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260 results on '"Jawahar M"'

1. Apraxia of lid opening

Author

Jebasingh Y, Menon K, Jawahar M, Gobinathan S, Balasubramanian S, and Shanbhogue K

Subjects
Apraxia of lid opening, levator palpebrae inhibition, Neurology. Diseases of the nervous system, RC346-429
Abstract

Apraxia of lid opening is a condition where patients do not have ptosis but have difficulty in overcoming levator palpebrae inhibition. We report a patient who presented with difficulty in opening eyelids with out diurnal variation, ptosis or blepharospasm. The diagnosis of Apraxia of lid opening is confirmed by electro physiology. The possibility of apraxia of lid opening should be considered in patients who present with difficulty in opening eyes. Various causes of Apraxia of lid opening are discussed.

Published
2006

2. 'Mirror agnosia' in a patient with right occipitotemporal infarct

Author

Menon Bijoy, Shanbhogue K, Mutharasu C, Gopinathan S, Balasubramanian S, Chandramohan, Kingsly Jebasingh, and Jawahar M

Subjects
Mirror agnosia, neglect, Neurology. Diseases of the nervous system, RC346-429
Abstract

We report the clinical profile and investigation of K, a patient suffering from agnosia for the concept of "mirrors". Normal people rarely confuse a mirror image for the real image. Our patient ′K′ has intact attention, memory and language with a left visual neglect with left hemianopia. When shown an object on his left with a mirror kept sagitally on his right, K grabs for the reflection and even gropes behind the mirror for it. Yet, when shown the mirror alone, he is able to tell that it is a mirror. Thus the concept of a ′mirror′ is lost in the experimental paradigm. An explanation for mirror ′agnosia′ is provided and recent controversies in the analysis of visuospatial functions in humans is highlighted.

Published
2006

3. Rifampicin-induced acute thrombocytopenia

Author

Banu Rekha V, Adhilakshmi A, and Jawahar M

Subjects
Tuberculosis, Treatment, Rifampicin, Thrombocytopenia, Diseases of the respiratory system, RC705-779
Abstract

Rifampicin is an essential component of the treatment regimen for tuberculosis. Extensive clinical experience has shown that the drug is well tolerated, but on rare occasions it can cause life threatening adverse reactions like acute renal failure and thrombocytopenia. At the Tuberculosis Research Centre, we have treated more than 8000 patients with pulmonary and extra-pulmonary tuberculosis with rifampicin-containing regimens over the past 30 years and we are reporting a case of acute thrombocytopenia probably rifampicin induced, in a patient who was retreated for tuberculosis. The physician treating tuberculosis patients must be aware of this rare life threatening complication, which if detected early, is completely reversible.

Published
2005

6. Effectiveness of BBV152/Covaxin and AZD1222/Covishield vaccines against severe COVID-19 and B.1.617.2/Delta variant in India, 2021: a multi-centric hospital-based case-control study

Author

Bhatnagar, Tarun, Chaudhuri, Sirshendu, Ponnaiah, Manickam, Yadav, Pragya D, Sabarinathan, R, Sahay, Rima R, Ahmed, Faheem, Aswathy, S, Bhardwaj, Pankaj, Bilimale, Anil, Kumar, M Santhosh, Logaraj, M., Narlawar, Uday, Palanivel, C, Patel, Prakash, Rai, Sanjay K, Saxena, Vartika, Singh, Arvind, Thangaraj, Jeromie WV, Agarwal, Ashwini, Alvi, Yasir, Amoghashree, Ashok, P, Babu, Dinesh, Bahurupi, Yogesh, Bhalavi, Sangita, Behera, Priyamadhaba, Biswas, Priyanka Pandit, Charan, Jaykaran, Chauhan, Nishant Kumar, Chetak, KB, Dar, Lalit, Das, Ayan, Deepashree, R, Dhar, Minakshi, Dhodapkar, Rahul, Dipu, TS, Dudeja, Mridu, Dudhmal, Manisha, Gadepalli, Ravisekhar, Garg, Mahendra Kumar, Gayathri, AV, Goel, Akhil Dhanesh, Gowdappa, H Basavana, Guleria, Randeep, Gupta, Manoj Kumar, Islam, Farzana, Jain, Mannu, Jain, Vineet, Jawahar, M Lanord Stanley, Joshi, Rajendra, Kant, Shashi, Kar, Sitanshu Sekhar, Kalita, Deepjyoti, Khapre, Meenakshi, Khichar, Satyendra, Kombade, Sarika Prabhakar, Kohli, Sunil, Kumar, Abhinendra, Kumar, Anil, Kumar, Deepak, Kulirankal, Kiran G, Leela, KV, Majumdar, Triparna, Mishra, Baijayantimala, Misra, Puneet, Misra, Sanjeev, Mohapatra, Prasanta Raghab, Murthy, M Narayana, Nyayanit, Dimpal A, Patel, Manish, Pathania, Monika, Patil, Savita, Patro, Binod Kumar, Jalandra, Ramniwas, Rathod, Pragati, Shah, Naimesh, Shete, Anita, Shukla, Deepak, Shwethashree, M, Sinha, Smita, Sumana, MN, Surana, Ashish, Trikha, Anjan, Tejashree, A, Venkateshan, Mahalingam, Vijaykrishnan, G, Wadhava, Sarita, Wig, Naveet, Gupta, Nivedita, Abraham, Priya, and Murhekar, Manoj V

