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The coronavirus disease 2019 (COVID-19) pandemic of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is causing considerable morbidity and mortality worldwide. Multiple reports have suggested that patients with heart failure (HF) are at a higher risk of severe disease and mortality with COVID-19. Moreover, evaluating and treating HF patients with comorbid COVID-19 represents a formidable clinical challenge as symptoms of both conditions may overlap and they may potentiate each other. Limited data exist regarding comprehensive management of HF patients with concomitant COVID-19. Since these issues pose serious new challenges for clinicians worldwide, HF specialists must develop a structured approach to the care of patients with COVID-19 and be included early in the care of these patients. Therefore, the Heart Failure Association of the European Society of Cardiology and the Chinese Heart Failure Association & National Heart Failure Committee conducted web-based meetings to discuss these unique clinical challenges and reach a consensus opinion to help providers worldwide deliver better patient care. The main objective of this position paper is to outline the management of HF patients with concomitant COVID-19 based on the available data and personal experiences of physicians from Asia, Europe and the United States. [ABSTRACT FROM AUTHOR]

Coronavirus is one of the serious threat and challenge for existing healthcare systems. Several prevention methods and precautions have been proposed by medical specialists to treat the virus and secure infected patients. Deep learning methods have been adopted for disease detection, especially for medical image classification. In this paper, we proposed a deep learning-based medical image classification for COVID-19 patients namely deep learning model for coronavirus (DLM-COVID-19). The proposed model improves the medical image classification and optimization for better disease diagnosis. This paper also proposes a mobile application for COVID-19 patient detection using a self-assessment test combined with medical expertise and diagnose and prevent the virus using the online system. The proposed deep learning model is evaluated with existing algorithms where it shows better performance in terms of sensitivity, specificity, and accuracy. Whereas the proposed application also helps to overcome the virus risk and spread. [ABSTRACT FROM AUTHOR]

The novel coronavirus (COVID-19) pandemic still has a significant impact on the worldwide population's health and well-being. Effective patient screening, including radiological examination employing chest radiography as one of the main screening modalities, is an important step in the battle against the disease. Indeed, the earliest studies on COVID-19 found that patients infected with COVID-19 present with characteristic anomalies in chest radiography. In this paper, we introduce COVID-ConvNet, a deep convolutional neural network (DCNN) design suitable for detecting COVID-19 symptoms from chest X-ray (CXR) scans. The proposed deep learning (DL) model was trained and evaluated using 21,165 CXR images from the COVID-19 Database, a publicly available dataset. The experimental results demonstrate that our COVID-ConvNet model has a high prediction accuracy at 97.43% and outperforms recent related works by up to 5.9% in terms of prediction accuracy. [ABSTRACT FROM AUTHOR]

Novel coronavirus (COVID-19) pandemic, has raised a serious situation across world human population and has become serious threat as contagious outbreak. This paper aims to overview the recently intelligent systems based on Artificial Intelligence using different medical imaging modalities like Computer Tomography (CT) and X-ray. This paper specifically discusses the machine learning techniques developed for COVID-19 diagnosis and provides insights on well-known data sets used to train these AI based networks. It also highlights the use of AI in COVID detection and classification at faster process where normal COVID testing takes couple of days to produce. Finally, we conclude by addressing the challenges associated with the use of Machine Leaming methods for COVID-19 detection and probable future trends in this research area. This paper is intended to provide experts (medical or otherwise) and technicians with new insights into the ways machine learning techniques are used and how they potentially further work in com batting the outbreak of CO VID-19. [ABSTRACT FROM AUTHOR]

Coronavirus is an infectious disease induced by extreme acute respiratory syndrome coronavirus 2. Novel coronaviruses can lead to mild to serious symptoms, like tiredness, nausea, fever, dry cough and breathlessness. Coronavirus symptoms are close to influenza, pneumonia and common cold. So Coronavirus can only be confirmed with a diagnostic test. 218 countries and territories worldwide have reported a total of 59.6 million active cases of the COVID-19 and 1.4 million deaths as of November 24, 2020. Rapid, accurate and early medical diagnosis of the disease is vital at this stage. Researchers analyzed the CT and X-ray findings from a large number of patients with coronavirus pneumonia to draw their conclusions. In this paper, we applied Support Vector Machine (SVM) classifier. After that we moved on to deep transfer learning models such as VGG16 and Xception which are implemented using Keras and Tensor flow to detect positive coronavirus patient using X-ray images. VGG16 and Xception show better performances as compared to SVM. In our work, Xception gained an accuracy of 97.46% with 98% f-score. [ABSTRACT FROM AUTHOR]

