11 research outputs found

    SlimFTL: a Small and Fast Page-level FTL using Hash Functions

    Get PDF
    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ์ปดํ“จํ„ฐ๊ณตํ•™๋ถ€, 2020. 8. ๊น€์ง€ํ™.As the capacity of an SSD increases, the amount of DRAM for managing the SSD increases proportionally. Since the DRAM cost directly affects the overall SSD price, it is important to minimize the DRAM size without degrading the SSD performance. In this paper, we propose a novel hash-based FTL mapping technique, SlimFTL, that meets this goal. SlimFTL overcomes the GC inefficiency problem of an existing hash-based FTL in two directions. By employing an efficient indirection layer between the logical page and its hashed physical block, SlimFTL reduces the block copy overhead during GC. SlimFTL exploits the spatial sequentiality among successive writes so that sequential writes can be mapped to the same physical block, which significantly reduces the number of valid copies during GC. Experimental results show that SlimFTL can achieve the same performance level of a page-level mapping scheme with only 44% of the DRAM capacity.Chpater 1. Introduction 1 1.1 Motivation 1 1.2 Contribution 3 1.3 Thesis Structure 6 Chapter 2. Background 7 2.1 Overview of Hash-based FTL 7 2.2 Existing Hash-based FTL 10 2.3 Evaluation Result of HPFTL 13 Chapter 3 SlimFTL 16 3.1 Overview of SlimFTL 16 3.2 Hash-based Mapping Table 18 3.3 Sequentiality-Aware Hasher 21 3.4 Hash Collision Handler 24 3.5 Garbage Collection 26 Chapter 4 Experiments 27 4.1 Experimental Setup 27 4.2 Experimental Results 29 Chapter 5 Related Works 34 5.1 Related Works 34 Chapter 6 Conclusions 36 6.1 Summary 36 6.2 Future Work 37 Bibliography 38 ์ดˆ๋ก 41Maste

    Feasibility of Coronary Artery Calcium Scoring on Dual-Energy Chest Computed Tomography: A Prospective Comparison with Electrocardiogram-Gated Calcium Score Computed Tomography

    Get PDF
    Rationale and Objectives: This study aimed to evaluate the feasibility of assessment using the coronary artery calcium score (CACS) in dual-energy chest computed tomography (CT). Materials and Methods: We prospectively enrolled 30 patients (19 male, 11 female; mean age, 63.73 ยฑ 9.40 years) who clinically required contrast-enhanced chest CT. The patients underwent electrocardiogram-gated cardiac calcium-scoring CT with a slice thickness of 2.5 mm followed by a sequentially non-gated contrast-enhanced dual-energy chest CT using 140/80 fast kVp switching technology with slice thicknesses of 1.25 mm and 2.5 mm. Virtual unenhanced (VUE) images were then reconstructed from the dual-energy CT using the material suppressed iodine (MSI) technique. Results: The mean heart rates were 63.33 ยฑ 12.01 beats per minute. The mean CACS on the coronary calcium-scoring CT was 361.1 ยฑ 435.5, and CACSs of the VUE images were 76.8 ยฑ 128.6 (2.5 mm slice) and 108.7 ยฑ 165.1 (1.25 mm slice). The correlation coefficients of CACS between the coronary calcium-scoring CT with the VUE 2.5 mm and 1.25 mm images were 0.888 and 0.904, respectively. The inter-observer agreements for the calcium score measurement between the calcium-scoring CT, VUE 2.5 mm, and VUE 1.25 mm were 1.000, 0.999, and 1.000, respectively. Conclusions: In conclusion, assessment of CACS using dual-energy chest CT might be feasible when using MSI virtual unenhanced dual-energy chest CT images with a slice thickness of 1.25 mm.ope

    ๋น„ ์กฐ์˜์ฆ๊ฐ• ํ‰๋ถ€ CT์—์„œ ๊ด€์ƒ๋™๋งฅ ์นผ์Š˜์Šค์ฝ”์–ด ์ธก์ •์„ ์œ„ํ•œ 16 cm ์ถ•์ƒ ์ดฌ์˜ ๊ธฐ๋ฒ•์˜ ์œ ์šฉ์„ฑ: ์ „ํ–ฅ์  ํƒ์ƒ‰์  ์—ฐ๊ตฌ