Published
2022
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7. Firewall Scheduling and Routing Using pfSense

Author

Muthukumar, M., Senthilkumar, P., Jawahar, M., Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Dash, Subhransu Sekhar, editor, Das, Swagatam, editor, and Panigrahi, Bijaya Ketan, editor

Published
2021
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9. An attention-based deep learning for acute lymphoblastic leukemia classification.

Author

Jawahar, M, Anbarasi, LJ, Narayanan, S, Gandomi, AH, Jawahar, M, Anbarasi, LJ, Narayanan, S, and Gandomi, AH

Abstract

The bone marrow overproduces immature cells in the malignancy known as Acute Lymphoblastic Leukemia (ALL). In the United States, about 6500 occurrences of ALL are diagnosed each year in both children and adults, comprising nearly 25% of pediatric cancer cases. Recently, many computer-assisted diagnosis (CAD) systems have been proposed to aid hematologists in reducing workload, providing correct results, and managing enormous volumes of data. Traditional CAD systems rely on hematologists' expertise, specialized features, and subject knowledge. Utilizing early detection of ALL can aid radiologists and doctors in making medical decisions. In this study, Deep Dilated Residual Convolutional Neural Network (DDRNet) is presented for the classification of blood cell images, focusing on eosinophils, lymphocytes, monocytes, and neutrophils. To tackle challenges like vanishing gradients and enhance feature extraction, the model incorporates Deep Residual Dilated Blocks (DRDB) for faster convergence. Conventional residual blocks are strategically placed between layers to preserve original information and extract general feature maps. Global and Local Feature Enhancement Blocks (GLFEB) balance weak contributions from shallow layers for improved feature normalization. The global feature from the initial convolution layer, when combined with GLFEB-processed features, reinforces classification representations. The Tanh function introduces non-linearity. A Channel and Spatial Attention Block (CSAB) is integrated into the neural network to emphasize or minimize specific feature channels, while fully connected layers transform the data. The use of a sigmoid activation function concentrates on relevant features for multiclass lymphoblastic leukemia classification The model was analyzed with Kaggle dataset (16,249 images) categorized into four classes, with a training and testing ratio of 80:20. Experimental results showed that DRDB, GLFEB and CSAB blocks' feature discrimination ability

Published
2024

23. General intelligence and executive functioning are overlapping but separable at genetic and molecular pathway levels: An analytical review of existing GWAS findings

Author

Ciobanu, Liliana G., primary, Stankov, Lazar, additional, Schubert, K. Oliver, additional, Amare, Azmeraw T., additional, Jawahar, M. Catharine, additional, Lawrence-Wood, Ellie, additional, Mills, Natalie T., additional, Knight, Matthew, additional, Clark, Scott R., additional, and Aidman, Eugene, additional

Published
2022
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27. ALNett: A cluster layer deep convolutional neural network for acute lymphoblastic leukemia classification.

Author

Jawahar, M, H, S, L, JA, Gandomi, AH, Jawahar, M, H, S, L, JA, and Gandomi, AH

Abstract

Acute Lymphoblastic Leukemia (ALL) is cancer in which bone marrow overproduces undeveloped lymphocytes. Over 6500 cases of ALL are diagnosed every year in the United States in both adults and children, accounting for around 25% of pediatric cancers, and the trend continues to rise. With the advancements of AI and big data analytics, early diagnosis of ALL can be used to aid the clinical decisions of physicians and radiologists. This research proposes a deep neural network-based (ALNett) model that employs depth-wise convolution with different dilation rates to classify microscopic white blood cell images. Specifically, the cluster layers encompass convolution and max-pooling followed by a normalization process that provides enriched structural and contextual details to extract robust local and global features from the microscopic images for the accurate prediction of ALL. The performance of the model was compared with various pre-trained models, including VGG16, ResNet-50, GoogleNet, and AlexNet, based on precision, recall, accuracy, F1 score, loss accuracy, and receiver operating characteristic (ROC) curves. Experimental results showed that the proposed ALNett model yielded the highest classification accuracy of 91.13% and an F1 score of 0.96 with less computational complexity. ALNett demonstrated promising ALL categorization and outperformed the other pre-trained models.

