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271 results on '"Raghavendra U"'

1. Interfacial molecular interactions of cellobiohydrolase Cel7A and its variants on cellulose

Author

Akshata R. Mudinoor, Peter M. Goodwin, Raghavendra U. Rao, Nardrapee Karuna, Alex Hitomi, Jennifer Nill, and Tina Jeoh

Subjects
Trichoderma reesei Cel7A, Super-resolution, Single-molecule imaging, Catalytic domain, Binding lifetime, Dissociation rate, Fuel, TP315-360, Biotechnology, TP248.13-248.65
Abstract

Abstract Background Molecular-scale mechanisms of the enzymatic breakdown of cellulosic biomass into fermentable sugars are still poorly understood, with a need for independent measurements of enzyme kinetic parameters. We measured binding times of cellobiohydrolase Trichoderma reesei Cel7A (Cel7A) on celluloses using wild-type Cel7A (WTintact), the catalytically deficient mutant Cel7A E212Q (E212Qintact) and their proteolytically isolated catalytic domains (CD) (WTcore and E212Qcore, respectively). The binding time distributions were obtained from time-resolved, super-resolution images of fluorescently labeled enzymes on cellulose obtained with total internal reflection fluorescence microscopy. Results Binding of WTintact and E212Qintact on the recalcitrant algal cellulose (AC) showed two bound populations: ~ 85% bound with shorter residence times of

Published
2020
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2. Role of Four-Chamber Heart Ultrasound Images in Automatic Assessment of Fetal Heart: A Systematic Understanding

Author

Anjan Gudigar, Raghavendra U., Jyothi Samanth, Akhila Vasudeva, Ashwal A. J., Krishnananda Nayak, Ru-San Tan, Edward J. Ciaccio, Chui Ping Ooi, Prabal Datta Barua, Filippo Molinari, and U. Rajendra Acharya

Subjects
computer-based diagnostic tool, congenital heart disease, deep learning approaches, ultrasound imagery, Information technology, T58.5-58.64
Abstract

The fetal echocardiogram is useful for monitoring and diagnosing cardiovascular diseases in the fetus in utero. Importantly, it can be used for assessing prenatal congenital heart disease, for which timely intervention can improve the unborn child’s outcomes. In this regard, artificial intelligence (AI) can be used for the automatic analysis of fetal heart ultrasound images. This study reviews nondeep and deep learning approaches for assessing the fetal heart using standard four-chamber ultrasound images. The state-of-the-art techniques in the field are described and discussed. The compendium demonstrates the capability of automatic assessment of the fetal heart using AI technology. This work can serve as a resource for research in the field.

Published
2022
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8. Interfacial molecular interactions of cellobiohydrolase Cel7A and its variants on cellulose

Author

Mudinoor, Akshata R, Goodwin, Peter M, Rao, Raghavendra U, Karuna, Nardrapee, Hitomi, Alex, Nill, Jennifer, and Jeoh, Tina

Subjects
Biochemistry and Cell Biology, Biological Sciences, Bioengineering, Trichoderma reesei Cel7A, Super-resolution, Single-molecule imaging, Catalytic domain, Binding lifetime, Dissociation rate, Heterogeneous interfacial enzyme kinetics, Chemical Engineering, Industrial Biotechnology, Biochemistry and cell biology, Industrial biotechnology
Abstract

BackgroundMolecular-scale mechanisms of the enzymatic breakdown of cellulosic biomass into fermentable sugars are still poorly understood, with a need for independent measurements of enzyme kinetic parameters. We measured binding times of cellobiohydrolase Trichoderma reesei Cel7A (Cel7A) on celluloses using wild-type Cel7A (WTintact), the catalytically deficient mutant Cel7A E212Q (E212Qintact) and their proteolytically isolated catalytic domains (CD) (WTcore and E212Qcore, respectively). The binding time distributions were obtained from time-resolved, super-resolution images of fluorescently labeled enzymes on cellulose obtained with total internal reflection fluorescence microscopy.ResultsBinding of WTintact and E212Qintact on the recalcitrant algal cellulose (AC) showed two bound populations: ~ 85% bound with shorter residence times of

