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Your search keyword '"Reddy, Prabhakar"' showing total 12 results
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12 results on '"Reddy, Prabhakar"'

1. Phase III Pivotal comparative clinical trial of intranasal (iNCOVACC) and intramuscular COVID 19 vaccine (Covaxin®)

Author

Singh, Chandramani, Verma, Savita, Reddy, Prabhakar, Diamond, Michael S., Curiel, David T., Patel, Chintan, Jain, Manish Kumar, Redkar, Sagar Vivek, Bhate, Amit Suresh, Gundappa, Vivek, Konatham, Rambabu, Toppo, Leelabati, Joshi, Aniket Chandrakant, Kushwaha, Jitendra Singh, Singh, Ajit Pratap, Bawankule, Shilpa, Ella, Raches, Prasad, Sai, Ganneru, Brunda, Chiteti, Siddharth Reddy, Kataram, Sreenivas, and Vadrevu, Krishna Mohan

Published
2023
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2. Persistence of immunity and impact of third dose of inactivated COVID-19 vaccine against emerging variants

Author

Vadrevu, Krishna Mohan, Ganneru, Brunda, Reddy, Siddharth, Jogdand, Harsh, Raju, Dugyala, Sapkal, Gajanan, Yadav, Pragya, Reddy, Prabhakar, Verma, Savita, Singh, Chandramani, Redkar, Sagar Vivek, Gillurkar, Chandra Sekhar, Kushwaha, Jitendra Singh, Mohapatra, Satyajit, Bhate, Amit, Rai, Sanjay Kumar, Ella, Raches, Abraham, Priya, Prasad, Sai, and Ella, Krishna

Published
2022
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3. Building an NLP based speech recognition technology for emergency call centers.

Author

Erukala, Sudarshan, Reddy, Prabhakar, Ramesh, Oruganti, Ramesh, Nagaram, Kumar, Atul, Prabhanjan, Bonthala, and Bolukonda, Prashanth

Subjects
*SPEECH perception, *ARTIFICIAL neural networks, *LANGUAGE models, *CALL centers, *GAUSSIAN mixture models, *NATURAL language processing, *AUTOMATIC speech recognition
Abstract

The approaches of automated speech identification for spoken conversations in emergencies call centres were explored and compared therefore in research. These methodology included acoustic and linguistic models, as well as labelling techniques. Currently present speech recognition algorithms perform poorly because contact centre discussion speech has special context and is spoken in loud, emotional contexts. Consequently, the primary components of speaker verification designs and acoustical training methodologies—as well such Various investigations and analyses of symmetrical information labelling methods were performed. Various variations of Deep Neural Network/Hidden Markov Model (DNN/ HMM) and Gaussian Mixture Model/Hidden Markov Model (GMM/HMM) approaches might have been implemented and tested in order to establish an efficient language framework for conversation information. Furthermore, useful conversation system language models developed Using intrinsic and extrinsic criteria, outlined Finally, when these recommended information labelling techniques with spelling correction are compared with typical labelling techniques, they dominate the other methodologies by a significant proportion. Using the investigation's findings as a guide, we found Showed the use of spelling adjustments prior to training information for a labelling approach, trigram with Kneser-Ney discounting for a language model, and DNN/HMM for an acoustic model are efficient setups for conversation voice recognition in emergency call centres. In order to be clear, this study was Done using two distinct datasets that were gathered from emergency calls: the Dialogue dataset (27 h), which comprises the speech of the call agents, and the Summary dataset (53 h), which contains spoken summaries of those conversations summarising emergency situations. Even if the remarks were taken from the Our strategies are loosely related to particular linguistic aspects despite the fact that the emergency contact centre is in the Turkic language family of Azerbaijani, which is spoken there. As a result, it is expected that the recommended ways will also work with the other languages in the same family. [ABSTRACT FROM AUTHOR]

Published
2024
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4. Efficacy, safety, and lot-to-lot immunogenicity of an inactivated SARS-CoV-2 vaccine (BBV152): interim results of a randomised, double-blind, controlled, phase 3 trial

