Application of MSCs therapy in COVID-19

The year 2019 ended with the official report of an unknown pneumonia outbreak in Wuhan, Hubei Province, China. Subsequently, this novel pneumonia was named COVID-19, which mainly attacks the respiratory system, causing severe damage. Although vaccination has relieved the stress of combating pandemics around the world after one year, there are still unknowns and challenges that come with hope. In this regard, stem cell therapy has been proposed as an effective approach to treating COVID-19. Mesenchymal stem cells (MSCs) can potentially be used as a hopeful tool in the cell-based therapy due to their ability to regenerate and regulate immune response. Although research and clinical results have shown encouraging achievement in patients who were treated with MSCs, drawbacks and challenges still exist in the face of new opportunities. This review aims to introduce the challenges of the COVID-19 vaccine and the possible clinical use of MSC-based therapy. Through analysis of COVID-19 and MSC-based therapy, the author aims to find the possibilities and feasibility of using MSCs to treat acute respiratory diseases, such as COVID. As a result, the author finds that MSC treatment is very practical, and it shows significant potential to treat COVID-19. © 2023 SPIE.

Machine Learning Techniques on X-ray Images for Covid-19 Classification

The Public Health Commission of Hubei Province, China, at the end of 2019reported cases of severe and unknown pneumonia, marked by fever, malaise, dry cough, dyspnea, and respiratory failure, that occurred in the urban area of Wuhan, according to the World Health Organization (WHO). The lung infection, SARS-CoV-2, also known as COVID-19, was caused by a brand-new coronavirus (coronavirus disease 2019). Since then, infections have increased exponentially, and the WHO labeled the outbreak a worldwide emergency at the beginning of March 2020. Infected and asymptomatic individuals who can spread the virus are the main sources of it. The transmission occurs mainly by airthrough the air through the droplets, however indirect transmission is also possible, such as through contact with infected surfaces. It becomes essential to identify viral carriers as soon as possible in order to stop the spread of the disease and reduce morbidity and mortality. Imaging examinations, which are among the specific tests used to make the definite diagnosis, are crucial in the patient's management when COVID-19 is suspected. Numerous papers that use machine learning techniques discuss the use of X-ray chest radiographs as a component that aids in diagnosis and permits disease follow-up. The goal of this work is to supply the scientific community with information on the most widely used Machine Learning algorithms applied to chest X-ray images. © 2022 IEEE.

Environment and health co-benefits of vehicle emission control policy in Hubei, China

Vehicle exhaust has been important source of atmospheric pollution in China. In terms of the environmental effects of vehicle emission control policies (VECPs), changes in air pollutants and greenhouse gases (GHG) emissions are receiving increasing attention. Hubei has implemented many traffic controls to accelerate pollution abatement. However, few studies have reported how they would affect pollutant emissions in Hubei in the future, as most concentrate on assessments during COVID-19. Further, there has been little research on whether these controls bring observable health benefits. Thus, this study comprehensively evaluates the emission of major air pollutants (including NOx, CO, VOCs, PM2.5, PM10, and PMTSP) and GHGs (CO2, CH4, and N2O) from the transportation sector concerning different VECPs in Hubei during 2015–2050, together with health outcomes. It highlights that individual VECPs contribute differently to environmental and health benefits, encouraging innovation in mechanisms and technologies to mitigate atmospheric pollution while generating health benefits.

Campaign-Style Paired Assistance: The Chinese Experience in Fighting the COVID-19 Pandemic

To cope with the COVID-19 pandemic, the Chinese government initiated a medical resource allocation and assistance mechanism that was characterized as a large-scale and regional mutual approach. Specifically, thirty provinces delivered medical resources (e.g., medical staff, medical supplies, and living materials) to "1+16” cities severely affected by the epidemic within a small amount of time, which solved the dilemma of medical collapse and governance "downtime” in epidemic areas, thereby changing the prevalence curve of the pandemic in China. "Campaign-style” targeted assistance can be interpreted based on the Chinese dual party-government model as well as the governance model of vertical accountability and horizontal competition, drawing from previous experience of normalized "designated assistance.” Consequently, paired assistance contributes to intergovernmental situations of decreasing divisibility and increasing cooperation. This study has the potential to bring insights to other countries around the world that are fighting the COVID-19 pandemic.

