요역학적 평가에 근거한 난치성 야뇨증에 대한 내시경적 치료

학위논문 (석사) -- 서울대학교 대학원 : 의과대학 의학과, 2020. 8. 박관진.Purpose: This study aimed to determine the urodynamic characteristics of refractory enuresis and explore whether they can be managed through differential endoscopic injection with botulinum toxin (BTX). Subjects and Methods: A total of 27 patients with nonmonosymptomatic enuresis (NME) who showed no response after conservative treatment for more than 12 months were included herein. Patients then underwent videourodynamic study (VUDS) and received a differential endoscopic injection of BTX within the same day. Reduced capacity (RC), detrusor overactivity (DO), and bladder neck widening (BNW) were the three major abnormal findings assessed during the filling phase, while sphincter hyperactivity (SH) was the only abnormality assessed during the emptying phase. Intravesical or intrasphincteric injection of BTX was attempted according to VUDS findings. Follow-up was conducted 1, 3, 6, and 12 months after treatment. Results: The median age was 10 (7–31) years. Although 19 and 8 patients had an overactive bladder or dysfunctional voiding prior to the procedure, respectively, more than half had a different diagnosis following VUDS. Those showing DO benefited from intravesical BTX injection, whereas those with only SH benefited from both intravesical and intrasphincteric injections. Treatment resistance to BTX seemed to have been attributed to BNW. Time had no apparent effect on efficacy, which remained 6 months after the injection. More than 80% of the patients retained the benefits of injection after 1 year. Conclusion: VUDS was useful in characterizing lower urinary tract dysfunction and determining appropriate treatment among patient with NME. Sphincter dysfunction plays a major role in refractoriness to conventional treatment.서론: 이 연구는 난치성 야뇨증의 요역학적 특성을 결정하고 보툴리눔 독소(BTX)를 이용한 내시경 주사를 통해 관리 될 수 있는지 여부를 조사하는 데 목적이 있다. 대상 및 방법: 12 개월 이상 동안 보존적 치료 후 반응을 보이지 않은 비단일증상 야뇨증 (NME)을 갖는 총 27 명의 서울대병원 어린이병원 환자가 포함되었다. 환자들은 비디오 요역동학검사 (VUDS)를 받았고 같은 날에 BTX의 내시경 주사를 받았다. 방광용적 감소 (RC), 배뇨근 과다활동(DO) 및 방광목 확대 (BNW)는 충만기에서 평가 된 3 가지 주요 비정상 결과였으며, 괄약근 과다활동 (SH)은 배뇨기 단계에서 유일하게 평가 된 이상이었다. VUDS 결과에 따라 BTX의 정맥 내 또는 괄약근 주사가 시도되었다. 치료 후 1, 3, 6 및 12 개월에 추적 관찰을 수행 하였다. 결과: 평균 연령은 10 세 (7-31)였다. 19 명과 8 명의 환자가 각각 수술 전에 과민성 방광 또는 기능 장애가 있었지만, 절반 이상이 VUDS에 따라 다른 진단을 받았다. DO를 나타내는 사람들은 방광내 BTX 주사에만 효과를 보인 반면, SH만을 가진 사람들은 방광내 및 괄약근 주사에 효과를 보였다. BTX에 대한 치료 내성은 BNW에 기인 한 것으로 보인다. 주사 후 6 개월 동안 지속되는 시간은 효능에 명백한 영향을 미치지 않았다. 환자의 80 % 이상이 1 년 후에도 주사의 이점을 유지했다. 결론: VUDS는 요로 기능 장애를 특성화하고 NME 환자의 적절한 치료를 결정하는 데 유용했다. 괄약근 기능 장애는 기존 치료법에 대한 난치성에 중요한 역할을 한다.Introduction 5 Subjects and Methods 9 Results 14 Discussion 18 Conclusion 22 References 23 Abstract in Korean 32Maste

