Intelligent Approach for Clustering Mutations’ Nature of COVID-19 Genome

In December 2019, a group of people in Wuhan city of Hubei province of China were found to be affected by an infection called dark etiology pneumonia. The outbreak of this pneumonia infection was declared a deadly disease by the China Center for Disease Control and Prevention on January 9, 2020, named Novel Coronavirus 2019 (nCoV-2019). This nCoV-2019 is now known as COVID-19. There is a big list of infections of this coronavirus which is present in the form of a big family. This virus can cause several diseases that usually develop with a serious problem. According to the World Health Organization (WHO), 2019-nCoV has been placed as the modern generation of Severe Acute Respiratory Syndrome (SARS) and Middle East Respiratory Syndrome (MERS) coronaviruses, so COVID-19 can repeatedly change its internal genome structure to extend its existence. Understanding and accurately predicting the mutational properties of the genome structure of COVID-19 can form a good leadership role in preventing and fighting against coronavirus. In this research paper, an analytical approach has been presented which is based on the k-means cluster technique of machine learning to find the clusters over the mutational properties of the COVID-19 viruses’ complete genome. This method would be able to act as a promising tool to monitor and track pathogenic infections in their stable and local genetics/hereditary varieties. This paper identifies five main clusters of mutations with as best in most cases in the coronavirus that could help scientists and researchers develop disease control vaccines for the transformation of coronaviruses.     

A Promising Anti-Cytokine-Storm Targeted Therapy for COVID-19:The Artificial-Liver Blood-Purification System

The coronavirus disease 2019(COVID-19)pan-epidemic,result-ing from infection with the 2019 novel coronavirus(2019-nCoV),also known as severe acute respiratory s...     

Integrated vaccination and non-pharmaceutical interventions based strategies in Ontario,Canada, as a case study: a mathematical modelling study

Recently, two coronavirus disease 2019 (COVID-19) vaccine products have been authorized in Canada. It is of crucial importance to model an integrated/combined package of non-pharmaceutical (physical/social distancing) and pharmaceutical (immunization) public health control measures. A modified epidemiological, compartmental SIR model was used and fit to the cumulative COVID-19 case data for the province of Ontario, Canada, from 8 September 2020 to 8 December 2020. Different vaccine roll-out strategies were simulated until 75% of the population was vaccinated, including a no-vaccination scenario. We compete these vaccination strategies with relaxation of non-pharmaceutical interventions. Non-pharmaceutical interventions were supposed to remain enforced and began to be relaxed on 31 January, 31 March or 1 May 2021. Based on projections from the data and long-term extrapolation of scenarios, relaxing the public health measures implemented by re-opening too early would cause any benefits of vaccination to be lost by increasing case numbers, increasing the effective reproduction number above 1 and thus increasing the risk of localized outbreaks. If relaxation is, instead, delayed and 75% of the Ontarian population gets vaccinated by the end of the year, re-opening can occur with very little risk. Relaxing non-pharmaceutical interventions by re-opening and vaccine deployment is a careful balancing act. Our combination of model projections from data and simulation of different strategies and scenarios, can equip local public health decision- and policy-makers with projections concerning the COVID-19 epidemiological trend, helping them in the decision-making process.     

Coronavirus disease 2019 (COVID-19) in two pediatric patients with kidney disease on chronic immunosuppression: A case series

Coronavirus disease 2019 (COVID-19) is a highly infectious disease caused by the severe acute respiratory syndrome coronavirus 2 virus (SARS-CoV-2). While children appear to experience less severe disease than adults, those with underlying conditions such as kidney disease may be more susceptible to infection. Limited data are present for children with kidney disease, and there are limited prior reports of pediatric hemodialysis patients with COVID-19. This report describes the mild clinical disease course of COVID-19 in two pediatric patients with chronic kidney disease, one on hemodialysis and both on chronic immunosuppression. We review treatment in these patients, as well as our measures to reduce transmission among our hemodialysis patients and staff.     

