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243 results on '"Kim, Byung J"'

13. Field evaluation of Digital Optical Method to quantify the visual opacity of plumes

Author

Du, Ke, Rood, Mark J., Kim, Byung J., Kemme, Michael R., Franek, Bill J., Mattison, Kevin, and Cook, Joan

Subjects
Digital photography -- Usage -- Optical properties -- Measurement, Plumes (Fluid dynamics) -- Optical properties -- Measurement -- Usage, Environmental services industry, Environmental issues, Science and technology
Abstract

ABSTRACT Visual Determination of the Opacity of Emissions from Stationary Sources (Method 9) is a reference method established by U.S. Environmental Protection Agency (EPA) to quantify plume opacity. However, Method [...]

Published
2007

16. Effect of substrate Henry's constant on biofilter performance

Author

Zhu, Xueqing, Suidan, Makram T., Pruden, Amy, Yang, Chunping, Alonso, Cristina, Kim, Byung J., and Kim, Byung R.

Subjects
Refuse and refuse disposal -- Research, Environmental services industry, Environmental issues, Science and technology, Research
Abstract

ABSTRACT Butanol, ether, toluene, and hexane, which have Henry's constants ranging from 0.0005 to 53, were used to investigate the effects of substrate solubility or availability on the removal of [...]

Published
2004

17. Removal of a volatile organic compound in a hybrid rotating drum biofilter

Author

Yang, Chunping, Suidan, Makram T., Zhu, Xueqing, and Kim, Byung J.

Subjects
Engineering and manufacturing industries, Environmental issues
Abstract

A hybrid bioreactor, combining an activated sludge process (ASP) and a rotating drum biofilter (RDB), was developed and evaluated for the treatment of volatile organic compounds (VOCs) in waste gas streams. The effects of the influent VOC concentration and the organic loading rate on the VOC removal efficiency and on the pattern of biomass accumulation were investigated. Toluene was used as the model VOC, the flow rate of the waste gas stream was 0.59 L/s, and the empty-bed retention time (EBRT) in the ASP portion was 46 s with an actual retention time of about 2 s. The EBRT in the RDB portion was 38 s based on the drum volume. When the VOC feed concentration increased from 221 to 884 mg toluene/[m.sup.3] (from 57.2 to 229 ppm), correspondingly the organic loading rate of the hybrid bioreactor increased from 1.58 to 6.32 kg chemical oxygen demand/[m.sup.3]/day (from 0.505 to 2.02 kg toluene/[m.sup.3]/day) based on the drum volume, both the ASP and RDB decreased, and the overall toluene removal efficiency declined from 99.8 to 74.1%. Biomass accumulation at different medium depths became more even when the organic loading rate was increased. Part of the applied VOC was biodegraded by the ASP, which suggests that this hybrid bioreactor could achieve longer runs between medium cleanings and higher VOC removal efficiencies than a single RDB bioreactor without an ASP portion at the same organic loading rate. CE Database subject headings: Activated sludge; Biofilm; Biological treatment; Biomass; Organic loads; Volatile organic chemicals; Abatement and removal; Filters.

Published
2004

22. Parameter estimation in biofilter systems

Author

Alonso, Cristina, Zhu, Xueqing, Suidan, Makram T., Kim, Byung R., and Kim, Byung J.

Subjects
Alcohol industry -- Research, Environmental issues, Science and technology, Ford Research Laboratory -- Research, University of Cincinnati -- Research
Abstract

Researchers from the Ford Research Laboratory, U.S. Army Construction Engineering Research Laboratory and the University of Cincinnati measured the unknown parameters of a mathematical biodegradation of volatile organic compounds (VOCs) model in a gas phase trickle-bed biofilter using results from a two-reactor pilot-scale system treating the VOC diethyl ether.

Published
2000

23. Model development

Author

Flora, Edna M.C.V., Suidan, Makram T., Flora, Joseph R.V., and Kim, Byung J.

