Development and Evaluation of an Index to Measure the Ability to Get Vaccinated for COVID-19.

The COVID-19 pandemic has been an historic challenge to public health, and to behavior change programs. There have been challenges in promoting vaccination in LMICs, including Nigeria. One important hypothesis deserving consideration is the ability to obtain vaccination as a potential barrier to vaccination uptake. The MOA (motivation, opportunity, and ability) framework, as illustrated by multiple theories such as COM-B, EAST, and the Fogg model, is a primary theoretical basis for the evaluation of this ability as a factor in vaccination uptake. There is little research on measuring the ability to get vaccinated in LMICs, including on the role of all of the MOA framework. The aim of this study was to develop and evaluate an ability factors index measured through social media-based data collected in Nigeria in late 2021 and early 2022. We present findings from an online survey of 8574 Nigerians and highlight new social media-based data collection techniques in this research. This study found that a new ability factors index comprising 12 items was associated with vaccine uptake independent of measures capturing other components of the MOA framework. This index may serve as a valuable research instrument for future studies. We conclude that a person's perceived ability to get vaccinated, measured by a newly validated index, is related to vaccination uptake and hesitancy, and that more research should be conducted in this area.

Tweet Classification and Sentiment Analysis of Covid 19 Epidemic by Applying Hybrid Based Techniques

World wide spread of COVID-19 pandemic, is throttling the normal life nearly for two years and claiming millions of life all over the globe. Starting from Wuhan of China it crosses more than 200 countries, thereby imposing a overwhelming challenge to health care system. On the other hand, there has been unprecedented advancement of the social media, namely, Twitter, Facebook, WhatsApp and Instagram etc. in an exponential manner. The essence of this paper is to extract and elucidate the opinion or sentiments of the people all around the globe regarding Coronavirus pandemic based on Twitter data. The analysis are based on both lexicon-based approach followed by machine learning algorithms and aims to express the state-of-the-art of the sentiment analysis on the current Coronavirus epidemic prevailing in the entire world and the awareness of the people regarding the disease, its symptoms and impact followed by the preventive measures that need to be undertaken. © 2022 IEEE.

A Simple Approach for Scripting in Air and Display using Computer Vision

Online teaching-learning platforms have become an integral part of life, during and post Covid-19 pandemic. In this regard, many teaching-learning accessories have been developed for use. AirPad, the work presented in this article is an application that helps draw one's imagination on screen by just capturing the motion of object of interest with a camera in air. Computer vision is concerned with the extraction of meaningful information from image data. Continued explorations on computer vision are often concerned with the development of computer algorithms for specific applications. Computer vision is a field of artificial intelligence that works on enabling computers to see, identify and process images in the same way as human vision does, and then provide the appropriate output. Three important tasks involved during computer vision processing are: 1) Detection 2) Tracking 3) Recognition. Computer vision algorithms are utilized to perform the task. The preferred language used is Python due to its exhaustive libraries and easy to use syntax, but can be implemented in any OpenCV supported language. Present work uses PyQt5 which is a python interface for Qt library. It is one of the most used modules in building GUI apps in Python, and that's due to its simplicity. Following features have been implemented in the present work: i. Functions to draw square, rectangle and circle. ii. Testing Tab to ensure proper traction of object of interest in each frame. iii. Export the work in multiple formats like images and pdf. The user requires a laptop with webcam and a virtual pen which could be a finger to use the application and enjoy its features.

In silico docking analysis revealed the potential of phytochemicals present in Phyllanthus amarus and Andrographis paniculata, used in Ayurveda medicine in inhibiting SARS-CoV-2.

The Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has resulted in outbreak of global pandemic, fatal pneumonia in human referred as Coronavirus Disease-2019 (Covid-19). Ayurveda, the age old practice of treating human ailments in India, can be considered against SARS-CoV-2. Attempt was made to provide preliminary evidences for interaction of 35 phytochemicals from two plants (Phyllanthus amarus and Andrographis paniculata used in Ayurveda) with SARS-CoV-2 proteins (open & closed state S protein, 3CLpro, PLpro and RdRp) through in silico docking analysis. The nucleotide analogue remdesivir, being used in treatment of SARS-CoV-2, was used as a positive control. The results revealed that 18 phytochemicals from P. amarus and 14 phytochemicals from A. paniculata shown binding energy affinity/dock score < - 6.0 kcal/mol, which is considered as minimum threshold for any compound to be used for drug development. Phytochemicals used for docking studies in the current study from P. amarus and A. paniculata showed binding affinity up to - 9.10 kcal/mol and - 10.60 kcal/mol, respectively. There was no significant difference in the binding affinities of these compounds with closed and open state S protein. Further, flavonoids (astragalin, kaempferol, quercetin, quercetin-3-O-glucoside and quercetin) and tannins (corilagin, furosin and geraniin) present in P. amarus have shown more binding affinity (up to - 10.60 kcal/mol) than remdesivir (up to - 9.50 kcal/mol). The pharmacokinetic predictions suggest that compounds from the two plants species studied in the current study are found to be non-carcinogenic, water soluble and biologically safe. The phytochemicals present in the extracts of P. amarus and A. paniculata might have synergistic effect with action on multiple target sites of SARS-CoV-2. The information generated here might serve as preliminary evidence for anti SARS-CoV-2 activity of phytochemicals present from P. amarus and A. paniculata and the potential of Ayurveda medicine in combating the virus. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s13205-020-02578-7.

In Silico Docking Analysis Revealed the Potential of Phytochemicals Present in Phyllanthus Amarus and Andrographis Paniculata, Used in Ayurveda Medicine in Inhibiting SARS-CoV-2 (preprint)

No therapeutics and vaccines are available against SARS-CoV-2 at present. In the current study we have made an attempt to provide preliminary evidences for interaction of 35 phytochemicals from two plants ( Phyllanthus amarus and Andrographis paniculata used in Ayurveda ) with SARS-CoV-2 proteins (S protein, 3CLpro, PLpro and RdRp) through in silico docking analysis. The docking was performed with the aid of AutoDock Vina and ADME and other pharmacokinetic properties were predicted using SWISSADME and admetSAR