A Dynamic-Static Dual Input Deep Neural Network Algorithm for Diagnosing COVID-19 by Cough

The COVID-19 (corona virus disease 2019) has caused serious impacts worldwide. Many scholars have done a lot of research on the prevention and control of the epidemic. The diagnosis of COVID-19 by cough is non-contact, low-cost, and easy-access, however, such research is still relatively scarce in China. Mel frequency cepstral coefficients (MFCC) feature can only represent the static sound feature, while the first-order differential MFCC feature can also reflect the dynamic feature of sound. In order to better prevent and treat COVID-19, the paper proposes a dynamic-static dual input deep neural network algorithm for diagnosing COVID-19 by cough. Based on Coswara dataset, cough audio is clipped, MFCC and first-order differential MFCC features are extracted, and a dynamic and static feature dual-input neural network model is trained. The model adopts a statistic pooling layer so that different length of MFCC features can be input. The experiment results show the proposed algorithm can significantly improve the recognition accuracy, recall rate, specificity, and F1-score compared with the existing models. © 2023 Chinese Institute of Electronics. All rights reserved.

The impact of COVID-19 pandemic on medical students: A cross-sectional study

Background: The coronavirus disease (COVID-19) pandemic has affected the medical education throughout the world. A study was done to assess the effect of education and psychological behavior on medical students. Aims and Objectives: The objective of the study is to evaluate the effect of COVID-19 on medical graduates in various aspects such as education, effect on clinical rotations, impact on the technology used for online classes, effect on quality of life, loneliness, sleep, and depressive symptoms. Material(s) and Method(s): A set of questions were distributed to Government Medical college, Suryapet students during November 2021-January 2022. Questionnaire aimed to study students' viewpoint of COVID-19's impact on their education, mental health, and willingness to participate clinically. Result(s): One hundred medical students from Government Medical College, Suryapet participated in this study. Most students (88%) agreed that pandemic had disrupted their medical education. About 64% agreed to attend clinical rotations and 68% of students accepting the risk of contracting COVID-19 in clinical rotations. COVID-19 had an impact on technology tools used for medical education. Students reported that COVID-19 had moderate impact on quality of life, sleep quality, anxiety, and depressive symptoms. Conclusion(s): The COVID-19 had an overall significant negative impact on undergraduate medical education. It is recommended that measures need to be taken to relieve students' stress.Copyright © 2023, Mr Bhawani Singh. All rights reserved.

Impact of comorbidities on the average length of stay and mortality of COVID-19 patients in a hospital: In India

ObjectiveTo estimate the length of stay and proportional mortality in COVID patients in a COVID-dedicated hospital.MethodsA retrospective record review was done using medical records of COVID-19 in-patients, capturing the demographic, clinical, and laboratory details of admitted COVID patients, including serial samples for RTPCR/CBNAAT for Coronavirus. These details from electronic medical records were compared and collated for patients of different comorbidities to arrive at the average length of stay and case fatality rate and time duration for patients to turn COVID-negative.ResultsPatients with Diabetes Mellitus (DM) had the highest Average Length of Stay (ALS) of 12.09 days in the hospital followed by patients with Hypertension (11.5 days). Patients without any comorbidities had ALS of 8.8 days. A combination of HTN, DM, coronary artery disease (CAD), and chronic kidney disease (CKD) had the highest ALS of 14.5 days. The average duration for patients to test negative is 16 days for patients without any comorbidities. The average duration between the first symptom and the negative test is the longest for DM (21 days) followed by HTN (19.5 days), cancer (19 days), and obesity (16 days). Among the 731 people who died in the observed time, the proportional mortality rate was highest with HTN (10.80%) followed by carcinoma (7.66%) and DM (6.56%), 32.55% had a combination of two or more comorbidities. 33.70% deceased didn't have any comorbidities.ConclusionThe COVID-19 pandemic has highlighted the importance of preparing for future outbreaks and sudden increases in cases. Based on our findings, we recommend Hospital administrators have a comprehensive approach to planning for the future, considering all relevant factors, including the epidemiology of the disease, the average length of stay, and mortality rates, to ensure that their hospitals are equipped to provide high-quality care to all patients.