Research on machine learning-based epidemic impact on Social Electricity Consumption model

This paper uses social electricity consumption data from 2015-2021 in a city in Hubei province, and uses some methods of artificial intelligence, for example, python function fitting and machine learning to construct an impact analysis and prediction model of the COVID-19 epidemic on Electricity Consumption. Through comparison with the effects of general linear regression and polynomial regression, a better model is developed which comprises four independent variables and uses polynomial regression. The model developed in this paper helps to quantify and measure the impact of the epidemic on society's electricity consumption, and ultimately enables users in the electricity industry to make convenient and rapid forecasts, helping them to make reasonable power supply plans, trading plans and dispatch plans, and to ensure safe and economic operation of the Electricity System. © 2022 ACM.

Analysis On the Challenges for Smart Elderly Care to Face in the Post-pandemic Era of Covid-19

Based on the investigation of the current situation of smart elderly care services in Wuhan, Hubei Province, combined with the results of field visits, this paper combs and analyzes the common problems faced by smart elderly care and the new problems and opportunities brought by the epidemic. Put forward countermeasures and suggestions to solve the problems faced by smart elderly care, so as to enhance the suitability and availability of smart elderly care services, and finally achieve the purpose of improving the well-being of the elderly and sharing the fruits of social development with the elderly. © 2022 ACM.

Analysis of the spatio-temporal process and humanistic influencing factors of COVID-19 spread in Hubei Province.

Since the outbreak of COVID-19, it has continued to impact on people' s work and life. Based on the analysis of the spatio-temporal spread process of the COVID-19 in Hubei Province, this study used regression analysis and other methods to analyze the factors affecting the spread of the epidemic, and put forward recommendations for epidemic prevention and control. This study found that the development of COVID-19 in Hubei Province had gone through four stages of slow rise, rapid rise, decline with fluctuation, and continuous declination. The severely affected areas were mainly concentrated in the surrounding areas of Wuhan, and the distance was attenuated. The spread and development of COVID-19 was the result of a combination of many factors, among them, the proportion of the floating population from Wuhan, the size of the population, the per capita green area, the per capita GDP, and the distance from other cities to Wuhan were the five main factors. And the proportion of the floating population from Wuhan was a significant factoc We hope that this research can provide a scientific basis for domestic and international COVID-19 prevention and control and help relevant departments to improve COVID-19 prevention measures and effectively control the spread of COVID-19. (English) [ FROM AUTHOR] 新冠肺炎疫情爆发以来, 持续影响民众的生产生活.本研究在分析湖北省新冠肺炎疫情扩散 的时空过程的基础上, 运用冋归分析等方法对疫情传播的影响因素进行分析, 并对疫情防控提出 建议.研究表明:湖北省新冠肺炎疫情的发展经历了新增确诊病例缓慢上升、快速上升、波动下降、 持续下降4个阶段;疫情严重区域主要集中在武汉市周边地区, 且呈现出距离衰减的特点;疫情的 传播和发展是多因素共同作用的结果, 其中从武汉流入人口比例、人口规模、人均绿地面积、人均 地区生产总值及城市至武汉距离5个因素影响较大, 且从武汉流入人口比例为显著影响因子.希望 本研究能为国内外疫情防控工作提供科学依据, 帮助相关部门进一步改进防疫措施, 有效控制疫 情传播. (Chinese) [ FROM AUTHOR] Copyright of Journal of Capital Normal University (Natural Science Edition) is the property of Journal of Capital Normal University (Natural Science Edition) Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Detection of pneumonia progression in lungs of individuals affected with covid 19 severely using deep learning techniques

