Clinical Significance of Reverse Redistribution on Tc-99m MIBI and T1-201 Myocardial Perfusion SPECT Images (Tc-99m MIBI와 T1-201 심근 SPECT에서 역재분포의 임상적 의의)
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- The Korean Journal of Nuclear Medicine
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- v.30 no.1
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- pp.95-103
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- 1996
Reverse redistribution(RRD) refers to a perfusion defect that develops or becomes more evident on rest imaging compared with the stress imaging. This phenomenon was not uncommonly noted on myocardial perfusion single photon emission computed tomography (SPECT). However, the clinical significance and pathophysiological mechanism of RRD were unclear. The aim of this study was to evaluate the incidence and clinical significance of RRD on either dipyridamole T1-201 or Tc-99m MIBI myocardial perfusion SPECT. RRD was defined as
To investigate the alteration of transferrin receptor (TfR) in the proliferating or transformed liver cells,
The purpose of this study was to compare the intake status of calcium, phosphorus, iron, and zinc of Korean adults residing in different regions. Subjects were recruited and divided into three groups according to the districts where they lived, which included rural (n=137), coastal (n=100), and urban district (n=117). Subjects were interviewed using a general questionnaire and 24-hour recall method for dietary intake. The average age of the subjects were 58.1 years for rural district, 57.7 years for coastal district, and 48.6 years for urban district. There was no significance in total food intake by regions. The food intakes from cereals, mushrooms, vegetables of rural district, that from fishes of coastal district, and those from sugars, milks, oils of urban area were the highest among three districts. The calcium, phosphorus, iron, and zinc intakes were
Appreciable radiation exposures certainly were occurred in the reactor burn-up, the nuelear fall-out and the surroundings of nuclear installations with radioactive effluents. Therefore, radioactive nuclides is not only potentially hazardous to workers of nuclear power plants and related industrials, but also the wokers who handle radioactive nuclides in biochemical research and nuclear medicine diagnostics. And in the case of occurring the nuclear accidents, the early medical treatment of radiation injury should be necessary but little is established medical procedures to decontaminate the victims of internal contamination of radioactive nuclides in korea. Accordingly, to achieve the basic data for protective roles and medical treatment of radiation injury, the present studies were carrid out to evaluate the decontamination of uranium by the chemical drugs. The results observed were summarized as follows: 1. The combined treatmet group of sodium bicarbonate and saline with uranyl nitrate injection simultaneously and the dithiothreitol group that was administered 30 minutes after uranyl nitrate injection were increased significantly in the change of body weight than uranyl nitrate-only group (P<0.005). 2. All the experimental groups were increased the fluid intake and urine volume on the uranyl nitrate-induced acute renal failure. but the combined treatment group of sodium bicarbonate and saline with uranyl nitrate injection simultaneously and the dithiothreitol group that was administered 30 minutes after uranyl nitrate injection have the higher increment of fluid intake and urine volume (P<0.05). 3. When sodium bicarbonate and saline was treated with uranyl nitrate injection simultaneously. and dithiothreitol was administered 30 minutes after uranyl nitrate injection. there was significantly reduced in BUN concentration (P<0.01). 4. When dithiothreitol was administered 30 minutes after uranyl nitrate injection. there was reduced more significantly on the increment of serum creatinine concentration than that observed in uranyl nitrate-only group(P<0.01). but when the combined treatment of sodium bicarbonate and saline with uranyl nitrate simultaneously, there was still. albeit much less marked. decrease in serum creatinine concentration. 5. The sodium bicarbonate and saline was treated with uranyl nitrate simultaneously and dithiothreitol was administered at 30 minutes after uranyl nitrate were excreted markedly higher urine creatinine concentration than the uranyl nitrate-only group. 6. Uranyl nitrate has been used in experimental animals to produce hydropic degeneration and swelling of proximal tubules, disappearance of microvilli and brush border or necrosis in the kidney and centrilobular necrosis, congestion, and telangiectasia of the liver. When the sodium bicarbonate and saline was treated with uranyl nitrate simultaneously, and dithiothreitol was administered. 30 minutes after uranyl nitrate, there was more marked the protective effect than uranyl nitrate-only group. Finally, if the sodium bicarbonate and saline may administered as quickly as possible each time that some risk for internal contamination, with uranium, and dithiothreitol is administered 30 minutes after uranium contamination, there ameliorates the course of uranyl nitrate-induced acute renal failure.and this effect is assocciated with prevention of uranium (heavy metal)-induced alterations in BUN, serum creatinine, urine creatinine, fluid intake, urine volume and body weight.
Recommender system has become one of the most important technologies in e-commerce in these days. The ultimate reason to shop online, for many consumers, is to reduce the efforts for information search and purchase. Recommender system is a key technology to serve these needs. Many of the past studies about recommender systems have been devoted to developing and improving recommendation algorithms and collaborative filtering (CF) is known to be the most successful one. Despite its success, however, CF has several shortcomings such as cold-start, sparsity, gray sheep problems. In order to be able to generate recommendations, ordinary CF algorithms require evaluations or preference information directly from users. For new users who do not have any evaluations or preference information, therefore, CF cannot come up with recommendations (Cold-star problem). As the numbers of products and customers increase, the scale of the data increases exponentially and most of the data cells are empty. This sparse dataset makes computation for recommendation extremely hard (Sparsity problem). Since CF is based on the assumption that there are groups of users sharing common preferences or tastes, CF becomes inaccurate if there are many users with rare and unique tastes (Gray sheep problem). This study proposes a new algorithm that utilizes Social Network Analysis (SNA) techniques to resolve the gray sheep problem. We utilize 'degree centrality' in SNA to identify users with unique preferences (gray sheep). Degree centrality in SNA refers to the number of direct links to and from a node. In a network of users who are connected through common preferences or tastes, those with unique tastes have fewer links to other users (nodes) and they are isolated from other users. Therefore, gray sheep can be identified by calculating degree centrality of each node. We divide the dataset into two, gray sheep and others, based on the degree centrality of the users. Then, different similarity measures and recommendation methods are applied to these two datasets. More detail algorithm is as follows: Step 1: Convert the initial data which is a two-mode network (user to item) into an one-mode network (user to user). Step 2: Calculate degree centrality of each node and separate those nodes having degree centrality values lower than the pre-set threshold. The threshold value is determined by simulations such that the accuracy of CF for the remaining dataset is maximized. Step 3: Ordinary CF algorithm is applied to the remaining dataset. Step 4: Since the separated dataset consist of users with unique tastes, an ordinary CF algorithm cannot generate recommendations for them. A 'popular item' method is used to generate recommendations for these users. The F measures of the two datasets are weighted by the numbers of nodes and summed to be used as the final performance metric. In order to test performance improvement by this new algorithm, an empirical study was conducted using a publically available dataset - the MovieLens data by GroupLens research team. We used 100,000 evaluations by 943 users on 1,682 movies. The proposed algorithm was compared with an ordinary CF algorithm utilizing 'Best-N-neighbors' and 'Cosine' similarity method. The empirical results show that F measure was improved about 11% on average when the proposed algorithm was used