During last two decades, the biggest global epidemic had been associated with middle east respiratory syndrome, severe acute respiratory syndrome, and novel coronavirus-19 (COVID-19) with clinical symptoms of bronchitis, pneumonia, and fetal respiratory illness. Infection caused by COVID-19 initially assumed to be milder in nature but consequently spreading across the globe and devastating mortality rate rapidly made it a pandemic. Having enormous challenges, many significant issues are yet to be addressed. Scientific community is engaged in designing and developing effective nano-biosensors for the quick detection of COVID-19, easy diagnosis as well as absolute tracking of infected population in order to prevent pandemic outbreak further. In this paper, key stages like suppressing the immune response of COVID-19 patients, diagnosis of COVID-19, and prevention of COVID-19 using nanomaterials have been discussed. Further, the unresolved challenges and drawbacks toward treatments and vaccine development at the earliest to win over this war have also been critically discussed. [ABSTRACT FROM AUTHOR]

The coronaviruses are a deadly family of epidemic viruses that can spread from one individual to another very quickly, infecting masses. The literature on epidemics indicates that the early diagnosis of a coronavirus infection can lead to a reduction in mortality rates. To prevent coronavirus disease 2019 (COVID-19) from spreading, the regular identification and monitoring of infected patients are needed. In this regard, wireless body area networks (WBANs) can be used in conjunction with machine learning and the Internet of Things (IoT) to identify and monitor the human body for health-related information, which in turn can aid in the early diagnosis of diseases. This paper proposes a novel coronavirus-body area network (CoV-BAN) model based on IoT technology as a real-time health monitoring system for the detection of the early stages of coronavirus infection using a number of wearable biosensors to examine the health status of the patient. The proposed CoV-BAN model is tested with five machine learning-based classification methods, including random forest, logistic regression, Naive Bayes, support vector machine and multi-layer perceptron classifiers, to optimize the accuracy of the diagnosis of COVID-19. For the long-term sustainability of the sensor devices, the development of energy-efficient WBAN is critical. To address this issue, a long-range (LoRa)-based IoT program is used to receive biosensor signals from the patient and transmit them to the cloud directly for monitoring. The experimental results indicate that the proposed model using the random forest classifier outperforms models using the other classifiers, with an average accuracy of 88.6%. In addition, power consumption is reduced when LoRa technology is used as a relay node. [ABSTRACT FROM AUTHOR]

Aim: The COVID‐19 pandemic led to widespread disruption of colorectal cancer services during 2020. Established cancer referral pathways were modified in response to reduced diagnostic availability. The aim of this paper is to assess the impact of COVID‐19 on colorectal cancer referral, presentation and stage. Methods: This was a single centre, retrospective cohort study performed at a tertiary referral centre. Patients diagnosed and managed with colorectal adenocarcinoma between January and December 2020 were compared with patients from 2018 and 2019 in terms of demographics, mode of presentation and pathological cancer staging. Results: In all, 272 patients were diagnosed with colorectal adenocarcinoma during 2020 compared with 282 in 2019 and 257 in 2018. Patients in all years were comparable for age, gender and tumour location (P > 0.05). There was a significant decrease in urgent suspected cancer referrals, diagnostic colonoscopy and radiological imaging performed between March and June 2020 compared with previous years. More patients presented as emergencies (P = 0.03) with increased rates of large bowel obstruction in 2020 compared with 2018–2019 (P = 0.01). The distribution of TNM grade was similar across the 3 years but more T4 cancers were diagnosed in 2020 versus 2018–2019 (P = 0.03). Conclusion: This study demonstrates that a relatively short‐term impact on the colorectal cancer referral pathway can have significant consequences on patient presentation leading to higher risk emergency presentation and surgery at a more advanced stage. It is therefore critical that efforts are made to make this pathway more robust to minimize the impact of other future adverse events and to consolidate the benefits of earlier diagnosis and treatment. [ABSTRACT FROM AUTHOR]