    Get PDF
    Purpose This study aimed to evaluate the utility of the 16-cm axial volume scan technique for calculating the coronary artery calcium score (CACS) using non-enhanced chest CT. Materials and Methods This study prospectively enrolled 20 participants who underwent both, non-enhanced chest CT (16-cm-coverage axial volume scan technique) and calcium-score CT, with the same parameters, differing only in slice thickness (in non-enhanced chest CT = 0.625, 1.25, 2.5 mm; in calcium score CT = 2.5 mm). The CACS was calculated using the conventional Agatston method. The difference between the CACS obtained from the two CT scans was compared, and the degree of agreement for the clinical significance of the CACS was confirmed through sectional analysis. Each calcified lesion was classified by location and size, and a one-to-one comparison of non-contrast-enhanced chest CT and calcium score CT was performed. Results The correlation coefficients of the CACS obtained from the two CT scans for slice thickness of 2.5, 1.25, and 0.625 mm were 0.9850, 0.9688, and 0.9834, respectively. The mean differences between the CACS were โˆ’21.4% at 0.625 mm, โˆ’39.4% at 1.25 mm, and โˆ’76.2% at 2.5 mm slice thicknesses. Sectional analysis revealed that 16 (80%), 16 (80%), and 13 (65%) patients showed agreement for the degree of coronary artery disease at each slice interval, respectively. Inter-reader agreement was high for each slice interval. The 0.625 mm CT showed the highest sensitivity for detecting calcified lesions. Conclusion The values in the non-contrast-enhanced chest CT, using the 16-cm axial volume scan technique, were similar to those obtained using the CACS in the calcium score CT, at 0.625 mm slice thickness without electrocardiogram gating. This can ultimately help predict cardiovascular risk without additional radiation exposure.ope

    Optimization of a chest computed tomography protocol for detecting pure ground glass opacity nodules: A feasibility study with a computer-assisted detection system and a lung cancer screening phantom

    Get PDF
    Objective: This study aimed to optimize computed tomography (CT) parameters for detecting ground glass opacity nodules (GGNs) using a computer-assisted detection (CAD) system and a lung cancer screening phantom. Methods: A lung cancer screening phantom containing 15 artificial GGNs (-630 Hounsfield unit [HU], 2-10 mm) in the left lung was examined with a CT scanner. Three tube voltages of 80, 100, and 120 kVp were used in combination with five tube currents of 25, 50, 100, 200, and 400 mA; additionally, three slice thicknesses of 0.625, 1.25, and 2.5 mm and four reconstruction algorithms of adaptive statistical iterative reconstruction (ASIR-V) of 30, 60, and 90% were used. For each protocol, accuracy of the CAD system was evaluated for nine target GGNs of 6, 8, or 10 mm in size. The cut-off size was set to 5 mm to minimize false positives. Results: Among the 180 combinations of tube voltage, tube current, slice thickness, and reconstruction algorithms, combination of 80 kVp, 200 mA, and 1.25-mm slice thickness with an ASIR-V of 90% had the best performance in the detection of GGNs with six true positives and no false positives. Other combinations had fewer than five true positives. In particular, any combinations with a 0.625-mm slice thickness had 0 true positive and at least one false positive result. Conclusion: Low-voltage chest CT with a thin slice thickness and a high iterative reconstruction algorithm improve the detection rate of GGNs with a CAD system in a phantom model, and may have potential in lung cancer screening.ope

    Differential Diagnosis of Thick Myocardium according to Histologic Features Revealed by Multiparametric Cardiac Magnetic Resonance Imaging

    Get PDF
    Left ventricular (LV) wall thickening, or LV hypertrophy (LVH), is common and occurs in diverse conditions including hypertrophic cardiomyopathy (HCM), hypertensive heart disease, aortic valve stenosis, lysosomal storage disorders, cardiac amyloidosis, mitochondrial cardiomyopathy, sarcoidosis and athlete's heart. Cardiac magnetic resonance (CMR) imaging provides various tissue contrasts and characteristics that reflect histological changes in the myocardium, such as cellular hypertrophy, cardiomyocyte disarray, interstitial fibrosis, extracellular accumulation of insoluble proteins, intracellular accumulation of fat, and intracellular vacuolar changes. Therefore, CMR imaging may be beneficial in establishing a differential diagnosis of LVH. Although various diseases share LV wall thickening as a common feature, the histologic changes that underscore each disease are distinct. This review focuses on CMR multiparametric myocardial analysis, which may provide clues for the differentiation of thickened myocardium based on the histologic features of HCM and its phenocopies.ope