What is Discussed about COVID-19: A Multi-Modal Framework for Analyzing Microblogs from Sina Weibo without Human Labeling

Starting from late 2019, the new coronavirus disease (COVID-19) has become a global crisis. With the development of online social media, people prefer to express their opinions and discuss the latest news online. We have witnessed the positive influence of online social media, which helped citizens and governments track the development of this pandemic in time. It is necessary to apply artificial intelligence (AI) techniques to online social media and automatically discover and track public opinions posted online. In this paper, we take Sina Weibo, the most widely used online social media in China, for analysis and experiments. We collect multi-modal microblogs about COVID-19 from 2020/1/1 to 2020/3/31 with a web crawler, including texts and images posted by users. In order to effectively discover what is being discussed about COVID-19 without human labeling, we propose a unified multi-modal framework, including an unsupervised short-text topic model to discover and track bursty topics, and a selfsupervised model to learn image features so that we can retrieve related images about COVID-19. Experimental results have shown the effectiveness and superiority of the proposed models, and also have shown the considerable application prospects for analyzing and tracking public opinions about COVID-19.     

Machine Learning and Classical Forecasting Methods Based Decision Support Systems for COVID-19

From late 2019 to the present day, the coronavirus outbreak tragically affected the whole world and killed tens of thousands of people. Many countries have taken very stringent measures to alleviate the effects of the coronavirus disease 2019 (COVID-19) and are still being implemented. In this study, various machine learning techniques are implemented to predict possible confirmed cases and mortality numbers for the future. According to these models, we have tried to shed light on the future in terms of possible measures to be taken or updating the current measures. Support Vector Machines (SVM), Holt-Winters, Prophet, and Long-Short Term Memory (LSTM) forecasting models are applied to the novel COVID-19 dataset. According to the results, the Prophet model gives the lowest Root Mean Squared Error (RMSE) score compared to the other three models. Besides, according to this model, a projection for the future COVID-19 predictions of Turkey has been drawn and aimed to shape the current measures against the coronavirus.     

Assessing the Effect of Global Travel and Contact Restrictions on Mitigating the COVID-19 Pandemic

Travel restrictions and physical distancing have been implemented across the world to mitigate the coronavirus disease 2019 (COVID-19) pandemic, but studies are needed to understand their effectiveness across regions and time. Based on the population mobility metrics derived from mobile phone geolocation data across 135 countries or territories during the first wave of the pandemic in 2020, we built a metapopulation epidemiological model to measure the effect of travel and contact restrictions on containing COVID-19 outbreaks across regions. We found that if these interventions had not been deployed, the cumulative number of cases could have shown a 97-fold (interquartile range 79–116) increase, as of May 31, 2020. However, their effectiveness depended upon the timing, duration, and intensity of the interventions, with variations in case severity seen across populations, regions, and seasons. Additionally, before effective vaccines are widely available and herd immunity is achieved, our results emphasize that a certain degree of physical distancing at the relaxation of the intervention stage will likely be needed to avoid rapid resurgences and subsequent lockdowns.     

Cognitive impairment in a patient with COVID-19 on hemodialysis: Too dangerous to neglect!

Neurological manifestations of coronavirus disease 2019 (COVID-19) often have tragic repercussions. Although many reports of neurological complications of severe acute respiratory syndrome coronavirus 2 infection exist, none of them are of patients on hemodialysis, who have a fivefold greater risk of stroke than the general population. In this report, we emphasize the importance of being vigilant for mild stroke in high risk populations—such as patients on hemodialysis—with COVID-19, since these conditions have overlapping symptoms.     

