624 results on '"Sabharwal A"'
Search Results
2. Exploring cardiovascular involvement in IgG4-related disease: a case series approach with cardiovascular magnetic resonance
- Author
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Henry, John Aaron, Xavier, Roshan, Selvaraj, Emmanuel, Burrage, Matthew, Thomas, Katharine E, Lukaschuk, Elena, Zhang, Qiang, Ferreira, Vanessa M, Piechnik, Stefan K, Sabharwal, Nikant, Neubauer, Stefan, Rider, Oliver, Culver, Emma L, and Lewis, Andrew
- Abstract
BackgroundIgG4-related disease (IgG4-RD) is a relapsing–remitting, fibroinflammatory, multisystem disorder. Cardiovascular involvement from IgG4-RD has not been systematically characterised. In this study, we sought to evaluate consecutive patients with IgG4-RD using a detailed multiparametric cardiovascular magnetic resonance (CMR) imaging protocol.MethodsWe prospectively enrolled 11 patients with histology-confirmed IgG4-RD; with active disease at time of scan. We undertook a detailed multiparametric CMR imaging protocol at 1.5T including cine imaging, native T1 and T2 mapping, stress perfusion imaging with inline quantitation of myocardial blood flow and late gadolinium enhancement (LGE) imaging.ResultsAll patients exhibited at least one abnormality on CMR imaging. Abnormal elevation of global or segmental left ventricular myocardial T1 and T2 values was present in four patients, suggesting myocardial oedema or inflammation. Abnormal LGE, suggesting myocardial scar fibrosis, was present in nine patients, with eight displaying a non-ischaemic pattern, and one showing an ischaemic pattern. Four patients fulfilled both Lake Louise Criteria for active myocardial inflammation, while a further six fulfilled one criterion. Myocardial perfusion reserve was normal in all evaluable patients. Ten patients had normal ventricular volumes, mass and systolic function. In addition, thoracic aortitis was identified in three patients who underwent 18F-flourodeoxyglucose PET/CT imaging, with resolution following anti-B-cell treatment.ConclusionsIn this cohort of patients with histology-confirmed IgG4-RD, multiparametric CMR revealed no changes in gross cardiac structure and function, but frequent myocardial tissue abnormalities. These data suggest a plausible pathophysiological link between IgG4-RD and cardiovascular involvement.
- Published
- 2025
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3. Wearable Blood Pressure Monitoring Devices: Understanding Heterogeneity in Design and Evaluation
- Author
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Cheung, Matt Y., Sabharwal, Ashutosh, Cote, Gerard L., and Veeraraghavan, Ashok
- Abstract
Objective: Rapid advances in cuffless blood pressure (BP) monitoring have the potential to radically transform clinical care for cardiovascular health. However, due to the large heterogeneity in device design and evaluation, it is difficult to critically and quantitatively evaluate research progress. In this two-part manuscript, we provide a principled way of describing and accounting for heterogeneity in device and study design. Methods: We first provide an overview of foundational elements and design principles of three critical aspects: 1) sensors and systems, 2) pre-processing and feature extraction, and 3) BP estimation algorithms. Then, we critically analyze the state-of-the-art methods via a systematic review. Results: First, we find large heterogeneity in study designs, making fair comparisons extremely challenging. Moreover, many study designs have data leakage and are underpowered. We suggest a first open-contribution BP estimation benchmark for standardization. Next, we observe that BP distribution in the study sample and the time between calibration and test in emerging personalized devices confound BP estimation error. We suggest accounting for these using a convenient metric coined “explained deviation”. Finally, we complement this manuscript with a website,
https://wearablebp.github.io , containing a bibliography, meta-analysis results, datasets, and benchmarks, providing a timely plaWorm to understand state-of-the-art devices. Conclusion: There is large heterogeneity in device and study design, which should be carefully accounted for when designing, comparing, and contrasting studies. Significance: Our findings will allow readers to parse out the heterogeneous literature and move toward promising directions for safer and more reliable devices in clinical practice and beyond.- Published
- 2024
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4. Adding 6 months of androgen deprivation therapy to postoperative radiotherapy for prostate cancer: a comparison of short-course versus no androgen deprivation therapy in the RADICALS-HD randomised controlled trial
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Parker, Chris C, Clarke, Noel W, Cook, Adrian D, Kynaston, Howard, Catton, Charles N, Cross, William R, Petersen, Peter M, Persad, Rajendra A, Saad, Fred, Bower, Lorna C, Logue, John, Payne, Heather, Forcat, Silvia, Goldstein, Cindy, Murphy, Claire, Anderson, Juliette, Barkati, Maroie, Bottomley, David M, Branagan, Jennifer, Choudhury, Ananya, Chung, Peter W M, Cogley, Lyn, Goh, Chee L, Hoskin, Peter, Khoo, Vincent, Malone, Shawn C, Masters, Lindsey, Morris, Stephen L, Nabid, Abdenour, Ong, Aldrich D, Raman, Rakesh, Tarver, Kathryn L, Tree, Alison C, Worlding, Jane, Wylie, James P, Zarkar, Anjali M, Parulekar, Wendy R, Parmar, Mahesh K B, Sydes, Matthew R, Amos, Claire, Beaney, Katherine, Bellenger, Katharine, Brawley, Chris, Chung, Christina, Cook, Adrian, Embleton, Andrew, Forcat, Silvia, Goldstein, Cindy, Grabey, Jenna, Hague, Dominic, Herasimtschuk, Anna, Islam, Zaheer, Jovic, Gordana, Maniatis, Christos, Masters, Lindsey, Mohamed, Fatima, Morgan, Rachel, Murphy, Claire, Noor, Dipa, Parmar, Max, Patterson, Paul, Pickering, Holly, Pugh, Cheryl, Rauchenberger, Mary, Ribeiro, Helena, Roach, Carol, Seray-Wurie, Fatimah, Shaji, Preetha, Sims, Hannah, Spittle, Ben, Sydes, Matthew, Tappenden, Nancy, Tsang, Lilian, Uscinska, Barbara, Brown, Teddy, Casey, Sue, Davidson, Cathy, Dellar, Conor, Fletcher, Mandy, Meyer, Ralph, Murphy, Karen, Parulekar, Wendy, Richardson, Harriet, Richardson, Karen, Whelan, Kate, Bissett, Donald, Bonner-Shand, Shelagh, Grant, Judith, Lynch, Rory, MacDonald, Graham, McWilliam, Marie, Moir, Rachel, Rodwell, Sue, Shearer, Kirsty, Smith, Margaret, Binns, Louise, Briggs, Maxine, Brown, Simon, Casanova, Nathalie, Clark, Katy, Hartley, Jasmine, Henry, Ann, Henson, Helen, Hill, Pipa, Jeyasanger, Ganesan, McAlister, Judy, Parkinson, Sharron, Shaw, Alison, Alcock, Christopher, Brady, Joanne, Camilleri, Philip, Carpenter, Janice, Hogbin, Emma, Hyde, Katherine, Kavanagh, Roisin, Ngumo, Alice, Padilla-Harris, Cheryl, Panakis, Niki, Pwint, Thinn, 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Bownie-Mukumbu, Godfrey, Bryan, Rachel, Corner, Victoria, Edwards, Abigail, Fazekasne Fulep, Adrienn, Guerrero-Urbano, Teresa, Gwilt, Julie, Happle, Liz, Jackman, Dee, Krysta, Beata, Paisey, Sangeeta, Shaffer, Richard, Smith, Jackie, Westley, Rosalyne, Yusof, Hilawati, Appleby, Rowan, Avis, Joanne, Beresford, Mark, Brydon-Hill, Ruth, Cox, Christine, Curtis, Samantha, Elwell, Christine, Frim, Olivera, Hamilton, Beatrice, Hudson, Zoe, Jenner, Abigail, Mantle, Mark, Mason, Jessica, Masson, Susan, McFarlane, Jonathan, Newman, Hugh, Pocock, Abigail, Robertson, Bryony, Tomlinson, Eve, Wassall, Rebecca, Williams (Née Allen), Sister Tania, Brown, Grace, Clarke, Peter, Cole, Aiden, Cousins, Jane, Gilchrist, Gail, Hagan, Chris, Hanna, Emma, Harney, Jackie, Hetherington, Stacey, Houghton, Fionnuala, Hynds, Sharon, Jain, Suneil, Johnston, Ruth, Keane, Patrick, Lyons, Ciara, McAleese, Jonathan, McAllister, Jo, McKenna, Karen, Mitchell, Darren, Murray, Stacey, O'Sullivan, Joe, Rajan, Nambi, Shum, Poh Lin, Stewart, David, Stranex, Stephen, Totten, Grace, Chandrashekara, Madhura, Drew, Ellen, Drew, Mary (Ellen), Fletcher, Julie, Gamble, Tina, Lokes, Rachel, Schumacher, Ann, Shetty, Kavitha, Sohanpal, Sundip, Whitehouse, James, Zarkar, Anjali, Ford, Daniel, Glaholm, John, Lenton, Daniel, O'Beney, Rachael, Sohanpal, Sundip, Taylor, Helen, Thomas, Helen, Whitehouse, James, Chetiyawardana, Samantha, Doherty, A, El-Modir, Ahmed, Ford, Daniel, Glaholm, John, Gunn, Melanie, James, Nicholas, Kedge, Liz, Lokes, Rachel, Mannan, Aliyah, Marange, Josephine, Morrison, Andrew, Okubanjo, Maryanne, Olaniyi-Oduntan, Darlletta, Peterkin, Zhane, Preece, George, Qureshi, Zayn, Ross, Liliam, Samra, Tarandip, Southgate, Elizabeth, Stephens, Rebekah, Stevenson, Robert, Watson-Jones, Phillip, Zarkar, Anjali, Abdulwahid, Danya, Ainsworth, Sarah, Ashworth, Sue, Beard, Karen, Charnley, Natalie, Desai, Parth, Fielding, Bethany, Flaherty, Jan, Forrest, Diane, Frankland, Helen, Haidar, Imran, Hargreaves, Jeanette, Henry, Joanne, Hugill, Angela, Jewers, Karen, Keith, Sarah, Kilroy, Stephen, King, Jennifer, Parikh, Omi, Riley, Karen, Ryan-Smith, Janet, Thomas, Jacqueline, Thompson, Catherine, Tickle, Vivienne, Waring, Lynsey, Wise, Marcus, Abdulwahid, Danya, Ainsworth, Sarah, Ashworth, Sue, Beard, Karen, Dawson, Louise, Desai, Parth, Ditchfield, Julie, Fielding, Bethany, Flaherty, Jan, Forrest, Diane, Frankland, Helen, Haidar, Imran, Hargreaves, Jeanette, Henry, Joanne, Hugill, Angela, Jewers, Karen, Keith, Sarah, Kilroy, Stephen, King, Jennifer, Parikh, Omi, Riley, Karen, Ryan-Smith, Janet, Thompson, Catherine, Tickle, Vivienne, Waring, Lynsey, Wise, Marcus, Al-Samarraie, Amir, Ali, Zulfiqer, Bolger, Annette, Davies, Llinos, Flavin, Aileen, Heron, Jane, Hughes, Faye, Jackson, Kim, Lewis, Joanne, Manley, Rachel, Oommen, Nikhil, Rutter, Charley-Anne, Smith, Nick, Srinivasan, Vaiku, Stockport, Jane, Tapping, Sister Hayley, Thomas, Heather, Toussi, H, Wood, Cathryn, Archer, Simon, Fletcher, Joanne, Ginnelly, Helen, Kirkby, Amy, Knight (n. Sherburn), Victoria, Lawrence nee Palmer, Tara, Mashegede, Beverley, Metcalf, Karen, Panades, Miguel, Sreenivasan, Thiagarajan, Szymiczek, Kinga, Thomas, Isobel, Young, Anita, Broadley, Eve, Brock, Sue, Davies, Joseph, Jones, Stephanie, McCully, Ruby, Naraine, Nicky, Preece, Kate, Purandare, Laura, Rabbi, Taslima, Savage, Sarah, Vamplew, Luke, Bamford, Linda, Benton, Richard, Cotterill, Lauren, Henry, Ann, Inman, Hayley, Karsera, Louise, Kay, Anne Marie, Kenyon, Lucille, Lazarte, Jannika, McNulty, Dawn, Moore, Eleanor, Nallathambi, Chandran, Naylor, Deirdre, Owen, Lisa, Parikh, Shefali, Pendrill, Amy, Price, Joanne, Robertshaw, Helen, Rodda, Sree, Sewell, Jane, Teo, Mark, Waldron, Eleanor, Bloomfield, David, Blythe, Tracy, Brown, Joanne, Carr, Philippa, Chambers, Carla, Dlodlo, Jessica, Frattaroli, Paul, Gilbert, Duncan, Hart, Andrew, Hodges, Samantha, Hooper, Simon, Hunter, Catherine, Lactao, Ranee, Leitao, Madalena, McKinna, Fiona, Nikapota, Ashok, Peterson, Jane, Robinson, Angus, Tremlett, Jean, Webb, Andrew, Wilkins, Marie, Young, Bobbie, Bahl, Amit, Baxter, Matt, Beresford, Mark, Bowers, Dawn, Challipalli, Amarnath, Coe, Marc, Cowley, Sue, Gillatt, David, Griffiths, Dorothy, Henson (nee Savidge), Verity, Hilman, Serena, Hollister, Robert, Kisanga, Mary, Lee, Vivienne, Lowe, Amelia, Macefield, Rhiannon, Masson, Susan, Pegler, Ruth, Robertson, Peter, Saldanha, Helen, Simmonds, Mary, Sims, Laura, Wheeler, Lucie, Wilson, Paula, Woods, Martin, Yarrow, Sue, Zelley, Sarah, Abd Alazeez, Mohamed, Alderton, Kate, Bahl, Amit, Beresford, Mark, Brain, Carol, Challipalli, Amarnath, Chilcott, Helen, Clarke, Samantha, Cobos-Arrivabene, Marta, Corderoy, Helen, Cousins, Rebecca, Garner, Victoria, Gillatt, David, Herbert, Chris, Johnson, Lyndsey, Jones, Kathryn, Kemp, Gail, Kirkby, Sarah, Kirkpatrick, Suriya, Koupparis, Anthony, Manning, Courtney, Oxley, Jon, Perry, Emily, Persad, Raj, Phipps, Charlotte, Richmond-Russell, Kerry, Rowe, Edward, Shelton, Luke, Shiridzinomwa, 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Thomas, Carys, Trigonis, Ioannis, Walker, Karen, Williams, Joanne, Williamson, Elizabeth, Wood, Marian, Wood, Pauline, Zurakovsky, Hilary, Bois (nee Harris), Elizabeth, Chan, Hin Fan, Chikopela, Loveness, Clark, Helen, Clements, Colette, Holliday, Samantha, Hughes, Owen, Jones, Clare, Kynaston, Howard, Mason, Malcolm, Narahari, Krishna, Pearse, Kevin, Staffurth, John, Austin, Mandy, Barnett, Christian, Camburn, Sister Tracey, Dawson, Elizabeth, Gibson, Sian, Hamid, Abdel, Kancherla, Kiran, Leone, Priscilla, Perkins, Lauren, Ramsay, Valerie, Singizi, Bryan, Allen, Julie, Anderson, Susan, Andrews (nee Moore), Eleanor, Bakawala, Rehana, Beazer, Sarah, Binks, Colin, Bowen, Jo, Broomfield, Barbara, Cook, Audrey, Crossley, Lin, Ford, Chris, Forkes, Janet, Hall, Julia, Healey-Mariano, Jennifer, Jenkins, Peter, Johnson, Nigel, Jonnada, Sai, Owen, Roger, Pratten, Elaine, Sayers, Rachel, Shimell, Chris, Skelton, Amy, Tan, Matthew, Trigg-Hogarth, Kate, Wronski, Sue, Aldous, Mary, Archer, Denise, 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Laverick, Rosaleen, Mansell, Gemma, Mislang, Albert, Ngwenya, Su, Panesar, Pritpal, Sanders, Kay, Simmons, Elaine, Sinfield, Melanie, Stanley, Laura, Steventon, Emily, Stockdale, Andrew, Sunny, Manju, Thind, Manreet, Tranter, Fiona, Tsalic, Medy, Worlding, Jane, Adamson, Vanessa, Bennion, Carole, Butler-Barnes, Jenny, Hough, Adele, Hough, Chris, Hughes, Joanne, Irwin, P, Javle, P, Logue, John, Mansfield, Carolyn, Smith, Rachel, Sumner, Lesley, Tomlinson, Annabel, Tran, Anna, Walker, Caroline, Wylie, James, Chakraborti, Prabir, Das, Prantik, Ennis, Dawn, Marriott, Sue, Muthukumar, Dakshinamoorthy, Pattu, Pugazhenthi, Ward, Colin, Alzouebi, Mymoona, Burlace, Barbara, Ferguson, Catherine, Firth, Alexandra, Hooton, Georgia, Jeffcutt, Nicole, Martindale, John, Neal, Amy, Pezaro, Carmel, Sivoglo, Virgil, Smedley (nee McCabe), Janine, Smith, Lucy, Taylor, Jennifer, Wilkinson, Nicola, Wood, Sister Kim, Afzal, Naveed, Anderson, Beverley, Andrews, Stephen, Bakker, Piet, Breakspear, Sally, Cornaby, Andrew, Crellin, Perric, Gibbins, Andrew, Gibbins, Jackie, Glen, Tracy, Goodsell, Josie, Horton, Sarah, Jones, Stephanie, Love, Sally, Masters, Benjamin, Rees, Andrew, Sharpe, Simon, Taylor, Kate, Wignall, Suzy, Harrow, Sister Kath, Jones, Lesley, Kanyi, Karen, Keng-Koh, Pek, McGarry, Karen, Ramachandra, Prakash, Smith, Lucy, Watts, Angela, Baumber, Shelley, Gilbert, Duncan, Howard, Joanna, Jones-Skipper, Kay, Manetta, Caroline, McCrisken, Lauren, McKinna, Fiona, Taylor, Jo-Anne, Williams, Amanda, Clark, Alison, Clarke, Maria, Egan, Lisa, Fiddes, Kathleen, Forman, Susan, Gardiner, Fiona, Jeffrey, David, Mariappan, Param, Mayne, Barbara, McLaren, Duncan, Rogers, Brian, Woods, Catherine, Young, Douglas, Anderson, John, Baines, Kizzy, Crundwell, M, Davey, Elizabeth, Ford, Victoria, Lawless, Theresa, Matkins, Ellen, McGinley, Shannon, McGrath, John, Piper, Jane, Roantree, Alison, Seath, Ingrid, Sheehan, Denise, Srinivasan, Rajaguru, Stott, Mark, Timmings, Katie, Webb, Claire, Al-hasso, Abdulla, Allan, Elaine, Barwell, Lorraine, Cowan, Chloe, Dodds, David, Finnegan, Niall, Griffen, Ailsa, Jones, Rob, Lawless, Claire, MacMillan, Antonia, Mahmood, Rana, Russell, Martin, Sadozye, Azmat, Shirley, Sian, Sidek, Norma, Wallace, Jan, Akhtar, Naeem, Al-hasso, Abdulla, Ansari, Jawaher, Bell, Karen, Humpreys, Louise, McWilliam, Donna, Moody, Emma, O'Neil, Rebecca, Peck, Suzannah, Turley, Claudia, Auladin, Raidah, Bird, Stephanie, Bissa, Caterina, Dabbs, Marianne, Di Maria, Fabio, Goh, Chee, Guerrero-Urbano, Teresa, Harding, Joshua, Jamieson, Ceri, Jones, Jules, Khaksar, Sara, Laing, Robert, Matthews, Laura, Medisetti, Aruna, Molony-Oates, Barbara, Money-Kyrle, Julian, Nobes, Jenny, Rabsztyn, Weronika, Shaffer, Richard, Sidi, Frances, Smale, Daisy May, Stone, Sarah, Tindall, Annie, Woods, Jane, Wu, Min, Daniel, Amelia, Holmes, Evelyn, Kellaway, Joanne, Lewis, Amanda, Light, Teresa, Melcher, Lucinda, Robinson, Lily, Staines, Nikki, Anderson, Susan, Carini, Serrafina, Cook, Audrey, Cooper, Sophie, Evans, Melanie, Follows, Josh, Forkes, Janet, Grant, Warren, Hedges, Andy, Hughes, Claire, Jones (Birch), Janine, King, Lisa, Lees, Laura, Mercer, Lily, Myles, Sophie, Roberts, Zara, Williamson, Nicola, Abbassi, Sara, Alonzi, Roberto, Anyamene, Nicola, Bici, Julia, Collins, Lucy, Dickson, Jeanette, Garrido-Perez, Sandra, Hoskin, Peter, Hughes, Robert, Jenkins, Suzanne, Kowalewska, Paulina, Milner, Jessica, Mitchell, Lesley, Ostler, Peter, Rashid, Aamna, Stapleton, Stephanie, Bashir, Faheem, Bone, Linzi, Clark, Diane, Dixit, Sanjay, Horne, Rhian, Jones, Dawn, Palmer, Sarah, Plowman, Kristian, Rawlings, Julie, Simms, Matthew, Addison, Anglise, Arnott, Seonaid, Borgaonkar, Sudhir, Brown, Sandra, Campbell, Audrey, Colligan, Steve, Cormack, Margaret, Doughty, Victoria, Kelly, Kay, Lister, Debbie, Macdonald, Alison, Macgregor, Carol, Mackay, Rachel, Madeleine, Jude, McNally, Morag, McPhail, Neil, Neville, Sean, Nicholls, Alison, Papakostidi, Aristoula, Simpson, Georgina, Sinclair, Glenda, Skene, Anna, Telfer, Sheena, Urquhart, Zoe, Whillis, David, Etheridge, Charlotte, Ford, Amanda, Ridley, Paul, Scrase, Christopher, Upson, Susan, Venkitaraman, Ramachandran, Capaldi, Lisa, Churn, Mark, Higgins, Linda, Knott, Helen, Stringer (pr. Davis), Sally, Taylor, Jacob, Tranter, Helen, Wollaston, Julie, Brogan (nee Roddie), Stephanie, Erskine, Susan, Hamill, Maureen, Prentice, Lynn, Russell, Martin, Sidek, Norma, Symon, Lesley, Teahan, Seamus, Todd, Anne, Tweedle, James, Young, Sally, Austin (nee Glover), Gemma, Boon, Ian, Bottomley, David, Breckin, Edmund, Clarke, Jude, Coyle, Catherine, Davies, Emily, Goulding, James, Henry, Ann, Kiltie, Anne, Loughrey, Carmel, Moore, Eleanor, Murad, Fatima, Pratt, Claire, Shuttleworth, Pam, Smith, Beccy, Smith, Gill, Branson (née Dineen), Amy, Christmas, Sallyanne, Cooke, Jill, Griffiths, Leyshon, Kent, Christopher, King, Amy, Kockelbergh, Roger, Potterton, Janet, Speed, Lesley, Vasanthan, Subramanian, Walker, Julia, Archer, Simon, Baria, Karin, Bateman, Samantha, Coombs, Sarah, Cunningham, Amy, Dahar, Nazeer, Daruwala, Pallon, Francis, Olesya, Ginnelly, Helen, Hall, Jane, Hoare, Kathryn, Key, Claire, Lockwood, Carol, Mark, Ian, Newton, Rachel, Okubanjo, Maryanne, Panades, Miguel, Sloan, Andrew, Sreenivasan, Thiagarajan, Bermingham, Nicola, Dean, Lynsey, Malik, Zafar, Pilkington, Pauline, Robson, Peter, Rogers, Heather, Rogers, Thomas, Treherne, Katy, Bourke, Amie, Chikkamuniyappa, Bindu, Dalton-Short, Ross, Edwards, Steve, Falconer, Alison, Ford, Amy, Harinarayanan, Sathish, Hassan, Nebah, Hornzee, Gillian, Magwaro, Sophia, McInerney, Sue, Pratap, Bhavesh, Rezkallah, Sarah, Sadare (Olaleye), Ibiyemi, Stancliffe, May, Stewart, Simon, Thoi, Anne-Maree, Westrop, Anna, Ahamadali, Sheefa, Arsanious, Nasr, Balogun, Shaki, Batish, Cheryl, Campbell, Yvonne, Crowley, Claire, Dunne, Emma, Ferreira, Miguel, Haldeos, Anne, Hijab, Adham, Huddart, Robert, John, Babbin, Michaelidou, Andriana, Nawrozzadeh, Mohammed, Pach, Jackie, Patel, Priyanka, Payne, Ann, Perry, Matthew, Sadare (Olaleye), Ibiyemi, Serra, Maria, Springall, Christine, Thompson, Jane, Thomson, Sister Jane, Tree, Alison, Aya, Viviana, Beaney, Ronald, Bower, Lorna, Conway, Lisa-Jane, Donaghey, Sally, Gordon, Hussain, Grau, Isabel, Guerrero-Urbano, Teresa, Harris, Sarah, Hogan, Vesna, Hore, Brendan, Hughes, Simon, Jones, Katie, Kuenzig, Greg, Morris, Stephen, Nathan, Kannon, Nunney, Dawn, Rashid, Jessica, Reynolds, Philip, Rooprai, Bali, Chikkamuniyappa, Bindu, Dalton-Short, Ross, Edwards, Steve, Hassan, Nebah, Magwaro, Sophia, Mangar, Stephen, Rezkallah, Sarah, Stewart, Simon, Thoi, Anne-Maree, Westrop, Anna, Azirar, Hanna, Caumont, Tom, Gupta, Nishi, Hill, Judy, Karp, Stephen, Kullane, Sagal, Macavoy, Tina, Melcher, Lucinda, Neave, Farhad, Newby, Jackie, Van Someren, Chloe, Anderson, Naomi, Annan, Lynda, Cooke, Juniebel, Douch, Emma, Fawcitt, Sara, Gore, Alexandra, Grieve, Sylvia, Jarvis, Claire, McCadden, Angela, Needleman, Sarah, Pigott, Katherine, Powell, Hannah, Ramtohul, Kaliyanee, Rosenfelder, Nicola, Vilarino-Varela, Maria, Ali, Jennyfa, Ali, Maryam, Banda, Laillah-Crystal, Bott, Trevor, Brown, Emmanuel, Bryant, Hanna, Carlino, Giulia, Grant, Cordelia, Hogan, Holly, Khoo, Vincent, Mojidra, Chintan, Montebello, Joseph, Morrison, Jennifer, Olabanji, Matthew, Pillai, Sijy, Siu, Bernard, Stafferton, Ruth, Storrs, Sarah, Townsend-Thorn, Debra, Tree, Alison, Van As, Nicholas, Vijayakumar, Vijitha, Welsh, Liam, Dearnaley, David, Gao, Annie, Horwich, Alan, Huddart, Robert, Lewington, Val, Marshall, John, Murphy, Louise, Parker, Chris, Venkitaraman, Ramachandran, Beaton, Kirsty, Bech-Nielsen, Nanette, Bolton, Olivia, Chetiyawardana, Samantha, Conibear, John, Dekaj, Fatjon, Hillman, Paul, Jayachandran, Resmi, Kaliski, Alexandre, Kiff, Janet, Nicholson, Alastair, Nixon, Jude, Oladimeji, Janet, Riches, Oscar, Smith, Dan, Tipples, Karen, Wells, Paula, Anderson, Chris, Daly, Deirdre, Dover, Serena, Gilmartin, Claire, Golden, Sophie, Gregg, Jane, Issa, Rami, Khoo, Vincent, Oni, Titilayo, Patel, Chandni, Perry, Matthew, Quarrell, Mark, Rolfe, Debbie, Tighe, Helen, Toledo, Jesusa, Tuazon, Juel, Varro, Robert, Bohra, Vikram, Caballes, Mark, Custins, Laura, Hornzee, Gillian, Ivie, Stephanie, Joshi, Manisha, Liau, Joy, Magwaro, Sophia, Naim, Farhan, Patel, Anup, Pathmanathan, Byiravey, Russell, Emily, Stewart, Simon, Thoi, Anne-Maree, Tippins, Anna, Westrop, Anna, Agdiran, Didem, Aggarwal, Ajay, Amoaten, Thomas, Aslam, Natasha, Bonsu, Nicole, Butt, Zainab, Danaswamy, Patricia, Davda, Reena, Gillesen, Annelies, Goel, Roshni, Hall, Julia, Khong, Rachel, Lowi, Suzy, Merrick, Richard, Mitra, Anita, Payne, Heather, Teh, Jonathan, Whinn, Samantha, Woolf, David, Zilkha, Helene, Bech-Nielsen, Nanette, Bolton, Olivia, Danaswamy, Patricia, Dawson-Athey, Linda, Foster, Emma, Gillian, Andrew, Green, James, Hillman, Paul, Hines, John, Holden, Simon, Jayachandran, Resmi, Kazingizi, Memory, O'Neill, John, Olaoni, Thompson, Peters, John, Philp, Timothy, Wells, Paula, Woolf, David, Zaidi, Sadaf, Adeyoju, Adebanji, Adinkra, Victoria, Brough, Richard, Brown, Stephen CW, Collins, Gerald, Hardstaff, Lisa, Hill, Pippa, Kujawa, Magda, McCurrie, Marilyn, Townley, Barbara, Woodhouse, Iain, Zafar, Waheed, Beesley, Sharon, Breen, Vivienne, Brulinski, Patryk, Burrage, Su, Calvert, Clare, Davison, Alison, Dias, Heather, Fossey, Gavin, Hotine, Jodie, Jones, Ifan, Jope, Carmel, Lines, Yvonne, Martins, Sarah, Mercier, Barbara, Mikropoulos, Christos, Murray, Jane, Neshiri, Innocent, Pamphlett, Ian, Phillips, Ann, Rai, Pavnish, Rezkallah, Sarah, Roberts, Verity, Taylor, Henry, Taylor, Katy, Thomas, Carys, Tribe, Lisa, Wood, Pauline, Bottomley, Ian, Buchan, Jonathan, Carpio, Viviana, Choudhury, Ananya, Conroy, Ruth, Cowan, Richard, Davison, Sue, Duncan, Ian, Elliott, Tony, Fair, Kim, Fallaize, Adrian, Flanagan, Laura, Green, Sarah, Harris, Catherine, Hart, Amber, Jones, Cathryn, Livsey, Jacqueline, Logue, John, McCaul, Damian, Nazari, Roonak, Newbery, Carol, O'Connor, Kate, O'Dwyer, Sister Jackie, Oliver, Joanne, Onoge, Ekugbe, Petsa, Maria, Redshaw, Catherine, Smith, Sarah-Ellen, Spoor, Willemijn, Thomas, Sister Viv, White, Holly, Worsley, Lucy, Wylie, James, Bailey, Linda, Bennett, Molly, Corless, Rebecca, Duffy, Annie, Fellows, Kathryn, Gipson, Anna, Haydock, Helen, Knox, WF, Liptrott, Sarah, McCarth, Fiona, McCartin, Fiona, Mistry, Heena, Mthethwa, Thobekile, Partington, Lillian, Piper, Lindsay, Ramani, Vijay, Sahin, Sarah, Sangar, Vijay, Slevin, Kathryn, Tuck, Jonathan, Wylie, James, Atkinson, Lorraine, Barnbrooke, Sarah, Barnes, Alison, Brownless, Caroline, Chadwick, David, Hardman, John, Harland, Keith, Long, Carol, McAuliffe, Sarah, McBride, Julia, McClurey, Sister Patricia, Mohan, Rita, Naylor, Lynne, Peedell, Clive, Robson, Paula, Shakespeare, Devadasan, Thompson, Emma, Van der Voet, Hans, Wilson, David, Barron, Chris, Frew, John, Lambert, Katy, McMenemin, Rhona, Pedley, Ian, Riseborough, Danielle, Roberts, Trevor, Stockley, Emma, Stoddart, Jonathan, Tate, Janine, Walker, Jayashree, Workman, Bridget, Barnett, Debra, Carter, Adam, Cox, Adam, Davey, Alison, Davies, Kirstin, Gilby, Lisa, Hodge, Simon, Kearney, S, Lavender, Helene, Marty, Janet, Nash, Maxine, Rahman, Syhed, Richards, Jayne, Simpson, Julie, Wall, Elaine, Webb, Paula, Wild, Karen, Williams, Rachel, Wilson, Jim, Branagan, Jenny, Camilleri, Philip, Dell, Gillian, Elwell, Christine, Foo, Maxine, Goyena, Ruby, Jones, Andrea, McBain, Hazel, McGrath, Katherine, O'Callaghan, Jane, Ramanathan, Dorai, Tee, Elizabeth, Thominson, Bronwen, Tighe (nee Bussey), Rachel, Archer, Denise, Beety, Jane, Birch, Richard, Bloomfield, Mark, Callam, Sara, Cooper, Adele, Darby, Helen, Healey, Gail, Heasley, Karen, Kapur, Guarav, London, Clare, Nobes, Jenny, Oosterom, Katrien, Ostrowski, Joe, Shobrook, Bridget, Turner, Sarah, Wade, Rob, Walker, Suzanne, Walton, Sharon, Websdale, Cheryl, White, Joanna, Alder, Leanne, Chivers, Rachael, Gooch, Carol, Howard, Lucy, Hutchinson, Camille, Jevons, Cody, Kumar, Daniel, Marley, Hazel, Mills, Jamie, Purves, Hanna, Sayers, Ian, Sundar, Santhanam, Szolin-Jones, Jacob, Walker, Georgina, Wood, Catherine, Choudhury, Ananya, Conroy, Ruth, Cook, Wendy, Gill, Lisa, Johnson, Joanne, Johnstone, Dawn, Jones, Richard, Livsey, Jacqueline, Mistry, Leena, Scarratt, Lyndsay, Aiku, Abimbola, Ajzensztejn, Daniel, Al-Chamali, Hiba, Andrade, Gerard, Barber, Dave, Bekulart, Sylwia, Bhatnagar, Gagan, Boutflower, Jane, Brewster, Simon, Butterfield, Anne, Camilleri, Philip, Carroll, Weronika, Cole, David J, Coutts, Tim, Doyle, Kerrie, Durrant, Lisa, Green, Trish, Hyde, Katherine, Jampana, Raj, Jastrzebska, Patrycja, Kiltie, Anne, Kyffin, Phillippa, Langridge, Nicole, Lawrey, Sarah, Markus, Sarah, Marston, Kerrie, Mooney, Matthew, Mukkath, Sandra, Murphy, Ann, Owens, Rob, Panakis, Niki, Rabsztyn, Weronika, Ruane, Sarah, Sabbagh, Ahmad, Sabharwal, Ami, Samkange, Grace, Sankighatta, Naveen, Skwarski, Michael, Stuart, Robert, Wellman, Sandie, Wilson, Jo, Cavanagh, Kerrie, Hollingdale, Abigail, Allen, Susan, Barter, Elisa, Benson, Richard, Gregory, Debbie, Ingle, Charlotte, Lawrence, Steph, Rooney, Paula-Joanne, Bishop, Emma, Brennan, Maria, Cogley, Lyn, Hammonds, John Christopher, McInerney, Paul, McLarty, Esther, Natale, Salvatore, Pascoe, Sarah, Purcell, Keith, Sells, Henry, Tyner, Sharah, yates, Victoria, Beamish, Neal, Blaney, Hilary, Brock, Sue, Clapp, Felicity, Clarke, Elizabeth, Coffin, Teresa, Crellin, Perric, Davies, Joe, Downs, Nichola, Gillespie, Sally, Goodsell, Josie, Harinarayanan, Sathish, Heckford, Louise, Iskender, Amanda, Orford, Sara, Pressdee, Sandy, Rix, Sophie, Wheelwright, Roger, Woodward, Elizabeth, Wright, Seonaid, Blight, Kathy, Dobson, Tracey, Hale, Jennifer, Harris-Burland, Angie, Khoury, Ghassan, Madhava, Kudingila, Meadows, Lorna, Nagar, Yoodhvir, Roca, Mila, Stacey, Wendy, Stephenson, Anna, Uherek, Maja, Virgo, Azarel, Watkinson, Catrin, Birtle, Alison, Chauhan, Zainab, Cornthwaite, Stephanie, Curtis, Sandra, Ellard, Rose, Fish, Nathan, Hefferon, Billy, Parikh, Omi, Searle, Claire, Spickett, Helen, Walmsley, Catherine, Wise, Marcus, Atkinson, Jane, Brown, Richard