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22,176 results on '"Pearse A"'

102. Cross-Sectional Survey to Assess Hospital System Readiness for Hemorrhage During and After Cesarean Delivery in Africa

Author

Crowther, Marcelle, Dyer, Robert A., Bishop, David G., Bulamba, Fred, Maswime, Salome, Pearse, Rupert M, Biccard, Bruce M., Adem, Mohammed, Belachew, Tizeta, Belay, Ermiyas, Boru, Yared, Busha, Turunesh, Daniel, Selam, Dawit, Abiy, Demissie, Brook, Dersso Mengistu, Degsew, Desta, Kokeb, Desta, Kelem, Galcha, Desta, Guchima, Nuroadis, Kenna, Peniel, Kifle, Fitsum, Kifleyohanes, Tewodros, Mulye, Betelehem, Zemdkun, Bezaye, Arendse, Gwen, Bedwell, Gillian J, Biccard, Bruce M, Bishop, David G, Crowther, Marcelle, Cunnama, Lucy, Desemela, Yamkela, Duvenage, Hanel, Duys, Rowan, Dyer, Robert A, Flint, Margot, Futshane, Aphelele, Gumede, Simphiwe, Hardy, Anneli, Kabambi, Kasandji F, Kinnes, Marian, Kluyts, Hyla-Louise, Mafana, Edson, Maswime, Salome, Molaoa, Steve, Moloi, Lebogang, Mrara, Busisiwe, Mtshabe, Lwandile, Ninise, Ezile J, Pohl, Linda, Prinsloo, Rosalind V, Mokotla, Catherine F, Mdunyelwa, Rev. Mzubanzi, Baker, Tim, Bandeke, Upendo J, Biyengo, Happines, Kaliza, Aneth C, Khalid, Karima, Kitwara, Juma S, Mkumbo, Elibariki, Sunguya, Bruno, Shayo, Rafael S, Barabona, Godfrey, Amunyo, Elvis, Bulamba, Fred, Gamubaka, Richard, Hewitt-Smith, Adam, Kamwesigwe, Assen, Kawiso, Muzamiru, Khanyalano, Justine, Kidulu, James, Magala, Peter, Nakibuule, Joanitah, Naluyima, Joan, Namutebi, Hasifah, Nandutu, Rose, Nanimambi, Juliana, Okanya, David, Steven Walutsyo, John, Kiwalya, Herbert, Bhangu, Aneel, Davies, Justine, Dias, Priyanthi, Ellis, Ryan, Fadipe, Charles, Fowler, Alexander J, Hamborg, Thomas, Mihaylova, Borislava, Moore, Jolene, Pearse, Rupert M, Stephens, Timothy J, Vickery, Nicola J, and Vindrola, Cecilia

Published
2024
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103. Impact of preoperative hypercoagulability on myocardial injury in overweight and obese patients undergoing lower limb arthroplasty: An observational study

Author

Gurunathan, Usha, Hines, Joel, Pearse, Bronwyn, McKenzie, Scott, Hay, Karen, Nandurkar, Harshal, and Eley, Victoria

Subjects
Diagnosis, Complications and side effects, Risk factors, Obesity -- Complications and side effects, Hypercoagulation -- Complications and side effects -- Diagnosis, Myocardial diseases -- Risk factors, Knee replacement arthroplasty -- Complications and side effects, Postoperative complications -- Risk factors, Hip replacement arthroplasty -- Complications and side effects, Heart diseases -- Risk factors, Cardiomyopathy -- Risk factors, Surgery -- Complications
Abstract

Author(s): Usha Gurunathan (corresponding author) [1,2]; Joel Hines [3]; Bronwyn Pearse [3,4]; Scott McKenzie [2,5]; Karen Hay [2,6]; Harshal Nandurkar [7]; Victoria Eley [2,8] INTRODUCTION Myocardial injury following noncardiac surgery [...]

Published
2024
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105. Non-native plant invasion after fire in western USA varies by functional type and with climate

Author

Prevéy, Janet S., Jarnevich, Catherine S., Pearse, Ian S., Munson, Seth M., Stevens, Jens T., Barrett, Kevin J., Coop, Jonathan D., Day, Michelle A., Firmage, David, Fornwalt, Paula J., Haynes, Katharine M., Johnston, James D., Kerns, Becky K., Krawchuk, Meg A., Miller, Becky A., Nietupski, Ty C., Roque, Jacquilyn, Springer, Judith D., Stevens-Rumann, Camille S., Stoddard, Michael T., and Tortorelli, Claire M.

Published
2024
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108. Choroidal Vascularity Index (CVI)--A Novel Optical Coherence Tomography Parameter for Monitoring Patients with Panuveitis?

Author

Rupesh Agrawal, Mohammed Salman, Kara-Anne Tan, Michael Karampelas, Dawn A Sim, Pearse A Keane, and Carlos Pavesio

Subjects
Medicine, Science
Abstract

PURPOSE:To compute choroidal vascularity index (CVI) using an image binarization tool on enhanced depth imaging (EDI)-optical coherence tomography (OCT) scans as a non-invasive optical tool to monitor progression in panuveitis and to investigate the utility of volumetric data from EDI-OCT scans using custom image analysis software. MATERIALS AND METHODS:In this retrospective cohort study, segmented EDI-OCT scans of both eyes in 19 patients with panuveitis were taken at baseline and at 3-month follow-up and were compared with EDI-OCT scans of normal eyes. Subfoveal choroidal area was segmented into luminal (LA) and stromal interstitial area (SA). Choroidal vascularity index (CVI) was defined as the proportion of LA to the total circumscribed subfoveal choroidal area (TCA). RESULTS:The mean choroidal thickness was 265.5±100.1μm at baseline and 278.4±102.6μm at 3 months follow up (p = 0.06). There was no statistically significant difference in TCA between study and control eyes (p = 0.08). CVI in the control group was 66.9±1.5% at baseline and 66.4±1.5% at follow up. CVI was 74.1±4.7% at baseline and 69.4±4.8% at 3 months follow up for uveitic eyes (p

Published
2016
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109. Optical Coherence Tomography in the UK Biobank Study - Rapid Automated Analysis of Retinal Thickness for Large Population-Based Studies.

Author

Pearse A Keane, Carlota M Grossi, Paul J Foster, Qi Yang, Charles A Reisman, Kinpui Chan, Tunde Peto, Dhanes Thomas, Praveen J Patel, and UK Biobank Eye Vision Consortium

Subjects
Medicine, Science
Abstract

PURPOSE:To describe an approach to the use of optical coherence tomography (OCT) imaging in large, population-based studies, including methods for OCT image acquisition, storage, and the remote, rapid, automated analysis of retinal thickness. METHODS:In UK Biobank, OCT images were acquired between 2009 and 2010 using a commercially available "spectral domain" OCT device (3D OCT-1000, Topcon). Images were obtained using a raster scan protocol, 6 mm x 6 mm in area, and consisting of 128 B-scans. OCT image sets were stored on UK Biobank servers in a central repository, adjacent to high performance computers. Rapid, automated analysis of retinal thickness was performed using custom image segmentation software developed by the Topcon Advanced Biomedical Imaging Laboratory (TABIL). This software employs dual-scale gradient information to allow for automated segmentation of nine intraretinal boundaries in a rapid fashion. RESULTS:67,321 participants (134,642 eyes) in UK Biobank underwent OCT imaging of both eyes as part of the ocular module. 134,611 images were successfully processed with 31 images failing segmentation analysis due to corrupted OCT files or withdrawal of subject consent for UKBB study participation. Average time taken to call up an image from the database and complete segmentation analysis was approximately 120 seconds per data set per login, and analysis of the entire dataset was completed in approximately 28 days. CONCLUSIONS:We report an approach to the rapid, automated measurement of retinal thickness from nearly 140,000 OCT image sets from the UK Biobank. In the near future, these measurements will be publically available for utilization by researchers around the world, and thus for correlation with the wealth of other data collected in UK Biobank. The automated analysis approaches we describe may be of utility for future large population-based epidemiological studies, clinical trials, and screening programs that employ OCT imaging.

