COVID-19 Detection Using CNN-ResNet-50 Model

COVID-19 is a deadly virus that originated in 2019 and could be easily transmitted from one geographical area to another. It affected the integral world, resulting in severe mortality due to its contagious effect on human life. The infection rate is continuously growing and it is becoming unmanageable since the virus moves easily from one human to another. Once we detect the COVID-19 virus in its early stages, we can easily reduce the death rate. The most common and widely used method of diagnosing COVID is through reverse transcription polymerase chain (RT-PCR). But the RT-PCR test is time consuming, inaccurate, and expensive. In this situation, the time period for the detection of viruses is valuable. Keeping these limitations in mind, we use an X-ray image of the chest to identify the COVID-19 infected patient. This procedure is achieved by using convolution neural network (CNN) in deep learning.

MACHINE LEARNING MODELS TO PREDICT COVID-19 VACCINATION INTENTION: AN INDIAN STUDY

Purpose: Covid 19 pandemic has taken the world by shock for last few years, and it has greatly impacted the livelihood of people across all walks of life and even the economies of many nations got greatly affected. Governments across the globe revived from the impact of covid-19 pandemic using many strategies and policies which were formulated under the guidance of the world health organization. One of the Prime weapons which helped the governments and public against covid -19 is vaccination. This research which was conducted August 2021 was done to understand the perception of the public towards the covid 19 vaccination and to predict the public intention to take up covid -19 vaccination using the health belief model constructs. Theoretical framework:The Study has used the variables of the health belief model namely the perceived severity, perceived susceptibility, Perceived Benefits, Cues to action and other socio-demographic variables to predict the intent of the respondents towards taking Covid-19 vaccination. Design/methodology/approach: Data was collected using a self-administered online questionnaire distributed to the respondents from Tamil Nadu, India who are above 18 years of age. Machine Learning Algorithms like Logistic Regression, Artificial Neural Networks were used to predict the public intent to take up covid 19 vaccination. Findings: From the Analysis of Logistic Regression and Artificial Neural Network, it was found that Health Belief Model Constructs Perceived Barriers, Perceived Benefits and Cues to action, were significant factors that affect the public intention to vaccinate. Research, Practical & Social implications:Findings of the research will help the government, stake holders to understand the factors impacting the respondent's intent to covid-19 vaccination which will guide them to plan better strategies for future vaccination drives Originality/value:The Study has used to two different machine learning algorithms to compare and corroborate the research findings and in turn identifying the significant predictors of covid-19 vaccination intent © 2022 AOS-Estratagia and Inovacao. All rights reserved.

SARS-CoV-2 neutralizing antibody epitopes are overlapping and highly mutated which raises the chances of escape variants and requires development of broadly reactive vaccines.

The rapid adaptation of SARS-CoV-2 within the host species and the increased viral transmission triggered the evolution of different SARS-CoV-2 variants. Though numerous monoclonal antibodies (mAbs) have been identified as prophylactic therapy for SARS-CoV-2, the ongoing surge in the number of SARS-CoV-2 infections shows the importance of understanding the mutations in the spike and developing novel vaccine strategies to target all variants. Here, we report the map of experimentally validated 74 SARS-CoV-2 neutralizing mAb binding epitopes of all variants. The majority (87.84%) of the potent neutralizing epitopes are localized to the receptor-binding domain (RBD) and overlap with each other, whereas limited (12.16%) epitopes are found in the N-terminal domain (NTD). Notably, 69 out of 74 mAb targets have at least one mutation at the epitope sites. The potent epitopes found in the RBD show higher mutations (4-10aa) compared to lower or modest neutralizing antibodies, suggesting that these epitopes might co-evolve with the immune pressure. The current study shows the importance of determining the critical mutations at the antibody recognition epitopes, leading to the development of broadly reactive immunogens targeting multiple SARS-CoV-2 variants. Further, vaccines inducing both humoral and cell-mediated immune responses might prevent the escape of SARS-CoV-2 variants from neutralizing antibodies.

