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1. The Impact of Bariatric Surgery on Coronary Microvascular Function Assessed Using Automated Quantitative Perfusion CMR

Author

Crane, James D., Joy, George, Knott, Kristopher D., Augusto, João B., Lau, Clement, Bhuva, Anish N., Seraphim, Andreas, Evain, Timothée, Brown, Louise A.E., Chowdhary, Amrit, Kotecha, Tushar, Fontana, Marianna, Plein, Sven, Ramar, Sasindran, Rubino, Francesco, Kellman, Peter, Xue, Hui, Pierce, Iain, Davies, Rhodri H., Moon, James C., Cruickshank, J. Kennedy, McGowan, Barbara M., and Manisty, Charlotte

Published
2024
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2. Prognostic utility and characterization of left ventricular hypertrophy using global thickness

Author

Lundin, Magnus, Heiberg, Einar, Nordlund, David, Gyllenhammar, Tom, Steding-Ehrenborg, Katarina, Engblom, Henrik, Carlsson, Marcus, Atar, Dan, van der Pals, Jesper, Erlinge, David, Borgquist, Rasmus, Khoshnood, Ardavan, Ekelund, Ulf, Nickander, Jannike, Themudo, Raquel, Nordin, Sabrina, Kozor, Rebecca, Bhuva, Anish N., Moon, James C., Maret, Eva, Caidahl, Kenneth, Sigfridsson, Andreas, Sörensson, Peder, Schelbert, Erik B., Arheden, Håkan, and Ugander, Martin

Published
2023
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3. [11C]metomidate PET-CT versus adrenal vein sampling for diagnosing surgically curable primary aldosteronism: a prospective, within-patient trial

Author

Wu, Xilin, Senanayake, Russell, Goodchild, Emily, Bashari, Waiel A., Salsbury, Jackie, Cabrera, Claudia P., Argentesi, Giulia, O’Toole, Samuel M., Matson, Matthew, Koo, Brendan, Parvanta, Laila, Hilliard, Nick, Kosmoliaptsis, Vasilis, Marker, Alison, Berney, Daniel M., Tan, Wilson, Foo, Roger, Mein, Charles A., Wozniak, Eva, Savage, Emmanuel, Sahdev, Anju, Bird, Nicholas, Laycock, Kate, Boros, Istvan, Hader, Stefan, Warnes, Victoria, Gillett, Daniel, Dawnay, Anne, Adeyeye, Elizabeth, Prete, Alessandro, Taylor, Angela E., Arlt, Wiebke, Bhuva, Anish N., Aigbirhio, Franklin, Manisty, Charlotte, McIntosh, Alasdair, McConnachie, Alexander, Cruickshank, J. Kennedy, Cheow, Heok, Gurnell, Mark, Drake, William M., and Brown, Morris J.

Published
2023
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4. Improving the generalizability of convolutional neural network-based segmentation on CMR images

Author

Chen, Chen, Bai, Wenjia, Davies, Rhodri H., Bhuva, Anish N., Manisty, Charlotte, Moon, James C., Aung, Nay, Lee, Aaron M., Sanghvi, Mihir M., Fung, Kenneth, Paiva, Jose Miguel, Petersen, Steffen E., Lukaschuk, Elena, Piechnik, Stefan K., Neubauer, Stefan, and Rueckert, Daniel

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

Convolutional neural network (CNN) based segmentation methods provide an efficient and automated way for clinicians to assess the structure and function of the heart in cardiac MR images. While CNNs can generally perform the segmentation tasks with high accuracy when training and test images come from the same domain (e.g. same scanner or site), their performance often degrades dramatically on images from different scanners or clinical sites. We propose a simple yet effective way for improving the network generalization ability by carefully designing data normalization and augmentation strategies to accommodate common scenarios in multi-site, multi-scanner clinical imaging data sets. We demonstrate that a neural network trained on a single-site single-scanner dataset from the UK Biobank can be successfully applied to segmenting cardiac MR images across different sites and different scanners without substantial loss of accuracy. Specifically, the method was trained on a large set of 3,975 subjects from the UK Biobank. It was then directly tested on 600 different subjects from the UK Biobank for intra-domain testing and two other sets for cross-domain testing: the ACDC dataset (100 subjects, 1 site, 2 scanners) and the BSCMR-AS dataset (599 subjects, 6 sites, 9 scanners). The proposed method produces promising segmentation results on the UK Biobank test set which are comparable to previously reported values in the literature, while also performing well on cross-domain test sets, achieving a mean Dice metric of 0.90 for the left ventricle, 0.81 for the myocardium and 0.82 for the right ventricle on the ACDC dataset; and 0.89 for the left ventricle, 0.83 for the myocardium on the BSCMR-AS dataset. The proposed method offers a potential solution to improve CNN-based model generalizability for the cross-scanner and cross-site cardiac MR image segmentation task., Comment: 15 pages, 8 figures

Published
2019
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5. Abstract 15044: The Relationship of Pericardial Fat With Metabolic Change and Myocardial Remodelling Following Bariatric Surgery

Author

Ardissino, Alessandra Maria, Joy, George, Crane, James D, Knott, Kristopher D, Augusto, Joao B, Lau, Clement, Bhuva, Anish N, Seraphim, Andreas, Chowdhary, Amrit, Fontana, Marianna, Plein, Sven, Ramar, Sasindran, Rubino, Francesco, Kellman, Peter, Xue, Hui, Pierce, Iain, Davies, Rhodri H, Moon, James C, Cruickshank, Kennedy, McGowan, Barbara M, and Manisty, Charlotte

Published
2023
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6. Quantitative, multiplexed, targeted proteomics for ascertaining variant specific SARS-CoV-2 antibody response

Author

Abbass, Hakam, Abiodun, Aderonke, Alfarih, Mashael, Alldis, Zoe, Altmann, Daniel M., Amin, Oliver E., Andiapen, Mervyn, Artico, Jessica, Augusto, João B., Baca, Georgina L., Bailey, Sasha N.L., Bhuva, Anish N., Boulter, Alex, Bowles, Ruth, Boyton, Rosemary J., Bracken, Olivia V., O’Brien, Ben, Brooks, Tim, Bullock, Natalie, Butler, David K., Captur, Gabriella, Carr, Olivia, Champion, Nicola, Chan, Carmen, Chandran, Aneesh, Coleman, Tom, Couto de Sousa, Jorge, Couto-Parada, Xose, Cross, Eleanor, Cutino-Moguel, Teresa, D’Arcangelo, Silvia, Davies, Rhodri H., Douglas, Brooke, Di Genova, Cecilia, Dieobi-Anene, Keenan, Diniz, Mariana O., Ellis, Anaya, Feehan, Karen, Finlay, Malcolm, Fontana, Marianna, Forooghi, Nasim, Francis, Sasha, Gibbons, Joseph M., Gillespie, David, Gilroy, Derek, Hamblin, Matt, Harker, Gabrielle, Hemingway, Georgia, Hewson, Jacqueline, Heywood, Wendy, Hickling, Lauren M., Hicks, Bethany, Hingorani, Aroon D., Howes, Lee, Itua, Ivie, Jardim, Victor, Lee, Wing-Yiu Jason, Jensen, Melaniepetra, Jones, Jessica, Jones, Meleri, Joy, George, Kapil, Vikas, Kelly, Caoimhe, Kurdi, Hibba, Lambourne, Jonathan, Lin, Kai-Min, Liu, Siyi, Lloyd, Aaron, Louth, Sarah, Maini, Mala K., Mandadapu, Vineela, Manisty, Charlotte, McKnight, Áine, Menacho, Katia, Mfuko, Celina, Mills, Kevin, Millward, Sebastian, Mitchelmore, Oliver, Moon, Christopher, Moon, James, Sandoval, Diana Muñoz, Murray, Sam M., Noursadeghi, Mahdad, Otter, Ashley, Pade, Corinna, Palma, Susana, Parker, Ruth, Patel, Kush, Pawarova, Mihaela, Petersen, Steffen E., Piniera, Brian, Pieper, Franziska P., Rannigan, Lisa, Rapala, Alicja, Reynolds, Catherine J., Richards, Amy, Robathan, Matthew, Rosenheim, Joshua, Rowe, Cathy, Royds, Matthew, West, Jane Sackville, Sambile, Genine, Schmidt, Nathalie M., Selman, Hannah, Semper, Amanda, Seraphim, Andreas, Simion, Mihaela, Smit, Angelique, Sugimoto, Michelle, Swadling, Leo, Taylor, Stephen, Temperton, Nigel, Thomas, Stephen, Thornton, George D., Treibel, Thomas A., Tucker, Art, Varghese, Ann, Veerapen, Jessry, Vijayakumar, Mohit, Warner, Tim, Welch, Sophie, White, Hannah, Wodehouse, Theresa, Wynne, Lucinda, Zahedi, Dan, Doykov, Ivan, Baldwin, Tomas, Spiewak, Justyna, Gilmour, Kimberly C., Áine McKnight, Treibel, Thomas, Moon, James C., Kevin Mills, and Heywood, Wendy E.

