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Institution

Kyungpook National University

EducationDaegu, South Korea
About: Kyungpook National University is a education organization based out in Daegu, South Korea. It is known for research contribution in the topics: Population & Large Hadron Collider. The organization has 20497 authors who have published 42107 publications receiving 834608 citations.


Papers
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Journal ArticleDOI
TL;DR: Pending confirmation in prospective studies, lipophilic xenobiotics, including brominated POPs stored in adipose tissue, may be involved in the pathogenesis of diabetes and metabolic syndrome.
Abstract: OBJECTIVE —Chlorinated persistent organic pollutants (POPs), endocrine disruptors accumulated in adipose tissue, were associated with diabetes and metabolic syndrome. Brominated flame retardants (BFRs), such as polybrominated diphenyl ethers (PBDEs) or polybrominated biphenyls (PBBs), are another class of POPs for which body burden is increasing. Cross-sectional associations of serum concentrations of BFRs with diabetes and metabolic syndrome were studied. RESEARCH DESIGN AND METHODS —In the National Health and Nutrition Examination Survey 2003–2004, 1,367 adults were examined with respect to diabetes status. Five PBDEs and one PBB were selected, detectable in ≥60% of participants. For the outcome metabolic syndrome, we restricted the analysis to 637 participants with a morning fasting sample. RESULTS —Compared with subjects with serum concentrations below the limit of detection, prevalent diabetes had differing dose-response associations with serum concentrations of PBB-153 and PBDE-153. Adjusted odds ratios across quartiles of serum concentrations for PBB-153 or PBDE-153 were 1.0, 0.7, 1.4, 1.6, and 1.9 ( P for trend <0.01) and 1.0, 1.6, 2.6, 2.7, and 1.8 ( P for quadratic term <0.01), respectively. PBB-153 was also positively associated with the prevalence of metabolic syndrome with adjusted odds ratios of 1.0, 1.5, 3.1, 3.1, and 3.1 ( P for trend<0.01). As in its association with diabetes, PBDE-153 showed an inverted U-shaped association with metabolic syndrome. CONCLUSIONS —Pending confirmation in prospective studies, lipophilic xenobiotics, including brominated POPs stored in adipose tissue, may be involved in the pathogenesis of diabetes and metabolic syndrome.

163 citations

Journal ArticleDOI
TL;DR: These findings highlight Fe4GeTe2 and its nanometer-thick crystals as a promising candidate for spin source operation at nearly room temperature and hold promise to further increase Tc in vdW ferromagnets by theory-guided material discovery.
Abstract: In spintronics, two-dimensional van der Waals crystals constitute a most promising material class for long-distance spin transport or effective spin manipulation at room temperature. To realize all-vdW-material–based spintronic devices, however, vdW materials with itinerant ferromagnetism at room temperature are needed for spin current generation and thereby serve as an effective spin source. We report theoretical design and experimental realization of a iron-based vdW material, Fe4GeTe2, showing a nearly room temperature ferromagnetic order, together with a large magnetization and high conductivity. These properties are well retained even in cleaved crystals down to seven layers, with notable improvement in perpendicular magnetic anisotropy. Our findings highlight Fe4GeTe2 and its nanometer-thick crystals as a promising candidate for spin source operation at nearly room temperature and hold promise to further increase Tc in vdW ferromagnets by theory-guided material discovery.

163 citations

Journal ArticleDOI
TL;DR: In this article, secondary alcohols (carbinols) react with primary alcohols in dioxane at 80 °C in the presence of a catalytic amount of RuCl2(PPh3)3 and KOH along with a sacrificial hydrogen acceptor.

163 citations

Journal ArticleDOI
TL;DR: Emerging evidence supporting the notion that extracellular HMGB1 acts as a proinflammatory danger signal is reviewed, and the potential therapeutic effects of a wide array ofHMGB1 inhibitors agents in sepsis and ischemic injury are discussed.
Abstract: High mobility group box 1 (HMGB1) is a highly conserved, ubiquitous protein present in the nuclei and cytoplasm of nearly all cell types. In response to infection or injury, HMGB1 is actively secreted by innate immune cells and/or released passively by injured or damaged cells. Thus, serum and tissue levels of HMGB1 are elevated during infection, and especially during sepsis. Sepsis is a systemic inflammatory response to disease and the most severe complication of infections, and HMGB1 acts as a potent proinflammatory cytokine and is involved in delayed endotoxin lethality and sepsis. Furthermore, the targeting of HMGB1 with antibodies or specific antagonists has been found to have protective effects in established preclinical inflammatory disease models, including models of lethal endotoxemia and sepsis. In the present study, emerging evidence supporting the notion that extracellular HMGB1 acts as a proinflammatory danger signal is reviewed, and the potential therapeutic effects of a wide array of HMGB1 inhibitors agents in sepsis and ischemic injury are discussed.