    ํฌ๋ ˆ์•„ํ‹ด ํ™”ํ•™์  ๊ตํ™˜ ํฌํ™” ์ฒœ์ด ๊ธฐ๋ฒ•์„ ์ด์šฉํ•œ ์‹ฌํ˜ˆ๊ด€ ์ž๊ธฐ๊ณต๋ช… ์˜์ƒ ๊ธฐ๋ฒ•์„ ํ†ตํ•œ ๋‹น๋‡จ๋ณ‘์„ฑ ์‹ฌ๊ทผ ๋ณ‘์ฆ ์ฅ๋ชจ๋ธ์—์„œ์˜ ์ด๋ฏธ์ง• ๋ฐ”์ด์˜ค๋งˆ์ปค ๊ตฌ์ถ•

    No full text
    ์‹คํ—˜ ๋ชฉ์  ๋‹น๋‡จ๋ณ‘์„ฑ ์‹ฌ๊ทผ ๋ณ‘์ฆ (diabetic cardiomyopathy, DCM) ์ฅ ๋ชจ๋ธ์„ ์‚ฌ์šฉํ•˜์—ฌ ์‹ฌ์žฅ ๊ธฐ๋Šฅ ๋ฐ ๊ตฌ์กฐ์˜ ๋ณ€ํ™” ์ „์— ์ผ์–ด๋‚˜๋Š” DCM ์ดˆ๊ธฐ์˜ ๋ฌผ์งˆ๋Œ€์‚ฌ ๋ณ€ํ™”๋ฅผ ๊ฐ์ง€ํ•˜๋Š” ๋ฐ ์žˆ์–ด ํฌ๋ ˆ์•„ํ‹ด ํ™”ํ•™์  ๊ตํ™˜ ํฌํ™” ์ฒœ์ด (creatine-chemical exchange saturation transfer, CrCEST) ์‹ฌํ˜ˆ๊ด€ ์ž๊ธฐ ๊ณต๋ช… (cardiovascular magnetic resonance, CMR) ์˜์ƒ์˜ ํšจ์œจ์„ฑ์„ ํ‰๊ฐ€ํ•œ๋‹ค. ์‹คํ—˜ ๋ฐฐ๊ฒฝ ๋‹น๋‡จ๋ณ‘์˜ ์œ ๋ณ‘๋ฅ ์€ ์ „ ์„ธ๊ณ„์ ์œผ๋กœ ์ฆ๊ฐ€ํ•˜๊ณ  ์žˆ์œผ๋ฉฐ ์ด๋Š” ์‹ฌ๋ถ€์ „์˜ ์ฃผ์š” ์›์ธ ์ค‘ ํ•˜๋‚˜์ด๋‹ค. ํ˜„์žฌ DCM์˜ ์ž„์ƒ ์ง„๋‹จ์€ ์‹ฌ์ดˆ์ŒํŒŒ ๋˜๋Š” ๊ธฐ์กด์˜ CMR ๊ธฐ๋ฒ•์„ ์‚ฌ์šฉํ•˜์—ฌ ๊ตฌ์กฐ์  ๋˜๋Š” ๊ธฐ๋Šฅ์  ์‹ฌ๊ทผ ์ด์ƒ์„ ๊ฐ์ง€ํ•˜๋Š” ๊ฒƒ์— ๊ธฐ๋ฐ˜ํ•œ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ๊ตฌ์กฐ์  ๋˜๋Š” ๊ธฐ๋Šฅ์  ์‹ฌ๊ทผ ๋ณ€ํ™”๋Š” DCM์ด ๋งค์šฐ ์ง„ํ–‰๋œ ๋‹จ๊ณ„์—์„œ๋‚˜ ๊ด€์ฐฐ๋œ๋‹ค. DCM์˜ ์กฐ๊ธฐ ์ง„๋‹จ๊ณผ ์‹ฌ๋ถ€์ „ ์˜ˆ๋ฐฉ์„ ์œ„ํ•œ ์น˜๋ฃŒ๋ฅผ ์œ„ํ•ด์„œ๋Š” DCM ์ดˆ๊ธฐ์˜ ์‹ฌ๊ทผ ๋ฌผ์งˆ๋Œ€์‚ฌ ๋ณ€ํ™”๋ฅผ ๊ฐ์ง€ํ•  ์ˆ˜ ์žˆ์–ด์•ผ ํ•œ๋‹ค. ์‹คํ—˜ ๋ฐฉ๋ฒ• 9์ฃผ๋ น ์ˆ˜์ปท Sprague-Dawley ์ฅ๋ฅผ ๋ฌด์ž‘์œ„๋กœ ๋ฐฐ์ •ํ•˜์—ฌ ๋‹น๋‡จ๋ณ‘ ์œ ๋ฐœ ์ „ (n = 12)๊ณผ ๋‹น๋‡จ๋ณ‘ ์œ ๋ฐœ ํ›„ 4, 8, 12, 16์ฃผ์ฐจ (๊ฐ๊ฐ n = 8, n = 7, n = 7, n = 6) ์— CMR์„ ์ดฌ์˜ํ–ˆ๋‹ค. ์ฅ์—๊ฒŒ ๋‹น๋‡จ๋ณ‘์„ ์œ ๋„ํ•˜๊ธฐ ์œ„ํ•ด ์ŠคํŠธ๋ ™ํ† ์กฐํ† ์‹  (Streptozotocin, STZ)์ด๋ผ๋Š” ์•ฝ๋ฌผ์„ ๋ณต๊ฐ• ๋‚ด๋กœ 65mg/kg ์šฉ๋Ÿ‰์œผ๋กœ ์ฃผ์‚ฌํ–ˆ๋‹ค. ์ฅ์˜ ์ฒด์ค‘๊ณผ ํ˜ˆ๋‹น ์ˆ˜์น˜๋Š” ๋งค์ฃผ ์ธก์ •ํ–ˆ๋‹ค. Cine, T1 ์ง€๋„ํ™” ์˜์ƒ ๋ฐ CrCEST ์˜์ƒ์„ 9.4 T ์ž๊ธฐ๊ณต๋ช…์˜์ƒ ์Šค์บ๋„ˆ๋ฅผ ์‚ฌ์šฉํ•˜์—ฌ ์ดฌ์˜ํ–ˆ๋‹ค. ์‹ฌ์žฅ ๊ธฐ๋Šฅ ๋ฐ ์‹ฌ๊ทผ strain์€ cine ์ด๋ฏธ์ง€์—์„œ ์ƒ์—…์ ์œผ๋กœ ์ด์šฉ ๊ฐ€๋Šฅํ•œ ์†Œํ”„ํŠธ์›จ์–ด๋ฅผ ์‚ฌ์šฉํ•˜์—ฌ ๋ฐ˜์ž๋™์œผ๋กœ ์ธก์ •ํ–ˆ๋‹ค. ์‹ฌ๊ทผ T1๊ฐ’, ์„ธํฌ์™ธ ๋ถ€ํ”ผ ๋ถ„์œจ (extracellular volume fraction, ECV) ๋ฐ CrCEST ์‹ ํ˜ธ ๋ถ„์„์„ ์‹œํ–‰ํ•  ์˜์—ญ์€ ์‹ฌ์‹ค ์ค‘๊ฒฉ์œผ๋กœ, ์†์œผ๋กœ ์˜์—ญ์„ ๊ทธ๋ ค์ฃผ๋ฉด ์—ญ์‹œ ์†ŒํŠธํ”„์›จ์–ด๊ฐ€ ๊ฐ’์„ ์ธก์ •ํ–ˆ๋‹ค. ๋ชจ๋“  ๋ฐ์ดํ„ฐ๋Š” ์„ ํ˜• ํ˜ผํ•ฉ ๋ชจ๋ธ์„ ์‚ฌ์šฉํ•˜์—ฌ ํ†ต๊ณ„ ๋ถ„์„ํ–ˆ๋‹ค. CrCEST ์‹ ํ˜ธ์— ๋Œ€ํ•œ ๊ด€์ฐฐ์ž ๋‚ด ๋ฐ ๊ด€์ฐฐ์ž ๊ฐ„ ์ผ์น˜๋„๋Š” Intraclass correlation coefficient (ICC)๋กœ ํ‰๊ฐ€ํ–ˆ๋‹ค. ์‹ฌ๊ทผ์˜ ์กฐ์งํ•™์  ํ‰๊ฐ€๋Š” ๊ฐ ๊ทธ๋ฃน์—์„œ ์ตœ์†Œ 3 ๋งˆ๋ฆฌ์˜ ์ฅ์— ๋Œ€ํ•ด ์ˆ˜ํ–‰ํ–ˆ๋‹ค. ์‹ฌ์žฅ ๊ธฐ๋Šฅ ๋ฐ ์‹ฌ๊ทผ strain, T1 ์ง€๋„ํ™” ์˜์ƒ, CrCEST ์˜์ƒ ๋ฐ ์กฐ์งํ•™์  ๊ฒ€์‚ฌ๋ฅผ 5๊ฐœ ๊ทธ๋ฃน์—์„œ ๋น„๊ตํ–ˆ๋‹ค. ์‹คํ—˜ ๊ฒฐ๊ณผ STZ ์ฃผ์‚ฌ ํ›„ ๋ชจ๋“  ์ฅ์—์„œ 1์ฃผ์ผ ๋‚ด์— ํ˜ˆ๋‹น ์ˆ˜์น˜๊ฐ€ ๊ธ‰๊ฒฉํžˆ ์ƒ์Šน ๋ฐ ์œ ์ง€๋˜์–ด ๋‹น๋‡จ๋ณ‘์€ ์„ฑ๊ณต์ ์œผ๋กœ ์œ ๋„๋˜์—ˆ๋‹ค. CrCEST ์‹ ํ˜ธ (magnetization transfer ratio asymmetry, MTRasym)๋Š” ๋‹น๋‡จ 4์ฃผ, 8์ฃผ, 12์ฃผ, 16์ฃผ์ฐจ ๊ตฐ์—์„œ (๊ฐ๊ฐ ํ‰๊ท  6.04 %, 3.76 %, 2.80 %, 3.34 %)์—์„œ ๋‹น๋‡จ ์œ ๋ฐœ ์ „ ๊ทธ๋ฃน (10.86 %)๋ณด๋‹ค ์œ ์˜๋ฏธํ•˜๊ฒŒ ๋‚ฎ์•˜๋‹ค (p<0.001). ๊ตฐ๊ฐ„ ๋Œ€์‘ ๋น„๊ต ๋ถ„์„์—์„œ๋Š” ๋‹น๋‡จ ์œ ๋ฐœ ์ „ ๊ทธ๋ฃน๊ณผ ๋‹น๋‡จ ๊ทธ๋ฃน ๊ฐ๊ฐ ์‚ฌ์ด์— ์œ ์˜๋ฏธํ•œ ์ฐจ์ด๋ฅผ ๋ณด์˜€๋‹ค (p<0.001). MTRasym์— ๋Œ€ํ•œ ๊ด€์ฐฐ์ž ๋‚ด ๋ฐ ๊ด€์ฐฐ์ž ๊ฐ„ ์ผ์น˜๋„ ํ‰๊ฐ€์—์„œ ICC๋Š” ๊ฐ๊ฐ 0.953 ๋ฐ 0.927์ด์—ˆ๋‹ค. ์ขŒ์‹ฌ์‹ค ๋ฐ•์ถœ์œจ์€ ๊ตฐ๊ฐ„ ์œ ์˜๋ฏธํ•œ ์ฐจ์ด๊ฐ€ ์žˆ์—ˆ๊ณ  (p<0.001), ๊ตฐ๊ฐ„ ๋Œ€์‘ ๋น„๊ต ๋ถ„์„์—์„œ ๋‹น๋‡จ ์œ ๋ฐœ ์ „ ๊ทธ๋ฃน๊ณผ ๋‹น๋‡จ 16์ฃผ์ฐจ ๊ทธ๋ฃน๊ฐ„์— ์œ ์˜๋ฏธํ•œ ์ฐจ์ด๋ฅผ ๋ณด์˜€๋‹ค (p<0.001). ์ผํšŒ ๋ฐ•์ถœ๋Ÿ‰, ์‹ฌ๋ฐ•์ถœ๋Ÿ‰ ๋ฐ ์ขŒ์‹ฌ์‹ค ์‹ฌ๊ทผ๋Ÿ‰์€ 5๊ฐœ ๊ทธ๋ฃน๊ฐ„์— ์œ ์˜๋ฏธํ•œ ์ฐจ์ด๊ฐ€ ์—†์—ˆ๋‹ค. Global radial, longitudinal peak strain์€ ๋‹น๋‡จ ์œ ๋ฐœ ์ „ ๊ทธ๋ฃน๊ณผ ๋น„๊ตํ–ˆ์„ ๋•Œ ๋‹น๋‡จ 12์ฃผ์ฐจ (๊ฐ๊ฐ p=0.049, p=0.004) ๋ฐ 16์ฃผ์ฐจ (๊ฐ๊ฐ p=0.003, p<0.001) ๊ทธ๋ฃน์—์„œ ์œ ์˜๋ฏธํ•˜๊ฒŒ ๊ฐ์†Œํ–ˆ๊ณ , Global circumferential peak strain์€ ๋‹น๋‡จ 16์ฃผ์ฐจ ๊ทธ๋ฃน (p=0.005) ๊ทธ๋ฃน์—์„œ ์œ ์˜๋ฏธํ•˜๊ฒŒ ๊ฐ์†Œํ–ˆ๋‹ค. ์‹ฌ๊ทผ์˜ native T1 ๊ฐ’๊ณผ ECV๋Š” ๋‹น๋‡จ ์œ ๋ฐœ ์ „ ๊ทธ๋ฃน์— ๋น„ํ•ด ๋‹น๋‡จ 12์ฃผ์ฐจ ๋ฐ 16์ฃผ์ฐจ ๊ทธ๋ฃน์—์„œ ์ฆ๊ฐ€ํ–ˆ๋‹ค (p<0.001). ์กฐ์งํ•™์  ์†Œ๊ฒฌ์ƒ ๋‹น๋‡จ 12์ฃผ์ฐจ ๋ฐ 16์ฃผ์ฐจ ์ฅ์—์„œ subendocardial fibrosis, inflammation์ด ๊ด€์ฐฐ๋˜์—ˆ๋‹ค. ํˆฌ๊ณผ์ „์žํ˜„๋ฏธ๊ฒฝ์—์„œ ๋ฏธํ† ์ฝ˜๋“œ๋ฆฌ์•„ ๋‚ด paracrystalline inclusion body๋Š” ๋‹น๋‡จ 4์ฃผ์ฐจ ์ฅ์—์„œ๋ถ€ํ„ฐ ๋ฐœ๊ฒฌ๋˜์—ˆ๊ณ , ๋ฏธํ† ์ฝ˜๋“œ๋ฆฌ์•„์˜ ํ‰๊ท  ๋ฉด์ ์€ ๋‹ค๋ฅธ ์ฅ๋“ค์— ๋น„ํ•ด์„œ ๋‹น๋‡จ 16์ฃผ์ฐจ ์ฅ์—์„œ ์ฆ๊ฐ€๋˜๋Š” ๊ฒƒ์„ ํ™•์ธํ–ˆ๋‹ค (p<0.001). ๊ฒฐ๋ก  ์šฐ๋ฆฌ์˜ ์—ฐ๊ตฌ๋Š” CrCEST CMR์ด ์‹ฌ์žฅ ๊ธฐ๋Šฅ ๋ฐ ๊ตฌ์กฐ์˜ ๋ณ€ํ™” ์ „์— ์ดˆ๊ธฐ DCM์—์„œ์˜ ๋ฌผ์งˆ๋Œ€์‚ฌ ๋ณ€ํ™”๋ฅผ ๊ฐ์ง€ํ•˜์—ฌ DCM์˜ ์กฐ๊ธฐ ์ง„๋‹จ์— ์œ ์šฉํ•˜๋‹ค๋Š” ๊ฒƒ์„ ๋ณด์—ฌ์ฃผ์—ˆ๋‹ค. Purpose: To evaluate the potential effectiveness of creatine (Cr) chemical exchange saturation transfer (CrCEST) cardiovascular magnetic resonance (CMR) imaging in detecting early metabolic changes before cardiac function and structural changes occur in a diabetic cardiomyopathy (DCM) rat model. Background: The clinical diagnosis of DCM is based on detecting structural or functional myocardial abnormalities with echocardiography or conventional CMR at the advanced stage of DCM. Detecting metabolic changes in the myocardium is necessary for the early diagnosis and management of DCM. Materials and methods: Nine-week-old male Sprague-Dawley rats were randomly assigned to undergo CMR scans before and at 4, 8, 12, and 16 weeks after being intraperitoneally injected with streptozotocin to induce diabetes. Cine, T1 mapping, and CrCEST MR images were obtained. Cardiac function and myocardial strain were semiautomatically assessed on the cine MR images. The regions of interest for myocardial T1 values, extracellular volume fraction, and CrCEST signals were located in the interventricular septum. Data were analyzed using the linear mixed model. The intra- and interobserver agreements on the CrCEST signal were assessed with the intraclass correlation coefficient (ICC). Shortly after the CMR scan, the myocardium of sacrificed rats in each group was histologically evaluated. Cardiac function, T1 mapping images, CrCEST images, and histological examination of myocardium of the five groups were compared. Results: The blood glucose levels of the rats increased rapidly 1 week after the streptozotocin injections. The CrCEST signals (magnetization transfer ratio asymmetry) were significantly lower in the diabetic groups than in the prediabetic group (P<0.001). The pairwise comparisons showed significant