Published
2022
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8. Operator and Centre Characteristics, And Choice of Pacing Mode

Author

Scott, Paul A., primary, Cannata, Antonio, additional, Shote, Aminat, additional, Wright, Ian J., additional, Bhuva, Anish N., additional, Lovell, Matthew J., additional, Plummer, Chris, additional, de Belder, Mark, additional, Dayer, Mark, additional, and Murgatroyd, Francis D., additional

Published
2024
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10. Blood transcriptional biomarkers of acute viral infection for detection of pre-symptomatic SARS-CoV-2 infection: a nested, case-control diagnostic accuracy study

Author

Abbass, Hakam, Abiodun, Aderonke, Alfarih, Mashael, Alldis, Zoe, Altmann, Daniel M, Amin, Oliver E, Andiapen, Mervyn, Artico, Jessica, Augusto, João B, Baca, Georgiana L, Bailey, Sasha NL, Bhuva, Anish N, Boulter, Alex, Bowles, Ruth, Boyton, Rosemary J, Bracken, Olivia V, O'Brien, Ben, Brooks, Tim, Bullock, Natalie, Butler, David K, Captur, Gabriella, Champion, Nicola, Chan, Carmen, Chandran, Aneesh, Collier, David, Couto de Sousa, Jorge, Couto-Parada, Xose, Cutino-Moguel, Teresa, Davies, Rhodri H, Douglas, Brooke, Di Genova, Cecilia, Dieobi-Anene, Keenan, Diniz, Mariana O, Ellis, Anaya, Feehan, Karen, Finlay, Malcolm, Fontana, Marianna, Forooghi, Nasim, Gaier, Celia, Gibbons, Joseph M, Gilroy, Derek, Hamblin, Matt, Harker, Gabrielle, Hewson, Jacqueline, Hickling, Lauren M, Hingorani, Aroon D, Howes, Lee, Hughes, Alun, Hughes, Gemma, Hughes, Rebecca, Itua, Ivie, Jardim, Victor, Lee, Wing-Yiu Jason, Jensen, Melaniepetra, Jones, Jessica, Jones, Meleri, Joy, George, Kapil, Vikas, Kurdi, Hibba, Lambourne, Jonathan, Lin, Kai-Min, Louth, Sarah, Maini, Mala K, Mandadapu, Vineela, Manisty, Charlotte, McKnight, Áine, Menacho, Katia, Mfuko, Celina, Mitchelmore, Oliver, Moon, Christopher, Moon, James C, Munoz Sandoval, Diana, Murray, Sam M, Noursadeghi, Mahdad, Otter, Ashley, Pade, Corinna, Palma, Susana, Parker, Ruth, Patel, Kush, Pawarova, Babita, Petersen, Steffen E, Piniera, Brian, Pieper, Franziska P, Pope, Daniel, Prossora, Maria, Rannigan, Lisa, Rapala, Alicja, Reynolds, Catherine J, Richards, Amy, Robathan, Matthew, Rosenheim, Joshua, Sambile, Genine, Schmidt, Nathalie M, Semper, Amanda, Seraphim, Andreas, Simion, Mihaela, Smit, Angelique, Sugimoto, Michelle, Swadling, Leo, Taylor, Stephen, Temperton, Nigel, Thomas, Stephen, Thornton, George D, Treibel, Thomas A, Tucker, Art, Veerapen, Jessry, Vijayakumar, Mohit, Welch, Sophie, Wodehouse, Theresa, Wynne, Lucinda, Zahedi, Dan, Gupta, Rishi K, Bell, Lucy C, Guerra-Assuncao, Jose A, Pollara, Gabriele, Whelan, Matthew, McKnight, Aine, and Chain, Benjamin M

Published
2021
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11. Diagnosis and risk stratification in hypertrophic cardiomyopathy using machine learning wall thickness measurement: a comparison with human test-retest performance

Author

Augusto, João B, Davies, Rhodri H, Bhuva, Anish N, Knott, Kristopher D, Seraphim, Andreas, Alfarih, Mashael, Lau, Clement, Hughes, Rebecca K, Lopes, Luís R, Shiwani, Hunain, Treibel, Thomas A, Gerber, Bernhard L, Hamilton-Craig, Christian, Ntusi, Ntobeko A B, Pontone, Gianluca, Desai, Milind Y, Greenwood, John P, Swoboda, Peter P, Captur, Gabriella, Cavalcante, João, Bucciarelli-Ducci, Chiara, Petersen, Steffen E, Schelbert, Erik, Manisty, Charlotte, and Moon, James C

Published
2021
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12. Use of quantitative cardiovascular magnetic resonance myocardial perfusion mapping for characterization of ischemia in patients with left internal mammary coronary artery bypass grafts

Author

Seraphim, Andreas, Knott, Kristopher D., Beirne, Anne-Marie, Augusto, Joao B., Menacho, Katia, Artico, Jessica, Joy, George, Hughes, Rebecca, Bhuva, Anish N., Torii, Ryo, Xue, Hui, Treibel, Thomas A., Davies, Rhodri, Moon, James C., Jones, Daniel A., Kellman, Peter, and Manisty, Charlotte

Published
2021
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14. OR02-02 Pre-operative Blood Pressure Response To Aldosterone Antagonists And Urinary Hybrid Steroid Ratios Predict Clinical Outcomes In Unilateral Primary Aldosteronism For At Least 2 Years Post-adrenalectomy

Author

Wu, Xilin, primary, Goodchild, Emily, additional, Senanayake, Russell, additional, Bashari, Waiel, additional, Salsbury, Jackie, additional, Cabrera, Claudia P, additional, Argentesi, Giulia, additional, O’Toole, Samuel M, additional, McFarlane, James, additional, Matson, Matthew, additional, Parvanta, Laila, additional, Hilliard, Nicholas, additional, Kosmoliaptsis, Vasilis, additional, Marker, Alison, additional, Berney, Daniel M, additional, Tan, Wilson, additional, Foo, Roger, additional, Mein, Charles A, additional, Wozniak, Eva, additional, Sahdev, Anju, additional, Bird, Nicholas, additional, Laycock, Kate, additional, Adeyeye, Elizabeth, additional, Dawnay, Anne, additional, Gillett, Daniel, additional, Prete, Alessandro, additional, Taylor, Angela E, additional, Arlt, Wiebke, additional, Bhuva, Anish N, additional, Manisty, Charlotte, additional, Cruickshank, Kennedy J, additional, Cheow, Heok, additional, Mark, Gurnell, additional, Drake, William, additional, and Brown, Morris J, additional

Published
2023
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15. The Prognostic Significance of Quantitative Myocardial Perfusion: An Artificial Intelligence–Based Approach Using Perfusion Mapping

Author

Knott, Kristopher D., Seraphim, Andreas, Augusto, Joao B., Xue, Hui, Chacko, Liza, Aung, Nay, Petersen, Steffen E., Cooper, Jackie A., Manisty, Charlotte, Bhuva, Anish N., Kotecha, Tushar, Bourantas, Christos V., Davies, Rhodri H., Brown, Louise A.E., Plein, Sven, Fontana, Marianna, Kellman, Peter, and Moon, James C.

Published
2020
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17. Automatic Measurement of the Myocardial Interstitium: Synthetic Extracellular Volume Quantification Without Hematocrit Sampling

Author

Treibel, Thomas A., Fontana, Marianna, Maestrini, Viviana, Castelletti, Silvia, Rosmini, Stefania, Simpson, Joanne, Nasis, Arthur, Bhuva, Anish N., Bulluck, Heerajnarain, Abdel-Gadir, Amna, White, Steven K., Manisty, Charlotte, Spottiswoode, Bruce S., Wong, Timothy C., Piechnik, Stefan K., Kellman, Peter, Robson, Matthew D., Schelbert, Erik B., and Moon, James C.