CKD-S versus CKD-M

학위논문(박사) -- 서울대학교대학원 : 의과대학 의학과, 2023. 8. 정창욱.Purpose: To analyze whether there is a difference in progression to end-stage renal disease (ESRD) and survival rate between surgically-induced chronic kidney disease (CKD-S) and medically-induced chronic kidney disease (CKD-M). Methods: Two different cohort studies were conducted. The first study was a multicenter hospital-based cohort, and patients who underwent partial or radical nephrectomy for renal cell carcinoma (RCC) without preoperative CKD were included in the CKD-S group. Patients enrolled in the Korean cohort study for Outcomes in Patients with Chronic Kidney Disease (KNOW-CKD) were included in the CKD-M group. The second study was a population-based cohort study using medical records, and estimated glomerular filtration rates in health checkups were extracted from the Korean National Health Insurance Service database. The primary outcome was progression to ESRD, defined as dialysis or kidney transplantation. The secondary outcome was all-cause mortality. Results: In the first study, patients with CKD-M were at higher risk of progression to ESRD (hazard ratio [HR]: 9.89, 95% confidence interval [CI]: 4.67–20.92, p5 years after adjusting for age, sex, body mass index, hypertension, and diabetes, the odds ratio of progression to ESRD or a 50% decrease in GFR within 5 years was significantly higher in the CKD-M group. In the second study, in the whole matched cohort without cardiovascular disease (CVD) history, patients with CKD-M were at higher risk of progression to ESRD (HR: 1.895, 95% CI: 1.044–3.442, p=0.0357) and CVD (HR: 1.167, 95% CI: 1.057–1.289, p=0.0023) than those with CKD-S. Patients with CKD-M were at lower risk of overall death; however, this observation was not statistically significant (HR: 0.922, 95% CI: 0.718–1.185, p=0.5268). Among patients with CKD grade ≥3 in the whole cohort, including CVD history, the CKD-M group was at significantly higher risk of progression to ESRD (HR: 2.208, 95% CI: 1.474–3.306, p=0.0001), CVD (HR: 1.318, 95% CI: 1.198–1.451, p<0.0001), and overall mortality (HR: 1.497, 95% CI: 1.208–1.856, p=0.0002). Conclusion: Patients with CKD-S appear to have a lower risk of developing ESRD than those with CKD-M in this study. Regarding mortality and progression to ESRD, it might not be accurate to conceive CKD-S and CKD-M as being on the same CKD spectrum.수술군은 내과군보다 만성신장병으로 진행할 위험도가 두 연구 모두에서 유의하게 낮았다. 또한 수술군은 내과군보다 사망 위험도가 두 연구의 결과를 종합하였을 때 낮은 경향을 보였다. 만성신장병이 수술로 인해 생긴 경우와 내과적으로 생긴 경우는 서로 동일하지 않은 질환 스펙트럼에 있는 것으로 보인다. 특히 당뇨가 있는 환자는 수술 후 신기능 저하가 발생하면 만성신부전 및 사망의 위험도가 유의하게 높으므로 신기능 관리에 각별한 주의가 요구된다.Introduction 1 Subjects and Methods 4 Results 8 Discussion 11 Conclusion 17 Figure Legends 18 Table Legends 26 Bibliography 36 Abstract in Korean 41박

Study of NIH3T3 cell migration on variable nano patterns

학위논문 (석사)-- 서울대학교 대학원 : 전기. 컴퓨터공학부, 2011.2. 전국진.Maste

Guehwan Jung

학위논문(석사)--아주대학교 일반대학원 :컴퓨터공학과,2014. 8콘텐트 중심 네트워킹 (Content-Centric Networking: CCN)은 기존의 IP 기반의 구조를 대체할 새로운 미래 인터넷 구조로 각광받고 있다. 대부분의 CCN 연구는 유선 환경에서 진행되었으나 최근에는 CCN의 장점을 활용하여 Throughput 및 지연 시간을 향상시키기 위한 연구가 진행되었다. 하지만 무선 환경의 특성상 쉽게 패킷 손실이 발생하므로 CCN의 특징을 반영한 전송 신뢰성을 향상시킬 수 있는 방안에 대한 연구가 필수적이다. 본 연구에서는 무선 콘텐트 중심 네트워킹에서 신뢰성 있는 패킷 전송을 위한 복구 기법을 제안한다. 무선 자원을 통해 패킷이 전송되기 위해서는 MAC 계층의 패킷 크기 제한을 만족해야 한다. 이를 위해 어플리케이션에서 전송된 패킷은 여러 개의 작은 조각으로 나누어져 수신자에게 전달된다. 하지만 수신자는 이 중 하나의 조각이라도 받지 못하면 원래의 패킷으로 조립할 수 없다. 이를 극복하기 위해 콘텐트 중심 네트워킹에서는 전송 받지 못한 콘텐트를 재전송 요청한다. 이 때, 송신자는 재전송 요청에 해당하는 모든 조각들을 수신자에게 다시 전송하여 이미 수신한 조각에 대해 중복 수신하는 비효율성이 발생한다. 이를 해결하기 위해 제안 기법은 수신자측에서 받지 못한 조각만 요청하여 효율적으로 무선 자원을 이용하도록 하였다.Ⅰ. Introduction 1 Ⅱ. The Motivation and Related Work 3 A. Interest Retransmission Problem 3 B. Related Works 4 Ⅲ. System Architecture 7 Ⅳ. Proposed Scheme 10 A. Period Setting 11 B. Aggregation of Missing Fragment Request 15 Ⅴ. Performance Evaluation 20 A. Experimental Environments 20 B. Experimental Results 22 Ⅵ. Conclusion 28 REFERENCES 29Maste

쇄항으로 교정수술을 받은 환자의 성인기 배변기능 평가

학위논문 (석사)-- 서울대학교 대학원 : 의학과 외과학전공, 2011.2. 정성은.Maste

Support Function-based Learning Methods with Kernels toward Large-scale Pattern Recognition Problems