Adverse events following immunization associated with the ChAdOx1nCoV-19 and BBV152 vaccine-a cross-sectional study

COVID-19, an infectious disease, has become a leading cause of death in many people. The rapid emergence of the pandemic prompted the development of a vaccine to mitigate the disease's harmful consequences. Vaccination is the only effective way to prevent infection from spreading and build immunity to the virus. However, developing adverse effects has become a major problem for vaccine reluctance. Accordingly, the interest has been shifted towards identifying the adverse effects developed following immunization. The current study objective is to assess and compare the intensity of adverse effects following 1st and 2nd dose of COVID-19 vaccination and the medication administered to relieve the symptoms associated with vaccination. A cross-sectional study was performed in a community over six months. A total of 836 participants were involved in the study. All the data regarding the vaccination were collected through a specially designed questionnaire form and analyzed in all the participants within the study group. According to the study, at least 1 AEFI was developed in about 90% of the study population. The most common systemic and local effect developed in the study population was fever (59.42%) and pain at the injection site (69.82%), respectively. With both vaccines (ChAdOx1 nCoV-19 and BBV152), the incidence and severity of AEFIs were lower after the second dose than after the first dose, and most of the symptoms associated with vaccination were alleviated by taking home remedies and symptomatic treatment. The adverse effects reported after receiving the ChAdOx1 nCoV-19 and BBV152 vaccines are typical of most vaccines, and the majority of them were tolerated, and most subsided in less than 24 hours.

A Hybrid Grey Wolf-Whale Optimization Algorithm for Classification of Corona Virus Genome Sequences using Deep Learning

Genome sequence data is widely accepted as complex data and is still growing in an exponential rate. Classification of genome sequences plays a crucial role as it finds its applications in the area of biology, medical and forensics etc. For classification, Genome sequences can be represented in terms of features. More number of less significant features leads to lower accuracy in classification task. Feature selection addresses this issue by selecting the most important features which aids to improve the accuracy and lessens the computational complexity. In this research, Hybrid Grey Wolf-Whale Optimization Algorithm (HGWWOA) is proposed for Genome sequence classification. The proposed algorithm is evaluated using 23 benchmark objective functions along with Convolutional Neural Network classifier and its efficiency is verified using a novel metric namely "Feature Reduction Rate”. The proposed optimization algorithm can be applied for any optimization problems. In this research work, the proposed algorithm is used for classification of Corona Virus genome sequences. Performance comparison of the proposed and existing algorithms was carried out and it is evident that the performance of proposed algorithm exceeds the previous algorithms with an accuracy of 98.2%. © 2023, Zarka Private University. All rights reserved.

A Study on Optimal Control of a COVID-19 Transmission Model with the Significance of Early Screening and Testing Measures

In this paper, we present a deterministic SEQIR mathematical model that describes the transmission dynamics of COVID-19 that also in-cludes testing procedures in the quarantine stage. The reproduction number R0 is derived with some properties of the model. The stabil-ity of equilibrium points is analyzed. An objective function is pro-posed and optimal control strategies are derived using Pontryagin's Maximum Principle. The existence and uniqueness of an optimal-ity system are demonstrated. Numerical simulations are presented in different scenarios with the control interventions early screening, prevention measures of COVID-19, and following a healthy lifestyle. The main objective of the paper is to eradicate the disease in exposed stage. The chosen control variables helps us to reduce the exposed population. (c) 2023 L&H Scientific Publishing, LLC. All rights reserved.

Prediction of Corona Virus Disease Outcome and Future Forecast the Trend of COVID-19 in India: Observational Study

This work is motivated by the disease caused by the novel corona virus Covid-19, rapid spread in India. An encyclopaedic search from India and worldwide social networking sites was performed between 1 March 2020 and 20 Jun 2020. Nowadays social network platform plays a vital role to track spreading behaviour of many diseases earlier then government agencies. Here we introduced the approach to predict and future forecast the disease outcome spread through corona virus in society to give earlier warning to save from life threats. We compiled daily data of Covid-19 incidence from all state regions in India. Five states (Maharashtra, Delhi, Gujarat, Rajasthan and Madhya-Pradesh) with higher incidence and other states considered for time series analysis to construct a predictive model based on daily incidence training data. In this study we have applied the predictive model building approaches like k-nearest neighbour technique, Random-Forest technique and stochastic gradient boosting technique in COVID-19 dataset and the simulated outcome compared with the observed outcome to validate model and measure the performance of model by accuracy (ACC) and Kappa measures. Further forecast the future trends in number of cases of corona virus deceased patients using the Holt Winters Method. Time series analysis is effective tool for predict the outcome of corona virus disease. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