Subjects
Nitrification -- Models, Microbial aggregation -- Models, Microbial mats -- Models, Engineering and manufacturing industries, Environmental issues
Abstract

Steady-state models of nitrifying biofilms are developed taking into account the mass transfer of neutral and ionic species, electroneutrality, pH-dependent Monod kinetics, chemical equilibrium, and the presence of a boundary layer. Due to the assumption of bulk conditions, the rigorous biofilm model often predicts the washout of one of the biofilm species involved in a series of sequential biofilm transformations such as nitrification. Coupling the rigorous bio film model with reactor mass balances yields the rigorous reactor model, which calculates the bulk conditions where both of the biofilm species remain and is thus more widely applicable. Substantial changes in the pH across the boundary layer and the biofilm are predicted; therefore, rates of nitrification may be improved by increasing bulk pH to high (alkaline) values, which results in optimum (neutral) pH within the biofilm. Varying influent conditions such as pH and buffer capacity can result in significant changes in reactor performance with respect to complete nitrification. The validity of the nitrification models are tested by comparison of profile predictions with that of experimental data, with encouraging results.

Published
1999

24. Sensitivity analysis

Author

Flora, Edna M.C.V., Suidan, Makram T., Flora, Joseph R.V., and Kim, Byung J.

Subjects
Nitrification -- Models, Microbial mats -- Models, Antitoxins -- Models, Engineering and manufacturing industries, Environmental issues
Abstract

Steady-state models of nitrifying biofilms have been developed that take into account the mass transfer of neutral and ionic species, electroneutrality, pH-dependent Monod kinetics, chemical equilibrium, and the presence of a boundary layer. Under the conditions investigated, the models predict significant changes in the biomass speciation and percent conversions in nitrifying biofilms as functions of pH, buffer capacity [in terms of total inorganic carbons], and, for the reactor models, volumetric flow rate per unit surface area of biofilm and total ammonium loading. To a lesser extent, the models also predict sensitivity to biofilm thickness, total ammonium, total nitrite, dissolved oxygen concentration, and total biomass density. Nitrogen conversions are only a very weak function of boundary layer thickness, total nitrate concentration, and the ratio of the chemical species' diffusivity in the biofilm to its diffusivity in water.

Published
1999

25. Model and analysis of belt drainage thickening

Author

Severin, Blaine F., Nye, Jeffrey V., and Kim, Byung J.

Subjects
Drainage -- Models, Sewage sludge -- Research, Engineering and manufacturing industries, Environmental issues
Abstract

A gravity drainage model was developed that included terms for cake resistance, filter resistance, and initial ponding. Laboratory scale batch drainage tests were performed at sludge volume loadings between 0.0 13 and 0.006 [m.sup.3]/[m.sup.2]. Results were compared with limits established from the model for ponded and nonponded sludge drainage At low volumetric loadings, the series of drainage curves represented a smooth family of curves with increased drainage rates at the volume was increased. However, at higher volumetric loadings, a discontinuity in the family of curves was observed in which the rate of filtrate flow decreased with an increase in sludge volume. The change in the shape of the filtrate curves corresponded with the observance of standing water on top of the forming sludge cake, or ponding. The effects of plowing during drainage were investigated. One effect of plows was to diminish the effects of ponding, noted as an increased drainage rate, for sludge loading conditions that had a tendency to pond in the absence of plows. A second effect was to cause the release of more water from sludges that were not prone to ponding than that seen in the absence of plows.

Published
1999

27. Dynamic mathematical model for the biodegradation of VOCs in a biofilter: biomass accumulation study

Author

Alonso, Cristina, Suidan, Makram T., Kim, Byung R., and Kim, Byung J.

Subjects
Volatile organic compounds -- Environmental aspects, Environmental monitoring -- Models, Environmental issues, Science and technology
Abstract

Mathematical modeling of processes in a packed trickle-bed air biofilter shows that a drop in removal efficiency may be observed when the amount of biomass accumulates in the reactor and causes a drop in the diffusion rate. Three reactor porous medium models were evaluated and results show that spheres and pipes were proven superior over parallel plates in explaining volatile organic compound removal rates and biofilter system performance.

