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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]

Real-time reverse transcriptase-polymerase chain reaction (RT-PCR)-based assays are the gold standard for virus diagnosis. Point-of-care (POC) technologies have shown great progress during this period. Herein, we propose a novel fuchsine dye-loaded polymersome for a colorimetric paper-based dot blot spike protein diagnostic assay for COVID-19 via smartphone-assisted sensing. The prepared platform aimed to create an adaptable tool that competes with traditional nanoparticle-based assays employing gold and silver. Analytical characterization and application of the testing platform showed high sensitivity (10 times better than gold nanoparticles), stability, fast turnaround, and reproducibility. The potential and possibilities demonstrated by the current platform could be observed in its adaptability for different markers and pathologies. In addition, smartphone-assisted sensing emphasizes the ability to use the tool at home by common peoples which can lower the burden on the healthcare facilities and reach more underdeveloped regions. • Facile, quick, and sensitive colorimetric paper-based biosensor for COVID-19. • Platform is based on functionalized dye-loaded polymersome. • Polymersomes were more sensitive than silver-enhanced gold nanoparticles. • Higher stability, repeatability, and reproducibility. • The biosensor is adjustable for other diseases. [ABSTRACT FROM AUTHOR]

The recent pandemic caused by the Coronavirus Disease (COVID-19) first surfaced in Wuhan, China in December 2019. This paper presents an expert system for the diagnosis of COVID-19 based on its symptoms to aid people in taking precautionary measures. When experts are not available, an expert system that can effectively diagnose the disease is crucial. It takes the place of one or more experts in decision-making and problem-solving. An expert system for diagnosis of COVID-19 is a system developed to recognize early COVID-19 symptoms that individuals may experience by allowing users to directly check the disease with results that can serve as a foundation for additional testing. This study's primary goal is to identify useful COVID-19 detection patterns or knowledge by examining the historical data we have obtained from the Kaggle dataset. The patterns are presented as rules, which are given to the expert system after consultation with a domain expert. A total of 1,048,575 pieces of data were used for model training and testing. To detect COVID-19 disease, we employ a PART rule-based algorithm, which performed 92.47% accurately in a 10-fold cross-validation test. We can therefore draw the conclusion that the algorithm produces a promising result and that the expert system aids in the diagnosis of the disease. The system offers a suggestion in line with the identified symptom. [ABSTRACT FROM AUTHOR]

Artificial neural networks (ANNs) ability to learn, correct errors, and transform a large amount of raw data into beneficial medical decisions for treatment and care has increased in popularity for enhanced patient safety and quality of care. Therefore, this paper reviews the critical role of ANNs in providing valuable insights for patients' healthcare decisions and efficient disease diagnosis. We study different types of ANNs in the existing literature that advance ANNs' adaptation for complex applications. Specifically, we investigate ANNs' advances for predicting viral, cancer, skin, and COVID-19 diseases. Furthermore, we propose a deep convolutional neural network (CNN) model called ConXNet, based on chest radiography images, to improve the detection accuracy of COVID-19 disease. ConXNet is trained and tested using a chest radiography image dataset obtained from Kaggle, achieving more than 97% accuracy and 98% precision, which is better than other existing state-of-the-art models, such as DeTraC, U-Net, COVID MTNet, and COVID-Net, having 93.1%, 94.10%, 84.76%, and 90% accuracy and 94%, 95%, 85%, and 92% precision, respectively. The results show that the ConXNet model performed significantly well for a relatively large dataset compared with the aforementioned models. Moreover, the ConXNet model reduces the time complexity by using dropout layers and batch normalization techniques. Finally, we highlight future research directions and challenges, such as the complexity of the algorithms, insufficient available data, privacy and security, and integration of biosensing with ANNs. These research directions require considerable attention for improving the scope of ANNs for medical diagnostic and treatment applications. [ABSTRACT FROM AUTHOR]

It has been validated beyond doubt that High-Resolution Computed Tomography (HRCT) chest and to some extent chest radiographs have a role in corona virus disease-19 (COVID-19). Much less is known about the role of lung ultrasonography (LUS) in COVID-19. In this paper, our main purpose was to gauge the relationship between LUS and chest HRCT in reverse transcriptase polymerase chain reaction (RT–PCR) documented cases of COVID-19, as well as in those with high suspicion of COVID-19 with negative RT–PCR. It was a prospective study carried out at our tertiary care hospital, namely, SKIMS Soura. The total number of patients in this study were 152 (200 patients were selected out of which only 152 had undergone both LUS and chest HRCT). The patients were subjected to both LUS and chest HRCT. The radiologist who performed LUS was blinded to clinical findings and HRCT was evaluated by a radiologist with about a decade of experience. The LUS findings compatible with the disease were subpleural consolidations, B-lines and irregular pleural lines. Findings that were compatible with COVID-19 on chest HRCT were bibasilar, subpleural predominant ground glass opacities, crazy paving and consolidations. COVID-19-positive patients were taken up for chest HRCT for disease severity stratification and were also subjected to LUS. On HRCT chest, the imaging abnormalities compatible with COVID-19 were evident in 110 individuals (72.37%), and on Lung Ultrasound they were observed in 120 individuals (78.95%). Imaging of COVID-19 patients assessed by both LUS and HRCT chest,, showed a positive correlation (p < 0.0001). The study revealed a sensitivity of 88%, a specificity of 76.62%, a positive predictive value of 78.57% and a negative predictive value of 86.76%. None of the individuals with a diagnosis of COVID-19 on HRCT were missed on LUS. An excellent correlation was derived between the LUS score and CT total severity score (p < 0.0001 with a kappa of 0.431). Similar precision compared with chest HRCT in the detection of chest flaws in COVID-19 patients was obtained on LUS. [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]

The COVID-19 pandemic has sparked a barrage of primary research and reviews. We investigated the publishing process, time and resource wasting, and assessed the methodological quality of the reviews on artificial intelligence techniques to diagnose COVID-19 in medical images. We searched nine databases from inception until 1 September 2020. Two independent reviewers did all steps of identification, extraction, and methodological credibility assessment of records. Out of 725 records, 22 reviews analysing 165 primary studies met the inclusion criteria. This review covers 174,277 participants in total, including 19,170 diagnosed with COVID-19. The methodological credibility of all eligible studies was rated as critically low: 95% of papers had significant flaws in reporting quality. On average, 7.24 (range: 0–45) new papers were included in each subsequent review, and 14% of studies did not include any new paper into consideration. Almost three-quarters of the studies included less than 10% of available studies. More than half of the reviews did not comment on the previously published reviews at all. Much wasting time and resources could be avoided if referring to previous reviews and following methodological guidelines. Such information chaos is alarming. It is high time to draw conclusions from what we experienced and prepare for future pandemics. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic shed light on the need for quick diagnosis tools in healthcare, leading to the development of several algorithmic models for disease detection. Though these models are relatively easy to build, their training requires a lot of data, storage, and resources, which may not be available for use by medical institutions or could be beyond the skillset of the people who most need these tools. This paper describes a data analysis and machine learning platform that takes advantage of high-performance computing infrastructure for medical diagnosis support applications. This platform is validated by re-training a previously published deep learning model (COVID-Net) on new data, where it is shown that the performance of the model is improved through large-scale hyperparameter optimisation that uncovered optimal training parameter combinations. The per-class accuracy of the model, especially for COVID-19 and pneumonia, is higher when using the tuned hyperparameters (healthy: 96.5%; pneumonia: 61.5%; COVID-19: 78.9%) as opposed to parameters chosen through traditional methods (healthy: 93.6%; pneumonia: 46.1%; COVID-19: 76.3%). Furthermore, training speed-up analysis shows a major decrease in training time as resources increase, from 207 min using 1 node to 54 min when distributed over 32 nodes, but highlights the presence of a cut-off point where the communication overhead begins to affect performance. The developed platform is intended to provide the medical field with a technical environment for developing novel portable artificial-intelligence-based tools for diagnosis support. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has been a global event affecting all aspects of human life and society, including acoustic aspects. In this Special Issue on COVID-19 and acoustics, we present 48 papers discussing the acoustical impacts of the pandemic and how we deal with it. The papers are divided into seven categories which include: physical masking and speech production, speech perception, noise, the underwater soundscape, the urban soundscape, pathogen transmissibility, and medical diagnosis. [ABSTRACT FROM AUTHOR]