COVID-19 Detection Using CNN-ResNet-50 Model

COVID-19 is a deadly virus that originated in 2019 and could be easily transmitted from one geographical area to another. It affected the integral world, resulting in severe mortality due to its contagious effect on human life. The infection rate is continuously growing and it is becoming unmanageable since the virus moves easily from one human to another. Once we detect the COVID-19 virus in its early stages, we can easily reduce the death rate. The most common and widely used method of diagnosing COVID is through reverse transcription polymerase chain (RT-PCR). But the RT-PCR test is time consuming, inaccurate, and expensive. In this situation, the time period for the detection of viruses is valuable. Keeping these limitations in mind, we use an X-ray image of the chest to identify the COVID-19 infected patient. This procedure is achieved by using convolution neural network (CNN) in deep learning. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Change in the Surgical Plan during Covid-19 Pandemic: A Systematic Review and Meta - Analysis

This section summarises the impact of changes on the surgical plan during Covid-19. This approaches surgical triage in times of the pandemic. However, the research has addressed several issues faced by hospitals to manipulate surgical planning during the pandemic. This, in turn, has explored the critical understanding of transitions addressed in "surgical planning" to get through the crises faced by hospitals due to a sudden change in the process of hospital settings. However, Covid-19 infection has been found to have a considerable influence on this surgical community. Copyright © 2022 Wolters Kluwer Medknow Publications. All rights reserved.

Hemolysis and COVID-19 infection: A fatal combination - An interesting series of three cases

COVID-19 has wreaked havoc ever since its inception and with the protean manifestations of the disease, it is imperative that progressively data are added to the literature. COVID-19 infection is a multisystem disorder with a wide range of clinical symptomatology. Recent information garnered has laid emphasis on pathological changes at microvascular level causing thrombotic/hemostatic defects, leading to the assorted clinical presentation. We present a consortium of three confirmed COVID-19 cases whose hospital course got convoluted with grave hematological complications in the form of hemolytic uremic syndrome and autoimmune hemolytic anemia. Regrettably, all three patients succumbed to their illness.

A Drug Repurposing Approach to Identify Therapeutics by Screening Pathogen Box Exploiting SARS-CoV-2 Main Protease.

Coronavirus disease-19 (COVID-19) is caused by severe acute respiratory syndrome coronavirus -2 (SARS-CoV-2) and is responsible for a higher degree of morbidity and mortality worldwide. There is a smaller number of approved therapeutics available to target the SARS-CoV-2 virus, and the virus is evolving at a fast pace. So, there is a continuous need for new therapeutics to combat COVID-19. The main protease (Mpro ) enzyme of SARS-CoV-2 is essential for replication and transcription of the viral genome, thus could be a potent target for the treatment of COVID-19. In the present study, we performed an in-silico screening analysis of 400 diverse bioactive inhibitors with proven antibacterial and antiviral properties against Mpro drug target. Ten compounds showed a higher binding affinity for Mpro than the reference compound (N3), with desired physicochemical properties. Furthermore, in-depth docking and superimposition revealed that three compounds (MMV1782211, MMV1782220, and MMV1578574) are actively interacting with the catalytic domain of Mpro . In addition, the molecular dynamics simulation study showed a solid and stable interaction of MMV178221-Mpro complex compared to the other two molecules (MMV1782220, and MMV1578574). In line with this observation, MM/PBSA free energy calculation also demonstrated the highest binding free energy of -115.8 kJ/mol for MMV178221-Mpro compound. In conclusion, the present in silico analysis revealed MMV1782211 as a possible and potent molecule to target the Mpro and must be explored in vitro and in vivo to combat the COVID-19.

COVID-19 Detection Using CNN-ResNet-50 Model

COVID-19 is a deadly virus that originated in 2019 and could be easily transmitted from one geographical area to another. It affected the integral world, resulting in severe mortality due to its contagious effect on human life. The infection rate is continuously growing and it is becoming unmanageable since the virus moves easily from one human to another. Once we detect the COVID-19 virus in its early stages, we can easily reduce the death rate. The most common and widely used method of diagnosing COVID is through reverse transcription polymerase chain (RT-PCR). But the RT-PCR test is time consuming, inaccurate, and expensive. In this situation, the time period for the detection of viruses is valuable. Keeping these limitations in mind, we use an X-ray image of the chest to identify the COVID-19 infected patient. This procedure is achieved by using convolution neural network (CNN) in deep learning. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Liver Injury at Presentation in COVID-19 is Associated with Immuno-Inflammatory Response