Published
2016
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18. A Multicenter, Scan-Rescan, Human and Machine Learning CMR Study to Test Generalizability and Precision in Imaging Biomarker Analysis

Author

Bhuva, Anish N., Bai, Wenjia, Lau, Clement, Davies, Rhodri H., Ye, Yang, Bulluck, Heeraj, McAlindon, Elisa, Culotta, Veronica, Swoboda, Peter P., Captur, Gabriella, Treibel, Thomas A., Augusto, Joao B., Knott, Kristopher D., Seraphim, Andreas, Cole, Graham D., Petersen, Steffen E., Edwards, Nicola C., Greenwood, John P., Bucciarelli-Ducci, Chiara, Hughes, Alun D., Rueckert, Daniel, Moon, James C., and Manisty, Charlotte H

Published
2019
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21. Left ventricular mass and global wall thickness – prognostic utility and characterization of left ventricular hypertrophy

Author

Lundin, Magnus, primary, Heiberg, Einar, additional, Nordlund, David, additional, Gyllenhammar, Tom, additional, Steding-Ehrenborg, Katarina, additional, Engblom, Henrik, additional, Carlsson, Marcus, additional, Atar, Dan, additional, van der Pals, Jesper, additional, Erlinge, David, additional, Borgquist, Rasmus, additional, Khoshnood, Ardavan, additional, Ekelund, Ulf, additional, Nickander, Jannike, additional, Themudo, Raquel, additional, Nordin, Sabrina, additional, Kozor, Rebecca, additional, Bhuva, Anish N, additional, Moon, James C, additional, Maret, Eva, additional, Caidahl, Kenneth, additional, Sigfridsson, Andreas, additional, Sörensson, Peder, additional, Schelbert, Erik B, additional, Arheden, Håkan, additional, and Ugander, Martin, additional

Published
2022
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22. Immune boosting by B.1.1.529 (Omicron) depends on previous SARS-CoV-2 exposure

Author

Reynolds, Catherine J., Pade, Corinna, Gibbons, Joseph M., Otter, Ashley D., Lin, Kai-Min, Muñoz Sandoval, Diana, Pieper, Franziska P., Butler, David K., Liu, Siyi, Joy, George, Forooghi, Nasim, Treibel, Thomas A., Manisty, Charlotte, Moon, James C., Semper, Amanda, Brooks, Tim, McKnight, Áine, Altmann, Daniel M., Boyton, Rosemary J., Abbass, Hakam, Abiodun, Aderonke, Alfarih, Mashael, Alldis, Zoe, Amin, Oliver E., Andiapen, Mervyn, Artico, Jessica, Augusto, João B., Baca, Georgina L., Bailey, Sasha N. L., Bhuva, Anish N., Boulter, Alex, Bowles, Ruth, Bracken, Olivia V., O’Brien, Ben, Bullock, Natalie, Captur, Gabriella, Carr, Olivia, Champion, Nicola, Chan, Carmen, Chandran, Aneesh, Coleman, Tom, Couto de Sousa, Jorge, Couto-Parada, Xose, Cross, Eleanor, Cutino-Moguel, Teresa, D’Arcangelo, Silvia, Davies, Rhodri H., Douglas, Brooke, Di Genova, Cecilia, Dieobi-Anene, Keenan, Diniz, Mariana O., Ellis, Anaya, Feehan, Karen, Finlay, Malcolm, Fontana, Marianna, Francis, Sasha, Gillespie, David, Gilroy, Derek, Hamblin, Matt, Harker, Gabrielle, Hemingway, Georgia, Hewson, Jacqueline, Heywood, Wendy, Hickling, Lauren M., Hicks, Bethany, Hingorani, Aroon D., Howes, Lee, Itua, Ivie, Jardim, Victor, Lee, Wing-Yiu Jason, Jensen, Melaniepetra, Jones, Jessica, Jones, Meleri, Kapil, Vikas, Kelly, Caoimhe, Kurdi, Hibba, Lambourne, Jonathan, Lloyd, Aaron, Louth, Sarah, Maini, Mala K., Mandadapu, Vineela, Menacho, Katia, Mfuko, Celina, Mills, Kevin, Millward, Sebastian, Mitchelmore, Oliver, Moon, Christopher, Moon, James, Murray, Sam M., Noursadeghi, Mahdad, Otter, Ashley, Palma, Susana, Parker, Ruth, Patel, Kush, Pawarova, Mihaela, Petersen, Steffen E., Piniera, Brian, Rannigan, Lisa, Rapala, Alicja, Richards, Amy, Robathan, Matthew, Rosenheim, Joshua, Rowe, Cathy, Royds, Matthew, Sackville West, Jane, Sambile, Genine, Schmidt, Nathalie M., Selman, Hannah, Seraphim, Andreas, Simion, Mihaela, Smit, Angelique, Sugimoto, Michelle, Swadling, Leo, Taylor, Stephen, Temperton, Nigel, Thomas, Stephen, Thornton, George D., Tucker, Art, Varghese, Ann, Veerapen, Jessry, Vijayakumar, Mohit, Warner, Tim, Welch, Sophie, White, Hannah, Wodehouse, Theresa, Wynne, Lucinda, Zahedi, Dan, Chain, Benjamin, and Medical Research Council (MRC)

Subjects
QR355, B-Lymphocytes, Science & Technology, Multidisciplinary, SARS-CoV-2, General Science & Technology, T-Lymphocytes, Immunization, Secondary, COVID-19, Cross Reactions, Antibodies, Viral, T-CELL IMMUNITY, Antibodies, Neutralizing, Multidisciplinary Sciences, Mice, INFECTION, Spike Glycoprotein, Coronavirus, Science & Technology - Other Topics, Animals, Humans, COVIDsortium Investigators§, COVIDsortium Immune Correlates Network§, BNT162 Vaccine
Abstract

INTRODUCTION B.1.1.529 (Omicron) and its subvariants pose new challenges for control of the COVID-19 pandemic. Although vaccinated populations are relatively protected from severe disease and death, countries with high vaccine uptake are experiencing substantial caseloads with breakthrough infection and frequent reinfection. RATIONALE We analyzed cross-protective immunity against B.1.1.529 (Omicron) in triple-vaccinated health care workers (HCWs) with different immune-imprinted histories of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection during the ancestral Wuhan Hu-1, B.1.1.7 (Alpha), and B.1.617.2 (Delta) waves and after infection during the B.1.1.529 (Omicron) wave in previously infection-naïve individuals and those with hybrid immunity, to investigate whether B.1.1.529 (Omicron) infection could further boost adaptive immunity. Spike subunit 1 (S1) receptor binding domain (RBD) and whole spike binding, live virus neutralizing antibody (nAb) potency, memory B cell (MBC) frequency, and T cell responses against peptide pools and naturally processed antigen were assessed. RESULTS B and T cell recognition and nAb potency were boosted against previous variants of concern (VOCs) in triple-vaccinated HCWs, but this enhanced immunity was attenuated against B.1.1.529 (Omicron) itself. Furthermore, immune imprinting after B.1.1.7 (Alpha) infection resulted in reduced durability of antibody binding against B.1.1.529 (Omicron), and S1 RBD and whole spike VOC binding correlated poorly with live virus nAb potency. Half of triple-vaccinated HCWs showed no T cell response to B.1.1.529 (Omicron) S1 processed antigen, and all showed reduced responses to the B.1.1.529 (Omicron) peptide pool, irrespective of SARS-CoV-2 infection history. Mapping T cell immunity in class II human leukocyte antigen transgenics showed that individual spike mutations could result in loss or gain of T cell epitope recognition, with changes to T cell effector and regulatory programs. Triple-vaccinated, previously infection-naïve individuals infected during the B.1.1.529 (Omicron) wave showed boosted cross-reactive S1 RBD and whole spike binding, live virus nAb potency, and T cell immunity against previous VOCs but less so against B.1.1.529 (Omicron) itself. Immune imprinting from prior Wuhan Hu-1 infection abrogated any enhanced cross-reactive antibody binding, T cell recognition, MBC frequency, or nAb potency after B.1.1.529 (Omicron) infection. CONCLUSION Vaccine boosting results in distinct, imprinted patterns of hybrid immunity with different combinations of SARS-CoV-2 infection and vaccination. Immune protection is boosted by B.1.1.529 (Omicron) infection in the triple-vaccinated, previously infection-naïve individuals, but this boosting is lost with prior Wuhan Hu-1 imprinting. This “hybrid immune damping” indicates substantial subversion of immune recognition and differential modulation through immune imprinting and may be the reason why the B.1.1.529 (Omicron) wave has been characterized by breakthrough infection and frequent reinfection with relatively preserved protection against severe disease in triple-vaccinated individuals.