differences between the prediabetic group and each of the four diabetic groups (P<0.001). The ICCs for intra- and interobserver agreements on the CrCEST signal were 0.953 and 0.927, respectively. The left ventricular ejection fraction was significantly different between the groups (P<0.001). The pairwise comparison showed a significant difference between the prediabetic and 16-week diabetic groups (P<0.001). Stroke volumes, cardiac output, and left ventricular myocardial mass were not significantly different between the five groups. The global radial peak strain and longitudinal peak strain were decreased significantly in the 12-week (P=0.049 and P=0.004, respectively) and 16-week diabetic groups (P=0.003 and P<0.001, respectively). The global circumferential peak strain was lower in the 16-week diabetic group (P=0.005) than in the prediabetic group. The myocardial native T1 values and extracellular volume fractions were higher in the 12- and 16-week diabetic groups than in the prediabetic group (P<0.001). Myocardial histology revealed subendocardial fibrosis in the 12- and 16-week diabetic rats. Transmission electron microscopy revealed mitochondrial paracrystalline inclusion bodies since 4 weeks after the onset of diabetes. The mean area of the mitochondria was significantly increased in the 16-week diabetic rats, compared to that of the other rat groups (P<0.001). Conclusions: CrCEST CMR revealed metabolic changes in early diabetic cardiomyopathy before cardiac function and structural changes occurred.open๋ฐ•

