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889 results on '"Chung, Jong Won"'

2. Effectiveness of endovascular treatment for in-hospital stroke vs. community-onset stroke: a propensity score-matched analysis

Author

Jung, Jae Wook, Kim, Kwang Hyun, Yun, Jaeseob, Nam, Hyo Suk, Heo, Ji Hoe, Baik, Minyoul, Yoo, Joonsang, Kim, Jinkwon, Park, Hyungjong, Sohn, Sung-Il, Hong, Jeong-Ho, Kim, Byung Moon, Kim, Dong Joon, Heo, JoonNyung, Bang, Oh Young, Seo, Woo-Keun, Chung, Jong-Won, Lee, Kyung-Yul, Jung, Yo Han, Lee, Hye Sun, Ahn, Seong Hwan, Shin, Dong Hoon, Choi, Hye-Yeon, Cho, Han-Jin, Baek, Jang-Hyun, Kim, Gyu Sik, Seo, Kwon-Duk, Kim, Seo Hyun, Song, Tae-Jin, Han, Sang Won, Park, Joong Hyun, Choi, Jin Kyo, and Kim, Young Dae

Published
2024
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3. Automated in-depth cerebral arterial labelling using cerebrovascular vasculature reframing and deep neural networks.

Author

Hong, Suk-Woo, Song, Ha-Na, Choi, Jong-Un, Cho, Hwan-Ho, Baek, In-Young, Lee, Ji-Eun, Kim, Yoon-Chul, Chung, Darda, Chung, Jong-Won, Bang, Oh-Young, Kim, Gyeong-Moon, Park, Hyun-Jin, Liebeskind, David, and Seo, Woo-Keun

Subjects
Humans, Neural Networks, Computer, Cerebral Arteries, Algorithms, Magnetic Resonance Angiography, Stroke
Abstract

Identifying the cerebral arterial branches is essential for undertaking a computational approach to cerebrovascular imaging. However, the complexity and inter-individual differences involved in this process have not been thoroughly studied. We used machine learning to examine the anatomical profile of the cerebral arterial tree. The method is less sensitive to inter-subject and cohort-wise anatomical variations and exhibits robust performance with an unprecedented in-depth vessel range. We applied machine learning algorithms to disease-free healthy control subjects (n = 42), patients with stroke with intracranial atherosclerosis (ICAS) (n = 46), and patients with stroke mixed with the existing controls (n = 69). We trained and tested 70% and 30% of each study cohort, respectively, incorporating spatial coordinates and geometric vessel feature vectors. Cerebral arterial images were analyzed based on the segmentation-stacking method using magnetic resonance angiography. We precisely classified the cerebral arteries across the exhaustive scope of vessel components using advanced geometric characterization, redefinition of vessel unit conception, and post-processing algorithms. We verified that the neural network ensemble, with multiple joint models as the combined predictor, classified all vessel component types independent of inter-subject variations in cerebral arterial anatomy. The validity of the categorization performance of the model was tested, considering the control, ICAS, and control-blended stroke cohorts, using the area under the receiver operating characteristic (ROC) curve and precision-recall curve. The classification accuracy rarely fell outside each images 90-99% scope, independent of cohort-dependent cerebrovascular structural variations. The classification ensemble was calibrated with high overall area rates under the ROC curve of 0.99-1.00 [0.97-1.00] in the test set across various study cohorts. Identifying an all-inclusive range of vessel components across controls, ICAS, and stroke patients, the accuracy rates of the prediction were: internal carotid arteries, 91-100%; middle cerebral arteries, 82-98%; anterior cerebral arteries, 88-100%; posterior cerebral arteries, 87-100%; and collections of superior, anterior inferior, and posterior inferior cerebellar arteries, 90-99% in the chunk-level classification. Using a voting algorithm on the queued classified vessel factors and anatomically post-processing the automatically classified results intensified quantitative prediction performance. We employed stochastic clustering and deep neural network ensembles. Ma-chine intelligence-assisted prediction of vessel structure allowed us to personalize quantitative predictions of various types of cerebral arterial structures, contributing to precise and efficient decisions regarding the cerebrovascular disease.

Published
2023

5. Multi-ancestry GWAS reveals excitotoxicity associated with outcome after ischaemic stroke

Author

Ibanez, Laura, Heitsch, Laura, Carrera, Caty, Farias, Fabiana HG, Del Aguila, Jorge L, Dhar, Rajat, Budde, John, Bergmann, Kristy, Bradley, Joseph, Harari, Oscar, Phuah, Chia Ling, Lemmens, Robin, Viana Oliveira Souza, Alessandro A, Moniche, Francisco, Cabezas-Juan, Antonio, Arenillas, Juan Francisco, Krupinksi, Jerzy, Cullell, Natalia, Torres-Aguila, Nuria, Muiño, Elena, Cárcel-Márquez, Jara, Marti-Fabregas, Joan, Delgado-Mederos, Raquel, Marin-Bueno, Rebeca, Hornick, Alejandro, Vives-Bauza, Cristofol, Navarro, Rosa Diaz, Tur, Silvia, Jimenez, Carmen, Obach, Victor, Segura, Tomas, Serrano-Heras, Gemma, Chung, Jong Won, Roquer, Jaume, Soriano-Tarraga, Carol, Giralt-Steinhauer, Eva, Mola-Caminal, Marina, Pera, Joanna, Lapicka-Bodzioch, Katarzyna, Derbisz, Justyna, Davalos, Antoni, Lopez-Cancio, Elena, Muñoz, Lucia, Tatlisumak, Turgut, Molina, Carlos, Ribo, Marc, Bustamante, Alejandro, Sobrino, Tomas, Castillo-Sanchez, Jose, Campos, Francisco, Rodriguez-Castro, Emilio, Arias-Rivas, Susana, Rodríguez-Yáñez, Manuel, Herbosa, Christina, Ford, Andria L, Gutierrez-Romero, Alonso, Uribe-Pacheco, Rodrigo, Arauz, Antonio, Lopes-Cendes, Iscia, Lowenkopf, Theodore, Barboza, Miguel A, Amini, Hajar, Stamova, Boryana, Ander, Bradley P, Sharp, Frank R, Kim, Gyeong Moon, Bang, Oh Young, Jimenez-Conde, Jordi, Slowik, Agnieszka, Stribian, Daniel, Tsai, Ellen A, Burkly, Linda C, Montaner, Joan, Fernandez-Cadenas, Israel, Lee, Jin Moo, and Cruchaga, Carlos