Published
2022

23. Quantitative, multiplexed, targeted proteomics for ascertaining variant specific SARS-CoV-2 antibody response

Author

Doykov, Ivan, Baldwin, Tomas, Spiewak, Justyna, Gilmour, Kimberly C., Gibbons, Joseph M., Pade, Corinna, Reynolds, Catherine J., McKnight, Áine, Noursadeghi, Mahdad, Maini, Mala K., Manisty, Charlotte, Treibel, Thomas, Captur, Gabriella, Fontana, Marianna, Boyton, Rosemary J., Altmann, Daniel M., Brooks, Tim, Semper, Amanda, Moon, James C., Mills, Kevin, Heywood, Wendy E., Abbass, Hakam, Abiodun, Aderonke, Alfarih, Mashael, Alldis, Zoe, Amin, Oliver E., Andiapen, Mervyn, Artico, Jessica, Augusto, João B., Baca, Georgina L., Bailey, Sasha N.L., Bhuva, Anish N., Boulter, Alex, Bowles, Ruth, Bracken, Olivia V., O’Brien, Ben, Bullock, Natalie, Butler, David K., Carr, Olivia, Champion, Nicola, Chan, Carmen, Chandran, Aneesh, Coleman, Tom, Couto de Sousa, Jorge, Couto-Parada, Xose, Cross, Eleanor, Cutino-Moguel, Teresa, D’Arcangelo, Silvia, Davies, Rhodri H., Douglas, Brooke, Di Genova, Cecilia, Dieobi-Anene, Keenan, Diniz, Mariana O., Ellis, Anaya, Feehan, Karen, Finlay, Malcolm, Forooghi, Nasim, Francis, Sasha, Gillespie, David, Gilroy, Derek, Hamblin, Matt, Harker, Gabrielle, Hemingway, Georgia, Hewson, Jacqueline, Heywood, Wendy, Hickling, Lauren M., Hicks, Bethany, Hingorani, Aroon D., Howes, Lee, Itua, Ivie, Jardim, Victor, Lee, Wing-Yiu Jason, Jensen, Melaniepetra, Jones, Jessica, Jones, Meleri, Joy, George, Kapil, Vikas, Kelly, Caoimhe, Kurdi, Hibba, Lambourne, Jonathan, Lin, Kai-Min, Liu, Siyi, Lloyd, Aaron, Louth, Sarah, Mandadapu, Vineela, Menacho, Katia, Mfuko, Celina, Millward, Sebastian, Mitchelmore, Oliver, Moon, Christopher, Moon, James, Sandoval, Diana Muñoz, Murray, Sam M., Otter, Ashley, Palma, Susana, Parker, Ruth, Patel, Kush, Pawarova, Mihaela, Petersen, Steffen E., Piniera, Brian, Pieper, Franziska P., Rannigan, Lisa, Rapala, Alicja, Richards, Amy, Robathan, Matthew, Rosenheim, Joshua, Rowe, Cathy, Royds, Matthew, West, Jane Sackville, Sambile, Genine, Schmidt, Nathalie M., Selman, Hannah, Seraphim, Andreas, Simion, Mihaela, Smit, Angelique, Sugimoto, Michelle, Swadling, Leo, Taylor, Stephen, Temperton, Nigel J., Thomas, Stephen, Thornton, George D., Treibel, Thomas A., Tucker, Art, Varghese, Ann, Veerapen, Jessry, Vijayakumar, Mohit, Warner, Tim, Welch, Sophie, White, Hannah, Wodehouse, Theresa, Wynne, Lucinda, Zahedi, Dan, Doykov, Ivan, Baldwin, Tomas, Spiewak, Justyna, Gilmour, Kimberly C., Gibbons, Joseph M., Pade, Corinna, Reynolds, Catherine J., McKnight, Áine, Noursadeghi, Mahdad, Maini, Mala K., Manisty, Charlotte, Treibel, Thomas, Captur, Gabriella, Fontana, Marianna, Boyton, Rosemary J., Altmann, Daniel M., Brooks, Tim, Semper, Amanda, Moon, James C., Mills, Kevin, Heywood, Wendy E., Abbass, Hakam, Abiodun, Aderonke, Alfarih, Mashael, Alldis, Zoe, Amin, Oliver E., Andiapen, Mervyn, Artico, Jessica, Augusto, João B., Baca, Georgina L., Bailey, Sasha N.L., Bhuva, Anish N., Boulter, Alex, Bowles, Ruth, Bracken, Olivia V., O’Brien, Ben, Bullock, Natalie, Butler, David K., Carr, Olivia, Champion, Nicola, Chan, Carmen, Chandran, Aneesh, Coleman, Tom, Couto de Sousa, Jorge, Couto-Parada, Xose, Cross, Eleanor, Cutino-Moguel, Teresa, D’Arcangelo, Silvia, Davies, Rhodri H., Douglas, Brooke, Di Genova, Cecilia, Dieobi-Anene, Keenan, Diniz, Mariana O., Ellis, Anaya, Feehan, Karen, Finlay, Malcolm, Forooghi, Nasim, Francis, Sasha, Gillespie, David, Gilroy, Derek, Hamblin, Matt, Harker, Gabrielle, Hemingway, Georgia, Hewson, Jacqueline, Heywood, Wendy, Hickling, Lauren M., Hicks, Bethany, Hingorani, Aroon D., Howes, Lee, Itua, Ivie, Jardim, Victor, Lee, Wing-Yiu Jason, Jensen, Melaniepetra, Jones, Jessica, Jones, Meleri, Joy, George, Kapil, Vikas, Kelly, Caoimhe, Kurdi, Hibba, Lambourne, Jonathan, Lin, Kai-Min, Liu, Siyi, Lloyd, Aaron, Louth, Sarah, Mandadapu, Vineela, Menacho, Katia, Mfuko, Celina, Millward, Sebastian, Mitchelmore, Oliver, Moon, Christopher, Moon, James, Sandoval, Diana Muñoz, Murray, Sam M., Otter, Ashley, Palma, Susana, Parker, Ruth, Patel, Kush, Pawarova, Mihaela, Petersen, Steffen E., Piniera, Brian, Pieper, Franziska P., Rannigan, Lisa, Rapala, Alicja, Richards, Amy, Robathan, Matthew, Rosenheim, Joshua, Rowe, Cathy, Royds, Matthew, West, Jane Sackville, Sambile, Genine, Schmidt, Nathalie M., Selman, Hannah, Seraphim, Andreas, Simion, Mihaela, Smit, Angelique, Sugimoto, Michelle, Swadling, Leo, Taylor, Stephen, Temperton, Nigel J., Thomas, Stephen, Thornton, George D., Treibel, Thomas A., Tucker, Art, Varghese, Ann, Veerapen, Jessry, Vijayakumar, Mohit, Warner, Tim, Welch, Sophie, White, Hannah, Wodehouse, Theresa, Wynne, Lucinda, and Zahedi, Dan

Abstract

Determining the protection an individual has to severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) variants of concern (VoCs) is crucial for future immune surveillance, vaccine development, and understanding of the changing immune response. We devised an informative assay to current ELISA-based serology using multiplexed, baited, targeted proteomics for direct detection of multiple proteins in the SARS-CoV-2 anti-spike antibody immunocomplex. Serum from individuals collected after infection or first- and second-dose vaccination demonstrates this approach and shows concordance with existing serology and neutralization. Our assays show altered responses of both immunoglobulins and complement to the Alpha (B.1.1.7), Beta (B.1.351), and Delta (B.1.617.1) VoCs and a reduced response to Omicron (B1.1.1529). We were able to identify individuals who had prior infection, and observed that C1q is closely associated with IgG1 (r > 0.82) and may better reflect neutralization to VoCs. Analyzing additional immunoproteins beyond immunoglobulin (Ig) G, provides important information about our understanding of the response to infection and vaccination.

Published
2022

24. Diagnostic performance of T1 and T2 mapping to detect intramyocardial hemorrhage in reperfused ST‐segment elevation myocardial infarction (STEMI) patients

Author

Bulluck, Heerajnarain, Rosmini, Stefania, Abdel‐Gadir, Amna, Bhuva, Anish N., Treibel, Thomas A., Fontana, Marianna, Gonzalez‐Lopez, Esther, Ramlall, Manish, Hamarneh, Ashraf, Sirker, Alex, Herrey, Anna S., Manisty, Charlotte, Yellon, Derek M., Moon, James C., and Hausenloy, Derek J.

Published
2017
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25. Pre-existing polymerase-specific T cells expand in abortive seronegative SARS-CoV-2

Author

Swadling, Leo, Diniz, Mariana O., Schmidt, Nathalie M., Amin, Oliver E., Chandran, Aneesh, Shaw, Emily, Pade, Corinna, Gibbons, Joseph M., Le Bert, Nina, Tan, Anthony T., Jeffery-Smith, Anna, Tan, Cedric C. S., Tham, Christine Y. L., Kucykowicz, Stephanie, Aidoo-Micah, Gloryanne, Rosenheim, Joshua, Davies, Jessica, Johnson, Marina, Jensen, Melanie P., Joy, George, McCoy, Laura E., Valdes, Ana M., Chain, Benjamin M., Goldblatt, David, Altmann, Daniel M., Boyton, Rosemary J., Manisty, Charlotte, Treibel, Thomas A., Moon, James C., Abbass, Hakam, Abiodun, Aderonke, Alfarih, Mashael, Alldis, Zoe, Andiapen, Mervyn, Artico, Jessica, Augusto, João B., Baca, Georgina L., Bailey, Sasha N. L., Bhuva, Anish N., Boulter, Alex, Bowles, Ruth, Bracken, Olivia V., O’Brien, Ben, Brooks, Tim, Bullock, Natalie, Butler, David K., Captur, Gabriella, Champion, Nicola, Chan, Carmen, Collier, David, de Sousa, Jorge Couto, Couto-Parada, Xose, Cutino-Moguel, Teresa, Davies, Rhodri H., Douglas, Brooke, Di Genova, Cecilia, Dieobi-Anene, Keenan, Ellis, Anaya, Feehan, Karen, Finlay, Malcolm, Fontana, Marianna, Forooghi, Nasim, Gaier, Celia, Gilroy, Derek, Hamblin, Matt, Harker, Gabrielle, Hewson, Jacqueline, Hickling, Lauren M., Hingorani, Aroon D., Howes, Lee, Hughes, Alun, Hughes, Gemma, Hughes, Rebecca, Itua, Ivie, Jardim, Victor, Lee, Wing-Yiu Jason, Jensen, Melanie petra, Jones, Jessica, Jones, Meleri, Kapil, Vikas, Kurdi, Hibba, Lambourne, Jonathan, Lin, Kai-Min, Louth, Sarah, Mandadapu, Vineela, McKnight, Áine, Menacho, Katia, Mfuko, Celina, Mitchelmore, Oliver, Moon, Christopher, Murray, Sam M., Noursadeghi, Mahdad, Otter, Ashley, Palma, Susana, Parker, Ruth, Patel, Kush, Pawarova, Babita, Petersen, Steffen E., Piniera, Brian, Pieper, Franziska P., Pope, Daniel, Prossora, Mary, Rannigan, Lisa, Rapala, Alicja, Reynolds, Catherine J., Richards, Amy, Robathan, Matthew, Sambile, Genine, Semper, Amanda, Seraphim, Andreas, Simion, Mihaela, Smit, Angelique, Sugimoto, Michelle, Taylor, Stephen, Temperton, Nigel J., Thomas, Stephen, Thornton, George D., Tucker, Art, Veerapen, Jessry, Vijayakumar, Mohit, Welch, Sophie, Wodehouse, Theresa, Wynne, Lucinda, Zahedi, Dan, Dorp, Lucy van, Balloux, Francois, McKnight, Áine, Bertoletti, Antonio, Maini, Mala K., Swadling, Leo [0000-0002-0537-6715], Schmidt, Nathalie M [0000-0002-9841-8418], Gibbons, Joseph M [0000-0002-7238-2381], Le Bert, Nina [0000-0003-0502-2527], Tham, Christine YL [0000-0002-2913-7591], Kucykowicz, Stephanie [0000-0002-8849-218X], Rosenheim, Joshua [0000-0003-0171-2053], McCoy, Laura E [0000-0001-9503-7946], Valdes, Ana M [0000-0003-1141-4471], Chain, Benjamin M [0000-0002-7417-3970], Goldblatt, David [0000-0002-0769-5242], Boyton, Rosemary J [0000-0002-5608-0797], van Dorp, Lucy [0000-0002-6211-2310], Balloux, Francois [0000-0003-1978-7715], Noursadeghi, Mahdad [0000-0002-4774-0853], Bertoletti, Antonio [0000-0002-2942-0485], Maini, Mala K [0000-0001-6384-1462], Apollo - University of Cambridge Repository, Medical Research Council (MRC), and Multiple Sclerosis Society

