Bio: Devvret Verma is an academic researcher from Graphic Era University. The author has contributed to research in topics: Computer science & Artificial intelligence. The author has an hindex of 3, co-authored 15 publications receiving 32 citations.
01 Nov 2021
TL;DR: In this paper, the authors highlight the microbial pollution of water with special reference to coliform and its nexus with the environment and highlight the major problems in the twenty-first century is providing everybody with safe drinking or domestic water.
Abstract: Water is essential for the life, but many people lack the accessibility to clean and healthy drinking water and die as a consequence of water-borne infections. Microorganism-mediated water pollution is considered as one of the great concerns to the aquatic environment across the globe. The effluent of fecal matter, hospitals, industry, and cattle farms increase the bacterial load in a water body. Coliform groups of bacteria have long been typically applied as an indicator organism of microbial contamination of the water and historically led to the public health security perception. Among the coliform, Escherichia coli is the indicator of fecal contamination. The multiple tube fermentation technique has been applied as a conventional way to detect coliform in water samples through the fermentation of lactose sugar with production of acid and gas. The potability of water has been measured by the absence or presence of coliform bacteria within the permissible limit referencing the most probable number index value (MPN/100 ml). As fecal pollution indicators, fecal streptococci and Clostridium perfringens are widely used as an alternative to coliform bacteria and have been confirmed via esculin hydrolyzing or catalase-negativity and sulfite reduction tests. Molecular (PCR-based) and enzymatic methods have been applied as a rapid way to detect indicators and other enteric isolates in water samples. Apart from that standard plate count (SPC) of heterotrophic bacteria and biochemical oxygen demand (BOD) techniques also determine the bacterial and organic pollution load in a water sample. Therefore, bacteriological analysis of water indicated that water is polluted by sewage to the extent that it is unsuitable for drinking and also unsuitable for recreation purposes. This is one of the big problems in the twenty-first century is providing everybody with safe drinking or domestic water. The main objective of this article is to highlight the microbial pollution of water with special reference to coliform and its nexus with the environment.
TL;DR: A framework to efficiently classify and discriminate between the PTB, Bacterial pneumonia and Viral Pneumonia from the collection of chest X-ray images is proposed and achieves remarkable high accuracy of 99.01%.
Abstract: The diagnosis of pulmonary diseases through a chest X-ray is a tough task and needs expertise. Many of the pulmonary diseases mimic each other, and the diagnoses becomes challenging. Discriminating pulmonary tuberculosis (PTB) from other pulmonary disease like pneumonia, lung cancer etc. is a major concern in the diagnosis of tuberculosis. Several cases have been recorded that were misdiagnosed and have faced severe complications. Therefore, in this paper, we have proposed a framework to efficiently classify and discriminate between the PTB, Bacterial pneumonia and Viral Pneumonia from the collection of chest X-ray images. The analysis has been performed by using neural network classifier. For the pre-processing of the data, various data augmentation methods were used that improves the validation and classification accuracy of the proposed model. The proposed framework was able to efficiently classify and discriminate different pulmonary infections and achieves remarkable high accuracy of 99.01%.
01 Jan 2021
TL;DR: In this paper, the molecular docking and dynamic of three pertinent medicinal plants i.e. Eurycoma harmandiana, Sophora flavescens and Andrographis paniculata phytocompounds against SARS-COV-2 papain-like protease (PLpro) and main protease(Mpro)/3-chymotrypsin-like proase (3CLpro) was performed through AutoDock-Vina visualized using PyMOL and BIOVIA-Discovery Studio 2020.
Abstract: The rapid spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) - coronavirus disease 2019 (COVID-19) has raised a severe global public health issue and creates a pandemic situation. The present work aims to study the molecular -docking and dynamic of three pertinent medicinal plants i.e. Eurycoma harmandiana, Sophora flavescens and Andrographis paniculata phyto-compounds against SARS-COV-2 papain-like protease (PLpro) and main protease (Mpro)/3-chymotrypsin-like protease (3CLpro). The interaction of protein targets and ligands was performed through AutoDock-Vina visualized using PyMOL and BIOVIA-Discovery Studio 2020. Molecular docking with canthin-6-one 9-O-beta-glucopyranoside showed highest binding affinity and less binding energy with both PLpro and Mpro/3CLpro proteases and was subjected to molecular dynamic (MD) simulations for a period of 100ns. Stability of the protein-ligand complexes was evaluated by different analyses. The binding free energy calculated using MM-PBSA and the results showed that the molecule must have stable interactions with the protein binding site. ADMET analysis of the compounds suggested that it is having drug-like properties like high gastrointestinal (GI) absorption, no blood-brain barrier permeability and high lipophilicity. The outcome revealed that canthin-6-one 9-O-beta-glucopyranoside can be used as a potential natural drug against COVID-19 protease.