Subjects
Epidemiology, Health Sciences, Biotechnology, Genetics, Cerebrovascular, Aging, Brain Disorders, Stroke, Neurosciences, Human Genome, 2.1 Biological and endogenous factors, Neurological, Bayes Theorem, Brain Ischemia, Genome-Wide Association Study, Humans, Ischemic Stroke, United States, ischaemic stroke, neuroprotection, genetics, NIHSS, Medical and Health Sciences, Psychology and Cognitive Sciences, Neurology & Neurosurgery, Biomedical and clinical sciences, Health sciences, Psychology
Abstract

During the first hours after stroke onset, neurological deficits can be highly unstable: some patients rapidly improve, while others deteriorate. This early neurological instability has a major impact on long-term outcome. Here, we aimed to determine the genetic architecture of early neurological instability measured by the difference between the National Institutes of Health Stroke Scale (NIHSS) within 6 h of stroke onset and NIHSS at 24 h. A total of 5876 individuals from seven countries (Spain, Finland, Poland, USA, Costa Rica, Mexico and Korea) were studied using a multi-ancestry meta-analyses. We found that 8.7% of NIHSS at 24 h of variance was explained by common genetic variations, and also that early neurological instability has a different genetic architecture from that of stroke risk. Eight loci (1p21.1, 1q42.2, 2p25.1, 2q31.2, 2q33.3, 5q33.2, 7p21.2 and 13q31.1) were genome-wide significant and explained 1.8% of the variability suggesting that additional variants influence early change in neurological deficits. We used functional genomics and bioinformatic annotation to identify the genes driving the association from each locus. Expression quantitative trait loci mapping and summary data-based Mendelian randomization indicate that ADAM23 (log Bayes factor = 5.41) was driving the association for 2q33.3. Gene-based analyses suggested that GRIA1 (log Bayes factor = 5.19), which is predominantly expressed in the brain, is the gene driving the association for the 5q33.2 locus. These analyses also nominated GNPAT (log Bayes factor = 7.64) ABCB5 (log Bayes factor = 5.97) for the 1p21.1 and 7p21.1 loci. Human brain single-nuclei RNA-sequencing indicates that the gene expression of ADAM23 and GRIA1 is enriched in neurons. ADAM23, a presynaptic protein and GRIA1, a protein subunit of the AMPA receptor, are part of a synaptic protein complex that modulates neuronal excitability. These data provide the first genetic evidence in humans that excitotoxicity may contribute to early neurological instability after acute ischaemic stroke.

Published
2022

7. Association of Antiplatelet Therapy, Including Cilostazol, With Improved Survival in Patients With Moyamoya Disease in a Nationwide Study

Author

Seo, Woo‐Keun, Kim, Jae‐Young, Choi, Eun‐Hyeok, Kim, Ye‐Sel, Chung, Jong‐Won, Saver, Jeffrey L, Bang, Oh Young, and Kim, Gyeong‐Moon

Subjects
Clinical Research, Clinical Trials and Supportive Activities, 6.1 Pharmaceuticals, Evaluation of treatments and therapeutic interventions, Adult, Cilostazol, Female, Follow-Up Studies, Humans, Male, Middle Aged, Moyamoya Disease, Platelet Aggregation Inhibitors, Population Surveillance, Republic of Korea, Retrospective Studies, Survival Rate, Young Adult, antiplatelet agent, cilostazol, moyamoya disease, stroke, survival, Cardiorespiratory Medicine and Haematology
Abstract

Background Although antiplatelet agents are frequently prescribed in moyamoya disease in routine clinical practice, there are no large-scale epidemiologic trials or randomized trial evidence to support their use in patients with moyamoya disease. Methods and Results Using the Korean National Health Insurance Service database, patients diagnosed with moyamoya disease between 2002 and 2016 were followed up for up to 14 years to assess, using time-dependent Cox regression in all patients and in a propensity score-matched cohort, the association of antiplatelet therapy and individual antiplatelet agents with survival. Among 25 978 patients with newly diagnosed moyamoya disease, mean age was 37.6±19.9 years, 61.6% were women, and total follow-up was 163 347 person-years. Among 9154 patients who were prescribed antiplatelet agents at least once during the follow-up period, the proportion prescribed cilostazol gradually increased from 5.5% in 2002 to 56.0% in 2016. Any antiplatelet use was associated with reduced risk of death (hazard ratio, 0.77; 95% CI, 0.70-0.84) in a multivariate model. Among individual antiplatelet agents, cilostazol was associated with greater reduction in mortality than the 5 other antiplatelet regimens. Subgroup analysis, according to the age group and history of ischemic stroke, and sensitivity analysis, using propensity score-matched analysis, revealed consistent results. Conclusions Antiplatelet therapy is associated with substantial improvement in survival in patients with moyamoya disease, and cilostazol is associated with greater survival benefit compared with other antiplatelet regimens. These results provisionally support the use of antiplatelet therapy in patients with moyamoya disease and the conduct of confirmatory randomized controlled trials.