Published
2016
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110. Use of Mechanical Turk as a MapReduce Framework for Macular OCT Segmentation

Author

Aaron Y. Lee, Cecilia S. Lee, Pearse A. Keane, and Adnan Tufail

Subjects
Ophthalmology, RE1-994
Abstract

Purpose. To evaluate the feasibility of using Mechanical Turk as a massively parallel platform to perform manual segmentations of macular spectral domain optical coherence tomography (SD-OCT) images using a MapReduce framework. Methods. A macular SD-OCT volume of 61 slice images was map-distributed to Amazon Mechanical Turk. Each Human Intelligence Task was set to $0.01 and required the user to draw five lines to outline the sublayers of the retinal OCT image after being shown example images. Each image was submitted twice for segmentation, and interrater reliability was calculated. The interface was created using custom HTML5 and JavaScript code, and data analysis was performed using R. An automated pipeline was developed to handle the map and reduce steps of the framework. Results. More than 93,500 data points were collected using this framework for the 61 images submitted. Pearson’s correlation of interrater reliability was 0.995 (p

Published
2016
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113. Subretinal fluid in eyes with active ocular toxoplasmosis observed using spectral domain optical coherence tomography.

Author

Yanling Ouyang, Fuqiang Li, Qing Shao, Florian M Heussen, Pearse A Keane, Nicole Stübiger, Srinivas R Sadda, and Uwe Pleyer

Subjects
Medicine, Science
Abstract

To describe the clinical finding of subretinal fluid (SRF) in the posterior pole by spectral domain optical coherence tomography (SD-OCT) in eyes with active ocular toxoplasmosis (OT).Retrospective case series.Thirty-nine eyes from 38 patients with active OT [corrected]..Eyes with active OT which underwent SD-OCT were reviewed. SRFs in the posterior pole were further analyzed.Presence of SRF; its accompanying features, e.g. retinal necrosis, cystoid macular edema (CME), choroidal neovascularization (CNV); and longitudinal changes of SRF, including maximum height and total volume before and after treatment.SRF presented in 45.5% (or 15/33) of eyes with typical active OT and in 51.3% (or 20/39) of eyes with active OT. The mean maximum height and total volume of SRF were 161.0 (range: 23-478) µm and 0.47 (range: 0.005-4.12) mm3, respectively. For 12 eyes with SRF related to active retinal necrosis, SRF was observed with complete absorption after conventional anti-toxoplasmosis treatment. The mean duration for observation of SRF clearance was 33.8 (range: 7-84) days. The mean rate of SRF clearance was 0.0128 (range: 0.0002-0.0665) mm3/day.SRF (i.e., serous retinal detachment) is a common feature in patients with active OT when SD-OCT is performed. The majority of SRF was associated with retinal necrosis and reacted well to conventional therapy, regardless of total fluid volume. However, SRF accompanying with CME or CNV responded less favorably or remained refractory to conventional or combined intravitreal treatment, even when the SRF was small in size.

Published
2015
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119. Associations of intraoperative end–tidal CO2 levels with postoperative outcome–secondary analysis of a worldwide observational study

