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.

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.

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.

In cervical cancer, the association between HPV infection and dysregulation of phosphoinositide 3-kinase (PI3K)/protein kinase B (AKT)/mammalian target of rapamycin (mTOR) pathway (PI3K/AKT/mTOR pathway) places mTOR as an attractive therapeutic target. The failure of current treatment modalities in advanced stages of this cancer and drawbacks of already available mTOR inhibitors demand for novel drug candidates. In the present study we identified the presence of a mTOR inhibitor in an active fraction of the ethyl acetate extract of Streptomyces sp OA293. The metabolites(s) in the active fraction completely inhibited mTORC1 and thereby suppressed activation of both of its downstream targets, 4E-BP1 and P70S6k, in cervical cancer cells. In addition, it also stalled Akt activation via inhibition of mTORC2. The mechanism of mTOR inhibition detailed in our study overcomes significant drawbacks of well known mTOR inhibitors such as rapamycin and rapalogs. The active fraction induced autophagy and Bax mediated apoptosis suggesting that mTOR inhibition resulted in programmed cell death of cancer cells. The molecular weight determination of the components in active fraction confirmed the absence of any previously known natural mTOR inhibitor. This is the first report of complete mTOR complex inhibition by a product derived from microbial source.

RNA modifications contribute to RNA and protein diversity in eukaryotes and lead to amino acid substitutions, deletions, and changes in gene expression levels. Several methods have developed to profile RNA modifications, however, a less laborious identification of inosine and pseudouridine modifications in the whole transcriptome is still not available. Herein, we address the first step of the above question by sequencing synthetic RNA constructs with inosine and pseudouridine modification using Oxford Nanopore Technology, which is a direct RNA sequencing platform for rapid detection of RNA modification in a relatively less labor-intensive manner. Our analysis of multiple nanopore parameters reveals mismatch error majorly distinguish unmodified versus modified nucleobase. Moreover, we have shown that acrylonitrile selective reactivity with inosine and pseudouridine generates a differential profile between the modified and treated construct. Our results offer a new methodology to harness selectively reactive chemical probe-based modification along with existing direct RNA sequencing methods to profile multiple RNA modifications on a single RNA.

COVID-19 is a viral disease that in the form of a pandemic has spread in the entire world, causing a severe impact on people’s well being. In fighting against this deadly disease, a pivotal step can prove to be an effective screening and diagnosing step to treat infected patients. This can be made possible through the use of chest X-ray images. Early detection using the chest X-ray images can prove to be a key solution in fighting COVID-19. Many computer-aided diagnostic (CAD) techniques have sprung up to aid radiologists and provide them a secondary suggestion for the same. In this study, we have proposed the notion of Pearson Correlation Coefficient (PCC) along with variance thresholding to optimally reduce the feature space of extracted features from the conventional deep learning architectures, ResNet152 and GoogLeNet. Further, these features are classified using machine learning (ML) predictive classifiers for multi-class classification among COVID-19, Pneumonia and Normal. The proposed model is validated and tested on publicly available COVID-19 and Pneumonia and Normal dataset containing an extensive set of 768 images of COVID-19 with 5216 training images of Pneumonia and Normal patients. Experimental results reveal that the proposed model outperforms other previous related works. While the achieved results are encouraging, further analysis on the COVID-19 images can prove to be more reliable for effective classification.

Rediscovery of known compounds and time consumed in identification, especially high molecular weight compounds with complex structure, have let down interest in drug discovery. In this study, whole-genome analysis of microbe and Global Natural Products Social (GNPS) molecular networking helped in initial understanding of possible compounds produced by the microbe. Genome data revealed 10 biosythethic gene clusters that encode for secondary metabolites with anticancer potential. NMR analysis of the pure compound revealed the presence of a four-ringed benz[a]anthracene, thus confirming angucycline; molecular networking further confirmed production of this class of compounds. The type II polyketide synthase gene identified in the microbial genome was matched with the urdamycin cluster by BLAST analysis. This information led to ease in identification of urdamycin E and a novel natural derivative, urdamycin V, purified from Streptomyces sp. OA293. Urdamycin E (Urd E) induced apoptosis and autophagy in cancer cell lines. Urd E exerted anticancer action through inactivation of the mTOR complex by preventing phosphorylation at Ser 2448 and Ser 2481 of mTORC1 and mTORC2, respectively. Significant reduction in phosphorylation of the major downstream regulators of both mTORC1 (p70s6k and 4e-bp1) and mTORC2 (Akt) were observed, thus further confirming complete inhibition of the mTOR pathway. Urd E presents itself as a novel mTOR inhibitor that employs a novel mechanism in mTOR pathway inhibition.