Introduction: The Coronavirus Disease 2019 (COVID 19) epidemic began in December 2019 in China and caused major concern. But no systematic review of COVID-19 infection control has been published. Aim: The aim of this study is to determine the COVID-19 disease control methods. Material and methods : We performed a systematic review of the literature using the keywords: 'coronavirus' and '2020,' 'COVID-19' in databases including ScienceDirect, PubMed, Springer and Scopus from January 1, 2020 to February 23, 2020. All observational studies, as well as case reports and editorials that were published in English were included. Data on the disease control methods of COVID- 19 were extracted by two researchers. Results and discussion: The preliminary search result was about 131 articles; 38 articles were retrieved for full-text screening, after screening articles by title and abstract. Finally, 17 papers having the study inclusion criteria were selected. Disease control is possible at three levels of prevention, diagnosis and treatment. Conclusions : COVID-19 is a new clinical infectious disease that can be controlled by adopting measures to prevent, diagnose and treat the disease. Further studies are needed to elucidate factors that may be effective in controlling the spread of the disease during recovery. [ABSTRACT FROM AUTHOR]

Background: Since the beginning of the COVID-19 outbreak in December 2019, a substantial body of COVID-19 medical literature has been generated. As of June 2020, gaps and longitudinal trends in the COVID-19 medical literature remain unidentified, despite potential benefits for research prioritisation and policy setting in both the COVID-19 pandemic and future large-scale public health crises.Methods: In this paper, we searched PubMed and Embase for medical literature on COVID-19 between 1 January and 24 March 2020. We characterised the growth of the early COVID-19 medical literature using evidence maps and bibliometric analyses to elicit cross-sectional and longitudinal trends and systematically identify gaps.Results: The early COVID-19 medical literature originated primarily from Asia and focused mainly on clinical features and diagnosis of the disease. Many areas of potential research remain underexplored, such as mental health, the use of novel technologies and artificial intelligence, pathophysiology of COVID-19 within different body systems, and indirect effects of COVID-19 on the care of non-COVID-19 patients. Few articles involved research collaboration at the international level (24.7%). The median submission-to-publication duration was 8 days (interquartile range: 4-16).Conclusions: Although in its early phase, COVID-19 research has generated a large volume of publications. However, there are still knowledge gaps yet to be filled and areas for improvement for the global research community. Our analysis of early COVID-19 research may be valuable in informing research prioritisation and policy planning both in the current COVID-19 pandemic and similar global health crises. [ABSTRACT FROM AUTHOR]

• The latest summary of the COVID-19 outbreak in China; • There might be an oral-fecal transmission of the virus; • Aggregates and consolidates the epidemiology, clinical manifestations, diagnosis, treatments and preventions of this new type of coronavirus. There is a current worldwide outbreak of the novel coronavirus Covid-19 (coronavirus disease 2019; the pathogen called SARS-CoV-2; previously 2019-nCoV), which originated from Wuhan in China and has now spread to 6 continents including 66 countries, as of 24:00 on March 2, 2020. Governments are under increased pressure to stop the outbreak from spiraling into a global health emergency. At this stage, preparedness, transparency, and sharing of information are crucial to risk assessments and beginning outbreak control activities. This information should include reports from outbreak site and from laboratories supporting the investigation. This paper aggregates and consolidates the epidemiology, clinical manifestations, diagnosis, treatments and preventions of this new type of coronavirus. [ABSTRACT FROM AUTHOR]