TL;DR: The severe acute respiratory syndrome coronavirus-2 (SARS CoV-2) is β-coronavirus that is responsible for the pandemic influenza virus disease 2019 (COVID-19) all over the world.
Abstract: The severe acute respiratory syndrome coronavirus-2 (SARS CoV-2) is β-coronavirus that is responsible for the pandemic coronavirus disease 2019 (COVID-19) all over the world. The rapid spread of th...
TL;DR: In this article, the authors showed that understanding the community composition, diversity, and gene regulation of AMF in the rice ecosystem played a critical role in maximizing As uptake and their potential in sustainable rice and other crops production.
Abstract: Arsenic (As) is a potentially toxic metalloid classified as a group 1 carcinogen, released in the soil environment because of natural as well as different anthropogenic activities. The presence of excess As content in soil and irrigation water enhances the As accumulation in rice grains. Millions of people who consume these contaminated grains are exposed to severe health issues. Increased arsenic uptake causes oxidative stress in plants, which combats by inducing the expression of several genes and signaling the biosynthesis of various antioxidants and phytochelatins. As toxicity reduces crop productivity, so it's critical to improve plant growth in As-contaminated environments while minimizing metal translocation to grains. Arbuscular mycorrhiza fungi (AMF) is considered a sustainable way to tolerate As toxicity. Organic pollutants metabolism by AMF, degradation of these soil contaminants by AMF exudation enzymes, and elimination of the pollutants by plant uptake and accumulation are the principal mechanisms of AMF mediated bioremediation. However, plant responses are established to vary with the host plant and the species of AMF. In our review, we showed that understanding the community composition, diversity, and gene regulation of AMF in the rice ecosystem played a critical role in maximizing As uptake and their potential in sustainable rice and other crops production. It has been reviewed that AMF has the potential to survive in an extremely As toxic condition and it potentially aids to improve the tolerance level of host plants.
TL;DR: In this paper, the authors developed the GI-Transit-Absorption Model (GITA Model) to analyze and predict the drug absorption kinetics by taking into account both the two factors, i.e., GI transit and drug absorbability including its site difference.
Abstract: The gastrointestinal (GI) absorption of orally administered drugs is determined by not only the permeability of GI mucosa but also the transit rate in the GI tract. It is well known that the gastric emptying rate is an important factor affecting the plasma concentration profile of orally administered drugs, and the intestinal transit rate also has a significant influence on the drug absorption, since it determines the residence time of the drug in the absorption site. The reason why the residence time is also a critical factor for drug absorption is that there is the site difference in absorbability for some drugs. We have developed the GI-Transit-Absorption Model (GITA Model) to analyze and predict the drug absorption kinetics by taking into account both the two factors, ie. GI transit and drug absorbability including its site difference. GITA Model has been already evidenced to be very useful for estimating the absorption kinetics of drugs with various characteristics and applied to assess the human data in combination with the gamma scintigraphy. In this review, the importance of GI transit rate in determining the absorption kinetics and the bioavailability of orally administered drugs is discussed mainly employing GITA Model and the results obtained by the model.
01 Feb 2021
TL;DR: InstaCovNet-19’s ability to detect COVID-19 without any human intervention at an economical cost with high accuracy can benefit humankind greatly in this age of Quarantine.