Author

Aggarwal, P., Aglawe, V., Ali, A., Anand, N., Awad, N., Bafna, V., Balasubramaniyam, G., Bandkar, A., Basha, P., Bharge, V., Bhate, A., Bhate, S., Bhavani, V., Bhosale, R., Chalapathy, DV, Chaubal, C., Chaudhary, D., Chavan, A., Desai, P., Dhodi, D., Dutta, S., Garg, R., Garg, K., George, M., Goyal, P., Guleria, R., Gupta, S., Jain, M., Jain, M.K., Jindal, S., Kalra, M., Kant, S., Khosla, P., Kulkarni, P., Kumar, P., Kumar, Y., Majumdar, A., Meshram, P., Mishra, V., Mohanty, S., Nair, J., Pandey, S., Panigrahi, S.K., Patil, B., Patil, V., Rahate, P., Raj, V., Ramanand, S., Rami, K., Ramraj, B., Rane, S., Rao, E.V., Rao, N., Raphael, R., Reddy, G., Redkar, V., Redkar, S., Sachdeva, A., Saha, J., Sahoo, J., Sampath, P., Savith, A., Shah, M., Shanmugam, L., Sharma, R., Sharma, P., Sharma, D., Singh, A., Singh, J., Singh, P., Sivaprakasam, S., Subramaniam, S., Sudheer, D., Tandon, S., Tariq, M., Tripathi, V., Vable, M., Verma, R., Waghmare, S., Ella, Raches, Reddy, Siddarth, Blackwelder, William, Potdar, Varsha, Yadav, Pragya, Sarangi, Vamshi, Aileni, Vinay K, Kanungo, Suman, Rai, Sanjay, Reddy, Prabhakar, Verma, Savita, Singh, Chandramani, Redkar, Sagar, Mohapatra, Satyajit, Pandey, Anil, Ranganadin, Pajanivel, Gumashta, Raghavendra, Multani, Manish, Mohammad, Shameem, Bhatt, Parul, Kumari, Laxmi, Sapkal, Gajanan, Gupta, Nivedita, Abraham, Priya, Panda, Samiran, Prasad, Sai, Bhargava, Balram, Ella, Krishna, and Vadrevu, Krishna Mohan

Published
2021
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6. Phase III Pivotal comparative clinical trial of intranasal (iNCOVACC) and intramuscular COVID 19 vaccine (Covaxin®).

Author

Singh, Chandramani, Verma, Savita, Reddy, Prabhakar, Diamond, Michael S., Curiel, David T., Patel, Chintan, Jain, Manish Kumar, Redkar, Sagar Vivek, Bhate, Amit Suresh, Gundappa, Vivek, Konatham, Rambabu, Toppo, Leelabati, Joshi, Aniket Chandrakant, Kushwaha, Jitendra Singh, Singh, Ajit Pratap, Bawankule, Shilpa, Ella, Raches, Prasad, Sai, Ganneru, Brunda, and Chiteti, Siddharth Reddy

Subjects
COVID-19 vaccines, SARS-CoV-2 Omicron variant, CLINICAL trials, IMMUNOGLOBULINS, ANTIBODY titer, T cells, INFECTIOUS disease transmission
Abstract

One of the most preferable characteristics for a COVID-19 vaccine candidate is the ability to reduce transmission and infection of SARS-CoV-2, in addition to disease prevention. Unlike intramuscular vaccines, intranasal COVID-19 vaccines may offer this by generating mucosal immunity. In this open-label, randomised, multicentre, phase 3 clinical trial (CTRI/2022/02/40065; ClinicalTrials.gov: NCT05522335), healthy adults were randomised to receive two doses, 28 days apart, of either intranasal adenoviral vectored SARS-CoV-2 vaccine (BBV154) or licensed intramuscular vaccine, Covaxin®. Between April 16 and June 4, 2022, we enrolled 3160 subjects of whom, 2971 received 2 doses of BBV154 and 161 received Covaxin. On Day 42, 14 days after the second dose, BBV154 induced significant serum neutralization antibody titers against the ancestral (Wuhan) virus, which met the pre-defined superiority criterion for BBV154 over Covaxin®. Further, both vaccines showed cross protection against Omicron BA.5 variant. Salivary IgA titers were found to be higher in BBV154. In addition, extensive evaluation of T cell immunity revealed comparable responses in both cohorts due to prior infection. However, BBV154 showed significantly more ancestral specific IgA-secreting plasmablasts, post vaccination, whereas Covaxin recipients showed significant Omicron specific IgA-secreting plasmablasts only at day 42. Both vaccines were well tolerated. Overall reported solicited reactions were 6.9% and 25.5% and unsolicited reactions were 1.2% and 3.1% in BBV154 and Covaxin® participants respectively. [ABSTRACT FROM AUTHOR]

Published
2023
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7. Immunogenicity and Tolerability of BBV154 (iNCOVACC®), an Intranasal SARS-CoV-2 Vaccine, Compared with Intramuscular Covaxin® in Healthy Adults: A Randomised, Open-Label, Phase 3 Clinical Trial

Author

Singh, Chandramani, primary, Verma, Savita, additional, Reddy, Prabhakar, additional, Diamond, Michael S., additional, Curiel, David T., additional, Patel, Chintan, additional, Jain, Manish Kumar, additional, Redkar, Sagar Vivek, additional, Bhate, Amit Suresh, additional, Gundoppa, Vivek, additional, Konatham, Rambabu, additional, Toppo, Leelabati, additional, Joshi, Aniket Chandrakant, additional, Kushwah, Jitendra Singh, additional, Singh, Ajit Pratap, additional, Gaidhane, Shilpa, additional, Vadrevu, Krishna Mohan, additional, and Group, anon, additional

Published
2023
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10. Persistence of immunity and impact of a third (booster) dose of an inactivated SARS-CoV-2 vaccine, BBV152; a phase 2, double-blind, randomised controlled trial