Published
2021

11. Efficacy and safety of oxiracetam in patients with vascular cognitive impairment: A multicenter, randomized, double-blinded, placebo-controlled, phase IV clinical trial

Author

Lim, Jae-Sung, Lee, Juneyoung, Kang, Yeonwook, Park, Hyun-Tae, Kim, Dong-Eog, Cha, Jae-Kwan, Park, Tai Hwan, Heo, Jae-Hyuk, Lee, Kyung Bok, Park, Jong-Moo, Oh, Mi Sun, Kim, Eung-Gyu, Chang, Dae-Il, Heo, Sung Hyuk, Park, Man-Seok, Park, HyunYoung, Yi, SangHak, Lee, Yeong Bae, Park, Kwang-Yeol, Lee, Soo Joo, Kim, Jae Guk, Lee, Jun, Cho, Kyung-Hee, Rha, Joung-Ho, Kim, Yeong-In, Lee, Jun Hong, Choi, Jay Chol, Oh, Kyung-Mi, Kwon, Jee-Hyun, Kim, Chulho, Park, Jong-Ho, Jung, Keun-Hwa, Sung, Sang Min, Chung, Jong-Won, Lee, Yong-Seok, Kim, Hahn Young, Cho, Hyun-Ji, Park, Jeong Wook, Moon, Won-Jin, and Bae, Hee-Joon

Published
2023
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12. A Multi-Center, Prospective, Observational Study to Evaluate the Therapeutic Effectiveness and Safety of an Olmesartan/Amlodipine Plus Rosuvastatin Combination Treatment in Patients with Concomitant Hypertension and Dyslipidemia.

Author

Lee, Bong-Ki, Kim, Byeong-Keuk, Park, Jae Hyoung, Chung, Jong-Won, Park, Chang Gyu, Kim, Jin Won, Kim, Young Dae, Park, Woo-Jung, Kim, Sang-Hyun, Cha, Jae-Kwan, Kim, Cheol Ho, Rha, Seung-Woon, Hong, Young Joon, Shin, Mi-Seung, Cho, Seong Wook, Sung, Young-Hee, Lee, Kiheon, Yu, Jae-Myung, Ryu, Dong-Ryeol, and Yu, Sungwook

Abstract

Introduction: This study assessed the therapeutic effectiveness of a single-pill combination (SPC) of olmesartan/amlodipine plus rosuvastatin for blood pressure (BP) and low-density lipoprotein cholesterol (LDL-C) in patients with hypertension and dyslipidemia. Methods: Adult patients with hypertension and dyslipidemia who were decided to be treated with the study drug were eligible. The primary endpoint was the proportion of patients who achieved BP, LDL-C and both BP and LDL-C treatment goals at weeks 24–48. Secondary endpoints were assessed at weeks 24–48 and included changes in BP and LDL-C levels from baseline; the proportion of patients who achieved treatment goals who were initially classified as uncontrolled at baseline; changes and percent changes in lipid parameters; changes in both BP and LDL-C levels among patients who reached treatment goals who were followed for more than 24 weeks; and the overall safety profile. Results: A total of 5476 patients were enrolled, and 4411 patients comprised the effectiveness evaluation set. The proportions of patients who reached the treatment goals for BP, LDL-C levels, and both BP and LDL-C levels were 67.93% [95% confidence interval (CI) 66.52–69.32], 80.19% [95% CI 78.85–81.49], and 58.07% [95% CI 56.43–59.7], respectively. Secondary endpoints showed statistically significant changes. Overall, the treatment was well tolerated. Conclusions: The treatment of patients with hypertension and dyslipidemia with the olmesartan/amlodipine plus rosuvastatin SPC was associated with significant decreases in SBP/DBP and LDL-C levels, and a high proportion of patients achieved the BP and LDL-C treatment goals. The finding of this study is worthwhile in that this study evaluated the effectiveness and safety in a broad patient population representative of those seen in everyday clinical practice. [ABSTRACT FROM AUTHOR]

Published
2025
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15. Automated detection of large vessel occlusion: a multicenter study validating efficacy and proving clinical implications

Author

Yum, Kyu Sun, primary, Chung, Jong-Won, additional, Ha, Sueyoung, additional, Park, Kwang-Yeol, additional, Shin, Dong-Ick, additional, Park, Hong-Kyun, additional, Cho, Yong-Jin, additional, Hong, Keun-Sik, additional, Kim, Jae Guk, additional, Lee, Soo-Joo, additional, Kim, Joon-Tae, additional, Bang, Oh Young, additional, Lee, Myungjae, additional, Kim, Dongmin, additional, Sunwoo, Leonard, additional, Bae, Hee-Joon, additional, Ryu, Wi-Sun, additional, and Kim, Beom Joon, additional

