Highly Sensitive Electrochemical Immunosensor Platforms for Dual Detection of SARS-CoV-2 Antigen and Antibody based on Gold Nanoparticle Functionalized Graphene Oxide Nanocomposites.

In this work, we report a facile synthesis of graphene oxide-gold (GO-Au) nanocomposites by electrodeposition. The fabricated electrochemical immunosensors are utilized for the dual detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antigen and SARS-CoV-2 antibody. The GO-Au nanocomposites has been characterized by UV-vis spectroscopy, X-ray diffraction (XRD), transmission electron microscopy (TEM), cyclic voltammetry (CV), differential pulse voltammetry (DPV), and electrochemical impedance spectroscopy (EIS) for its biosensing properties. The linear detection range of the SARS-CoV-2 antigen immunosensor is 10.0 ag mL-1 to 50.0 ng mL-1, whereas that for the antibody immunosensor ranges from 1.0 fg mL-1 to 1.0 ng mL-1. The calculated limit of detection (LOD) of the SARS-CoV-2 antigen immunosensor is 3.99 ag mL-1, and that for SARS-CoV-2 antibody immunosensor is 1.0 fg mL-1 with high sensitivity. The validation of the immunosensor has also been carried out on patient serum and patient swab samples from COVID-19 patients. The results suggest successful utilization of the immunosensors with a very low detection limit enabling its use in clinical samples. Further work is needed for the standardization of the results and translation in screen-printed electrodes for use in portable commercial applications.

Recurrent Neural Network and Reinforcement Learning Model for COVID-19 Prediction.

Detection and prediction of the novel Coronavirus present new challenges for the medical research community due to its widespread across the globe. Methods driven by Artificial Intelligence can help predict specific parameters, hazards, and outcomes of such a pandemic. Recently, deep learning-based approaches have proven a novel opportunity to determine various difficulties in prediction. In this work, two learning algorithms, namely deep learning and reinforcement learning, were developed to forecast COVID-19. This article constructs a model using Recurrent Neural Networks (RNN), particularly the Modified Long Short-Term Memory (MLSTM) model, to forecast the count of newly affected individuals, losses, and cures in the following few days. This study also suggests deep learning reinforcement to optimize COVID-19's predictive outcome based on symptoms. Real-world data was utilized to analyze the success of the suggested system. The findings show that the established approach promises prognosticating outcomes concerning the current COVID-19 pandemic and outperformed the Long Short-Term Memory (LSTM) model and the Machine Learning model, Logistic Regresion (LR) in terms of error rate.