Post-traumatic growth and perceived social support in young adolescents during the COVID-19 pandemic

Objectives: Pandemics would certainly have a negative impact on mental health. Positive modifications as well as negative alterations have been documented in earlier viral pandemic according to previous investigations. Teenagers face a variety of challenges during adolescence. Adolescents may become more concerned if this time coincides with other worries. This study aims to investigate the positive changes that occur in a young adolescent's life after COVID-19 pandemic, and to see how they relate to perceived social support. Methods: This cross-sectional study was performed on adolescents who were randomly selected from high schools in Kerman, Iran 2020 during the COVID-19. the sample size was 108 and for sampling wes used multi-stage random sampling at the end the data was analyzed by Pearson correlation test. Demographic information, the Multidimensional Scale of Perceived Social Support (MSPSS) and Posttraumatic Growth Inventory short form (PTGI-SF) were used to collect data. The data were subjected to descriptive and analytical statistical tests (Pearson correlation) using SPSS software version 24. Results: Statistically a positive correlation was found between the PTG total score and young homeschooled adolescents, parents working remotely, income loss and COVID-19 experience. Moreover, during the COVID-19 pandemic, a positive association was found between perceived social support and PTG total scores in young adolescents. There were also substantial positive connections between the MSPSS subscales and the PTGI overall score. Conclusion: Based on the findings, an overall growth in all areas of PTG was observed during the COVID-19 among young adolescents. Perceived social support scores have a positive and significant relationship with COVID-19 effects. In the crises we face throughout life, intimate family members and friends play a significant supporting role in adapting to these situations. © 2023 The Author(s).

Classification of COVID-19 and Nodule in CT Images using Deep Convolutional Neural Network

Distinguishing between coronavirus disease 2019 (COVID-19) and nodule as an early indicator of lung cancer in Computed Tomography (CT) images has been a challenge that radiologists have faced sinceCOVID-19 was announced as a pandemic. The similarity between these two infections is the main reason that brings dilemmas for them and may lead to a misdiagnosis. As a result, manual classification is not as efficient as automated classification. This paper proposes an automated approach to classify COVID-19 infections from nodules in CT images. Convolutional Neural Networks (CNNs) have significantly meliorated automated image classification tasks, particularly for medical images. Accordingly, we propose a refined CNN-based architecture through modifications in the network layers to reduce complexity. Furthermore, to vanquish the lack of training data, data augmentation approaches are utilized. In our method, Multi Layer Perceptron (MLP) is obligated to categorize the feature vectors extracted from denoised input images by convolutional layers into two main classes of COVID-19 infections and nodules. To the best of our knowledge, other state-of-the-art methods can only classify one of the two classes listed above. Compared to the mentioned counterparts, our proposed method has a promising performance with an accuracy of 97.80%. © 2022 IEEE.

Molecular dynamic simulation with protein and detection of repurposable drugs for COVID-19

In December 2019, the outbreak of acute respiratory illness caused by a novel coronavirus (2019-nCoV) keeps spreading at a rapid pace around the world. Lack of an effective vaccine, repurposing inhibitors, or de novo drug design might provide a long-term plan to combat this and potential infections due to specific virus conditions. The emergence of highly contagious COVID-19 and its high mortality rate among human populations has recently been declared a deadly pandemic that has provoked economic chaos and severe health problems. SARS-CoV-2 is an essential virus within its proteome, with several druggable components. The disease is a worldwide health issue that is instigated by severe acute coronavirus-2 syndrome (SARS-CoV-2) in the respiratory system. It is therefore of interest to research the binding features of 1615 drugs with FDA approval on the newly discovered main protease structure of 2019 novel coronavirus having strong sequence homology to that of SARS-CoV. © 2022 Elsevier Inc. All rights reserved.