The Efficacy and Safety of Triazavirin for COVID-19: A Trial Protocol

The coronavirus disease 2019 (COVID-19), a pneumonia caused by a novel coronavirus, was reported in December 2019. COVID-19 is highly contagious and has rapidly developed from a regional epidemic into a global pandemic. As yet, no effective drugs have been found to treat this virus. This study, an ongoing multicenter and blind randomized controlled trial (RCT), is being conducted at ten study sites in Heilongjiang Province, China, to investigate the efficacy and safety of Triazavirin (TZV) versus its placebo in COVID-19 patients. A total of 240 participants with COVID-19 are scheduled to be enrolled in this trial. Participants with positive tests of throat swab virus nucleic acid are randomized (1:1) into two groups: standard therapy plus TZV or standard therapy plus placebo for a 7-day treatment with a 21-day follow-up. The primary outcome is the time to clinical improvement of the subjects. Secondary outcomes include clinical improvement rate, time to alleviation of fever, mean time and proportion of obvious inflammatory absorption in the lung, conversion rate of repeated negative virus nucleic acid tests, mortality rate, and conversion rate to severe and critically severe patients. Adverse events, serious adverse events, liver function, kidney function, and concurrent treatments will be monitored and recorded throughout the trial. The results of this trial should provide evidence-based recommendations to clinicians for the treatment of COVID-19.     

Prediction of COVID-19 Confirmed Cases Using Gradient Boosting Regression Method

The fast spread of coronavirus disease (COVID-19) caused by SARSCoV-2 has become a pandemic and a serious threat to the world. As of May 30, 2020, this disease had infected more than 6 million people globally, with hundreds of thousands of deaths. Therefore, there is an urgent need to predict confirmed cases so as to analyze the impact of COVID-19 and practice readiness in healthcare systems. This study uses gradient boosting regression (GBR) to build a trained model to predict the daily total confirmed cases of COVID-19. The GBR method can minimize the loss function of the training process and create a single strong learner from weak learners. Experiments are conducted on a dataset of daily confirmed COVID-19 cases from January 22, 2020, to May 30, 2020. The results are evaluated on a set of evaluation performance measures using 10-fold cross-validation to demonstrate the effectiveness of the GBR method. The results reveal that the GBR model achieves 0.00686 root mean square error, the lowest among several comparative models.     

Superhydrophobic,photo-sterilize,and reusable mask based on graphene nanosheet-embedded carbon(GNEC)film

The 2019 coronavirus disease(COVID-19)has affected more than 200 countries.Wearing masks can effectively cut off the virus spreading route since the coronavirus is mainly spreading by respiratory droplets.However,the common surgical masks cannot be reused,resulting in the increasing economic and resource consumption around the world.Herein,we report a superhydrophobic,photo-sterilize,and reusable mask based on graphene nanosheet-embedded carbon(GNEC)film,with high-density edges of standing structured graphene nanosheets.The GNEC mask exhibits an excellent hydrophobic ability(water contact angle:157.9°)and an outstanding filtration efficiency with 100%bacterial filtration efficiency(BFE).In addition,the GNEC mask shows the prominent photo-sterilize performance,heating up to 110℃quickly under the solar illumination.These high performances may facilitate the combat against the COVID-19 outbreaks,while the reusable masks help reducing the economic and resource consumption.     

Enlightenment from the COVID-19 Pandemic:The Roles of Environmental Factors in Future Public Health Emergency Response

The coronavirus disease 2019(COVID-19)pandemic is challenging the current public health emergency response systems(PHERSs)of many countries.Although environment...     

The Scientific Foundation of Chinese Herbal Medicine against COVID-19

The recent coronavirus disease 2019 (COVID-19) pandemic outbreak has caused a serious global health emergency. Supporting evidence shows that COVID-19 shares a genomic similarity with other coronaviruses, such as severe acute respiratory syndrome coronavirus (SARS-CoV) and Middle East respiratory syndrome coronavirus (MERS-CoV), and that the pathogenesis and treatment strategies that were applied 17 years ago in combating SARS-CoV and other viral infections could be taken as references in today’s antiviral battle. According to the clinical pathological features of COVID-19 patients, patients can suffer from five steps of progression, starting with severe viral infection and suppression of the immune system and eventually progressing to cytokine storm, multi-organ damage, and lung fibrosis, which is the cause of mortality. Therefore, early prevention of disease progression is important. However, no specific effective drugs and vaccination are currently available, and the World Health Organization is urging the development of novel prevention and treatment strategies. Traditional Chinese medicine could be used as an alternative treatment option or in combination with Western medicine to treat COVID-19, due to its basis on historical experience and holistic pharmacological action. Here, we summarize the potential uses and therapeutic mechanisms of Chinese herbal formulas (CHFs) from the reported literature, along with patent drugs that have been recommended by institutions at the national and provincial levels in China, in order to verify their scientific foundations for treating COVID-19. In perspective, more basic and clinical studies with multiple high-tech and translational technologies are suggested to further confirm the therapeutic efficacies of CHFs.     