B, Cartwright, Debbie, Coomber, Kristy, Hunt, Allison, Kowalczyk-Williams, Wioletta, Lewis, Christina, Parr, Stephen, Purdon, Helen, Rogers, Paul, Vowell, Emma, Capaldi, Lisa, Featherstone, Wendy, Hamilton, Joanna, Harrison, Alison, Hindle, Margaret, Hodson, Hayley, Isaew, Andrea, Knapp, Jeanette, Moss, Sarah, Gibbs, Stephanie, Gujral, Sandeep, Jannapureddy, Revanth, Kelkar, Anand, Martinou, Maria, Mills-Baldock, Tina, Nicholson, Alastair, O'Brien, Neale, Subramaniam, Ramachandran, Tahir, Saad, Vandal, Mohammad Tanvir, Vu, Thi, Allen, Rachael, Betts, Chris, Bowmer, Amanda, Clarke, Noel, Cordwell, Amanda, Cowan, Richard, Dharmaprasad, Soney, Duncan (nee Keatley), Claire, Elliott, Tony, Farnworth, Christine, Farrell, Helen, George, Siny, Goulden, Kay, Harter, Leah, Kaushal, Malan, Kirk, Sister Sarah, Lau, Maurice, Lydon, Anne Marie, O'Flynn, Kieran, Platt, Danielle, Redshaw, Catherine, Richardson, Karen, Shackley, David, Stevenson, Garry, Taylor, Sister Melanie, Thompson, Chloe-Anne, Tran, Anna, Vinod, Cellins, Wadsworth, Oliver, Youd, Sister Jill, Attlee, Julie, Bhatnagar, Adityanarayan, Bunn, Sarah, Campbell, Allister, El-Saghir, Mohammed, Fennelly, Ruth, Guy, Peter, Gwilt, Julie, Mattocks, Lehentha, Mcintosh, Gregor, Murphy, Brenda, Reed, Catherine, Strong-Sheldrake, Sophia, Tedd, Gemma, Dent, Kathleen, Dixit, Sanjay, Hood, Marion, Martin, Karen, Pearson, Sandra, Potoczna, Dorota, Spencer, Sue, Towse, Jo, Whotton, Nikita, Bowen, Katie, Clark, Su, Clarke, Rachael, Ferguson, Catherine, Firth, Alexandra, Gibbins, Kate, Kirkbride, Peter, Martin, Jackie, Martindale, John, Smedley (nee McCabe), Janine, Smith, Suzanne, Spalton, Catherine, Tay, Jessica, Wood, Sister Kim, Adams, Marion, Bates, Emma, Chennattukungu, Indukala, Clancy, Joanna, Cross, Vanessa, Hughes, Hayley, Jose, Sanal, Kurian-Downer, Sunita, Michael, Elena, Moore, Sister Helen, Nicholas, Karen, Pope, Suzanne, Potts, Sally, Prashant, Ravi, Singh, Harpreet, Smith, Sandra, Srihari, Narayanan, Weaver, Emma, Abbas, Ali, Barnes, Nicky, Brown, Richard B, Dallas, Nicola, Jackson, Sister Ann, Kader, Nicole, Karim, Omer, Laniado, M, Litchfield, Jayne, Mahmood, Sana, O'Donnell, Helen, Robinson, Victoria, Sears, Alison, Sharif, Shahid, Sierra Pala, Ana, Sinclair, Julie-Ann, Smith, Sister Catherine, Ashcroft, Jan, Boardman, John, Bowes-Cavanagh, Jeanette, Brown, Rebecca, Chedham, Alison, Darch, Flora, Farrar, Michelle, Foot, Jayne, Goldsworthy, Simon, Graham, John, Gray, Emma, Green, Sara, Jelski, Joseph, Kalejaiye, Odunayo, Keni, Manjusha, Locke, Angela, Mahoney, Sue, McMeekin, Faith, Periasamy, Manivannan, Russell, Tamlyn, Ruszala, Elizabeth, Segaran, Surayne, Tighe, Mary, Varughese, Mohini, Wallbutton, Rebecca, Webster, Christine, Whitcher, Alison, Wiggins, Sarah, Wrigley, Martha, Zietz, Maria, Abab, Julie, Cumming, Kirsty, Galloway, Lucy, Ganabady, Malavika, Gwilt, Julie, Haskell, Annelise, Heath, Catherine, James, Naomi, Kennedy, Julie, McFarlane, Victoria, Morales-Azofra, Fabiola, Morton, Susan, Mundy, Carina, Noble, Mark, Paschalis, Alec, Patrick, Julie, Reader, Leanne, Stephenson, Anna, Taylor, Lisa, Wakefield, Shauna, Ahmed, Imtiaz, Bowman, Sue, Chan, Olivia, Cyriac, Abby, Davies, Tracey, Maitland, Katrina, Prejbisz, Jan, Reece, Sheila, Shires, Heather, Tsang, David, Anscombe, Eleanor, Brough, Richard, Brough, WA, Clitheroe, Pat, Cocks, Tracie, Collins, Gerald, Connolly nee McKenna, Sarah, Corcoran, Sam, Coughlan, Tricia, Gilmour, Christina, Goodwin, Emma, Graham, Susan, Hatimy, Umi, Haydock, Helen, Hodgkinson, Sheila, Kilmartin, John, Logue, John, Orrell, Lucy, Rotherham, Carol, Smallwood, Sarah, Taylor, Jill, Wiss, Elliott, Adab, Fawzi, Bhana, Rajanee, Dhinakaran, Kathirvelu, Evans, Marion, Myatt, Alison, Parkinson, Katrina, Peake, Angela, Sellars, Elizabeth, Smith, Rowena, Storer, Julie, Thomasson, Georgia, Vengalil, Salil, Walton, Jenny, Ward, Angela, Boam, Samantha, Burgoyne, Jamie-Rae, Chadwick, Eliot, Gill, Shafiq, Haigh, Steve, Hollis, Keri, Lim, Jun, McCabe, Alastair, Munson, Lyndsey, Nash, Dominic, Saunders, Daniel, Smith, Susan, Sundar, Santhanam, Wade, Lynne, Walker, Georgina, Evans, Elizabeth, Franklin, Alex, Harris (n. Marchant), Emily, Johnstone, Maria, Jones, Lewis, Lemon, Nicola, Phan, Mau-Don, Pudney, Delia, Spence, Rachael, Stretch, Alison, Tait, Ellen, Williams, Bethan, Belcher, Rebecca, Cole, David J, De Silva-Minor, Shiroma, Dodge, Sister Jan, Grayland, Sarah, Jones, Ania, Khan, Omar, Owen, Tim, Palmer, Debbie, Parajes, Cerila, Pegler, Suzannah, Poole, Abbie, Sargent, Sister Tracey, Starling, Ellen, Taylor, Becky, Winter, Sister Helen, Zinyemba, Vivian, Allison, Michele, Blundell, Martyn, Brookman, Catherine, Buckley, Jon, Chamberlain, Shelley, Cuffe, Donna, Davies, Stacey, Fairfax, Claire, Greedus, Helen, Griffin, Hannah, Koehler, Ingrid, Lydon, Anna, Michels, Jorg, Pym, Julia, Roberts, Fiona, Srinivasan, Rajaguru, Thornton, Lorraine, Vandecandalaere, Elaine, Watts, Erica, Welsh, Linda, Anderson, Jim, Anderson, Juliette, Ball, Julie, Benton, Louise, Bowers, Richard, Burlace, Barbara, Casanova, Nathalie, Chahal, Rohit, Coyle, Catherine, Dickinson, Peter, Fosker, Chris, Heeley, Janine, Henry, Ann, Kanaga-Sundaram, Subramanian, Littler, Stephen, Rodda, Sree, Slack, Jonathan, Sundaram, Subramanian Kanaga, Taylor, Beverley, Weston, Philip, Chan, Andrew, Chettle, Judith, Gamble, Tina, Griffiths, Theresa, Hartwell, Lyn, Johnson-Rollings, Ashley, Jones, Julia, Millage, Helen, O'Neill, Eilish, Simmons, Elaine, Stockdale, Andrew, Walsh, Donna, Webb, Kerrie, Williams, Jo, Anderson, John, Dymond, Harvey, Eleftheriadis, Symeon, Hilman, Serena, Lloyd-Jones, Hugh, Wells, Tom, Bennett, Sarah, Dunn, Sharon, Fildes, Diane, Hassall, Alison, Herbert, Gaynor, Hughes, Kathryn, Innes, Helen, Kelly, Alison, Littler, John, Lomax, Laurie, Miller, Nikki, Nutman, Chris, Stott, Matthew, Syndikus, Isabel, Tolan, Shaun, Weston, Alison, Baker, Ivanna, Carter, Vanda, Chatfield, Jenny, Churn, Mark, Cooke, Peter, Grant, Anna, Homer, David, Keng-Koh, Pek, Kirk, Christine, Lomas, Claire, McCormick, Marian, Mohseeni, Arizoo, Pawaroo, Renita, Radford, Liz, Rogers, Jason, Samanci, Ali, Sayers, Ian, Sharman, Emma, Spencer, Hazel, Spruce, Debbie, Worthington, Hannah, Bak, Dagmara, Bowen, Jo, Capaldi, Lisa, Cleary, Kristy, Davies, Sue, Forkes, Janet, Gauntlett, Monica, Gupta, Kamalnayan, Healey-Mariano, Jennifer, Hodson, Hayley, Holdsworth, Amanda, Ledger, Kate, Morrow, Hugh, Perry, Heather, Rimell, Patricia, Rosoman, Alison, Taylor, Jacob, Tranter, Helen, Tyler, Jayne, White, Ann, Bloomfield, David, Crowe (nee Hughes), Dawn, Flynn-Batham, Marian, Folkes, Linda, Funnell, Sarah, Gilbert, Jeanette, Gomez-Marcos, Raquel, Gonzalez, Celia, Hoskins, Chloe, Moore, Sally, Nikapota, Ashok, Plataniotis, George, Rippin, Sarah, Smith, Matthew, Tsawayo, Tan, Turner, Nikki, Wakelen, Paula, Brady, Joanne, Camilleri, Philip, Carpenter, Janice, Cserbane, Anita, Fernandez, Claire, Hogbin, Emma, Hyde, Katherine, Joshi, Manisha, Kavanagh, Roisin, Kurup, Rahul, Manyangadze, Sarah, McLain-Smith, Susan, Ngumo, Alice, Panakis, Niki, Protheroe, Andrew, Pwint, Thinn, Sabharwal, Ami, Trewsmith, Neil, Wynn, Hazel, Allison, Joanna, Barron, Claire, Beaumont, Erica, Beer, Nigel, Beesley, Kate, Donaldson, David, Duckett, Tracey, Fox, Shirley, Gray, Emma, Kotze, Michelle, Kumar, Shiyam, Macrory, Joanne, Perry, Jess, Pippard, Lucy, Porter, Tim, Rennie, Kerry, Sparrow, Geoffrey, Williams-Yesson, Barbara, Balderson, Jane, Bottomley, David, Bowling, Lorna, Brittain, Paul, Brookes, Claire, Campbell, Hilary, Campos, Monica, Conway, Rachel, Davies, Flor, Donnison, Michelle, Elliott, Mark, Evans, Richard, Fallah, Fereshteh, Fearnley, Mark, Giddings, Emma, Gollapothu, Srilakshmi, Harvey, Sally, Holbrook, Andy, Howard, Lucy, Ingham, Jo, Jakeman, Christine, Joseph, Joji, Mole, Lisa, Musunuru, Hima Bindu, Pashley, Sarah, Saleem, Shamila, Stower, Michael, Strider, Paula, Taylor, Carol, Thackray, Louis, Todd, Joanna, Wightman, John, Wilson, Russ, Youngs, Nora, Byrne, Mary Marg, Deenihan, Emma, Durkan, Garret, Jennings, Marian, Mahoney, Catriona, Martin, Joseph, McInerney, Veronica, Raftery, Liz, Rogers, Eamonn, Spillane, Maria, Sullivan, Frank, Teh, Chiaw Woon, Wright, Ann, Harving, Niels, Hejlesen, Finn, Kempel, Mette Moe, Steffensen, Kirsten, Bentzen, Lise, Borre, Michael, Buus, Simon, Fode, Kirsten, Kaa Bach, Birgit, Lassen, Yasmin, Laursen, Vibeke, Lemng Kruse, Helle, Lucakova, Slavka, Nørmark Iversen, Helle, Brasso, Klaus, Christensen, Anne Juel, Gruschy, Lisa, Klovgaard, Karina, Lunau, Anne Sofie, Meidhal Petersen, Peter, Moller Christensen, Trine, Poder, Lone Lund, Ribers, Kristine, Stilling, Sine, Thim, Stine, Torin Lehmann, Anna, Vandborg, Maria, Christensen, Tine, Kokholm, Kathrine Friser, Lindberg, Henriette, Nybom, Kirstine, Rønnengart, Eva, Saxe, Charlotte, Baird, Noelle, Braun, Michael W, Cheung, Arthur, Fourt, Monica, Jackson, Cathy, Jepson, Debbie, Johnson, Arlissa, Karvat, Anand, Kwan, Winkle, McIndoe, Sue, Mitchell, Donna, Sandhu, Sandeep, Serpanchy, Rosanne, Wu, Helen, Beausoleil, Rachelle, Crawford, Melanie, DiMarco, Christine, Ding, Ian, Gladwish, Adam, MacIsaac, Patricia, MarshGray, Kimberley, Morton, Gerard, Murphy, Cara, Pascoe, Caitlin, Pokhrel, Sujata, Stevens, Christiaan, Yoon, Frederick, Yu, Jason, Dubey, Arbind, Sivananthan, Gokulan, Bahary, JeanPaul, Barkati, Maroie, Beauchemin, MarieClaude, Benth, Silvine, Carbonaro, Adriana, Delouya, Guila, ElSokhn, Nidale, Frazzi, Alexandra, Lambert, Carole, Lavertu, Sophie, Menard, Cynthia, Pan, Siew Siew, Rousseau, Pierre, Saad, Fred, Taussky, Daniel, Trudel, Diane, Amanie, John Oliver, Batz, Margaret, Borynec, Carol, Danielson, Brita, Garcia, Leni Santiago, Gramlich, Colin, Lang, Monika, Larson, Beverly, Mallett, Debbie, McCrudden, Sylvia, Murtha, Albert, Parker, Shelley, Parliament, Matthew B, Patel, Samir, Pearcey, Robert, Pervez, Nadeem, Rose, Jim, Shewchuk, Caroline, Smith, Carlie, Usmani, Nawaid, Williams, Candra, Yee, Don, AlRashdan, Abdulla, Avery, Alison, Beaton, Heather, Bond, Brittany, BrucePayne, Kim, Burbridge, Susan, Bursey, Kara, Carvery, Lane, Dill, Kendra, Dugas, Stevie, Harding, Claudia, Hodder, June, Hollenhorst, Helmut, Hubley, Lynn, Joseph, Paul K, Little, Erin, MacDonald, Angela, MacIsaac, Kathy, MacVicar, Jennifer, McCracken, Jillian, Moffatt, Emily, Nieforth, Joan, Patil, Nikhilesh, Rijt, Kelsey van der, Roberts, Victoria, Rose, Tina, Rutledge, Robert DH, Salyzyn, Camryn, Simpson, Robin, Sutherland, Donna, Walker, Heather, Wilke, Derek, Yeo, Tanya, Yunace, Lorrie, Zinck, Connie, Bier, Bianca, Bucci, Catherine, Corbett, Thomas B, Dayes, Ian S, Eady, Robin, Gudelis, Susan O, Hill, Elaine, Kinrade, Yvonne, Laughlin, Anne, Lukka, Himu, Makepeace, Barbara, Marshall, Lynn, Nielsen, Linda, Oliverio, Carmela, Patel, Malti Behn, Tsakirdis, Thedoros, Wong, Jason W T, Allen, Kirsten, Bachand, Francois, Carefoot, Layton, Cotts, Shannon, Creelman, Brianna, Crook, Juanita, GeorgeChayka, Danielle, Halperin, Ross, Hartt, Nancy, Hayes, Jill A, Kim, David, Kim, TuongVan, Kronstal, Flo, Lee, Bernard, Lew, Tanya, McClelland, Marie, Opitz, Debbie, Overend, Aldyn, Reed, Melanie, Watson, Michaella, Wilkie, Karen, Winn, Carol, Yanchuk, Janet, Bonner, Scott, Brander, Mike, Brundage, Michael, Carlson, Cindy, Edwards, Jackie, ElKerdawy, Hala, Harpell, Rhonda, Hartman, Carrie, Kalyvas, Maria, KirbyBello, Denise, Kumar, Vikaash, Leach, Deborah, MacVicar, Karen, Mahmud, Aamer, Maize, Christine, Moelker, Yvonne, Murano, Abby, Paul, Nancy, Ross, Ashley, RossSmith, Marleen, Shenfield, Carey, Spencer, Craig, Tennant, Virginia, Vermette, Tracy, Willing, Stephanie, Zaza, Khaled O, Zeltser, Fred, Gearing, Jennifer, Girolametto, Carla, Gopaul, Darindra, Hamilton, Ronald, Hendel, Debra, Janke, Susan, Knackstedt, Jochen, Kuk, Joda, Morrison, MaryBeth, Mumin, Anissa, Nemirov, Stephanie, Rachakonda, Ramana, Shantz, Brenda S, Siddiqui, Atif, Thallury, Sheeba, Ahmad, Belal, Bailey, Laura, Bauman, Glenn, Camara, Mary Jane, Corkery, Danylle, DSouza, David, DiLullo, Elenor, Eichenberger, Lois, Francis, Peggy, Grant, Susan, Gratton, Albert, Gurzanski, Christina, Hare, Jennifer A, Johnson, Craig, Kenny, Shannon, Lock, Michael I, Loughlin, Kate, Mardell, Stephen, Mayer, Heather, Rodrigues, George B, Sax, Lori, Sebborn, Kes, Sexton, Tracy, Venkatesan, Varagur M, tenHaaf, Darlene, Chatelain, AnneMarie, Fortin, Jacqueline, Houle, Stefanie, Khaouam, Nader, Lachance, Nicole, Laliberte, Benoit, Lemaire, Celine, Nguyen, David H A, Sylvestre, Alma, Vavassis, Peter, Yassa, Michael, Adnaan, Jamila, Briand, Wayne, Casey, Linda, Chipman, Penny, Corwin, Charlotte, Duclos, Marie, Faria, Sergio Luiz, Ghoul, Mokhtar, Joncas, MarieClaude, Koulouris, Zoe, Larocque, Stephanie, Lee, Brenda, Olha, Carolynna, Robert, MarieFrance, Sharma, Rajesh, Shenouda, George, Souhami, Luis, Albrecht, Virginia, Froese, Jane, Froude, Nancy, Howitt, Pamela, Iqbal, Fawaad, Kift, Shannon, Koll, Wayne, Li, Audrey, MacGregor, Barbara, Manganaro, Aileen, McKee, Dianne, Mendoza, Edeliza, Mui, Jimmy, Myrehaug, Sten, Singson, Angelina, Slessor, Janet, Somasiri, Ashane, Stevens, Nicole, Berghout, Koralee, Bourque, JeanMarc, Carkner, Amy, E, Choan, Eapen, Libni, Eze, Nancy, Fish, Matt, Haddad, Alain, Hicks, Kimberly, Iordanidi, Elina, Kanji, Femina, Kendal, Wayne S, MacMullin, Lisa, MacRae, Robert M, Malone, Shawn, Morgan, Scott, Nguyen, Julie, OBrien, Anna, Page, Nancy, Perry, Gad A, Reeves, MarieClaude, Roach, Joanne, Sanchez, Maria, Spearman, Mary, Spencer, Patti, Tikoo, Archana, Turriff, Lisa, Wright, Natalie, Bedoya, Luis