Published
2024
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20. Efficacy and safety of nerinetide for the treatment of acute ischaemic stroke (ESCAPE-NA1): a multicentre, double-blind, randomised controlled trial

Author

Demchuk, Andrew, Barber, Philip, Smith, Eric, Bal, Simerpreet, Subramaniam, Suresh, Peters, Steven, Couillard, Phillippe, Klein, Gary, Stys, Peter, Menon, Bijoy, Coutts, Shelagh, Almekhlafi, Mohammed, Hill, Michael, Goyal, Mayank, Wong, John, Mitha, Alim, Eesa, Muneer, Morrish, William, Alqatani, Saad, Kashani, Nima, Joshi, Manish, Zerna, Charlotte, Ospel, Johanna, Venkatesan, Prasanna, Teleg, Erika, Sitaram, Amith, Graham, Brett, Van Gaal, Stephen, Moussaddy, Aimen, Chakraborty, Debabrata, Maraj, Nicholas, Loockey, Andrew, Chen, Shuo, Singh, Ravinder, Alsultan, Abdulaziz, Asuncion, Ria, Tse, Dominic, Doshi, Darshan, Volny, Ondrej, Ojha, Piyush, Wadhwa, Ankur, Marko, Martha, Singh, Nishita, Wasyliw, Sanchea, Ryckborst, Karla, Kenney, Carol, Save, Supriya, Jambula, Anitha, Newcommon, Nancy, Hull, Gavin, Blackstock, Darcy, Kiszczak, Sharon, Zimmel, Leslie, Wright, Michelle, Jahraus, Cari, Andersen, Linda, Bohn, Shelly, Paul, Joseph, Zhang, Oiao, Doram, Craig, Lowerison, Mark, Hanley, Andrea, Campbell, Lori, Ure, Ashley, Taylor, Francis, Hul, Dominic, Wiebe, Samuel, Saluzzi, Marina, Blenkin, Nicole, Frayne, Richard, Buck, Brian, Butcher, Kenneth, Shuaib, Ashfaq, Jeerakathil, Tom, Jickling, Glen, Emery, Derek, Rempel, Jeremy, Owen, Richard, Ashforth, Robert, Yeo, Tom, Kotylak, Trevor, O'Kelly, Cian, Chow, Michael, Siddiqui, Muzaffer, Saqqur, Maher, D'Souza, Atlantic, Lloret, Mar, Butt, Asif, Nomani, Ali, Kalashyan, Hayrapet, Thirunavukkarasu, Sibi, Jabs, Juline, Fairall, Paige, Piquette, Lori, Phillips, Stephen, Green, A. Laine, Gubitz, Gordon, Heidenreich, Jens, Huynh, Thien, Shankar, Jai, Maloney, William, Vandorpe, Robert, Schmidt, Matthias, Pickett, Gwynedd, Weeks, Adrienne, Jarrett, Judith, MacDonald, Debbie, Arsenault, Joanna, Kinnear, Ruth, Mandzia, Jennifer, Mayich, Michael, Boulton, Melfort, Bullrich, Maria, Fridman, Sebastian, Kiwan, Ruba, Lee, Donald, Lownie, Stephen, Khaw, Alexander, Pandey, Sachin, Sharma, Manas, Sposato, Luciano, Wade, Kevin, Beauchamp, Beth, Lambourn, Lindsay, Amato-Marziali, Belinda, Poppe, Alexandre, Roy, Daniel, Daneault, Nicole, Deschaintre, Yan, Gioia, Laura, Jacquin, Grégory, Odier, Céline, Stapf, Christian, Iancu, Daniela, Raymond, Jean, Weill, Alain, Lapierre, Marlène, Jadil, Nadia, Jolteus, Judlène, Teitelbaum, Jeanne, Angle, Mark, Hannouche, Mathew, Badawy, Mohamed, Letourneau, Justin, Cortes, Maria, Linnell, Grant, Tampieri, Donateela, Vieira, Lucy, Legault, Catherine, Durcan, Liam, Moore, Angela, Cole, Erin, Magnussen, Claire, Salmon, Kristiana, Khalil, Salma, Desloges, Isabelle, Waxman, Lindsay, Abdon, Wynne, Lai, Sonia, Dowlatshahi, Dar, Shamy, Michel, Stotts, Grant, Blacquiere, Dylan, Lesiuk, Howard, Quateen, Aiman, Drake, Brian, Dewar, Brian, Daham, Zeinab, Kelly, Michael, Hunter, Gary, Peeling, Lissa, Whelan, Ruth, Garner, Aaron, Urroz, Lilian, Maley, Sharleen, Silver, Frank, Casaubon, Leanne, Pikula, Aleksandra, Del Campo, Martin, Schaafsma, Joanna, Jaigobin, Cheryl, Krings, Timo, Pereira, Vitor, Agid, Ronit, Farb, Richard, Kalman, Libby, Cayley, Anne, Williams, Janice, Stewart, Tim, Misquitta, Karen, Bharatha, Aditya, Selchen, Daniel, Marotta, Thomas, Sarma, Dipanka, Montanera, Walter, Spears, Juluan, Saposnik, Gustavo, Kostyrko, Pawel, Li, Yangmei, Parra-Farina, Carmen, Bengzon Diestro, Jose Danilo, Camden, Marie-Christine, Verreault, Steve, Mackey, Ariane, Audet, Marie-Eve, Milot, Geneviève, Lavoie, Pascale, Gariépy, Jean-Luc, Collard, Karine, Haché, Annette, Van Adel, Brian, Sahlas, Demetrios, Alshanbari, Ahmed, Baldwin, Josephine, Catanese, Luciana, Chen, Bing, De Sa Boasquevisque, Danielle, Deshmukh, Aviraj, Knapman, Jordan, Lamers, Shana, Larrazabal, Ramiro, MacMillan, Susan, Mai, Lauren, Mattia, Alicia, Nayar, Sumiti, Ng, Kuan, Oczkowski, Wieslaw, Perera, Kanjana, Shawawrah, Mays, Shoamanesh, Ashkan, Sobhi, Gita, Syed, Nabeel, Taher, Noran, Vandervelde, Cheyanne, Wang Pharm, Di, Field, Thalia, Teal, Philip, Benavente, Oscar, Wilson, Laura, Mann, Sharanpal, Yip, Samuel, Murphy, Colleen, Heran, Manraj, Rohr, Axel, Settecase, Fabio, Redekop, Gary, Haw, Charles, Maclean, Genoveva, Murray, Karina, Chiu, Rosalin, Flexman, Alana, Strecha, Eilidh, Gayton, Yolanda, Yip, Judy, Swartz, Richard, Boulos, Mark, Gladstone, David, Yu, Amy, Hopyan, Julia, Khosravani, Houman, Manosalva