Research progress on the regulation of endothelial cells by natural products of traditional Chinese medicine in the prevention and treatment of atherosclerosis

[] Atherosclerosis (As) is the pathological basis of coronary heart disease, and vascular endothelial injury is the initiating factor of coronary atherosclerosis. Vascular endothelial cells are a single layer of cells located in the inner layer of blood vessels and regulates exchanges between the blood stream and the surrounding tissues, and their integrity is very important. Many active monomers and the derivatives in natural products of traditional Chinese medicine modulate the function of endothelial cells by intervening oxidative stress, regulating the release of vasoactive substances, reducing inflammation, and equilibrating coagulation and anticoagulant system. They have the advantages of multi-pathway, multi-link and multi-target regulation in protecting from endothelial injury and attenuating atherogenesis. They have also been used to protect against corona virus disease 2019 (COVID-19) induced endothelial injury and atheroslerosis. This article reviews the research progress of the above issues in this field. © 2023, Editorial Office of Chinese Journal of Arteriosclerosis. All rights reserved.

Potential targets and traditional Chinese medicine treatment strategy of cough after SARS-CoV-2 infection.

The severe acute respiratory syndrome corona virus 2 (SARS-CoV-2) Omicron variants BA.5.2 and BF.7 have become the main epidemic strains in China since the quarantine policy was lifted in 7th December 2022. Cough is one of the main symptoms induced by SARS-CoV-2 infection. SARS-CoV-2 infection-associated cough injuries the lung and upper respiratory tract, while the infected people cough out virus and liquid which forms virus-containing aerosols, a medium for quickly spreading. Furthermore, cough is one of primary sequelae of discharged patients in corona virus disease 2019 (COVID-19). By now, there are no efficacious drugs for treatment of upper respiratory tract infection associated cough induced by omicron. Traditional Chinese medicine (TCM) has a long history on treating cough. By reviewing the mechanisms of the occurrence of cough after SARS-CoV-2 infection, potential therapeutic targets and cough suppressant herbs with significant efficacy in clinical and basic research, we provide a reference for the treatment of cough after SARS-Cov-2 infection and a basis for the majority of infected patients to select appropriate herbs for cough relief under guidance of physicians.Copyright © 2023 Editorial Office of Chinese Traditional and Herbal Drugs. All rights reserved.

Examining the Growth of Online Entrepreneurship Effects on Family Income in Jordan during the Corona Virus

According to the significant impacts of social media and the internet on all facets of our lives in general and the business world in particular, many business owners and entrepreneurs who are looking to expand their clientele or start their own ventures have moved to the virtual world, particularly when they want to advance their careers. For the reasons mentioned above, this article aims to ascertain how social media networks impact business operations, with a focus on how they affect entrepreneurship growth. Facebook and Instagram are the two most useful social media platforms. The findings indicate that Facebook and Instagram have a significant impact on increasing entrepreneurship and household income in Jordan. © 2023 IEEE.

Customers' Acceptance of E-banking During the COVID-19 Pandemic: The Case of Oman

The Omani banking industry still lags behind in keeping up with the full adoption of e-banking technologies in delivering its services. Nevertheless, this might have changed to some extent with the spread of the Corona Virus (COVID-19) which forced financial institutions around the world to temporary close most of their branches and operate mostly on an online basis. The purpose of the study is to investigate customers' perception of E-Banking Adoption in the Omani context during the COVID-19 pandemic. Specifically, the focus is on determining the aspects that would potentially increase the acceptance and usage of E-banking services from the customer's perspective. A questionnaire was developed with the purpose of collecting data from total sample of 200 banks' customers in Oman. In terms of data analysis, linear regression and t-tests were used. The results indicated that bank customers in Oman have a high willingness to use E-banking services. On the other hand, the findings revealed that perceived ease of use, uncertainty, facilitating conditions and self-efficacy has a significant effect on E-banking adoption and hence are have the potential to increase the adoption of E-banking services in Oman. The study contributes significantly to the behavioural and innovation adoption theories. In addition, it contributes in developing policies and setting improvement measures in the banking industry. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022.