Diaz de, Cossette, Danielle, Cusan, Leonello, Desrosiers, Isabelle, Dufour, Nathalie, Foster, William, Gomez, JoseLuis, Hathout, Lara, Lajoie, MarieAndree, Martin, AndreGuy, Pouliot, Sophie, Vigneault, Eric, Bouchard, Myriam, Bujold, Rachel, Carmel, Michel, Cote, Genevieve, Dion, Helene, Ebacher, Annie, Gauthier, Isabelle C, Ladouceur, Cynthia, Lamoureux, Alexandra, Larouche, Julie, Marquis, Tania, Nabid, Abdenour, Pearson, Diane, Sabbagh, Robert, TetreaultLaflamme, Audrey, Baker, Erin, Blackwood, Geoffrey, Galway, Denise, Greenland, Jonathan, Hannaford, Zeta, Hoskins, Ashley, House, Gail, Kamran, Asim, Manning, Kimberley, Nelder, Matthew, Noel, Lorilee, Norman, Alia, ODea, Jamie, Pinnell, Erin, Pochini, Craig, Putt, Dawne, Rahman, Arifur, Rickert, Lauren, Sathya, Jinka R, Teasdale, Breanne, Thoms, John, Whiteway, Chrystal, Whittle, Stacy, Cheung, Patrick CF, Chu, William, Chung, Hans T, Commisso, Angela, Danjoux, Cyril, Escueta, Vivette, Klotz, Laurence, Leung, Michael, Liu, Stanley, Loblaw, Douglas Andrew, Malek, Leila, Morton, Gerard, Nam, Robert K, Szumacher, Ewa, Tseng, Eric Chia Lin, Vesprini, Danny, Abed, Jessy, Ang, Daniel, Bayley, Andrew J, Berlin, Alejandro, Bristow, Robert, Catton, Charles, Chan, Heidi, Chui, Amadeus, Chung, Peter, Degendorfer, Sarah, Gospodarowicz, Mary, Gregorcic, Emily, Gumapac, Nicole, Helou, Joelle, Hsien, John, Kong, Vickie, Lalani, Nafisha, Lao, Bernadeth, Lawson, Daeria, Menard, Cynthia, Merante, Pat, Milosevic, Michael, Mirmooji, Ramesh, Muraj, Zaynab, Peres, Sarin, Petroff, Avi, Petrovska, Aleksandra, Pineda, Sandra, Quintos, Judy, Raman, Srinivas, Ramotar, Sarah, Ruiz, Maria Jose Gaitan, Sabate, Kathryn, Shukla, Arundhati, Tharahan, Nanthini, Thevarajah, Vaishaali, Tiessen, Kyoko, Topalovich, Aleksandra, Torres, Cynthia, Tse, Karen, Tsuji, Debbie, Velec, Erin, Wallace, Kris, Warde, Padraig, Bisson, MarieEve, Dahmane, Rafika, Gagne, Vanessa, Harvey, Julie, Lebrun, Benoit, Marineau, Line, Montour, Julie, Samson, Julie, Sicard, MarieEve, Thibeault, Andreanne, Vincent, Francois, Vincent, LindaSuzanne, Blood, Paul A, Canavan, Joycelin, Clements, Mia AnnaMaria, Glick, Daniel, Holloway, Caroline, Holwerda, Eleanor, Joe, Howard, Lacey, Cathy, Lam, Jacqueline, Leong, Nelson, Lim, Jan, Lomas, Alia, Pai, Howard H, Priestley, Emilie, Shahid, Negin, Vallieres, Isabelle, Duncan, Graeme, Gleave, Martin E, Goldenberg, S Larry, Karunakaran, Subashini, Keyes, Mira, KimSing, Charmaine, McKenzie, Michael, Morris, William James, Nakashima, Lynne, Peacock, Michael, Pickles, Thomas A, Rodda, Sree, Sibug, Sheena, So, Alan I, Tyldesley, Scott, Zhang, Tina Wanting, Ahmed, Shahida, Ammeter, Barb, Anderson, Leanne, Ann, Taehee, Bakija, Zeljka, Bashir, Bashir, Benjaminson, Patricia, Bergen, Leah, Cadiz, Lovely, Chahine, Jacqueline, Cherepak, Kathy, Chornopyski, Tracy, Chowdhury, Amitava D, Davies, Heather, Dawe, David, Drachenberg, Darrel E, DuMontier, Joelle, Dubey, Arbind, Eska, Tiffani, Francisco, Cristina, Gamez, Raquel, Garrett, Gina, Giovannini, Julia, Graham, Jeffrey, Guarino, Robyn, Hunter, William, Johnston, Laurel, Kitkowski, Caitlin, Klapp, Kathi, Koul, Rashmi, Kuzyk, Valerie, Last, Debbie, Li, KuanLin, Long, Heather, Majumdar, Arpita, Marek, Kathleen, McNish, Jennifer, Mills, Laurie, Morales, Maria, Nashed, Maged, Nichol, Rhonda, Ong, Aldrich Dixon, Ouelette, Ashley, Patel, DeepKumar, Pawl, Marilyn, Peak, Lisa, Priebe, Shauna, Quon, Harvey, Rizzuto, Stacey, Rudnicki, Rayleen, Santos, Priscilla, Sharma, Atul, Sivananthan, Gokulan, Squires, Mandy, Trakalo, Kathy, Turner, Christy, Vaughn, Jamie, Walker, Lori, Watson, Melanie R, Wei, Joyce, Weir, JoAnn, Woloshyn, Rose, Wood, Allison, Zhang, Hanbo, and Zwarych, Darlene
- Abstract
Previous evidence indicates that adjuvant, short-course androgen deprivation therapy (ADT) improves metastasis-free survival when given with primary radiotherapy for intermediate-risk and high-risk localised prostate cancer. However, the value of ADT with postoperative radiotherapy after radical prostatectomy is unclear.
- Published
- 2024
- Full Text
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5. Duration of androgen deprivation therapy with postoperative radiotherapy for prostate cancer: a comparison of long-course versus short-course androgen deprivation therapy in the RADICALS-HD randomised trial
- Author
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Parker, Chris C, Kynaston, Howard, Cook, Adrian D, Clarke, Noel W, Catton, Charles N, Cross, William R, Petersen, Peter M, Persad, Rajendra A, Pugh, Cheryl A, Saad, Fred, Logue, John, Payne, Heather, Bower, Lorna C, Brawley, Chris, Rauchenberger, Mary, Barkati, Maroie, Bottomley, David M, Brasso, Klaus, Chung, Hans T, Chung, Peter W M, Conroy, Ruth, Falconer, Alison, Ford, Vicky, Goh, Chee L, Heath, Catherine M, James, Nicholas D, Kim-Sing, Charmaine, Kodavatiganti, Ravi, Malone, Shawn C, Morris, Stephen L, Nabid, Abdenour, Ong, Aldrich D, Raman, Rakesh, Rodda, Sree, Wells, Paula, Worlding, Jane, Parulekar, Wendy R, Parmar, Mahesh K B, Sydes, Matthew R, Amos, Claire, Beaney, Katherine, Bellenger, Katharine, Brawley, Chris, Chung, Christina, Cook, Adrian, Embleton, Andrew, Forcat, Silvia, Goldstein, Cindy, Grabey, Jenna, Hague, Dominic, Herasimtschuk, Anna, Islam, Zaheer, Jovic, Gordana, Maniatis, Christos, Masters, Lindsey, Mohamed, Fatima, Morgan, Rachel, Murphy, Claire, Noor, Dipa, 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Stephanie, Joshi, Manisha, Liau, Joy, Magwaro, Sophia, Naim, Farhan, Patel, Anup, Pathmanathan, Byiravey, Russell, Emily, Stewart, Simon, Thoi, Anne-Maree, Tippins, Anna, Westrop, Anna, Agdiran, Didem, Aggarwal, Ajay, Amoaten, Thomas, Aslam, Natasha, Bonsu, Nicole, Butt, Zainab, Danaswamy, Patricia, Davda, Reena, Gillesen, Annelies, Goel, Roshni, Hall, Julia, Khong, Rachel, Lowi, Suzy, Merrick, Richard, Mitra, Anita, Payne, Heather, Teh, Jonathan, Whinn, Samantha, Woolf, David, Zilkha, Helene, Bech-Nielsen, Nanette, Bolton, Olivia, Danaswamy, Patricia, Dawson-Athey, Linda, Foster, Emma, Gillian, Andrew, Green, James, Hillman, Paul, Hines, John, Holden, Simon, Jayachandran, Resmi, Kazingizi, Memory, O'Neill, John, Olaoni, Thompson, Peters, John, Philp, Timothy, Wells, Paula, Woolf, David, Zaidi, Sadaf, Adeyoju, Adebanji, Adinkra, Victoria, Brough, Richard, Brown, Stephen CW, Collins, Gerald, Hardstaff, Lisa, Hill, Pippa, Kujawa, Magda, McCurrie, Marilyn, Townley, Barbara, Woodhouse, Iain, Zafar, Waheed, Beesley, Sharon, Breen, Vivienne, Brulinski, Patryk, Burrage, Su, Calvert, Clare, Davison, Alison, Dias, Heather, Fossey, Gavin, Hotine, Jodie, Jones, Ifan, Jope, Carmel, Lines, Yvonne, Martins, Sarah, Mercier, Barbara, Mikropoulos, Christos, Murray, Jane, Neshiri, Innocent, Pamphlett, Ian, Phillips, Ann, Rai, Pavnish, Rezkallah, Sarah, Roberts, Verity, Taylor, Henry, Taylor, Katy, Thomas, Carys, Tribe, Lisa, Wood, Pauline, Bottomley, Ian, Buchan, Jonathan, Carpio, Viviana, Choudhury, Ananya, Conroy, Ruth, Cowan, Richard, Davison, Sue, Duncan, Ian, Elliott, Tony, Fair, Kim, Fallaize, Adrian, Flanagan, Laura, Green, Sarah, Harris, Catherine, Hart, Amber, Jones, Cathryn, Livsey, Jacqueline, Logue, John, McCaul, Damian, Nazari, Roonak, Newbery, Carol, O'Connor, Kate, O'Dwyer, Sister Jackie, Oliver, Joanne, Onoge, Ekugbe, Petsa, Maria, Redshaw, Catherine, Smith, Sarah-Ellen, Spoor, Willemijn, Thomas, Sister Viv, White, Holly, Worsley, Lucy, Wylie, James, Bailey, Linda, Bennett, Molly, Corless, Rebecca, Duffy, Annie, Fellows, Kathryn, Gipson, Anna, Haydock, Helen, Knox, WF, Liptrott, Sarah, McCarth, Fiona, McCartin, Fiona, Mistry, Heena, Mthethwa, Thobekile, Partington, Lillian, Piper, Lindsay, Ramani, Vijay, Sahin, Sarah, Sangar, Vijay, Slevin, Kathryn, Tuck, Jonathan, Wylie, James, Atkinson, Lorraine, Barnbrooke, Sarah, Barnes, Alison, Brownless, Caroline, Chadwick, David, Hardman, John, Harland, Keith, Long, Carol, McAuliffe, Sarah, McBride, Julia, McClurey, Sister Patricia, Mohan, Rita, Naylor, Lynne, Peedell, Clive, Robson, Paula, Shakespeare, Devadasan, Thompson, Emma, Van der Voet, Hans, Wilson, David, Barron, Chris, Frew, John, Lambert, Katy, McMenemin, Rhona, Pedley, Ian, Riseborough, Danielle, Roberts, Trevor, Stockley, Emma, Stoddart, Jonathan, Tate, Janine, Walker, Jayashree, Workman, Bridget, Barnett, Debra, Carter, Adam, Cox, Adam, Davey, Alison, Davies, Kirstin, Gilby, Lisa, Hodge, Simon, Kearney, S, Lavender, Helene, Marty, Janet, Nash, Maxine, Rahman, Syhed, Richards, Jayne, Simpson, Julie, Wall, Elaine, Webb, Paula, Wild, Karen, Williams, Rachel, Wilson, Jim, Branagan, Jenny, Camilleri, Philip, Dell, Gillian, Elwell, Christine, Foo, Maxine, Goyena, Ruby, Jones, Andrea, McBain, Hazel, McGrath, Katherine, O'Callaghan, Jane, Ramanathan, Dorai, Tee, Elizabeth, Thominson, Bronwen, Tighe (nee Bussey), Rachel, Archer, Denise, Beety, Jane, Birch, Richard, Bloomfield, Mark, Callam, Sara, Cooper, Adele, Darby, Helen, Healey, Gail, Heasley, Karen, Kapur, Guarav, London, Clare, Nobes, Jenny, Oosterom, Katrien, Ostrowski, Joe, Shobrook, Bridget, Turner, Sarah, Wade, Rob, Walker, Suzanne, Walton, Sharon, Websdale, Cheryl, White, Joanna, Alder, Leanne, Chivers, Rachael, Gooch, Carol, Howard, Lucy, Hutchinson, Camille, Jevons, Cody, Kumar, Daniel, Marley, Hazel, Mills, Jamie, Purves, Hanna, Sayers, Ian, Sundar, Santhanam, Szolin-Jones, Jacob, Walker, Georgina, Wood, Catherine, Choudhury, Ananya, Conroy, Ruth, Cook, Wendy, Gill, Lisa, Johnson, Joanne, Johnstone, Dawn, Jones, Richard, Livsey, Jacqueline, Mistry, Leena, Scarratt, Lyndsay, Aiku, Abimbola, Ajzensztejn, Daniel, Al-Chamali, Hiba, Andrade, Gerard, Barber, Dave, Bekulart, Sylwia, Bhatnagar, Gagan, Boutflower, Jane, Brewster, Simon, Butterfield, Anne, Camilleri, Philip, Carroll, Weronika, Cole, David J, Coutts, Tim, Doyle, Kerrie, Durrant, Lisa, Green, Trish, Hyde, Katherine, Jampana, Raj, Jastrzebska, Patrycja, Kiltie, Anne, Kyffin, Phillippa, Langridge, Nicole, Lawrey, Sarah, Markus, Sarah, Marston, Kerrie, Mooney, Matthew, Mukkath, Sandra, Murphy, Ann, Owens, Rob, Panakis, Niki, Rabsztyn, Weronika, Ruane, Sarah, Sabbagh, Ahmad, Sabharwal, Ami, Samkange, Grace, Sankighatta, Naveen, Skwarski, Michael, Stuart, Robert, Wellman, Sandie, Wilson, Jo, Cavanagh, Kerrie, Hollingdale, Abigail, Allen, Susan, Barter, Elisa, Benson, Richard, Gregory, Debbie, Ingle, Charlotte, Lawrence, Steph, Rooney, Paula-Joanne, Bishop, Emma, Brennan, Maria, Cogley, Lyn, Hammonds, John Christopher, McInerney, Paul, McLarty, Esther, Natale, Salvatore, Pascoe, Sarah, Purcell, Keith, Sells, Henry, Tyner, Sharah, yates, Victoria, Beamish, Neal, Blaney, Hilary, Brock, Sue, Clapp, Felicity, Clarke, Elizabeth, Coffin, Teresa, Crellin, Perric, Davies, Joe, Downs, Nichola, Gillespie, Sally, Goodsell, Josie, Harinarayanan, Sathish, Heckford, Louise, Iskender, Amanda, Orford, Sara, Pressdee, Sandy, Rix, Sophie, Wheelwright, Roger, Woodward, Elizabeth, Wright, Seonaid, Blight, Kathy, Dobson, Tracey, Hale, Jennifer, Harris-Burland, Angie, Khoury, Ghassan, Madhava, Kudingila, Meadows, Lorna, Nagar, Yoodhvir, Roca, Mila, Stacey, Wendy, Stephenson, Anna, Uherek, Maja, Virgo, Azarel, Watkinson, Catrin, Birtle, Alison, Chauhan, Zainab, Cornthwaite, Stephanie, Curtis, Sandra, Ellard, Rose, Fish, Nathan, Hefferon, Billy, Parikh, Omi, Searle, Claire, Spickett, Helen, Walmsley, Catherine, Wise, Marcus, Atkinson, Jane, Brown, Richard B, Cartwright, Debbie, Coomber, Kristy, Hunt, Allison, Kowalczyk-Williams, Wioletta, Lewis, Christina, Parr, Stephen, Purdon, Helen, Rogers, Paul, Vowell, Emma, Capaldi, Lisa, Featherstone, Wendy, Hamilton, Joanna, Harrison, Alison, Hindle, Margaret, Hodson, Hayley, Isaew, Andrea, Knapp, Jeanette, Moss, Sarah, Gibbs, Stephanie, Gujral, Sandeep, Jannapureddy, Revanth, Kelkar, Anand, Martinou, Maria, Mills-Baldock, Tina, Nicholson, Alastair, O'Brien, Neale, Subramaniam, Ramachandran, Tahir, Saad, Vandal, Mohammad Tanvir, Vu, Thi, Allen, Rachael, Betts, Chris, Bowmer, Amanda, Clarke, Noel, Cordwell, Amanda, Cowan, Richard, Dharmaprasad, Soney, Duncan (nee Keatley), Claire, Elliott, Tony, Farnworth, Christine, Farrell, Helen, George, Siny, Goulden, Kay, Harter, Leah, Kaushal, Malan, Kirk, Sister Sarah, Lau, Maurice, Lydon, Anne Marie, O'Flynn, Kieran, Platt, Danielle, Redshaw, Catherine, Richardson, Karen, Shackley, David, Stevenson, Garry, Taylor, Sister Melanie, Thompson, Chloe-Anne, Tran, Anna, Vinod, Cellins, Wadsworth, Oliver, Youd, Sister Jill, Attlee, Julie, Bhatnagar, Adityanarayan, Bunn, Sarah, Campbell, Allister, El-Saghir, Mohammed, Fennelly, Ruth, Guy, Peter, Gwilt, Julie, Mattocks, Lehentha, Mcintosh, Gregor, Murphy, Brenda, Reed, Catherine, Strong-Sheldrake, Sophia, Tedd, Gemma, Dent, Kathleen, Dixit, Sanjay, Hood, Marion, Martin, Karen, Pearson, Sandra, Potoczna, Dorota, Spencer, Sue, Towse, Jo, Whotton, Nikita, Bowen, Katie, Clark, Su, Clarke, Rachael, Ferguson, Catherine, Firth, Alexandra, Gibbins, Kate, Kirkbride, Peter, Martin, Jackie, Martindale, John, Smedley (nee McCabe), Janine, Smith, Suzanne, Spalton, Catherine, Tay, Jessica, Wood, Sister Kim, Adams, Marion, Bates, Emma, Chennattukungu, Indukala, Clancy, Joanna, Cross, Vanessa, Hughes, Hayley, Jose, Sanal, Kurian-Downer, Sunita, Michael, Elena, Moore, Sister Helen, Nicholas, Karen, Pope, Suzanne, Potts, Sally, Prashant, Ravi, Singh, Harpreet, Smith, Sandra, Srihari, Narayanan, Weaver, Emma, Abbas, Ali, Barnes, Nicky, Brown, Richard B, Dallas, Nicola, Jackson, Sister