Alzate, Herbert, Southwell, Alisia, Kamra, Maneesha, Lopes, Kaitlyn, Kapoor, Arunima, da Costa, Leodante, Yang, Victor, Dyer, Erin, Shah, Ruchir, Knowles, Brenda, Nichols, Jennifer, Patterson, Jennifer, Cope, Krista, Dickerson, Lee, Barton, Katrina, Gray, Kimberly, Massengale, Ticey, Miller, Melanie, Simpson, Cindy, Walker, Sabrina, Tennyson-Yemm, Chlin, Devlin, Thomas, Frei, Donald, Bellon, Richard, Atchie, Benjamin, Kaminsky, Ian, Wilson, Duandelyn, Schraeder, Nicolle, Arias, Eric, Kodis, Lisa, Talley, Mark, Edinger, Alex, Talley, Tiffany, Dempsey, Ita, Williams, Laurie, Williams, Scott, Kupniewski, Sonny, Fasbinder, Brad, Snead, Joanna, Choe, Hana, Mackenzie, Larami, Weisman, Davi, Kozak, Osman, Shah, Qaisar, Gzesh, Dan, Kulandaivel, Kandan, Khoury, John, Klein, Brad, Bussinger, Patricia, Griffin, Lisa, dePalmo, Ashley, Oliva, Cynthia, Jadhav, Ashutosh, Jovin, Tudor, Kenmuir, Cynthia, Jankowitz, Brian, Gross, Bradley, Rocha, Marcelo, Starr, Matthew, Brown, Merritt, Hawkes, Christine, Shah, Kavit, Tememe, Danoushka, Walker, Gregory, Patel, Pratit, Klein, Bradley, Ziayee, Habibullah, Limaye, Kaustubh, Baxendell, Lisa, Gilchrist, Vicki, Feineigle, Patricia, Toseki Haibach, Kelsea, Van Every, Cathy, Desai, Shashyat, Zulfiqar, Maryam, Gibson, Linda, Barrett, Sean, Turkel-Parrella, David, Arcot, Karthikeyan, DiCrescento, Steven, Farkas, Jeffrey, Filipowski, Gregory, Frontera, Jennifer, Joset, Danielle, Liff, Jeremy, Scher, Erica, McDougall, Cameron, Kelly, Cheryl, Patel, Akshal, Monteith, Stephen, Starkman, Sidney, Atchaneeyasakul, Kunakorn, Burgos, Adrian, Chua, Janice Anne, Gaines, Nathan, Grunberg, Ileana, Guzy, Judy, Liu, Zuolu, Maluste, Neil, Ramirez, Lucas, Sharma, Latisha, Heck, Donald, Brown, Morry, McDonald, Colin, Calderon, Mateo, Hargis, Mitch, Roels, Christina, Emmady, Prabhu, Alvi, Talat, McTaggart, Ryan, Jayaraman, Mahesh, Cutting, Shawna, Smith, Wendy, Foley, Susan, Paolucci, Gino, Has, Richard, Quinn, Katie, Fuller, Lindsey, Brierley, Rebecca, Watkins, Christina, Demir, Nicole, Lopez, George, Vargas, Alejandro, Osteraas, Nicholas, Holtz, Becky, Sauvageau, Eric, Hanel, Ricardo, Aghaebrahim, Amin, Chmayssani, Mohamad, Naval, Neeraj, Day, Jason, Dellorso, Scott, Ludwig, Benjamin, Schemmel, Derek, Ebreo, Nancy, Bell, Karen, Lewis, Lanai, Delucia, Marjorie, Nogueira, Raul, Haussen, Diogo, Frankel, Michael, Bhatt, Nirav, Bianchi, Nicolas, Anderson, Aaron, Belagaje, Samir, Liberato, Bernardo, Rangaraju, Srikant, Al-Bayati, Alhamza, Grossberg, Jonathan, Craft, Leah, Schindler, Kiva, Schaad, Erin, Schultz, Meagan, Southerly, Lorretta, Doppelheuer, Shannon, Charlton, Jacquelyn, Faggard, Jason, Barbret, Zuzana, DukSoo Han, Ethan, Walters, Robin, Dolia, Jaydevsinh, El-Jamal, Sleiman, Eby, Brendan, Saleem, Yasir, Pearl, Harrison, Patel, Kishan, Rahman, Haseeb, Butt, Reema, English, Stephen, Puri, Ajit, Howk, Mary, Singh, Jasmeet, Massari, Francesco, DeMacedo Rodrigues, Katyucia, Kuhn, Anna, English, Joey, Barazangi, Nobl, Telischak, Nick, Kim, Warren, Ross, Josh, DeVole, Nata, Redford, Jessica, Ferreira, Milena, Ponting, Katie, Shen, Helen, Bedenk, Ann, Patel, Susila, Fernandess, Julia, Ke, Michael, Spokoyny, Ilanit, Gao, Billy, Tong, David, Chen, Charlene, Wong, Christine, Choe, Jessica, Martin, Coleman, Summers, Debbie, Boutwell, Christine, Olds, Karin, Crandall, Suzanne, Eatman, John, Akhtar, Naveed, Holloway, William, Halpin, Jared, Donegan, Brett, Schindler, Joseph, Hwang, David, Gilmore, Emily, Petersen, Nils, Sheth, Kevin, Brown, Stacy, Beekman, Rachel, George, Benjamin, Kaddouh, Firas, Falcone, Guido, Wira, Charles, Matouk, Charles, Loomis, Caitlin, Amin, Hardik, Stretz, Hans Christoph, Narula, Reshma, Jasne, Adam, Sansing, Lauren, Agarwal, Tijil, Jasak, Sara, Fontaine, Briana, King, Zachary, Kuohn, Lindsey, Orgass, Hailey, Leary, Megan, Nye, Joan, Halbert, Kelsey, Nystrom, Karin, Neuschatz, Kaile, Petrucci, Dawn, Coppola, Anna, Tarpley, Jason, Joyce, Treasure, Hou, Sam, Umekubo, Mark, Nakamura, Catrice, Ovando, Renee, Zuniga, Diana, Nien, Yih Lin, Basto, Fernando Mayor, Arch, Allison, Jong, Laura, Conrad, William, Abbott, Tara, Yao, Jay, Caganap, Scott, Jordan, John, Teitelbaum, George, Darflinger, Robert, Gupta, Rishi, Bain, Marianne, Budzik, Ronald, Czerniak, Jennifer, Groezinger, Katherine, Hicks