163 citations

Journal ArticleDOI
Ittai Dayan1, Holger R. Roth2, Aoxiao Zhong1, Ahmed Harouni2, Amilcare Gentili, Anas Z. Abidin2, Andrew Liu2, Anthony Costa3, Bradford J. Wood4, Chien-Sung Tsai5, Chih-Hung Wang5, Chun-Nan Hsu6, C. K. Lee2, Peiying Ruan2, Daguang Xu2, Dufan Wu1, Eddie Huang2, Felipe Kitamura7, Griffin Lacey2, Gustavo César de Antônio Corradi7, Gustavo Nino, Hao-Hsin Shin8, Hirofumi Obinata, Hui Ren1, Jason C. Crane9, Jesse Tetreault2, Jiahui Guan2, John Garrett10, Joshua D. Kaggie11, Jung Gil Park12, Keith J. Dreyer1, Krishna Juluru8, Kristopher Kersten2, Marcio Aloisio Bezerra Cavalcanti Rockenbach, Marius George Linguraru4, Marius George Linguraru13, Masoom A. Haider14, Masoom A. Haider15, Meena AbdelMaseeh14, Nicola Rieke2, Pablo F. Damasceno9, Pedro Mário Cruz e Silva2, Pochuan Wang16, Sheng Xu4, Shuichi Kawano, Sira Sriswasdi17, Soo-Young Park18, Thomas M. Grist10, Varun Buch, Watsamon Jantarabenjakul19, Watsamon Jantarabenjakul17, Weichung Wang16, Won Young Tak18, Xiang Li1, Xihong Lin1, Young Joon Kwon3, Abood Quraini2, Andrew Feng2, Andrew N. Priest11, Baris Turkbey4, Benjamin S. Glicksberg3, Bernardo Bizzo, Byung Seok Kim20, Carlos Tor-Díez4, Chia-Cheng Lee5, Chia-Jung Hsu5, Chin Lin5, Chiu-Ling Lai, Christopher P. Hess9, Colin B. Compas2, Deepeksha Bhatia2, Eric K. Oermann, Evan Leibovitz, Hisashi Sasaki, Hitoshi Mori, Isaac Yang2, Jae Ho Sohn9, Krishna Nand Keshava Murthy8, Li-Chen Fu16, Matheus Ribeiro Furtado de Mendonça7, Mike Fralick, Min Kyu Kang12, Mohammad Adil2, Natalie Gangai8, Peerapon Vateekul17, Pierre Elnajjar8, Sarah E Hickman11, Sharmila Majumdar9, Shelley McLeod15, Sheridan Reed4, Stefan Gräf11, Stephanie Harmon4, Tatsuya Kodama, Thanyawee Puthanakit17, Thanyawee Puthanakit19, Tony Mazzulli21, Tony Mazzulli15, Vitor Lavor7, Yothin Rakvongthai17, Yu Rim Lee18, Yuhong Wen2, Fiona J. Gilbert11, Mona Flores2, Quanzheng Li1 
TL;DR: In this article, the authors used federated learning to predict future oxygen requirements of symptomatic patients with COVID-19 using inputs of vital signs, laboratory data and chest X-rays.
Abstract: Federated learning (FL) is a method used for training artificial intelligence models with data from multiple sources while maintaining data anonymity, thus removing many barriers to data sharing. Here we used data from 20 institutes across the globe to train a FL model, called EXAM (electronic medical record (EMR) chest X-ray AI model), that predicts the future oxygen requirements of symptomatic patients with COVID-19 using inputs of vital signs, laboratory data and chest X-rays. EXAM achieved an average area under the curve (AUC) >0.92 for predicting outcomes at 24 and 72 h from the time of initial presentation to the emergency room, and it provided 16% improvement in average AUC measured across all participating sites and an average increase in generalizability of 38% when compared with models trained at a single site using that site’s data. For prediction of mechanical ventilation treatment or death at 24 h at the largest independent test site, EXAM achieved a sensitivity of 0.950 and specificity of 0.882. In this study, FL facilitated rapid data science collaboration without data exchange and generated a model that generalized across heterogeneous, unharmonized datasets for prediction of clinical outcomes in patients with COVID-19, setting the stage for the broader use of FL in healthcare. Federated learning, a method for training artificial intelligence algorithms that protects data privacy, was used to predict future oxygen requirements of symptomatic patients with COVID-19 using data from 20 different institutes across the globe.

162 citations


Authors

Showing all 20671 results

NameH-indexPapersCitations
Hyun-Chul Kim1764076183227
David R. Jacobs1651262113892
Yang Yang1642704144071
Yongsun Kim1562588145619
Jongmin Lee1502257134772
Inkyu Park1441767109433
Christopher George Tully1421843111669
Teruki Kamon1422034115633
Manfred Paulini1411791110930
Kazuhiko Hara1411956107697
Luca Lista1402044110645
Dong-Chul Son138137098686
Christoph Paus1371585100801
Frank Filthaut1351684103590
Andreas Warburton135157897496
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202375
2022317
20213,152
20203,071
20192,763
20182,664