According to the World Health Organization (WHO), the coronavirus (COVID-19) pandemic is putting even the best healthcare systems across the world under tremendous pressure. The early detection of this type of virus will help in relieving the pressure of the healthcare systems. Chest X-rays has been playing a crucial role in the diagnosis of diseases like Pneumonia. As COVID-19 is a type of influenza, it is possible to diagnose using this imaging technique. With rapid development in the area of Machine Learning (ML) and Deep learning, there had been intelligent systems to classify between Pneumonia and Normal patients. This paper proposes the machine learning-based classification of the extracted deep feature using ResNet152 with COVID-19 and Pneumonia patients on chest X-ray images. SMOTE is used for balancing the imbalanced data points of COVID-19 and Normal patients. This non-invasive and early prediction of novel coronavirus (COVID-19) by analyzing chest X-rays can further be used to predict the spread of the virus in asymptomatic patients. The model is achieving an accuracy of 0.973 on Random Forest and 0.977 using XGBoost predictive classifiers. The establishment of such an approach will be useful to predict the outbreak early, which in turn can aid to control it effectively.

Chrysomycin A isolated from Streptomyces sp. OA161 is bactericidal to Mycobacterium tuberculosis, methicillin resistant Staphylococcus aureus and vancomycin resistant Enterococcus faecalis.

Artificial Intelligence (AI) is being widely recognized these days for natural product research. In this article, we highlight the importance of AI and its application in various stages of natural product identification and characterization.

Nanopore direct RNA sequencing (dRNA-Seq) reads reveal RNA modifications through consistent error profiles specific to a modified nucleobase. However, a null data set is required to identify actual RNA modification-associated errors for distinguishing it from confounding highly intrinsic sequencing errors. Here, we reveal that inosine creates a signature mismatch error in dRNA-Seq reads and obviates the need for a null data set by harnessing the selective reactivity of acrylonitrile for validating the presence of actual inosine modifications. Selective reactivity of acrylonitrile toward inosine altered multiple dRNA-Seq parameters like signal intensity and trace value. We also deduced the stoichiometry of inosine modification through deviation in signal intensity and trace value using this chemical biology approach. Furthermore, we devised Nano ICE-Seq, a protocol to overcome the low coverage issue associated with direct RNA sequencing. Taken together, our chemical probe-based approach may facilitate the knockout-free detection of disease-associated RNA modifications in clinical scenarios.