Coronary Pneumonia Research Articles

Stability analysis of the SEIRS epidemic model with infectious dynamics during latent and infectious periods

Coronary pneumonia caused by the SARS-CoV-2 virus was one of the most significant public health threats in recent years. In this paper, we develop and investigate an SEIRS epidemic model that incorporates infectivity during both the latent and infectious stages to characterize the transmission dynamics of COVID-19. By calculating the basic reproduction number (R0) and applying monotonic dynamical system theory along with geometric methods, we validate the threshold theorem. Our analysis demonstrates that the disease-free equilibrium is globally stable when R0 < 1, while the endemic equilibrium becomes globally stable when R0 > 1.

Growth of Internet Users with Special Emphasis on the Impact of New Coronary Pneumonia

Growth of Internet Users with Special Emphasis on the Impact of New Coronary Pneumonia

X-ray Image Recognition of Pneumonia Based on Three Different Neural Networks

With the outbreak of new coronary pneumonia, a variety of pneumonia diseases are emerging, doctors often have misdiagnoses and omissions. To assist doctors in better clinical diagnosis, a variety of high-performance neural network structures have been applied to the X-ray recognition of pneumonia, and the X-ray image recognition accuracy will be improved, will effectively improve the efficiency of the hospital detection, at the same time, can also avoid the patient to miss the best rescue time. To this end, this paper compares and analyses the recognition performance of the three currently used neural network structures in the recognition of pneumonia X-ray images, uses some publicly available datasets and a mixed set composed of different datasets to conduct experiments, and summarises the advantages of the models and the direction of possible improvement. The three neural network models are presented, compared, and analysed to suggest useful references for the recognition of pneumonia X-ray images. Finally, an analysis and outlook for future neural network models for pneumonia x-ray image recognition is presented.

Analysis of Pfizer Profitability Based on Dupont Analysis Method

As a leader in the pharmaceutical industry, Pfizer has maintained a long-term leading position in the industry through technology R&D investment and product innovation. Especially in recent years, the drugs and vaccines produced by Pfizer have been widely used worldwide to treat new coronary pneumonia. Pfizer's sales have increased significantly and occupied a significant market share. The rapid accumulation of Pfizer's wealth and the improvement of its operational capabilities have attracted more and more attention from investors. The goal of an enterprise is to obtain profit, and profitability is an essential indicator for evaluating the profit of an enterprise. No matter which stakeholder of the enterprise pays excellent attention to the profitability of the enterprise, standard analysis methods need to convey more information to investors. This article analyzes Pfizer's 2020-2022 financial statements using the DuPont analysis method to find out the problems that Pfizer has in terms of profitability and puts forward some opinions for investors' reference.

Inhibition of CYP1A1 Alleviates Colchicine-Induced Hepatotoxicity.

Colchicine, a natural compound extracted from Colchicum autumnale, is a phytotoxin, but interestingly, it also has multiple pharmacological activities. Clinically, colchicine is widely used for the treatment of gouty arthritis, familial Mediterranean fever, cardiovascular dysfunction and new coronary pneumonia. However, overdose intake of colchicine could cause lethal liver damage, which is a limitation of its application. Therefore, exploring the potential mechanism of colchicine-induced hepatotoxicity is meaningful. Interestingly, it was found that CYP1A1 played an important role in the hepatotoxicity of colchicine, while it might also participate in its metabolism. Inhibition of CYP1A1 could alleviate oxidative stress and pyroptosis in the liver upon colchicine treatment. By regulating CYP1A1 through the CASPASE-1-GSDMD pathway, colchicine-induced liver injury was effectively relieved in a mouse model. In summary, we concluded that CYP1A1 may be a potential target, and the inhibition of CYP1A1 alleviates colchicine-induced liver injury through pyroptosis regulated by the CASPASE-1-GSDMD pathway.

Utilizing time series for forecasting the development trend of coronavirus: A validation process

