RESUMEN
Corona Virus Disease (COVID-19) caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is a pandemic that has claimed so far over half a million human life across the globe. Researchers all over the world are exploring various molecules including phytochemicals to get a potential anti-COVID-19 drug. Certain phytochemicals present in some spices are claimed to possess antiviral, anti-bacterial, and anti-fungal properties. Hence, an in-silico study was done by selecting eighteen well reported antiviral phytochemicals from some spices commonly used in Indian kitchen viz. Curcuma longa (Turmeric), Nigella sativa (Black cumin), Piper nigrum (Black pepper), Trachyspermum ammi (Carom) and Zingiber officinale (Ginger) to find out whether they can prevent SARS-CoV-2 infection. Firstly, we predicted the structure of TMPRSS2 (transmembrane protease serine 2), a host protein that truncates spike protein of SARS-CoV-2 thereby facilitating its endocytosis, and then docked against its catalytic domain the selected phytochemicals and camostat (a well-known synthetic inhibitor of TMPRSS2). Thereafter, stability of seven best docked phytochemicals and camostat were scrutinized by Molecular Dynamic Simulation (MDS). MDS analysis indicated bisdemethoxycurcumin (BDMC), carvacrol and thymol as better inhibitors than the camostat due to their stable binding with TMPRSS2 in its oxyanion hole and inducing subtle modification in the spatial arrangement of the catalytic triad residues. Among these three phytochemicals, carvacrol appeared to be the best inhibitor, followed by BDMC, whereas thymol was least effective.Communicated by Ramaswamy H. Sarma.
RESUMEN
Since the arrival of the novel Covid-19, several types of researches have been initiated for its accurate prediction across the world. The earlier lung disease pneumonia is closely related to Covid-19, as several patients died due to high chest congestion (pneumonic condition). It is challenging to differentiate Covid-19 and pneumonia lung diseases for medical experts. The chest X-ray imaging is the most reliable method for lung disease prediction. In this paper, we propose a novel framework for the lung disease predictions like pneumonia and Covid-19 from the chest X-ray images of patients. The framework consists of dataset acquisition, image quality enhancement, adaptive and accurate region of interest (ROI) estimation, features extraction, and disease anticipation. In dataset acquisition, we have used two publically available chest X-ray image datasets. As the image quality degraded while taking X-ray, we have applied the image quality enhancement using median filtering followed by histogram equalization. For accurate ROI extraction of chest regions, we have designed a modified region growing technique that consists of dynamic region selection based on pixel intensity values and morphological operations. For accurate detection of diseases, robust set of features plays a vital role. We have extracted visual, shape, texture, and intensity features from each ROI image followed by normalization. For normalization, we formulated a robust technique to enhance the detection and classification results. Soft computing methods such as artificial neural network (ANN), support vector machine (SVM), K-nearest neighbour (KNN), ensemble classifier, and deep learning classifier are used for classification. For accurate detection of lung disease, deep learning architecture has been proposed using recurrent neural network (RNN) with long short-term memory (LSTM). Experimental results show the robustness and efficiency of the proposed model in comparison to the existing state-of-the-art methods.