AIMS: This review is focused on the laboratory diagnoses of the coronavirus disease 2019 (COVID-19) by recognizing the antigen of the causative agent SARS-CoV-2 virus. Various antigen tests are available in this moment and these tests are being further developed in order to reach a better diagnostic value. The issue is reviewed in a complex view. METHODS: In this work, a complex survey of the current literature was made. The relevant and recent papers related to antigen tests of COVID-19 are discussed and cited. Basic specifications of the antigen tests and competitive methods were also scrutinized in the current literature. RESULTS: The survey of the current literature (years 2019 - 2021) was made and diagnostic methods like lateral flow tests (lateral flow immunochromatographic assay) and various types of biosensors were specified as tools for COVID-19 diagnosis and their application to be used as a point-of-care test is considered. CONCLUSIONS: Small hand-held assays applicable in the point-of-care conditions for diagnosis of COVID-19 by analysis of SARS-CoV-2 antigen are the means of a growing interest and these means undergo a significant development leading to the improvements of their specifications and applicability to the current praxis. Merit of the assays is discussed in this paper. [ABSTRACT FROM AUTHOR]

Since the first case of COVID‐19 was reported in December 2019, many studies have been carried out on artificial intelligence for the rapid diagnosis of the disease to support health services. Therefore, in this study, we present a powerful approach to detect COVID‐19 and COVID‐19 findings from computed tomography images using pre‐trained models using two different datasets. COVID‐19, influenza A (H1N1) pneumonia, bacterial pneumonia and healthy lung image classes were used in the first dataset. Consolidation, crazy‐paving pattern, ground‐glass opacity, ground‐glass opacity and consolidation, ground‐glass opacity and nodule classes were used in the second dataset. The study consists of four steps. In the first two steps, distinctive features were extracted from the final layers of the pre‐trained ShuffleNet, GoogLeNet and MobileNetV2 models trained with the datasets. In the next steps, the most relevant features were selected from the models using the Sine–Cosine optimization algorithm. Then, the hyperparameters of the Support Vector Machines were optimized with the Bayesian optimization algorithm and used to reclassify the feature subset that achieved the highest accuracy in the third step. The overall accuracy obtained for the first and second datasets is 99.46% and 99.82%, respectively. Finally, the performance of the results visualized with Occlusion Sensitivity Maps was compared with Gradient‐weighted class activation mapping. The approach proposed in this paper outperformed other methods in detecting COVID‐19 from multiclass viral pneumonia. Moreover, detecting the stages of COVID‐19 in the lungs was an innovative and successful approach. [ABSTRACT FROM AUTHOR]

This paper proposes a new deep learning (DL) framework for the analysis of lung diseases, including COVID-19 and pneumonia, from chest CT scans and X-ray (CXR) images. This framework is termed optimized DenseNet201 for lung diseases (LDDNet). The proposed LDDNet was developed using additional layers of 2D global average pooling, dense and dropout layers, and batch normalization to the base DenseNet201 model. There are 1024 Relu-activated dense layers and 256 dense layers using the sigmoid activation method. The hyper-parameters of the model, including the learning rate, batch size, epochs, and dropout rate, were tuned for the model. Next, three datasets of lung diseases were formed from separate open-access sources. One was a CT scan dataset containing 1043 images. Two X-ray datasets comprising images of COVID-19-affected lungs, pneumonia-affected lungs, and healthy lungs exist, with one being an imbalanced dataset with 5935 images and the other being a balanced dataset with 5002 images. The performance of each model was analyzed using the Adam, Nadam, and SGD optimizers. The best results have been obtained for both the CT scan and CXR datasets using the Nadam optimizer. For the CT scan images, LDDNet showed a COVID-19-positive classification accuracy of 99.36%, a 100% precision recall of 98%, and an F1 score of 99%. For the X-ray dataset of 5935 images, LDDNet provides a 99.55% accuracy, 73% recall, 100% precision, and 85% F1 score using the Nadam optimizer in detecting COVID-19-affected patients. For the balanced X-ray dataset, LDDNet provides a 97.07% classification accuracy. For a given set of parameters, the performance results of LDDNet are better than the existing algorithms of ResNet152V2 and XceptionNet. [ABSTRACT FROM AUTHOR]

Since two years ago, the COVID-19 virus has spread strongly in the world and has killed more than 6 million people directly and has affected the lives of more than 500 million people. Early diagnosis of the virus can help to break the chain of transmission and reduce the death rate. In most cases, the virus spreads in the infected person's chest. Therefore, the analysis of a chest CT scan is one of the most efficient methods for diagnosing a patient. Until now, various methods have been presented to diagnose COVID-19 disease in chest CT-scan images. Most recent studies have proposed deep learning-based methods. But handcrafted features provide acceptable results in some studies too. In this paper, an innovative approach is proposed based on the combination of low-level and deep features. First of all, local neighborhood difference patterns are performed to extract handcrafted texture features. Next, deep features are extracted using MobileNetV2. Finally, a two-level decision-making algorithm is performed to improve the detection rate especially when the proposed decisions based on the two different feature set are not the same. The proposed approach is evaluated on a collected dataset of chest CT scan images from June 1, 2021, to December 20, 2021, of 238 cases in two groups of patient and healthy in different COVID-19 variants. The results show that the combination of texture and deep features can provide better performance than using each feature set separately. Results demonstrate that the proposed approach provides higher accuracy in comparison with some state-of-the-art methods in this scope. [ABSTRACT FROM AUTHOR]

