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Institution

Manipal University Jaipur

About: Manipal University Jaipur is a based out in . It is known for research contribution in the topics: Cloud computing & Population. The organization has 1266 authors who have published 2217 publications receiving 11814 citations.


Papers
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Journal ArticleDOI
TL;DR: It is believed that silver nanoparticles can be engineered so as to increase their efficacy, stability, specificity, biosafety and biocompatibility, and ascertaining the susceptibility of cytoxicity, genotoxicity, and inflammatory response to human cells upon AgNPs exposure.
Abstract: Multidrug resistance of the pathogenic microorganisms to the antimicrobial drugs has become a major impediment toward successful diagnosis and management of infectious diseases. Recent advancements in nanotechnology-based medicines have opened new horizons for combating multidrug resistance in microorganisms. In particular, the use of silver nanoparticles (AgNPs) as a potent antibacterial agent has received much attention. The most critical physico-chemical parameters that affect the antimicrobial potential of AgNPs include size, shape, surface charge, concentration and colloidal state. AgNPs exhibits their antimicrobial potential through multifaceted mechanisms. AgNPs adhesion to microbial cells, penetration inside the cells, ROS and free radical generation, and modulation of microbial signal transduction pathways have been recognized as the most prominent modes of antimicrobial action. On the other side, AgNPs exposure to human cells induces cytotoxicity, genotoxicity and inflammatory response in human cells in a cell-type dependent manner. This has raised concerns regarding use of AgNPs in therapeutics and drug delivery. We have summarized the emerging endeavors that address current challenges in relation to safe use of AgNPs in therapeutics and drug delivery platforms. Based on research done so far, we believe that AgNPs can be engineered so as to increase their efficacy, stability, specificity, biosafety and biocompatibility. In this regard, three perspectives research directions have been suggested that include 1) synthesizing AgNPs with controlled physico-chemical properties, 2) examining microbial development of resistance towards AgNPs, and 3) ascertaining the susceptibility of cytoxicity, genotoxicity, and inflammatory response to human cells upon AgNPs exposure.

1,112 citations

Journal ArticleDOI
TL;DR: A convolutional neural networks (CNN) is used to classify the COVID-19-infected patients as infected (+ve) or not (−ve) and extensive analysis shows that the proposed model can classify the chest CT images at a good accuracy rate.
Abstract: Early classification of 2019 novel coronavirus disease (COVID-19) is essential for disease cure and control. Compared with reverse-transcription polymerase chain reaction (RT-PCR), chest computed tomography (CT) imaging may be a significantly more trustworthy, useful, and rapid technique to classify and evaluate COVID-19, specifically in the epidemic region. Almost all hospitals have CT imaging machines; therefore, the chest CT images can be utilized for early classification of COVID-19 patients. However, the chest CT-based COVID-19 classification involves a radiology expert and considerable time, which is valuable when COVID-19 infection is growing at rapid rate. Therefore, an automated analysis of chest CT images is desirable to save the medical professionals' precious time. In this paper, a convolutional neural networks (CNN) is used to classify the COVID-19-infected patients as infected (+ve) or not (-ve). Additionally, the initial parameters of CNN are tuned using multi-objective differential evolution (MODE). Extensive experiments are performed by considering the proposed and the competitive machine learning techniques on the chest CT images. Extensive analysis shows that the proposed model can classify the chest CT images at a good accuracy rate.

473 citations

Journal ArticleDOI
TL;DR: A DenseNet201 based deep transfer learning (DTL) is proposed to classify the patients as COVID infected or not i.e. COVID-19 or COVID (-).
Abstract: Deep learning models are widely used in the automatic analysis of radiological images. These techniques can train the weights of networks on large datasets as well as fine tuning the weights of pre...

390 citations

Proceedings ArticleDOI
13 Apr 2016
TL;DR: This study attempts to review the diversity of the field, starting with the history of nanotechnology, the properties of the nanoparticle, various strategies of synthesis, the many advantages and disadvantages of different methods and its application.
Abstract: The nanotechnology and biomedical sciences opens the possibility for a wide variety of biological research topics and medical uses at the molecular and cellular level. The biosynthesis of nanoparticles has been proposed as a cost-effective and environmentally friendly alternative to chemical and physical methods. Plant-mediated synthesis of nanoparticles is a green chemistry approach that connects nanotechnology with plants. Novel methods of ideally synthesizing NPs are thus thought that are formed at ambient temperatures, neutral pH, low costs and environmentally friendly fashion. Keeping these goals in view nanomaterials have been synthesized using various routes. Among the biological alternatives, plants and plant extracts seem to be the best option. Plants are nature’s “chemical factories”. They are cost efficient and require low maintenance. The advantages and disadvantages of nanotechnology can be easily enumerated. This study attempts to review the diversity of the field, starting with the history of nanotechnology, the properties of the nanoparticle, various strategies of synthesis, the many advantages and disadvantages of different methods and its application.

295 citations

Journal ArticleDOI
03 Jul 2020-Irbm
TL;DR: An automated deep transfer learning-based approach for detection of COVID-19 infection in chest X-rays is developed by using the extreme version of the Inception (Xception) model, which performs significantly better as compared to the existing models.
Abstract: The most widely used novel coronavirus (COVID-19) detection technique is a real-time polymerase chain reaction (RT-PCR) However, RT-PCR kits are costly and take 6-9 hours to confirm infection in the patient Due to less sensitivity of RT-PCR, it provides high false-negative results To resolve this problem, radiological imaging techniques such as chest X-rays and computed tomography (CT) are used to detect and diagnose COVID-19 In this paper, chest X-rays is preferred over CT scan The reason behind this is that X-rays machines are available in most of the hospitals X-rays machines are cheaper than the CT scan machine Besides this, X-rays has low ionizing radiations than CT scan COVID-19 reveals some radiological signatures that can be easily detected through chest X-rays For this, radiologists are required to analyze these signatures However, it is a time-consuming and error-prone task Hence, there is a need to automate the analysis of chest X-rays The automatic analysis of chest X-rays can be done through deep learning-based approaches, which may accelerate the analysis time These approaches can train the weights of networks on large datasets as well as fine-tuning the weights of pre-trained networks on small datasets However, these approaches applied to chest X-rays are very limited Hence, the main objective of this paper is to develop an automated deep transfer learning-based approach for detection of COVID-19 infection in chest X-rays by using the extreme version of the Inception (Xception) model Extensive comparative analyses show that the proposed model performs significantly better as compared to the existing models

215 citations


Authors

Showing all 1314 results

NameH-indexPapersCitations
Ashok Kumar1515654164086
Sanjay Jain10388146880
Ashish Sharma7590920460
Mauro Conti6050713741
Ajay Kumar5380912181
Abhishek Sharma524269715
Dharmendra Tripathi371884298
Luis Gómez-Chova341445257
Vijay Kumar331473811
Ankur Srivastava312424127
Jordi Muñoz-Marí301224905
Pooja Singh282493173
Dilbag Singh27771723
Tanmoy Chakraborty263192782
Manjit Kaur23571403
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
20235
2022150
2021646
2020478
2019344
2018231