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1,162 results on '"Khan, Kashif A."'

8. Reviewing the Materials, Techniques, and Characteristics of PEEK in the Context of Additive Manufacturing (3D Printing)

Author

Arora, Pawan Kumar, Ahmad, Fahad, Khan, Kashif, Jha, Ashish Kumar, Tehami, Nashit, Chaari, Fakher, Series Editor, Gherardini, Francesco, Series Editor, Ivanov, Vitalii, Series Editor, Haddar, Mohamed, Series Editor, Cavas-Martínez, Francisco, Editorial Board Member, di Mare, Francesca, Editorial Board Member, Kwon, Young W., Editorial Board Member, Tolio, Tullio A. M., Editorial Board Member, Trojanowska, Justyna, Editorial Board Member, Schmitt, Robert, Editorial Board Member, Xu, Jinyang, Editorial Board Member, Yadav, Sanjay, editor, Shrivastava, Yogesh, editor, and Rab, Shanay, editor

Published
2024
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10. Critical Factors Influencing MOOCs Retention: The Mediating Role of Information Technology

Author

Khan, Amin Ullah, Khan, Kashif Ullah, Atlas, Fouzia, Akhtar, Sadia, and Khan, Farhan

Abstract

In the current digital era, Massive Open Online Courses (MOOCs) are considered as an educational revolution specifically in the current situation of COVID-19. Although students from all over the globe register for MOOCs, only a small proportion of participants complete the online courses. MOOCs at present encounter the problem of low retention rates, thus providing numerous research gaps. This paper investigates the impact of critical factors such as the instructor's role, course relevancy, learning outcomes on MOOCs retention. This study also analyzes the mediating role of IT (Social Media) between critical factors and MOOCs retention and provides feasible solutions for the improvement of MOOCs retention rates. Data were collected from Pakistani participants who attended, registered, or completed any MOOC. The findings of this study indicate that critical factors such as the instructor's role, course relevancy, learning outcomes have a positive impact on MOOC participants' retention. The results also confirm the important mediating role of IT (Social Media). The universities and the instructors who offer MOOCs should consider these critical factors when offering an online course. These critical factors can enhance MOOCs participants' retention leading them to take the courses until certification.

Published
2021

11. Report of the first seven agents in the I-SPY COVID trial: a phase 2, open label, adaptive platform randomised controlled trial

Author

Consortium, The I-SPY COVID, Files, D Clark, Aggarwal, Neil, Albertson, Timothy, Auld, Sara, Beitler, Jeremy R, Berger, Paul, Burnham, Ellen L, Calfee, Carolyn S, Cobb, Nathan, Crippa, Alessio, Discacciati, Andrea, Eklund, Martin, Esserman, Laura, Friedman, Eliot, Gandotra, Sheetal, Khan, Kashif, Koff, Jonathan, Kumar, Santhi, Liu, Kathleen D, Martin, Thomas R, Matthay, Michael A, Meyer, Nuala J, Obermiller, Timothy, Robinson, Philip, Russell, Derek, Thomas, Karl, Wong, Fum, Wunderink, Richard G, Wurfel, Mark M, Yen, Albert, Youssef, Fady A, Darmanian, Anita, Dzierba, Amy L, Garcia, Ivan, Gosek, Katarzyna, Madahar, Purnema, Mittel, Aaron M, Muir, Justin, Rosen, Amanda, Schicchi, John, Serra, Alexis L, Wahab, Romina, Gibbs, Kevin W, Landreth, Leigha, LaRose, Mary, Parks, Lisa, Wynn, Adina, Ittner, Caroline AG, Mangalmurti, Nilman S, Reilly, John P, Harris, Donna, Methukupally, Abhishek, Patel, Siddharth, Boerger, Lindsie, Kazianis, John, Higgins, Carrie, McKeehan, Jeff, Daniel, Brian, Fields, Scott, Hurst-Hopf, James, Jauregui, Alejandra, Swigart, Lamorna Brown, Blevins, Daniel, Nguyen, Catherine, Suarez, Alexis, Tanios, Maged A, Sarafian, Farjad, Shah, Usman, Adelman, Max, Creel-Bulos, Christina, Detelich, Joshua, Harris, Gavin, Nugent, Katherine, Spainhour, Christina, Yang, Philip, Haczku, Angela, Hardy, Erin, Harper, Richart, Morrissey, Brian, Sandrock, Christian, Budinger, GR Scott, Donnelly, Helen K, Singer, Benjamin D, Moskowitz, Ari, Coleman, Melissa, Levitt, Joseph, Lu, Ruixiao, Henderson, Paul, Asare, Adam, Dunn, Imogene, and Barragan, Alejandro Botello