Background: Coronavirus disease 2019 (COVID-19) has become a pandemic since its first appearance in late 2019. Deaths caused by COVID-19 are still increasing day by day and early diagnosis has become crucial. Since current diagnostic methods have many disadvantages, new investigations are needed to improve the performance of diagnosis.Methods: A novel method is proposed to automatically diagnose COVID-19 by using Electrocardiogram (ECG) data with deep learning for the first time. Moreover, a new and effective method called hexaxial feature mapping is proposed to represent 12-lead ECG to 2D colorful images. Gray-Level Co-Occurrence Matrix (GLCM) method is used to extract features and generate hexaxial mapping images. These generated images are then fed into a new Convolutional Neural Network (CNN) architecture to diagnose COVID-19.Results: Two different classification scenarios are conducted on a publicly available paper-based ECG image dataset to reveal the diagnostic capability and performance of the proposed approach. In the first scenario, ECG data labeled as COVID-19 and No-Findings (normal) are classified to evaluate COVID-19 classification ability. According to results, the proposed approach provides encouraging COVID-19 detection performance with an accuracy of 96.20% and F1-Score of 96.30%. In the second scenario, ECG data labeled as Negative (normal, abnormal, and myocardial infarction) and Positive (COVID-19) are classified to evaluate COVID-19 diagnostic ability. The experimental results demonstrated that the proposed approach provides satisfactory COVID-19 prediction performance with an accuracy of 93.00% and F1-Score of 93.20%. Furthermore, different experimental studies are conducted to evaluate the robustness of the proposed approach.Conclusion: Automatic detection of cardiovascular changes caused by COVID-19 can be possible with a deep learning framework through ECG data. This not only proves the presence of cardiovascular changes caused by COVID-19 but also reveals that ECG can potentially be used in the diagnosis of COVID-19. We believe the proposed study may provide a crucial decision-making system for healthcare professionals.Source Code: All source codes are made publicly available at: https://github.com/mkfzdmr/COVID-19-ECG-Classification. [ABSTRACT FROM AUTHOR]

A comprehensive medical image-based diagnosis is usually performed across various image modalities before passing a final decision; hence, designing a deep learning model that can use any medical image modality to diagnose a particular disease is of great interest. The available methods are multi-staged, with many computational bottlenecks in between. This paper presents an improved end-to-end method of multimodal image classification using deep learning models. We present top research methods developed over the years to improve models trained from scratch and transfer learning approaches. We show that when fully trained, a model can first implicitly discriminate the imaging modality and then diagnose the relevant disease. Our developed models were applied to COVID-19 classification from chest X-ray, CT scan, and lung ultrasound image modalities. The model that achieved the highest accuracy correctly maps all input images to their respective modality, then classifies the disease achieving overall 91.07% accuracy. [ABSTRACT FROM AUTHOR]

The coronavirus disease (COVID-19) is an infectious disease caused by the SARS-CoV-2 virus. COVID-19 is found to be the most infectious disease in last few decades. This disease has infected millions of people worldwide. The inadequate availability and the limited sensitivity of the testing kits have motivated the clinicians and the scientist to use Computer Tomography (CT) scans to screen COVID-19. Recent advances in technology and the availability of deep learning approaches have proved to be very promising in detecting COVID-19 with increased accuracy. However, deep learning approaches require a huge labeled training dataset, and the current availability of benchmark COVID-19 data is still small. For the limited training data scenario, the CNN usually overfits after several iterations. Hence, in this work, we have investigated different pre-trained network architectures with transfer learning for COVID-19 detection that can work even on a small medical imaging dataset. Various variants of the pre-trained ResNet model, namely ResNet18, ResNet50, and ResNet101, are investigated in the current paper for the detection of COVID-19. The experimental results reveal that transfer learned ResNet50 model outperformed other models by achieving a recall of 98.80% and an F1-score of 98.41%. To further improvise the results, the activations from different layers of best performing model are also explored for the detection using the support vector machine, logistic regression and K-nearest neighbor classifiers. Moreover, a classifier fusion strategy is also proposed that fuses the predictions from the different classifiers via majority voting. Experimental results reveal that via using learned image features and classification fusion strategy, the recall, and F1-score have improvised to 99.20% and 99.40%. [ABSTRACT FROM AUTHOR]

Laboratory medicine plays a fundamental role in the detection, diagnosis and management of COVID-19 infection. Recent observations of the morphology of cells circulating in blood found the presence of particular reactive lymphocytes (COVID-19 RL) in some of the infected patients and demonstrated that it was an indicator of a better prognosis of the disease. Visual morphological analysis is time consuming, requires smear review by expert clinical pathologists, and is prone to subjectivity. This paper presents a convolutional neural network system designed for automatic recognition of COVID-19 RL. It is based on the Xception71 structure and is trained using images of blood cells from real infected patients. An experimental study is carried out with a group of 92 individuals. The input for the system is a set of images selected by the clinical pathologist from the blood smear of a patient. The output is the prediction whether the patient belongs to the group associated with better prognosis of the disease. A threshold is obtained for the classification system to predict that the smear belongs to this group. With this threshold, the experimental test shows excellent performance metrics: 98.3% sensitivity and precision, 97.1% specificity, and 97.8% accuracy. The system does not require costly calculations and can potentially be integrated into clinical practice to assist clinical pathologists in a more objective smear review for early prognosis. [ABSTRACT FROM AUTHOR]

The outbreak of coronavirus disease (COVID-19) has been a nightmare to citizens, hospitals, healthcare practitioners, and the economy in 2020. The overwhelming number of confirmed cases and suspected cases put forward an unprecedented challenge to the hospital’s capacity of management and medical resource distribution. To reduce the possibility of cross-infection and attend a patient according to his severity level, expertly diagnosis and sophisticated medical examinations are often required but hard to fulfil during a pandemic. To facilitate the assessment of a patient’s severity, this paper proposes a multi-modality feature learning and fusion model for end-to-end covid patient severity prediction using the blood test supported electronic medical record (EMR) and chest computerized tomography (CT) scan images. To evaluate a patient’s severity by the co-occurrence of salient clinical features, the High-order Factorization Network (HoFN) is proposed to learn the impact of a set of clinical features without tedious feature engineering. On the other hand, an attention-based deep convolutional neural network (CNN) using pre-trained parameters are used to process the lung CT images. Finally, to achieve cohesion of cross-modality representation, we design a loss function to shift deep features of both-modality into the same feature space which improves the model’s performance and robustness when one modality is absent. Experimental results demonstrate that the proposed multi-modality feature learning and fusion model achieves high performance in an authentic scenario. [ABSTRACT FROM AUTHOR]

Due to false negative results of the real-time Reverse Transcriptase-Polymerase Chain Reaction (RT-PCR) test, the complemental practices such as computed tomography (CT) and X-ray in combination with RT-PCR are discussed to achieve a more accurate diagnosis of COVID-19 in clinical practice. Since radiology includes visual understanding as well as decision making under limited conditions such as uncertainty, urgency, patient burden, and hospital facilities, mistakes are inevitable. Therefore, there is an immediate requirement to carry out further investigation and develop new accurate detection and identification methods to provide automatically quantitative evaluation of COVID-19. In this paper, we propose a new computer-aided diagnosis application for COVID-19 detection using deep learning techniques. A new technique, which receives symmetric X-ray data as the input, is presented in this study by combining Convolutional Neural Networks (CNN) with Ant Lion Optimization Algorithm (ALO) and Multiclass Naïve Bayes Classifier (NB). Moreover, several other classifiers such as Softmax, Support Vector Machines (SVM), K-Nearest Neighbors (KNN) and Decision Tree (DT) are combined with CNN. The promising results of these classifiers are evaluated and presented for accuracy, precision, and F1-score metrics. NB classifier with Ant Lion Optimization Algorithm and CNN produced the best results with 98.31% accuracy, 100% precision and 98.25% F1-score and with the lowest execution time. [ABSTRACT FROM AUTHOR]

