ABSTRACT
Purpose: Since the start of the COVID-19 pandemic, various manifestations have been reported, including ophthalmic symptoms, especially with the different mutations and variants that have occurred over the last few years. In view of this, our study was conducted to gauge the knowledge, attitude, and practices of patients toward the ophthalmic manifestations of COVID-19. Methods: This was a hospital-based, cross-sectional, observational study. Patients who had tested positive for COVID-19 were asked to answer a detailed questionnaire about their knowledge of COVID-19 ophthalmic symptoms, their experience with the symptoms, and their attitude and practice toward the same. The data collected was analyzed using Microsoft Excel, and the Chi-squared test was used to determine significant differences in the results among different demographic profiles. Results: Our study found that 82 (39%) of the 210 participants were aware that COVID-19 could present with symptoms in the eyes. A total of 47 participants had experienced eye symptoms of COVID-19. Among them, only 15 (31.91%) consulted and received treatment from an ophthalmologist or general physician for the same. Most of them (59.57%) did not seek any treatment, and 8.5% self-medicated or used non-allopathic forms of medicine. The most common symptom was redness of the eyes, reported by 57.44% of those who had eye symptoms. Conclusion: Most people were unaware of ocular manifestations of COVID-19 and most of those who were aware were medical professionals. Amongst those who developed symptoms, only a minority sought medical treatment.
Subject(s)
COVID-19 , COVID-19/epidemiology , Cross-Sectional Studies , Health Knowledge, Attitudes, Practice , Humans , Pandemics , Tertiary Care CentersABSTRACT
The 2019 novel coronavirus (COVID-19), with a starting point in Wuhan, China, has spread rapidly among people living in other countries and is approaching approximately 4.5 million cases worldwide according to the statistics of the European Centre for Disease Prevention and Control. There are a limited number of COVID-19 test kits available in hospitals due to the increasing number of cases daily. Therefore, it is necessary to implement an automatic detection system as a quick alternative diagnosis option to prevent COVID-19 from spreading among people. In this study, three different convolutional neural network-based models (ResNet50, InceptionV3, and InceptionResNetV2) have been proposed for the detection of Coronavirus Pneumonia infected patients using chest X-ray radiographs. ROC analyses and confusion matrices by these three models are given and analyzed using 5-fold cross-validation. Considering the performance results obtained, it is seen that the pre-trained ResNet50 model provides the highest classification performance with 98% accuracy among the other two proposed models (97% accuracy for InceptionV3 and 87% accuracy for Inception-ResNetV2). The result is based on the data available in the repository of GitHub, Kaggle, and Open-i as per their validated X-ray images.
ABSTRACT
Treatment of SARS-CoV-2 targeting its RNA dependent RNA polymerase (RdRp) is of current interest. Remdesivir has been approved for the treatment of COVID-19 around the world. However, the drug has been linked with pharmacological limitations like adverse effects and reduced efficiency. Nevertheless, recent advancements have depicted molnupiravir as an effective therapeutic agent to target the SARS-CoV-2 RdRp. The drug has cleared both in vitro and in vivo screening. It is in phase-III clinical trial. Nonetheless, there are no data on themolecular binding interaction of molnupiravir with RdRp. Therefore, it is of interest to report the binding interaction of molnupiravir using molecular docking. It is also of interest to show its stability during interaction using molecular dynamics and binding free energy calculations along with drug likeliness and pharmacokinetic properties in comparison with remdesivir.