Published
1998

28. Bottle-in-bag for gas sampling with analysis by autoheadspace GC

Author

Brixie, Julia M., Neilson, Leslie M., Kim, Byung J., and Severin, Blaine F.

Subjects
Air quality -- Analysis, Air quality management -- Methods, Air sampling apparatus -- Innovations, Air pollution -- Control, Environmental issues, Science and technology
Abstract

An innovation in gas sampling and analysis has been developed in the bottle-in-bag technique that has been proven to be accurate, effective and easy to use. The gas sampling technique relies on a headspace vial placed inside a sealable bag. Gas is pumped into the bag, and the headspace vial is closed through the bag. Subsequent samplings will use new bags and vials. The bottle-in-bag has been found to outperform the Tedlar bag and manual gas syringe gas collection methods.

Published
1997

29. Microcomputer model for design and location of runway exits

Author

Hobeika, Antoine G., Trani, Antonio A., Sherali, Hanif D., and Kim, Byung J.

Subjects
Airports -- Runways, Air traffic control, Airplanes -- Dispatching, Engineering and manufacturing industries, Science and technology, Transportation industry
Abstract

An interactive microcomputer program referred to as runway exit design interactive model (REDIM) has been developed to optimally locate and design high-speed runway exits at airports. The model uses kinematic equations to characterize the aircraft landing dynamics and a polynomial-time dynamic programming algorithm to find the optimal locations of the high-speed exits. The objective of the optimization algorithm is to minimize the weighted average runway occupancy time (ROT) of an aircraft mix selected by the user. A data base of aircraft characteristics for four terminal airspace procedure (TERP) categories has been included in the model to facilitate its use in a large variety of airport environments and conditions. The data-base file relieves the user's data-input burden, specially when the aircraft mix consists of many different types of aircraft. The program is developed to be used as a design and a planning instrument by engineers. Hence, considerable effort has been devoted to user interface such as menu system, interactive data editing, and graphical and tabular outputs.

Published
1993

46. MATHEMATICAL MODEL OF BIOFILTRATION OF VOCs: EFFECT OF NITRATE CONCENTRATION AND BACKWASHING

Author

Alonso, Christina, Zhu, Xueqing, Suidan, Makram T., Kim, Byung R., and Kim, Byung J.

Subjects
Biodegradation -- Analysis, Environmental engineering -- Research, Mathematical models -- Usage, Volatile organic compounds -- Waste management, Filtration -- Analysis, Engineering and manufacturing industries, Environmental issues
Abstract

The affect of nitrate concentration and reactor backwashing on biofilter performance is evaluated using a dynamic mathematical model of the biodegradation process of volatile organic compounds in a trickle bed biofilter packed with uniform synthetic solids. Experimental observations from a bench-scale biofilter system treating ether were used to develop and validate the model. Experience acquired in biofiltration of volatile organic compounds has demonstrated that although these two factors--nitrate and backwashing--are secondary when organic packing material is used, they are essential when the packing media is synthetic. The operation of a synthetic media packed reactor requires the addition of nutrients necessary for biodegradation. Since nitrate was utilized as the nitrogen source in this system, it was included in the model as a limiting substrate (nutrient). A negative affect of excessive accumulation of biomass in the reactor on biofilter performance has also been observed in highly loaded synthetic media biofilters. This problem was solved by removing excessive biomass via full media fluidization and backwashing of the reactor. The affect of periodic backwashing was included in the model as a reduction in the biofilm thickness and a new approach to calculate the reactor specific surface area after backwashing. The unknown model parameters that correspond to nitrate limitations were estimated. The mathematical model was then used for simulation and analyses of the affect of these two factors on the biodegradation process.

Published
2001

48. Biodistribution and Clearance of Stable Superparamagnetic Maghemite Iron Oxide Nanoparticles in Mice Following Intraperitoneal Administration.