COVID-19 cases are still rising globally in the middle of the tuberculosis epidemic. Several countries have reported TB-COVID-19 coinfection that could pose a double burden in the health care facilities in developing countries. We reported two pulmonary tuberculosis patients coinfected with COVID-19 with an overlapping clinical manifestation of tuberculosis and COVID-19 with a good prognosis at the end of COVID-19 treatment. This paper aims to discuss TB patients' susceptibility against SARS-COV-2 infection, the clinical profile of TB-COVID-19 coinfection, and the disease's prognosis. The clinician should be aware of both common disease symptoms that appear in a patient and should be confirmed and treat promptly. [ABSTRACT FROM AUTHOR]

Since the outbreak of COVID-19 in 2019, the rapid spread of the epidemic has brought huge challenges to medical institutions. If the pathological region in the COVID-19 CT image can be automatically segmented, it will help doctors quickly determine the patient's infection, thereby speeding up the diagnosis process. To be able to automatically segment the infected area, we proposed a new network structure and named QC-HC U-Net. First, we combine residual connection and dense connection to form a new connection method and apply it to the encoder and the decoder. Second, we choose to add Hypercolumns in the decoder section. Compared with the benchmark 3D U-Net, the improved network can effectively avoid vanishing gradient while extracting more features. To improve the situation of insufficient data, resampling and data enhancement methods are selected in this paper to expand the datasets. We used 63 cases of MSD lung tumor data for training and testing, continuously verified to ensure the training effect of this model, and then selected 20 cases of public COVID-19 data for training and testing. Experimental results showed that in the segmentation of COVID-19, the specificity and sensitivity were 85.3% and 83.6%, respectively, and in the segmentation of MSD lung tumors, the specificity and sensitivity were 81.45% and 80.93%, respectively, without any fitting. [ABSTRACT FROM AUTHOR]

In order to understand how these studies are evolving to respond to COVID-19 and to facilitate the containment of COVID-19, this paper accurately extracted the spatial and topic information from the metadata of papers related to COVID-19 using text mining techniques, and with the extracted information, the research evolution was analyzed from the temporal, spatial, and topic perspectives. From a temporal view, in the three months after the emergence of COVID-19, the number of published papers showed an obvious growth trend, and it showed a relatively stable cyclical trend in the later period, which is basically consistent with the development of COVID-19. Spatially, most of the authors who participated in related research are concentrated in the United States, China, Italy, the United Kingdom, Spain, India, and France. At the same time, with the continuous spread of COVID-19 in the world, the distribution of the number of authors has gradually expanded, showing to be correlated with the severity of COVID-19 at a spatial scale. From the perspective of topic, the early stage of COVID-19 emergence, the related research mainly focused on the origin and gene identification of the virus. After the emergence of the pandemic, studies related to the diagnosis and analysis of psychological health, personal security, and violent conflict are added. Meanwhile, some categories are most closely related to the control and prevention of the epidemic, such as pathology analysis, diagnosis, and treatment; epidemic situation and coping strategies; and prediction and assessment of epidemic situation. In most time periods, the majority of studies focused on these three categories. [ABSTRACT FROM AUTHOR]

This paper focuses on the application of deep learning (DL) in the diagnosis of coronavirus disease (COVID-19). The novelty of this work is in the introduction of optimized InceptionResNetV2 for COVID-19 (CO-IRv2) method. A part of the CO-IRv2 scheme is derived from the concepts of InceptionNet and ResNet with hyperparameter tuning, while the remaining part is a new architecture consisting of a global average pooling layer, batch normalization, dense layers, and dropout layers. The proposed CO-IRv2 is applied to a new dataset of 2481 computed tomography (CT) images formed by collecting two independent datasets. Data resizing and normalization are performed, and the evaluation is run up to 25 epochs. Various performance metrics, including precision, recall, accuracy, F1-score, area under the receiver operating characteristics (AUC) curve are used as performance metrics. The effectiveness of three optimizers known as Adam, Nadam and RMSProp are evaluated in classifying suspected COVID-19 patients and normal people. Results show that for CO-IRv2 and for CT images, the obtained accuracies of Adam, Nadam and RMSProp optimizers are 94.97%, 96.18% and 96.18%, respectively. Furthermore, it is shown here that for the case of CT images, CO-IRv2 with Nadam optimizer has better performance than existing DL algorithms in the diagnosis of COVID-19 patients. Finally, CO-IRv2 is applied to an X-ray dataset of 1662 images resulting in a classification accuracy of 99.40%. [ABSTRACT FROM AUTHOR]

This paper analyses COVID-19 patients' dynamics during the first wave in the region of Castilla y León (Spain) with around 2.4 million inhabitants using multi-state competing risk survival models. From the date registered as the start of the clinical process, it is assumed that a patient can progress through three intermediate states until reaching an absorbing state of recovery or death. Demographic characteristics, epidemiological factors such as the time of infection and previous vaccinations, clinical history, complications during the course of the disease and drug therapy for hospitalised patients are considered as candidate predictors. Regarding risk factors associated with mortality and severity, consistent results with many other studies have been found, such as older age, being male, and chronic diseases. Specifically, the hospitalisation (death) rate for those over 69 is 27.2% (19.8%) versus 5.3% (0.7%) for those under 70, and for males is 14.5%(7%) versus 8.3%(4.6%)for females. Among patients with chronic diseases the highest rates of hospitalisation are 26.1% for diabetes and 26.3% for kidney disease, while the highest death rate is 21.9% for cerebrovascular disease. Moreover, specific predictors for different transitions are given, and estimates of the probability of recovery and death for each patient are provided by the model. Some interesting results obtained are that for patients infected at the end of the period the hazard of transition from hospitalisation to ICU is significatively lower (p < 0.001) and the hazard of transition from hospitalisation to recovery is higher (p < 0.001). For patients previously vaccinated against pneumococcus the hazard of transition to recovery is higher (p < 0.001). Finally, internal validation and calibration of the model are also performed. [ABSTRACT FROM AUTHOR]

Invasive pulmonary aspergillosis (IPA) has always been a challenging diagnosis and risk factors an important guide to investigate specific population, especially in Intensive Care Unit. Traditionally recognized risk factors for IPA have been haematological diseases or condition associated with severe immunosuppression, lately completed by chronic conditions (such as obstructive pulmonary disease, liver cirrhosis, chronic kidney disease and diabetes), influenza infection and Intensive Care Unit (ICU) admission. Recently, a new association with SARS-CoV2 infection, named COVID-19-associated pulmonary aspergillosis (CAPA), has been reported worldwide, even if its basic epidemiological characteristics have not been completely established yet. In this narrative review, we aimed to explore the potential risk factors for the development of CAPA and to evaluate whether previous host factors or therapeutic approaches used in the treatment of COVID-19 critically ill patients (such as mechanical ventilation, intensive care management, corticosteroids, broad-spectrum antibiotics, immunomodulatory agents) may impact this new diagnostic category. Reviewing all English-language articles published from December 2019 to December 2020, we identified 21 papers describing risk factors, concerning host comorbidities, ICU management, and COVID-19 therapies. Although limited by the quality of the available literature, data seem to confirm the role of previous host risk factors, especially respiratory diseases. However, the attention is shifting from patients' related risk factors to factors characterizing the hospital and intensive care course, deeply influenced by specific features of COVID treatment itself. Prolonged invasive or non-invasive respiratory support, as well as the impact of corticosteroids and/or immunobiological therapies seem to play a pivotal role. ICU setting related factors, such as environmental factors, isolation conditions, ventilation systems, building renovation works, and temporal spread with respect to pandemic waves, need to be considered. Large, prospective studies based on new risk factors specific for CAPA are warranted to guide surveillance and decision of when and how to treat this particular population. [ABSTRACT FROM AUTHOR]

