Twitter-Based Sentiment Analysis and Topic Modeling of Social Media Posts using Natural Language Processing, to Understand People's Perspectives Regarding COVID-19 Omicron Subvariants XBB.1.5 and BF.7

Concerns about an increase in cases during the COVID-19 pandemic have been heightened by the emergence of a new Omicron subvariant XBB.1.5 that joined the previously reported BF.7 as a source of public health concern. COVID-19 cases have been on the rise intermittently throughout the ongoing pandemic, likely because of the continuous introduction of SARS-CoV-2 subtypes. The present study analyzed the Indian citizen's perceptions of the latest covid variants XBB.1.5 and BF.7 using the natural language processing technique, especially topic modeling and sentiment analysis. The tweets posted by Indian citizens regarding this issue were analyzed and used for this study. Government authorities, policymakers, and healthcare officials will be better able to implement the necessary policy effectively to tackle the XBB 1.5 and BF.7 crises if they are aware of the people's sentiments and concerns about the crisis. A total of 8,54,312 tweets have been used for this study. Our sentiment analysis study has revealed that out of those 8,54,312 tweets, the highest number of tweets (n = 3,19,512 tweets (37.3%)) about COVID variants XBB.1.5 and BF.7 had neutral sentiments, 3,16,951 tweets (37.1%) showed positive sentiments and 2,17,849 tweets (25.4%) had negative sentiments. Fear of the future and concerns about the immunity of the vaccines are of prime concerns to tackle the ongoing pandemic. Copyright © The Author(s) 2023.

Artificial intelligence: A key for detecting COVID-19 using chest radiography

An impending branch of computer science is artificial intelligence. It plays an important role in the construction of smart machines that are capable of performing sophisticated operations. One of the key characteristics of artificial intelligence is its ability to make decisions on its own and rationalize the solution, helping us to achieve a certain goal. Our human race has faced many threats in the form of epidemics and pandemics, which have proved to be almost incurable in the past. Nevertheless, science and its evolving technologies have given us some hope to fight such threats. One such pandemic that our human race is facing in the current times is COVID-19. This deadly disease is rapidly spreading across the whole world endangering the lives of humans. Amid the chaos, we desperately need to stop the spread, or at least take adequate counter-active measures to detect this virus at its early stage. Deep learning, a subset of artificial intelligence provides many models which helps in the automation of the task of detecting viruses in humans mainly with the help of image processing. In detecting COVID-19, deep learning is a breakthrough, which has helped us in our proposed system. This system makes use of chest radiographs (CXR) to detect the presence of the virus in the human body thereby lowering the risk of spread which is fairly high in manual detection methods. The CXRs are one of the most common imaging tests in the clinical field, which helps in detecting the presence of cold, cough, shortness of breath in the lungs, and so on. The proposed model is very efficient when it comes to detecting problems in the lungs with the help of image processing. We propose an improvised neural network derived from the Convolutional Neural Network which works similar to the human brain structure to detect and process the CXR images efficiently and at faster rates. The neural network mimics the functioning of the brain, where self-learning and decision making are its key features. The image data sets are a collection of CXR images which have a RGB value of 1. This approach is proven to be safer and better than the manual testing methods that are currently deployed. As the traditional methods for detecting COVID-19 virus is tedious, and not fairly accurate, automating this task can help in giving accurate results with reduced risk of spread of disease through physical contact. © 2022 Scrivener Publishing LLC.

Stage Wise Prediction of Covid-19 Pneumonia from CT images using VGG-16 and SVM

In COVID-19 time, finding medication was the tedious process. Proposed work explains about the segregation of covid-19 CT scan images into categories like mild, moderate and severe on the basis of pneumonia. The dataset uses 227 CT scan images which have been collected manually from hospitals. At first, the CT scan input images are preprocessed using K-means clustering algorithm. Then Watershed algorithm is used for the segmentation of the pre-processed images to get the affected region. After getting the affected region, VGG-16 model is used for feature extraction process, for model training 53 CT scan images are used as the testing dataset from 185 CT images. Using extracted feature, SVM model will classify the Covid19 pneumonia as mild, moderate, or severe. Finally the classifier has given an accuracy of 96.15% for the prediction of Covid-19 pneumonia stages. © 2022 IEEE.