Author

Pham, Binh T. T., Colvin, Emily K., Pham, Nguyen T. H., Kim, Byung J., Fuller, Emily S., Moon, Elizabeth A., Barbey, Raphael, Yuen, Samuel, Rickman, Barry H., Bryce, Nicole S., Bickley, Stephanie, Tanudji, Marcel, Jones, Stephen K., Howell, Viive M., and Hawkett, Brian S.

Subjects
MAGHEMITE, NANOMEDICINE, NANOPARTICLES, SUPERPARAMAGNETIC materials, IRON oxide nanoparticles, PRUSSIAN blue, ATOMIC absorption spectroscopy
Abstract

Nanomedicine is an emerging field with great potential in disease theranostics. We generated sterically stabilized superparamagnetic iron oxide nanoparticles (s-SPIONs) with average core diameters of 10 and 25 nm and determined the in vivo biodistribution and clearance profiles. Healthy nude mice underwent an intraperitoneal injection of these s-SPIONs at a dose of 90 mg Fe/kg body weight. Tissue iron biodistribution was monitored by atomic absorption spectroscopy and Prussian blue staining. Histopathological examination was performed to assess tissue toxicity. The 10 nm s-SPIONs resulted in higher tissue-iron levels, whereas the 25 nm s-SPIONs peaked earlier and cleared faster. Increased iron levels were detected in all organs and body fluids tested except for the brain, with notable increases in the liver, spleen, and the omentum. The tissue-iron returned to control or near control levels within 7 days post-injection, except in the omentum, which had the largest and most variable accumulation of s-SPIONs. No obvious tissue changes were noted although an influx of macrophages was observed in several tissues suggesting their involvement in s-SPION sequestration and clearance. These results demonstrate that the s-SPIONs do not degrade or aggregate in vivo and intraperitoneal administration is well tolerated, with a broad and transient biodistribution. In an ovarian tumor model, s-SPIONs were shown to accumulate in the tumors, highlighting their potential use as a chemotherapy delivery agent. [ABSTRACT FROM AUTHOR]

Published
2018
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49. Ticagrelor in patients with diabetes and stable coronary artery disease with a history of previous percutaneous coronary intervention (THEMIS-PCI): a phase 3, placebo-controlled, randomised trial