Published
2020

35. A comparative evaluation of six sigma metrics and quality goal index ratio 3 months prior to first lockdown due to COVID-19 pandemic and 3 months during lockdown in a NABL accredited central laboratory

Author

Shirin Sulthana P.K., Raghavendra U., Shaheena Yassir, Ganesh Prasad V., and Mohammed Ansar

Subjects
General Medicine, General Biochemistry, Genetics and Molecular Biology
Abstract

Introduction and Aim: Sigma represents Standard Deviation (SD) which indicates the degree of variation in a process, where the higher sigma value implies that less likely the laboratory reports false test results. Using a newer parameter called Quality Goal Index (QGI) we can find the reason behind the lower sigma value. Our study aimed to compare the six-sigma metric and QGI ratio 3 months prior to first lockdown due to COVID-19 pandemic and 3 months during the first lockdown. Methodology: A retrospective study was used to compare the six-sigma metric and QGI ratio 3 months prior to first lockdown due to COVID-19 pandemic and 3 months during the first lockdown for the selected ten analytes from 1st of January 2020 to 30th of June 2020 from the clinical biochemistry section of Yenepoya Medical College Hospital, Deralakatte, Mangalore. Results: The sigma metrics from January to March (level 1) indicated that urea, TSH, beta-HCG fell short of meeting Six Sigma quality performance and from April to June, glucose, creatinine, urea and ALT had metrics less than 3 at both the Internal Quality Control levels. QGI ratio indicated that from January to March, the problem was imprecision for urea, TSH and beta-HCG (QGI < 0.8). From April to June, urea and creatinine showed imprecision, glucose and ALT showed inaccuracy, urea and ALT showed both imprecision and inaccuracy. Conclusion: This study highlights the necessity for stringent Internal Quality Control and External Quality Assurance monitoring even during the lockdown period of the pandemic. By implementing six sigma and finding QGI ratio, quality of laboratory services can be improved immensely.

Published
2022
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40. Extracellular vesicle-mediated approaches for the diagnosis and therapy of MASLD: current advances and future prospective

Author

Swasthika Gurjar, Ramanarayana Bhat A, Raghavendra Upadhya, and Revathi P. Shenoy

Subjects
Metabolic dysfunction-associated steatotic liver disease, Liver diseases, Extracellular vesicles, Biomarker, Targeted therapy, Nutritional diseases. Deficiency diseases, RC620-627
Abstract

Abstract Metabolic dysfunction-associated steatotic liver disease (MASLD) is an asymptomatic, multifaceted condition often associated with various risk factors, including fatigue, obesity, insulin resistance, metabolic syndrome, and sleep apnea. The increasing burden of MASLD underscores the critical need for early diagnosis and effective therapies. Owing to the lack of efficient therapies for MASLD, early diagnosis is crucial. Consequently, noninvasive biomarkers and imaging techniques are essential for analyzing disease risk and play a pivotal role in the global diagnostic process. The use of extracellular vesicles has emerged as promising for early diagnosis and therapy of various liver ailments. Herein, a comprehensive summary of the current diagnostic modalities for MASLD is presented, highlighting their advantages and limitations while exploring the potential of extracellular vesicles (EVs) as innovative diagnostic and therapeutic tools for MASLD. With this aim, this review emphasizes an in-depth understanding of the origin of EVs and the pathophysiological alterations of these ectosomes and exosomes in various liver diseases. This review also explores the therapeutic potential of EVs as key components in the future management of liver disease. The dual role of EVs as biomarkers and their therapeutic utility in MASLD essentially highlights their clinical integration to improve MASLD diagnosis and treatment. While EV-based therapies are still in their early stages of development and require substantial research to increase their therapeutic value before they can be used clinically, the diagnostic application of EVs has been extensively explored. Moving forward, developing diagnostic devices leveraging EVs will be crucial in advancing MASLD diagnosis. Thus, the literature summarized provides suitable grounds for clinicians and researchers to explore EVs for devising diagnostic and treatment strategies for MASLD.

Published
2025
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42. Brain tumor detection and screening using artificial intelligence techniques: Current trends and future perspectives.

