Targeted sequencing showed one variant c.3373G>A (p.Val1125Met) in the studied family following autosomal dominant inheritance. Computational programs predicted a high score of pathogenicity. Secondary structure of the region surrounding p.Val1125 was changed to a shortened beta-strand based on prediction of I-TASSER and Phyre2 servers with high confidence value for the mutation. cMyBP-C protein was modeled to 3dmkA. Our findings suggest that one single mutation of MYBPC3 may have different effects on the cellular mechanisms based of its zygosity. Various factors might be considered for explaining this phenomenon. This gene may have an important role in Iranian DCM and HCM patients.

Since the first emergence of the novel coronavirus in December 2019 in Wuhan, China, and the subsequent pandemic, the disease has become a major matter of concern for global health and affected millions of people all over the world. SARS coronavirus 2 (SARS-CoV-2), the virus responsible for COVID-19, targets the upper and lower respiratory tracts in most cases. Their involvement would cause pneumonia with symptoms such as dry cough, dyspnea, fever, sputum production, and rhinorrhea. 1 One of the significant complications related to this virus is the ability of the disease to affect other organs with a wide range of signs and symptoms. Gastrointestinal manifestations (e.g., nausea, vomiting, diarrhea, anorexia, etc.), neurological manifestations (e.g., anosmia, ageusia, headache, altered mental status, dizziness, etc.), and multiorgan dysfunction (e.g., metabolic or electrolyte imbalance, etc.) may occur in COVID-19 patients. 2 In this case presentation, we aim to focus on the cardiovascular complications of COVID-19. They can present in different forms and severities, from nonspecific palpitations and chest tightness to life-threatening events such as left ventricular dysfunction (heart failure), acute

In this study, the ability of radiomics features extracted from myocardial perfusion imaging with SPECT (MPI-SPECT) was investigated for the prediction of ejection fraction (EF) post-percutaneous coronary intervention (PCI) treatment. A total of 52 patients who had undergone pre-PCI MPI-SPECT were enrolled in this study. After normalization of the images, features were extracted from the left ventricle, initially automatically segmented by k-means and active contour methods, and finally edited and approved by an expert radiologist. More than 1700 2D and 3D radiomics features were extracted from each patient’s scan. A cross-combination of three feature selections and seven classifier methods was implemented. Three classes of no or dis-improvement (class 1), improved EF from 0 to 5% (class 2), and improved EF over 5% (class 3) were predicted by using tenfold cross-validation. Lastly, the models were evaluated based on accuracy, AUC, sensitivity, specificity, precision, and F-score. Neighborhood component analysis (NCA) selected the most predictive feature signatures, including Gabor, first-order, and NGTDM features. Among the classifiers, the best performance was achieved by the fine KNN classifier, which yielded mean accuracy, AUC, sensitivity, specificity, precision, and F-score of 0.84, 0.83, 0.75, 0.87, 0.78, and 0.76, respectively, in 100 iterations of classification, within the 52 patients with 10-fold cross-validation. The MPI-SPECT-based radiomic features are well suited for predicting post-revascularization EF and therefore provide a helpful approach for deciding on the most appropriate treatment.