Abstract: Recently, the whole world became infected by the newly discovered coronavirus (COVID-19). SARS-CoV-2, or widely known as COVID-19, has proved to be a hazardous virus severely affecting the health of people. It causes respiratory illness, especially in people who already suffer from other diseases. Limited availability of test kits as well as symptoms similar to other diseases such as pneumonia has made this disease deadly, claiming the lives of millions of people. Artificial intelligence models are found to be very successful in the diagnosis of various diseases in the biomedical field In this paper, an integrated stacked deep convolution network InstaCovNet-19 is proposed. The proposed model makes use of various pre-trained models such as ResNet101, Xception, InceptionV3, MobileNet, and NASNet to compensate for a relatively small amount of training data. The proposed model detects COVID-19 and pneumonia by identifying the abnormalities caused by such diseases in Chest X-ray images of the person infected. The proposed model achieves an accuracy of 99.08% on 3 class (COVID-19, Pneumonia, Normal) classification while achieving an accuracy of 99.53% on 2 class (COVID, NON-COVID) classification. The proposed model achieves an average recall, F1 score, and precision of 99%, 99%, and 99%, respectively on ternary classification, while achieving a 100% precision and a recall of 99% on the binary class., while achieving a 100% precision and a recall of 99% on the COVID class. InstaCovNet-19's ability to detect COVID-19 without any human intervention at an economical cost with high accuracy can benefit humankind greatly in this age of Quarantine.
TL;DR: It is suggested that farmers and consumers face variable implications with forecasted precipitation scenarios and calls for research on management practices to facilitate climate adaptation for sustainable crop production.
Abstract: Climate change is impacting agro-ecosystems, crops, and farmer livelihoods in communities worldwide. While it is well understood that more frequent and intense climate events in many areas are resulting in a decline in crop yields, the impact on crop quality is less acknowledged, yet it is critical for food systems that benefit both farmers and consumers through high-quality products. This study examines tea (Camellia sinensis; Theaceae), the world's most widely consumed beverage after water, as a study system to measure effects of seasonal precipitation variability on crop functional quality and associated farmer knowledge, preferences, and livelihoods. Sampling was conducted in a major tea producing area of China during an extreme drought through the onset of the East Asian Monsoon in order to capture effects of extreme climate events that are likely to become more frequent with climate change. Compared to the spring drought, tea growth during the monsoon period was up to 50% higher. Concurrently, concentrations of catechin and methylxanthine secondary metabolites, major compounds that determine tea functional quality, were up to 50% lower during the monsoon while total phenolic concentrations and antioxidant activity increased. The inverse relationship between tea growth and concentrations of individual secondary metabolites suggests a dilution effect of precipitation on tea quality. The decrease in concentrations of tea secondary metabolites was accompanied by reduced farmer preference on the basis of sensory characteristics as well as a decline of up to 50% in household income from tea sales. Farmer surveys indicate a high degree of agreement regarding climate patterns and the effects of precipitation on tea yields and quality. Extrapolating findings from this seasonal study to long-term climate scenario projections suggests that farmers and consumers face variable implications with forecasted precipitation scenarios and calls for research on management practices to facilitate climate adaptation for sustainable crop production.
TL;DR: In this paper, a modified MobileNet was proposed to solve the gradient vanishing problem and improve the classification performance through dynamically combining features in different layers of a CNN, which achieved 99.6% test accuracy on the five-category CXR image dataset and 99.3% test performance on the CT image dataset.
Abstract: Understanding and classifying Chest X-Ray (CXR) and computerised tomography (CT) images are of great significance for COVID-19 diagnosis. The existing research on the classification for COVID-19 cases faces the challenges of data imbalance, insufficient generalisability, the lack of comparative study, etc. To address these problems, this paper proposes a type of modified MobileNet to classify COVID-19 CXR images and a modified ResNet architecture for CT image classification. In particular, a modification method of convolutional neural networks (CNN) is designed to solve the gradient vanishing problem and improve the classification performance through dynamically combining features in different layers of a CNN. The modified MobileNet is applied to the classification of COVID-19, Tuberculosis, viral pneumonia (with the exception of COVID-19), bacterial pneumonia and normal controls using CXR images. Also, the proposed modified ResNet is used for the classification of COVID-19, non-COVID-19 infections and normal controls using CT images. The results show that the proposed methods achieve 99.6% test accuracy on the five-category CXR image dataset and 99.3% test accuracy on the CT image dataset. Six advanced CNN architectures and two specific COVID-19 detection models, i.e., COVID-Net and COVIDNet-CT are used in comparative studies. Two benchmark datasets and a CXR image dataset which combines eight different CXR image sources are employed to evaluate the performance of the above models. The results show that the proposed methods outperform the comparative models in classification accuracy, sensitivity, and precision, which demonstrate their potential in computer-aided diagnosis for healthcare applications.