Subjects
Male, Transcription, Genetic, 631/250/1619/554, medicine.disease_cause, DISEASE, Neutralization, Cohort Studies, 13/1, 631/250/2152/1566/1571, INFECTION, Coronaviridae, Asymptomatic Infections, Polymerase, Coronavirus, Multidisciplinary, biology, article, virus diseases, DNA-Directed RNA Polymerases, Multidisciplinary Sciences, 13/31, Seroconversion, Cohort, Science & Technology - Other Topics, VIRUS, Female, 82/75, HEALTH-CARE WORKERS, Antibody, ANTIBODY-RESPONSES, General Science & Technology, Health Personnel, 13/106, IMMUNITY, Evolution, Molecular, Memory T Cells, In vivo, Multienzyme Complexes, medicine, Humans, EXPOSURE, 631/326/596/4130, COVIDsortium Investigators, Cell Proliferation, PATHOGENS, Science & Technology, SARS-CoV-2, MEMORY, CORONAVIRUSES, COVID-19, Membrane Proteins, 631/250/254, biology.organism_classification, Virology, biology.protein
Abstract

Individuals with potential exposure to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) do not necessarily develop PCR or antibody positivity, suggesting that some individuals may clear subclinical infection before seroconversion. T cells can contribute to the rapid clearance of SARS-CoV-2 and other coronavirus infections1–3. Here we hypothesize that pre-existing memory T cell responses, with cross-protective potential against SARS-CoV-2 (refs. 4–11), would expand in vivo to support rapid viral control, aborting infection. We measured SARS-CoV-2-reactive T cells, including those against the early transcribed replication–transcription complex (RTC)12,13, in intensively monitored healthcare workers (HCWs) who tested repeatedly negative according to PCR, antibody binding and neutralization assays (seronegative HCWs (SN-HCWs)). SN-HCWs had stronger, more multispecific memory T cells compared with a cohort of unexposed individuals from before the pandemic (prepandemic cohort), and these cells were more frequently directed against the RTC than the structural-protein-dominated responses observed after detectable infection (matched concurrent cohort). SN-HCWs with the strongest RTC-specific T cells had an increase in IFI27, a robust early innate signature of SARS-CoV-2 (ref. 14), suggesting abortive infection. RNA polymerase within RTC was the largest region of high sequence conservation across human seasonal coronaviruses (HCoV) and SARS-CoV-2 clades. RNA polymerase was preferentially targeted (among the regions tested) by T cells from prepandemic cohorts and SN-HCWs. RTC-epitope-specific T cells that cross-recognized HCoV variants were identified in SN-HCWs. Enriched pre-existing RNA-polymerase-specific T cells expanded in vivo to preferentially accumulate in the memory response after putative abortive compared to overt SARS-CoV-2 infection. Our data highlight RTC-specific T cells as targets for vaccines against endemic and emerging Coronaviridae.

Published
2021

26. Blood transcriptional biomarkers of acute viral infection for detection of pre-symptomatic SARS-CoV-2 infection: a nested, case-control diagnostic accuracy study

Author

Gupta, Rishi K, primary, Rosenheim, Joshua, additional, Bell, Lucy C, additional, Chandran, Aneesh, additional, Guerra-Assuncao, Jose A, additional, Pollara, Gabriele, additional, Whelan, Matthew, additional, Artico, Jessica, additional, Joy, George, additional, Kurdi, Hibba, additional, Altmann, Daniel M, additional, Boyton, Rosemary J, additional, Maini, Mala K, additional, McKnight, Aine, additional, Lambourne, Jonathan, additional, Cutino-Moguel, Teresa, additional, Manisty, Charlotte, additional, Treibel, Thomas A, additional, Moon, James C, additional, Chain, Benjamin M, additional, Noursadeghi, Mahdad, additional, Abbass, Hakam, additional, Abiodun, Aderonke, additional, Alfarih, Mashael, additional, Alldis, Zoe, additional, Amin, Oliver E, additional, Andiapen, Mervyn, additional, Augusto, João B, additional, Baca, Georgiana L, additional, Bailey, Sasha NL, additional, Bhuva, Anish N, additional, Boulter, Alex, additional, Bowles, Ruth, additional, Bracken, Olivia V, additional, O'Brien, Ben, additional, Brooks, Tim, additional, Bullock, Natalie, additional, Butler, David K, additional, Captur, Gabriella, additional, Champion, Nicola, additional, Chan, Carmen, additional, Collier, David, additional, Couto de Sousa, Jorge, additional, Couto-Parada, Xose, additional, Davies, Rhodri H, additional, Douglas, Brooke, additional, Di Genova, Cecilia, additional, Dieobi-Anene, Keenan, additional, Diniz, Mariana O, additional, Ellis, Anaya, additional, Feehan, Karen, additional, Finlay, Malcolm, additional, Fontana, Marianna, additional, Forooghi, Nasim, additional, Gaier, Celia, additional, Gibbons, Joseph M, additional, Gilroy, Derek, additional, Hamblin, Matt, additional, Harker, Gabrielle, additional, Hewson, Jacqueline, additional, Hickling, Lauren M, additional, Hingorani, Aroon D, additional, Howes, Lee, additional, Hughes, Alun, additional, Hughes, Gemma, additional, Hughes, Rebecca, additional, Itua, Ivie, additional, Jardim, Victor, additional, Lee, Wing-Yiu Jason, additional, Jensen, Melaniepetra, additional, Jones, Jessica, additional, Jones, Meleri, additional, Kapil, Vikas, additional, Lin, Kai-Min, additional, Louth, Sarah, additional, Mandadapu, Vineela, additional, Manisty,, Charlotte, additional, McKnight, Áine, additional, Menacho, Katia, additional, Mfuko, Celina, additional, Mitchelmore, Oliver, additional, Moon, Christopher, additional, Moon,, James C, additional, Munoz Sandoval, Diana, additional, Murray, Sam M, additional, Otter, Ashley, additional, Pade, Corinna, additional, Palma, Susana, additional, Parker, Ruth, additional, Patel, Kush, additional, Pawarova, Babita, additional, Petersen, Steffen E, additional, Piniera, Brian, additional, Pieper, Franziska P, additional, Pope, Daniel, additional, Prossora, Maria, additional, Rannigan, Lisa, additional, Rapala, Alicja, additional, Reynolds, Catherine J, additional, Richards, Amy, additional, Robathan, Matthew, additional, Sambile, Genine, additional, Schmidt, Nathalie M, additional, Semper, Amanda, additional, Seraphim, Andreas, additional, Simion, Mihaela, additional, Smit, Angelique, additional, Sugimoto, Michelle, additional, Swadling, Leo, additional, Taylor, Stephen, additional, Temperton, Nigel, additional, Thomas, Stephen, additional, Thornton, George D, additional, Tucker, Art, additional, Veerapen, Jessry, additional, Vijayakumar, Mohit, additional, Welch, Sophie, additional, Wodehouse, Theresa, additional, Wynne, Lucinda, additional, and Zahedi, Dan, additional