Background: Severe acute respiratory syndrome-coronavirus (SARS-CoV) and SARS-CoV-2 might affect the hypothalamic-pituitary-adrenal axis. This paper presents a rare case of pituitary insufficiency diagnosed after CoV disease (COVID)-19. Case Report: On December 7, 2020, a 67-year-old male patient presented to the Endocrinology Department of Lokman Hekim University with weakness, weight loss, and abdominal pain lasting for 3 weeks. In his medical history, he had a positive real-time polymerase chain reaction test result for SARS-CoV-2 based on the nasopharyngeal swab analyzed on September 30. Central hypothyroidism, secondary adrenal insufficiency, hypogonadotropic hypogonadism, and a low dehydroepiandrosterone-sulfate level were detected in the laboratory examinations. Steroid therapy was initiated. Conclusion: Non-specific adrenal insufficiency symptoms such as fatigue and weakness, and psychosomatic symptoms can be seen during the course of COVID-19 or the post-COVID-19 period. Physicians should be aware about the systemic effects of SARS-CoV-2 infection and follow-up patients with a history of COVID-19 accordingly. [ABSTRACT FROM AUTHOR]

Background: In medical diagnosis and clinical practice, diagnosing a disease early is crucial for accurate treatment, lessening the stress on the healthcare system. In medical imaging research, image processing techniques tend to be vital in analyzing and resolving diseases with a high degree of accuracy. This paper establishes a new image classification and segmentation method through simulation techniques, conducted over images of COVID-19 patients in India, introducing the use of Quantum Machine Learning (QML) in medical practice.Methods: This study establishes a prototype model for classifying COVID-19, comparing it with non-COVID pneumonia signals in Computed tomography (CT) images. The simulation work evaluates the usage of quantum machine learning algorithms, while assessing the efficacy for deep learning models for image classification problems, and thereby establishes performance quality that is required for improved prediction rate when dealing with complex clinical image data exhibiting high biases.Results: The study considers a novel algorithmic implementation leveraging quantum neural network (QNN). The proposed model outperformed the conventional deep learning models for specific classification task. The performance was evident because of the efficiency of quantum simulation and faster convergence property solving for an optimization problem for network training particularly for large-scale biased image classification task. The model run-time observed on quantum optimized hardware was 52 min, while on K80 GPU hardware it was 1 h 30 min for similar sample size. The simulation shows that QNN outperforms DNN, CNN, 2D CNN by more than 2.92% in gain in accuracy measure with an average recall of around 97.7%.Conclusion: The results suggest that quantum neural networks outperform in COVID-19 traits' classification task, comparing to deep learning w.r.t model efficacy and training time. However, a further study needs to be conducted to evaluate implementation scenarios by integrating the model within medical devices. [ABSTRACT FROM AUTHOR]

Artificial intelligence techniques are used on chest X-ray images for accurate detection of diseases and this paper aims to develop a process which is capable of diagnosing COVID-19 using deep learning methods on X-ray images. For this purpose, we used Mask R-CNN method to train and test on the dataset to classify between patients infected and non-infected with COVID-19. The dataset used here contains a large number of frontal views of X-ray images which are an essential resource for the algorithms used in the development of tools for the detection of COVID-19. Using 668 chest Xray images, the proposed model achieved an accuracy as high as 96.98%, specificity of 97.36% with the precision of 96.60%. The entire process is presented in detail. When a comparison table on the AIbased techniques is prepared, it is noticed that the Mask R-CNN technique on chest X-ray images provides better efficiency in the detection of COVID-19. The Mask R-CNN method is found to be accurate and robust in the detection of COVID-19 from chest X-ray images. [ABSTRACT FROM AUTHOR]

Background: Mortality is a key component of the natural history of COVID-19 infection. Surveillance data on COVID-19 deaths and case diagnoses are widely available in the public domain, but they are not used to model time to death because they typically do not link diagnosis and death at an individual level. This paper demonstrates that by comparing the unlinked patterns of new diagnoses and deaths over age and time, age-specific mortality and time to death may be estimated using a statistical method called deconvolution.Methods: Age-specific data were analysed on 816 deaths among 6235 cases over age 50 years in Victoria, Australia, from the period January through December 2020. Deconvolution was applied assuming logistic dependence of case fatality risk (CFR) on age and a gamma time to death distribution. Non-parametric deconvolution analyses stratified into separate age groups were used to assess the model assumptions.Results: It was found that age-specific CFR rose from 2.9% at age 65 years (95% CI:2.2 - 3.5) to 40.0% at age 95 years (CI: 36.6 - 43.6). The estimated mean time between diagnosis and death was 18.1 days (CI: 16.9 - 19.3) and showed no evidence of varying by age (heterogeneity P = 0.97). The estimated 90% percentile of time to death was 33.3 days (CI: 30.4 - 36.3; heterogeneity P = 0.85). The final age-specific model provided a good fit to the observed age-stratified mortality patterns.Conclusions: Deconvolution was demonstrated to be a powerful analysis method that could be applied to extensive data sources worldwide. Such analyses can inform transmission dynamics models and CFR assessment in emerging outbreaks. Based on these Australian data it is concluded that death from COVID-19 occurs within three weeks of diagnosis on average but takes five weeks in 10% of fatal cases. Fatality risk is negligible in the young but rises above 40% in the elderly, while time to death does not seem to vary by age. [ABSTRACT FROM AUTHOR]

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) outbreak poses a major global threat to the public health worldwide. The infectious disease caused by the virus that affected the entire world was named as the Coronavirus disease-2019 (COVID-19). The knowledge regarding the wide clinico-biological aspects of the COVID-19 continues to evolve very rapidly, given the growing data from all over the world. During this complicated process, healthcare professionals have benefited from each other's experiences in combatting against the COVID-19 syndrome. COVID-19 related studies have been performed by a wide variety of research groups in Turkey as well as the rest of the world. The aim of this paper is to outline Turkish COVID-19 research indexed in the LitCovid system. LitCovid is a curated literature hub for tracking up-to-date scientific data about the SARS-CoV-2. COVID-19's first case was detected in Turkey, on March 11th, 2020. Six months after the first case was observed, the total number of COVID-19 patients was reported to be as 286,455, and the total number of deaths due to SARS-CoV-2 was 6895. The genetic sequence of the novel coronavirus showed significant identity to SARS-CoV and MERS-CoV. Numerous drugs including lopinavir/ritonavir, favipiravir, neuraminidase inhibitors, remdesivir, umifenovir, azithromycin, and chloroquine have been suggested for the management of COVID-19 although the exact treatment is yet to be determined. [ABSTRACT FROM AUTHOR]