Author

Bhatt, Deepak L, Steg, Philippe Gabriel, Mehta, Shamir R, Leiter, Lawrence A, Simon, Tabassome, Fox, Kim, Held, Claes, Andersson, Marielle, Himmelmann, Anders, Ridderstråle, Wilhelm, Chen, Jersey, Song, Yang, Diaz, Rafael, Goto, Shinya, James, Stefan K, Ray, Kausik K, Parkhomenko, Alexander N, Kosiborod, Mikhail N, McGuire, Darren K, Harrington, Robert A, Santos, Vladimir, Jain, Ashit, Lendel, Irina, Russo, Michael, Haught, W H, Bouza, Manuel, Gogia, Harinder, Banerjee, Supratim, Kichura, George, Kantaros, Louis, Padron, Francisco, Passi, Rakesh, Stone, Jay, Pursley, Michael, D'Urso, Michael, Gardner, Timothy, Bennett, James, Nour, Khaled, Saini, Satinder, Zhang, Wenwu, Kumbhani, Dharam, Thomas, Dustin, Angiolillo, Dominick, Bertolet, Barry, Roman-Miranda, Amaury, Black, Robert, Manshadi, Ramin, Vaca, Carlos, Blanco, Antonio, Napoli, Mark, Brabham, David, Akyea-Djamson, Ayim, Desai, Pratik, Prasada, Sudhir, Khaira, Ajit, Forgosh, Leslie, Lieber, Ira, Umpierrez, Guillermo, Singal, Dinesh, Londono, Juan, Fraser, Neil, Ruiz, Jose, Vega, Damaris, Rodriguez, Lilia, Brown, Christopher, Syed, Faizullah, Aggarwala, Guarav, Eaves, William, Foster, Michael, Gupta, Dinesh, Avino, David, Asfour, Wail, Tonnessen, Glen, Zhao, Xue-Qiao, Singh, Narendra, Brockmyre, Andrew, Lepor, Norman, Shammas, Nicolas, Blick, David, Hearne, Steven, Prodafikas, John, Carell, Edgar, Izzo, Mark, Karim, Amin, Zakhary, Bosh, Atieh, Mahmoud, Leichter, Steven, Meadows, Charles, Hotchkiss, David, Abu-Fadel, Mazen, Wiseman, Alan, Bander, Jeffrey, Shah, Mahesh, Banerjee, Subhash, Ganim, Ricky, Sopko, Karen, Khan, Misal, Lloret, Ramon, Weirick, Troy, Mehta, Rajendra, Thadani, Udho, Bhargava, Anuj, Kosiborod, Mikhail, Moya, Jaynier, Staniloae, Cezar, Guerra, Yamirka D, Chhabra, Anil, Kosmicki, Douglas, Shaheen, Wassim, Mohammed, Akber, Bitters, J'Cinda, Pattanayak, Jan, Javier, Julian, Srivastava, Sunny, Phillips, Roland, Al-Amin, Jessie, Lillestol, Michael, Simpson, Patrick, Hazan, Lydie, Amin, Amit, Shah, Gopi, Korpas, Denes, Platt, Bruce, Dickert, Jim, Puente, Orlando, Hiotis, Louis, Doyle, Timothy, Rajan, Raj, Meholick, Alan, Gring, Christian, Hage-Korban, Elie, Feldman, Robert, Colfer, Harry, Butman, Samuel, Foster, Malcolm, Hart, Terence, Huling, Randall, Eshaghian, Shervin, Quintana, Ofsman, Cheung, Deanna, Handel, Franklin, Rodriguez, Mara, Suh, David, Gordon, Paul, Pressman, Gregg, Bauer, Michael, French, William, Barettella, Mark, Chatrathi, Sridhar, Suresh, Damodhar, Goldberg, Ronald, Huth, Mark, Younis, Liwa, Rahman, Aref, Mascolo, Richard, Welch, Michelle, Suneja, Randeep, Smith, Stephen, Shurmur, Scott, Agaiby, John, Jingo, Ahmad, Johnston, Janice, Beth, Mary, Vlastaris, Anthony, Kemp, Susan, Taheri, Hamid, Pereira, Edward, Deyoung, Michael, Hawa, Zafir, Smith, Ray, Galski, Thomas, Garas, Samer, Reddy, M, Sharma, Susheel, Hargrove, Joeseph, Treasure