Published
2021
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28. Evidence to support magnetic resonance conditional labelling of all pacemaker and defibrillator leads in patients with cardiac implantable electronic devices

Author

Bhuva, Anish N, primary, Moralee, Russell, additional, Brunker, Tamara, additional, Lascelles, Karen, additional, Cash, Lizette, additional, Patel, Kush P, additional, Lowe, Martin, additional, Sekhri, Neha, additional, Alpendurada, Francisco, additional, Pennell, Dudley J, additional, Schilling, Richard, additional, Lambiase, Pier D, additional, Chow, Anthony, additional, Moon, James C, additional, Litt, Harold, additional, Baksi, A John, additional, and Manisty, Charlotte H, additional

Published
2021
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29. Additional file 1 of Use of quantitative cardiovascular magnetic resonance myocardial perfusion mapping for characterization of ischemia in patients with left internal mammary coronary artery bypass grafts

Author

Seraphim, Andreas, Knott, Kristopher D., Beirne, Anne-Marie, Augusto, Joao B., Menacho, Katia, Artico, Jessica, Joy, George, Hughes, Rebecca, Bhuva, Anish N., Torii, Ryo, Xue, Hui, Treibel, Thomas A., Davies, Rhodri, Moon, James C., Jones, Daniel A., Kellman, Peter, and Manisty, Charlotte

Abstract

Additional file 1: Figure S1. Bullseye plot of the left ventricle, demonstrating the American Heart Association model territories used for analysis. Figure S2. Top: Comparison of arterial time delay (TA) between healthy subjects and patients with prior CABG. Bottom: Percentage increase in MBF by extending allowable TA to 5 s in healthy subjects and patients with prior CABG. Table S1. Predictors of myocardial perfusion reserve (MPR) in the LIMA–LAD territory.

Published
2021
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30. Diagnosis and risk stratification in hypertrophic cardiomyopathy using machine learning wall thickness measurement: a comparison with human test-retest performance.

Author

UCL - SSS/IREC/CARD - Pôle de recherche cardiovasculaire, UCL - (SLuc) Service de pathologie cardiovasculaire, Augusto, João B, Davies, Rhodri H, Bhuva, Anish N, Knott, Kristopher D, Seraphim, Andreas, Alfarih, Mashael, Lau, Clement, Hughes, Rebecca K, Lopes, Luís R, Shiwani, Hunain, Treibel, Thomas A, Gerber, Bernhard, Hamilton-Craig, Christian, Ntusi, Ntobeko A B, Pontone, Gianluca, Desai, Milind Y, Greenwood, John P, Swoboda, Peter P, Captur, Gabriella, Cavalcante, João, Bucciarelli-Ducci, Chiara, Petersen, Steffen E, Schelbert, Erik, Manisty, Charlotte, Moon, James C, UCL - SSS/IREC/CARD - Pôle de recherche cardiovasculaire, UCL - (SLuc) Service de pathologie cardiovasculaire, Augusto, João B, Davies, Rhodri H, Bhuva, Anish N, Knott, Kristopher D, Seraphim, Andreas, Alfarih, Mashael, Lau, Clement, Hughes, Rebecca K, Lopes, Luís R, Shiwani, Hunain, Treibel, Thomas A, Gerber, Bernhard, Hamilton-Craig, Christian, Ntusi, Ntobeko A B, Pontone, Gianluca, Desai, Milind Y, Greenwood, John P, Swoboda, Peter P, Captur, Gabriella, Cavalcante, João, Bucciarelli-Ducci, Chiara, Petersen, Steffen E, Schelbert, Erik, Manisty, Charlotte, and Moon, James C

Abstract

Left ventricular maximum wall thickness (MWT) is central to diagnosis and risk stratification of hypertrophic cardiomyopathy, but human measurement is prone to variability. We developed an automated machine learning algorithm for MWT measurement and compared precision (reproducibility) with that of 11 international experts, using a dataset of patients with hypertrophic cardiomyopathy. 60 adult patients with hypertrophic cardiomyopathy, including those carrying hypertrophic cardiomyopathy gene mutations, were recruited at three institutes in the UK from August, 2018, to September, 2019: Barts Heart Centre, University College London Hospital (The Heart Hospital), and Leeds Teaching Hospitals NHS Trust. Participants had two cardiovascular magnetic resonance scans (test and retest) on the same day, ensuring no biological variability, using four cardiac MRI scanner models represented across two manufacturers and two field strengths. End-diastolic short-axis MWT was measured in test and retest by 11 international experts (from nine centres in six countries) and an automated machine learning method, which was trained to segment endocardial and epicardial contours on an independent, multicentre, multidisease dataset of 1923 patients. Machine learning MWT measurement was done with a method based on solving Laplace's equation. To assess test-retest reproducibility, we estimated the absolute test-retest MWT difference (precision), the coefficient of variation (CoV) for duplicate measurements, and the number of patients reclassified between test and retest according to different thresholds (MWT >15 mm and >30 mm). We calculated the sample size required to detect a prespecified MWT change between pairs of scans for machine learning and each expert. 1440 MWT measurements were analysed, corresponding to two scans from 60 participants by 12 observers (11 experts and machine learning). Experts differed in the MWT they measured, ranging from 14·9 mm (SD 4·2) to 19·0 mm (4·7; p<0·0001

Published
2021

31. Blood transcriptional biomarkers of acute viral infection for detection of pre-symptomatic SARS-CoV-2 infection: a nested, case-control diagnostic accuracy study

Author

Gupta, Rishi K, Rosenheim, Joshua, Bell, Lucy C, Chandran, Aneesh, Guerra-Assuncao, Jose A, Pollara, Gabriele, Whelan, Matthew, Artico, Jessica, Joy, George, Kurdi, Hibba, Altmann, Daniel M, Boyton, Rosemary J, Maini, Mala K, McKnight, Aine, Lambourne, Jonathan, Cutino-Moguel, Teresa, Manisty, Charlotte, Treibel, Thomas A, Moon, James C, Chain, Benjamin M, Noursadeghi, Mahdad, Abbass, Hakam, Abiodun, Aderonke, Alfarih, Mashael, Alldis, Zoe, Amin, Oliver E, Andiapen, Mervyn, Augusto, João B, Baca, Georgiana L, Bailey, Sasha NL, Bhuva, Anish N, Boulter, Alex, Bowles, Ruth, Bracken, Olivia V, O'Brien, Ben, Brooks, Tim, Bullock, Natalie, Butler, David K, Captur, Gabriella, Champion, Nicola, Chan, Carmen, Collier, David, Couto de Sousa, Jorge, Couto-Parada, Xose, Davies, Rhodri H, Douglas, Brooke, Di Genova, Cecilia, Dieobi-Anene, Keenan, Diniz, Mariana O, Ellis, Anaya, Feehan, Karen, Finlay, Malcolm, Fontana, Marianna, Forooghi, Nasim, Gaier, Celia, Gibbons, Joseph M, Gilroy, Derek, Hamblin, Matt, Harker, Gabrielle, Hewson, Jacqueline, Hickling, Lauren M, Hingorani, Aroon D, Howes, Lee, Hughes, Alun, Hughes, Gemma, Hughes, Rebecca, Itua, Ivie, Jardim, Victor, Lee, Wing-Yiu Jason, Jensen, Melaniepetra, Jones, Jessica, Jones, Meleri, Kapil, Vikas, Lin, Kai-Min, Louth, Sarah, Mandadapu, Vineela, Manisty,, Charlotte, McKnight, Áine, Menacho, Katia, Mfuko, Celina, Mitchelmore, Oliver, Moon, Christopher, Moon,, James C, Munoz Sandoval, Diana, Murray, Sam M, Otter, Ashley, Pade, Corinna, Palma, Susana, Parker, Ruth, Patel, Kush, Pawarova, Babita, Petersen, Steffen E, Piniera, Brian, Pieper, Franziska P, Pope, Daniel, Prossora, Maria, Rannigan, Lisa, Rapala, Alicja, Reynolds, Catherine J, Richards, Amy, Robathan, Matthew, Sambile, Genine, Schmidt, Nathalie M, Semper, Amanda, Seraphim, Andreas, Simion, Mihaela, Smit, Angelique, Sugimoto, Michelle, Swadling, Leo, Taylor, Stephen, Temperton, Nigel J., Thomas, Stephen, Thornton, George D, Tucker, Art, Veerapen, Jessry, Vijayakumar, Mohit, Welch, Sophie, Wodehouse, Theresa, Wynne, Lucinda, Zahedi, Dan, Gupta, Rishi K, Rosenheim, Joshua, Bell, Lucy C, Chandran, Aneesh, Guerra-Assuncao, Jose A, Pollara, Gabriele, Whelan, Matthew, Artico, Jessica, Joy, George, Kurdi, Hibba, Altmann, Daniel M, Boyton, Rosemary J, Maini, Mala K, McKnight, Aine, Lambourne, Jonathan, Cutino-Moguel, Teresa, Manisty, Charlotte, Treibel, Thomas A, Moon, James C, Chain, Benjamin M, Noursadeghi, Mahdad, Abbass, Hakam, Abiodun, Aderonke, Alfarih, Mashael, Alldis, Zoe, Amin, Oliver E, Andiapen, Mervyn, Augusto, João B, Baca, Georgiana L, Bailey, Sasha NL, Bhuva, Anish N, Boulter, Alex, Bowles, Ruth, Bracken, Olivia V, O'Brien, Ben, Brooks, Tim, Bullock, Natalie, Butler, David K, Captur, Gabriella, Champion, Nicola, Chan, Carmen, Collier, David, Couto de Sousa, Jorge, Couto-Parada, Xose, Davies, Rhodri H, Douglas, Brooke, Di Genova, Cecilia, Dieobi-Anene, Keenan, Diniz, Mariana O, Ellis, Anaya, Feehan, Karen, Finlay, Malcolm, Fontana, Marianna, Forooghi, Nasim, Gaier, Celia, Gibbons, Joseph M, Gilroy, Derek, Hamblin, Matt, Harker, Gabrielle, Hewson, Jacqueline, Hickling, Lauren M, Hingorani, Aroon D, Howes, Lee, Hughes, Alun, Hughes, Gemma, Hughes, Rebecca, Itua, Ivie, Jardim, Victor, Lee, Wing-Yiu Jason, Jensen, Melaniepetra, Jones, Jessica, Jones, Meleri, Kapil, Vikas, Lin, Kai-Min, Louth, Sarah, Mandadapu, Vineela, Manisty,, Charlotte, McKnight, Áine, Menacho, Katia, Mfuko, Celina, Mitchelmore, Oliver, Moon, Christopher, Moon,, James C, Munoz Sandoval, Diana, Murray, Sam M, Otter, Ashley, Pade, Corinna, Palma, Susana, Parker, Ruth, Patel, Kush, Pawarova, Babita, Petersen, Steffen E, Piniera, Brian, Pieper, Franziska P, Pope, Daniel, Prossora, Maria, Rannigan, Lisa, Rapala, Alicja, Reynolds, Catherine J, Richards, Amy, Robathan, Matthew, Sambile, Genine, Schmidt, Nathalie M, Semper, Amanda, Seraphim, Andreas, Simion, Mihaela, Smit, Angelique, Sugimoto, Michelle, Swadling, Leo, Taylor, Stephen, Temperton, Nigel J., Thomas, Stephen, Thornton, George D, Tucker, Art, Veerapen, Jessry, Vijayakumar, Mohit, Welch, Sophie, Wodehouse, Theresa, Wynne, Lucinda, and Zahedi, Dan