The Impact of Covid-19 Vaccines on Sperm Quality

Objective: To determine the impact of Covid-19 vaccines on sperm quality. Study Design: Case control study Place and Duration of Study: Department of Diabetes & Endocrinology, Chandka Medical College Hospital Larkana from 1st July 2022 to 31st December 2022. Methodology: Patients were enrolled as 50 those who had PCR confirmed Covid 19 history and 50 those who never got Covid-19. On this basis those cases who had a Covid-19 history were placed in group A while those who did not had Covid-19 history were placed in Group B. Patients clinical history including anamnesis, marital status, cryptorchidism, operative varicocele, or any chronic ailment were documented. A counting chamber was used for sperm count in a 100 square area. Spermatozoa was measured as either rapid-progressively motile (Type a), or as slow-progressively-motile (Type b), or as situ motile (Type c), and finally as immobile (Type d). The total semen sperm count was gained by multiplication of concentration of sperm with its volume. Result(s): Volume and concentration was significantly different in both study groups. Difference in tail anomaly was also observed. In group A, it was 29.20 +/- 10.26 while 27.59 +/- 12.31 was the value of group B. Almost equal number of participants were married. Azoospermia was only found among Covid patients. Conclusion(s): Azoospermia was only found in Covid patients and no such results were obtained from Covid negative patients.Copyright © 2023 Lahore Medical And Dental College. All rights reserved.

Forecasting major impacts of COVID-19 pandemic on country-driven sectors: challenges, lessons, and future roadmap.

The pandemic caused by the coronavirus disease 2019 (COVID-19) has produced a global health calamity that has a profound impact on the way of perceiving the world and everyday lives. This has appeared as the greatest threat of the time for the entire world in terms of its impact on human mortality rate and many other societal fronts or driving forces whose estimations are yet to be known. Therefore, this study focuses on the most crucial sectors that are severely impacted due to the COVID-19 pandemic, in particular reference to India. Considered based on their direct link to a country's overall economy, these sectors include economic and financial, educational, healthcare, industrial, power and energy, oil market, employment, and environment. Based on available data about the pandemic and the above-mentioned sectors, as well as forecasted data about COVID-19 spreading, four inclusive mathematical models, namely-exponential smoothing, linear regression, Holt, and Winters, are used to analyse the gravity of the impacts due to this COVID-19 outbreak which is also graphically visualized. All the models are tested using data such as COVID-19 infection rate, number of daily cases and deaths, GDP of India, and unemployment. Comparing the obtained results, the best prediction model is presented. This study aims to evaluate the impact of this pandemic on country-driven sectors and recommends some strategies to lessen these impacts on a country's economy.

COVID-19 prediction based on hybrid Inception V3 with VGG16 using chest X-ray images.

The Corona Virus was first started in the Wuhan city, China in December of 2019. It belongs to the Coronaviridae family, which can infect both animals and humans. The diagnosis of coronavirus disease-2019 (COVID-19) is typically detected by Serology, Genetic Real-Time reverse transcription-Polymerase Chain Reaction (RT-PCR), and Antigen testing. These testing methods have limitations like limited sensitivity, high cost, and long turn-around time. It is necessary to develop an automatic detection system for COVID-19 prediction. Chest X-ray is a lower-cost process in comparison to chest Computed tomography (CT). Deep learning is the best fruitful technique of machine learning, which provides useful investigation for learning and screening a large amount of chest X-ray images with COVID-19 and normal. There are many deep learning methods for prediction, but these methods have a few limitations like overfitting, misclassification, and false predictions for poor-quality chest X-rays. In order to overcome these limitations, the novel hybrid model called "Inception V3 with VGG16 (Visual Geometry Group)" is proposed for the prediction of COVID-19 using chest X-rays. It is a combination of two deep learning models, Inception V3 and VGG16 (IV3-VGG). To build the hybrid model, collected 243 images from the COVID-19 Radiography Database. Out of 243 X-rays, 121 are COVID-19 positive and 122 are normal images. The hybrid model is divided into two modules namely pre-processing and the IV3-VGG. In the dataset, some of the images with different sizes and different color intensities are identified and pre-processed. The second module i.e., IV3-VGG consists of four blocks. The first block is considered for VGG-16 and blocks 2 and 3 are considered for Inception V3 networks and final block 4 consists of four layers namely Avg pooling, dropout, fully connected, and Softmax layers. The experimental results show that the IV3-VGG model achieves the highest accuracy of 98% compared to the existing five prominent deep learning models such as Inception V3, VGG16, ResNet50, DenseNet121, and MobileNet.

