The mRNA-Based Coronavirus Disease-2019 Vaccine-induced Severe Cardiomyopathy: A Rare Incident.

We are presenting a case of severe dilated cardiomyopathy (DCM) post-mRNA-based coronavirus disease-2019 (COVID-19) vaccine (Pfizer-BioNTech) in a young healthy man. After the second dose of the vaccine, his health rapidly declined, and he developed severe DCM with reduced ejection fraction. Investigations for other causes of DCM were unremarkable. Severe DCM following COVID-19 vaccine injection is a rare but serious condition. It also highlights the importance of close follow-up of pericarditis and perimyocarditis cases post-COVID-19 vaccine and we recommend clinicians have a low threshold for using echocardiography for early diagnosis and management.

Facial Expression Recognition of Instructor Using Deep Features and Extreme Learning Machine.

Classroom communication involves teacher's behavior and student's responses. Extensive research has been done on the analysis of student's facial expressions, but the impact of instructor's facial expressions is yet an unexplored area of research. Facial expression recognition has the potential to predict the impact of teacher's emotions in a classroom environment. Intelligent assessment of instructor behavior during lecture delivery not only might improve the learning environment but also could save time and resources utilized in manual assessment strategies. To address the issue of manual assessment, we propose an instructor's facial expression recognition approach within a classroom using a feedforward learning model. First, the face is detected from the acquired lecture videos and key frames are selected, discarding all the redundant frames for effective high-level feature extraction. Then, deep features are extracted using multiple convolution neural networks along with parameter tuning which are then fed to a classifier. For fast learning and good generalization of the algorithm, a regularized extreme learning machine (RELM) classifier is employed which classifies five different expressions of the instructor within the classroom. Experiments are conducted on a newly created instructor's facial expression dataset in classroom environments plus three benchmark facial datasets, i.e., Cohn-Kanade, the Japanese Female Facial Expression (JAFFE) dataset, and the Facial Expression Recognition 2013 (FER2013) dataset. Furthermore, the proposed method is compared with state-of-the-art techniques, traditional classifiers, and convolutional neural models. Experimentation results indicate significant performance gain on parameters such as accuracy, F1-score, and recall.