The coronavirus sickness (COVID-19) is a worldwide pandemic that was detected in December 2019 by a Chinese physician in Wuhan, Hubei Province, mainland China. There is presently no licensed human vaccine to combat it. When individuals are close together, COVID-19 spreads more fast. As a consequence, travel constraints are in place to minimize the disease from spreading, and regular Washing the hands is encouraged so that the infections that occur due to the virus. Other indications and symptoms include chest discomfort, sputum production, and a sore throat. COVID19 could lead to a very dangerous disease which is pneumonia which occurs due to the virus. When employing CT scans or Xrays to identify the symptoms that are occuring due to the cause of covid-19 in the last region of the lungs then the accuracy is better than when utilizing RT-PCR. But as there are very less radiologists as compared to the new residents or the people that have come and aslo there has been seen many re examinations occurring of these patients. To solve this kind of the issues or problems that is limiting the CT scans and the x-rays the speed of this procedure must be boosted. This may be done by adding artificial intelligence (AI) approaches into contemporary diagnostic systems. The main motive of the paper is to provide the best accuracy to detect the disease using CNN along with a comparison with the transfer learning approach. © 2022 IEEE.

Predicting Future, Past, and Misinterpreted COVID-19 Cases Using Bidirectional LSTM Model for Proper Health Monitoring: Advances in Data Analytics

The first coronavirus case was reported in Hubei province of China, and within three months, it affected almost all the countries in the world. under such circumstances, World Health Organization (WHO) declared 2019 novel coronavirus as a global pandemic. Even though its fatality is low, the transmission rate makes it more dangerous. Similar to previous disease outbreaks in the human history, COVID-19 also exhibits certain transmission patterns. Mathematical models can be used to analyze these patterns and forecast the upcoming COVID-19 cases. Such forecasting methods could help governments to take further actions to stop those cases from occurring. Most of the previous studies used past infections to forecast future infections. However, they completely neglected the unreported cases while making predictions. By knowing the initially reported cases, we can understand the dynamics of the epidemic more precisely. In order to capture the transmission dynamics, we proposed a novel deep learning model called a B-LSTM (Bidirectional Long Short-Term Memory) model. In order to recalculate the past or missing infections, we applied a masking technique to our B-LSTM model. Results obtained from our model shows that end point of this pandemic in India will be around next year. However, by November the rate of infections will decrease linearly. In addition to that, we compared the forecasting accuracies of B-LSTM with statistical-based ARIMA and LSTM models. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

The Impact of China's Paired Assistance Policy on the COVID-19 Crisis-An Empirical Case Study of Hubei Province.

To control the coronavirus pandemic (COVID-19), China implemented the Paired Assistance Policy (PAP). Local responders in 16 cities in Hubei Province were paired with expert teams from 19 provinces and municipalities. Fully supported by the country's top-down political system, PAP played a significant role in alleviating the COVID-19 pandemic in Hubei Province and China as a whole. In this study, we examined PAP using a two-way fixed effects model with the cumulative number of medical support personnel and cumulative duration as measurements. The results show personnel and material support played an active role in the nation's response to the COVID-19 public health crisis.

Study on Urban Travel Volume During the Outbreak of COVID-19

The distribution and change of travel intensity reflect the pattern of the city and the activity of trip population. It is important to understand the pattern of the city and the activity of trip flow for urban planning and government decision-making. This paper constructs a Bayesian hierarchical spatiotemporal model with three effects: space, time, and space-time, which uses the travel intensity data during the outbreak of the novel coronavirus (COVID-19) in Hubei province (2020.01.01–2020.05.02). With the help of Markoff’s Monte Carlo method, this paper analyzes the distribution and fluctuation of traffic flow in each city of Hubei province. The results show that the space-time model does not deteriorate compared with the main space model. The study found that nearly 41% of cities with a spatial effect higher than 1 were active during the epidemic in Hubei province and the time effect of travel intensity in Hubei province dropped rapidly from 2 to 0.5 after cities in Hubei province issued measures to close the cities one after another, which lasted nearly a month. Strict social distance intervention is one of the important reasons for Hubei province to control the epidemic effectively in a few months. At the same time, in the stability analysis of the city, we found that Wuhan belongs to an unstable area, which is unfavorable to the control of COVID-19. The research results provide a certain perspective for COVID-19 prevention and control: when there are confirmed patients in the province, we believe that the government should first pay attention to those cities with high spatial effect and instability. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Immunoinformatics Tools: A boon in vaccine Development Against Covid-19

