Sex-based Differences in the Phenotypic Expression and Prognosis of Idiopathic Non-ischemic Cardiomyopathy: A Cardiovascular Magnetic Resonance Study.

AIMS: We sought to characterize sex-related differences in CMR-based cardiovascular phenotypes and prognosis in patients with idiopathic non-ischemic cardiomyopathy (NICM). METHODS AND RESULTS: Patients with NICM enrolled in the Cardiovascular Imaging Registry of Calgary (CIROC) between 2015 and 2021 were identified. Z-score values for chamber volumes and function were calculated as standard deviation from mean values of 157 sex-matched healthy volunteers, ensuring reported differences were independent of known sex-dependencies. Patients were followed for the composite outcome of all-cause mortality, heart failure admission, or ventricular arrhythmia.A total of 747 patients were studied, 531 (71%) males. By Z-score values, females showed significantly higher left ventricular (LV) ejection fraction (EF; median difference 1 SD) and right ventricular (RV) EF (difference 0.6 SD) with greater LV mass (difference 2.1 SD; p-value<0.01 for all) versus males despite similar chamber volumes. Females had a significantly lower prevalence of mid-wall striae (MWS) fibrosis (23% versus 36%; p-value<0.001). Over a median follow-up of 4.7 years, 173 patients (23%) developed the composite outcome, with equal distribution in males and females. LV EF and MWS were significant independent predictors of the outcome (respective HR [95% CI] 0.97 [0.95-0.99] and 1.6 [1.2-2.3]; p-value=0.003 and 0.005). There was no association of sex with the outcome. CONCLUSIONS: In a large contemporary cohort, NICM was uniquely expressed in females versus males. Despite similar chamber dilation, females demonstrated greater concentric remodelling, lower reductions in bi-ventricular function, and a lower burden of replacement fibrosis. Overall, their prognosis remained similar to male patients with NICM.

FCM-DNN: diagnosing coronary artery disease by deep accuracy fuzzy C-means clustering model.

Cardiovascular disease is one of the most challenging diseases in middle-aged and older people, which causes high mortality. Coronary artery disease (CAD) is known as a common cardiovascular disease. A standard clinical tool for diagnosing CAD is angiography. The main challenges are dangerous side effects and high angiography costs. Today, the development of artificial intelligence-based methods is a valuable achievement for diagnosing disease. Hence, in this paper, artificial intelligence methods such as neural network (NN), deep neural network (DNN), and fuzzy C-means clustering combined with deep neural network (FCM-DNN) are developed for diagnosing CAD on a cardiac magnetic resonance imaging (CMRI) dataset. The original dataset is used in two different approaches. First, the labeled dataset is applied to the NN and DNN to create the NN and DNN models. Second, the labels are removed, and the unlabeled dataset is clustered via the FCM method, and then, the clustered dataset is fed to the DNN to create the FCM-DNN model. By utilizing the second clustering and modeling, the training process is improved, and consequently, the accuracy is increased. As a result, the proposed FCM-DNN model achieves the best performance with a 99.91% accuracy specifying 10 clusters, i.e., 5 clusters for healthy subjects and 5 clusters for sick subjects, through the 10-fold cross-validation technique compared to the NN and DNN models reaching the accuracies of 92.18% and 99.63%, respectively. To the best of our knowledge, no study has been conducted for CAD diagnosis on the CMRI dataset using artificial intelligence methods. The results confirm that the proposed FCM-DNN model can be helpful for scientific and research centers.

CNN-KCL: Automatic myocarditis diagnosis using convolutional neural network combined with k-means clustering.

Myocarditis is the form of an inflammation of the middle layer of the heart wall which is caused by a viral infection and can affect the heart muscle and its electrical system. It has remained one of the most challenging diagnoses in cardiology. Myocardial is the prime cause of unexpected death in approximately 20% of adults less than 40 years of age. Cardiac MRI (CMR) has been considered a noninvasive and golden standard diagnostic tool for suspected myocarditis and plays an indispensable role in diagnosing various cardiac diseases. However, the performance of CMR depends heavily on the clinical presentation and features such as chest pain, arrhythmia, and heart failure. Besides, other imaging factors like artifacts, technical errors, pulse sequence, acquisition parameters, contrast agent dose, and more importantly qualitatively visual interpretation can affect the result of the diagnosis. This paper introduces a new deep learning-based model called Convolutional Neural Network-Clustering (CNN-KCL) to diagnose Myocarditis. In this study, we used 47 subjects with a total number of 98,898 images to diagnose myocarditis disease. Our results demonstrate that the proposed method achieves an accuracy of 97.41% based on 10 fold-cross validation technique with 4 clusters for diagnosis of Myocarditis. To the best of our knowledge, this research is the first to use deep learning algorithms for the diagnosis of myocarditis.

Fusion of convolution neural network, support vector machine and Sobel filter for accurate detection of COVID-19 patients using X-ray images.