Background: SARS-CoV-2 has been shown to affect liver and GI tract in addition to primary involvement of lungs. Liver injury in COVID-19 is hypothesized to be multifactorial with 14- 73% of patients showing evidence of deranged liver functional. Data regarding the liver injury due to SARS CoV-2 infection in a large cohort of unselected patients, is limited, especially from India. So, we conducted a retrospective study among consecutive patients admitted with COVID-19 disease to a tertiary care hospital during the first wave of pandemic. Methods: It was a retrospective observational study. Consecutive patients infected with SARS- CoV-2 and admitted to the COVID-19 ward or ICU of our hospital between 1/4/2020 to 30/6/2020 were included. Patients, < 18 years of age, pregnant ladies and those with underlying liver disease were excluded. Detail history along with data on laboratory parameters, treatment given and outcomes (need for oxygen therapy, ICU admission, need for ventilatory support and in-hospital mortality) was collected and analysed. Results: Data on 303 patients was analysed after exclusions. The mean age was 47.9(15.9) years and 214(69.5%) were males. Out of 303 patients 149 (49.2%) had liver injury. Mild liver injury was present in 95(31.3%), moderate to severe liver injury in 54 (17.8%) patients and only 5 (1.6%) had severe liver injury. Pure cholestatic liver injury was present 19 (6.2%) cases. Male sex (82.1% vs 58.5%;P<0.001) and presence of symptoms (97.3% vs 90.8%;P= 0.01) were associated with presence of liver injury. Patients who had liver injury had significantly longer duration of symptoms before presentation [6 (3-8) days vs 4 (3-7) days);P=0.02] and higher serum ferritin levels [322(156-552) vs 151(44.9-299.5) ng/ml;P=0.02]. On multivariate analysis, serum ferritin was the only factor, independently associated with liver injury (OR- 1.002;95% CI- 1.001-1.004;P=0.006). Serum ferritin had a positive correlation with AST [r=0.416;P=0.0001] and ALT [R=0.458;P =0.0001] in the entire cohort. Liver injury was not significantly associated with need of oxygen therapy, ICU stay, mechanical ventilation or mortality but patients with moderate-severe liver injury had a longer hospital stay than those without [12.2 (5.07) vs 10.3 (4.84) days;P=0.01]. Conclusion: In COVID-19 patients, liver injury at presentation is common in symptomatic male patients and occurs around the end of first week and correlates strongly with serum ferritin levels, suggesting that it might be driven by immuno-inflammation.

Post-disaster recovery for family firms: The role of owner motivations, firm resources, and dynamic capabilities

Natural disasters (e.g., earthquakes and pandemics) negatively affect firms and their stakeholders. These disasters disrupt the operations of firms and lives of people by generating a shock in the system. Small firms are especially vulnerable to the shocks and disturbances resulting from these disasters. Since small firms, especially family firms, are key economic contributors and agents of recovery in any community, understanding their post-disaster recovery processes is critical. Therefore, this study examines the post-disaster recovery processes of small family firms. We utilize a grounded theory approach to analyze and propose that resources and socioemotional wealth priorities influence the post-disaster recovery of small family firms. Utilizing the 8.8 Richter scale earthquake in Chile in 2010 as a natural disaster, we examine the eight-year lagged data of 20 small family firms with disrupted operations. Our findings have important implications for small firms experiencing the negative consequences of disruptions, including those experiencing the COVID-19 pandemic-induced disruption.

Sustainable municipal solid waste management (MSWM) in the smart cities in Indian context

Purpose: Smart cities in India are going to be a reality very soon by turning challenges into opportunities for the society. However, due to rapid increase in population burden, fast urbanization and growing demand of advanced services in the smart cities, the quantity of per capita municipal solid waste (MSW) has escalated. Moreover, the COVID-19 pandemic has further challenged the municipal solid waste management (MSWM) system with the increasing amount of infectious wastes coming from households (HHs), quarantine centers, healthcare facilities, vaccination centers, etc. Therefore, the present study attempts to explore and analyze the various dimensions of sustainable MSWM system in the smart cities. Design/methodology/approach: The study identifies 13 factors of sustainable MSWM system from the literature, field surveys and stakeholders' opinions. Thereafter, stakeholders' opinions are collected and analyzed using total interpretive structural modeling (TISM) approach to explore the interrelationships among the factors of sustainable MSWM system. These relationships are further validated through the empirical investigation of the real-life case study of Rourkela Municipal Corporation (RMC), Odisha, India. Findings: The TISM approach places all 13 factors into six levels in the hierarchical digraph depending upon the inputs received from the various stakeholders on their interrelationships. Study also validates the proposed TISM model by collecting the data of RMC, Odisha, on the development of MSWM system over the period of 2015–2021. Practical implications: The study also highlights various implications for the other developing cities and stakeholders to set up the roadmap for developing the sustainable MSWM system. Study defines “IT platform” and “awareness among citizens” as the base of the sustainable MSWM system in any smart city. Originality/value: The present study is the first of its kind to explore the interrelationships among the factors of sustainable MSWM system by using TISM approach. Moreover, the proposed TISM framework is further validated through the empirical journey of one of the smart cities in India. © 2021, Emerald Publishing Limited.

