Uncertainty-Aware Semi-supervised Method using Large Unlabelled and Limited Labeled COVID-19 Data

This work was partly supported by the MINECO/ FEDER under the RTI2018-098913-B100, CV20-45250 and A-TIC-080-UGR18 projects.The new coronavirus has caused more than 1 million deaths and continues to spread rapidly. This virus targets the lungs, causing respiratory distress which can be mild or severe. The X-ray or computed tomography (CT) images of lungs can reveal whether the patient is infected with COVID-19 or not. Many researchers are trying to improve COVID-19 detection using artificial intelligence. In this paper, relying on Generative Adversarial Networks (GAN), we propose a Semi-supervised Classification using Limited Labelled Data (SCLLD) for automated COVID-19 detection. Our motivation is to develop learning method which can cope with scenarios that preparing labelled data is time consuming or expensive. We further improved the detection accuracy of the proposed method by applying Sobel edge detection. The GAN discriminator output is a probability value which is used for classification in this work. The proposed system is trained using 10,000 CT scans collected from Omid hospital. Also, we validate our system using the public dataset. The proposed method is compared with other state of the art supervised methods such as Gaussian processes. To the best of our knowledge, this is the first time a COVID-19 semi-supervised detection method is presented. Our method is capable of learning from a mixture of limited labelled and unlabelled data where supervised learners fail due to lack of sufficient amount of labelled data. Our semi-supervised training method significantly outperforms the supervised training of Convolutional Neural Network (CNN) in case labelled training data is scarce. Our method has achieved an accuracy of 99.60%, sensitivity of 99.39%, and specificity of 99.80% where CNN (trained supervised) has achieved an accuracy of 69.87%, sensitivity of 94%, and specificity of 46.40%.Spanish Government RTI2018-098913-B100 CV20-45250 A-TIC-080UGR1

Risk factors prediction, clinical outcomes, and mortality in COVID-19 patients

Preventing communicable diseases requires understanding the spread, epidemiology, clinical features, progression, and prognosis of the disease. Early identification of risk factors and clinical outcomes might help in identifying critically ill patients, providing appropriate treatment, and preventing mortality. We conducted a prospective study in patients with flu-like symptoms referred to the imaging department of a tertiary hospital in Iran between March 3, 2020, and April 8, 2020. Patients with COVID-19 were followed up after two months to check their health condition. The categorical data between groups were analyzed by Fisher's exact test and continuous data by Wilcoxon rank-sum test. Three hundred and nineteen patients (mean age 45.48 ± 18.50 years, 177 women) were enrolled. Fever, dyspnea, weakness, shivering, C-reactive protein, fatigue, dry cough, anorexia, anosmia, ageusia, dizziness, sweating, and age were the most important symptoms of COVID-19 infection. Traveling in the past 3 months, asthma, taking corticosteroids, liver disease, rheumatological disease, cough with sputum, eczema, conjunctivitis, tobacco use, and chest pain did not show any relationship with COVID-19. To the best of our knowledge, a number of factors associated with mortality due to COVID-19 have been investigated for the first time in this study. Our results might be helpful in early prediction and risk reduction of mortality in patients infected with COVID-19. © 2020 Wiley Periodicals LL

Combining a convolutional neural network with autoencoders to predict the survival chance of COVID-19 patients.

COVID-19 has caused many deaths worldwide. The automation of the diagnosis of this virus is highly desired. Convolutional neural networks (CNNs) have shown outstanding classification performance on image datasets. To date, it appears that COVID computer-aided diagnosis systems based on CNNs and clinical information have not yet been analysed or explored. We propose a novel method, named the CNN-AE, to predict the survival chance of COVID-19 patients using a CNN trained with clinical information. Notably, the required resources to prepare CT images are expensive and limited compared to those required to collect clinical data, such as blood pressure, liver disease, etc. We evaluated our method using a publicly available clinical dataset that we collected. The dataset properties were carefully analysed to extract important features and compute the correlations of features. A data augmentation procedure based on autoencoders (AEs) was proposed to balance the dataset. The experimental results revealed that the average accuracy of the CNN-AE (96.05%) was higher than that of the CNN (92.49%). To demonstrate the generality of our augmentation method, we trained some existing mortality risk prediction methods on our dataset (with and without data augmentation) and compared their performances. We also evaluated our method using another dataset for further generality verification. To show that clinical data can be used for COVID-19 survival chance prediction, the CNN-AE was compared with multiple pre-trained deep models that were tuned based on CT images

Staphylococcus aureus versus neutrophil: Scrutiny of ancient combat

Staphylococcus aureus (S.aureus)is a Gram-positive bacterium that causes many infections and diseases. This pathogen can cause many types of infections such as impetigo, toxic shock syndrome toxin (TSST1), pneumonia, endocarditis, and autoimmune diseases like lupus erythematosus and can infect other healthy individuals. In the pathogenic process, colonization is a main risk factor for invasive diseases. Various factors including the cell wall-associated factors and receptors of the epithelial cells facilitate adhesion and colonization of this pathogen. S. aureus has many enzymes, toxins, and strategies to evade from the immune system either by an enzyme that lyses cellular component or by hiding from the immune system via surface antigens like protein A and second immunoglobulin-binding protein (Sbi). The strategies of this bacterium can be divided into five groups: A: Inhibit neutrophil recruitment B: Inhibit phagocytosis C: Inhibit killing by ROS, D: Neutrophil killing, and E: Resistance to antimicrobial peptide. On the other hand, innate immune system via neutrophils, the most important polymorphonuclear leukocytes, fights against bacterial cells by neutrophil extracellular trap (NET). In this review, we try to explain the role of each factor in immune evasion. © 2019 Elsevier Lt