II, William, Kaskar, Omran, Katz, Brian, Loochtan, Aaron, Pema, Peter, Rai, Vivek, Vora, Nirav, Brown, Amanda, Goodman, Diane, Danenbergs, Barb, Gossett, Monica, Bang, Oh Young, Chung, Jong-Won, Jeon, Pyoung, Kim, Keon Ha, Lim, Yun Jeong, Heo, Ji Hoe, Nam, Hyo Suk, Kim, Young Dae, Park, HyungJong, Hwang, In Gun, Ha, Wooseok, Choi, Jin Kyo, Jeong, Kyungsun, Rha, Joung-Ho, Park, Hee-Kwon, Yoon, Cindy, Kim, Bo-Kyung, Sohn, Sung-Il, Kim, Chang-hyun, Yoo, Joonsang, Hong, Jeong-Ho, Park, Hyungjong, Kim, Sohyeon, Choi, Moonkyung, Bae, Hyoeun, Lee, Jun Seok, Lee, Jae-Joon, Jun, Go-Eun, Jeon, Sujeong, Campbell, Bruce, Mitchell, Peter, Yassi, Nawaf, Davis, Stephen, Donnan, Geoffrey, Parsons, Mark, Yan, Bernard, Dowling, Richard, Bush, Steven, Wu, Teddy, Shah, Darshan, Zhao, Henry, Salvaris, Patrick, Alemseged, Fana, Ng, Felix, Williams, Cameron, Balabanski, Anna, Dos Santos, Angela, Ng, Jo-Lyn, McDonald, Amy, Jackson, David, Tsoleridis, Jessica, Pesavento, Lauren, Kleinig, Timothy John, Nurs, Roy Drew, Cranefield, Jennifer, Scroop, Rebecca, Cagi, Lavenia, Harvey, Jackson, Waters, Michael, Thornton, John, Brennan, Robert, O'Hare, Alan, Power, Sarah, Williams, David, Boyle, Karl, Moynihan, Barry, Liddy, Ann-Marie, Large, Margaret, Cullen, Ailbhe, Walsh, Roisin, Martin, Emma, Lynch, Julie, McElroy, Sinéad, Murphy, Seán, Coveney, Sarah, Deegan, Caroline, Horgan, Gillian, Kelly, Peter, Laffan, Aoife, Llamas-Osorio, Yudy, Lynch, Catherine, Mac Mahon, Peter, Marnane, Michael, McCabe, John, Burns, Paul, Tauro, Suzanne, Cuddy, Sarah, Rennie, Ian, Smyth, Graham, Flynn, Peter, Wiggam, Ivan, Kerr, Enda, Gordon, Patricia, Fearon, Patricia, Roberts, Geralt, Patterson, Cathy, Adams, Karen, Wells, Brian, McFarland, Margaret, Holmin, Staffan, Moreira, Tiago, Sjöstrand, Christina, Mazya, Michael, Almqvist, Håkan, Wahlgren, Nils, Steinberg, Anna, Cooray, Charith, Eriksson, Einar, Thorén, Magnus, Keselman, Boris, Ahmed, NIaz, Holmberg, Åke, Axelsson, Maria, Berglund, Annika, Thomalla, Götz, Golsari, Amir, Hoppe, Julia, Deb-Chatterji, Milani, Cheng, Bastian, Barrow, Ewgenia, Schlemm, Eckhard, Lettow, Iris, Jensen, Märit, Otto, Dagmar, Jaramillo, Kirsten, Appelbohm, Hannes, Fiehler, Jens, Bester, Maxim, Schönfeld, Michael, Brekenfeld, Caspar, Holst, Brigitte, Wortmann, Ginette, Koch, Friederike, Puetz, Volker, Gerber, Johannes, Abramyuk, Andrij, Kaiser, Daniel, Winzer, Simon, Prakapenia, Alexandra, Pallesen, Lars-Peder, Siepmann, Timo, Barlinn, Kristian, Haase, Kathrin, Sauer, Angela, Psychogios, Marios-Nikos, Liman, Jan, Brehm, Alex, Maus, Volker, Hollstein, Nicole, Reinke, Annika, Neitz, Gustav, Schnieder, Marlena, Schwarz, Margret, Ibrahim, Allam, Müller, Julia, Meister, Stefanie, Bäumle, Berit, Chapot, René, Pilgram-Pastor, Sara, Wallocha, Marta, Stauder, Michael, Celik, Ekin, Stracke, Paul, Müller, Nicole, Weber, Ralph, Velktamp, Roland, Nagel, Simon, Ringleb, Peter Arthur, Heyse, Miriam, Amiri, Hemasse, Mundiyanapurath, Sibu, Chen, Min, Bevrle, Elisabeth, Beck, Perdita, Möhlenbruch, Markus alfred, Tymianski, Michael, Potvin, Alphonse, Harris, Cameron, Wang, Chong, Adams, Corey, Mayor, Diana, Garman, Dave, Chau, Davis, Wu, Jane, Lunn, Janet, Heard, Kathy, Bermanis, Liat, Teyes, Lucy, Constant, Marc, Galsim, Maria, Helbin, Michael, Leroux, Michelle, Felix, Nicole, Bendre, Nilima, Cuthbert, Sandra, Brokx, Steve, Zhang, Yichuan, Lingham, Trudy, Kohli, Yatika, Salter, Michael, Liebeschuetz, Joseph, Hofmann, Frieder, Constant, John, Soeder, Tom, Snoke, Kara, Bologa, Monica, Stahl, Kimberly, Graziewicz, Maria, Hill, Michael D, Menon, Bijoy K, Nogueira, Raul G, McTaggart, Ryan A, Demchuk, Andrew M, Poppe, Alexandre Y, Buck, Brian H, Field, Thalia S, van Adel, Brian A, Swartz, Richard H, Shah, Ruchir A, Ospel, Johanna M, Almekhlafi, Mohammed A, Ryckborst, Karla J, Lowerison, Mark W, Garman, David, Cutting, Shawna M, Coutts, Shelagh B, Rempel, Jeremy L, Rohr, Axel CR, Sahlas, Demetrios J, Yu, Amy Y X, Devlin, Thomas G, Hanel, Ricardo A, Silver, Frank L, Campbell, Bruce C V, Mandzia, Jennifer L, Kleinig, Timothy J, Kelly, Michael E, Frei, Donald F, Tarpley, Jason W, McDougall, Cameron G, Puri, Ajit S, Phillips, Stephen J, Schindler, Joseph L, Budzik, Ronald F, Martin, Coleman O, Burns, Paul A, and Lopez, George A

