Identification of Leading Compounds from Euphorbia neriifolia (Dudsor) Extracts as a Potential Inhibitor of SARS-CoV-2 ACE2-RBDS1 Receptor Complex: An Insight from Molecular Docking ADMET Profiling and MD-simulation Studies.

Coronavirus disease-19 (COVID-19) are deadly and infectious disease that impacts individuals in a variety of ways. Scientists have stepped up their attempts to find an antiviral drug that targets the spike protein (S) of Angiotensin converting enzyme 2 (ACE2) (receptor protein) as a viable therapeutic target for coronavirus. The most recent study examines the potential antagonistic effects of 17 phytochemicals present in the plant extraction of Euphorbia neriifolia on the anti-SARS-CoV-2 ACE2 protein. Computational techniques like molecular docking, absorption, distribution, metabolism, excretion, and toxicity (ADMET) investigations, and molecular dynamics (MD) simulation analysis were used to investigate the actions of these phytochemicals. The results of molecular docking studies showed that the control ligand (2-acetamido-2-deoxy-ß-D-glucopyranose) had a binding potential of -6.2 kcal/mol, but the binding potentials of delphin, ß-amyrin, and tulipanin are greater at -10.4, 10.0, and -9.6 kcal/mol. To verify their drug-likeness, the discovered hits were put via Lipinski filters and ADMET analysis. According to MD simulations of the complex run for 100 numbers, delphin binds to the SARS-CoV-2 ACE2 receptor's active region with good stability. In root-mean-square deviation (RMSD) and root mean square fluctuation (RMSF) calculations, delphinan, ß-amyrin, and tulipanin showed reduced variance with the receptor binding domain subunit 1(RBD S1) ACE2 protein complex. The solvent accessible surface area (SASA), radius of gyration (Rg), molecular surface area (MolSA), and polar surface area (PSA) validation results for these three compounds were likewise encouraging. The convenient binding energies across the 100 numbers binding period were discovered by using molecular mechanics of generalized born and surface (MM/GBSA) to estimate the ligand-binding free energies to the protein receptor. All things considered, the information points to a greater likelihood of chemicals found in Euphorbia neriifolia binding to the SARS-CoV-2 ACE2 active site. To determine these lead compounds' anti-SARS-CoV-2 potential, in vitro and in vivo studies should be conducted. How to cite this article: Islam MN, Pramanik MEA, Hossain MA, et al. Identification of Leading Compounds from Euphorbia Neriifolia (Dudsor) Extracts as a Potential Inhibitor of SARS-CoV-2 ACE2-RBDS1 Receptor Complex: An Insight from Molecular Docking ADMET Profiling and MD-simulation Studies. Euroasian J Hepato-Gastroenterol 2023;13(2):89-107.

Adverse effects of COVID-19 vaccination: machine learning and statistical approach to identify and classify incidences of morbidity and post-vaccination reactogenicity

Good vaccine safety and reliability are essential to prevent infectious disease spread. A small but significant number of apparent adverse reactions to the new COVID-19 vaccines have been reported. Here, we aim to identify possible common causes for such adverse reactions with a view to enabling strategies that reduce patient risk by using patient data to classify and characterise patients those at risk of such reactions. We examined patient medical histories and data documenting post-vaccination effects and outcomes. The data analyses were conducted by different statistical approaches followed by a set of machine learning classification algorithms. In most cases, similar features were significantly associated with poor patient reactions. These included patient prior illnesses, admission to hospitals and SARS-CoV-2 reinfection. The analyses indicated that patient age, gender, allergic history, taking other medications, type-2 diabetes, hypertension and heart disease are the most significant pre-existing factors associated with risk of poor outcome and long duration of hospital treatments, pyrexia, headache, dyspnoea, chills, fatigue, various kind of pain and dizziness are the most significant clinical predictors. The machine learning classifiers using medical history were also able to predict patients most likely to have complication-free vaccination with an accuracy score above 85%. Our study identifies profiles of individuals that may need extra monitoring and care (e.g., vaccination at a location with access to comprehensive clinical support) to reduce negative outcomes through classification approaches. Important classifiers achieving these reactions notably included allergic susceptibility and incidence of heart disease or type-2 diabetes.

Early Detection of Coronavirus Cases Using Chest X-ray Images Employing Machine Learning and Deep Learning Approaches

This study aims to propose a deep learning model to detect COVID-19 positive cases more precisely utilizing chest X-ray images. We have collected and merged all the publicly available chest X-ray datasets of COVID-19 infected patients from Kaggle and Github, and pre-processed it using random sampling approach. Then, we proposed and applied an enhanced convolutional neural network (CNN) model to this dataset and obtained a 94.03% accuracy, 95.52% AUC and 94.03% f-measure for detecting COVID-19 positive patients. We have also performed a comparative performance between our proposed CNN model with several state-of-the-art machine learning classifiers including support vector machine, random forest, k-nearest neighbor, logistic regression, gaussian naive bayes, bernoulli naive bayes, decision tree, Xgboost, multilayer perceptron, nearest centroid and perceptron as well as deep learning and pre-trained models such as deep neural network, residual neural network, visual geometry group network 16, and inception network V3 were employed, where our model yielded outperforming results compared to all other models. While evaluating the performance of our models, we have emphasized on specificity along with accuracy to identify non-COVID-19 individuals more accurately, which may potentially facilitate the early detection of COVID-19 patients for their preliminary screening, especially in under-resourced health infrastructure with insufficient PCR testing systems and testing facilities. Moreover, this model could also be applicable to the cases of other lung infections.

Real-time time-series modelling for prediction of COVID-19 spread and intervention assessment

Substantial amount of data about the COVID-19 pandemic is generated every day. Yet, data streaming, while considerably visualized, is not accompanied with advanced modelling techniques to provide real-time insights. This study introduces a unified platform which integrates visualization capabilities with advanced statistical methods for predicting the virus spread in the short run, using real-time data. The platform is backed up by advanced time series models to capture any possible non-linearity in the data which is enhanced by the capability of measuring the expected impact of preventive interventions such as social distancing and lockdowns. The platform enables lay users, and experts, to examine the data and develop several customized models with different restriction such as models developed for specific time window of the data. Our policy assessment of the case of Australia, shows that social distancing and travel ban restriction significantly affect the reduction of number of cases, as an effective policy.