Author

Kroell, Wolfgang, Metzler, Helfried, Struber, Gerd, Wegscheider, Thomas, Gombotz, Hans, Hiesmayr, Michael, Schmid, Werner, Urbanek, Bernhard, Kahn, David, Momeni, Mona, Pospiech, Audrey, Lois, Fernande, Forget, Patrice, Grosu, Irina, Poelaert, Jan, van Mossevelde, Veerle, van Malderen, Marie-Claire, Dylst, Dimitri, van Melkebeek, Jeroen, Beran, Maud, de Hert, Stefan, De Baerdemaeker, Luc, Heyse, Bjorn, Van Limmen, Jurgen, Wyffels, Piet, Jacobs, Tom, Roels, Nathalie, De Bruyne, Ann, van de Velde, Stijn, Leva, Brigitte, Damster, Sandrine, Plichon, Benoit, JurosZovko, Marina, Djonovic-Omanoviċ, Dejana, Pernar, Selma, Zunic, Josip, Miskovic, Petar, Zilic, Antonio, Kvolik, Slavica, Ivic, Dubravka, Azenic-Venzera, Darija, Skiljic, Sonja, Vinkovic, Hrvoje, Oputric, Ivana, Juricic, Kazimir, Frkovic, Vedran, Kopic, Jasminka, Mirkovic, Ivan, Karanovic, Nenad, Carev, Mladen, Dropulic, Natasa, Saric, Jadranka Pavicic, Erceg, Gorjana, Dvorscak, Matea Bogdanovic, Mazul-Sunko, Branka, Pavicic, Anna Marija, Goranovic, Tanja, Maldini, Branka, Radocaj, Tomislav, Gavranovic, Zeljka, Mladic-Batinica, Inga, Sehovic, Mirna, Stourac, Petr, Harazim, Hana, Smekalova, Olga, Kosinova, Martina, Kolacek, Tomas, Hudacek, Kamil, Drab, Michal, Brujevic, Jan, Vitkova, Katerina, Jirmanova, Katerina, Volfova, Ivana, Dzurnakova, Paula, Liskova, Katarina, Dudas, Radovan, Filipsky, Radek, El Kafrawy, Samir, Abdelwahab, Hisham Hosny, Metwally, Tarek, Abdel-Razek, Ahmed, El-Shaarawy, Ahmed Mostafa, Hasan, Wael Fathy, Ahmed, Ahmed Gouda, Yassin, Hany, Magdy, Mohamed, Abdelhady, Mahdy, Mahran, Mohamed, Herodes, Eiko, Kivik, Peeter, Oganjan, Juri, Aun, Annika, Sormus, Alar, Sarapuu, Kaili, Mall, Merilin, Karjagin, Juri, Futier, Emmanuel, Petit, Antoine, Gerard, Adeline, Marret, Emmanuel, Solier, Marc, Jaber, Samir, Prades, Albert, Krassler, Jens, Merzky, Simone, de Abreu, Marcel Gama, Uhlig, Christopher, Kiss, Thomas, Bundy, Anette, Bluth, Thomas, Gueldner, Andreas, Spieth, Peter, Scharffenberg, Martin, Thiem, Denny Tran, Koch, Thea, Treschan, Tanja, Schaefer, Maximilian, Bastin, Bea, Geib, Johann, Weiss, Martin, Kienbaum, Peter, Pannen, Benedikt, Gottschalk, Andre, Konrad, Mirja, Westerheide, Diana, Schwerdtfeger, Ben, Wrigge, Hermann, Simon, Philipp, Reske, Andreas, Nestler, Christian, Valsamidis, Dimitrios, Stroumpoulis, Konstantinos, Antholopoulos, Georgios, Andreou, Antonis, Karapanos, Dimitris, Theodoraki, Kassiani, Gkiokas, Georgios, Tasoulis, Marios-Konstantinos, Sidiropoulou, Tatiana, Zafeiropoulou, Foteini, Florou, Panagiota, Pandazi, Aggeliki, Tsaousi, Georgia, Nouris, Christos, Pourzitaki, Chryssa, Bystritski, Dmitri, Pizov, Reuven, Eden, Arieh, Pesce, Caterina Valeria, Campanile, Annamaria, Marrella, Antonella, Grasso, Salvatore, De Michele, Michele, Bona, Francesco, Giacoletto, Gianmarco, Sardo, Elena, Giancarlo, Luigi, Sottosanti, Vicari, Solca, Maurizio, Volta, Carlo Alberto, Spadaro, Savino, Verri, Marco, Ragazzi, Riccardo, Zoppellari, Roberto, Cinnella, Gilda, Raimondo, Pasquale, La Bella, Daniela, Mirabella, Lucia, D’antini, Davide, Pelosi, Paolo, Molin, Alexandre, Brunetti, Iole, Gratarola, Angelo, Pellerano, Giulia, Sileo, Rosanna, Pezzatto, Stefano, Montagnani, Luca, Pasin, Laura, Landoni, Giovanni, Zangrillo, Alberto, Beretta, Luigi, Di Parma, Ambra Licia, Tarzia, Valentina, Dossi, Roberto, Sassone, Marta Eugenia, Sances, Daniele, Tredici, Stefano, Spano, Gianluca, Castellani, Gianluca, Delunas, Luigi, Peradze, Sopio, Venturino, Marco, Arpino, Ines, Sher, Sara, Tommasino, Concezione, Rapido, Francesca, Morelli, Paola, Vargas, Maria, Servillo, Giuseppe, Cortegiani, Andrea, Raineri, Santi Maurizio, Montalto, Francesca, Russotto, Vincenzo, Giarratano, Antonino, Baciarello, Marco, Generali, Michela, Cerati, Giorgia, Leykin, Yigal, Bressan, Filippo, Bartolini, Vittoria, Zamidei, Lucia, Brazzi, Luca, Liperi, Corrado, Sales, Gabriele, Pistidda, Laura, Severgnini, Paolo, Brugnoni, Elisa, Musella, Giuseppe, Bacuzzi, Alessandro, Muhardri, Dalip, Gecaj-Gashi, Agreta, Sada, Fatos, Bytyqi, Adem, Karbonskiene, Aurika, Aukstakalniene, Ruta, Teberaite, Zivile, Salciute, Erika, Tikuisis, Renatas, Miliauskas, Povilas, Jurate, Sipylaite, Kontrimaviciute, Egle, Tomkute, Gabija, Xuereb, John, Bezzina, Maureen, Borg, Francis Joseph, Hemmes, Sabrine, Schultz, Marcus, Hollmann, Markus, Wiersma, Irene, Binnekade, Jan, Bos, Lieuwe, Boer, Christa, Duvekot, Anne, In’t Veld, Bas, Werger, Alice, Dennesen, Paul, Severijns, Charlotte, De Jong, Jasper, Hering, Jens, van Beek, Rienk, Ivars, Stefan, Jammer, Ib, Breidablik, Alena, Hodt, Katharina Skirstad, Fjellanger, Frode, Avalos, Manuel Vico, MellinOlsen, Jannicke, Andersson, Elisabeth, Shafi-Kabiri, Amir, Molina, Ruby, Wutai, Stanley, Morais, Erick, Tareco, Gloria, Ferreira, Daniel, Amaral, Joana, de Lurdes Goncalves Castro, Maria, Cadilha, Susana, Appleton, Sofia, Parente, Suzana, Correia, Mariana, Martins, Diogo, Monteirosa, Angela, Ricardo, Ana, Rodrigues, Sara, Horhota, Lucian, Grintescu, Ioana Marina, Mirea, Liliana, Grintescu, Ioana Cristina, Corneci, Dan, Negoita, Silvius, Dutu, Madalina, Garotescu, Ioana Popescu, Filipescu, Daniela, Prodan, Alexandru Bogdan, Droc, Gabriela, Fota, Ruxandra, Popescu, Mihai, Tomescu, Dana, Petcu, Ana Maria, Tudoroiu, Marian Irinel, Moise, Alida, Guran, Catalin-Traian, Gherghina, Iorel, Costea, Dan, Cindea, Iulia, Copotoiu, Sanda-Maria, Copotoiu, Ruxandra, Barsan, Victoria, Tolcser, Zsolt, Riciu, Magda, Moldovan, Septimiu Gheorghe, Veres, Mihaly, Gritsan, Alexey, Kapkan, Tatyana, Gritsan, Galina, Korolkov, Oleg, Kulikov, Alexander, Lubnin, Andrey, Ovezov, Alexey, Prokoshev, Pavel, Lugovoy, Alexander, Anipchenko, Natalia, Babayants, Andrey, Komissarova, Irina, Zalina, Karginova, Likhvantsev, Valery, Fedorov, Sergei, Lazukic, Aleksandra, Pejakovic, Jasmina, Mihajlovic, Dunja, Kusnierikova, Zuzana, Zelinkova, Maria, Bruncakova, Katarina, Polakovicova, Lenka, Sobona, Villiam, Novak-Supe, Barbka, Pekle-Golez, Ana, Jovanov, Miroljub, Strazisar, Branka, Markovic-Bozic, Jasmina, Novak-Jankovic, Vesna, Voje, Minca, Grynyuk, Andriy, Kostadinov, Ivan, Spindler-Vesel, Alenka, Moral, Victoria, Unzueta, Mari Carmen, Puigbo, Carlos, Fava, Josep, Canet, Jaume, Moret, Enrique, Nunez, Monicá Rodriguez, Sendra, Mar, Brunelli, Andrea, Rodenas, Frederic, Monedero, Pablo, Martinez, Francisco Hidalgo, Temino, Maria Jose Yepes, Simon, Antonio Martínez, de Abajo Larriba, Ana, Lisi, Alberto, Perez, Gisela, Martinez, Raquel, Granell, Manuel, Vivo, Jose Tatay, Ruiz, Cristina Saiz, de Andrés Ibañez, Jose Antonio, Pastor, Ernesto, Soro, Marina, Ferrando, Carlos, Defez, Mario, Alvares-Santullano, Cesar Aldecoa, Perez, Rocio, Rico, Jesus, Jawad, Monir, Saeed, Yousif, Gillberg, Lars, Bengisun, Zuleyha Kazak, Kazbek, Baturay Kansu, Coskunfirat, Nesil, Boztug, Neval, Sanli, Suat, Yilmaz, Murat, Hadimioglu, Necmiye, Senturk, Nuzhet Mert, Camci, Emre, Kucukgoncu, Semra, Sungur, Zerrin, Sivrikoz, Nukhet, Ozgen, Serpil Ustalar, Toraman, Fevzi, Selvi, Onur, Senturk, Ozgur, Yildiz, Mine, Kuvaki, Bahar, Gunenc, Ferim, Kucukguclu, Semih, Ozbilgin, Şule, Maral, Jale, Canli, Seyda, Arun, Oguzhan, Saltali, Ali, Aydogan, Eyup, Akgun, Fatma Nur, Sanlikarip, Ceren, Karaman, Fatma Mine, Mazur, Andriy, Vorotyntsev, Sergiy, Rousseau, Guy, Barrett, Colin, Stancombe, Lucia, Shelley, Ben, Scholes, Helen, Limb, James, Rafi, Amir, Wayman, Lisa, Deane, Jill, Rogerson, David, Williams, John, Yates, Susan, Rogers, Elaine, Pulletz, Mark, Moreton, Sarah, Jones, Stephanie, Venkatesh, Suresh, Burton, Maudrian, Brown, Lucy, Goodall, Cait, Rucklidge, Matthew, Fuller, Debbie, Nadolski, Maria, Kusre, Sandeep, Lundberg, Michael, Everett, Lynn, Nutt, Helen, Zuleika, Maka, Carvalho, Peter, Clements, Deborah, Creagh-Brown, Ben, Watt, Philip, Raymode, Parizade, Pearse, Rupert, Mohr, Otto, Raj, Ashok, Creary, Thais, Chishti, Ahmed, Bell, Andrea, Higham, Charley, Cain, Alistair, Gibb, Sarah, Mowat, Stephen, Franklin, Danielle, West, Claire, Minto, Gary, Boyd, Nicholas, Mills, Gary, Calton, Emily, Walker, Rachel, Mackenzie, Felicity, Ellison, Branwen, Roberts, Helen, Chikungwa, Moses, Jackson, Clare, Donovan, Andrew, Foot, Jayne, Homan, Elizabeth, Montgomery, Jane, Portch, David, Mercer, Pauline, Palmer, Janet, Paddle, Jonathan, Fouracres, Anna, Datson, Amanda, Andrew, Alyson, Welch, Leanne, Rose, Alastair, Varma, Sandeep, Simeson, Karen, Rambhatla, Mrutyunjaya, Susarla, Jaysimha, Marri, Sudhakar, Kodaganallur, Krishnan, Das, Ashok, Algarsamy, Shivarajan, Colley, Julie, Davies, Simon, Szewczyk, Margaret, Smith, Thomas, Fernandez-Bustamante, Ana, Luzier, Elizabeth, Almagro, Angela, Melo, Marcos Vidal, Fernando, Luiz, Sulemanji, Demet, Sprung, Juraj, Weingarten, Toby, Kor, Daryl, Scavonetto, Federica, Tze, Yeo, Nasa, Prashant, van Meenen, David M.P., Paulus, Frederique, de Abreu, Marcelo Gama, Bossers, Sebastiaan M., Schober, Patrick, Schultz, Marcus J., Neto, Ary Serpa, and Hemmes, Sabrine N.T.