A time series prediction model was developed to predict the number of confirmed cases from October 2022 to November 2022 based on the number of confirmed cases of New Coronary Pneumonia from January 20, 2021 to September 20, 2022. We will analyze the number of confirmed cases in the Philippines from January 1, 2020 to September 20, 2022 to build a prediction model and make predictions. Among the works of other scholars, it can be shown that time series is an excellent forecasting model, particularly around dates. The study in this work begins with the original data for inference, and each phase of inference is based on objective criteria, such as smooth data analysis utilising ADF detection and ACF graph analysis, and so on. When comparing the performance of algorithms with functions for time series models, hundreds of algorithms are evaluated one by one on the basis of the same data source in order to find the best method. Following the acquisition of the methods, ADF detection and ACF graph analysis are undertaken to validate them, resulting in a closed-loop research. Although the dataset in this study was generated from publicly available data from the Philippines (our data world for coronaviruses), the ARIMA model used to predict data beyond September 20, 2022 exhibited unusually high accuracy. This model was used to compare the performance of several algorithms, each evaluated using the same training data. Finally, the best R2 for the ARIMA model was 92.56% or higher, and iterative optimization of the function produced a predictive model with an R2 of 97.6%. This reveals the potential trajectory of coronaviruses in the Philippines. Finally, the model with the greatest performance is chosen as the prediction model. In actual implementations, several subjective and objective elements, such as the government’s epidemic defence measures, the worldwide pandemic condition, and whether the data source distributes the data in a timely way, might restrict the prediction’s accuracy. Such prediction findings can be used as a foundation for data releases by health agencies.

The Impact of Smoking on COVID-19 Analyzed by Hierarchical Linear Model

COVID-19 has been of great concern since it broke out. Many countries have put more effort into securing vaccines and therapeutic medicines. In the meantime, there has been a large number of scholarly efforts in figuring out the relationship between smoking and new coronary pneumonia. This study firstly collected daily data (the number of people hospitalized with new coronary pneumonia, severe cases, and the number of people double-vaccinated against new coronary pneumonia in various countries or regions) and annual data (the human development index and smoking rate of each country or region). We collected data from 160 countries from the Our Data World database. The time is for January 1, 2020 through June 30, 2022. We divided daily and annual data into the first-level and second-level data. Finally, we checked the relationship between smoking and COVID-19 in a Hierarchical Linear Model (HLM). The result shows that smoking raises the number of new cases of infection and death among people fully vaccinated per hundred. There is a partial mediating effect of new cases smoothed per million over new deaths smoothed per million. Smoking significantly increases the effect of new cases smoothed per million over new deaths smoothed per million. Our findings have high generalizability around the world.

The intercultural communication competence improvement for pre-service CSL teachers: A blended learning method based on SVVR

BACKGROUND: With the growing global influence of new coronary pneumonia and an increasing number of successful education case, spherical video-based virtual reality (SVVR) has arisen as an application for effective integration with teaching and learning. OBJECTIVE: This is a mixed-methods study on SVVR-based training for pre-service CSL teachers to more effectively develop their cross-cultural communication and interaction abilities. METHODS: An experimental strategy was adopted in this work. First, the processing of VR technology for designed TVET courses, including practical training courses, had been completed; The second experiment was a group experiment of one month; Finally, the hypotheses were tested by an independent sample t-test. RESULTS: Based on experiment data of the learners, this study build a multi-modal hybrid training and recognition model and confirm hybrid approach effectively improves the intercultural communication abilities of pre-service CSL teachers. CONCLUSIONS: The findings of this study confirm that information technology plays an important role in pre-service CSL teacher training, and teachers should incorporate more information technology, design more hands-on interactive activities and scenarios, and take into account the individual differences of learners in the teaching process. Several policy proposals are offered to ensure that ICT is better integrated into classroom instruction.

Exploring the Behavioral Intention of Vaccination against COVID-19 Using the Theory of Planned Behavior

People’s attitudes towards vaccines and beliefs about infectious disease prevention are closely related to the design and effectiveness of prevention strategies. This study explores Taiwanese people’s views on COVID-19 vaccines, beliefs about infectious disease prevention, and people's acceptance of vaccinations extent and influencing factors. This study is mainly aimed at the people in the Greater Taipei area as the research subject. The convenience sampling method is used to investigate the willingness of the people to receive the new coronary pneumonia vaccine. The relevant data is collected through the google online questionnaire. The study found that people’s attitudes, subjective norms, perceived behavioral control and behavioral intentions, social trust, health awareness, and expected emotions towards vaccination against COVID-19 were partially significant due to demographic variables. People’s attitudes, subjective norms, and perceived behavioral control have positive effects on behavioral intentions.

The impact of the digital economy on young people’s consumption in the context of the new coronary pneumonia epidemic

This paper analyzes digital economy data from Beijing and 1 Chinese province and city by a panel model and a fixed impact model. The findings are as follows: First, the development of digital participatory finance has positively influenced China’s consumption level, consumption structure, consumption areas and development, mainly because it has affected the consumer goods of Chinese citizens. Second, by analyzing digital economy indicators and data at different stages of economic development, it is believed that the size and reach of the digital economy will have a more positive impact on per capita consumption in China. China’s digital economy mainly affects the urban-rural structure, regional structure, and consumption habits of residents to realize the impact of the digital economy. The digital economy has a much greater impact on growth-oriented household consumption than on household consumption. Based on the findings, recommendations for action are also provided. The government should strengthen digital infrastructure, improve digital economy products and derivative financial services, improve the quality of current financial services for citizens, and increase the affordability of citizens to successfully develop the digital economy.