Abstract

Background We hypothesised that host-response biomarkers of viral infections might contribute to early identification of individuals infected with SARS-CoV-2, which is critical to breaking the chains of transmission. We aimed to evaluate the diagnostic accuracy of existing candidate whole-blood transcriptomic signatures for viral infection to predict positivity of nasopharyngeal SARS-CoV-2 PCR testing.Methods We did a nested case-control diagnostic accuracy study among a prospective cohort of health-care workers (aged ≥18 years) at St Bartholomew’s Hospital (London, UK) undergoing weekly blood and nasopharyngeal swab sampling for whole-blood RNA sequencing and SARS-CoV-2 PCR testing, when fit to attend work. We identified candidate blood transcriptomic signatures for viral infection through a systematic literature search. We searched MEDLINE for articles published between database inception and Oct 12, 2020, using comprehensive MeSH and keyword terms for “viral infection”, “transcriptome”, “biomarker”, and “blood”. We reconstructed signature scores in blood RNA sequencing data and evaluated their diagnostic accuracy for contemporaneous SARS-CoV-2 infection, compared with the gold standard of SARS-CoV-2 PCR testing, by quantifying the area under the receiver operating characteristic curve (AUROC), sensitivities, and specificities at a standardised Z score of at least 2 based on the distribution of signature scores in test-negative controls. We used pairwise DeLong tests compared with the most discriminating signature to identify the subset of best performing biomarkers. We evaluated associations between signature expression, viral load (using PCR cycle thresholds), and symptom status visually and using Spearman rank correlation. The primary outcome was the AUROC for discriminating between samples from participants who tested negative throughout the study (test-negative controls) and samples from participants with PCR-confirmed SARS-CoV-2 infection (test-positive part

Published
2021

35. Evidence to support magnetic resonance conditional labelling of all pacemaker and defibrillator leads in patients with cardiac implantable electronic devices.

Author

Bhuva, Anish N, Moralee, Russell, Brunker, Tamara, Lascelles, Karen, Cash, Lizette, Patel, Kush P, Lowe, Martin, Sekhri, Neha, Alpendurada, Francisco, Pennell, Dudley J, Schilling, Richard, Lambiase, Pier D, Chow, Anthony, Moon, James C, Litt, Harold, Baksi, A John, and Manisty, Charlotte H

Subjects
ARTIFICIAL implants, CARDIAC pacemakers, ELECTRONIC equipment, MAGNETIC resonance, DEFIBRILLATORS, CARDIAC patients, MAGNETIC resonance imaging
Abstract

Aims Many cardiac pacemakers and defibrillators are not approved by regulators for magnetic resonance imaging (MRI). Even following generator exchange to an approved magnetic resonance (MR)-conditional model, many systems remain classified 'non-MR conditional' due to the leads. This classification makes patient access to MRI challenging, but there is no evidence of increased clinical risk. We compared the effect of MRI on non-MR conditional and MR-conditional pacemaker and defibrillator leads. Methods and results Patients undergoing clinical 1.5T MRI with pacemakers and defibrillators in three centres over 5 years were included. Magnetic resonance imaging protocols were similar for MR-conditional and non-MR conditional systems. Devices were interrogated pre- and immediately post-scan, and at follow-up, and adverse clinical events recorded. Lead parameter changes peri-scan were stratified by MR-conditional labelling. A total of 1148 MRI examinations were performed in 970 patients (54% non-MR conditional systems, 39% defibrillators, 15% pacing-dependent) with 2268 leads. There were no lead-related adverse clinical events, and no clinically significant immediate or late lead parameter changes following MRI in either MR-conditional or non-MR conditional leads. Small reductions in atrial and right ventricular sensed amplitudes and impedances were similar between groups, with no difference in the proportion of leads with parameter changes greater than pre-defined thresholds (7.1%, 95% confidence interval: 6.1–8.3). Conclusions There was no increased risk of MRI in patients with non-MR conditional pacemaker or defibrillator leads when following recommended protocols. Standardizing MR conditions for all leads would significantly improve access to MRI by enabling patients to be scanned in non-specialist centres, with no discernible incremental risk. [ABSTRACT FROM AUTHOR]

Published
2022
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36. ENDGAMES

Author

Bhuva, Anish N, Lota, Harpreet K, Viswanathan, Jayaraj, Au-Yong, Amy, Au-Yong, Iain T H, and Sedgwick, Philip

Published
2012

37. Non-invasive assessment of ventriculo-arterial coupling using aortic wave intensity analysis combining central blood pressure and phase-contrast cardiovascular magnetic resonance

Author

Bhuva, A, D'Silva, A, Torlasco, C, Nadarajan, N, Jones, S, Boubertakh, R, Van Zalen, J, Scully, P, Knott, K, Benedetti, G, Augusto, J, Bastiaenen, R, Lloyd, G, Sharma, S, Moon, J, Parker, K, Manisty, C, Hughes, A, Bhuva, Anish N, Augusto, J B, Bastiaenen, Rachel, Moon, J C, Parker, K H, Manisty, C H, Hughes, Alun D, Bhuva, A, D'Silva, A, Torlasco, C, Nadarajan, N, Jones, S, Boubertakh, R, Van Zalen, J, Scully, P, Knott, K, Benedetti, G, Augusto, J, Bastiaenen, R, Lloyd, G, Sharma, S, Moon, J, Parker, K, Manisty, C, Hughes, A, Bhuva, Anish N, Augusto, J B, Bastiaenen, Rachel, Moon, J C, Parker, K H, Manisty, C H, and Hughes, Alun D

Abstract

BACKGROUND: Wave intensity analysis (WIA) in the aorta offers important clinical and mechanistic insight into ventriculo-arterial coupling, but is difficult to measure non-invasively. We performed WIA by combining standard cardiovascular magnetic resonance (CMR) flow-velocity and non-invasive central blood pressure (cBP) waveforms. METHODS AND RESULTS: Two hundred and six healthy volunteers (age range 21-73 years, 47% male) underwent sequential phase contrast CMR (Siemens Aera 1.5 T, 1.97 × 1.77 mm2, 9.2 ms temporal resolution) and supra-systolic oscillometric cBP measurement (200 Hz). Velocity (U) and central pressure (P) waveforms were aligned using the waveform foot, and local wave speed was calculated both from the PU-loop (c) and the sum of squares method (cSS). These were compared with CMR transit time derived aortic arch pulse wave velocity (PWVtt). Associations were examined using multivariable regression. The peak intensity of the initial compression wave, backward compression wave, and forward decompression wave were 69.5 ± 28, -6.6 ± 4.2, and 6.2 ± 2.5 × 104 W/m2/cycle2, respectively; reflection index was 0.10 ± 0.06. PWVtt correlated with c or cSS (r = 0.60 and 0.68, respectively, P < 0.01 for both). Increasing age decade and female sex were independently associated with decreased forward compression wave (-8.6 and -20.7 W/m2/cycle2, respectively, P < 0.01) and greater wave reflection index (0.02 and 0.03, respectively, P < 0.001). CONCLUSION: This novel non-invasive technique permits straightforward measurement of wave intensity at scale. Local wave speed showed good agreement with PWVtt, and correlation was stronger using the cSS than the PU-loop. Ageing and female sex were associated with poorer ventriculo-arterial coupling in healthy individuals.