Subjects
Health Services, Clinical Trials and Supportive Activities, Clinical Research, Good Health and Well Being, I-SPY COVID Consortium, Acute lung injury, Clinical trial, Respiratory insufficiency, SARS-CoV-2
Abstract

BackgroundAn urgent need exists to rapidly screen potential therapeutics for severe COVID-19 or other emerging pathogens associated with high morbidity and mortality.MethodsUsing an adaptive platform design created to rapidly evaluate investigational agents, hospitalised patients with severe COVID-19 requiring ≥6 L/min oxygen were randomised to either a backbone regimen of dexamethasone and remdesivir alone (controls) or backbone plus one open-label investigational agent. Patients were enrolled to the arms described between July 30, 2020 and June 11, 2021 in 20 medical centres in the United States. The platform contained up to four potentially available investigational agents and controls available for randomisation during a single time-period. The two primary endpoints were time-to-recovery (

Published
2023

26. Addressing the Brain Drain Crisis among Healthcare Professionals

Author

Kashif Khan Kashif Khan and Anny Ashiq Ali

Subjects
Brain draining, Healthcare professionals, Healthcare professionals' crises, Nursing, RT1-120
Abstract

Many of our healthcare professionals are growing increasingly frustrated and experiencing various psychological conditions, leading to feelings of depression and stress within our society. This editorial will explore the reasons behind the brain draining of healthcare professionals in Pakistan.

Published
2024
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27. I-SPY COVID adaptive platform trial for COVID-19 acute respiratory failure: rationale, design and operations

Author

Files, Daniel Clark, Matthay, Michael A, Calfee, Carolyn S, Aggarwal, Neil R, Asare, Adam L, Beitler, Jeremy R, Berger, Paul A, Burnham, Ellen L, Cimino, George, Coleman, Melissa H, Crippa, Alessio, Discacciati, Andrea, Gandotra, Sheetal, Gibbs, Kevin W, Henderson, Paul T, Ittner, Caroline AG, Jauregui, Alejandra, Khan, Kashif T, Koff, Jonathan L, Lang, Julie, LaRose, Mary, Levitt, Joe, Lu, Ruixiao, McKeehan, Jeffrey D, Meyer, Nuala J, Russell, Derek W, Thomas, Karl W, Eklund, Martin, Esserman, Laura J, Liu, Kathleen D, Mittel, Aaron M, Yen, Albert F, Suarez, Alexis E, Serra, Alexis L, Amin, Alpesh N, Rosen, Amanda, Dzierba, Amy L, Haczku, Angela, Barker, Anna D, Weisman, Ariel R, Daniel, Brian M, Morrissey, Brian M, Jones, Chayse, Creel-Bulos, Christina, Angelucci, Christina M, Files, Daniel C, Ng, Diana, Youssef, Fady A, Chaparro-Rojas, Fredy, Harris, Gavin H, Barot, Harsh V, Su, Heny, Garcia, Ivan, Sutter, Jacqueline B, Dodin, Jamal, Lee, Jerry S, Kazianis, John, Reilly, John P, Detelich, Joshua F, Lang, Julie E, Muir, Justin, Gosek, Katarzyna, Nugent, Katherine L, Yee, Kimberly, Rodrigues, Laura G, Macias, Laura R, Orr, Lindsey A, Boerger, Lindsie L, Rosario-Remigio, Lissette, Kufa, Lucia, Huerta, Luis E, Tanios, Maged, Reyes, Maria B, Adelman, Max W, Juarez, Maya M, Jung, Michelle, Meyers, Michelle, Sternlieb, Mitchell P, Cobb, Nathan K, and Aggarwal, Neil