Published
2025
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124. Preoperative N-terminal pro-B-type natriuretic peptide and myocardial injury after stopping or continuing renin–angiotensin system inhibitors in noncardiac surgery: a prespecified analysis of a phase 2 randomised controlled multicentre trial

Author

Ackland, Gareth, Martin, Tim, Fernandez, Maria, Seidu, Fatima, Pakats, Mari-Liis, Mahr, Otto, MacDonald, Neil, Santos, Filipa Dos, Garcia, Amaia Arrieta, Uddin, Ruzena, Begum, Salma, Pearse, Rupert, Subhedar, Emily, Wan, Yize, Patel, Akshaykumar, Shahid, Tasnin, Gooneratne, Mevan, Trainer, Charlotte, Griffiths, Bethan, Dunkley, Steven, May, Shaun, Walker, Sophie, Fowler, Alexander, Stephens, Timothy, Oliveira, Monica, Januszewska, Marta, Niebrzegowska, Edyta, Amaral, Vanessa, Kassam, Jamila, Young, Sophie, Ahmad, Shanaz, Whalley, Jan, Haines, Ryan, Hui, Sara, Hammond, Rob, Crane, David, Brealey, David, Bampoe, Sohail, Stephens, Robert, Reyes, Anna, Martir, Gladys, Diaz, Chimverly, Cleland, Stuart, Minto, Gary, Wilmshurst, Natasha, Affleck, Debbie-Claire, Ward, Tracy, Werrett, Gavin, Cummins, Susan, Amber, Alan, Biffen, Andrew, Boumphrey, Stephen, Cann, Elizabeth, Eglinton, Charlotte, Jones, Elaine, Mwadeyi, Memory, Piesley, Sam, Cowan, Richard, Alderton, Julie, Reed, Fiona, Smith, Joanne, Turner, Amy, Madziva, Lorraine, Patrick, Abigail, Harris, Penny, Lang, Harry, Middleditch, Alexander, Pickering, Anthony, O’Donovan, Catherine, Houlihan, Rebecca, Jarvis, Rosina, Shrimpton, Andrew, Farmery, Toni, Tucker, Katy, Davis, Danielle, Somanth, Sameer, Duncan, Louise, Melsom, Helen, Clark, Sarah, Kent, Melanie, Wood, Michelle, Laidlaw, Ami, Matheson-Smith, Tracy, Potts, Kathryn, Kay, Andrea, Hobson, Stefanie, Sear, John, Kapil, Vikas, Archbold, Andrew, Wilson, Matt, Dndrejaj, Drilona, Ly, Dennis, Gutierrez del Arroyo, Ana, Abbott, Tom E.F., Dias, Priyanthi, Somanath, Sameer, Pearse, Rupert M., and Ackland, Gareth L.

Published
2024
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125. Robustness of an Artificial Intelligence Solution for Diagnosis of Normal Chest X-Rays

Author

Dyer, Tom, Smith, Jordan, Dissez, Gaetan, Tay, Nicole, Malik, Qaiser, Morgan, Tom Naunton, Williams, Paul, Garcia-Mondragon, Liliana, Pearse, George, and Rasalingham, Simon

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Artificial Intelligence
Abstract

Purpose: Artificial intelligence (AI) solutions for medical diagnosis require thorough evaluation to demonstrate that performance is maintained for all patient sub-groups and to ensure that proposed improvements in care will be delivered equitably. This study evaluates the robustness of an AI solution for the diagnosis of normal chest X-rays (CXRs) by comparing performance across multiple patient and environmental subgroups, as well as comparing AI errors with those made by human experts. Methods: A total of 4,060 CXRs were sampled to represent a diverse dataset of NHS patients and care settings. Ground-truth labels were assigned by a 3-radiologist panel. AI performance was evaluated against assigned labels and sub-groups analysis was conducted against patient age and sex, as well as CXR view, modality, device manufacturer and hospital site. Results: The AI solution was able to remove 18.5% of the dataset by classification as High Confidence Normal (HCN). This was associated with a negative predictive value (NPV) of 96.0%, compared to 89.1% for diagnosis of normal scans by radiologists. In all AI false negative (FN) cases, a radiologist was found to have also made the same error when compared to final ground-truth labels. Subgroup analysis showed no statistically significant variations in AI performance, whilst reduced normal classification was observed in data from some hospital sites. Conclusion: We show the AI solution could provide meaningful workload savings by diagnosis of 18.5% of scans as HCN with a superior NPV to human readers. The AI solution is shown to perform well across patient subgroups and error cases were shown to be subjective or subtle in nature.

Published
2022

126. Enhancing Early Lung Cancer Detection on Chest Radiographs with AI-assistance: A Multi-Reader Study

Author

Dissez, Gaetan, Tay, Nicole, Dyer, Tom, Tam, Matthew, Dittrich, Richard, Doyne, David, Hoare, James, Pat, Jackson J., Patterson, Stephanie, Stockham, Amanda, Malik, Qaiser, Morgan, Tom Naunton, Williams, Paul, Garcia-Mondragon, Liliana, Smith, Jordan, Pearse, George, and Rasalingham, Simon

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Artificial Intelligence
Abstract

Objectives: The present study evaluated the impact of a commercially available explainable AI algorithm in augmenting the ability of clinicians to identify lung cancer on chest X-rays (CXR). Design: This retrospective study evaluated the performance of 11 clinicians for detecting lung cancer from chest radiographs, with and without assistance from a commercially available AI algorithm (red dot, Behold.ai) that predicts suspected lung cancer from CXRs. Clinician performance was evaluated against clinically confirmed diagnoses. Setting: The study analysed anonymised patient data from an NHS hospital; the dataset consisted of 400 chest radiographs from adult patients (18 years and above) who had a CXR performed in 2020, with corresponding clinical text reports. Participants: A panel of readers consisting of 11 clinicians (consultant radiologists, radiologist trainees and reporting radiographers) participated in this study. Main outcome measures: Overall accuracy, sensitivity, specificity and precision for detecting lung cancer on CXRs by clinicians, with and without AI input. Agreement rates between clinicians and performance standard deviation were also evaluated, with and without AI input. Results: The use of the AI algorithm by clinicians led to an improved overall performance for lung tumour detection, achieving an overall increase of 17.4% of lung cancers being identified on CXRs which would have otherwise been missed, an overall increase in detection of smaller tumours, a 24% and 13% increased detection of stage 1 and stage 2 lung cancers respectively, and standardisation of clinician performance. Conclusions: This study showed great promise in the clinical utility of AI algorithms in improving early lung cancer diagnosis and promoting health equity through overall improvement in reader performances, without impacting downstream imaging resources.

