ABSTRACT
With the growth of smart medical devices and applications in smart hospitals, home care facilities, nursing, and the Internet of Medical Things (IoMT) are becoming more ubiquitous. It uses smart medical devices and cloud computing services, and basic Internet of Things (IoT) technology, to detect key body indicators, monitor health situations, and generate multivariate data to provide just-in-time healthcare services. In this article, we present a novel collaborative disease detection system based on IoMT amalgamated with captured image data. The system can be based on intelligent agents, where every agent explores the interaction between different medical data obtained by smart sensor devices using reinforcement learning as well as targets to detect diseases. The agents then collaborate to make a reliable conclusion about the detected diseases. Intensive experiments were conducted using medical data. The results show the importance of using intelligent agents for disease detection in healthcare decision-making. Moreover, collaboration increases the detection rate, with numerical results showing the superiority of the proposed framework compared with baseline solutions for disease detection. © 2001-2012 IEEE.
ABSTRACT
COVID-19 caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged first around December 2019 in the city of Wuhan, China. Since then, several variants of the virus have emerged with different biological properties. This pandemic has so far led to widespread infection cycles with millions of fatalities and infections globally. In the recent cycle, a new variant omicron and its three sub-variants BA.1, BA.2 and BA.3 have emerged which seems to evade host immune defences and have brisk infection rate. Particularly, BA.2 variant has shown high transmission rate over BA.1 strain in different countries including India. In the present study, we have evaluated a set of eighty drugs/compounds using in silico docking calculations in omicron and its variants. These molecules were reported previously against SARS-CoV-2. Our docking and simulation analyses suggest differences in affinity of these compounds in omicron and BA.2 compared to SARS-CoV-2. These studies show that neohesperidin, a natural flavonoid found in Citrus aurantium makes a stable interaction with spike receptor domain of omicron and BA.2 compared to other variants. Free energy binding analyses further validates that neohesperidin forms a stable complex with spike RBD in omicron and BA.2 with a binding energy of −237.9 ± 18.7 kJ/mol and −164.1 ± 17.5 kJ/mol respectively. Key residual differences in the RBD interface of these variants form the basis for differential interaction affinities with neohesperidin as drug binding site overlaps with RBD-human ACE2 interface. These data might be useful for the design and development of novel scaffolds and pharmacophores to develop specific therapeutic strategies against these novel variants. © 2022