Author

Raghavendra, U, Gudigar, A, Paul, A, Goutham, TS, Inamdar, MA, Hegde, A, Devi, A, Ooi, CP, Deo, RC, Barua, PD, Molinari, F, Ciaccio, EJ, Acharya, UR, Raghavendra, U, Gudigar, A, Paul, A, Goutham, TS, Inamdar, MA, Hegde, A, Devi, A, Ooi, CP, Deo, RC, Barua, PD, Molinari, F, Ciaccio, EJ, and Acharya, UR

Abstract

A brain tumor is an abnormal mass of tissue located inside the skull. In addition to putting pressure on the healthy parts of the brain, it can lead to significant health problems. Depending on the region of the brain tumor, it can cause a wide range of health issues. As malignant brain tumors grow rapidly, the mortality rate of individuals with this cancer can increase substantially with each passing week. Hence it is vital to detect these tumors early so that preventive measures can be taken at the initial stages. Computer-aided diagnostic (CAD) systems, in coordination with artificial intelligence (AI) techniques, have a vital role in the early detection of this disorder. In this review, we studied 124 research articles published from 2000 to 2022. Here, the challenges faced by CAD systems based on different modalities are highlighted along with the current requirements of this domain and future prospects in this area of research.

Published
2023

43. YOLOv5s-CAM: A Deep Learning Model for Automated Detection and Classification for Types of Intracranial Hematoma in CT Images

Author

Vidhya, V, Raghavendra, U, Gudigar, A, Basak, S, Mallappa, S, Hegde, A, Menon, GR, Barua, PD, Salvi, M, Ciaccio, EJ, Molinari, F, Acharya, UR, Vidhya, V, Raghavendra, U, Gudigar, A, Basak, S, Mallappa, S, Hegde, A, Menon, GR, Barua, PD, Salvi, M, Ciaccio, EJ, Molinari, F, and Acharya, UR

Abstract

Intracranial hematoma due to traumatic brain injury is a serious health concern with rates of morbidity and mortality that are increasing worldwide. Manual identification is slow, subject to observer variabilities, and the existing automated techniques for intracranial hematoma detection in non-contrast computed tomography images cannot effectively detect multiple lesions of irregular sizes and shapes. Therefore, a computer-aided system using different window settings, YOLOv5s, cascaded attention module, and spatial pyramid pooling-fast is proposed to detect hematoma types, namely acute intraparenchymal, intraventricular, subdural, epidural, subarachnoid, and chronic subdural. Firstly, the computed tomography images are pre-processed using a window-based stacking approach wherein a three-channel image is generated by stacking grayscale images obtained with the aid of multiple window settings, i.e, brain, bone, and subdural. Secondly, a cascaded attention module is constructed in the neck of the YOLOv5s model to improve its detection performance by placing the convolution block attention module in serial with the efficient channel attention module. The cascaded attention module enriches the feature representation of various hematoma types in complex backgrounds especially when they are small and inconspicuous. The spatial pyramid pooling is replaced by a spatial pyramid pooling-fast to reduce the computational parameters and accelerate the feature fusion ability. The proposed deep learning model is trained, validated, and tested with 15,921 images from the brain haemorrhage extended dataset and it achieved overall precision, recall, F1-score, and mean average precision at 0.5, and mean average precision at 0.5:0.95 of 0.935, 0.908, 0.921, 0.943 and 0.65 respectively. The experimental results show that in comparison to the original YOLOv5s model and state-of-the-art methods, the model was able to localize and classify the acute or chronic instances of five hematoma su

Published
2023

44. A Novel Attention-Based Model for Semantic Segmentation of Prostate Glands Using Histopathological Images

Author

Inamdar, MA, Raghavendra, U, Gudigar, A, Bhandary, S, Salvi, M, Deo, RC, Barua, PD, Ciaccio, EJ, Molinari, F, Acharya, UR, Inamdar, MA, Raghavendra, U, Gudigar, A, Bhandary, S, Salvi, M, Deo, RC, Barua, PD, Ciaccio, EJ, Molinari, F, and Acharya, UR