The Interplay of the Unfolded Protein Response in Neurodegenerative Diseases: A Therapeutic Role of Curcumin.

Abnormal accumulation of misfolded proteins in the endoplasmic reticulum and their aggregation causes inflammation and endoplasmic reticulum stress. This promotes accumulation of toxic proteins in the body tissues especially brain leading to manifestation of neurodegenerative diseases. The studies suggest that deregulation of proteostasis, particularly aberrant unfolded protein response (UPR) signaling, may be a common morbific process in the development of neurodegeneration. Curcumin, the mixture of low molecular weight polyphenolic compounds from turmeric, Curcuma longa has shown promising response to prevents many diseases including current global severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and neurodegenerative disorders. The UPR which correlates positively with neurodegenerative disorders were found affected by curcumin. In this review, we examine the evidence from many model systems illustrating how curcumin interacts with UPR and slows down the development of various neurodegenerative disorders (ND), e.g., Alzheimer's and Parkinson's diseases. The recent global increase in ND patients indicates that researchers and practitioners will need to develop a new pharmacological drug or treatment to manage and cure these neurodegenerative diseases.

Cross sectional survey of medical student volunteering and education during the coronavirus pandemic in the United Kingdom

Introduction: COVID-19 led to global disruption of healthcare and many students volunteered to provide clinical support. Volunteering to work was a unique medical education opportunity;however, it is unknown whether this was a positive learning experience. Methods: The COVID Ready 2 study is a national cross-sectional study of all medical students at UK medical schools. We will compare opinions of those who did and did not volunteer to determine the educational benefit and issues they faced. We will use thematic analysis to identify themes in qualitative responses, in addition to quantitative analysis. Results: The primary objective is to explore the effect of volunteering during the pandemic on medical education in comparison to those who did not volunteer. Our secondary objectives are to identify: whether students would be willing to assume similar roles in a non-pandemic setting;if students found the experience more or less beneficial than traditional hospital placements and reasons for this;what the perceived benefits and disadvantages of volunteering were;the difference in perceived preparedness between students who did and did not volunteer for foundation training year one and the next academic year;training received by volunteers;and to explore issues associated with volunteering, including safety issues and issues with role and competence. Conclusions: We anticipate this study will help identify volunteer structures that have been beneficial for students, so that similar infrastructures can be used in the future;and help determine whether formal voluntary roles should be introduced into the non-pandemic medical curriculum.

Temporary business model innovation – SMEs’ innovation response to the Covid-19 crisis

The Covid-19 crisis has hit SMEs particularly hard. Numerous business models (BM) have been limited or rendered downright impossible due to decreased social contact. SMEs can respond to this exogenous crisis via temporary business model innovation (BMI). This empirical study investigates these temporary BMs using a multiple case study approach based on five SMEs in Austria, Germany, and Liechtenstein who within a short period of time applied their core competencies and networks to integrate new BMs, which were in some cases very different from existing ones. These had a positive effect on strategic flexibility, and if desired can also be incorporated into the firm long-term. The paper contributes to SME crisis management during the Covid-19 pandemic by pointing out and developing a successful management mechanism that allows to survive a crisis or even improve during this time. Moreover, we contribute to BMI literature by explaining temporary BMI as a new form of BMI. It also makes clear to managers that temporary BMs add value to firms and create new revenue streams.

SARS-CoV-2 Cellular Entry Is Independent of the ACE2 Cytoplasmic Domain Signaling.