Ann, Kader, Nicole, Karim, Omer, Laniado, M, Litchfield, Jayne, Mahmood, Sana, O'Donnell, Helen, Robinson, Victoria, Sears, Alison, Sharif, Shahid, Sierra Pala, Ana, Sinclair, Julie-Ann, Smith, Sister Catherine, Ashcroft, Jan, Boardman, John, Bowes-Cavanagh, Jeanette, Brown, Rebecca, Chedham, Alison, Darch, Flora, Farrar, Michelle, Foot, Jayne, Goldsworthy, Simon, Graham, John, Gray, Emma, Green, Sara, Jelski, Joseph, Kalejaiye, Odunayo, Keni, Manjusha, Locke, Angela, Mahoney, Sue, McMeekin, Faith, Periasamy, Manivannan, Russell, Tamlyn, Ruszala, Elizabeth, Segaran, Surayne, Tighe, Mary, Varughese, Mohini, Wallbutton, Rebecca, Webster, Christine, Whitcher, Alison, Wiggins, Sarah, Wrigley, Martha, Zietz, Maria, Abab, Julie, Cumming, Kirsty, Galloway, Lucy, Ganabady, Malavika, Gwilt, Julie, Haskell, Annelise, Heath, Catherine, James, Naomi, Kennedy, Julie, McFarlane, Victoria, Morales-Azofra, Fabiola, Morton, Susan, Mundy, Carina, Noble, Mark, Paschalis, Alec, Patrick, Julie, Reader, Leanne, Stephenson, Anna, Taylor, Lisa, Wakefield, Shauna, Ahmed, Imtiaz, Bowman, Sue, Chan, Olivia, Cyriac, Abby, Davies, Tracey, Maitland, Katrina, Prejbisz, Jan, Reece, Sheila, Shires, Heather, Tsang, David, Anscombe, Eleanor, Brough, Richard, Brough, WA, Clitheroe, Pat, Cocks, Tracie, Collins, Gerald, Connolly nee McKenna, Sarah, Corcoran, Sam, Coughlan, Tricia, Gilmour, Christina, Goodwin, Emma, Graham, Susan, Hatimy, Umi, Haydock, Helen, Hodgkinson, Sheila, Kilmartin, John, Logue, John, Orrell, Lucy, Rotherham, Carol, Smallwood, Sarah, Taylor, Jill, Wiss, Elliott, Adab, Fawzi, Bhana, Rajanee, Dhinakaran, Kathirvelu, Evans, Marion, Myatt, Alison, Parkinson, Katrina, Peake, Angela, Sellars, Elizabeth, Smith, Rowena, Storer, Julie, Thomasson, Georgia, Vengalil, Salil, Walton, Jenny, Ward, Angela, Boam, Samantha, Burgoyne, Jamie-Rae, Chadwick, Eliot, Gill, Shafiq, Haigh, Steve, Hollis, Keri, Lim, Jun, McCabe, Alastair, Munson, Lyndsey, Nash, Dominic, Saunders, Daniel, Smith, Susan, Sundar, Santhanam, Wade, Lynne, Walker, Georgina, Evans, Elizabeth, Franklin, Alex, Harris (n. Marchant), Emily, Johnstone, Maria, Jones, Lewis, Lemon, Nicola, Phan, Mau-Don, Pudney, Delia, Spence, Rachael, Stretch, Alison, Tait, Ellen, Williams, Bethan, Belcher, Rebecca, Cole, David J, De Silva-Minor, Shiroma, Dodge, Sister Jan, Grayland, Sarah, Jones, Ania, Khan, Omar, Owen, Tim, Palmer, Debbie, Parajes, Cerila, Pegler, Suzannah, Poole, Abbie, Sargent, Sister Tracey, Starling, Ellen, Taylor, Becky, Winter, Sister Helen, Zinyemba, Vivian, Allison, Michele, Blundell, Martyn, Brookman, Catherine, Buckley, Jon, Chamberlain, Shelley, Cuffe, Donna, Davies, Stacey, Fairfax, Claire, Greedus, Helen, Griffin, Hannah, Koehler, Ingrid, Lydon, Anna, Michels, Jorg, Pym, Julia, Roberts, Fiona, Srinivasan, Rajaguru, Thornton, Lorraine, Vandecandalaere, Elaine, Watts, Erica, Welsh, Linda, Anderson, Jim, Anderson, Juliette, Ball, Julie, Benton, Louise, Bowers, Richard, Burlace, Barbara, Casanova, Nathalie, Chahal, Rohit, Coyle, Catherine, Dickinson, Peter, Fosker, Chris, Heeley, Janine, Henry, Ann, Kanaga-Sundaram, Subramanian, Littler, Stephen, Rodda, Sree, Slack, Jonathan, Sundaram, Subramanian Kanaga, Taylor, Beverley, Weston, Philip, Chan, Andrew, Chettle, Judith, Gamble, Tina, Griffiths, Theresa, Hartwell, Lyn, Johnson-Rollings, Ashley, Jones, Julia, Millage, Helen, O'Neill, Eilish, Simmons, Elaine, Stockdale, Andrew, Walsh, Donna, Webb, Kerrie, Williams, Jo, Anderson, John, Dymond, Harvey, Eleftheriadis, Symeon, Hilman, Serena, Lloyd-Jones, Hugh, Wells, Tom, Bennett, Sarah, Dunn, Sharon, Fildes, Diane, Hassall, Alison, Herbert, Gaynor, Hughes, Kathryn, Innes, Helen, Kelly, Alison, Littler, John, Lomax, Laurie, Miller, Nikki, Nutman, Chris, Stott, Matthew, Syndikus, Isabel, Tolan, Shaun, Weston, Alison, Baker, Ivanna, Carter, Vanda, Chatfield, Jenny, Churn, Mark, Cooke, Peter, Grant, Anna, Homer, David, Keng-Koh, Pek, Kirk, Christine, Lomas, Claire, McCormick, Marian, Mohseeni, Arizoo, Pawaroo, Renita, Radford, Liz, Rogers, Jason, Samanci, Ali, Sayers, Ian, Sharman, Emma, Spencer, Hazel, Spruce, Debbie, Worthington, Hannah, Bak, Dagmara, Bowen, Jo, Capaldi, Lisa, Cleary, Kristy, Davies, Sue, Forkes, Janet, Gauntlett, Monica, Gupta, Kamalnayan, Healey-Mariano, Jennifer, Hodson, Hayley, Holdsworth, Amanda, Ledger, Kate, Morrow, Hugh, Perry, Heather, Rimell, Patricia, Rosoman, Alison, Taylor, Jacob, Tranter, Helen, Tyler, Jayne, White, Ann, Bloomfield, David, Crowe (nee Hughes), Dawn, Flynn-Batham, Marian, Folkes, Linda, Funnell, Sarah, Gilbert, Jeanette, Gomez-Marcos, Raquel, Gonzalez, Celia, Hoskins, Chloe, Moore, Sally, Nikapota, Ashok, Plataniotis, George, Rippin, Sarah, Smith, Matthew, Tsawayo, Tan, Turner, Nikki, Wakelen, Paula, Brady, Joanne, Camilleri, Philip, Carpenter, Janice, Cserbane, Anita, Fernandez, Claire, Hogbin, Emma, Hyde, Katherine, Joshi, Manisha, Kavanagh, Roisin, Kurup, Rahul, Manyangadze, Sarah, McLain-Smith, Susan, Ngumo, Alice, Panakis, Niki, Protheroe, Andrew, Pwint, Thinn, Sabharwal, Ami, Trewsmith, Neil, Wynn, Hazel, Allison, Joanna, Barron, Claire, Beaumont, Erica, Beer, Nigel, Beesley, Kate, Donaldson, David, Duckett, Tracey, Fox, Shirley, Gray, Emma, Kotze, Michelle, Kumar, Shiyam, Macrory, Joanne, Perry, Jess, Pippard, Lucy, Porter, Tim, Rennie, Kerry, Sparrow, Geoffrey, Williams-Yesson, Barbara, Balderson, Jane, Bottomley, David, Bowling, Lorna, Brittain, Paul, Brookes, Claire, Campbell, Hilary, Campos, Monica, Conway, Rachel, Davies, Flor, Donnison, Michelle, Elliott, Mark, Evans, Richard, Fallah, Fereshteh, Fearnley, Mark, Giddings, Emma, Gollapothu, Srilakshmi, Harvey, Sally, Holbrook, Andy, Howard, Lucy, Ingham, Jo, Jakeman, Christine, Joseph, Joji, Mole, Lisa, Musunuru, Hima Bindu, Pashley, Sarah, Saleem, Shamila, Stower, Michael, Strider, Paula, Taylor, Carol, Thackray, Louis, Todd, Joanna, Wightman, John, Wilson, Russ, Youngs, Nora, Byrne, Mary Marg, Deenihan, Emma, Durkan, Garret, Jennings, Marian, Mahoney, Catriona, Martin, Joseph, McInerney, Veronica, Raftery, Liz, Rogers, Eamonn, Spillane, Maria, Sullivan, Frank, Teh, Chiaw Woon, Wright, Ann, Harving, Niels, Hejlesen, Finn, Kempel, Mette Moe, Steffensen, Kirsten, Bentzen, Lise, Borre, Michael, Buus, Simon, Fode, Kirsten, Kaa Bach, Birgit, Lassen, Yasmin, Laursen, Vibeke, Lemng Kruse, Helle, Lucakova, Slavka, Nørmark Iversen, Helle, Brasso, Klaus, Christensen, Anne Juel, Gruschy, Lisa, Klovgaard, Karina, Lunau, Anne Sofie, Meidhal Petersen, Peter, Moller Christensen, Trine, Poder, Lone Lund, Ribers, Kristine, Stilling, Sine, Thim, Stine, Torin Lehmann, Anna, Vandborg, Maria, Christensen, Tine, Kokholm, Kathrine Friser, Lindberg, Henriette, Nybom, Kirstine, Rønnengart, Eva, Saxe, Charlotte, Baird, Noelle, Braun, Michael W, Cheung, Arthur, Fourt, Monica, Jackson, Cathy, Jepson, Debbie, Johnson, Arlissa, Karvat, Anand, Kwan, Winkle, McIndoe, Sue, Mitchell, Donna, Sandhu, Sandeep, Serpanchy, Rosanne, Wu, Helen, Beausoleil, Rachelle, Crawford, Melanie, DiMarco, Christine, Ding, Ian, Gladwish, Adam, MacIsaac, Patricia, MarshGray, Kimberley, Morton, Gerard, Murphy, Cara, Pascoe, Caitlin, Pokhrel, Sujata, Stevens, Christiaan, Yoon, Frederick, Yu, Jason, Dubey, Arbind, Sivananthan, Gokulan, Bahary, JeanPaul, Barkati, Maroie, Beauchemin, MarieClaude, Benth, Silvine, Carbonaro, Adriana, Delouya, Guila, ElSokhn, Nidale, Frazzi, Alexandra, Lambert, Carole, Lavertu, Sophie, Menard, Cynthia, Pan, Siew Siew, Rousseau, Pierre, Saad, Fred, Taussky, Daniel, Trudel, Diane, Amanie, John Oliver, Batz, Margaret, Borynec, Carol, Danielson, Brita, Garcia, Leni Santiago, Gramlich, Colin, Lang, Monika, Larson, Beverly, Mallett, Debbie, McCrudden, Sylvia, Murtha, Albert, Parker, Shelley, Parliament, Matthew B, Patel, Samir, Pearcey, Robert, Pervez, Nadeem, Rose, Jim, Shewchuk, Caroline, Smith, Carlie, Usmani, Nawaid, Williams, Candra, Yee, Don, AlRashdan, Abdulla, Avery, Alison, Beaton, Heather, Bond, Brittany, BrucePayne, Kim, Burbridge, Susan, Bursey, Kara, Carvery, Lane, Dill, Kendra, Dugas, Stevie, Harding, Claudia, Hodder, June, Hollenhorst, Helmut, Hubley, Lynn, Joseph, Paul K, Little, Erin, MacDonald, Angela, MacIsaac, Kathy, MacVicar, Jennifer, McCracken, Jillian, Moffatt, Emily, Nieforth, Joan, Patil, Nikhilesh, Rijt, Kelsey van der, Roberts, Victoria, Rose, Tina, Rutledge, Robert DH, Salyzyn, Camryn, Simpson, Robin, Sutherland, Donna, Walker, Heather, Wilke, Derek, Yeo, Tanya, Yunace, Lorrie, Zinck, Connie, Bier, Bianca, Bucci, Catherine, Corbett, Thomas B, Dayes, Ian S, Eady, Robin, Gudelis, Susan O, Hill, Elaine, Kinrade, Yvonne, Laughlin, Anne, Lukka, Himu, Makepeace, Barbara, Marshall, Lynn, Nielsen, Linda, Oliverio, Carmela, Patel, Malti Behn, Tsakirdis, Thedoros, Wong, Jason W T, Allen, Kirsten, Bachand, Francois, Carefoot, Layton, Cotts, Shannon, Creelman, Brianna, Crook, Juanita, GeorgeChayka, Danielle, Halperin, Ross, Hartt, Nancy, Hayes, Jill A, Kim, David, Kim, TuongVan, Kronstal, Flo, Lee, Bernard, Lew, Tanya, McClelland, Marie, Opitz, Debbie, Overend, Aldyn, Reed, Melanie, Watson, Michaella, Wilkie, Karen, Winn, Carol, Yanchuk, Janet, Bonner, Scott, Brander, Mike, Brundage, Michael, Carlson, Cindy, Edwards, Jackie, ElKerdawy, Hala, Harpell, Rhonda, Hartman, Carrie, Kalyvas, Maria, KirbyBello, Denise, Kumar, Vikaash, Leach, Deborah, MacVicar, Karen, Mahmud, Aamer, Maize, Christine, Moelker, Yvonne, Murano, Abby, Paul, Nancy, Ross, Ashley, RossSmith, Marleen, Shenfield, Carey, Spencer, Craig, Tennant, Virginia, Vermette, Tracy, Willing, Stephanie, Zaza, Khaled O, Zeltser, Fred, Gearing, Jennifer, Girolametto, Carla, Gopaul, Darindra, Hamilton, Ronald, Hendel, Debra, Janke, Susan, Knackstedt, Jochen, Kuk, Joda, Morrison, MaryBeth, Mumin, Anissa, Nemirov, Stephanie, Rachakonda, Ramana, Shantz, Brenda S, Siddiqui, Atif, Thallury, Sheeba, Ahmad, Belal, Bailey, Laura, Bauman, Glenn, Camara, Mary Jane, Corkery, Danylle, DSouza, David, DiLullo, Elenor, Eichenberger, Lois, Francis, Peggy, Grant, Susan, Gratton, Albert, Gurzanski, Christina, Hare, Jennifer A, Johnson, Craig, Kenny, Shannon, Lock, Michael I, Loughlin, Kate, Mardell, Stephen, Mayer, Heather, Rodrigues, George B, Sax, Lori, Sebborn, Kes, Sexton, Tracy, Venkatesan, Varagur M, tenHaaf, Darlene, Chatelain, AnneMarie, Fortin, Jacqueline, Houle, Stefanie, Khaouam, Nader, Lachance, Nicole, Laliberte, Benoit, Lemaire, Celine, Nguyen, David H A, Sylvestre, Alma, Vavassis, Peter, Yassa, Michael, Adnaan, Jamila, Briand, Wayne, Casey, Linda, Chipman, Penny, Corwin, Charlotte, Duclos, Marie, Faria, Sergio Luiz, Ghoul, Mokhtar, Joncas, MarieClaude, Koulouris, Zoe, Larocque, Stephanie, Lee, Brenda, Olha, Carolynna, Robert, MarieFrance, Sharma, Rajesh, Shenouda, George, Souhami, Luis, Albrecht, Virginia, Froese, Jane, Froude, Nancy, Howitt, Pamela, Iqbal, Fawaad, Kift, Shannon, Koll, Wayne, Li, Audrey, MacGregor, Barbara, Manganaro, Aileen, McKee, Dianne, Mendoza, Edeliza, Mui, Jimmy, Myrehaug, Sten, Singson, Angelina, Slessor, Janet, Somasiri, Ashane, Stevens, Nicole, Berghout, Koralee, Bourque, JeanMarc, Carkner, Amy, E, Choan, Eapen, Libni, Eze, Nancy, Fish, Matt, Haddad, Alain, Hicks, Kimberly, Iordanidi, Elina, Kanji, Femina, Kendal, Wayne S, MacMullin, Lisa, MacRae, Robert M, Malone, Shawn, Morgan, Scott, Nguyen, Julie, OBrien, Anna, Page, Nancy, Perry, Gad A, Reeves, MarieClaude, Roach, Joanne, Sanchez, Maria, Spearman, Mary, Spencer, Patti, Tikoo, Archana, Turriff, Lisa, Wright, Natalie, Bedoya, Luis Diaz de, Cossette, Danielle, Cusan, Leonello, Desrosiers, Isabelle, Dufour, Nathalie, Foster, William, Gomez, JoseLuis, Hathout, Lara, Lajoie, MarieAndree, Martin, AndreGuy, Pouliot, Sophie, Vigneault, Eric, Bouchard, Myriam, Bujold, Rachel, Carmel, Michel, Cote, Genevieve, Dion, Helene, Ebacher, Annie, Gauthier, Isabelle C, Ladouceur, Cynthia, Lamoureux, Alexandra, Larouche, Julie, Marquis, Tania, Nabid, Abdenour, Pearson, Diane, Sabbagh, Robert, TetreaultLaflamme, Audrey, Baker, Erin, Blackwood, Geoffrey, Galway, Denise, Greenland, Jonathan, Hannaford, Zeta, Hoskins, Ashley, House, Gail, Kamran, Asim, Manning, Kimberley, Nelder, Matthew, Noel, Lorilee, Norman, Alia, ODea, Jamie, Pinnell, Erin, Pochini, Craig, Putt, Dawne, Rahman, Arifur, Rickert, Lauren, Sathya, Jinka R, Teasdale, Breanne, Thoms, John, Whiteway, Chrystal, Whittle, Stacy, Cheung, Patrick CF, Chu, William, Chung, Hans T, Commisso, Angela, Danjoux, Cyril, Escueta, Vivette, Klotz, Laurence, Leung, Michael, Liu, Stanley, Loblaw, Douglas Andrew, Malek, Leila, Morton, Gerard, Nam, Robert K, Szumacher, Ewa, Tseng, Eric Chia Lin, Vesprini, Danny, Abed, Jessy, Ang, Daniel, Bayley, Andrew J, Berlin, Alejandro, Bristow, Robert, Catton, Charles, Chan, Heidi, Chui, Amadeus, Chung, Peter, Degendorfer, Sarah, Gospodarowicz, Mary, Gregorcic, Emily, Gumapac, Nicole, Helou, Joelle, Hsien, John, Kong, Vickie, Lalani, Nafisha, Lao, Bernadeth, Lawson, Daeria, Menard, Cynthia, Merante, Pat, Milosevic, Michael, Mirmooji, Ramesh, Muraj, Zaynab, Peres, Sarin, Petroff, Avi, Petrovska, Aleksandra, Pineda, Sandra, Quintos, Judy, Raman, Srinivas, Ramotar, Sarah, Ruiz, Maria Jose Gaitan, Sabate, Kathryn, Shukla, Arundhati, Tharahan, Nanthini, Thevarajah, Vaishaali, Tiessen, Kyoko, Topalovich, Aleksandra, Torres, Cynthia, Tse, Karen, Tsuji, Debbie, Velec, Erin, Wallace, Kris, Warde, Padraig, Bisson, MarieEve, Dahmane, Rafika, Gagne, Vanessa, Harvey, Julie, Lebrun, Benoit, Marineau, Line, Montour, Julie, Samson, Julie, Sicard, MarieEve, Thibeault, Andreanne, Vincent, Francois, Vincent, LindaSuzanne, Blood, Paul A, Canavan, Joycelin, Clements, Mia AnnaMaria, Glick, Daniel, Holloway, Caroline, Holwerda, Eleanor, Joe, Howard, Lacey, Cathy, Lam, Jacqueline, Leong, Nelson, Lim, Jan, Lomas, Alia, Pai, Howard H, Priestley, Emilie, Shahid, Negin, Vallieres, Isabelle, Duncan, Graeme, Gleave, Martin E, Goldenberg, S Larry, Karunakaran, Subashini, Keyes, Mira, KimSing, Charmaine, McKenzie, Michael, Morris, William James, Nakashima, Lynne, Peacock, Michael, Pickles, Thomas A, Rodda, Sree, Sibug, Sheena, So, Alan I, Tyldesley, Scott, Zhang, Tina Wanting, Ahmed, Shahida, Ammeter, Barb, Anderson, Leanne, Ann, Taehee, Bakija, Zeljka, Bashir, Bashir, Benjaminson, Patricia, Bergen, Leah, Cadiz, Lovely, Chahine, Jacqueline, Cherepak, Kathy, Chornopyski, Tracy, Chowdhury, Amitava D, Davies, Heather, Dawe, David, Drachenberg, Darrel E, DuMontier, Joelle, Dubey, Arbind, Eska, Tiffani, Francisco, Cristina, Gamez, Raquel, Garrett, Gina, Giovannini, Julia, Graham, Jeffrey, Guarino, Robyn, Hunter, William, Johnston, Laurel, Kitkowski, Caitlin, Klapp, Kathi, Koul, Rashmi, Kuzyk, Valerie, Last, Debbie, Li, KuanLin, Long, Heather, Majumdar, Arpita, Marek, Kathleen, McNish, Jennifer, Mills, Laurie, Morales, Maria, Nashed, Maged, Nichol, Rhonda, Ong, Aldrich Dixon, Ouelette, Ashley, Patel, DeepKumar, Pawl, Marilyn, Peak, Lisa, Priebe, Shauna, Quon, Harvey, Rizzuto, Stacey, Rudnicki, Rayleen, Santos, Priscilla, Sharma, Atul, Sivananthan, Gokulan, Squires, Mandy, Trakalo, Kathy, Turner, Christy, Vaughn, Jamie, Walker, Lori, Watson, Melanie R, Wei, Joyce, Weir, JoAnn, Woloshyn, Rose, Wood, Allison, Zhang, Hanbo, and Zwarych, Darlene