Body composition and physical activity of university students during COVID-19 lockdowns.

[Purpose] Behavioral restrictions during the corona virus disease 2019 (COVID-19) pandemic may have affected the physical activity levels of college students. We aimed to characterize the body composition and physical activity of college students during these behavioral restrictions. [Participants and Methods] The body composition (height, weight, body mass index, body fat mass, body fat percentage, total body muscle mass, free-fat muscle index [FFMI], and fat mass index [FMI]), physical activity, amount the of walking, amount of daily activity, and the number of steps were measured in 52 university students. [Results] For both male and females, the number of steps taken was lower than the average steps reported by the Ministry of Health, Labour and Welfare. In males, FFMI had a strong positive correlation with physical activity, amount of walking, and the number of steps taken. In females, FFMI had a strong positive correlation with physical activity and the amount of walking, as well as a moderate positive correlation with the amount of daily activity. [Conclusion] Since physical activity and walking of university students during COVID-19 affect FFMI, it is necessary to develop an exercise program that considers behavioral patterns.

[Interpretation of the 2021 American Academy of Allergy, Asthma and Immunology expert consensus on the treatment of postinfectious olfactory dysfunction].

Respiratory tract viruses are the second leading cause of olfactory dysfunction. Between 2019 to 2022, the world has been plagued by the problem of olfaction caused by the COVID-19. As we learn more about the impact of severe acute respiratory syndrome coronavirus 2（SARS-CoV-2）, with the recognition that olfactory dysfunction is a key symptom of this disease process, there is a greater need than ever for evidence-based management of postinfectious olfactory dysfunction（PIOD）. The Clinical Olfactory Working Group has proposed theconsensus on the roles of PIOD. This paper is the detailed interpretation of the consensus.

Time to Extubation among ARDS Subjects with and without COVID-19 Pneumonia.

BACKGROUND: Pneumonia from COVID-19 that results in ARDS may require invasive mechanical ventilation. This retrospective study assessed the characteristics and outcomes of subjects with COVID-19-associated ARDS versus ARDS (non-COVID) during the first 6 months of the COVID-19 pandemic in 2020. The primary objective was to determine whether mechanical ventilation duration differed between these cohorts and identify other potential contributory factors. METHODS: We retrospectively identified 73 subjects admitted between March 1 and August 12, 2020, with either COVID-19-associated ARDS (37) or ARDS (36) who were managed with the lung protective ventilator protocol and required > 48 h of mechanical ventilation. Exclusion criteria were the following: <18 years old or the patient required tracheostomy or interfacility transfer. Demographic and baseline clinical data were collected at ARDS onset (ARDS day 0), with subsequent data collected on ARDS days 1-3, 5, 7, 10, 14, and 21. Comparisons were made by using the Wilcoxon rank-sum test (continuous variables) and chi-square test (categorical variables) stratified by COVID-19 status. A Cox proportional hazards model assessed the cause-specific hazard ratio for extubation. RESULTS: The median (interquartile range) mechanical ventilation duration among the subjects who survived to extubation was longer in those with COVID-19-ARDS versus the subjects with non-COVID ARDS: 10 (6-20) d versus 4 (2-8) d; P < .001. Hospital mortality was not different between the two groups (22% vs 39%; P = .11). The competing risks Cox proportional hazard analysis (fit among the total sample, including non-survivors) revealed that improved compliance of the respiratory system and oxygenation were associated with the probability of extubation. Oxygenation improved at a lower rate in the subjects with COVID-19-associated ARDS than in the subjects with non-COVID ARDS. CONCLUSIONS: Mechanical ventilation duration was longer in subjects with COVID-19-associated ARDS compared with the subjects with non-COVID ARDS, which may be explained by a lower rate of improvement in oxygenation status.