The first evidences of SARS Covid 19 virus were reported from Labs in Wuhan, China's Hubei Province, at the end of 2019. It spread very quickly throughout China, leading in an epidemic and a global pandemic. A large population was affected and died due to the pandemic in 2019. It shares genetic similarities with SARS-CoV-2 and MERS-COV. The development of an effective SARS-CoV-2 vaccine is important for reducing COVID-19 deaths and giving immunological protection to the worldwide community. The lengthy and expensive process of vaccine production can be shortened by using immunoinformatics approaches. immunoinformatics tools such as Vaxijen, IEDB, NetCTL 1.2, PEP-FOLD etc have previously been used in reverse vaccinology for SARS-CoV-2 vaccine development in areas such as antigen selection, toxicity, predicting vaccine targets, allergenicity prediction and selection of MHC-I and II binding epitopes etc. In this review, we summarize some of the most useful immunoinformatics tools like vexijen, Bepipred 2.0, SVMTrip, FNepitope etc and their role in the development of covid 19 vaccines. The characteristics of such tools have been thoroughly reviewed, and which may provide experimental biologists with prediction insights that may enhance active research attempts to identify therapies for the infectious COVID-19 illness. © 2022 IEEE.

Automated COVID-19 detection using Deep Convolutional Neural Network and Chest X-ray Images

COVID-19 was previously identified as 2019-nCoV, however it was reclassified as severe acute respiratory syndrome coronavirus 2 by the International Committee on Taxonomy of Viruses (ICTV) (SARS-CoV-2). It was first discovered in Wuhan, China's Hubei Province, and has since spread all over the world. The scientific community is working to develop COVID-19 detection technologies that are both quick and accurate. Chest x-ray imaging can aid in the early diagnosis of COVID-19 patients. In COVID-19 individuals, chest x-rays can indicate a variety of lung abnormalities, including lung consolidation, ground-glass opacity, and others. The COVID-19 biomarkers, however, must be identified by qualified and experienced radiologists. Each report must be inspected by the radiologist, which is a time-consuming procedure. The medical infrastructure is currently overburdened due to the huge volume of patients. In this study, we propose automatic COVID-19 identification in chest x-rays using a deep learning technique. COVID-19, pneumonia, and healthy x-rays are included in the dataset for the studies. The proposed model had an average accuracy and sensitivity of 97 percent. The obtained findings demonstrate that the model can compete with existing state-of-the-art models. © 2021 IEEE.

The impact of COVID-19 on aquaculture in China and recommended strategies for mitigating the impact.

We carried out a preliminary investigation to study the impact of COVID-19 on aquaculture in China and identify the strategies and measures that have been taken by the Chinese Government. The investigation involved questionnaire surveys designed for all stakeholders along the industrial chain, including grow-out farmers, seed producers, fish processors, fish traders, and feed companies engaged in the catfish sector in Hubei Province and the tilapia sector in Guangdong Province during the strict period of control and after these control measures were lifted. We also attempted to summarize the government interventions and measures taken by different stakeholders along the value chain to minimize the damage caused by COVID-19 and support the recovery of different sectors in the aquaculture industry. We found that due to delayed harvesting, fish stocks were held-up in ponds and normal farming was interrupted. Farmers and traders were more severely impacted by the pandemic than other sectors. Furthermore, a series of strategies and measures are recommended to cope with the pandemic and other similar risks in the future. We expect that this study will provide good evidence for international societies to support the aquaculture industry in minimizing the impact of the pandemic and the rapid recovery of the industry in the post-pandemic period.