Published
2022

129. Sex dependence of postoperative pulmonary complications – A post hoc unmatched and matched analysis of LAS VEGAS

Author

Kroell, Wolfgang, Metzler, Helfried, Struber, Gerd, Wegscheider, Thomas, Gombotz, Hans, Hiesmayr, Michael, Schmid, Werner, Urbanek, Bernhard, Kahn, David, Momeni, Mona, Pospiech, Audrey, Lois, Fernande, Forget, Patrice, Grosu, Irina, Poelaert, Jan, van Mossevelde, Veerle, van Malderen, Marie-Claire, Dylst, Dimitri, van Melkebeek, Jeroen, Beran, Maud, de Hert, Stefan, De Baerdemaeker, Luc, Heyse, Bjorn, Van Limmen, Jurgen, Wyffels, Piet, Jacobs, Tom, Roels, Nathalie, De Bruyne, Ann, van de Velde, Stijn, Leva, Brigitte, Damster, Sandrine, Plichon, Benoit, Juros-Zovko, Marina, Djonoviċ-Omanoviċ, Dejana, Pernar, Selma, Zunic, Josip, Miskovic, Petar, Zilic, Antonio, Kvolik, Slavica, Ivic, Dubravka, Azenic-Venzera, Darija, Skiljic, Sonja, Vinkovic, Hrvoje, Oputric, Ivana, Juricic, Kazimir, Frkovic, Vedran, Kopic, Jasminka, Mirkovic, Ivan, Karanovic, Nenad, Carev, Mladen, Dropulic, Natasa, Saric, Jadranka Pavicic, Erceg, Gorjana, Dvorscak, Matea Bogdanovic, Mazul-Sunko, Branka, Pavicic, Anna Marija, Goranovic, Tanja, Maldini, Branka, Radocaj, Tomislav, Gavranovic, Zeljka, Mladic-Batinica, Inga, Sehovic, Mirna, Stourac, Petr, Harazim, Hana, Smekalova, Olga, Kosinova, Martina, Kolacek, Tomas, Hudacek, Kamil, Drab, Michal, Brujevic, Jan, Vitkova, Katerina, Jirmanova, Katerina, Volfova, Ivana, Dzurnakova, Paula, Liskova, Katarina, Dudas, Radovan, Filipsky, Radek, el Kafrawy, Samir, Abdelwahab, Hisham Hosny, Metwally, Tarek, Abdel-Razek, Ahmed, El-Shaarawy, Ahmed Mostafa, Hasan, Wael Fathy, Ahmed, Ahmed Gouda, Yassin, Hany, Magdy, Mohamed, Abdelhady, Mahdy, Mahran, Mohamed, Herodes, Eiko, Kivik, Peeter, Oganjan, Juri, Aun, Annika, Sormus, Alar, Sarapuu, Kaili, Mall, Merilin, Karjagin, Juri, Futier, Emmanuel, Petit, Antoine, Gerard, Adeline, Marret, Emmanuel, Solier, Marc, Jaber, Samir, Prades, Albert, Krassler, Jens, Merzky, Simone, de Abreu, Marcel Gama, Uhlig, Christopher, Kiss, Thomas, Bundy, Anette, Bluth, Thomas, Gueldner, Andreas, Spieth, Peter, Scharffenberg, Martin, Thiem, Denny Tran, Koch, Thea, Treschan, Tanja, Schaefer, Maximilian, Bastin, Bea, Geib, Johann, Weiss, Martin, Kienbaum, Peter, Pannen, Benedikt, Gottschalk, Andre, Konrad, Mirja, Westerheide, Diana, Schwerdtfeger, Ben, Wrigge, Hermann, Simon, Philipp, Reske, Andreas, Nestler, Christian, Valsamidis, Dimitrios, Stroumpoulis, Konstantinos, Antholopoulos, Georgios, Andreou, Antonis, Karapanos, Dimitris, Theodoraki, Kassiani, Gkiokas, Georgios, Tasoulis, Marios-Konstantinos, Sidiropoulou, Tatiana, Zafeiropoulou, Foteini, Florou, Panagiota, Pandazi, Aggeliki, Tsaousi, Georgia, Nouris, Christos, Pourzitaki, Chryssa, Bystritski, Dmitri, Pizov, Reuven, Eden, Arieh, Pesce, Caterina Valeria, Campanile, Annamaria, Marrella, Antonella, Grasso, Salvatore, De Michele, Michele, Bona, Francesco, Giacoletto, Gianmarco, Sardo, Elena, Sottosanti, Luigi Giancarlo Vicari, Solca, Maurizio, Volta, Carlo Alberto, Spadaro, Savino, Verri, Marco, Ragazzi, Riccardo, Zoppellari, Roberto, Cinnella, Gilda, Raimondo, Pasquale, La Bella, Daniela, Mirabella, Lucia, D'antini, Davide, Pelosi, Paolo, Molin, Alexandre, Brunetti, Iole, Gratarola, Angelo, Pellerano, Giulia, Sileo, Rosanna, Pezzatto, Stefano, Montagnani, Luca, Pasin, Laura, Landoni, Giovanni, Zangrillo, Alberto, Beretta, Luigi, Di Parma, Ambra Licia, Tarzia, Valentina, Dossi, Roberto, Sassone, Marta Eugenia, Sances, Daniele, Tredici, Stefano, Spano, Gianluca, Castellani, Gianluca, Delunas, Luigi, Peradze, Sopio, Venturino, Marco, Arpino, Ines, Sher, Sara, Tommasino, Concezione, Rapido, Francesca, Morelli, Paola, Vargas, Maria, Servillo, Giuseppe, Cortegiani, Andrea, Raineri, Santi Maurizio, Montalto, Francesca, Russotto, Vincenzo, Giarratano, Antonino, Baciarello, Marco, Generali, Michela, Cerati, Giorgia, Leykin, Yigal, Bressan, Filippo, Bartolini, Vittoria, Zamidei, Lucia, Brazzi, Luca, Liperi, Corrado, Sales, Gabriele, Pistidda, Laura, Severgnini, Paolo, Brugnoni, Elisa, Musella, Giuseppe, Bacuzzi, Alessandro, Muhardri, Dalip, Gecaj-Gashi, Agreta, Sada, Fatos, Bytyqi, Adem, Karbonskiene, Aurika, Aukstakalniene, Ruta, Teberaite, Zivile, Salciute, Erika, Tikuisis, Renatas, Miliauskas, Povilas, Jurate, Sipylaite, Kontrimaviciute, Egle, Tomkute, Gabija, Xuereb, John, Bezzina, Maureen, Borg, Francis Joseph, Hemmes, Sabrine, Schultz, Marcus, Hollmann, Markus, Wiersma, Irene, Binnekade, Jan, Bos, Lieuwe, Nijbroek, Sunny, Boer, Christa, Duvekot, Anne, ’t Veld, Bas in, Werger, Alice, Dennesen, Paul, Severijns, Charlotte, De Jong, Jasper, Hering, Jens, van Beek, Rienk, Ivars, Stefan, Jammer, Ib, Breidablik, Alena, Hodt, Katharina Skirstad, Fjellanger, Frode, Avalos, Manuel Vico, Mellin-Olsen, Jannicke, Andersson, Elisabeth, Shafi-Kabiri, Amir, Molina, Ruby, Wutai, Stanley, Morais, Erick, Tareco, Glória, Ferreira, Daniel, Amaral, Joana, de Lurdes Goncalves Castro, Maria, Cadilha, Susana, Appleton, Sofia, Parente, Suzana, Correia, Mariana, Martins, Diogo, Monteirosa, Angela, Ricardo, Ana, Rodrigues, Sara, Horhota, Lucian, Grintescu, Ioana Marina, Mirea, Liliana, Grintescu, Ioana Cristina, Corneci, Dan, Negoita, Silvius, Dutu, Madalina, Garotescu, Ioana Popescu, Filipescu, Daniela, Prodan, Alexandru Bogdan, Droc, Gabriela, Fota, Ruxandra, Popescu, Mihai, Tomescu, Dana, Petcu, Ana Maria, Tudoroiu, Marian Irinel, Moise, Alida, Guran, Catalin-Traian, Gherghina, Iorel, Costea, Dan, Cindea, Iulia, Copotoiu, Sanda-Maria, Copotoiu, Ruxandra, Barsan, Victoria, Tolcser, Zsolt, Riciu, Magda, Moldovan, Septimiu Gheorghe, Veres, Mihaly, Gritsan, Alexey, Kapkan, Tatyana, Gritsan, Galina, Korolkov, Oleg, Kulikov, Alexander, Lubnin, Andrey, Ovezov, Alexey, Prokoshev, Pavel, Lugovoy, Alexander, Anipchenko, Natalia, Babayants, Andrey, Komissarova, Irina, Zalina, Karginova, Likhvantsev, Valery, Fedorov, Sergei, Lazukic, Aleksandra, Pejakovic, Jasmina, Mihajlovic, Dunja, Kusnierikova, Zuzana, Zelinkova, Maria, Bruncakova, Katarina, Polakovicova, Lenka, Sobona, Villiam, Novak-Supe, Barbka, Pekle-Golez, Ana, Jovanov, Miroljub, Strazisar, Branka, Markovic-Bozic, Jasmina, Novak-Jankovic, Vesna, Voje, Minca, Grynyuk, Andriy, Kostadinov, Ivan, Spindler-Vesel, Alenka, Moral, Victoria, Unzueta, Mari Carmen, Puigbo, Carlos, Fava, Josep, Canet, Jaume, Moret, Enrique, Nunez, Mónica Rodriguez, Sendra, Mar, Brunelli, Andrea, Rodenas, Frederic, Monedero, Pablo, Martinezv, Francisco Hidalgo, Temino, Maria Jose Yepes, Simon, Antonio Martínez, de Abajo Larriba, Ana, Lisi, Alberto, Perez, Gisela, Martinez, Raquel, Granell, Manuel, Vivo, Jose Tatay, Ruiz, Cristina Saiz, de Andrés Ibañez, Jose Antonio, Pastor, Ernesto, Soro, Marina, Ferrando, Carlos, Defez, Mario, Alvares-Santullano, Cesar Aldecoa, Perez, Rocio, Rico, Jesus, Jawad, Monir, Saeed, Yousif, Gillberg, Lars, Hedenstierma, Göran, Bengisun, Zuleyha Kazak, Kazbek, Baturay Kansu, Coskunfirat, Nesil, Boztug, Neval, Sanli, Suat, Yilmaz, Murat, Hadimioglu, Necmiye, Senturk, Nuzhet Mert, Camci, Emre, Kucukgoncu, Semra, Sungur, Zerrin, Sivrikoz, Nukhet, Ozgen, Serpil Ustalar, Toraman, Fevzi, Selvi, Onur, Senturk, Ozgur, Yildiz, Mine, Kuvaki, Bahar, Gunenc, Ferim, Kucukguclu, Semih, Ozbilgin, Şule, Maral, Jale, Canli, Seyda, Arun, Oguzhan, Saltali, Ali, Aydogan, Eyup, Akgun, Fatma Nur, Sanlikarip, Ceren, Karaman, Fatma Mine, Mazur, Andriy, Vorotyntsev, Sergiy, Rousseau, Guy, Barrett, Colin, Stancombe, Lucia, Shelley, Ben, Scholes, Helen, Limb, James, Rafi, Amir, Wayman, Lisa, Deane, Jill, Rogerson, David, Williams, John, Yates, Susan, Rogers, Elaine, Pulletz, Mark, Moreton, Sarah, Jones, Stephanie, Venkatesh, Suresh, Burton, Maudrian, Brown, Lucy, Goodall, Cait, Rucklidge, Matthew, Fuller, Debbie, Nadolski, Maria, Kusre, Sandeep, Lundberg, Michael, Everett, Lynn, Nutt, Helen, Zuleika, Maka, Carvalho, Peter, Clements, Deborah, Creagh-Brown, Ben, Watt, Philip, Raymode, Parizade, Pearse, Rupert, Mohr, Otto, Raj, Ashok, Creary, Thais, Chishti, Ahmed, Bell, Andrea, Higham, Charley, Cain, Alistair, Gibb, Sarah, Mowat, Stephen, Franklin, Danielle, West, Claire, Minto, Gary, Boyd, Nicholas, Mills, Gary, Calton, Emily, Walker, Rachel, Mackenzie, Felicity, Ellison, Branwen, Roberts, Helen, Chikungwa, Moses, Jackson, Clare, Donovan, Andrew, Foot, Jayne, Homan, Elizabeth, Montgomery, Jane, Portch, David, Mercer, Pauline, Palmer, Janet, Paddle, Jonathan, Fouracres, Anna, Datson, Amanda, Andrew, Alyson, Welch, Leanne, Rose, Alastair, Varma, Sandeep, Simeson, Karen, Rambhatla, Mrutyunjaya, Susarla, Jaysimha, Marri, Sudhakar, Kodaganallur, Krishnan, Das, Ashok, Algarsamy, Shivarajan, Colley, Julie, Davies, Simon, Szewczyk, Margaret, Smith, Thomas, Fernandez-Bustamante, Ana, Luzier, Elizabeth, Almagro, Angela, Melo, Marcos Vidal, Fernando, Luiz, Sulemanji, Demet, Sprung, Juraj, Weingarten, Toby, Kor, Daryl, Scavonetto, Federica, Tze, Yeo, Vermeulen, Tom D., Hol, Liselotte, Swart, Pien, Mills, Gary H., Putensen, Christian, Serpa Neto, Ary, Vidal Melo, Marcos F., Hollmann, Markus W., Gama de Abreu, Marcelo, Schultz, Marcus J., Hemmes, Sabrine N., and van Meenen, David M.

Published
2024
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130. Evaluation of cystoid change phenotypes in ocular toxoplasmosis using optical coherence tomography.

Author

Yanling Ouyang, Uwe Pleyer, Qing Shao, Pearse A Keane, Nicole Stübiger, Antonia M Joussen, Srinivas R Sadda, and Florian M Heussen

Subjects
Medicine, Science
Abstract

To present unique cystoid changes occurring in patients with ocular toxoplasmosis observed in spectral domain optical coherence tomography (OCT).Forty-six patients (80 eyes) with a diagnosis of ocular toxoplasmosis, who underwent volume OCT examination between January 2005 and October 2012, were retrospectively collected. Review of clinical examination findings, fundus photographs, fluorescein angiograms (FA) and OCT image sets obtained at initial visits and follow-up. Qualitative and quantitative analyses of cystoid space phenotypes visualized using OCT.Of the 80 eyes included, 17 eyes (15 patients) demonstrated cystoid changes in the macula on OCT. Six eyes (7.5%) had cystoid macular edema (CME), 2 eyes (2.5%) had huge outer retinal cystoid space (HORC), 12 eyes (15%) had cystoid degeneration and additional 3 eyes (3.75%) had outer retinal tubulation due to age related macular degeneration. In one eye with HORC, the lesion was seen in the photoreceptor outer segment, accompanied by photoreceptor elongation and splitting. Three eyes presented with paravascular cystoid degeneration in the inner retina without other macular OCT abnormality.In this study, different phenotypes of cystoid spaces seen in eyes with ocular toxoplasmosis using spectral domain OCT (SD-OCT) were demonstrated. CME presented as an uncommon feature, consistently with previous findings. Identification of rare morphological cystoid features (HORC with/without photoreceptor enlongation or splitting) on clinical examination had provided evidence to previous experimental models, which may also expand the clinical spectrum of the disease. Cystoid degeneration in the inner retina next to the retinal vessels in otherwise "normal" looking macula was observed, which may suggest more often clinical evaluation for those patients. Further studies are needed to verify the relevance of cystoid features seen on SD-OCT in assisting with the diagnosis and management of ocular toxoplasmosis.