Almost 80% of people confronting COVID-19 recover from COVID-19 disease without any particular treatments. They experience heterogeneous symptoms; a wide range of respiratory symptoms, cough, dyspnea, fever, and viral pneumonia. However, some others need urgent intervention and special treatment to get rid of this widespread disease. So far, there isn't any unique drug for the potential treatment of COVID 19. However, some available therapeutic drugs used for other diseases seem beneficial for the COVID-19 treatment. On the other hand, there is a robust global concern for developing an efficient COVID-19 vaccine to control the COVID-19 pandemic sustainably. According to the WHO report, since 8 October 2021, 320 vaccines have been in progress. 194 vaccines are in the pre-clinical development stage that 126 of them are in clinical progression. Here, in this paper, we have comprehensively reviewed the most recent and updated information about coronavirus and its mutations, all the potential therapeutic approaches for treating COVID-19, developed diagnostic systems for COVID- 19 and the available COVID-19 vaccines and their mechanism of action. [Display omitted] [ABSTRACT FROM AUTHOR]

Coronavirus is an infectious life-threatening disease and is mainly transmitted through infected person coughs, sneezes, or exhales. This disease is a global challenge that demands advanced solutions to address multiple dimensions of this pandemic for health and wellbeing. Different types of medical and technological-based solutions have been proposed to control and treat COVID-19. Machine learning is one of the technologies used in Magnetic Resonance Imaging (MRI) classification whereas nature-inspired algorithms are also adopted for image optimization. In this paper, we combined the machine learning and nature-inspired algorithm for brain MRI images of COVID-19 patients namely Machine Learning and Nature Inspired Model for Coronavirus (MLNI-COVID-19). This model improves the MRI image classification and optimization for better diagnosis. This model will improve the overall performance especially the area of brain images that is neglected due to the unavailability of the dataset. COVID-19 has a serious impact on the patient brain. The proposed model will help to improve the diagnosis process for better medical decisions and performance. The proposed model is evaluated with existing algorithms and achieved better performance in terms of sensitivity, specificity, and accuracy. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) has shown immense potential to fight COVID-19 in many ways. This paper focuses primarily on AI's role in managing COVID-19 using digital images, clinical and laboratory data analysis, and a summary of the most recent articles published last year. We surveyed the use of AI for COVID-19 detection, screening, diagnosis, the progression of severity, mortality, drug repurposing, and other tasks. We started with the technical overview of all models used to fight the COVID-19 pandemic and ended with a brief statement of the current state-of-the-art, limitations, and challenges. [ABSTRACT FROM AUTHOR]

Considering the current pandemic, caused by the spreading of the novel Coronavirus disease, there is the urgent need for methods to quickly and automatically diagnose infection. To assist pathologists and radiologists in the detection of the novel coronavirus, in this paper we propose a two-tiered method, based on formal methods (to the best of authors knowledge never previously introduced in this context), aimed to (i) detect whether the patient lungs are healthy or present a generic pulmonary infection; (ii) in the case of the previous tier, a generic pulmonary disease is detected to identify whether the patient under analysis is affected by the novel Coronavirus disease. The proposed approach relies on the extraction of radiomic features from medical images and on the generation of a formal model that can be automatically checked using the model checking technique. We perform an experimental analysis using a set of computed tomography medical images obtained by the authors, achieving an accuracy of higher than 81% in disease detection. [ABSTRACT FROM AUTHOR]

Coronavirus disease 2019 (COVID-19), due to the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), has become an epidemiological threat and a worldwide concern. SARS-CoV-2 has spread to 210 countries worldwide and more than 6,500,000 confirmed cases and 384,643 deaths have been reported, while the number of both confirmed and fatal cases is continually increasing. COVID-19 is a viral disease that can affect every age group—from infants to the elderly—resulting in a wide spectrum of various clinical manifestations. COVID-19 might present different degrees of severity—from mild or even asymptomatic carriers, even to fatal cases. The most common complications include pneumonia and acute respiratory distress syndrome. Fever, dry cough, muscle weakness, and chest pain are the most prevalent and typical symptoms of COVID-19. However, patients might also present atypical symptoms that can occur alone, which might indicate the possible SARS-CoV-2 infection. The aim of this paper is to review and summarize all of the findings regarding clinical manifestations of COVID-19 patients, which include respiratory, neurological, olfactory and gustatory, gastrointestinal, ophthalmic, dermatological, cardiac, and rheumatologic manifestations, as well as specific symptoms in pediatric patients. [ABSTRACT FROM AUTHOR]