, Charles, Emerson, Ronald, Haddad, Tariq, Rohr, Kathryn, Levinson, Larry, Gaona, Raul, Uretsky, Barry, Maheshwari, Hiralal, Lee, Denny, Kinnaman, Stephanie, Singal, Robert, Geohas, Jeffrey, Gigliotti, Osvaldo, Raisinghani, Ajit, Khurana, Charanjit, Hella, Brent, Kelberman, Michael, Voyce, Steven, Singh, Sanjay, Lo, Eric, Singh, Pradeep, Goodfellow, Ross, Fischer, Stuart, Lorraine, Richard, Turner, Traci, Shanes, Jeffrey, Busch, Robert, Broker, Robert, Zaniewski, Michelle, Pounds, Kevin, Debs-Perez, Giselle, Ong, Stephen, Frandsen, Brad, Fullington, Douglas, Jaffrani, Naseem, Khan, Ahtaram, Lee, Marcus, Pouzar, Joe, Revtyak, George, Gonzalez, Julian, Nakhle, Samer, Murillo, Abel, Young, Douglas, Makam, Sashi, Syed, Mushtaq, Woolf, Kevin, Grena, Paul, Alfata, Sarab, Mahal, Sharan, Hoffman, David, Kizhakekuttu, Tinoy, Deering, Joseph, Bhavsar, Janak, Mikesell, Scott, Wilson, William, Wilson, Vance, El, Salah, Spinale, Francis, Kannarkat, Vinod, Rao, Sunder, Hanson, Lenita, Bertsch, John, Gonzalez-Ortiz, Elena, Severino, Norma, Willis, John, Schock, Joel, Bakhtari, Ladan, Gazmuri, Raul, Ansari, Saadat, Hall, Jason, Mehta, Arvind, Shealy, Neal, Zarich, Stuart, Singh, Deovrat, Vora, Kishor, Andrawis, Nabil, Molter, Darron, Maron, David, Cardona, Jose, D'Agostino, Ronald, Arshad, Tamjeed, Samaan, Rodney, Jones, David, Presser, Dale, Heath, John, Green, Sandy, Bittar, George, Henry, Sheldon, Korn, David, Schmedtje, John, Nadar, Venkatesh, Graham, Bruce, Labroo, Ajay, Clavijo, Leonardo, Roseman, Hal, Ledesma, Gilbert, Rosen, Robert, Dor, Isaac, Kirby, William, Sutton, Jennefer, Eder, Frank, Iteld, Bruce, Gomez-Cortes, Jose, Buchbinder, Maurice, Kasper, Joseph, Terrelonge, Antonio, Torres, Gustavo, Jagielo, Ted, Alvarez, Jose, Handelsman, Yehuda, Guillen, Mario, Richwine, Randall, Lewy-Alterbaum, Lorena, Corder, Clinton, Arvind, Moogali, Bolshoun, David, Mikhail, Magdy, Minton, Stephen, Alvarado, Odilon, Abbott, J, Cauthen, Brett, Welter, Ryan, Mintz, Randy, Cox, John, Quick, Annette, Weiss, Melvin, Dy, Johnny, Zebrack, James, Gandelman, Glenn, Hegde, Vinayak, Silver, Marc, DeGregorio, Michele, Lawson, William, Paa, Christopher, Bortnick, Anna, Krolick, Merrill, Sotolongo, Rodolfo, Cheirif, Jorge, Kumar, Priya, Nadar, Venkatesh, Jetty, Preetham, Patel, Ambar, Kruk, Mariusz, Kobielusz-Gembala, Iwona, Rewerska, Barbara, Madrzejewski, Adam, Milewski, Krzysztof, Cygler, Jerzy, Petryka-Mazurkiewicz, Joanna, Jastrzebski, Waldemar, Korecki, Janusz, Fil, Wojciech, Prokopczuk, Janusz, Bochenek, Anna, Wujkowski, Marek, Witek, Robert, Konczakowski, Piotr, Miekus, Pawel, Szczasny, Marcin, Musial, Wlodzimierz, Cymerman, Krzysztof, Lampart, Jacek, Mikosinski, Jacek, Szynal, Slawomir, Fares, Issa, Opolski, Grzegorz, Mazur, Stanislaw, Wozakowska-Kaplon, Beata, Bijata-Bronisz, Renata, Wierucki, Lukasz, Losa, Beata, Drelich, Grzegorz, Konieczny, Marek, Starczewski, Pawel, Pawlowicz, Lidia, Jesionowski, Pawel, Jurowiecki, Jaroslaw, Gniot, Jacek, Czyzycki, Mariusz, Stania, Karol, Kucharczyk-Bauman, Izabela, Busz-Papiez, Benita, Karczmarczyk, Agnieszka, Sudnik, Wanda, Koszek, Alina, Kolodziej, Piotr, Skwarna, Bartosz, Jaramillo, Nicolás, Jankowski, Maciej, Czochra, Wojciech, Kinasz, Leszek, Miklaszewicz, Beata, Stasinska, Teresa, Pluta, Wladyslaw, Basiak, Marcin, Rusicka, Teresa, Niedbal-Yahfouf, Izabela, Popenda, Grazyna, Korzeniak, Romuald, Mirek, Agnieszka, Mariankowski, Rafal, Wojnowski, Lukasz, Korol, Marek, Baszak, Jacek, Podolec, Piotr, Piesiewicz, Wojciech, Zurakowski, Aleksander, Luengas, Carlos, Skura, Marek, Pilecki, Piotr, Majchrzak, Piotr, Krzyzagórska, Ewa, Drozd, Marcin, Kaczmarek, Barbara, Sliwinska, Teresa, Zelazowska, Katarzyna, Sztembis, Rafal, Landa, Katarzyna, Matyszczak-Toniak, Lidia, Strojek, Krzysztof, Piepiorka, Marek, Malinowski, Robert, Górska, Maria, Stolarczyk-Sowa, Edyta, Romanowski, Leszek, Zinka, Elzbieta, Reszka, Zygfryd, Skierkowska, Joanna, Uzunow, Anna, Laskowska-Derlaga, Ewa, Puntus, Ekaterina, Kosmacheva, Elena D, Koziolova, Natalia, Pavlov, Prokhor, Supryadkina, Tatiana, Didenko, Yury, Kopylov, Philipp, Kazakov, Andrei, Aksentiev, Sergei, Vishneva, Elena, Repin, Alexey, Smolenskaya, Olga, Mantserova, Olga, Khrustalev, Oleg, Privalova, Elena, Konstantinov, Vladimir, Boldueva, Svetlana, Ezhov, Andrey, Chernyavsky, Alexander, Kamalov, Gadel, Galyavich, Albert, Zubeeva, Galina, Nechaeva, Galina, Shustov, Sergey, Dzhaiani, Nino, Treshkur, Tatiana, Osokina, Nataliya, Panov, Alexey, Shutemova, Elena, Makukhin, Valeriy, Kropotina, Tatiana, Tsyba, Larisa, Karpov, Yuri, Sizova, J M, Ballyuzek, Marina, Tarasov, Nikolay, Demchenko, Elena, Barbarash, Olga, Moiseev, Valentin, Markov, Valentin, Kuznetsov, Vadim, Viktorova, Inna, Sergienko, Igor, Ermoshkina, Lyudmila, Khasanov, Niyaz, Khlevchuk, Tatiana, Baglikov, Andrey, Shalaev, Sergey, Zonova, Elena, Reznik, Elena, Haisheva, Larisa, Morugova, Tatyana, Lomakin, Nikita, Vishnevsky, Alexander, Shvarts, Yuri, Magnitskaya, Olga, Mikhailusova, Marina, Pavlysh, Elena, Libov, Igor, Zateyschikova, Anna, Kostenko, Victor, Edin, Anton, Khovaeva, Yaroslava, Zakharov, Konstantin, Stryuk, Raisa, Khirmanov, Vladimir, Kanorskiy, Sergey, Yakushin, Sergey, Barabashkina, Anna, Li, Hongwei, Zhao, Qiang, Zhang, Jian, Ma, Jianhua, He, Yong, Luo, Ming, Zhang, Aidong, Zhang, Ningru, Chai, Yingru, Ma, Genshan, Wang, Hao, Liu, Zhigang, He, Lanjie, Song, Zhifang, Dong, Xiaolin, Tao, Liang, Li, Zhanquan, Su, Xi, Kong, Xiangqing, Niu, Heping, Ge, Junbo, Luo, Zhurong, Huang, Wenjun, Peng, Daoquan, Yuan, Zuyi, Milanova, Maria, Tenev, Doncho, Gogov, Antoni, Karageorgiev, Dimitar, Kolchev, Todor, Rusev, Nikolay, Georgieva, Nikolina, Kondov, Rumen, Rusinov, Veselin, Petrov, Ivo, Stanchev, Georgi, Konteva, Mariana, Dincheva, Adriana, Yaneva, Zdravka, Vatova, Ralitsa, Ilieva, Katya, Runev, Nikolai, Kolomanov, Borislav, Petrov, Ivan, Iliev, Nikolay, Tisheva, Snezhanka, Chompalova, Boryana, Tokmakova, Maria, Raev, Dimitar, Byanov, Kolyo, Markov, Dimitar, Mihov, Lenko, Mihov, Atanas, Milcheva, Nora, Minchev, Milen, Mollov, Mihail, Borisov, Borislav, Tihchev, Tihomir, Karakolev, Venelin, Dimov, Bojidar, Georgiev, Svetoslav, Smilov, Lachezar, Koo, Bon