Published
2014
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132. VAFO-Loss: VAscular Feature Optimised Loss Function for Retinal Artery/Vein Segmentation

Author

Zhou, Yukun, Xu, Moucheng, Hu, Yipeng, Blumberg, Stefano B., Zhao, An, Wagner, Siegfried K., Keane, Pearse A., and Alexander, Daniel C.

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

Estimating clinically-relevant vascular features following vessel segmentation is a standard pipeline for retinal vessel analysis, which provides potential ocular biomarkers for both ophthalmic disease and systemic disease. In this work, we integrate these clinical features into a novel vascular feature optimised loss function (VAFO-Loss), in order to regularise networks to produce segmentation maps, with which more accurate vascular features can be derived. Two common vascular features, vessel density and fractal dimension, are identified to be sensitive to intra-segment misclassification, which is a well-recognised problem in multi-class artery/vein segmentation particularly hindering the estimation of these vascular features. Thus we encode these two features into VAFO-Loss. We first show that incorporating our end-to-end VAFO-Loss in standard segmentation networks indeed improves vascular feature estimation, yielding quantitative improvement in stroke incidence prediction, a clinical downstream task. We also report a technically interesting finding that the trained segmentation network, albeit biased by the feature optimised loss VAFO-Loss, shows statistically significant improvement in segmentation metrics, compared to those trained with other state-of-the-art segmentation losses., Comment: 13 pages, 6 figures, 3 tables

Published
2022

135. Grading of Age-Related Macular Degeneration: Comparison between Color Fundus Photography, Fluorescein Angiography, and Spectral Domain Optical Coherence Tomography

Author

Nils F. Mokwa, Tina Ristau, Pearse A. Keane, Bernd Kirchhof, Srinivas R. Sadda, and Sandra Liakopoulos

Subjects
Ophthalmology, RE1-994
Abstract

Purpose. To compare color fundus photography (FP), fluorescein angiography (FA), and spectral domain optical coherence tomography (SDOCT) for the detection of age-related macular degeneration (AMD), choroidal neovascularisation (CNV), and CNV activity. Methods. FPs, FAs, and SDOCT volume scans from 120 eyes of 66 AMD and control patients were randomly collected. Control eyes were required to show no AMD, but other retinal pathology was allowed. The presence of drusen, pigmentary changes, CNV, and signs for CNV activity was independently analyzed for all imaging modalities. Results. AMD was diagnosed based on FP in 75 eyes. SDOCT and FA showed sensitivity (specificity) of 89% (76%) and 92% (82%), respectively. CNV was present on FA in 68 eyes. Sensitivity (specificity) was 78% (100%) for FP and 94% (98%) for SDOCT. CNV activity was detected by SDOCT or FA in 60 eyes with an agreement in 46 eyes. Sensitivity was 88% for SDOCT and 88% for FA. FP showed sensitivity of 38% and specificity of 98%. Conclusions. CNV lesions and activity may be missed by FP alone, but FP may help identifying drusen and pigmentary changes. SDOCT is highly sensitive for the detection of AMD, CNV, and CNV activity; however, it cannot fully replace FA.

Published
2013
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137. Development of Anti-VEGF Therapies for Intraocular Use: A Guide for Clinicians

Author

Pearse A. Keane and Srinivas R. Sadda

Subjects
Ophthalmology, RE1-994
Abstract

Angiogenesis is the process by which new blood vessels form from existing vessel networks. In the past three decades, significant progress has been made in our understanding of angiogenesis; progress driven in large part by the increasing realization that blood vessel growth can promote or facilitate disease. By the early 1990s, it had become clear that the recently discovered “vascular endothelial growth factor” (VEGF) was a powerful mediator of angiogenesis. As a result, several groups targeted this molecule as a potential mediator of retinal ischemia-induced neovascularization in disorders such as diabetic retinopathy and retinal vein occlusion. Around this time, it also became clear that increased intraocular VEGF production was not limited to ischemic retinal diseases but was also a feature of choroidal vascular diseases such as neovascular age-related macular degeneration (AMD). Thus, a new therapeutic era emerged, utilizing VEGF blockade for the management of chorioretinal diseases characterized by vascular hyperpermeability and/or neovascularization. In this review, we provide a guide for clinicians on the development of anti-VEGF therapies for intraocular use.

Published
2012
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138. Early lysosome defects precede neurodegeneration with amyloid-β and tau aggregation in NHE6-null rat brain.

Author

Lee, YouJin, Miller, Morgan R, Fernandez, Marty A, Berg, Elizabeth L, Prada, Adriana M, Ouyang, Qing, Schmidt, Michael, Silverman, Jill L, Young-Pearse, Tracy L, and Morrow, Eric M

Subjects
Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Acquired Cognitive Impairment, Brain Disorders, Alzheimer's Disease, Neurosciences, Neurodegenerative, Rare Diseases, Aging, Dementia, 2.1 Biological and endogenous factors, Aetiology, Neurological, Alzheimer Disease, Amyloid beta-Peptides, Animals, Ataxia, Brain, Disease Models, Animal, Epilepsy, Genetic Diseases, X-Linked, Hippocampus, Intellectual Disability, Lysosomes, Male, Microcephaly, Ocular Motility Disorders, Rats, Sodium-Hydrogen Exchangers, tau Proteins, rat model, lysosomes, neurodegeneration, tau, amyloid beta, Medical and Health Sciences, Psychology and Cognitive Sciences, Neurology & Neurosurgery
Abstract

Loss-of-function mutations in the X-linked endosomal Na+/H+ exchanger 6 (NHE6) cause Christianson syndrome in males. Christianson syndrome involves endosome dysfunction leading to early cerebellar degeneration, as well as later-onset cortical and subcortical neurodegeneration, potentially including tau deposition as reported in post-mortem studies. In addition, there is reported evidence of modulation of amyloid-β levels in experimental models wherein NHE6 expression was targeted. We have recently shown that loss of NHE6 causes defects in endosome maturation and trafficking underlying lysosome deficiency in primary mouse neurons in vitro. For in vivo studies, rat models may have an advantage over mouse models for the study of neurodegeneration, as rat brain can demonstrate robust deposition of endogenously-expressed amyloid-β and tau in certain pathological states. Mouse models generally do not show the accumulation of insoluble, endogenously-expressed (non-transgenic) tau or amyloid-β. Therefore, to study neurodegeneration in Christianson syndrome and the possibility of amyloid-β and tau pathology, we generated an NHE6-null rat model of Christianson syndrome using CRISPR-Cas9 genome-editing. Here, we present the sequence of pathogenic events in neurodegenerating NHE6-null male rat brains across the lifespan. NHE6-null rats demonstrated an early and rapid loss of Purkinje cells in the cerebellum, as well as a more protracted neurodegenerative course in the cerebrum. In both the cerebellum and cerebrum, lysosome deficiency is an early pathogenic event, preceding autophagic dysfunction. Microglial and astrocyte activation also occur early. In the hippocampus and cortex, lysosome defects precede loss of pyramidal cells. Importantly, we subsequently observed biochemical and in situ evidence of both amyloid-β and tau aggregation in the aged NHE6-null hippocampus and cortex (but not in the cerebellum). Tau deposition is widely distributed, including cortical and subcortical distributions. Interestingly, we observed tau deposition in both neurons and glia, as has been reported in Christianson syndrome post-mortem studies previously. In summary, this experimental model is among very few examples of a genetically modified animal that exhibits neurodegeneration with deposition of endogenously-expressed amyloid-β and tau. This NHE6-null rat will serve as a new robust model for Christianson syndrome. Furthermore, these studies provide evidence for linkages between endolysosome dysfunction and neurodegeneration involving protein aggregations, including amyloid-β and tau. Therefore these studies may provide insight into mechanisms of more common neurodegenerative disorders, including Alzheimer's disease and related dementias.