Trends and characteristics of COVID-19 and cardiovascular disease related studies.

Introduction: The new coronavirus has caused a pandemic that has infected hundreds of millions of people around the world since its outbreak. But the cardiovascular damage caused by the new coronavirus is unknown. We have analyzed the current global scenario and the general pattern of growth. After summarizing the known relationship between cardiovascular diseases and new coronary pneumonia, relevant articles are analyzed through bibliometrics and visualization. Methods: Following our pre-designed search strategy, we selected publications on COVID-19 and cardiovascular disease in the Web of Science database. In our relevant bibliometric visualization analysis, a total of 7,028 related articles in the WOS core database up to 20th October 2022 were summarized, and the most prolific authors, the most prolific countries, and the journals and institutions that published the most articles were summarized and quantitatively analyzed. Results: SARS-CoV-2 is more infectious than SARS-CoV-1 and has significant involvement in the cardiovascular system in addition to pulmonary manifestations, with a difference of 10.16% (20.26%/10.10%) in the incidence of cardiovascular diseases. The number of cases increases in winter and decreases slightly in summer with temperature changes, but the increase in cases tends to break out of seasonality across the region as mutant strains emerge. The co-occurrence analysis found that with the progress of the epidemic, the research keywords gradually shifted from ACE2 and inflammation to the treatment of myocarditis and complications, indicating that the research on the new crown epidemic has entered the stage of prevention and treatment of complications. Conclusion: When combined with the current global pandemic trend, how to improve prognosis and reduce human body damage could become a research focus. At the same time, timely detection, prevention, and discovery of new mutant strains have also become key tasks in the fight against the epidemic, and full preparations have been made to prevent the spread of the next wave of mutant strains, and still need to continue to pay attention to the differential performance of the variant "omicron."

Privacy-Preserving-Enabled Lightweight COVID-19 Simulation Model for Mobile Intelligent Application

In order to control the first wave of COVID-19 pandemic in 2020, many models have shown effectiveness in predicting the spread of new coronary pneumonia and the different interventions. However, few models can collect large amounts of high-quality real-time data faster under the premise of protecting privacy, considering the impact of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variant and the mass vaccination program as a new intervention. Therefore, we developed a mobile intelligent application that can collect a large amount of real-time data while protecting privacy and conducted a feasibility study by defining a new COVID-19 mathematical model SEMCVRD. By simulating different intervention measures, the prediction model of the mobile intelligent application used in this article simulates the epidemic situation in the U.K. as an example. The findings are as below: the optimal intervention strategy is to suppress the intervention at <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$P=3$ </tex-math></inline-formula> (intervention intensity: the average number of contacts per person per day) before the end of March 2021, then gradually release the intervention intensity at a rate of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$P+2$ </tex-math></inline-formula> , and finally release the intensity to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$P=9$ </tex-math></inline-formula> in June 2021. The COVID-19 pandemic will end at the end of June 2021, when the total number of deaths will reach 128772. This strategy will be able to balance the tradeoff between loss of life and economic loss. Compared with the official statistics released by the U.K. government on May 31, 2021, our model can accurately predict the relative error rate of the total number of cases is less than 6.9%, and the relative error rate of the total number of deaths is less than 1%. Furthermore, the model is also suitable for collecting data from countries/regions around the world.

The Impact of the COVID-19 Pandemic on China's Economic Structure the Stay-at-Home Economy

The impact of the new coronavirus outbreak on China's industrial economy is enormous. the outbreak of the new coronavirus at the end of 2019 coinciding with the Chinese New Year interrupted the current economic development plan of China. It also had an impact on the macroeconomic, meso-industrial and micro-individual development. To solve the current development problems and to avoid the phenomenon of economic cliff, which is the main policy choice of the grassroots industrial organizations and related departments at this stage. This paper analyzes the impact of the new coronary pneumonia on China's economy, compares the online consumption before and after the epidemic, theorizes the "home economy" under the epidemic, and explores the relevant countermeasures after the epidemic, in order to boost market confidence, support steady economic growth, and lay a solid economic foundation for the country to achieve the general tone of "seeking progress while maintaining stability". In this way, this paper can boost market confidence, support steady economic growth, and lay a solid economic foundation for the country to achieve the general keynote of seeking progress while maintaining stability.

