Diagnosis of Autism Disorder Based on Deep Network Trained by Augmented EEG Signals.

Autism spectrum disorder is a neurodevelopmental disorder typically characterized by abnormalities in social interaction and stereotyped and repetitive behaviors. Diagnosis of autism is mainly based on behavioral tests and interviews. In recent years, studies involving the diagnosis of autism based on analysis of EEG signals have increased. In this paper, recorded signals from people suffering from autism and healthy individuals are divided to without overlap windows considered as images and these images are classified using a two-dimensional Deep Convolution Neural Network (2D-DCNN). Deep learning models require a lot of data to extract the appropriate features and automate data classification. But, in most neurological studies, preparing a large number of measurements is difficult (a few 1000s as compared to million natural images), due to the cost, time, and difficulty of recording these signals. Therefore, to make the appropriate number of data, in our proposed method, some of the data augmentation methods are used. These data augmentation methods are mainly introduced for image databases and should be generalized for EEG-as-an-image database. In this paper, one of the nonlinear image mixing methods is used that mixes the rows of two images. According to the fact that any row in our image is one channel of EEG signal, this method is named channel combination. The result is that in the best case, i.e., augmentation according to channel combination, the average accuracy of 88.29% is achieved in the classification of short signals of healthy people and ASD ones and 100% for ASD and epilepsy ones, using 2D-DCNN. After the decision on joined windows related to each subject, we could achieve 100% accuracy in detecting ASD subjects, according to long EEG signals.

COVID-19 pandemic and death anxiety among intensive care nurses working at the Hospitals Affiliated to Tehran University of Medical Science.

INTRODUCTION: The unpredictable challenges and conditions of COVID-19 can cause mental health problems. In such a situation, one of the most important psychological problems is the fear and anxiety of death. Death anxiety can affect the quality of patient care services and the job satisfaction and mental health of nurses. METHODS: This is a descriptive cross-sectional study in which 110 nurses working in the intensive care units of hospitals affiliated to the Tehran University of Medical Sciences were selected by the convenience sampling method from April to September 2016. The data collection tools used in the study include a demographic questionnaire and a Templer death-anxiety questionnaire. FINDINGS: The results showed that the level of death anxiety in nurses working at COVID-19 intensive care units is associated with age, working hours per week, childbearing, several patients needing end-of-life care, cases of direct participation in resuscitation operations, cases of patient death observations, and satisfaction with personal protective equipment (P < o.o5). CONCLUSION: Increasing the nurses' awareness of the critical situations of COVID-19, management measures, improving the working environment, social support, and increasing personal protective equipment seem to be the effective factors in protecting the intensive care unit nurses against COVID-19 and reducing death anxiety.

Postoperative COVID-19 Pneumonia in an Asymptomatic Patient: A Case Report.

BACKGROUND: The recent outbreak of the coronavirus disease (COVID-19) in China has rapidly spread throughout the world and there are many reports of symptoms ranging from malaise to acute respiratory distress syndrome (ARDS) caused by this infection. However, few reports have been discussed surgical outcomes in COVID-19 patients. CASE PRESENTATION: In this report, we described a case of an elderly female developed with postoperative pulmonary complications after uneventful elective minor surgery. The patient was asymptomatic before the operation with no history of cough or fever. After surgery, the patient developed respiratory distress and chest radiological imaging revealed bilateral ground-glass opacities. It seems any type of surgeries requiring local anesthesia or general anesthesia may contribute to worsening outcomes in patients with covid19.