Background: In Lebanon, HIV is concentrated in both native and refugee communities of men who have sex with men (MSM). For over 10 years, the National AIDS Program (NAP) has offered HIV voluntary counselling and testing through a partnership with nongovernmental organizations (NGOs). In 2018, implementation of HIV self-tests (HIVST) was introduced, and this self-care intervention has been further scaled up during the coronavirus disease 2019 (COVID-19) pandemic. This paper (1) describes the effectiveness of implementing HIVST in Lebanon, and (2) discusses how the success of HIVST implementation has been reflected during the COVID-19 pandemic.Methods: The NAP conducted a series of workshops (July-November 2018) to introduce HIVST services for healthcare workers working at different NGOs. The workshops highlighted that HIVST would be distributed for free, that it would be confidential and voluntary, and that participants were encouraged to notify the NGOs of their results, which would be kept strictly confidential. NGOs collected data anonymously and confidentially from beneficiaries (age, consistency of condom use and HIV testing history), who were asked to call back with the results of their HIVST. At the NAP, data were combined, aggregated and analysed.Results: In 2019, the NGOs distributed 1103/1380 (79.9%) HIVST kits to their beneficiaries. The NGOs collected feedback on 111 kit results, of which two were HIV-positive. Feedback about HIVST results from beneficiaries was low (111/1103) due to noncompliance of beneficiaries and the lack of human and financial resources in the NGOs. From January through May 2020, a total of 625/780 HIVST kits (80.1%) were distributed. This period was divided into pre-COVID-19 and during COVID-19. The follow-up with the beneficiaries during COVID-19 was much improved because of the absence of on-site activities, shifting more efforts towards HIVST (449/625). There have been no reports of social harm related to HIVST.Conclusion: HIVST implementation in Lebanon serves as an example of introducing a self-care intervention as part of a community-led effort. In order to maintain HIVST services at the same improved level, reorganization of care is needed within each NGO following the adaptation process due to COVID-19, along with continuous monitoring and evaluation of HIVST reported data. [ABSTRACT FROM AUTHOR]

AIMS: Aims: The Coronavirus Disease 2019 (COVID-19) caused not only global pandemic, but it also pointed at unprepared health care systems and countermeasures were introduced under the pressure of urgent circumstances. This review is focused on discussion and critical evaluation of instrumental tools for COVID-19 diagnosis that were developed in the last months. METHODS: Survey of actual literature and scientific reports was made. The most substantial analytical and diagnostical methods were identified and described. Principles and limitations of the methods are described, and actual papers are cited in this review. RESULTS: Analytical and diagnostical methods like Polymerase Chain Reaction (PCR), Loop-mediated isothermal Amplification (LAMP), Lateral Flow Immunochromatography Assay (LFIA), Enzyme-Linked Immunosorbent Assay (ELISA), biosensors and ChemiLuminescence ImmunoAssay (CLIA) are discussed for assay of viral particles, antigens and specific host antibodies in blood, serum, plasma, nasopharyngeal swab and other samples in order to diagnose COVID-19. CONCLUSIONS: The Coronavirus Disease 2019 (COVID-19) is an emerging disease that has spread over the world since the end of year 2019. The global epidemic pointed at the necessity to introduce sensitive methods for instrumental diagnosis of COVID-19 and distinguishing it from the other viral diseases. (Tab. 2, Ref. 96). [ABSTRACT FROM AUTHOR]

AIMS: Aims: The Coronavirus Disease 2019 (COVID-19) caused not only global pandemic, but it also pointed at unprepared health care systems and countermeasures were introduced under the pressure of urgent circumstances. This review is focused on discussion and critical evaluation of instrumental tools for COVID-19 diagnosis that were developed in the last months. METHODS: Survey of actual literature and scientific reports was made. The most substantial analytical and diagnostical methods were identified and described. Principles and limitations of the methods are described, and actual papers are cited in this review. RESULTS: Analytical and diagnostical methods like Polymerase Chain Reaction (PCR), Loop-mediated isothermal Amplification (LAMP), Lateral Flow Immunochromatography Assay (LFIA), Enzyme-Linked Immunosorbent Assay (ELISA), biosensors and ChemiLuminescence ImmunoAssay (CLIA) are discussed for assay of viral particles, antigens and specific host antibodies in blood, serum, plasma, nasopharyngeal swab and other samples in order to diagnose COVID-19. CONCLUSIONS: The Coronavirus Disease 2019 (COVID-19) is an emerging disease that has spread over the world since the end of year 2019. The global epidemic pointed at the necessity to introduce sensitive methods for instrumental diagnosis of COVID-19 and distinguishing it from the other viral diseases. (Tab. 2, Ref. 96). [ABSTRACT FROM AUTHOR]

Introduction: Our aim in this study was to summarize information on the use of intelligent models for predicting and diagnosing the Coronavirus disease 2019 (COVID-19) to help early and timely diagnosis of the disease. Material and Methods: A systematic literature search included articles published until 20 April 2020 in PubMed, Web of Science, IEEE, ProQuest, Scopus, bioRxiv, and medRxiv databases. The search strategy consisted of two groups of keywords: A) Novel coronavirus, B) Machine learning. Two reviewers independently assessed original papers to determine eligibility for inclusion in this review. Studies were critically reviewed for risk of bias using prediction model risk of bias assessment tool. Results: We gathered 1650 articles through database searches. After the full-text assessment 31 articles were included. Neural networks and deep neural network variants were the most popular machine learning type. Of the five models that authors claimed were externally validated, we considered external validation only for four of them. Area under the curve (AUC) in internal validation of prognostic models varied from .94 to .97. AUC in diagnostic models varied from 0.84 to 0.99, and AUC in external validation of diagnostic models varied from 0.73 to 0.94. Our analysis finds all but two studies have a high risk of bias due to various reasons like a low number of participants and lack of external validation. Conclusion: Diagnostic and prognostic models for COVID-19 show good to excellent discriminative performance. However, these models are at high risk of bias because of various reasons like a low number of participants and lack of external validation. Future studies should address these concerns. Sharing data and experiences for the development, validation, and updating of COVID-19 related prediction models is needed. [ABSTRACT FROM AUTHOR]

In this paper, we describe a case series of four patients who were admitted with emergencies related to aortic aneurysms over a 3-day period and were treated with endovascular repair. The first patient was an 81-year-old female with a history of abdominal pain and a ruptured aortic aneurysm diagnosed by AngioCT-scan. The second patient was a 63-year-old male with a history of oral digestive bleeding and an AngioCT-scan showing an aortoenteric fistula. The third patient was a 77-year-old female with sudden-onset abdominal pain and ruptured right common iliac aneurysm. The fourth patient presented with abdominal pain and an AngioCT-scan showed aortic rupture. All four patients were discharged with no major complications or surgical mortality. These case series show that despite the Covid-19 pandemic situation, since elective surgeries decreased, vascular emergencies have increased. [ABSTRACT FROM AUTHOR]