COVID-19 GEO-SPATIAL BIG DATA ANALYSIS of POPULATION MORTALITY for COUNTRIES SITUATED at LATITUDES between 64°N and 35°S

The outbreak of the Covid-19 emerged from Wuhan, Hubei province of China, spread geo-spatially in more than 210 countries causing more than 96.7 million people of the global population infected and 2.06 million deaths (as on 20th January 2021) from 25.416 million people infected and 0.851 million deaths (as on 30th August 2020), which is still spreading in geo-spatiotemporal way to the new geographical locations. There are marked variations in the spectrum of daily new cases of covid-19 between different countries. People do not receive sufficient sunlight to retain adequate vitamin D levels during winter in countries situated at the latitude beyond 35°N. Vitamin D is important in preventing the cytokine storm and subsequent acute respiratory distress syndrome that is commonly the cause of mortality. The global spreading of covid-19 caused marked variations in population mortality between different countries situated at different latitudes, which suggest establishing the correlation between latitude and the severity of the covid-19 outbreak. In this paper, geo-spatial big data analysis has been carried out for determining the impact of latitude and the role of vitamin-D on population mortality for 52 countries situated between the latitude 64°N and 35°S, based on population mortality data from 15th April 2020 to 30th June 2021, which shows relatively lower population mortality in countries that lie below the latitude 38°N. This paper explains the variability factor of population mortality from 3rd May 2020 to 30th January 2021 with respect to population mortality on 15th April 2020 for determining the severity of the covid-19, which shows the significant severity of the covid-19 outbreak in the country such as South Africa, Colombia, Russia, Kuwait, India, Mexico and Ukraine during 30th September 2020 to 30th January 2021 and sudden rise of variability factor for Romania, Serbia, Slovenia, Austria and Poland. © ACRS 2021.All right reserved.

Epidemiological and clinical characteristics of 40 cases of COVID-19 outside Hubei

To explore characteristics of patients with pneumonia infected by 2019 Novel Coronavirus (COVID-19) in 2019 outside Hubei Province, China. 40 patients with pneumonia infected by COVID-19 which were confirmed by COVID-19 nucleic acid test were included. Procalcitonin (PCT), serum amyloid A (SAA), C-reactive protein (CRP) and computed tomography (CT) manifestations were analyzed. 40% of patients had clear contact history with Wuhan or other areas of Hubei Province. 60% of patients were clustered diseases and 40% were imported cases. 75% of patients had initial fever, 7.5% had cough, 5% had sore throat at first. 45% had decreased lymphocyte count, 72.5% and 55% patients had increased levels of SAA and CRP. 72.5% of the patients showed multiple ground glass lesions in one or two lungs on chest CT. 90% of the patients with pneumonia are of the common type, and alpha-interferon atomization inhalation combined with Lopinavir/Ritonavir tablets were given to patients during treatment. 62.5% of the patients were treated with antibiotics, and 15% with hormone. All patients improved after treatment, and 14 patients were cured and discharged. Family cluster infection and asymptomatic infection may be the main way of spreading of COVID-19 pneumonia outside Hubei Province in China.

A Study on Effect of COVID-19 and AI tools to Detect Coronavirus from Chest Medical Images

In December 2019, an outbreak of a series of severe respiratory illness was found in Wuhan, Hubei Province, China. It was due to a novel coronavirus, now identified as SARS-CoV-2. The virus is human-to-human transmissible and that is why it has created a pandemic. Due to the continuous increasing death toll, several governments have been compelled to execute complete lockdown throughout the countries and followed by a social separation. The lack of tailored treatment remains an issue. Usually, patients above the age of 65 are more vulnerable to serious illnesses, according to epidemiological studies, whereas children have lesser symptoms. Depending on the present scenario of coronavirus disease, World Health Organization (WHO) advised to implement precautionary measures to defend self and other population. It has been also instructed to take legal action if some careless personnel do not abide with the guidelines of WHO and respective government of his country. A covid detection mechanism from X-ray images is presented in this paper, where a deep convolutional neural network has been utilized to determine whether a person is a covid patient or not. The proposed model accomplishes more than 96% accuracy, which proofs the goodness of the proposed work. © 2021 IEEE.