The coronavirus (COVID-19) is currently the most common contagious disease which is prevalent all over the world. The main challenge of this disease is the primary diagnosis to prevent secondary infections and its spread from one person to another. Therefore, it is essential to use an automatic diagnosis system along with clinical procedures for the rapid diagnosis of COVID-19 to prevent its spread. Artificial intelligence techniques using computed tomography (CT) images of the lungs and chest radiography have the potential to obtain high diagnostic performance for Covid-19 diagnosis. In this study, a fusion of convolutional neural network (CNN), support vector machine (SVM), and Sobel filter is proposed to detect COVID-19 using X-ray images. A new X-ray image dataset was collected and subjected to high pass filter using a Sobel filter to obtain the edges of the images. Then these images are fed to CNN deep learning model followed by SVM classifier with ten-fold cross validation strategy. This method is designed so that it can learn with not many data. Our results show that the proposed CNN-SVM with Sobel filter (CNN-SVM + Sobel) achieved the highest classification accuracy, sensitivity and specificity of 99.02%, 100% and 95.23%, respectively in automated detection of COVID-19. It showed that using Sobel filter can improve the performance of CNN. Unlike most of the other researches, this method does not use a pre-trained network. We have also validated our developed model using six public databases and obtained the highest performance. Hence, our developed model is ready for clinical application.

Impact of high dietary energy on obesity and oxidative stress in domestic pigeons.

Obesity is associated with increased risk of oxidative stress in humans and laboratory animals but information regarding obesity-induced oxidative stress in birds is lacking. Therefore, this study aimed to investigate the influence of high-energy diets (HED) on obesity and oxidative stress in domestic pigeons. Forty-five adult clinically healthy-domestic male pigeons were randomly assigned to three equal dietary groups including low (2,850 kcal/kg), medium (3,150 kcal/kg) and high (3,450 kcal/kg) energy diets (named low energy diet, medium-energy diet and HED, respectively). All birds received formulated diets for 60 consecutive days. Several parameters such as feed intake, body weight (BW), average weight gain (AWG) and total weight gain were determined. Serum concentrations of triglyceride (TG), total cholesterol (TC), high-, low- and very-low-density lipoprotein cholesterols, alanine aminotransferase (ALT), aspartate aminotransferase (AST) and alkaline phosphatase (ALP) were evaluated at days 0, 30 and 60; and serum levels of total antioxidant capacity (T-AOC), malondialdehyde (MDA) and cortisol were also measured at day 60. On day 60, five pigeons from each group were randomly euthanized and some parameters such as weight and relative weight of liver, breast muscle, and abdominal fat were determined. Furthermore, hepatic total fat content was also evaluated. BW, AWG, total weight, and circulating TG, TC, ALT, AST, ALP, MDA and cortisol in HED were significantly higher than other groups. Serum T-AOC in HED was significantly lower than the other groups. In conclusion, this study showed that increasing dietary energy up to 3,450 kcal/kg in pigeons led to obesity and oxidative stress in them. Accordingly, it could be stated that HED and obesity induce oxidative stress in pigeon and controlling the dietary energy intake of pigeons is necessary to prevent oxidative stress in them.

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.

Diagnostic Value of Diffusion Weighted Magnetic Resonance Imaging in Evaluation of Metastatic Axillary Lymph Nodes in a Sample of Iranian Women with Breast Cancer

Objective: To evaluate the diagnostic value of diffusion weighted magnetic resonance imaging (DW-MRI) in assessment of metastases in axillary lymph nodes (ALNs) in a sample of Iranian women with breast cancer. Methods: A total of 50 axillary lymph nodes from 30 female patients with histologically verified breast cancer were assessed by 1.5 T MRI. DWI was implemented at b-values of 50, 400 and 800 s/mm2. Short axis diameter, presence of fatty hilum and apparent diffusion coefficient (ADC) values (min, max and mean) of metastatic and non-metastatic ALNs was compared. Cutoff ADC values to discriminate between benign and malignant axillary lymph nodes were analyzed with receiver coefficient characteristic (ROC) curves. Result: The final histopathological examination revealed 46% (n=23) metastatic and 54% (n=27) non-metastatic ALNs. There was no statistically significant difference in short axis diameter between the two groups (p = 0.537). However there was significantly correlation between loss of fatty hilum and presence of metastases (p < 0.001) and ADC values (0.255 ± 0.19×10-3 mm2/s vs 0.616 ±0.3×10-3 mm2/s (ADC min), 1.088 ± 0.22×10-3 mm2/s vs 1.497 ± 0.24×10-3 mm2/s (ADC max) and 0.824 ± 0.103 ×10-3 mm2/s vs 1.098 ± 0.23 ×10-3 mm2/s (ADC mean)) of metastatic ALNs were significantly lower than those of non-metastatic ALNs (p < 0.001). The optimal mean ADC cut-off value for differentiation between metastatic and non-metastatic ALNs was 0.904×10-3 mm2/s which had a higher specificity (88.9%) and accuracy (91.8%) as compared with ADC min and ADC max. Conclusion: DWI-MRI and ADC values are promising imaging methods which can assess metastatic ALNs in breast cancer with high sensitivity, specificity and accuracy.