- Abstract
Previous evidence supports androgen deprivation therapy (ADT) with primary radiotherapy as initial treatment for intermediate-risk and high-risk localised prostate cancer. However, the use and optimal duration of ADT with postoperative radiotherapy after radical prostatectomy remains uncertain.
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- 2024
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6. From F to A on the New York Regents Science Exams--An Overview of the Aristo Project
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Clark, Peter, Etzioni, Oren, Khashabi, Daniel, Khot, Tushar, Mishra, Bhavana Dalvi, Richardson, Kyle, Sabharwal, Ashish, Schoenick, Carissa, Tafjord, Oyvind, Tandon, Niket, Bhakthavatsalam, Sumithra, Groeneveld, Dirk, Guerquin, Michal, and Schmitz, Michael
- Subjects
Artificial intelligence -- Usage ,Standardized tests -- Technology application ,Natural language interfaces -- Usage ,Computational linguistics -- Usage ,Language processing -- Usage ,Sciences education -- Technology application ,Artificial intelligence ,Technology application ,Business - Abstract
Artificial intelligence has achieved remarkable mastery over games such as Chess, Go, and poker, and even Jeopardy!, but the rich variety of standardized exams has remained a landmark challenge. Even [...]
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- 2020
7. MODERN MUSASHI: JOCKO WILLINK's Commentaries on the Martial Way
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Sabharwal, Harinder Singh
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General interest ,Sports and fitness - Abstract
My obsession with Miyamoto Musashi began when I was 13 years old, right after my sensei gave me a copy of Go Rin No Sho, The Book of Five Rings. [...]
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- 2020
8. Exploring the impact of cloud and edge computing in education: Addressing challenges and unveiling opportunities
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Arun Kumar, K., Varun Chand, H., John, Neethu Maria, Emmanuel, Sruthy, Polamarasetty, Sarojarani, and Sabharwal, Seema
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- 2024
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9. Bivalent Omicron BA.4/BA.5 BNT162b2 Vaccine in 6-Month- to <12-Year-Olds
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Sher, Lawrence D, Boakye-Appiah, Justice K, Hill, Sungeen, Wasserman, Emily, Xu, Xia, Maldonado, Yvonne, Walter, Emmanuel B, Muñoz, Flor M, Paulsen, Grant C, Englund, Janet A, Talaat, Kawsar R, Barnett, Elizabeth D, Kamidani, Satoshi, Senders, Shelly, Simões, Eric A F, Belanger, Kelly, Parikh, Vrunda, Ma, Hua, Wang, Xingbin, Lu, Claire, Cooper, David, Koury, Kenneth, Anderson, Annaliesa S, Türeci, Özlem, Şahin, Uğur, Swanson, Kena A, Gruber, William C, Gurtman, Alejandra, Kitchin, Nicholas, and Sabharwal, Charu
- Abstract
The use of safe and effective COVID-19 vaccines in pediatric populations remains important. In this clinical trial, safety and immunogenicity data support the favorable benefit-risk profile for a variant-adapted bivalent BNT162b2 in children 6 months to 12 years old.
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- 2024
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10. Seeing Beneath the Skin with Computational Photography: Optical imaging technologies hold powerful potential in healthcare: Optical imaging technologies hold powerful potential in healthcare.
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NOWARA, EWA, MCDUFF, DANIEL, SABHARWAL, ASHUTOSH, and VEERARAGHAVAN, ASHOK
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COMPUTATIONAL photography ,OPTICAL coherence tomography ,MEDICAL care ,HEART beat ,BLOOD sugar monitoring ,HEMODYNAMICS ,CAMERAS - Abstract
This article presents a look at various types of computational photography, including Near-infrared or NIR cameras, radio waves, and optical coherence tomography. Areas of medical care where these methods are applied include cardiac cycle measurement, retinal imaging, blood pressure determination, and glucose monitoring. Computational photography allows for remote access to the devices as well as safety and comfort for patients but disadvantages include limited amount of light scattering and potentially inaccurate measurement in patient's with dark skin types.
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- 2022
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11. QUEEN OF ALL MOVES: Want to Maximize Your Fighting Ability? Try Combat Chess!: One of Bruce Lee's preferred techniques was the straight right lead (shown). He also taught the lead-hand finger jab, which is technically faster, has a greater reach and requires less force to be effective
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Sabharwal, Harinder Singh
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Chess -- Methods ,Intelligence gathering ,General interest ,Sports and fitness - Abstract
Allow me to intercept those who would object to the title of this article. I'm not claiming that there's a secret move, shortcut or hack that will give you the [...]
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- 2020
12. Jeet Kune Do's How to Apply Combat the time-Tested Philosophy Teachings to Any Art!
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Sabharwal, Harinder Singh
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Martial arts ,Philosophy ,Tae kwon do ,Intelligence gathering ,Karate ,General interest ,Sports and fitness - Abstract
Jeet Kune Do, or 'way of the intercepting fist,' is Bruce Lee's gift to the martial arts world. It's always been my opinion, however, that to fully grasp its concepts, [...]
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- 2019
13. Herman, Ana-Maria. Reconfiguring the museum: the politics of digital display
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Sabharwal, A.
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Reconfiguring the Museum: The Politics of Digital Display (Nonfiction work) -- Herman, Ana-Maria ,Books -- Book reviews ,Library and information science ,Literature/writing - Abstract
Herman, Ana-Maria. Reconfiguring the museum: the politics of digital display. McGill-Queen's, 2023. 272p bibl index ISBN 9780228014256 cloth, $80.00; ISBN 9780228015260 ebook, contact publisher for price 61-0330 AM7 MARC This [...]
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- 2023
14. Inflammatory risk and cardiovascular events in patients without obstructive coronary artery disease: the ORFAN multicentre, longitudinal cohort study
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Chan, Kenneth, Wahome, Elizabeth, Tsiachristas, Apostolos, Antonopoulos, Alexios S, Patel, Parijat, Lyasheva, Maria, Kingham, Lucy, West, Henry, Oikonomou, Evangelos K, Volpe, Lucrezia, Mavrogiannis, Michail C, Nicol, Edward, Mittal, Tarun K, Halborg, Thomas, Kotronias, Rafail A, Adlam, David, Modi, Bhavik, Rodrigues, Jonathan, Screaton, Nicholas, Kardos, Attila, Greenwood, John P, Sabharwal, Nikant, De Maria, Giovanni Luigi, Munir, Shahzad, McAlindon, Elisa, Sohan, Yogesh, Tomlins, Pete, Siddique, Muhammad, Kelion, Andrew, Shirodaria, Cheerag, Pugliese, Francesca, Petersen, Steffen E, Blankstein, Ron, Desai, Milind, Gersh, Bernard J, Achenbach, Stephan, Libby, Peter, Neubauer, Stefan, Channon, Keith M, Deanfield, John, Antoniades, Charalambos, Thomas, Sheena, Denton, Jon, Farral, Robyn, Taylor, Carolyn, Qin, Wendy, Kasongo, Mary, Anthony, Susan, Banning, Adrian, Xie, Cheng, Kharbanda, Rajesh K, Pritchard, Amy, Halborg, Thomas, Syed, Nigar, Fry, Sam, Mathers, Chris, Rose, Anne, Hudson, George, Bajaj, Amrita, Das, Intrajeet, Deshpande, Aparna, Rao, Praveen, Lawday, Dan, Mirsadraee, Saeed, Hudson, Benjamin, Berry, Colin, Marwan, Mohamed, Maurovich-Horvat, Pál, He, Guo-Wei, Lin, Wen-Hua, Fan, Li-Juan, Takahashi, Naohiko, Kondo, Hidekazu, Dai, Neng, Ge, Junbo, Koo, Bon-Kwon, Guglielmo, Marco, Pontone, Gianluca, Huck, Daniel, Benedek, Theodora, Rajani, Ronak, Vilic, Dijana, Aljazzaf, Haleema, Mun, Mak S, Benedetti, Giulia, Preston, Rebecca L, Raisi-Estabragh, Zahra, Connolly, Derek L, Sharma, Vinoda, Grenfell, Rebecca, Bradlow, William, Schmitt, Matthias, Serfaty, Fabiano, Gottlieb, Ilan, Neves, Mario FT, Newby, David E, Dweck, Marc R, Hatem, Stéphane, Redheuil, Alban, Benetos, Georgios, Beer, Meinrad, Granillo, Gastón AR, Selvanayagam, Joseph, Lopez-Jimenez, Francisco, De Bosscher, Ruben, Tavildari, Alain, Figtree, Gemma, Danad, Ibrahim, Shantouf, Ronney, Kietselaer, Bas, Tousoulis, Dimitris, Dangas, George, Mehta, Nehal N, Kontanidis, Christos, Kunadian, Vijay, and Fairbairn, Timothy A
- Abstract
Coronary computed tomography angiography (CCTA) is the first line investigation for chest pain, and it is used to guide revascularisation. However, the widespread adoption of CCTA has revealed a large group of individuals without obstructive coronary artery disease (CAD), with unclear prognosis and management. Measurement of coronary inflammation from CCTA using the perivascular fat attenuation index (FAI) Score could enable cardiovascular risk prediction and guide the management of individuals without obstructive CAD. The Oxford Risk Factors And Non-invasive imaging (ORFAN) study aimed to evaluate the risk profile and event rates among patients undergoing CCTA as part of routine clinical care in the UK National Health Service (NHS); to test the hypothesis that coronary arterial inflammation drives cardiac mortality or major adverse cardiac events (MACE) in patients with or without CAD; and to externally validate the performance of the previously trained artificial intelligence (AI)-Risk prognostic algorithm and the related AI-Risk classification system in a UK population.
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- 2024
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15. Effects of a stabilized stannous fluoride dentifrice on clinical, immunomodulatory, and microbial outcomes in a human experimental gingivitis model
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Fine, Noah, Barbour, Abdelahhad, Kaura, Kamini, Kerns, Kristopher A., Chen, Dandan, Trivedi, Harsh M., Gomez, Juliana, Sabharwal, Amarpreet, McLean, Jeffrey S., Darveau, Richard P., and Glogauer, Michael
- Abstract
Stannous fluoride dentifrice is well established for its beneficial clinical effects. In this study, we evaluated the effects of stannous fluoride on inflammation and oral microbiome. In this randomized, parallel‐arm, double‐blind, controlled clinical trial, we compared clinical resolution of experimental gingivitis by evaluating bleeding on probing, gingival index, and plaque index between stannous fluoride stabilized with zinc phosphate (test) and sodium fluoride (control) dentifrices. Further, these groups were compared for oral neutrophil counts, systemic priming of neutrophils, gingival crevicular fluid (GCF) expression of inflammatory markers, and the oral microbiome. We found significant reduction in bleeding on probing in the test group compared to the control group in experimental gingivitis when participants used the test dentifrice prior to induction of experimental gingivitis. The test group also showed significant reductions in GCF levels of inflammatory markers (matrix metalloproteinase 8 [MMP8], receptor activator of nuclear factor kappa‐Β ligand [RANKL]), oral polymorphonuclear neutrophil (PMN) counts, and systemic neutrophil priming (CD11b expression) during experimental gingivitis. Further, significant reductions in the gram‐negative genera Porphyromonas, Tannerella, and Treponemawere noted in the test group. The stannous fluoride stabilized with zinc phosphate dentifrice formulation demonstrated clinical reduction in gingival inflammation and a beneficial effect on microbiome and immune markers. This intervention should be explored as a preventive aid in the progression of plaque‐induced gingivitis to periodontitis.