Staphylococcus aureus versus neutrophil: Scrutiny of ancient combat

Staphylococcus aureus (S.aureus) is a Gram-positive bacterium that causes many infections and diseases. This pathogen can cause many types of infections such as impetigo, toxic shock syndrome toxin (TSST1), pneumonia, endocarditis, and autoimmune diseases like lupus erythematosus and can infect other healthy individuals. In the pathogenic process, colonization is a main risk factor for invasive diseases. Various factors including the cell wall-associated factors and receptors of the epithelial cells facilitate adhesion and colonization of this pathogen. S. aureus has many enzymes, toxins, and strategies to evade from the immune system either by an enzyme that lyres cellular component or by hiding from the immune system via surface antigens like protein A and second immunoglobulin-binding protein (Sbi). The strategies of this bacterium can be divided into five groups: A: Inhibit neutrophil recruitment B: Inhibit phagocytosis C: Inhibit killing by ROS, D: Neutrophil killing, and E: Resistance to antimicrobial peptide. On the other hand, innate immune system via neutrophils, the most important polymorphonuclear leukocytes, fights against bacterial cells by neutrophil extracellular trap (NET). In this review, we try to explain the role of each factor in immune evasion

Oxidative stress and Parkinson's disease: conflict of oxidant-antioxidant systems

Parkinson's disease (PD) is defined as a chronic neurodegenerative disorder which is diagnosed mostly by its clinical manifestations. Reactive oxygen species (ROS) are considered as key modulators in the development of PD. Despite the intensive investigations, antioxidant-dependent molecular mechanisms of initiation and development of PD are controversial. Free radicals cause serious damage and death of dopamine producing cells when antioxidant capacity of the cells is reduced against oxidative stress (OxS). Many intracellular reactions create ROS, including activation of NADPH oxidase (NOX), mitochondrial dysfunction, and hydrogen peroxide (H�O�) decomposition. On the contrary, natural antioxidants, vitamins, proteins, and antioxidant signaling pathways are major factors to neutralize ROS and its destructive effects. The functional role of nuclear factor E2-related factor 2, Heme oxygenase-1, and selenium against ROS-dependent initiation and progression of PD is elucidated. In this review, we collected multiple factors that play the main role in the initiation, development, and pathogenesis of PD and we discussed their function in the PD. © 2019 Elsevier B.V

The correlation between cardiac magnetic resonance T2* and left ventricular global longitudinal strain in people with β-thalassemia

Background: Heart failure is the biggest cause of mortality and morbidity in people with thalassemia, and iron deposition in cardiac tissue impairs cardiovascular function. Therefore, early detection of cardiac involvement is important to improve the prognosis in these individuals. Method: Two- and three-dimensional echocardiography was performed to evaluate left ventricular ejection fraction (LVEF), left ventricular volumes and diameters, and global longitudinal strain (GLS) in 130 individuals with β-thalassemia using the speckle tracking method. Magnetic resonance imaging (MRI) was carried out on both the heart and liver. The participants were divided into 2 groups based on cardiac T2* values (normal and abnormal cardiac iron load), and the correlation between cardiac T2* MRI and GLS was evaluated. Results: The statistical analysis showed a significant correlation between cardiac T2* MRI and left ventricular global longitudinal strain. There was a significant difference in global longitudinal strain (P <.0001), liver MRI T2*(P <.0001), and left ventricular ejection fraction (P <.001) between the 2 groups. The optimal cutoff value for GLS was �18.5 with sensitivity and specificity 73.0 and 63.0, respectively (postitive predictive value = 50, negative predictive value = 82.3, AUC = 0.742, std. error = 0.046) which predicts T2* value of <20 ms, according to cardiac MRI. Conclusions: The participants with cardiac iron overload had a lower GLS than those without one. This suggests that GLS may be a useful method to predict myocardial iron overload particularly in β-thalassemia patients with subclinical cardiac involvement. © 2018, Wiley Periodicals, Inc

The correlation between cardiac magnetic resonance T2* and left ventricular global longitudinal strain in people with β-thalassemia

Background: Heart failure is the biggest cause of mortality and morbidity in people with thalassemia, and iron deposition in cardiac tissue impairs cardiovascular function. Therefore, early detection of cardiac involvement is important to improve the prognosis in these individuals. Method: Two- and three-dimensional echocardiography was performed to evaluate left ventricular ejection fraction (LVEF), left ventricular volumes and diameters, and global longitudinal strain (GLS) in 130 individuals with β-thalassemia using the speckle tracking method. Magnetic resonance imaging (MRI) was carried out on both the heart and liver. The participants were divided into 2 groups based on cardiac T2* values (normal and abnormal cardiac iron load), and the correlation between cardiac T2* MRI and GLS was evaluated. Results: The statistical analysis showed a significant correlation between cardiac T2* MRI and left ventricular global longitudinal strain. There was a significant difference in global longitudinal strain (P <.0001), liver MRI T2*(P <.0001), and left ventricular ejection fraction (P <.001) between the 2 groups. The optimal cutoff value for GLS was �18.5 with sensitivity and specificity 73.0 and 63.0, respectively (postitive predictive value = 50, negative predictive value = 82.3, AUC = 0.742, std. error = 0.046) which predicts T2* value of <20 ms, according to cardiac MRI. Conclusions: The participants with cardiac iron overload had a lower GLS than those without one. This suggests that GLS may be a useful method to predict myocardial iron overload particularly in β-thalassemia patients with subclinical cardiac involvement. © 2018, Wiley Periodicals, Inc