Author

Vadrevu, Krishna Mohan, primary, Ganneru, Brunda, additional, Reddy, Siddharth, additional, Jogdand, Harsh, additional, Raju, Dugyala, additional, Praturi, Usha, additional, Sapkal, Gajanan, additional, Yadav, Pragya, additional, Reddy, Prabhakar, additional, Verma, Savita, additional, Singh, Chandramani, additional, Redkar, Sagar Vivek, additional, Gillurkar, Chandra Sekhar, additional, Kushwaha, Jitendra Singh, additional, Mohapatra, Satyajit, additional, Bhate, Amit, additional, Rai, Sanjay, additional, Ella, Raches, additional, Abraham, Priya, additional, Prasad, Sai, additional, and Ella, Krishna, additional

Published
2022
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11. Efficacy, safety, and lot-to-lot immunogenicity of an inactivated SARS-CoV-2 vaccine (BBV152): interim results of a randomised, double-blind, controlled, phase 3 trial

Author

Ella, Raches, primary, Reddy, Siddarth, additional, Blackwelder, William, additional, Potdar, Varsha, additional, Yadav, Pragya, additional, Sarangi, Vamshi, additional, Aileni, Vinay K, additional, Kanungo, Suman, additional, Rai, Sanjay, additional, Reddy, Prabhakar, additional, Verma, Savita, additional, Singh, Chandramani, additional, Redkar, Sagar, additional, Mohapatra, Satyajit, additional, Pandey, Anil, additional, Ranganadin, Pajanivel, additional, Gumashta, Raghavendra, additional, Multani, Manish, additional, Mohammad, Shameem, additional, Bhatt, Parul, additional, Kumari, Laxmi, additional, Sapkal, Gajanan, additional, Gupta, Nivedita, additional, Abraham, Priya, additional, Panda, Samiran, additional, Prasad, Sai, additional, Bhargava, Balram, additional, Ella, Krishna, additional, Vadrevu, Krishna Mohan, additional, Aggarwal, P., additional, Aglawe, V., additional, Ali, A., additional, Anand, N., additional, Awad, N., additional, Bafna, V., additional, Balasubramaniyam, G., additional, Bandkar, A., additional, Basha, P., additional, Bharge, V., additional, Bhate, A., additional, Bhate, S., additional, Bhavani, V., additional, Bhosale, R., additional, Chalapathy, DV, additional, Chaubal, C., additional, Chaudhary, D., additional, Chavan, A., additional, Desai, P., additional, Dhodi, D., additional, Dutta, S., additional, Garg, R., additional, Garg, K., additional, George, M., additional, Goyal, P., additional, Guleria, R., additional, Gupta, S., additional, Jain, M., additional, Jain, M.K., additional, Jindal, S., additional, Kalra, M., additional, Kant, S., additional, Khosla, P., additional, Kulkarni, P., additional, Kumar, P., additional, Kumar, Y., additional, Majumdar, A., additional, Meshram, P., additional, Mishra, V., additional, Mohanty, S., additional, Nair, J., additional, Pandey, S., additional, Panigrahi, S.K., additional, Patil, B., additional, Patil, V., additional, Rahate, P., additional, Raj, V., additional, Ramanand, S., additional, Rami, K., additional, Ramraj, B., additional, Rane, S., additional, Rao, E.V., additional, Rao, N., additional, Raphael, R., additional, Reddy, G., additional, Redkar, V., additional, Redkar, S., additional, Sachdeva, A., additional, Saha, J., additional, Sahoo, J., additional, Sampath, P., additional, Savith, A., additional, Shah, M., additional, Shanmugam, L., additional, Sharma, R., additional, Sharma, P., additional, Sharma, D., additional, Singh, A., additional, Singh, J., additional, Singh, P., additional, Sivaprakasam, S., additional, Subramaniam, S., additional, Sudheer, D., additional, Tandon, S., additional, Tariq, M., additional, Tripathi, V., additional, Vable, M., additional, Verma, R., additional, and Waghmare, S., additional

Published
2021
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12. Association between Heavy Metal Exposure and Idiopathic Aplastic Anemia in Adolescents and Adults:a Case Control Study from Northern India

Author

Jain, Arihant, Wadhera, Sarthak, Malhotra, Pankaj, Singh, Charanpreet, Lad, Deepesh Pravinkumar, Khadwal, Alka Rani, Prakash, Gaurav, Varma, Neelam, and Reddy, Prabhakar

Abstract

Introduction:Compared to western world the incidence of AA in South Asian countries is higher. There are studies to suggest that incidence of this disease is 2-3 times higher in LMICs. Besides immune mediated pathophysiology and inherited presdispostion, toxic insult to the hematopoietic elements due to chemotherapy, radiation exposure, drugs,and environmental toxins such as pesticides and heavy metals have been associated with Aplastic Anemia.The higher incidence of AA in lower socio-economic status and its association with drinking water habits, industrial exposure suggests a possible link with heavy metals in the pathogenesis of disease.

Published
2023
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