Recently emerged severe acute respiratory syndrome coronavirus (SARS-CoV)-1 and -2 initiate virus infection by binding of their spike glycoprotein with the cell-surface receptor angiotensin-converting enzyme 2 (ACE2) and enter into the host cells mainly via the clathrin-mediated endocytosis pathway. However, the internalization process post attachment with the receptor is not clear for both SARS-CoV-1 and -2. Understanding the cellular factor/s or pathways used by these CoVs for internalization might provide insights into viral pathogenesis, transmission, and development of novel therapeutics. Here, we demonstrated that the cytoplasmic tail of ACE2 is not essential for the entry of SARS-CoV-1 and -2 by using bioinformatics, mutational, confocal imaging, and pseudotyped SARS-CoVs infection studies. ACE2 cytoplasmic domain (cytACE2) contains a conserved internalization motif and eight putative phosphorylation sites. Complete cytoplasmic domain deleted ACE2 (∆cytACE2) was properly synthesized and presented on the surface of HEK293T and BHK21 cells like wtACE2. The SARS-CoVs S1 or RBD of spike protein binds and colocalizes with the receptors followed by internalization into the host cells. Moreover, pseudotyped SARS-CoVs entered into wtACE2- and ∆cytACE2-transfected cells but not into dipeptidyl peptidase 4 (DPP4)-expressing cells. Their entry was significantly inhibited by treatment with dynasore, a dynamin inhibitor, and NH4Cl, an endosomal acidification inhibitor. Furthermore, SARS-CoV antibodies and the soluble form of ACE2-treated pseudotyped SARS-CoVs were unable to enter the wtACE2 and ∆cytACE2-expressing cells. Altogether, our data show that ACE2 cytoplasmic domain signaling is not essential for the entry of SARS-CoV-1 and -2 and that SARS-CoVs entry might be mediated via known/unknown host factor/s.

Design of a Medical Prototype Robot for Nurse Assistance

Robotics in the medical field is an ongoing trend in both research and commercial sectors. Robots are used in every hospital department for assistance in delivering things, surgery, checking vital signs, telepresence, etc. Medical prototype robot is a scenario-based assistive robot with a customized design to help the hospital staff fight against Covid-19 (Coronavirus disease) outbreak, ensuring social distancing. It has basic features like delivering medicine and small handheld devices, remote temperature sensing using IR (infrared) and UVC (ultraviolet type C) disinfection unit. The main aim of this prototype is to make the nurse not to handle the devices which was handled by the patients in which we can convey the information through an audio system (which is already available in the hospital) or a nurse will be assisting the initial instructions required (by ensuring the social distance) that is in the isolated ward so that the patient can do the task properly. For this prototype, we are using the basic microcontroller, that is, Arduino UNO. We were successful in taking readings with the help of a temperature sensor and were able to supply power to the UVC lamp in which it sterilized the objects inside the unit when it was exposed for 2-3 minutes. And finally, the robot was able to move successfully with the help of Arduino and Bluetooth setup. © 2021 IEEE.

Neural net pattern recognition based auscultation of croup cough and pertussis using phase portrait features

Cough signal analysis for understanding the pathological condition has become important from the outset of the exigency posed by the epidemic COVID-19. The present work suggests a surrogate approach for the classification of cough signals - croup cough (CC) and pertussis (PT) – based on spectral, fractal, and nonlinear time-series techniques. The spectral analysis of CC reveals the presence of more frequency components in the short duration cough sound compared to PT. The musical nature of CC is unveiled not only through the spectral analysis but also through the phase portrait features – sample entropy (S), maximal Lyapunov exponent (L), and Hurst exponent (Hb). The modifications in the internal morphology of the respiratory tract, giving rise to more frequency components associated with the complex airflow dynamics, get staged through the higher fractal dimension of CC. Among the two supervised classification tools, cubic KNN (CKNN) and neural net pattern recognition (NNPR), used for classifying the CC and PT signals based on nonlinear time series parameters, NNPR is found better. Thus, the study opens the possibility of identification of pulmonary pathological conditions through cough sound signal analysis. © 2021

Is SARS CoV-2 a Multifractal?-Unveiling the Fractality and Fractal Structure

A first report of unveiling the fractality and fractal nature of severe acute respiratory syndrome coronavirus (SARS CoV-2) responsible for the pandemic disease widely known as coronavirus disease 2019 (COVID 19) is presented. The fractal analysis of the electron microscopic and atomic force microscopic images of 40 coronaviruses (CoV), by the normal and differential box-counting method, reveals its fractal structure. The generalised dimension indicates the multifractal nature of the CoV. The higher value of fractal dimension and lower value of Hurst exponent (H) suggest higher complexity and greater roughness. The statistical analysis of generalised dimension and H is understood through the notched box plot. The study on CoV clusters also confirms its fractal nature. The scale-invariant value of the box-counting fractal dimension of CoV yields a value of 1.820. The study opens the possibility of exploring the potential of fractal analysis in the medical diagnosis of SARS CoV-2.