Published
2020
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23. Deep learning-based automatic classification of ischemic stroke subtype using diffusion-weighted images

Author

Ryu, Wi-Sun, primary, Schellingerhout, Dawid, additional, Lee, Hoyeon, additional, Lee, Keon-Joo, additional, Kim, Chi Kyung, additional, Kim, Beom Joon, additional, Chung, Jong-Won, additional, Lim, Jae-Sung, additional, Kim, Joon-Tae, additional, Kim, Dae-Hyun, additional, Cha, Jae-Kwan, additional, Sunwoo, Leonard, additional, Kim, Dongmin, additional, Suh, Sang-il, additional, Bang, Oh Young, additional, Bae, Hee-Joon, additional, and Kim, Dong-Eog, additional

Published
2024
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27. Comorbidity index for predicting mortality at 6 months after reperfusion therapy

Author

Nam, Hyo Suk, Kim, Young Dae, Yoo, Joonsang, Park, Hyungjong, Kim, Byung Moon, Bang, Oh Young, Kim, Hyeon Chang, Han, Euna, Kim, Dong Joon, Heo, Joonyung, Kim, Minyoung, Choi, Jin Kyo, Lee, Kyung-Yul, Lee, Hye Sun, Shin, Dong Hoon, Choi, Hye-Yeon, Sohn, Sung-Il, Hong, Jeong-Ho, Lee, Jong Yun, Baek, Jang-Hyun, Kim, Gyu Sik, Seo, Woo-Keun, Chung, Jong-Won, Kim, Seo Hyun, Song, Tae-Jin, Han, Sang Won, Park, Joong Hyun, Kim, Jinkwon, Jung, Yo Han, Cho, Han-Jin, Ahn, Seong Hwan, Lee, Sung Ik, Seo, Kwon-Duk, and Heo, Ji Hoe