Published
2022

36. AN INTERESTING CASE OF METASTATIC BRAIN ABSCESS

Author

Jawahar M, Lakshmi Narasimhan Ranganathan, Balasubramanian S, Nithyanandam A, Chandramouleeswaran V, and Kannan V

Subjects
Pathology, medicine.medical_specialty, business.industry, medicine, business, medicine.disease, Brain abscess
Published
2019
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40. Effectiveness of BBV152/Covaxin and AZD1222/Covishield Vaccines Against Severe COVID-19 and B.1.617.2/Delta Variant in India, 2021: A Multi-Centric Hospital-Based Case-Control Study

Author

Bhatnagar, Tarun, primary, Chaudhuri, Sirshendu, additional, Ponnaiah, Manickam, additional, Yadav, Pragya D., additional, Sabarinathan, R., additional, Sahay, Rima R., additional, Ahmed, Faheem, additional, Aswathy, S, additional, Bhardwaj, Pankaj, additional, Bilimale, Anil, additional, Muthusamy, Santhosh Kumar, additional, Logaraj, M, additional, Narlawar, Uday, additional, Palanivel, C., additional, Patel, Prakash B., additional, Rai, Sanjay K., additional, Saxena, Vartika, additional, Singh, Arvind, additional, Thangaraj, Jeromie Wesley Vivian, additional, Agarwal, Ashwini, additional, Alvi, Yasir, additional, ., Amoghashree, additional, Ashok, P, additional, Babu, Dinesh, additional, Bahurupi, Yogesh, additional, Bhalavi, Sangita, additional, Behera, Priyamadhaba, additional, Biswas, Priyanka Pandit, additional, Charan, Jaykaran, additional, Chauhan, Nishant Kumar, additional, Chetak, KB, additional, Dar, Lalit, additional, Das, Ayan, additional, Deepashree, R, additional, Dhar, Minakshi, additional, Dhodapkar, Rahul, additional, Dipu, TS, additional, Dudeja, Mridu, additional, Dudhmal, Manisha, additional, Gadepalli, Ravisekhar, additional, Garg, Mahendra Kumar, additional, Gayathri, AV, additional, Goel, Akhil Dhanesh, additional, Gowdappa, Basavana, additional, Guleria, Randeep, additional, Gupta, Manoj Kumar, additional, Islam, Farzana, additional, Jain, Mannu, additional, Jain, Vineet, additional, Jawahar, M. Lanord Stanley, additional, Joshi, Rajendra, additional, Kant, Shashi, additional, Kar, Sitanshu Sekhar, additional, Kalita, Deepjyoti, additional, Khapre, Meenakshi, additional, Khichar, Satyendra, additional, Kombade, Sarika Prabhakar, additional, Kohli, Sunil, additional, Kumar, Abhinendra, additional, Kumar, Anil, additional, Kumar, Deepak, additional, Kulirankal, Kiran G., additional, Leela, KV, additional, Majumdar, Triparna, additional, Mishra, Baijayantimala, additional, Misra, Puneet, additional, Misra, Sanjeev, additional, Mohapatra, Prasanta Raghab, additional, Murthy, M. Narayana, additional, Nyayanit, Dimpal A., additional, Patel, Manish, additional, Pathania, Monika, additional, Patil, Savita, additional, Patro, Binod Kumar, additional, Jalandra, Ramniwas, additional, Rathod, Pragati, additional, Shah, Naimesh, additional, Shete, Anita, additional, Shukla, Deepak, additional, Shwethashree, M, additional, Sinha, Smita, additional, Surana, Ashish, additional, Trikha, Anjan, additional, Tejashree, A, additional, Venkateshan, Mahalingam, additional, Vijaykrishnan, G, additional, Wadhava, Sarita, additional, Wig, Naveet, additional, Gupta, Nivedita, additional, Abraham, Priya, additional, and Murhekar, Manoj V., additional

Published
2021
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