Published
2022

139. Management of Neovascular Age-Related Macular Degeneration in Clinical Practice: Initiation, Maintenance, and Discontinuation of Therapy

Author

Pearse A. Keane, Adnan Tufail, and Praveen J. Patel

Subjects
Ophthalmology, RE1-994
Abstract

Neovascular age-related macular degeneration (AMD) is a leading cause of irreversible visual loss in elderly populations. In recent years, pharmacological inhibition of vascular endothelial growth factor (VEGF), via intravitreal injection of ranibizumab (Lucentis) or bevacizumab (Avastin), has offered the first opportunity to improve visual outcomes in patients diagnosed with this disorder. In this paper, we provide recommendations on how bevacizumab and ranibizumab may be best applied in current clinical practice, with an emphasis on their underlying pharmacology and efficacy. In addition, we review current guidelines for the initiation, maintenance, and discontinuation of anti-VEGF therapies, as well as emerging treatment strategies and future directions in the field.

Published
2011
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146. The Association of Urinary Sodium Excretion with Glaucoma and Related Traits in a Large United Kingdom Population

Author

Aschard, Hugues, Chia, Mark, Chua, Sharon, Do, Ron, Foster, Paul, Kang, Jae, Kastner, Alan, Khawaja, Anthony, Kim, Jihye, Lentjes, Marleen, Luben, Robert, Madjedi, Kian, Montesano, Giovanni, Pasquale, Louis, Stuart, Kelsey, Warwick, Alasdair, Wiggs, Janey, Allen, Naomi, Aslam, Tariq, Atan, Denize, Barman, Sarah, Barrett, Jenny, Bishop, Paul, Black, Graeme, Braithwaite, Tasanee, Carare, Roxana, Chakravarthy, Usha, Chan, Michelle, Day, Alexander, Desai, Parul, Dhillon, Bal, Dick, Andrew, Doney, Alexander, Egan, Cathy, Ennis, Sarah, Fruttiger, Marcus, Garway-Heath, David (Ted), Gibson, Jane, Guggenheim, Jeremy, Hammond, Chris, Hardcastle, Alison, Harding, Simon, Hogg, Ruth, Hysi, Pirro, Keane, Pearse, Khaw, Peng Tee, Lascaratos, Gerassimos, Littlejohns, Thomas, Lotery, Andrew, Luthert, Phil, MacGillivray, Tom, Mackie, Sarah, McGuinness, Bernadette, McKay, Gareth, McKibbin, Martin, Moore, Tony, Morgan, James, O'Sullivan, Eoin, Oram, Richard, Owen, Chris, Patel, Praveen, Paterson, Euan, Peto, Tunde, Petzold, Axel, Pontikos, Nikolas, Rahi, Jugnoo, Rudnicka, Alicja, Sattar, Naveed, Self, Jay, Sergouniotis, Panagiotis, Sivaprasad, Sobha, Steel, David, Stratton, Irene, Strouthidis, Nicholas, Sudlow, Cathie, Sun, Zihan, Tapp, Robyn, Thomas, Dhanes, Trucco, Emanuele, Tufail, Adnan, Viswanathan, Ananth, Vitart, Veronique, Weedon, Mike, Williams, Katie, Williams, Cathy, Woodside, Jayne, Yates, Max, Yip, Jennifer, Zheng, Yalin, Aung, Tin, Burdon, Kathryn, Chen, Li, Cheng, Ching-Yu, Craig, Jamie, Cree, Angela, de Vries, Victor, Driessen, Sjoerd, Fingert, John, Gharahkhani, Puya, Hammond, Christopher, Hayward, Caroline, Hewitt, Alex, Jansonius, Nomdo, Jonansson, Fridbert, Jonas, Jost, Kass, Michael, Khor, Chiea, Klaver, Caroline, Koh, Jacyline, MacGregor, Stuart, Mackey, David, Mitchell, Paul, Pang, Calvin, Pasutto, Francesca, Pfeiffer, Norbert, Polašek, Ozren, Ramdas, Wishal, Schuster, Alexander, Segrè, Ayellet, Stefansson, Einer, Stefánsson, Kári, Thorleifsson, Gudmar, Thorsteinsdottir, Unnur, van Duijn, Cornelia, Vergroesen, Joëlle, Vithana, Eranga, Wilson, James, Wojciechowski, Robert, Wong, Tien, Young, Terri, Stuart, Kelsey V., Biradar, Mahantesh I., Luben, Robert N., Dhaun, Neeraj, Wagner, Siegfried K., Warwick, Alasdair N., Madjedi, Kian M., Pasquale, Louis R., Wiggs, Janey L., Kang, Jae H., Lentjes, Marleen A.H., Foster, Paul J., and Khawaja, Anthony P.

Published
2024
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148. Evolutionary ecology of masting: mechanisms, models, and climate change

Author

Bogdziewicz, Michal, Kelly, Dave, Ascoli, Davide, Caignard, Thomas, Chianucci, Francesco, Crone, Elizabeth E., Fleurot, Emilie, Foest, Jessie J., Gratzer, Georg, Hagiwara, Tomika, Han, Qingmin, Journé, Valentin, Keurinck, Léa, Kondrat, Katarzyna, McClory, Ryan, LaMontagne, Jalene M., Mundo, Ignacio A., Nussbaumer, Anita, Oberklammer, Iris, Ohno, Misuzu, Pearse, Ian S., Pesendorfer, Mario B., Resente, Giulia, Satake, Akiko, Shibata, Mitsue, Snell, Rebecca S., Szymkowiak, Jakub, Touzot, Laura, Zwolak, Rafal, Zywiec, Magdalena, and Hacket-Pain, Andrew J.

Published
2024
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150. Risk Factors for Age-Related Maculopathy

Author

Paul P. Connell, Pearse A. Keane, Evelyn C. O'Neill, Rasha W. Altaie, Edward Loane, Kumari Neelam, John M. Nolan, and Stephen Beatty

Subjects
Ophthalmology, RE1-994
Abstract

Age-related maculopathy (ARM) is the leading cause of blindness in the elderly. Although beneficial therapeutic strategies have recently begun to emerge, much remains unclear regarding the etiopathogenesis of this disorder. Epidemiologic studies have enhanced our understanding of ARM, but the data, often conflicting, has led to difficulties with drawing firm conclusions with respect to risk for this condition. As a consequence, we saw a need to assimilate the published findings with respect to risk factors for ARM, through a review of the literature appraising results from published cross-sectional studies, prospective cohort studies, case series, and case control studies investigating risk for this condition. Our review shows that, to date, and across a spectrum of epidemiologic study designs, only age, cigarette smoking, and family history of ARM have been consistently demonstrated to represent risk for this condition. In addition, genetic studies have recently implicated many genes in the pathogenesis of age-related maculopathy, including Complement Factor H, PLEKHA 1, and LOC387715/HTRA1, demonstrating that environmental and genetic factors are important for the development of ARM suggesting that gene-environment interaction plays an important role in the pathogenesis of this condition.

Published
2009
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