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- 2024
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16. Serum Troponin I Assessments in 5- to 30-Year-Olds After BNT162b2 Vaccination
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Albertson, Timothy E., Hansen, Caitlin, Bihari, Smiti, Gayed, Juleen, Xu, Xia, Simón-Campos, J. Abraham, Dever, Michael E., Cardona, Jose F., Mitha, Essack, Baker, Jeffrey B., Keep, Georgina, Oladipupo, Islamiat, Mensa, Federico J., Feng, Ye, Ma, Hua, Koury, Kenneth, Mather, Susan, Ianos, Claudia Ana, Anderson, Annaliesa S., Türeci, Özlem, Şahin, Uǧur, Gruber, William C., Gurtman, Alejandra, Sabharwal, Charu, and Kitchin, Nicholas
- Abstract
Introduction: Rare myocarditis and pericarditis cases have occurred in coronavirus disease 2019 (COVID-19) messenger RNA (mRNA) vaccine recipients. Troponin levels, a potential marker of myocardial injury, were assessed in healthy participants before and after BNT162b2 vaccination. Methods: Vaccine-experienced 12- to 30-year-olds in phase 3 crossover C4591031 Substudy B (NCT04955626) who had two or three prior BNT162b2 30-μg doses were randomized to receive BNT162b2 30 μg followed by placebo, or placebo followed by BNT162b2 30 µg, 1 month apart. A participant subset, previously unvaccinated against COVID-19, in the phase 3 C4591007 study (NCT04816643) received up to three vaccinations (BNT162b2 10 μg or placebo [5- to 11-year-olds]) or open-label BNT162b2 30 μg (12- to 15-year-olds). Blood samples collected pre-vaccination, 4 days post-vaccination, and 1-month post-vaccination (C4591031 Substudy B only) were analyzed. Frequencies of elevated troponin I levels (male, > 35 ng/l; female, > 17 ng/l) were assessed. Results: Percentages of 12- to 30-year-olds (n= 1485) in C4591031 Substudy B with elevated troponin levels following BNT162b2 or placebo receipt were 0.5% and 0.8% before vaccination, 0.7% and 1.0% at day 4, and 0.7% and 0.5% at 1 month, respectively. In Study C4591007 (n= 1265), elevated troponin I levels were observed in 0.2, 0.4, and 0.2% of 5- to 11-year-old BNT162b2 recipients at baseline and 4 days post-dose 2 and 3, respectively; corresponding values in 12- to 15-year-olds were 0.4, 0.4, and 0.7%. No 5- to 11-year-old placebo recipients had elevated troponin levels. No myocarditis or pericarditis cases or deaths were reported. Conclusions: Among 5- to < 30-year-olds in both studies, troponin levels were rarely elevated (≤ 1.0%) and similar before and post-vaccination; troponin levels were also similar between BNT162b2 and placebo in 12- to 30-year-old and 5- to 11-year-old recipients in the respective studies. No myocarditis or pericarditis cases were reported. These findings did not provide evidence that BNT162b2 causes troponin elevations. No utility of routine measurement of troponin levels in asymptomatic BNT162b2 recipients was identified.
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- 2024
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17. Users and Gratification Theory Approach to Understand Why People Use Digital Media Mainly During Lockdown
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Sabharwal, Dhruv
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This article seeks to find the uses and gratification that consumers receive by using digital media during the lockdown when there is a situation of Covid. This article tries to provide a more comprehensive understanding of why consumers use digital media. Exploratory research was conducted. In-depth interviews were conducted with over 30 individuals who use social media or digital media during the lockdown. This study identified eight implications of uses and gratifications for using digital media. They are social interaction, seeking information, time-pass, entertainment, relaxation, expression of opinion, information sharing, and knowledge about others.
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- 2024
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18. Assessment of biomass of moss Thuidium cymbifoliumDozy & Molk. for removal of Eriochrome Black T and Auramine-O dyes from aqueous solutions: Kinetics, Isotherms, Thermodynamics, and Microtoxicity studies
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Secrain, Swati, Pannu, Anshul, Dogra, Neha, Sabharwal, Hiteshi, and Kapila, Sunita
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Graphical abstract:
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- 2024
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19. Effectiveness of neuromuscular exercises and neuromuscular electrical stimulation on pain, function and balance in patients with knee osteoarthritis – a randomised controlled trial
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Sabharwal, J. and Joshi, S.
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- 2024
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20. Exploration of Drought Tolerant PGPR and Their role in Regulating Antioxidant Enzymes in Maize (Zea maysL.)
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Rai, Piyush Kant, Sabharwal, Usha, Singh, Shilpi, Yadav, Ajar Nath, and Choure, Kamlesh
- Abstract
Maize in water-deficient regions faces growth challenges due to drought stress, disrupting water relations and nutrient absorption. This study aims to evaluate the potential of isolated plant growth-promoting rhizobacteria (PGPR) in alleviating drought stress effects and regulating antioxidant enzymes in maize. In an in vitro study, eight rhizobacterial isolates were assessed for drought tolerance. Among these isolates, three PGPR strains, namely Pseudomonas fluorescence(MZ723944): PKR1, Bacillus subtilis(MZ960306): PKR5, and Achromobacter xylosoxidans(OK272512): PKR6, were identified. These strains were exposed to 20% (w/v)PEG stress levels to simulate drought conditions, and their impact on maize germination and antioxidant enzyme activity was evaluated. The three isolated PGPR strains demonstrated resistance to 20% (w/v)PEG stress, resulting in a notable improvement of 40–50% in maize germination under stress conditions. Furthermore, these PGPR strains positively influenced the activity of antioxidant enzymes in maize seedlings. They enhanced superoxide dismutase and catalase activity, thereby regulating plant defence mechanisms. Additionally, enzymes such as peroxidases and glutathione reductases were found to reduce oxidative damage and enhance stress tolerance in maize plants. This study demonstrates that specific PGPR strains can mitigate drought stress and regulate antioxidant enzymes in maize, providing a sustainable approach to agriculture.
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- 2024
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21. Solving Linear Inverse Problems Using Higher-Order Annealed Langevin Diffusion
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Zilberstein, Nicolas, Sabharwal, Ashutosh, and Segarra, Santiago
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We propose a solution for linear inverse problems based on higher-order Langevin diffusion. More precisely, we propose pre-conditioned second-order and third-order Langevin dynamics that provably sample from the posterior distribution of our unknown variables of interest while being computationally more efficient than their first-order counterpart and the non-conditioned versions of both dynamics. Moreover, we prove that both pre-conditioned dynamics are well-defined and have the same unique invariant distributions as the non-conditioned cases. We also incorporate an annealing procedure that has the double benefit of further accelerating the convergence of the algorithm and allowing us to accommodate the case where the unknown variables are discrete. Numerical experiments in two different tasks in communications (MIMO symbol detection and channel estimation) and in three tasks for images showcase the generality of our method and illustrate the high performance achieved relative to competing approaches (including learning-based ones) while having comparable or lower computational complexity.
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- 2024
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22. In-Band Full-Duplex: The Physical Layer
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Smida, Besma, Wichman, Risto, Kolodziej, Kenneth E., Suraweera, Himal A., Riihonen, Taneli, and Sabharwal, Ashutosh
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In this article, we review the key concepts and the progress in the design of physical-layer aspects of in-band full-duplex (IBFD) communications. One of the fundamental challenges in realizing IBFD is self-interference that can be up to 100 dB stronger than signals of interest. Thus, we start by reviewing state-of-the-art research in self-interference cancellation, addressing both model-based and emerging machine learning-based methods. Then, we turn our attention to new wireless systems with many degrees of freedom for which the traditional IBFD designs do not gracefully scale and, hence, require many innovations to enable IBFD. We provide an extensive review of basic concepts and state of the art in massive multiple-input–multiple-output IBFD. Then, we consider the mmWave band IBFD and review advanced physical-layer architectures. The above review provides the proper context to discuss IBFD innovations and new challenges for sixth-generation networks and beyond, where wireless networks are envisioned to be multifunctional, combining communications with functions such as sensing, cognitive radios, physical-layer security, and wireless power transfer. We conclude this article with a status update on the adoption of IBFD in communication standards.
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- 2024
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23. Automated identification of fleck lesions in Stargardt disease using deep learning enhances lesion detection sensitivity and enables morphometric analysis of flecks
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Sabharwal, Jasdeep, Liu, Tin Yan Alvin, Antonio-Aguirre, Bani, Abousy, Mya, Patel, Tapan, Cai, Cindy X, Jones, Craig K, and Singh, Mandeep S
- Abstract
PurposeTo classify fleck lesions and assess artificial intelligence (AI) in identifying flecks in Stargardt disease (STGD).MethodsA retrospective study of 170 eyes from 85 consecutive patients with confirmed STGD. Fundus autofluorescence images were extracted, and flecks were manually outlined. A deep learning model was trained, and a hold-out testing subset was used to compare with manually identified flecks and for graders to assess. Flecks were clustered using K-means clustering.ResultsOf the 85 subjects, 45 were female, and the median age was 37 years (IQR 25–59). A subset of subjects (n=41) had clearly identifiable fleck lesions, and an AI was successfully trained to identify these lesions (average Dice score of 0.53, n=18). The AI segmentation had smaller (0.018 compared with 0.034 mm2, p<0.001) but more numerous flecks (75.5 per retina compared with 40.0, p<0.001), but the total size of flecks was not different. The AI model had higher sensitivity to detect flecks but resulted in more false positives. There were two clusters of flecks based on morphology: broadly, one cluster of small round flecks and another of large amorphous flecks. The per cent frequency of small round flecks negatively correlated with subject age (r=−0.31, p<0.005).ConclusionsAI-based detection of flecks shows greater sensitivity than human graders but with a higher false-positive rate. With further optimisation to address current shortcomings, this approach could be used to prescreen subjects for clinical research. The feasibility and utility of quantifying fleck morphology in conjunction with AI-based segmentation as a biomarker of progression require further study.
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- 2024
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24. Measuring the impact of eco-friendly or green marketing on buying behaviour of Indian consumers
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Sabharwal, Dhruv and Narula, Sumit
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In today's competitive market where companies and organisations are looking for new ways of marketing, the current study focuses at measuring the impact of green marketing mix on green purchase behaviour of Indian consumers. In this research paper, 150 respondents were interviewed face-to-face using a self-prepared questionnaire. Convenience sampling technique was used to collect data from the respondents. In this research paper, structural equation modelling (SEM) is being used to conduct confirmatory factor analysis. To test the structural model, AMOS 23.0 software was used. Research analysed that four elements of green marketing mix named as green product, green price, green place and green promotion have an important and crucial impact on the green buying behaviour of Indian consumers. Moreover, the study shows that green products have the highest and green place has the lowest impact on consumers' buying behaviour. Current research will help advertisers to understand the concept of green marketing mix and design the tools of green marketing mix according to the need of consumers who purchase green products. In addition, the study will show the concept of sustainable marketing, so that modern marketers can use this technique as a strategic tool to persuade consumers' green purchasing behaviour.
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- 2024
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25. Human face identification after plastic surgery using SURF, Multi-KNN and BPNN techniques
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Sabharwal, Tanupreet and Gupta, Rashmi
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Facial identification for surgical and non-surgical datasets is getting popular. The reason behind this popularity is the growing need of a robust facial recognition system which is consistent to occlusion, spoofing attacks and last but most important plastic surgery effects. Plastic therapies are undertaken by individuals to beautify their external appearance but it is also undertaken by impostors to commit crimes and falsify their true identities. This research work aims at developing a facial recognition system which can identify genuine and impostor pairs. The projected methodology optimizes face detection via Back-Propagation Neural Network (BPNN) and dimensionality reduction by means of Speeded Up Robust Features followed by Multi-K-Nearest-Neighbor technique. The novelty is the production of an innovative-fangled T-Database which trains the BPNN. Thus, BPNN converges faster and achieves higher recognition. The proposed scheme is not applied till date on a medically altered dataset. We have applied five distance metrics and integrated them to acquire T-Dataset, which is fed to the BPNN. This scheme is tested on surgical and non-surgical datasets and it is deduced that higher recognition is achieved with non-surgical databases as compared to surgical ones. For both surgical and non-surgical datasets, the computational cost attained is the modest.
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- 2024
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26. Estimating Traffic Rates in CSMA/CA Networks: A Feasibility Analysis for a Class of Eavesdroppers
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Cheng, Yirong, Graves, Eric, Swami, Ananthram, and Sabharwal, Ashutosh
- Abstract
Estimation of transmission rates by a malicious user can serve as a stepping stone to further attacks on the network. In this paper, we aim to investigate the general problem of estimating traffic transmission rates in a CSMA/CA network using a class of passive eavesdropping methods. We consider the case where a single eavesdropper passively monitors all active network nodes but cannot observe all packet transmissions due to spatial-reuse collisions. To enable tractable analysis, we first propose an approximate statistical model that can help the eavesdropper estimate transmission rates with partial measurements under spatial reuse. We next consider a class of eavesdroppers that become increasingly more capable, and develop a framework to demonstrate that two classes of eavesdropper capabilities are sufficient to achieve a consistent transmission rate estimator. We provide numerical tests of our proposed estimators under practical network cases using the NS-3 simulator that validate the theoretical results.
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- 2023
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27. RUTA 40 - BOGOTA GIRARDOT - COLOMBIE LA STABILISATION DES TALUS ROCHEUX DE MELGAR.
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VARILLON, JÉRÔME and SABHARWAL, ABHISHEK
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WEATHER ,EMBANKMENTS ,ENGINEERING design ,BEDROCK ,STRUCTURAL engineering ,LANDSLIDES - Abstract
Copyright of Travaux (00411906) is the property of COM'1 EVIDENCE and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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- 2023
28. Direct-acting agents cure hepatitis C in children
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Sabharwal, Vishakha, Moloney, Carole, and Pelton, Stephen I.
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Diseases ,Sofosbuvir ,Velpatasvir ,Patient compliance ,Virus diseases ,Hepatitis C virus - Abstract
Between 23,000 and 46,000 U.S. children live with chronic hepatitis C virus with a prevalence of 0.17% anti-hepatitis C virus (HCV) antibody positivity in those aged 6-11 years and 0.39% [...]
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- 2021
29. Accuracy of noncycloplegic photorefraction using Spot photoscreener in detecting amblyopia risk factors in preschool children in an Indian eye clinic
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Sharma, Manasvini, Ganesh, Suma, Tibrewal, Shailja, Sabharwal, Shalinder, Sachdeva, Neha, Adil, Mohd, Chaudhary, Jyotsana, and Siddiqui, Zeeshan
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Risk factors ,Pediatrics ,Amblyopia -- Risk factors ,Setting (Literature) - Published
- 2020
30. Coded Estimation: Design of Backscatter Array Codes for 3D Orientation Estimation
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Rammal, Mohamad Rida, Diggavi, Suhas, and Sabharwal, Ashutosh
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We consider the problem of estimating the orientation of a 3D object with the assistance of configurable backscatter tags. We explore the idea of designing tag response codes to improve the accuracy of orientation estimation. To minimize the difference between the true and estimated orientation, we propose two code design criteria. We also derive a lower bound on the worst-case error using Le Cam’s method and provide simulation results for multiple scenarios including line-of-sight only and multipath, comparing the theoretical bounds to those achieved by the designs.
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- 2023
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31. Guest Editorial Full Duplex and its Applications
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Smida, Besma, Sabharwal, Ashutosh, Fodor, Gabor, Alexandropoulos, George C., Suraweera, Himal A., and Chae, Chan-Byoung
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The capability of nodes to transmit and receive data simultaneously within the same frequency band, referred to as in-band FD, disrupts the conventional assumptions underlying wireless network design. This new feature enhances spectral efficiency and reduces latency, which are essential drivers in advancing next-generation networks. In the past few years, full-duplex (FD) has evolved from being a laboratory idea to being incorporated into telecommunications standards and proof of concepts. From 2010 to 2020, considerable research and development efforts were devoted to advancing FD wireless communications. By 2015, the cable modem industry had already implemented in-band FD technology to establish the DOCSIS 4.0 standard, enabling next-generation cable modems to operate in FD mode. By 2020, FD wireless products started to emerge in the market.
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- 2023
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32. Full-Duplex Wireless for 6G: Progress Brings New Opportunities and Challenges
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Smida, Besma, Sabharwal, Ashutosh, Fodor, Gabor, Alexandropoulos, George C., Suraweera, Himal A., and Chae, Chan-Byoung
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The use of in-band full-duplex (FD) enables nodes to simultaneously transmit and receive on the same frequency band, which challenges the traditional assumption in wireless network design. The full-duplex capability enhances spectral efficiency and decreases latency, which are two key drivers pushing the performance expectations of next-generation mobile networks. In less than ten years, in-band FD has advanced from being demonstrated in research labs to being implemented in standards, presenting new opportunities to utilize its foundational concepts. Some of the most significant opportunities include using FD to enable wireless networks to sense the physical environment, integrate sensing and communication applications, develop integrated access and backhaul solutions, and work with smart signal propagation environments powered by reconfigurable intelligent surfaces. However, these new opportunities also come with new challenges for large-scale commercial deployment of FD technology, such as managing self-interference, combating cross-link interference in multi-cell networks, and coexistence of dynamic time division duplex, subband FD and FD networks.
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- 2023
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33. Safety and Efficacy of the Hilzo Esophageal Stent for the Management of Dysphagia in Malignant Esophageal Obstruction
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Lagha, Eamon, Mulholland, Douglas, Tadros, Catharine, Faris, Mohamad, Sabharwal, Tarun, Diamantopoulos, Athanasios, and Ahmed, Irfan
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- 2023
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34. Coronary Atherosclerotic Plaque Activity and Future Coronary Events
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Moss, Alastair, Daghem, Marwa, Tzolos, Evangelos, Meah, Mohammed N., Wang, Kang-Ling, Bularga, Anda, Adamson, Philip D., Kwiecinski, Jacek, Fletcher, Alison, Dawson, Dana, Arumugam, Parthiban, Sabharwal, Nikant, Greenwood, John P., Townend, Jon N., Calvert, Patrick A., Rudd, James H. F., Berman, Dan, Verjans, Johan, Slomka, Piotr, Dey, Damini, Forsyth, Laura, Murdoch, Lauren, Lee, Robert J., Lewis, Steff, Mills, Nicholas L., van Beek, Edwin J. R., Williams, Michelle C., Dweck, Marc R., and Newby, David E.