Published
2021
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28. Interpretable machine learning for early neurological deterioration prediction in atrial fibrillation-related stroke

Author

Kim, Seong-Hwan, Jeon, Eun-Tae, Yu, Sungwook, Oh, Kyungmi, Kim, Chi Kyung, Song, Tae-Jin, Kim, Yong-Jae, Heo, Sung Hyuk, Park, Kwang-Yeol, Kim, Jeong-Min, Park, Jong-Ho, Choi, Jay Chol, Park, Man-Seok, Kim, Joon-Tae, Choi, Kang-Ho, Hwang, Yang Ha, Kim, Bum Joon, Chung, Jong-Won, Bang, Oh Young, Kim, Gyeongmoon, Seo, Woo-Keun, and Jung, Jin-Man

Published
2021
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33. Association of statin pretreatment with collateral circulation and final infarct volume in acute ischemic stroke patients: A meta-analysis

Author

Malhotra, Konark, Safouris, Apostolos, Goyal, Nitin, Arthur, Adam, Liebeskind, David S., Katsanos, Aristeidis H., Sargento-Freitas, João, Ribo, Marc, Molina, Carlos, Chung, Jong-Won, Bang, Oh Young, Magoufis, Georgios, Cheema, Ahmad, Shook, Steven J., Uchino, Ken, Alexandrov, Andrei V., and Tsivgoulis, Georgios

Published
2019
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34. Corrigendum to “D-dimer to fibrinogen ratio predicts early neurological deterioration in ischemic stroke with atrial fibrillation” [Thromb. Res. 229 (2023) 219–224]

Author

Nam, Ki-Woong, primary, Kim, Chi Kyung, additional, Yu, Sungwook, additional, Oh, Kyungmi, additional, Chung, Jong-Won, additional, Oh, Young Bang, additional, Kim, Gyeong-Moon, additional, Jung, Jin-Man, additional, Song, Tae-Jin, additional, Kim, Yong-Jae, additional, Kim, Bum Joon, additional, Heo, Sung Hyuk, additional, Park, Kwang-Yeol, additional, Kim, Jeong-Min, additional, Park, Jong-Ho, additional, Choi, Jay Chol, additional, Park, Man-Seok, additional, Kim, Joon-Tae, additional, Choi, Kang-Ho, additional, Hwang, Yang Ha, additional, and Seo, Woo-Keun, additional

Published
2024
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35. Machine Learning-Based Etiologic Subtyping of Ischemic Stroke Using Circulating Exosomal microRNAs.

Author

Bang, Ji Hoon, Kim, Eun Hee, Kim, Hyung Jun, Chung, Jong-Won, Seo, Woo-Keun, Kim, Gyeong-Moon, Lee, Dong-Ho, Kim, Heewon, and Bang, Oh Young

Subjects
ISCHEMIC stroke, STROKE patients, EXOSOMES, MICRORNA, EXTRACELLULAR vesicles
Abstract

Ischemic stroke is a major cause of mortality worldwide. Proper etiological subtyping of ischemic stroke is crucial for tailoring treatment strategies. This study explored the utility of circulating microRNAs encapsulated in extracellular vesicles (EV-miRNAs) to distinguish the following ischemic stroke subtypes: large artery atherosclerosis (LAA), cardioembolic stroke (CES), and small artery occlusion (SAO). Using next-generation sequencing (NGS) and machine-learning techniques, we identified differentially expressed miRNAs (DEMs) associated with each subtype. Through patient selection and diagnostic evaluation, a cohort of 70 patients with acute ischemic stroke was classified: 24 in the LAA group, 24 in the SAO group, and 22 in the CES group. Our findings revealed distinct EV-miRNA profiles among the groups, suggesting their potential as diagnostic markers. Machine-learning models, particularly logistic regression models, exhibited a high diagnostic accuracy of 92% for subtype discrimination. The collective influence of multiple miRNAs was more crucial than that of individual miRNAs. Additionally, bioinformatics analyses have elucidated the functional implications of DEMs in stroke pathophysiology, offering insights into the underlying mechanisms. Despite limitations like sample size constraints and retrospective design, our study underscores the promise of EV-miRNAs coupled with machine learning for ischemic stroke subtype classification. Further investigations are warranted to validate the clinical utility of the identified EV-miRNA biomarkers in stroke patients. [ABSTRACT FROM AUTHOR]

Published
2024
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40. Multi-scale ordering in highly stretchable polymer semiconducting films

Author

Xu, Jie, Wu, Hung-Chin, Zhu, Chenxin, Ehrlich, Anatol, Shaw, Leo, Nikolka, Mark, Wang, Sihong, Molina-Lopez, Francisco, Gu, Xiaodan, Luo, Shaochuan, Zhou, Dongshan, Kim, Yun-Hi, Wang, Ging-Ji Nathan, Gu, Kevin, Feig, Vivian Rachel, Chen, Shucheng, Kim, Yeongin, Katsumata, Toru, Zheng, Yu-Qing, Yan, He, Chung, Jong Won, Lopez, Jeffrey, Murmann, Boris, and Bao, Zhenan

Published
2019
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