Published
2020

38. Measurement of T1 Mapping in Patients With Cardiac Devices: Off-Resonance Error Extends Beyond Visual Artifact but Can Be Quantified and Corrected

Author

Bhuva, Anish N., primary, Treibel, Thomas A., additional, Seraphim, Andreas, additional, Scully, Paul, additional, Knott, Kristopher D., additional, Augusto, João B., additional, Torlasco, Camilla, additional, Menacho, Katia, additional, Lau, Clement, additional, Patel, Kush, additional, Moon, James C., additional, Kellman, Peter, additional, and Manisty, Charlotte H., additional

Published
2021
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40. Repeatability of Cardiac Magnetic Resonance Radiomics: A Multi-Centre Multi-Vendor Test-Retest Study

Author

Raisi-Estabragh, Zahra, primary, Gkontra, Polyxeni, additional, Jaggi, Akshay, additional, Cooper, Jackie, additional, Augusto, João, additional, Bhuva, Anish N., additional, Davies, Rhodri H., additional, Manisty, Charlotte H., additional, Moon, James C., additional, Munroe, Patricia B., additional, Harvey, Nicholas C., additional, Lekadir, Karim, additional, and Petersen, Steffen E., additional

Published
2020
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41. Healthcare Workers Bioresource: Study outline and baseline characteristics of a prospective healthcare worker cohort to study immune protection and pathogenesis in COVID-19

Author

Augusto, João B, primary, Menacho, Katia, additional, Andiapen, Mervyn, additional, Bowles, Ruth, additional, Burton, Maudrian, additional, Welch, Sophie, additional, Bhuva, Anish N, additional, Seraphim, Andreas, additional, Pade, Corinna, additional, Joy, George, additional, Jensen, Melanie, additional, Davies, Rhodri H, additional, Captur, Gabriella, additional, Fontana, Marianna, additional, Montgomery, Hugh, additional, O’Brien, Ben, additional, Hingorani, Aroon D, additional, Cutino-Moguel, Teresa, additional, McKnight, Áine, additional, Abbass, Hakam, additional, Alfarih, Mashael, additional, Alldis, Zoe, additional, Baca, Georgina L, additional, Boulter, Alex, additional, Bracken, Olivia V, additional, Bullock, Natalie, additional, Champion, Nicola, additional, Chan, Carmen, additional, Couto-Parada, Xose, additional, Dieobi-Anene, Keenan, additional, Feehan, Karen, additional, Figtree, Gemma, additional, Figtree, Melanie C, additional, Finlay, Malcolm, additional, Forooghi, Nasim, additional, Gibbons, Joseph M, additional, Griffiths, Peter, additional, Hamblin, Matt, additional, Howes, Lee, additional, Itua, Ivie, additional, Jones, Meleri, additional, Jardim, Victor, additional, Kapil, Vikas, additional, Jason Lee, Wing-Yiu, additional, Mandadapu, Vineela, additional, Mfuko, Celina, additional, Mitchelmore, Oliver, additional, Palma, Susana, additional, Patel, Kush, additional, Petersen, Steffen E, additional, Piniera, Brian, additional, Raine, Rosalind, additional, Rapala, Alicja, additional, Richards, Amy, additional, Sambile, Genine, additional, Couto de Sousa, Jorge, additional, Sugimoto, Michelle, additional, Thornton, George D, additional, Artico, Jessica, additional, Zahedi, Dan, additional, Parker, Ruth, additional, Robathan, Mathew, additional, Hickling, Lauren M, additional, Ntusi, Ntobeko, additional, Semper, Amanda, additional, Brooks, Tim, additional, Jones, Jessica, additional, Tucker, Art, additional, Veerapen, Jessry, additional, Vijayakumar, Mohit, additional, Wodehouse, Theresa, additional, Wynne, Lucinda, additional, Treibel, Thomas A, additional, Noursadeghi, Mahdad, additional, Manisty, Charlotte, additional, and Moon, James C, additional

Published
2020
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42. Improving the Generalizability of Convolutional Neural Network-Based Segmentation on CMR Images

Author

Chen, Chen, primary, Bai, Wenjia, additional, Davies, Rhodri H., additional, Bhuva, Anish N., additional, Manisty, Charlotte H., additional, Augusto, Joao B., additional, Moon, James C, additional, Aung, Nay, additional, Lee, Aaron M., additional, Sanghvi, Mihir M., additional, Fung, Kenneth, additional, Paiva, Jose Miguel, additional, Petersen, Steffen E., additional, Lukaschuk, Elena, additional, Piechnik, Stefan K., additional, Neubauer, Stefan, additional, and Rueckert, Daniel, additional

Published
2020
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43. Age matters: differences in exercise-induced cardiovascular remodelling in young and middle aged healthy sedentary individuals

Author

Torlasco, Camilla, primary, D’Silva, Andrew, additional, Bhuva, Anish N, additional, Faini, Andrea, additional, Augusto, Joao B, additional, Knott, Kristopher D, additional, Benedetti, Giulia, additional, Jones, Siana, additional, Zalen, Jet Van, additional, Scully, Paul, additional, Lobascio, Ilaria, additional, Parati, Gianfranco, additional, Lloyd, Guy, additional, Hughes, Alun D, additional, Manisty, Charlotte H, additional, Sharma, Sanjay, additional, and Moon, James C, additional

Published
2020
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44. Reply

Author

Bhuva, Anish N., primary, D’Silva, Andrew, additional, Hughes, Alun D., additional, Moon, James C., additional, and Manisty, Charlotte H., additional

Published
2020
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45. Cardiovascular Remodeling Experienced by Real-World, Unsupervised, Young Novice Marathon Runners

Author

D’Silva, Andrew, primary, Bhuva, Anish N., additional, van Zalen, Jet, additional, Bastiaenen, Rachel, additional, Abdel-Gadir, Amna, additional, Jones, Siana, additional, Nadarajan, Niromila, additional, Menacho Medina, Katia D., additional, Ye, Yang, additional, Augusto, Joao, additional, Treibel, Thomas A., additional, Rosmini, Stefania, additional, Ramlall, Manish, additional, Scully, Paul R., additional, Torlasco, Camilla, additional, Willis, James, additional, Finocchiaro, Gherardo, additional, Papatheodorou, Efstathios, additional, Dhutia, Harshil, additional, Cole, Della, additional, Chis Ster, Irina, additional, Hughes, Alun D., additional, Sharma, Rajan, additional, Manisty, Charlotte, additional, Lloyd, Guy, additional, Moon, James C., additional, and Sharma, Sanjay, additional

Published
2020
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46. Training for a First-Time Marathon Reverses Age-Related Aortic Stiffening

Author

Bhuva, Anish N., primary, D’Silva, Andrew, additional, Torlasco, Camilla, additional, Jones, Siana, additional, Nadarajan, Niromila, additional, Van Zalen, Jet, additional, Chaturvedi, Nish, additional, Lloyd, Guy, additional, Sharma, Sanjay, additional, Moon, James C., additional, Hughes, Alun D., additional, and Manisty, Charlotte H., additional

Published
2020
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49. Non-invasive assessment of ventriculo-arterial coupling using aortic wave intensity analysis combining central blood pressure and phase-contrast cardiovascular magnetic resonance

Author

Bhuva, Anish N, primary, D’Silva, A, primary, Torlasco, C, primary, Nadarajan, N, primary, Jones, S, primary, Boubertakh, R, primary, Van Zalen, J, primary, Scully, P, primary, Knott, K, primary, Benedetti, G, primary, Augusto, J B, primary, Bastiaenen, Rachel, primary, Lloyd, G, primary, Sharma, S, primary, Moon, J C, primary, Parker, K H, primary, Manisty, C H, primary, and Hughes, Alun D, primary

Published
2019
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50. Sex and regional differences in myocardial plasticity in aortic stenosis are revealed by 3D model machine learning

Author

Bhuva, Anish N, primary, Treibel, Thomas A, additional, De Marvao, Antonio, additional, Biffi, Carlo, additional, Dawes, Timothy J W, additional, Doumou, Georgia, additional, Bai, Wenjia, additional, Patel, Kush, additional, Boubertakh, Redha, additional, Rueckert, Daniel, additional, O’Regan, Declan P, additional, Hughes, Alun D, additional, Moon, James C, additional, and Manisty, Charlotte H, additional

Published
2019
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