Federated learning based Covid-19 detection.

The world is affected by COVID-19, an infectious disease caused by the SARS-CoV-2 virus. Tests are necessary for everyone as the number of COVID-19 affected individual's increases. So, the authors developed a basic sequential CNN model based on deep and federated learning that focuses on user data security while simultaneously enhancing test accuracy. The proposed model helps users detect COVID-19 in a few seconds by uploading a single chest X-ray image. A deep learning-aided architecture that can handle client and server sides efficiently has been proposed in this work. The front-end part has been developed using StreamLit, and the back-end uses a Flower framework. The proposed model has achieved a global accuracy of 99.59% after being trained for three federated communication rounds. The detailed analysis of this paper provides the robustness of this work. In addition, the Internet of Medical Things (IoMT) will improve the ease of access to the aforementioned health services. IoMT tools and services are rapidly changing healthcare operations for the better. Hopefully, it will continue to do so in this difficult time of the COVID-19 pandemic and will help to push the envelope of this work to a different extent.

Applications of machine learning for COVID-19 misinformation: a systematic review.

The inflammable growth of misinformation on social media and other platforms during pandemic situations like COVID-19 can cause significant damage to the physical and mental stability of the people. To detect such misinformation, researchers have been applying various machine learning (ML) and deep learning (DL) techniques. The objective of this study is to systematically review, assess, and synthesize state-of-the-art research articles that have used different ML and DL techniques to detect COVID-19 misinformation. A structured literature search was conducted in the relevant bibliographic databases to ensure that the survey was solely centered on reproducible and high-quality research. We reviewed 43 papers that fulfilled our inclusion criteria out of 260 articles found from our keyword search. We have surveyed a complete pipeline of COVID-19 misinformation detection. In particular, we have identified various COVID-19 misinformation datasets and reviewed different data processing, feature extraction, and classification techniques to detect COVID-19 misinformation. In the end, the challenges and limitations in detecting COVID-19 misinformation using ML techniques and the future research directions are discussed.

Determination of potential inhibitors based on isatin derivatives against SARS-CoV-2 main protease (mpro): a molecular docking, molecular dynamics and structure-activity relationship studies.

SARS-COV-2, the novel coronavirus and root of global pandemic COVID-19 caused a severe health threat throughout the world. Lack of specific treatments raised an effort to find potential inhibitors for the viral proteins. The recently invented crystal structure of SARS-CoV-2 main protease (Mpro) and its key role in viral replication; non-resemblance to any human protease makes it a perfect target for inhibitor research. This article reports a computer-aided drug design (CADD) approach for the screening of 118 compounds with 16 distinct heterocyclic moieties in comparison with 5 natural products and 7 repurposed drugs. Molecular docking analysis against Mpro protein were performed finding isatin linked with a oxidiazoles (A2 and A4) derivatives to have the best docking scores of -11.22 kcal/mol and -11.15 kcal/mol respectively. Structure-activity relationship studies showed a good comparison with a known active Mpro inhibitor and repurposed drug ebselen with an IC50 value of -0.67 µM. Molecular Dynamics (MD) simulations for 50 ns were performed for A2 and A4 supporting the stability of the two compounds within the binding pocket, largely at the S1, S2 and S4 domains with high binding energy suggesting their suitability as potential inhibitors of Mpro for SARS-CoV-2.