K, Ahn, Taehoon, Hong, Soon J, Yoon, Junghan, Oh, Seok K, Jeong, Myung H, Kim, Doo-Il, Chang, Kiyuk, Kim, Weon, Hahn, Joo-Yong, Cha, Kwang S, Lee, Jung-Hee, Choi, Si-Wan, Nam, Chang-Wook, Chae, In-Ho, Park, Yong H, Tahk, Seung-Jea, Shin, Won-Yong, Chae, Jei-Keon, Kim, Byung J, Bae, Jang-Whan, Park, Woo J, Rha, Seung W, Choi, Young J, Hwang, Jin-Yong, Park, Hun S, Baracioli, Luciano, Guimaraes, Fabio, Vasconcellos, Eduardo, Saraiva, José, Pereira, Adamastor, Santos, Queulla, Rossi, Paulo, Maia, Lilia, Madeira, Miguel, Pereira, Marcio, Botelho, Roberto, Reis, Gilmar, Eliaschewitz, Freddy, Borges, Joao, Nascimento, Carlos, Fortes, Jose A, de Souza, Weimar, Pimentel, Pedro, Hissa, Miguel, Franchetti, Marcos, Precoma, Dalton, Ortiz, Costantino, Hernandes, Mauro, Saporito, Wladmir, dos Santos, Fabio R, Kormann, Adrian, Neuenschwander, Fernando, Dutra, Oscar, Rassi, Nelson, Tanajura, Luiz, Souza, Juliana, Junior, Delcio S, Leaes, Paulo, Forte, Adriana, Bonansea, Teresa C, Marin, Jose, Machado, Bruno, Cerqueira, Maria J, Silva, Frederico, Michalaros, Yorghos, Manenti, Euler, Cercato, Cintia, Figueiredo, Estevao, Liu, Ming-En, Wang, Yi-Chih, Lee, Tsung-Ming, Fang, Chih, Wu, Yen-Wen, Ueng, Kwo-Chang, Sheu, Huey-Herng, Lai, Wen T, Hsieh, I-Chang, Chen, Zhih-Cherng, Lee, Mu-Jang, Chiang, Chern, Shyu, Kou-Gi, Hsia, Chien-Hsun, Mar, Guang-Yuan, Chan, Shih-Hung, Wu, Chih-Cheng, Tseng, Wei-Kung, Chang, Kuan-Cheng, Yeh, Hung-I, Wang, Ji-Hung, Hou, Charles, Sorokina, Inna, Dolzhenko, Maryna, Horoshko, Olha, Karpenko, Oleksandr, Rudenko, Leonid, Vakaliuk, Igor, Kulyk, Anna, Levchenko, Olena, Prokhorov, Oleksandr, Reshotko, Dmytro, Sorokivskyy, Mykhaylo, Velichko, Nataliia, Maslovskyi, Valentyn, Teliatnikova, Zinaida, Dotsenko, Sergiy, Krakhmalova, Olena, Kraiz, Igor, Zharinova, Viktoria, Bula, Larysa, Kaydashev, Igor, Molodtsov, Valeriy, Rasputina, Lesya, Pidlisna, Viktoriya, Lysunets, Olena, 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Anders, Leonsson-Zachrisson, Maria, and Ridderstråle, Wilhelm

Abstract

Patients with stable coronary artery disease and diabetes with previous percutaneous coronary intervention (PCI), particularly those with previous stenting, are at high risk of ischaemic events. These patients are generally treated with aspirin. In this trial, we aimed to investigate if these patients would benefit from treatment with aspirin plus ticagrelor.

Published
2019
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