Fabrication of a High-Performance and Reusable Planar Face Mask in Response to the COVID-19 Pandemic

The coronavirus disease 2019(COVID-19)pandemic has caused a surge in demand for face masks,with the massive consumption of masks leading to an increase in resou...     

Adaptive Wall-Based Attachment Ventilation:A Comparative Study on Its Effectiveness in Airborne Infection Isolation Rooms with Negative Pressure

The transmission of coronavirus disease 2019(COVID-19)has presented challenges for the control of the indoor environment of isolation wards.Scientific air distr...     

Strategies and Advances in Combating COVID-19 in China

Coronavirus disease 2019 (COVID-19)—the third in a series of coronavirus infections—has caused a global public health event in the 21st century, resulting in substantial global morbidity and mortality. Building on its legacy of managing severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS), China has played a key role in the scientific community by revealing the viral transmission routes and clinical characteristics of COVID-19 and developing novel therapeutic interventions and vaccines. Despite these rapid scientific and technological advances, uncertainties remain in tracing the original sources of infection, determining the routes of transmission and pathogenesis, and addressing the lack of targeted clinical management of COVID-19. Here, we summarize the major COVID-19 research advances in China in order to provide useful information for global pandemic control.     

2019新型冠状病毒的核酸检测

2019冠状病毒病(COVID-19)疫情已成为国际关注的突发公共卫生事件。为应对突发病毒疫情事件,加快病毒检测并提高检测准确性变得非常重要。中华人民共和国国家卫生健康委员会《新型冠状病毒肺炎诊疗方案》规定了核酸检测和基因测序作为确诊病例的方法,检测结果是对潜伏期人群、疑似病例人群和隔离期人群的新型冠状病毒(2019-nCoV/SARS-CoV-2)进行确诊的重要依据。对实时荧光RT-PCR检测、数字PCR检测及基因测序方法进行了综述,并对后续监控和生物安全测量(生物计量)标准体系的建立进行了展望。     

Machine Learning Approach for COVID-19 Detection on Twitter

Social networking services (SNSs) provide massive data that can be a very influential source of information during pandemic outbreaks. This study shows that social media analysis can be used as a crisis detector (e.g., understanding the sentiment of social media users regarding various pandemic outbreaks). The novel Coronavirus Disease-19 (COVID-19), commonly known as coronavirus, has affected everyone worldwide in 2020. Streaming Twitter data have revealed the status of the COVID-19 outbreak in the most affected regions. This study focuses on identifying COVID-19 patients using tweets without requiring medical records to find the COVID-19 pandemic in Twitter messages (tweets). For this purpose, we propose herein an intelligent model using traditional machine learning-based approaches, such as support vector machine (SVM), logistic regression (LR), naïve Bayes (NB), random forest (RF), and decision tree (DT) with the help of the term frequency inverse document frequency (TF-IDF) to detect the COVID-19 pandemic in Twitter messages. The proposed intelligent traditional machine learning-based model classifies Twitter messages into four categories, namely, confirmed deaths, recovered, and suspected. For the experimental analysis, the tweet data on the COVID-19 pandemic are analyzed to evaluate the results of traditional machine learning approaches. A benchmark dataset for COVID-19 on Twitter messages is developed and can be used for future research studies. The experiments show that the results of the proposed approach are promising in detecting the COVID-19 pandemic in Twitter messages with overall accuracy, precision, recall, and F1 score between 70% and 80% and the confusion matrix for machine learning approaches (i.e., SVM, NB, LR, RF, and DT) with the TF-IDF feature extraction technique.