Computational Approaches for Novel Therapeutic and Diagnostic Designing to Mitigate SARS-CoV2 Infection: Revolutionary Strategies to Combat Pandemics

Computational Approaches for Novel Therapeutic and Diagnostic Designing to Mitigate SARS-CoV2 Infection: Revolutionary Strategies to Combat Pandemics compiles information about various computational bioinformatic approaches that can help combat viral infection. The book includes working knowledge of various molecular docking and molecular dynamic simulation approaches that have been exploited for drug repurposing and drug designing purpose. In addition, it sheds light on reverse vaccinomics and immunoinformatic approaches for vaccine designing against SARS-CoV2 infection. This book is an essential resource for researchers, bioinformaticians, computational biologists, computational chemists and pharmaceutical companies who are working on the development of effective and specific therapeutic interventions and point-of-care diagnostic devices using various computational approaches. © 2022 Elsevier Inc. All rights reserved.

Advancing deep learning for supply chain optimization of covid-19 vaccination in rural communities

Covid19 is a global pandemic that brought lots of disruptions in day-to-day life, affected economies, closed millions of businesses, and took a lot of precious lives. Along with social distancing and wearing masks, the effective way to eradicate the virus is to administer vaccines. To prevent the spread of disease and avoid deaths, it is essential to prioritize vaccine distribution. At the request of CDC, National Academies of Science, Engineering and Medicine published the Framework for fair distribution of COVID-19 Vaccine. This paper focuses on studying the rate of vaccination in urban and rural communities and identifying gaps in the Covid19 vaccine supply chain using data science. Demand forecasting using deep learning is proposed for planning vaccine allocation and distribution. Deep learning refers to multilayer neural networks that can learn extremely complex patterns using hidden layers between inputs and outputs. Long Short-Term Memory neural networks will be used to forecast vaccine demand. © 2021 IEEE.

Nano-enabled biosensing systems for intelligent healthcare: towards COVID-19 management.

Biosensors are emerging as efficient (sensitive and selective) and affordable analytical diagnostic tools for early-stage disease detection, as required for personalized health wellness management. Low-level detection of a targeted disease biomarker (pM level) has emerged extremely useful to evaluate the progression of disease under therapy. Such collected bioinformatics and its multi-aspects-oriented analytics is in demand to explore the effectiveness of a prescribed treatment, optimize therapy, and correlate biomarker level with disease pathogenesis. Owing to nanotechnology-enabled advancements in sensing unit fabrication, device integration, interfacing, packaging, and sensing performance at point-of-care (POC) has rendered diagnostics according to the requirements of disease management and patient disease profile i.e. in a personalized manner. Efforts are continuously being made to promote the state of art biosensing technology as a next-generation non-invasive disease diagnostics methodology. Keeping this in view, this progressive opinion article describes personalized health care management related analytical tools which can provide access to better health for everyone, with overreaching aim to manage healthy tomorrow timely. Considering accomplishments and predictions, such affordable intelligent diagnostics tools are urgently required to manage COVID-19 pandemic, a life-threatening respiratory infectious disease, where a rapid, selective and sensitive detection of human beta severe acute respiratory system coronavirus (SARS-COoV-2) protein is the key factor.