A study on the statistical distribution and regression analysis of novel coronavirus in India

The novel Coronavirus Disease (COVID-19) pandemic has affected all the parts of the world. India has seen two peaks in the first and second waves, and currently in second place globally after USA. The peak in the first wave was observed in the mid of September, 2020 and in the second wave, the peak is said to be crossed during early May, 2021. Although India is visualizing declining trend and crossed peak on 20th January 2022, many research studies and scientists are still recommending not to lower guard due to possibility of new variants. Proper modelling of COVID-19 distribution may help in determining the course of pandemic and planning the future requirements. The purpose of this study is to analyse the COVID-19 data in India using various probabilistic distributions and identify the best fit model. Further, regression analysis is also carried out to predict the relation among variables like daily cases, deaths, number of people vaccinated and reproduction rate. The effect of current vaccines against the new variants like Omicron in India are discussed at the end. © 2022 Nickan Research Institute. All rights reserved.

Pneumonia_Net Model: Deep Learning Based Intelligent System forPneumonia Classification using Chest X-ray Images

Radiologists are faced with a challenging problem whenever they have to classify the anomalies shown on chest x-rays. Because of this, throughout the course of the last few decades, computer aided diagnostic (CAD) systems have been created to extract meaningful information from X-rays in order to assist medical professionals in gaining a quantitative understanding of an X-ray.Because radiology is such an important field, most of the time the analysis of radiologist images is carried out by trained medical professionals. This is due to the fact that patients seek the highest possible level of treatment in addition to the highest possible quality, regardless of how much it costs.However, its complexity and the subjective nature of the visuals limit its usefulness. There is a great deal of diversity between different translators and a great deal of exhaustion in human professional image processing. Our main goal is to classify lung disorders utilizing diagnostic X-ray images analysed using deep learning and images exploited using Pandas, Keras, Open CV, Tensor Flow, etc. Chest radiographs are still diagnosed by doctors and radiologists using manual and visual methods. As a result, a system capable of diagnosing chest X-rays must be developed that is both smart and automated. The goal of this study is to classify chest X-ray images into normal and pathological using a deep neural network model called Pneumonia Net. It is trained and evaluated using chest X-rays taken from publicly available databases that include both normal and pathological radiographs. Due to their capacity to automatically extract high-level representations from large data sets, CNN-based deep learning categorization approaches outperform existing picture classification methods in this regard. Three different network models are compared depending on their performance. In experiments, it was found that the Pneumonia Net model had a good generalisation capacity in identifying unseen chest X-rays as normal or anomalous, and that its performance was better than that of other network models.

In-Silico Drug Designing of Spike Receptor with Its ACE2 Receptor and Nsp10/Nsp16 MTase Complex Against SARS-CoV-2.

The realm Riboviria constitutes Coronaviruses, which led to the emergence of the pandemic COVID 19 in the twenty-first century affected millions of lives. At present, the management of COVID 19 largely depends on antiviral therapeutics along with the anti-inflammatory drug. The vaccine is under the final clinical phase, and emergency use is available. We aim at ACE2 and Nsp10/Nsp16 MTase as potential drug candidate in COVID 19 management in the present work. For drug designing, various computational simulation strategies have been employed like Swiss-Model, Hawk Dock, HDOCK, py Dock, and PockDrug for homology modeling, binding energies of the molecule with a target, simulate the conformation and binding poses, statistics of protein lock with target key and drug ability, respectively. The current in-silico screening depicts that the spike protein receptor is complementary to the target when bound to each other and forms a stable complex. The MMGBSA free energy binding property of receptor and ligand is critical. The intermolecular Statistics with the target Nsp10/Nsp16 MTase complex are plausible. We have also observed a high-affinity pocket binding site with the target. Therefore, the favorable intermolecular interactions and Physico-chemical properties emanate as a drug candidate treating COVID-19. This study has approached computational tools to analyze the conformation, binding affinity, and drug ability of receptor-ligand. Thus, the spike receptor with its ACE2 receptor with Nsp10/Nsp16 MTase complex would be a potent drug against SARS CoV-2 and can cure the infection as per consensus scoring.

Distinctive therapeutic strategies against Corona Virus-19 (COVID-19): A pharmacological review

Coronaviruses (CoVs) are RNA viruses threatened the global health. The causative microorganism, causing coronavirus disease-2019 (COVID-19), is known as (novel coronavirus) or (severe acute respiratory syndrome coronavirus-2) (SARS-CoV-2). Furthermore, respiratory illnesses such as pneumonia and breathing failure are caused by COVID-19, first reported in Wuhan, China. So far, over 939,968COVID-19 death cases out of approximately 29,765,666 confirmed cases have been globally reported.However, global research institutions and companies have exerted tremendous effort to develop vaccines and drugs against SARS-CoV-2. Finally, our aim is to comprehensively review epidemiology, etiology, pathophysiology as well as the outlined the several strategies (preclinical/clinical) of COVID-19 treatment at the molecular level.