Research on emergency logistics location-routing problem for public health emergencies

At present, the world's public health emergencies are becoming more and more frequent, bringing great threats to people's life and health safety and economic development. In the new crown pneumonia epidemic that affected the world in 2020, due to the shortage of medical resources and poor transportation of materials, the comprehensive control of the epidemic encountered great difficulties. In order to minimize the impact and loss caused by public health emergencies, it is necessary to study the location of emergency logistics centers and transportation route planning. This paper studies the positioning-routing problem in the emergency logistics system after public health emergencies, applies genetic algorithm and the Traveling Salesman Problem (TSP) to solve the positioning-routing problem in this paper, takes the current Wuhan map as an example, combines the p-center theory to take the location of each new coronary pneumonia directional receiving hospital as the basis for determining the material distribution center and transportation route, and uses actual cases to study the positioning-route problem.

Analysis on the Impact of the COVID-19 on Higher Education in China

The outbreak of novel coronary pneumonia in late 2019 was characterized by a highly contagious virus. In response, the national government reacted quickly and took proactive measures, which played a very important role in effectively controlling the spread of the outbreak. The prevention and control of the Newcastle pneumonia epidemic has led to advances in a number of areas, including higher education. The content of higher education has been expanded, the way higher education is taught has been reformed, and reforms in the way universities are governed have been promoted. From the perspective of education model, the epidemic is the touchstone of educational information. Under the epidemic situation, the loopholes of traditional teaching mode have been exposed. Online education has ushered in new opportunities by virtue of its immersive advantages, but it also faces many challenges. This paper provides an in-depth discussion of the impact of COVID-19 on higher education in both negative and positive aspects, and proposes solutions towards alleviating the digital divide, and put forward possible suggestions from three aspects of colleges, teachers and students.

Application of SHEL Model in the Management of Respiratory Tract Exposure in Fangcang Shelter Hospitals for COVID-19.

To explore the application effect of the (software factors, hardware factors, environmental factors, parties and other factors, SHEL) model in respiratory tract exposure protection of staff in temporary COVID-19 hospitals. 207 Staff members working in the isolation units of Fangcang shelter hospitals between 20 May 2022 and 5 June 2022 were selected as research subjects. The SHEL model was used to protect and manage the respiratory exposure of the isolation unit staff to the novel coronavirus. The incidence of respiratory exposure among the staff in the isolation units was compared before the SHEL model's implementation (20 May 2022-28 May 2022) and afterwards the SHEL model's implementation (29 May 2022-5 June 2022). Before the implementation of the SHEL model, a total of nine cases (4.35%) from 207 workers had respiratory exposure. Occurrence location: six cases in the isolation room (one-out room, level-one protection zone) and three cases in the drop-off area for patients outside the ward. After implementation, a total of two cases (0.97%) of respiratory tract exposure occurred among the 207 staff members; both occurred in the unprotected zone (two-out room, level-two protection zone), and the difference was statistically significant before and after the implementation (P < 0.05). New coronary pneumonia Fangcang shelter hospitals should use the SHEL model to manage the respiratory exposure of their isolation unit staff to reduce the respiratory exposure risk to staff in isolation units.

Research on epidemic spread model based on cold chain input

In recent years, the new type of coronary pneumonia (COVID-19) has become a highly contagious disease worldwide, posing a serious threat to the public health. This paper is based on the SEIR model of the new coronavirus pneumonia, considering the impact of cold chain input and re-positive on the spread of the virus in the COVID-19. In the process of model design, the food cold chain and re-positive are used as parameters, and its stability is analyzed and simulated. The experimental results show that taking into account the cold chain input and re-positive can effectively simulate the spread of the epidemic. The research results have important research value and practical significance for the prevention and control of the COVID-19 and the prediction of important time nodes.

Epidemiological clustered characteristics of coronavirus disease 2019 (COVID-19) in three phases of transmission in Jilin Province, China.

