ABSTRACT
The coronavirus spread that started in Wuhan, China and spread across the world, affecting the best of the healthcare systems from the Lombardy region of Italy to India, the US, and the UK, required accurate diagnosis. A rapid assessment to ascertain whether or not a patient has COVID-19 is required by frontline clinicians. In this paper, we propose to deduce the presence of COVID-19 using X-ray images of the lungs through feature extraction. A convolution network model is built for binary classification of images into corona positive and negative using the deep learning framework on Python, Keras. Various studies using different classifiers such as CART, XGB-L and XGB Tree were studied, which used machine learning for detection of COVID-19 and yielded a very accurate diagnosis. In this particular CNN model, Google Colab is used to execute the algorithm. The dataset is trained and the validation accuracy obtained is more than 96%. This is a very cost-effective way of using machine learning for the classification of infected and non-infected cases since working on Google Colab doesn't require enormous computational resources. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
BackgroundWith the rapid increase in COVID-19 cases and the discovery of new viral variants within India over multiple waves, the expensive reagents and time-consuming sample pretreatment required for qPCR analysis have led to slower detection of the disease. The vast Indian population demands an inexpensive and competent sample preparation strategy for rapid detection of the disease facilitating early and efficient containment of the disease. MethodsIn this study, we have surveyed the spread of COVID-19 infection over Faridabad, Haryana, India for 6 months. We also devised a simple single-step method for total RNA extraction using a single tube and compared its efficacy with the commercially available RNA isolation kits. FindingsOur findings suggest that determining Ct values for samples subjected to the One Pot RNA extraction method was as efficient as the commercially available kits but delivers a subtle advantage in a way, by minimizing the cost, labor and sample preparation time. ConclusionThis novel crude RNA extraction method is stable and capable of operating in developing countries like India for low resource settings, without the use of expensive reagents and instruments. Additionally, this method can be further adapted to pooling samples strategies owing to its high sensitivity.
ABSTRACT
The new Corona-virus, recently called the severe acute respiratory syndrome Coronavirus (SARS-CoV-2) appears for the first time in China and more precisely in Wuhan (December 2019). This infection can be deadly. Seniors, and individuals with other restorative conditions may be more helpless and ended up truly sick. Typically why inquire about into drugs to treat this contamination remains basic in a few investigate laboratories. Natural home grown cures have long been the most, in case not the as it were, cure within the verbal convention for treating sicknesses. Advanced pharmaceutical has known its victory much appreciated to conventional medication, the adequacy of which determines from therapeutic plants. The objective of this think about is to decide in case the components of common root have an anti-viral impact and which can anticipate people from disease by this coronavirus utilizing the foremost dependable strategy is atomic docking, which utilized to discover the interaction between considered atoms and the protein, in our case we based on the inhibitor of Coronavirus (nCoV-2019) primary protease. Chinese Pharmaceutical herbs might be a profitable pool for distinguishing active compounds for treating disease of 2019-nCoV. In this think about, we summarize several dynamic compounds, counting baicalin, Scutellarin, Hesperetin, Nicotianamine and glycyrrhizin that may have potential anti-2019-nCoV effects. We conduct atomic docking to anticipate their capacity for authoritative ACE2, which may anticipate the 2019-nCoV disease. We propose that these chosen compounds worth assist examination for avoiding 2019-nCoV. © 2020, SILAE (Italo-Latin American Society of Ethnomedicine). All rights reserved.