracy of 90.8%, sensitivity of 94.5%, and specificity of 86.0%.