ABSTRACT
The primary requirement for early detection of COVID and control of the virus's spread is rapid and precise diagnosis. Computer vision and deep learning-based models can be used to assist this COVID diagnosis process through chest X-ray scans. This study performs a comparative analysis on different deep learning models which can be used to diagnose COVID from chest X-ray scans. For this work, the following deep learning models were selected: VGG16, Xception, ResNet, DenseNet, and MobileNet. This research looks not only at COVID, but also at other SARS-CoV-2-related diseases such as SARS and MERS. The dataset used consists of five categories: normal, COVID, pneumonia, SARS, and MERS. The comparative study showed that both the MobileNet and DenseNet models were able to deliver the best performance, with the highest accuracy and minimal loss. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
The primary requirement for early detection of COVID and control of the virus’s spread is rapid and precise diagnosis. Computer vision and deep learning-based models can be used to assist this COVID diagnosis process through chest X-ray scans. This study performs a comparative analysis on different deep learning models which can be used to diagnose COVID from chest X-ray scans. For this work, the following deep learning models were selected: VGG16, Xception, ResNet, DenseNet, and MobileNet. This research looks not only at COVID, but also at other SARS-CoV-2-related diseases such as SARS and MERS. The dataset used consists of five categories: normal, COVID, pneumonia, SARS, and MERS. The comparative study showed that both the MobileNet and DenseNet models were able to deliver the best performance, with the highest accuracy and minimal loss. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
While policy makers around the globe have meticulously organised mass immunisation against Coronavirus Disease 2019 (COVID-19), its safety concerns and adverse events that need prompt evaluation are also emerging. Acute Transverse Myelitis (TM) is a rare neurological phenomenon where motor, sensory or autonomic disturbance occurs as a result of spinal cord injury. The aetiology of transverse myelitis is thought to be immune-mediated as a result of infection, parainfectious disorder, autoimmune disease or malignancy. Though a rare disease, acute TM warrants prompt recognition and aggressive therapy for favourable neurological patient outcomes. Hereby, authors presented this case of a 61-year-old male patient who developed symptoms of acute TM, 20 days after receiving an adenovirus vectored ChAdOx1 nCoV-19 vaccine against SARS-CoV-2. The patient was treated with intravenous steroids, supportive care with Foley's catheterisation and his weakness and bladder control improved over 1 week. Copyright © 2022 Journal of Clinical and Diagnostic Research. All rights reserved.
ABSTRACT
This chapter provides an overview of the content in this volume on East Asian economic integration. It outlines debates about the nature and value of trade and economic integration, and examines whether East Asia will follow a similar evolution in the debate, as well as how East Asia will be affected by the growing anti-trade and anti-globalisation policy debates. The overview also includes policy discussions on the development of the coronavirus disease (COVID-19) pandemic and its implications for East Asia. Following this discussion, it reviews the chapters of the volume in terms of four areas of focus relevant for the future development of East Asia: (i) new regional and multilateral trading arrangements: analysis and evaluation;(ii) production networks, investment policies, and global value chains;(iii) structural transformation, skills, and domestic capacity;and (iv) infrastructure, digital technologies, regional security, and environment. © The Editors and Contributors Severally 2021.
ABSTRACT
Epidemiological models are a system of partial differential equations that model the spread of any epidemics in a closed population. These models are crucial tools for public health policy makers and medical practitioners. Reliable model descriptions often demand optimal parameter estimations. The model parameters are often estimated using numerical methods and traditional optimization algorithms. The inherent stochasticity in real-world outbreaks demand powerful optimizers for parameter estimation. Such ill-defined problems have been potential candidates for meta-heuristic optimization algorithms. The objectives of the proposed study include formulating parameter estimation as an optimization problem and finding optimal/near-optimal parameters for existing COVID models and to analyze the COVID epidemiological models (with optimal model parameters) based on their prediction efficacy. Using the parameters, forecasts for upcoming days can be produced. This paper compares epidemiological models with different machine learning models based on evaluation techniques. The top-five heavily affected states of India having the highest number of cases are considered for the study. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
The coronavirus disease – 2019 (COVID-19) pandemic, caused by severe acute respiratory syndrome coronavirus virus – 2 (SARS-CoV-2), set it foots in China during December 2019 as a high-alert public health emergency. This malady had thereafter spread rapidly across the globe in more than 215 countries, affecting more than 50 million people and causing the death of nearly 1.3 million as of 9th November, 2020 and resulted in a massive panic, fear, and economic crashes in most of the world. A better understanding of the disease, the virus, structural biology, clinical manifestations, risk factors, transmission, diagnosis, treatment, and management can be extrapolated from the literature review of the research up to date. In addition, deliberations on animal linkages, spillover and zoonotic implications for exploring the actual origin of the disease and all possible animal-human interfaces, intermediate host;diagnosis for devising specific and sensitive tests of ease, accessibility and affordability;advances in the development of safe and effective vaccines and therapeutics for prevention and treatment;management of COVID-19 practicable in all countries;application of traditional or regularly used modalities including plant-based products and medicinal herbs against SARS-COV-2;nutritious dietary foods against this disease;and socio-economic impacts of COVID-19 can provide valuable information on these various aspects. Most of the research currently focuses on disease, development of a vaccine or therapeutic modalities. But the future mortality rate and virulence of virus not only depends on the evolution of the virus, but also on how we develop preventive measures and effective treatment as well as in advance preparedness. The present review highlights salient aspects of SARS-CoV-2 / COVID-19, pathology, risk factors, transmission, diagnosis, potential treatment, and alternative / supportive therapeutic options. © 2020, Editorial board of Journal of Experimental Biology and Agricultural Sciences. All rights reserved.
ABSTRACT
Newly emerging severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causing coronavirus disease 2019 (COVID-19) pandemic has now spread across the globe in past few months while affecting 26 million people and leading to more than 0.85 million deaths as on 2nd September, 2020. Severity of SARS-CoV-2 infection increases in COVID-19 patients due to pre-existing health co-morbidities. This mini-review has focused on the three significant co-morbidities viz., heart disease, hypertension, and diabetes, which are posing high health concerns and increased mortality during this ongoing pandemic. The observed co-morbidities have been found to be associated with the increasing risk factors for SARS-CoV-2 infection and COVID-19 critical illness as well as to be associated positively with the worsening of the health condition of COVID-19 suffering individuals resulting in the high risk for mortality. SARS-CoV-2 enters host cell via angiotensin-converting enzyme 2 receptors. Regulation of crucial cardiovascular functions and metabolisms like blood pressure and sugar levels are being carried out by ACE2. This might be one of the reasons that contribute to the higher mortality in COVID-19 patients having co-morbidities. Clinical investigations have identified higher levels of creatinine, cardiac troponin I, alanine aminotransferase, NT-proBNP, creatine kinase, D-dimer, aspartate aminotransferase and lactate dehydrogenase in patients who have succumbed to death from COVID-19 as compared to recovered individuals. More investigations are required to identify the modes behind increased mortality in COVID-19 patients having co-morbidities of heart disease, hypertension, and diabetes. This will enable us to design and develop suitable therapeutic strategies for reducing the mortality. More attention and critical care need to be paid to such high risk patients suffering from co-morbidities during COVID-19 pandemic.