Since COVID-19 pandemic has been continuously rising and spreading, several original contributions and review articles on COVID-19 started to appear in the literature. The review articles are mainly focus on the current status of the pandemic along with current status of the corona diagnosis and treatment process. Due to some disadvantages of the currently used methods, the improvement on the novel promising diagnosis and treatment methods of corona virus is very important issue. In this review, after briefly discussing the status of current diagnosis and treatment methods, we present to the scientific community, novel promising methods in the diagnosis and treatment of COVID-19. As with other novel approaches, first, the diagnosis potential of mass spectroscopy and optical spectroscopic methods such as UV/visible, infrared, and Raman spectroscopy coupled with chemometrics will be discussed for the corona virus infected samples based on the relevant literature. In vibrational spectroscopy studies, due to complexity of the data, multivariate analysis methods are also applied to data. The application of multivariate analysis tools that can be used to extract useful information from the data for diagnostic and characterisation purposes is also included in this review. The reviewed methods include hierarchical cluster analysis, principal component analysis, linear and quadratic discriminant analysis, support vector machine algorithm, and one form of neural networks namely deep learning method. Second, novel treatment methods such as photodynamic therapy and the use of nanoparticles in the in-corona virus therapy will be discussed. Finally, the advantages of novel promising diagnosis and treatment methods in COVID-19, over standard methods will be discussed. One of the main aims of this paper is to encourage the scientific community to explore the potential of this novel tools for their use in corona virus characterization, diagnosis, and treatment. [ABSTRACT FROM AUTHOR]

Coronavirus disease 2019 (COVID-19) dissemination occurred from December 2019 and quickly spread to all countries. Infected patients with COVID-19 have had a wide range of symptoms, ranging from mild to severe illness. The most mortality was observed in patients with underlying disease and over 45 years. World statistics have shown that the COVID-19 outbreak is most expanded in Middle Eastern, West Asian, European, North, and South American countries, and is least expanded in African countries. Therefore, the aim of the paper was the evaluation of six African countries including Mali, Mauritania, Niger, Guinea, Togo, and Djibouti to find why this disease is least expanded in African countries. Study was conducted by Questioner for countries health organizers to define their different aspect exposure and fight with COVID-19 including epidemiology, clinical aspects of the disease, case definitions, diagnosis laboratory confirmation, and referral of cases by the portal of entry, case management, and disease prevention in these countries. According to this opinion review, due to the low international flights and low domestic travel, the spread, and prevalence of COVID-19 was low and the return of the immigrants of these countries has caused the spread of COVID-19 among these countries. Experience, preparation, and impact of previous infections epidemic such as the Ebola virus epidemic would have beneficial, which have promoted certain reflexes among people that cause low dissemination in these countries. [ABSTRACT FROM AUTHOR]

This paper presents a study of early epidemiological assessment of COVID-19 transmission dynamics in Indonesia. The aim is to quantify heterogeneity in the numbers of secondary infections. To this end, we estimate the basic reproduction number R 0 and the overdispersion parameter K at two regions in Indonesia: Jakarta–Depok and Batam. The method to estimate R 0 is based on a sequential Bayesian method, while the parameter K is estimated by fitting the secondary case data with a negative binomial distribution. Based on the first 1288 confirmed cases collected from both regions, we find a high degree of individual-level variation in the transmission. The basic reproduction number R 0 is estimated at 6.79 and 2.47, while the overdispersion parameter K of a negative-binomial distribution is estimated at 0.06 and 0.2 for Jakarta–Depok and Batam, respectively. This suggests that superspreading events played a key role in the early stage of the outbreak, i.e., a small number of infected individuals are responsible for large numbers of COVID-19 transmission. This finding can be used to determine effective public measures, such as rapid isolation and identification, which are critical since delay of diagnosis is the most common cause of superspreading events. [ABSTRACT FROM AUTHOR]

COVID-19 is a rapidly growing pandemic that has grown from a few cases in Wuhan, China to millions of infections and hundreds of thousands of deaths worldwide within a few months. Sub-Saharan Africa is not spared. Radiology has a key role to play in the diagnosis and management of COVID-19 as literature from Wuhan and Italy demonstrates. We therefore share some critical knowledge and practice areas for radiological suspicion and diagnosis. In addition, emphasis on how guarding against healthcare acquired infections (HAIs) by applying "red" and "green" principle is addressed. Given that pandemics such as COVID-19 can worsen the strain on the scantily available radiological resources in this region, we share some practical points that can be applied to manage these precious resources also needed for other essential services. We have noted that radiology does not feature in many main COVID-19 guidelines, regionally and internationally. This paper therefore suggests areas of collaboration for radiology with other clinical and management teams. We note from our local experience that radiology can play a role in COVID-19 surveillance. [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]

The global spread of COVID-19 has caused a substantial societal burden and become a major global public health issue. The COVID-19 elderly population with hypertension, diabetes, cardiovascular, and cerebrovascular diseases are at risk. Mortality rates are highest in these individuals if infected with COVID-19. Although the lungs are the main organs involved in acute respiratory distress syndrome caused by COVID-19 infection, COVID-19 triggers inflammatory and immune mechanisms, inducing a "cytokine storm" that aggravates disease progression and may lead to death. Presently, effective drugs are lacking, although current studies have confirmed that drugs with therapeutic potential include redaciclovir, lopinavir/ritonavir combined with interferon-β, convalescent plasma, and monoclonal antibodies. Currently, the most reasonable and effective way to prevent COVID-19 is to control the source of infection, terminate routes of transmission, and protect susceptible populations. With the rise of COVID-19 in China and worldwide, further prevention, diagnosis, and treatment measures are a critical unmet need. Cerebrovascular disease has high incidence, disability rate, and fatality rate. COVID-19 patient outcomes may also be complicated with acute stroke. This paper summarizes the influence of COVID-19 on cerebrovascular disease and discusses possible pathophysiological mechanisms to provide new angles for the prevention and diagnosis of this disease. [ABSTRACT FROM AUTHOR]

Background: Actual level of knowledge of treatment of COVID-19 disease caused by a type of coronavirus is that the disease cannot at present be cured by targeted therapy. Worldwide research is aiming to find a specific treatment, such as a vaccine or drug, for this type of coronavirus; this may help improve the situation, but it is highly expensive and time-consuming. The purpose of this paper is to review drug therapies approved in different parts of the world to treat COVID-19 and draw attention to ototoxicity as one of the adverse side-effects. Material and methods: Review of current literature was done in the scientific databases PubMed, ResearchGate, GoogleScholar, and Science-Direct. Studies were reviewed with reference to the inclusion criteria, then graded to assess the internal and external validity, leaving 50 studies for review. Results: According to scientific reports, possible antiviral pharmacological agents to treat COVID-19 consist of chloroquine, hydroxychloroquine, azitromycine, oseltamivir, and tocilizumab. In some cases, certain combinations may lead to additive ototoxicity as an adverse effect. Ototoxicity may be manifested by sensory and nervous hearing loss, tinnitus, imbalance, and cochlear-mandibular symptoms, which are sometimes temporary but sometimes permanent. Conclusion: Drug ototoxicity is well known as a cause of cochlear hearing loss, and so the use of new pharmacotherapy methods and drug combinations in the fight against the new coronavirus may have harmful effects. Ototoxicity needs to be taken into account. [ABSTRACT FROM AUTHOR]