Subjects
Clinical Research, Clinical Trials and Supportive Activities, Good Health and Well Being, Bayes Theorem, COVID-19, Humans, Pandemics, Respiratory Distress Syndrome, Respiratory Insufficiency, SARS-CoV-2, Treatment Outcome, ISPY COVID Adaptive Platform Trial Network, undefined, adult intensive & critical care, clinical trials, Clinical Sciences, Public Health and Health Services, Other Medical and Health Sciences
Abstract

IntroductionThe COVID-19 pandemic brought an urgent need to discover novel effective therapeutics for patients hospitalised with severe COVID-19. The Investigation of Serial studies to Predict Your Therapeutic Response with Imaging And moLecular Analysis (ISPY COVID-19 trial) was designed and implemented in early 2020 to evaluate investigational agents rapidly and simultaneously on a phase 2 adaptive platform. This manuscript outlines the design, rationale, implementation and challenges of the ISPY COVID-19 trial during the first phase of trial activity from April 2020 until December 2021.Methods and analysisThe ISPY COVID-19 Trial is a multicentre open-label phase 2 platform trial in the USA designed to evaluate therapeutics that may have a large effect on improving outcomes from severe COVID-19. The ISPY COVID-19 Trial network includes academic and community hospitals with significant geographical diversity across the country. Enrolled patients are randomised to receive one of up to four investigational agents or a control and are evaluated for a family of two primary outcomes-time to recovery and mortality. The statistical design uses a Bayesian model with 'stopping' and 'graduation' criteria designed to efficiently discard ineffective therapies and graduate promising agents for definitive efficacy trials. Each investigational agent arm enrols to a maximum of 125 patients per arm and is compared with concurrent controls. As of December 2021, 11 investigational agent arms had been activated, and 8 arms were complete. Enrolment and adaptation of the trial design are ongoing.Ethics and disseminationISPY COVID-19 operates under a central institutional review board via Wake Forest School of Medicine IRB00066805. Data generated from this trial will be reported in peer-reviewed medical journals.Trial registration numberNCT04488081.

Published
2022

31. Genome Editing and Cardiac Regeneration

Author

Bassawon, Rayhaan, Khan, Kashif, Siddique, Ateeque, Shum-Tim, Dominique, Crusio, Wim E., Series Editor, Dong, Haidong, Series Editor, Radeke, Heinfried H., Series Editor, Rezaei, Nima, Series Editor, Steinlein, Ortrud, Series Editor, and Xiao, Junjie, Series Editor

Published
2023
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47. Adversarial Learning on the Latent Space for Diverse Dialog Generation

Author

Khan, Kashif, Sahu, Gaurav, Balasubramanian, Vikash, Mou, Lili, and Vechtomova, Olga

Subjects
Computer Science - Computation and Language
Abstract

Generating relevant responses in a dialog is challenging, and requires not only proper modeling of context in the conversation but also being able to generate fluent sentences during inference. In this paper, we propose a two-step framework based on generative adversarial nets for generating conditioned responses. Our model first learns a meaningful representation of sentences by autoencoding and then learns to map an input query to the response representation, which is in turn decoded as a response sentence. Both quantitative and qualitative evaluations show that our model generates more fluent, relevant, and diverse responses than existing state-of-the-art methods., Comment: Accepted to COLING 2020

Published
2019

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