- Abstract
IMPORTANCE: Recurrent coronary events in patients with recent myocardial infarction remain a major clinical problem. Noninvasive measures of coronary atherosclerotic disease activity have the potential to identify individuals at greatest risk. OBJECTIVE: To assess whether coronary atherosclerotic plaque activity as assessed by noninvasive imaging is associated with recurrent coronary events in patients with myocardial infarction. DESIGN, SETTING, AND PARTICIPANTS: This prospective, longitudinal, international multicenter cohort study recruited participants aged 50 years or older with multivessel coronary artery disease and recent (within 21 days) myocardial infarction between September 2015 and February 2020, with a minimum 2 years’ follow-up. INTERVENTION: Coronary 18F-sodium fluoride positron emission tomography and coronary computed tomography angiography. MAIN OUTCOMES AND MEASURES: Total coronary atherosclerotic plaque activity was assessed by 18F-sodium fluoride uptake. The primary end point was cardiac death or nonfatal myocardial infarction but was expanded during study conduct to include unscheduled coronary revascularization due to lower than anticipated primary event rates. RESULTS: Among 2684 patients screened, 995 were eligible, 712 attended for imaging, and 704 completed an interpretable scan and comprised the study population. The mean (SD) age of participants was 63.8 (8.2) years, and most were male (601 [85%]). Total coronary atherosclerotic plaque activity was identified in 421 participants (60%). After a median follow-up of 4 years (IQR, 3-5 years), 141 participants (20%) experienced the primary end point: 9 had cardiac death, 49 had nonfatal myocardial infarction, and 83 had unscheduled coronary revascularizations. Increased coronary plaque activity was not associated with the primary end point (hazard ratio [HR], 1.25; 95% CI, 0.89-1.76; P = .20) or unscheduled revascularization (HR, 0.98; 95% CI, 0.64-1.49; P = .91) but was associated with the secondary end point of cardiac death or nonfatal myocardial infarction (47 of 421 patients with high plaque activity [11.2%] vs 19 of 283 with low plaque activity [6.7%]; HR, 1.82; 95% CI, 1.07-3.10; P = .03) and all-cause mortality (30 of 421 patients with high plaque activity [7.1%] vs 9 of 283 with low plaque activity [3.2%]; HR, 2.43; 95% CI, 1.15-5.12; P = .02). After adjustment for differences in baseline clinical characteristics, coronary angiography findings, and Global Registry of Acute Coronary Events score, high coronary plaque activity was associated with cardiac death or nonfatal myocardial infarction (HR, 1.76; 95% CI, 1.00-3.10; P = .05) but not with all-cause mortality (HR, 2.01; 95% CI, 0.90-4.49; P = .09). CONCLUSIONS AND RELEVANCE: In this cohort study of patients with recent myocardial infarction, coronary atherosclerotic plaque activity was not associated with the primary composite end point. The findings suggest that risk of cardiovascular death or myocardial infarction in patients with elevated plaque activity warrants further research to explore its incremental prognostic implications.
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- 2023
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35. Perspectives of patients, care partners, and primary care physicians on management of mild cognitive impairment and mild Alzheimer’s disease dementia
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Brunton, Stephen, Pruzin, Jeremy J., Alford, Susan, Hamersky, Carol, Sabharwal, Anup, and Gopalakrishna, Ganesh
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ABSTRACTObjectivesEarly diagnosis of mild cognitive impairment (MCI) and mild Alzheimer’s disease (AD) dementia is crucial for effective disease management and optimizing patient outcomes. We sought to better understand the MCI and mild AD dementia medical journey from the perspective of patients, care partners, and physicians.MethodsWe conducted online surveys in the United States among patients/care partners and physicians in 2021.Results103 patients with all-cause MCI or mild AD dementia aged 46–90 years, 150 care partners for someone with all-cause MCI or mild AD dementia, and 301 physicians (101 of which were primary care physicians, [PCPs]) completed surveys. Most patient/care partners reported that experiencing forgetfulness (71%) and short-term memory loss (68%) occurred before talking to a healthcare professional. Most patients (73%) followed a common medical journey, in which the initial discussion with a PCP took place 15 months after symptom onset. However, only 33% and 39% were diagnosed and treated by a PCP, respectively. Most (74%) PCPs viewed themselves as coordinators of care for their patients with MCI and mild AD dementia. Over one-third (37%) of patients/care partners viewed PCPs as the care coordinator.ConclusionsPCPs play a vital role in the timely diagnosis and treatment of MCI and mild AD dementia but often are not considered the care coordinator. For the majority of patients, the initial discussion with a PCP took place 15 months after symptom onset; therefore, it is important to educate patients/care partners and PCPs on MCI and AD risk factors, early symptom recognition, and the need for early diagnosis and treatment. PCPs could improve patient care and outcomes by building their understanding of the need for early AD diagnosis and treatment and improving the efficiency of the patient medical journey by serving as coordinators of care.
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- 2023
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36. FUTUREPROOF PLANT STRATEGIES A HOW-TO GUIDE.
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Sabharwal, Amish
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INDUSTRIAL organization (Economic theory) ,SUSTAINABILITY ,DIGITAL transformation ,DIGITAL twin ,EPCM contractors - Published
- 2024
37. In-Band Full Duplex
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Sabharwal, Ashutosh and Smida, Besma
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The global wireless industry works like clockwork. Every decade, the global community ratifies a new generation of cellular standards relying on advances from the past decades; a similar rhythm drives Wi-Fi standardization. In all standards, cellular and Wi-Fi, a core design principle is that wireless nodescan either transmit or receive in a given frequency band. However, a node cannot simultaneously transmit and receive in the same frequency band due to high self-interference from its own transmitted signal that can drown out the receive signal. As a result, to enable bidirectional communications between nodes, network designs have relied on partitioning time and frequency using a mix of time-division duplex and frequency-division duplex. In short, wireless designs have not considered in-band full duplex (IBFD) as a building block.
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- 2024
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38. Direct-acting agents cure hepatitis C in children
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Sabharwal, Vishakha, Moloney, Carole, and Pelton, Stephen I.
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Diseases ,Glecaprevir ,Antiviral agents ,Patient compliance ,Hepatitis C virus - Abstract
Between 23,000 and 46,000 U.S. children live with chronic hepatitis C virus with a prevalence of 0.17% anti-hepatitis C virus (HCV) antibody positivity in those aged 6-11 years and 0.39% [...]
- Published
- 2020
39. Is diversity management sufficient? Organizational inclusion to further performance
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Sabharwal, Meghna
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Management ,Influence ,Public relations ,Surveys ,Company public relations ,Company business management ,Workplace diversity -- Surveys -- Management ,Organizational effectiveness -- Surveys -- Management ,Government officials -- Public relations -- Surveys ,Goal setting -- Surveys -- Influence ,Workplace multiculturalism -- Surveys -- Management ,Public officers -- Public relations -- Surveys - Abstract
Introduction Public organizations are hiring women and minorities to create a diverse workforce that reflects the demographics of the nation. Furthermore, they are providing diversity training that focuses on handling [...], This study focuses on the concept of organizational inclusion, which goes beyond diversity management, the dominant paradigm in the field of public administration. Although several studies in public administration mention the importance of inclusion, none of these studies have empirically tested its association with performance beyond diversity management. Data for this study comes from a survey conducted among public managers in Texas agencies. The study finds that diversity management alone is insufficient for improving workplace performance. What is required instead is an approach that promotes greater inclusion of employees in ways that takes their views into account and promotes self-esteem. The results show that productive workplaces exist when employees are encouraged to express their opinions, and their input is sought before making important organizational decisions. This requires supportive leadership and empowering employees with information and resources that will help them make important decisions about their jobs. Keywords organizational inclusion, diversity, diversity management, organizational performance, leadership
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- 2014
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40. Book Excerpt: Anger is a short madness
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Sharma, Anjana and Sabharwal, Gopa
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Kolkata, India -- Political aspects ,Partition of India, 1947 ,The Psychological Impact of the Partition of India (Essay collection) -- Excerpts ,Religious leaders -- Demonstrations and protests -- Homes and haunts ,Hindu-Muslim relations -- Political aspects ,Government - Abstract
Byline: Anjana Sharma and Gopa Sabharwal The Psychological Impact of the Partition of India Edited by Sanjeev Jain and Alok Sarin Sage Publications, 260 pages, Rs 850 This essay seeks [...]
- Published
- 2018
41. CFTR Modulators for the Gastroenterologist
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Sabharwal, Sabina and Lusman, Sarah Shrager
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- 2023
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42. Maternal, Infant, and Breast Milk Antibody Response Following COVID-19 Infection in Early Versus Late Gestation
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Wachman, Elisha M., Snyder-Cappione, Jennifer, Devera, Jean, Boateng, Jeffery, Dhole, Yashoda, Clarke, Katherine, Yuen, Rachel R., Parker, Samantha E., Hunnewell, Jessica, Ferraro, Renee, Jean-Sicard, Sigride, Woodard, Elizabeth, Cruikshank, Alice, Sinha, Bharati, Bartolome, Ruby, Barnett, Elizabeth D., Yarrington, Christina, Taglauer, Elizabeth S., and Sabharwal, Vishakha
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- 2023
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43. Interferon Gamma Release Assay Results and Testing Trends Among Patients Younger Than 2 Years Old at Two US Health Centers
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Tabatneck, Mary E., He, Wei, Lamb, Gabriella S., Sun, Mingwei, Goldmann, Don, Sabharwal, Vishakha, Sandora, Thomas J., Haberer, Jessica E., and Campbell, Jeffrey I.
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- 2023
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44. Legal Evolution of a Law Against Weight Discrimination in the United States: A Focus on Massachusetts
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Campoverde Reyes, Karen J., Sabharwal, Shreya, and Stanford, Fatima Cody
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Weight discrimination heightens health inequalities, particularly among racial and ethnically diverse populations. We aim to research the legal evolution of the law against weight discrimination (S.2495) and raise awareness among lawmakers in the Commonwealth of Massachusetts. We invited officials (n = 199) to attend a legislative briefing, and 25.6% completed a 14-question anonymous survey upon arrival. Contrary to our hypothesis, this first-of-its-kind study found that most policymakers are aware of weight biases. While S.2495 did not pass, the current bill S.2669, prohibiting body size discrimination, has recently been reported favorably by the Joint Committee on the Judiciary and referred to the committee on Senate Ways and Means.
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- 2023
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45. LensFD: Using Lenses for Improved Sub-6 GHz Massive MIMO Full-Duplex
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Chen, Zhican, Barati, C. Nicolas, Veihl, Jon, Shepard, Clayton, and Sabharwal, Ashutosh
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Massive MIMO has emerged as a key-enabler to deliver the high data-rates in current and next-generation systems. Many massive MIMO deployments use time-division duplex (TDD), thereby opening the door for the potential use of full-duplex at base-stations. In this paper, we present LensFD, a scalable method to enable cost-effective massive MIMO full-duplex operation in sub-6 GHz frequencies with a focus on the CBRS band of 3.5 GHz. LensFD uses a low-cost lens array combined with transmit precoding to reshape the effective beam pattern of antennas in massive MIMO arrays to reduce self-interference. We tested three lens configurations in LensFD experimentally with a software-defined 40-antenna base-station and demonstrate a significant improvement in self-interference reduction and hence overall increased system capacity to 1.6× over the TDD counterpart. As an important contribution, we will open-source the large measurement dataset from our experiments.
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- 2023
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46. Healing of acute anterior cruciate ligament rupture on MRI and outcomes following non-surgical management with the Cross Bracing Protocol
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Filbay, Stephanie R, Dowsett, Matthew, Chaker Jomaa, Mohammad, Rooney, Jane, Sabharwal, Rohan, Lucas, Phil, Van Den Heever, Andrew, Kazaglis, James, Merlino, Justin, Moran, Mick, Allwright, Maggie, Kuah, Donald E K, Durie, Ra, Roger, Greg, Cross, Mervyn, and Cross, Tom
- Abstract
ObjectiveInvestigate MRI evidence of anterior cruciate ligament (ACL) healing, patient-reported outcomes and knee laxity in patients with acute ACL rupture managed non-surgically with the Cross Bracing Protocol (CBP).MethodsEighty consecutive patients within 4 weeks of ACL rupture were managed with CBP (knee immobilisation at 90° flexion in brace for 4 weeks, followed by progressive increases in range-of-motion until brace removal at 12 weeks, and physiotherapist-supervised goal-oriented rehabilitation). MRIs (3 months and 6 months) were graded using the ACL OsteoArthritis Score (ACLOAS) by three radiologists. Mann-Whitney U tests compared Lysholm Scale and ACL quality of life (ACLQOL) scores evaluated at median (IQR) of 12 months (7–16 months) post-injury, and χ2tests compared knee laxity (3-month Lachman’s test and 6-month Pivot-shift test), and return-to-sport at 12 months between groups (ACLOAS grades 0–1 (continuous±thickened ligament and/or high intraligamentous signal) versus ACLOAS grades 2–3 (continuous but thinned/elongated or complete discontinuity)).ResultsParticipants were aged 26±10 years at injury, 39% were female, 49% had concomitant meniscal injury. At 3 months, 90% (n=72) had evidence of ACL healing (ACLOAS grade 1: 50%; grade 2: 40%; grade 3: 10%). Participants with ACLOAS grade 1 reported better Lysholm Scale (median (IQR): 98 (94–100) vs 94 (85–100)) and ACLQOL (89 (76–96) vs 70 (64–82)) scores, compared with ACLOAS grades 2–3. More participants with ACLOAS grade 1 had normal 3-month knee laxity (100% vs 40%) and returned to pre-injury sport (92% vs 64%), compared with participants with an ACLOAS grades 2–3. Eleven patients (14%) re-injured their ACL.ConclusionAfter management of acute ACL rupture with the CBP, 90% of patients had evidence of healing on 3-month MRI (continuity of the ACL). More ACL healing on 3-month MRI was associated with better outcomes. Longer-term follow-up and clinical trials are needed to inform clinical practice.
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- 2023
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47. Machine learning to develop a predictive model of pressure injury in persons with spinal cord injury
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Luther, Stephen L., Thomason, Susan S., Sabharwal, Sunil, Finch, Dezon K., McCart, James, Toyinbo, Peter, Bouayad, Lina, Lapcevic, William, Hahm, Bridget, Hauser, Ronald G., Matheny, Michael E., and Powell-Cope, Gail
- Abstract
Study design: A 5-year longitudinal, retrospective, cohort study. Objectives: Develop a prediction model based on electronic health record (EHR) data to identify veterans with spinal cord injury/diseases (SCI/D) at highest risk for new pressure injuries (PIs). Setting: Structured (coded) and text EHR data, for veterans with SCI/D treated in a VHA SCI/D Center between October 1, 2008, and September 30, 2013. Methods: A total of 4709 veterans were available for analysis after randomly selecting 175 to act as a validation (gold standard) sample. Machine learning models were created using ten-fold cross validation and three techniques: (1) two-step logistic regression; (2) regression model employing adaptive LASSO; (3) and gradient boosting. Models based on each method were compared using area under the receiver-operating curve (AUC) analysis. Results: The AUC value for the gradient boosting model was 0.62 (95% CI = 0.54–0.70), for the logistic regression model it was 0.67 (95% CI = 0.59–0.75), and for the adaptive LASSO model it was 0.72 (95% CI = 0.65–80). Based on these results, the adaptive LASSO model was chosen for interpretation. The strongest predictors of new PI cases were having fewer total days in the hospital in the year before the annual exam, higher vs. lower weight and most severe vs. less severe grade of injury based on the American Spinal Cord Injury Association (ASIA) Impairment Scale. Conclusions: While the analyses resulted in a potentially useful predictive model, clinical implications were limited because modifiable risk factors were absent in the models.
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- 2023
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48. Annealed Langevin Dynamics for Massive MIMO Detection
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Zilberstein, Nicolas, Dick, Chris, Doost-Mohammady, Rahman, Sabharwal, Ashutosh, and Segarra, Santiago
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Solving the optimal symbol detection problem in multiple-input multiple-output (MIMO) systems is known to be NP-hard. Hence, the objective of any detector of practical relevance is to get reasonably close to the optimal solution while keeping the computational complexity in check. In this work, we propose a MIMO detector based on an annealed version of Langevin (stochastic) dynamics. More precisely, we define a stochastic dynamical process whose stationary distribution coincides with the posterior distribution of the symbols given our observations. In essence, this allows us to approximate the maximum a posteriori estimator of the transmitted symbols by sampling from the proposed Langevin dynamic. Furthermore, we carefully craft this stochastic dynamic by gradually adding a sequence of noise with decreasing variance to the trajectories, which ensures that the estimated symbols belong to a pre-specified discrete constellation. Based on the proposed MIMO detector, we also design a robust version of the method by unfolding and parameterizing one term– the score of the likelihood– by a neural network. Through numerical experiments in both synthetic and real-world data, we show that our proposed detector yields state-of-the-art symbol error rate performance and the robust version becomes noise-variance agnostic.
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- 2023
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49. Corporate social responsibility: a discourse
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Kumar, Vinod and Sabharwal, Shivani
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Corporate social responsibility -- Social aspects ,Economics ,Political science - Abstract
Introduction We live in an era where corporate entities and those at the helm of managing their affairs --managers are not judged only by their financial performance anymore but also [...]
- Published
- 2013
50. Raab, Nigel A.: The humanities in transition from postmodernism into the digital age
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Sabharwal, A.
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The Humanities in Transition from Postmodernism into the Digital Age (Nonfiction work) -- Raab, Nigel A. ,Books -- Book reviews ,Library and information science ,Literature/writing - Abstract
Raab, Nigel A. The humanities in transition from postmodernism into the digital age. Routledge, 2020. 218p bibl Index ISBN 9780367896799 cloth, $160.00; ISBN 9781003020493 ebook, $48.95 59-0022 AZ105 MARC Testing [...]
- Published
- 2021
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