Abstract

One of the foremost causes of death in males worldwide is prostate cancer. The identification, detection and diagnosis of the same is very crucial in saving lives. In this paper, we present an efficient gland segmentation model using digital histopathology and deep learning. These methods have the potential to revolutionize medicine by identifying hidden patterns within the image. The recent improvements in data acquisition, processing and analysis of Deep Learning Models has made Artificial Intelligence driven healthcare a very lucrative area, in terms of data inference and delivering meaningful insights. This study presents an automated method for segmenting histopathological images of human prostate glands. The main focus is developing new methods for segmenting histopathological images of prostate gland using a multi-channel algorithm with an attention mechanism to detect important areas. We compare our results with a host of contemporary techniques and show that our method performs better at the segmentation task for histopathological imagery. Our method is able to delineate gland and background parts with an average Dice-coefficient of 0.9168. In this attention-based model we propose for semantic segmentation of prostate glands the potential to provide accurate segmentation versus tumor features, which has significant implications for medical screening applications.

Published
2023

45. Endoscopy, video capsule endoscopy, and biopsy for automated celiac disease detection: A review

Author

Jahmunah, V, En Wei Koh, J, Sudarshan, VK, Raghavendra, U, Gudigar, A, Lih Oh, S, Wen Loh, H, Faust, O, Datta Barua, P, Ciaccio, EJ, Rajendra Acharya, U, Jahmunah, V, En Wei Koh, J, Sudarshan, VK, Raghavendra, U, Gudigar, A, Lih Oh, S, Wen Loh, H, Faust, O, Datta Barua, P, Ciaccio, EJ, and Rajendra Acharya, U

Abstract

Celiac Disease (CD) is a common ailment that affects approximately 1% of the world population. Automated CD detection can help experts during the diagnosis of this condition at an early stage and bring significant benefits to both patients and healthcare providers. For this purpose, scientists have created automatic and semi-automatic CD diagnostic support systems. In this study, we performed information extraction methods that were found useful for efforts to differentiate CD versus non-CD. To focus the review process, only methods for endoscopy, video capsule endoscopy (VCE) and biopsy image analyses were considered. As described herein, we have learned that statistical and non-linear methods are most important for information extraction. These information extraction tools might benefit clinical workflows by reducing intra- and inter-observer variability. However, bias, introduced by resolving design choices during the creation of diagnostic support systems, may limit the general validity of the performance results, impacting the transferability of study outcomes. Therefore, having am overview of information extraction tools. Together with their general and specific limitations, might be assistive in improving the information extraction process. We hope our review results will provide a foundation for the design of next-generation statistical and nonlinear methods that can be used in CD detection systems. We have also compared various review articles and discussed recommendations to improve CD diagnosis. From this review, it is evident that CD diagnosis is slowly moving away from conventional techniques towards advanced deep learning techniques.

Published
2023

48. A Novel Face Recognition Method Using PCA, LDA and Support Vector Machine

Author

Raghavendra, U., Mahesh, P. K., Gudigar, Anjan, Akan, Ozgur, Series editor, Bellavista, Paolo, Series editor, Cao, Jiannong, Series editor, Dressler, Falko, Series editor, Ferrari, Domenico, Series editor, Gerla, Mario, Series editor, Kobayashi, Hisashi, Series editor, Palazzo, Sergio, Series editor, Sahni, Sartaj, Series editor, Shen, Xuemin (Sherman), Series editor, Stan, Mircea, Series editor, Xiaohua, Jia, Series editor, Zomaya, Albert, Series editor, Coulson, Geoffrey, Series editor, Meghanathan, Natarajan, editor, Chaki, Nabendu, editor, and Nagamalai, Dhinaharan, editor

Published
2012
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49. YOLOv5s-CAM: A Deep Learning Model for Automated Detection and Classification for Types of Intracranial Hematoma in CT Images

Author

Vidhya, V., primary, Raghavendra, U., additional, Gudigar, Anjan, additional, Basak, Sudipta, additional, Mallappa, Sankalp, additional, Hegde, Ajay, additional, Menon, Girish R., additional, Barua, Prabal Datta, additional, Salvi, Massimo, additional, Ciaccio, Edward J., additional, Molinari, Filippo, additional, and Acharya, U. Rajendra, additional

Published
2023
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