The current epidemiological status of the new coronary pneumonia epidemic in China is being explored to prevent and control the localized dissemination of aggregated outbreaks. This study analyzed the characteristics of new outbreaks of coronavirus disease 2019 (COVID-19) at three stages of aggregated outbreaks in Jilin Province, China, to provide a reference for the prevention and control of aggregated outbreaks. Case information were collected from all patients in Jilin Province from January 12, 2020 to the present. The epidemic was divided into three stages according to the time of onset. The first stage comprised 97 cases reported from January 12, 2020 to February 19, 2020, during which 17 aggregated outbreaks occurred. The second comprised 43 cases reported from April 25, 2020 and May 23, 2020, involving one aggregated outbreak. The third comprised 435 cases reported on January 10, 2021 and February 9, 2021, involving one aggregated outbreak. The relationship between aggregated and non-aggregated cases in the first phase of the outbreak and the difference between imported and local cases during the aggregated outbreak were assess using statistical analysis, and the differences in the baseline information between the three phases were analyzed. The incubation periods of the three phases were 10 days, 8 days, and 5 days. The number of aggregated epidemic events in Jilin Province tended to increase and then decrease over time. The clustered events in Jilin Province were divided into four categories: household contact (14 times, 51 cases); household contact and public places (one time, three cases); household contact, public places, and gatherings (one time, six cases); and household contact, public places, gatherings, and work (three times, 495 cases). Clustered events occurred mainly between January 22, 2020, and February 4, 2020. Among all cases in the first phase of the outbreak, the method of detection and the time from diagnosis to discharge were longer in aggregated cases than in non-aggregated cases, and that the source of infection and renewal cases were more frequent and more likely to be detected in the outpatient clinics during aggregated outbreaks than the imported cases. The second phase of the epidemic showed significant spatial variability (Moran's I<0, P<0.05). The third stage of the epidemic occurred in a higher proportion of individuals aged 50-90 years and within a shorter incubation period compared with the first two stages. The current focus of prevention and control of the COVID-19 epidemic in Jilin Province is to strictly implement the restrictions on gatherings and to perform timely screening and isolation of close contacts of infectious sources while strengthening the supervision of the inflow of people from outside the region. Simultaneously, more targeted prevention and control measures can be implemented for different age groups and occupations.

TDD-UNet:Transformer with double decoder UNet for COVID-19 lesions segmentation

The outbreak of new coronary pneumonia has brought severe health risks to the world. Detection of COVID-19 based on the UNet network has attracted widespread attention in medical image segmentation. However, the traditional UNet model is challenging to capture the long-range dependence of the image due to the limitations of the convolution kernel with a fixed receptive field. The Transformer Encoder overcomes the long-range dependence problem. However, the Transformer-based segmentation approach cannot effectively capture the fine-grained details. We propose a transformer with a double decoder UNet for COVID-19 lesions segmentation to address this challenge, TDD-UNet. We introduce the multi-head self-attention of the Transformer to the UNet encoding layer to extract global context information. The dual decoder structure is used to improve the result of foreground segmentation by predicting the background and applying deep supervision. We performed quantitative analysis and comparison for our proposed method on four public datasets with different modalities, including CT and CXR, to demonstrate its effectiveness and generality in segmenting COVID-19 lesions. We also performed ablation studies on the COVID-19-CT-505 dataset to verify the effectiveness of the key components of our proposed model. The proposed TDD-UNet also achieves higher Dice and Jaccard mean scores and the lowest standard deviation compared to competitors. Our proposed method achieves better segmentation results than other state-of-the-art methods.

Clinical features and effectiveness of Chinese medicine in patients with COVID-19 from overseas: A retrospective study in Xiamen, China.

COVID-19, referred to as new coronary pneumonia, is an acute infectious disease caused by a new type of coronavirus SARS-CoV-2. To evaluate the effect of integrated Chinese medicine and Western medicine in patients with COVID-19 from overseas. Data were collected from 178 COVID-19 patients overseas at First Affiliated Hospital of Xiamen University from April 1, 2021 to July 31, 2021. These patients received therapy of integrated Chinese medicine and western medicine. Demographic data and clinical characteristics were extracted and analyzed. In addition, the prescription which induced less length of PCR positive days and hospitalization days than the median value was obtained. The top 4 frequently used Chinese medicine and virus-related genes were analyzed by network pharmacology and bioinformatics analysis. According to the chest computed tomography (CT) measurement, abnormal lung findings were observed in 145 subjects. The median length of positive PCR/hospitalization days was 7/7 days for asymptomatic subjects, 14/24 days for mild subjects, 10/15 days for moderate subjects, and 14/20 days for severe subjects. The most frequently used Chinese medicine were Scutellaria baicalensis (Huangqin), Glycyrrhiza uralensis (Gancao), Bupleurum chinense (Chaihu), and Pinellia ternata (Banxia). The putative active ingredients were baicalin, stigmasterol, sigmoidin-B, cubebin, and troxerutin. ACE, SARS-CoV-2 3CL, SARS-CoV-2 Spike, SARS-CoV-2 ORF7a, and caspase-6 showed good binding properties to active ingredients. In conclusion, the clinical results showed that integrated Chinese medicine and Western medicine are effective in treating COVID-19 patients from overseas. Based on the clinical outcomes, the putative ingredients from Chinese medicine and the potential targets of SARS-CoV-2 were provided, which could provide a reference for the clinical application of Chinese medicine in treating COVID-19 worldwide.