The detection of the COronaVIrus Disease 2019 (COVID-19) from Computed Tomography (CT) scans has become a very important task in modern medical diagnosis. Unfortunately, typical resolutions of state-of-the-art CT scans are still not adequate for reliable and accurate automatic detection of COVID-19 disease. Motivated by this consideration, in this paper, we propose a novel architecture that jointly affords the Single-Image Super-Resolution (SISR) and the reliable classification problems from Low Resolution (LR) and noisy CT scans. Specifically, the proposed architecture is based on a couple of Twinned Residual Auto-Encoders (TRAE), which exploits the feature vectors and the SR images recovered by a Master AE for performing transfer learning and then improves the training of a "twinned" Follower AE. In addition, we also develop a Task-Aware (TA) version of the basic TRAE architecture, namely the TA-TRAE, which further utilizes the set of feature vectors generated by the Follower AE for the joint training of an additional auxiliary classifier, so to perform automated medical diagnosis on the basis of the available LR input images without human support. Experimental results and comparisons with a number of state-of-the-art CNN/GAN/CycleGAN benchmark SISR architectures, performed by considering × 2 , × 4 , and × 8 super-resolution (i.e., upscaling) factors, support the effectiveness of the proposed TRAE/TA-TRAE architectures. In particular, the detection accuracy attained by the proposed architectures outperforms the corresponding ones of the implemented CNN, GAN and CycleGAN baselines up to 9.0%, 6.5%, and 6.0% at upscaling factors as high as × 8. • High-performance automatic COVID-19 disease detection. • Twinned Residual Auto-Encoders for CT scans classification and super-resolution. • Task-aware architecture for joint CT scans classification and super-resolution. [ABSTRACT FROM AUTHOR]

Novel Coronavirus disease (COVID-19) has been widely described as the cause for a proinflammatory and hypercoagulable state. The inflammatory process involving the alveolar vascular endothelium in the respiratory system, is a determining factor for the onset of primary Pulmonary Artery Thrombosis (PAT) even in patients with heparin prophylactic treatment. Little is known about the efficacy of the anticoagulant therapy during the course of PAT caused by COVID-19. In this paper we describe the results obtained in patients with moderate COVID-19 disease, previously threated with prophylactic enoxaparin, who then received full Anticoagulant treatment after diagnosis of PAT. After three months Computed Tomography Pulmonary Angiography demonstrated a complete resolution of the vascular obstructive lesions in all patients, while all the coagulation tests were normal. [ABSTRACT FROM AUTHOR]

Coronavirus disease 2019 (COVID-19) is a pandemic and rapidly progressing infectious disease that represents a global health care emergency due to severe acute respiratory syndrome. Rapid tests detect antigen or antibody presence, which is useful for patient care and for assisting in disease surveillance and epidemiological research. The complicating aspect of the COVID-19 spread may be due to asymptomatic individuals. Unfortunately, asymptomatic individuals are not detected in the early stages of infection, which could help to prevent community spread. The present paper shares an opinion through the proposal of a research model for detecting COVID-19 among asymptomatic carriers. [ABSTRACT FROM AUTHOR]

• The study described the case of a 7-week-old suckling baby infected withSARS-CoV-2 virus. • Only culture isolation allowed the identification of the cytopathogenic agent. • Cell culture still remains the only reference method for emerging viruses. SARS-CoV-2 emerged in China in December 2019 and has now been declared a pandemic by the World Health Organization. This paper described the case of a 7-week-old suckling baby from Italy who was SARS-CoV-2-positive only by the cell culture method, with no clinical suspicion of and/or risk factors for SARS-CoV-2 infection. The baby was referred to hospital, with signs and symptoms of upper respiratory tract infection, before the virus had spread to the province. Nasal and pharyngeal swabs and a nasopharyngeal aspirate were used for conventional and molecular diagnostic assays not including the SARS-CoV-2 virus. Bacteria referred to the resident population were revealed in nasal and pharyngeal swabs. No viruses were detected using both immunofluorescence assay and nucleic acid amplification assays in the nasopharyngeal aspirate. The baby was discharged in good condition after 3 days of hospitalisation. Later, a cytopathic effect on the cell monolayers currently used for respiratory viruses was observed and the viral particles were identified as Coronaviridae by transmission electron microscopy. SARS-CoV-2 was identified by RT-PCR performed both on cell culture and on the stored aliquot of the original sample. The virus isolate was named SARS-Cov-2/human/Parma/1/2020. Cell culture still remains the only reference diagnostic method also for emerging viruses, allowing it to reveal cytopathogenic viruses and demonstrate their infectivity. [ABSTRACT FROM AUTHOR]

To manage the propagation of infectious diseases, particularly fast-spreading pandemics, it is necessary to provide information about possible infected places and individuals, however, it needs diagnostic tests and is time-consuming and expensive. To smooth these issues, and motivated by the current Coronavirus disease (COVID-19) pandemic, in this paper, we propose a learning-based system and a hidden Markov model (i) to assess hazardous places of a contagious disease, and (ii) to predict the probability of individuals' infection. To this end, we track the trajectories of individuals in an environment. For evaluating the models and the approaches, we use the Covid-19 outbreak in an urban environment as a case study. Individuals in a closed population are explicitly represented by their movement trajectories over a period of time. The simulation results demonstrate that by adjusting the communicable disease parameters, the detector system and the predictor system are able to correctly assess the hazardous places and determine the infection possibility of individuals and cluster them accurately with high probability, i.e., on average more than 96%. In general, the proposed approaches to assessing hazardous places and predicting the infection possibility of individuals can be applied to contagious diseases by tailoring them to the influential features of the disease. • Utilizing the movement trajectories of individuals in a city to manage infection disease. • Proposing a learning-based system to assess hazardous places of a contagious disease. • Proposing a hidden Markov model to predict the probability of individuals infection. • Applying the Covid-19 outbreak in an urban environment as a case study. [Display omitted] [ABSTRACT FROM AUTHOR]

To manage the propagation of infectious diseases, particularly fast-spreading pandemics, it is necessary to provide information about possible infected places and individuals, however, it needs diagnostic tests and is time-consuming and expensive. To smooth these issues, and motivated by the current Coronavirus disease (COVID-19) pandemic, in this paper, we propose a learning-based system and a hidden Markov model (i) to assess hazardous places of a contagious disease, and (ii) to predict the probability of individuals' infection. To this end, we track the trajectories of individuals in an environment. For evaluating the models and the approaches, we use the Covid-19 outbreak in an urban environment as a case study. Individuals in a closed population are explicitly represented by their movement trajectories over a period of time. The simulation results demonstrate that by adjusting the communicable disease parameters, the detector system and the predictor system are able to correctly assess the hazardous places and determine the infection possibility of individuals and cluster them accurately with high probability, i.e., on average more than 96%. In general, the proposed approaches to assessing hazardous places and predicting the infection possibility of individuals can be applied to contagious diseases by tailoring them to the influential features of the disease. • Utilizing the movement trajectories of individuals in a city to manage infection disease. • Proposing a learning-based system to assess hazardous places of a contagious disease. • Proposing a hidden Markov model to predict the probability of individuals infection. • Applying the Covid-19 outbreak in an urban environment as a case study. [Display omitted] [ABSTRACT FROM AUTHOR]