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1.
Energies ; 14(21):7023, 2021.
Article in English | MDPI | ID: covidwho-1488523

ABSTRACT

Recent developments regarding the Internet of Things (IoT), Artificial Intelligence (AI), and Machine Learning (ML) opened new horizons of healthcare opportunities. Moreover, these technological advancements give strength to face upcoming healthcare challenges. One of such challenges is the advent of COVID-19, which has adverse effects beyond comprehension. Therefore, utilizing the basic functionalities of IoT, this work presents a real-time rule-based Fuzzy Logic classifier for COVID-19 Detection (FLCD). The proposed model deploys the IoT framework to collect real-time symptoms data from users to detect symptomatic and asymptomatic Covid-19 patients. Moreover, the proposed framework is also capable of monitoring the treatment response of infected people. FLCD constitutes three components: symptom data collection using wearable sensors, data fusion through Rule-Based Fuzzy Logic classifier, and cloud infrastructure to store data with a possible verdict (normal, mild, serious, or critical). After extracting the relevant features, experiments with a synthetic COVID-19 symptom dataset are conducted to ensure effective and accurate detection of COVID-19 cases. As a result, FLCD successfully acquired 95% accuracy, 94.73% precision, 93.35% recall, and showed a minimum error rate of 2.52%.

2.
J Biomol Struct Dyn ; 40(6): 2851-2864, 2022 04.
Article in English | MEDLINE | ID: covidwho-1026871

ABSTRACT

Ivermectin (IVM) is a broad-spectrum antiparasitic agent, having inhibitory potential against wide range of viral infections. It has also been found to hamper SARS-CoV-2 replication in vitro, and its precise mechanism of action against SARS-CoV-2 is yet to be understood. IVM is known to interact with host importin (IMP)α directly and averts interaction with IMPß1, leading to the prevention of nuclear localization signal (NLS) recognition. Therefore, the current study seeks to employ molecular docking, molecular mechanics generalized Born surface area (MM-GBSA) analysis and molecular dynamics simulation studies for decrypting the binding mode, key interacting residues as well as mechanistic insights on IVM interaction with 15 potential drug targets associated with COVID-19 as well as IMPα. Among all COVID-19 targets, the non-structural protein 9 (Nsp9) exhibited the strongest affinity to IVM showing -5.30 kcal/mol and -84.85 kcal/mol binding energies estimated by AutoDock Vina and MM-GBSA, respectively. However, moderate affinity was accounted for IMPα amounting -6.9 kcal/mol and -66.04 kcal/mol. Stability of the protein-ligand complexes of Nsp9-IVM and IMPα-IVM was ascertained by 100 ns trajectory of all-atom molecular dynamics simulation. Structural conformation of protein in complex with docked IVM exhibited stable root mean square deviation while root mean square fluctuations were also found to be consistent. In silico exploration of the potential targets and their interaction profile with IVM can assist experimental studies as well as designing of COVID-19 drugs. Communicated by Ramaswamy H. Sarma.


Subject(s)
COVID-19 Drug Treatment , Ivermectin , Antiviral Agents/chemistry , Humans , Ivermectin/pharmacology , Ivermectin/therapeutic use , Molecular Docking Simulation , SARS-CoV-2 , alpha Karyopherins
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