The computational screening of inhibitor for black fungus and white fungus by D-glucofuranose derivatives using in silico and SAR study

Black fungus is the foremost life-threatening disease during the SARS-CoV-2 affected patients and spreading quickly in the region of the subcontinent of India although there was no prescribed proper medication. As the Dglucofuranose and its derivatives are reported to show strong antifungal activity, this study has been designed with them for their computational investigation. Firstly, the overallprediction of activity spectra for substances (PASS) value illustrates a goodprobability to be active(Pa) and probability to be inactive (Pi) value. Next, pharmacokinetics parameters including drug-likeness and Lipinski's rules, absorption, distribution, metabolism, excretion, and toxicity (ADMET) parameters, and overall quantum calculation of computational approaches by Density Functional Theory (DFT) have graduallybeen performed to analyze quantum calculations. After the analysis of docking score, it is found at -9.4 kcal/mol, -7.5 kcal/mol, -7.8 kcal/mol, -8.5 kcal/mol against the strain of black fungus protein strains Mycolicibacterium smegmatis, Mucor lusitanicus, Rhizomucor mieh, and white fungus protein Candida Auris, Aspergillus luchuensis and Candida albicans. Next, the molecular dynamics of docked complexes have been performed to check their stability in biological systems with water ranging 100 ns calculating the Root Mean Square Deviation (RMSD) and Root Mean Square Fluctuation (RMSF)where the minimum RMSD and RMSF value indicated the higher stable configuration of docked complexes.These compounds have perfectly matched all the pharmacokinetics criteria to be a good drug candidate against both black and white fungus, and they are non-carcinogenic, low solubility, low toxic for both aquatic and non-aquatic. In addition, the quantum calculation using DFT conveys the strongest support and information about their chemical stability and biological significance. Finally, it could be concluded that the carboxylic group and methyl group inthe benzene ring causes higher binding affinity against black and white fungus protein strain through the formation of hydrogen and hydrophobic bonds. © 2021 ACG Publications All rights reserved

COVID-19 impact on reproduction and fertility.

The COVID-19 pandemic is an unexpected worldwide situation, and all countries have implemented their own policies to curb the spread of the virus. The pathophysiology of COVID-19 has opened numerous hypotheses of functional alterations in different physiological aspects. The direct impact of SARS-CoV-2 on the urogenital organs of males and females is still to be assessed. Nevertheless, based on biological similarities between SARS-CoV and SARS-CoV-2, several hypotheses have been proposed. In this study, we will discuss the possible mechanism of action, and potential effects on the male/female reproductive system and fertility.

Incidence of COVID-19 disease in pediatric kidney transplant recipients: A report of the improving renal outcomes collaborative

Background: The impact of COVID-19 disease on previously healthy children has been minimal, yet there is limited data on the impact of COVID-19 on children and adolescents with kidney transplants. Methods: We used the existing infrastructure of the Improving Renal Outcomes Collaborative (IROC) learning health system to develop and rapidly implement a webbased registry for collecting clinical and outcomes data about COVID-19 disease in pediatric transplant recipients. We distributed the registry to 32 U.S. pediatric kidney transplant centers and requested clinical and outcomes data from all recipients suspected of having COVID-19 disease. Here, we present an interim analysis of the first 6 weeks of registry data. Results: Between April 6 and May 27, 2020, 18 IROC centers entered data on 99 pediatric kidney transplant recipients who had PCR based testing for COVID-19. 54 patients were tested due to symptoms of COVID-19 (most commonly fever and cough), 7 asymptomatic patients had a known COVID exposure. 34 patients were tested per hospital policy (e.g. pre-anesthesia), and 4 did not have a reported testing indication. Overall, 10/99 (10%) tested positive for COVID-19, 6 of whom had any symptoms, 3 had a known exposure with a COVID+ individual, and 1 was diagosed by a pre-anesthesia screen. Thus far, the clinical course and outcomes are known in 8/10 COVID-19+ patients: 5 received outpatient supportive care alone, 2 were admitted to intensive care and 1 was admitted to a non-intensive care inpatient unit. Transplant outcomes were excellent in all COVID-19+ patients. There were no cases with respiratory failure, acute kidney injury, or allograft rejection/failure. There were no deaths due to COVID-19 disease. Conclusions: In this interim analysis of the IROC learning health system, pediatric kidney transplant recipients had a relatively low incidence of COVID-19 disease and excellent short-term outcomes.