    USP13์— ์˜ํ•œ HMGB1 ์•ˆ์ •์„ฑ๊ณผ ์œ„์น˜์ด๋™ ์กฐ์ ˆ

    No full text
    Department of Medical ScienceHigh Mobility Group Box 1(HMGB1)์€ ์†์ƒ ๊ด€๋ จ ๋ถ„์ž์  ํŒจํ„ด์œผ๋กœ์„œ ์„ ์ฒœ๋ฉด์—ญ์— ์žˆ์–ด์„œ ํ•ต์‹ฌ์ ์ธ ์—ญํ• ์„ ๋‹ด๋‹นํ•œ๋‹ค. HMGB1์€ ์—ผ์ฆ์„ฑ ์ž๊ทน์— ๋Šฅ๋™์ ์œผ๋กœ ๋ถ„๋น„๋˜์–ด ์—ผ์ฆ๋ฐ˜์‘์˜ ํ›„๊ธฐ ๋งค๊ฐœ์ž๋กœ์„œ ์ž‘์šฉํ•œ๋‹ค. ์•„์„ธํ‹ธํ™”, ์ธ์‚ฐํ™”, ๊ทธ๋ฆฌ๊ณ  ์‚ฐํ™” ๊ฐ™์€ ๋ช‡๋ช‡ ๋ฒˆ์—ญ ํ›„ ๋ณ€ํ˜•์ด HMGB1 ๋ถ„๋น„๋ฅผ ์กฐ์ ˆํ•œ๋‹ค๊ณ  ์•Œ๋ ค์ ธ ์žˆ์œผ๋‚˜, HMGB1์ด ํƒˆ์œ ๋น„ํ€ดํ‹ดํ™”์— ์˜ํ•ด์„œ ์–ด๋–ป๊ฒŒ ์กฐ์ ˆ๋˜๋Š”์ง€๋Š” ๋ฐํ˜€์ง„ ๋ฐ” ์—†๋‹ค. ์ด ์—ฐ๊ตฌ์—์„œ๋Š” ubiquitin-specific-protease (USP13)๊ฐ€ HMGB1์˜ ์ƒˆ๋กœ์šด ๊ฒฐํ•ฉ ํŒŒํŠธ๋„ˆ์ด๋ฉฐ, ํƒˆ์œ ๋น„ํ€ดํ‹ดํ™”๋ฅผ ํ†ตํ•˜์—ฌ ๊ทธ ์•ˆ์ •์„ฑ์„ ์กฐ์ ˆํ•จ์„ ๋ฐํ˜”๋‹ค. ๋˜ํ•œ ๊ณผ๋ฐœํ˜„๋œ USP13์€ HMGB1์˜ ์„ธํฌ์งˆ๋กœ์˜ ์ด๋™๊ณผ ์„ธํฌ ๋ฐ– ๋ถ„๋น„๋„ ์œ ๋„ํ–ˆ๋Š”๋ฐ, ํƒˆ์œ ๋น„ํ€ดํ‹ดํ™” ํšจ์†Œ ํ™œ์„ฑ์ด ์ œ๊ฑฐ๋œ ๋Œ์—ฐ๋ณ€์ด ํ˜•์˜ USP13๋„ HMGB1 ๋ถ„๋น„๋ฅผ ์ด‰์ง„์‹œํ‚ด์„ ๊ด€์ฐฐํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ์ด๋Š” USP13์ด HMGB1์˜ ์„ธํฌ ๋‚ด ์œ„์น˜์™€ ์ด๋™์„ ํƒˆ์œ ๋น„ํ€ดํ‹ดํ™” ํšจ์†Œ ํ™œ์„ฑ๊ณผ๋Š” ๋ฌด๊ด€ํ•˜๊ฒŒ ์กฐ์ ˆํ•œ๋‹ค๋Š” ๊ฒƒ์„ ์˜๋ฏธํ•œ๋‹ค. ๊ฒฐ๋ก ์ ์œผ๋กœ, USP13์€ HMGB1์˜ ์•ˆ์ •์„ฑ๊ณผ ๊ทธ ๋ถ„๋น„๋กœ ์ด์–ด์ง€๋Š” ์œ„์น˜ ์ด๋™์„ ์กฐ์ ˆํ•˜๋Š” ๋‘ ๊ฐ€์ง€ ์—ญํ• ์„ ํ•œ๋‹ค๋Š” ๊ฒƒ์„ ๋ณด๊ณ ํ•˜๋Š” ๋ฐ”์ด๋‹ค.open์„

    Generating a Process Simulation Model and Optimal Schedule for Steelmaking Process using Process Mining

    No full text
    MasterDigital twin is an advanced simulation environment that can reflect real-world models into the virtual world. By applying digital twins to manufacturing processes, production prediction and process optimization can be achieved to improve process efficiency. Simulation model generation and process optimization technologies are key technologies in digital twin. In this study, three steps were taken to generate process simulation model and optimal schedule for steelmaking process. First, an event log error repair method that reflects the characteristics of the process was presented. After that, a simulation model that reflects the characteristics of the steelmaking process, such as idle patterns, queue rules, and continuous activity patterns, was constructed, and the effectiveness of the model was confirmed by comparing the simulation log with the actual log. Finally, the process optimization problem was defined by simplifying the entire process, and an optimization model was built using the process mining method used to generate simulation model. The performance of the optimization model was verified by comparing the simulation logs based on the optimization schedule and original schedule

    Optimization of Hydrogen Refueling Stations Deployment and Supply Chain Networks: Current Status and Research Suggestions

    No full text
    Hydrogen infrastructure consisting of hydrogen refueling stations and supply chain network is critical in the hydrogen mobility economy. This paper overviews the important key concepts of the hydrogen mobility economy and investigates the status of hydrogen vehicles and refueling stations in Korea and other countries. It also reviews the methodologies for hydrogen station and supply chain network optimization and suggests research agenda for the hydrogen mobility economy.22Nkc
    corecore