Prevalence and associated factors of depressive symptoms among the young adults during the post-epidemic period - Evidence from the first wave of COVID-19 in Hubei Province, China.

INTRODUCTION: China emerged from the first wave of COVID-19 in a short period of time and returned to normal economic and living order nationwide, making China's entry into the post-COVID-19 epidemic period since April 2020. However, the COVID-19 epidemic had a great impact on young adults' psychological status and may continue into the post-epidemic period. The enormous economic, employment and entrepreneurship pressures of this period may exacerbate this negative impact. This study investigated the depression status of the young adults and put forward the suggestions on how to strengthen the psychological crisis intervention and social security to cultivate the resilience of the young adults after major public health emergencies. METHODS: This study conducted a questionnaire survey to identify the prevalence of depressive symptoms and explore the associated factors of depressive symptoms among 1069 young adults in X City, Hubei province in September 2020. And the multistage stratified random sampling method was used for sampling. Depressive symptoms were measured using the 10-item version of the Center for Epidemiological Studies Depression Scale (CES-D-10). Descriptive statistics and logistic regression analysis were adopted for statistical analysis. RESULTS: 1069 respondents (67.68% male; mean age = 28.87 ± 4.18 years; age range = 18-35 years) were included in final analyses. About 25.9% of the respondents reported depressive symptoms (CES-D-10 score = 7.28 ± 3.85). Age, marital status, employment status, monthly disposable income, the cognition, experience and social relationship of the COVID-19 epidemic, and regional discrimination were significantly associated with depressive symptoms. Being male (P = 0.025), age of 25-29 years (P = 0.011), having a household size with 4-5 (P = 0.01) and more than 8 (P = 0.012) family members, a little pessimism about the prospect of COVID-19 epidemic prevention and control (P = 0.044), often (P = 0.018) or always (P = 0.009) participation in anti-epidemic volunteer work were likely to lead to depressive symptoms. CONCLUSIONS: In the post-COVID-19 epidemic period, the psychological status of young people is generally stable, but some of them are depressed. Life, work and mental stress affect the generation of depressive symptoms among the young adults.

The Common Ancestor Time Estimation of SARS-CoV-2 Based on Phylogenetic Analysis

At the end of 2019, a novel coronavirus SARS-CoV-2 was first identified in Wuhan, Hubei Province, China. So far, the virus has spread globally. The rapid transmission and mutation have undoubtedly increased the difficulty of exploring the source of SARS-CoV-2, and it has made the origins study meaningful. This study uses 40,280 SARS-CoV-2 genetic sequences from the world for phylogenetic analysis. It is inferred that the time to Most Recent Common Ancestor (tMRCA) of global SARS-CoV-2 is roughly in early December 2019. Similarly, the tMRCA of SARS-CoV-2 in each continent is estimated, and sort out the timeline of the virus's entry into human society. Also, it is found that some regions outside Asia have COVID-19 cases in December 2019. © 2021 IEEE.

Prediction and Analysis of COVID-19 Epidemic Situation via Modified SEIR Model with Asymptomatic Infection

This paper deals with the prediction and analysis of COVID-19 epidemic situation based on a modified SEIR model with asymptomatic infection. First, by considering the self-isolation and asymptomatic infection, a modified SEIR model is proposed to predict and evaluate the epidemic situation of COVID-19 in Hubei Province, China. Then, based on the daily data reported by the Health Commission of Hubei Province, the modified SEIR model is solved numerically, and the parameters of the modified model are inverted by the least square method. Third, based on the modified model, the epidemic situation of COVID-19 in Hubei Province is predicted and verified. The simulation results show that the modified SEIR model is significant and reliable to describe the spread property of the COVID-19, thereby providing a potential theoretical support for the decision-making of epidemic prevention and control in the future. © 2021 IEEE.