The impact of COVID-19 on the value of coastal recreational resource from the perspective of recreational behaviour change: Evident from China.

The Corona Virus Disease 2019 (COVID-19) outbreak took a heavy toll on the global tourism industry in 2020, and affected the value realization of coastal recreational ecosystem service. From the micro perspective, this paper combines travel cost method with contingent behaviour method to obtain residents' actual behaviour and contingent behaviour data, and discusses the impact of the outbreak of COVID-19 on the value realization of coastal recreational resources from the perspective of the change in residents' recreational behaviour in Qingdao, China. Residents are observed to significantly reduce their outdoor activities in response to the COVID-19. The number of visits to the beach decreases by 25.2% when there is an outbreak, and decreases by 0.064% for every 1% increase in the number of confirmed cases, which is used to represent the severity of the epidemic. The asymmetries effects of epidemic situation on residents' recreational behaviour show that the improvements lead to larger and more significant impacts than the deteriorations. The disappearance of the pandemic crisis will provide considerable welfare for the citizens in Qingdao, which reaches to 1.9323 billion CNY/year. If the number of confirmed cases deteriorates to 900, the environmental welfare loss will be 0.3366 billion CNY/year. Additionally, we test the effects of residents' cognitive variables, and find that risk perception can strengthen the negative impacts of COVID-19 cases. Furthermore, the deteriorations in the environmental attributes are found to have stronger impacts on the number of visits than the improvements. This paper provides empirical-support results about the change of coastal recreational value through the evaluation of recreational behaviours in the post-epidemic period, which will give important implications for government's marine ecosystem restoration and coastal management work.

Sars-Related Corona Viruses-A Challenge

SARS (Severe acute respiratory syndrome)-related corona viruses was first of all discovered 18 years ago in china from bats. Previously some study shown that bats are infected to animal kingdom and from animal this virus spread in human. As per report of identification and characterization of novel corona virus which is responsible for epidemic of acute respiratory syndrome in human beings. First of all this protein of novel SARS are seen in Wuhan city of, China in January 2020.Copyright © 2019, Advanced Scientific Research. All rights reserved.

A Contact Matrix-Based Approach for Predicting COVID-19 Using Influenza Data

The global pandemic Corona Virus Disease 2019 (COVID-19) has become one of the deadliest epidemics in human history, bringing enormous harm to human society. To help health policymakers respond to the threat of COVID-19, prediction of outbreaks is needed. Research on COVID-19 prediction usually uses data-driven models and mechanism models. However, in the early stages of the epidemic, there were not enough data to establish a data-driven model. The inadequate understanding of the virus that causes COVID-19, SARS-COV-2, has also led to the inaccuracies of the mechanism model. This has left the government with the toughest Non-pharmaceutical interventions (NPIs) to curb the spread of the virus, such as the lockdown of Wuhan in 2020. Yet man is a social animal, and social relations and interactions are necessary for his existence. The novel coronavirus and containment measures have challenged human and community interactions, affecting the lives of individuals and collective societies. To help governments take appropriate and necessary actions in the early stages of an epidemic, and to mitigate its impact on people's psychology and lives, we used the COVID-19 pandemic as an example to develop a model that uses surveillance data from one epidemic to predict the development trend of another. Based on the fact that both influenza and COVID-19 are transmitted through infectious respiratory droplets, we hypothesized that they may have the same underlying contact structure, and we proposed the influenza data-based COVID-19 prediction (ICP) model. In this model, the underlying contact pattern is firstly inferred by using a singular value decomposition method from influenza surveillance data. Then the contact matrix was used to simulate the influenza virus transmission through close contact of people, and the influenza virus transmission model was established. In order to be able to simulate the spread of COVID-19 virus using influenza transmission models, we used influenza contact matrix and COVID-19 infection data to estimate the risk of a population contracting COVID-19, i.e. force of infection of COVID-19. Finally, we used force of infection and influenza virus transmission model to simulate and predict the spread of COVID-19 in the population. We obtained age-disaggregated influenza and COVID-19 infection data for the United States in 2020, as well as data for Europe, which was not disaggregated by age. We use correlation coefficients as an evaluation indicator, and the final results prove that the predicted value and the actual value are positively correlated. So, the development trend of COVID-19 can be predicted using influenza surveillance data. © 2022 IEEE.