DoctorIn the last decade, the kernel methods have contributed to significantadvances in research areas such as statistics, probability theory,data mining, pattern recognition and articial intelligence with practicalsuccess in their application to regression, classication, clustering,ranking and visualization problems. By utilizing techniques andtheories from optimization, statistics, and most importantly functionalanalysis in the feature space, kernel machines have provided principledways of solving these problems with exibility, generality and accuracy.Among various kernel methods, Support Vector Machines (SVMs) andGaussian Processes(GPs) have been extensively studied and applied todiverse problems in machine learning with state-of-the-art performance.However, as the size of data to be analyzed by these kernel machinesgrows explosively due to the development of automated datacollecting, processing and storing techniques, the eciency has emergedas a critical issue. In this circumstance, nonlinear training cost of thekernel machines to solve quadratic programming for SVMs and eigendecompositionproblem for GPs inhibits their practical advantages. Andwhen the runtime complexity of the solution is a primary concern as inmany real-time applications, these kernel methods are less attractivethan the conventional linear models since the run time complexity ofkernel machines are generally proportional to the size of training data.To this end, many researches in kernel methods have been recently devotedto the development of fast algorithms for ecient training andsparse kernel machines for online testing. Even though some of themachieved fair success, most of them leads to another issues to be solvedsuch as local minimum, performance degrade and sensitivity to the setting.In this thesis, we aim to provide a new class of methods which enhancethe eciency of conventional kernel machines without compensatingthe performance and robustness. By utilizing important conceptsfrom nonlinear dynamics, the proposed methods are mainly based on themulti-basin system constructed by kernel support functions. These includereduced set construction method for sparser kernel machines, fastlabeling method for clustering, domain described machine for rankingand support learning for pattern denoising. Through various experimentson synthetic and real-world problems, we show the eectiveness of the proposed methods by comparing the performance with other related methods. We expect the proposed methods will pave the wayfor the kernel machines to be applied eciently to the emerging largescaleapplications such as high-denition image analysis, web documentsranking and gene database clustering

Quantile clustering for inductive and robust learning

2

Barrier Option Pricing with Model Averaging Methods under Local Volatility Models

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Computer-aided Detection of Subsolid Nodules at Chest CT: Improved Performance with Deep Learning-based CT Section Thickness Reduction

Background: Studies on the optimal CT section thickness for detecting subsolid nodules (SSNs) with computer-aided detection (CAD) are lacking. Purpose: To assess the effect of CT section thickness on CAD performance in the detection of SSNs and to investigate whether deep learning-based super-resolution algorithms for reducing CT section thickness can improve performance. Materials and Methods: CT images obtained with 1-, 3-, and 5-mm-thick sections were obtained in patients who underwent surgery between March 2018 and December 2018. Patients with resected synchronous SSNs and those without SSNs (negative controls) were retrospectively evaluated. The SSNs, which ranged from 6 to 30 mm, were labeled ground-truth lesions. A deep learning-based CAD system was applied to SSN detection on CT images of each section thickness and those converted from 3- and 5-mm section thickness into 1-mm section thickness by using the super-resolution algorithm. The CAD performance on each section thickness was evaluated and compared by using the jackknife alternative free response receiver operating characteristic figure of merit. Results: A total of 308 patients (mean age 6 standard deviation, 62 years 6 10; 183 women) with 424 SSNs (310 part-solid and 114 nonsolid nodules) and 182 patients without SSNs (mean age, 65 years 6 10; 97 men) were evaluated. The figures of merit differed across the three section thicknesses (0.92, 0.90, and 0.89 for 1, 3, and 5 mm, respectively; P = .04) and between 1- and 5-mm sections (P = .04). The figures of merit varied for nonsolid nodules (0.78, 0.72, and 0.66 for 1, 3, and 5 mm, respectively; P &lt; .001) but not for part-solid nodules (range, 0.93-0.94; P = .76). The super-resolution algorithm improved CAD sensitivity on 3- and 5-mm-thick sections (P = .02 for 3 mm, P &lt; .001 for 5 mm). Conclusion: Computer-aided detection (CAD) of subsolid nodules performed better at 1-mm section thickness CT than at 3- and 5-mm section thickness CT, particularly with nonsolid nodules. Application of a super-resolution algorithm improved the sensitivity of CAD at 3- and 5-mm section thickness CT. (C) RSNA, 202

Deep learning-based differentiation of invasive adenocarcinomas from preinvasive or minimally invasive lesions among pulmonary subsolid nodules

Objectives: To evaluate a deep learning-based model using model-generated segmentation masks to differentiate invasive pulmonary adenocarcinoma (IPA) from preinvasive lesions or minimally invasive adenocarcinoma (MIA) on CT, making comparisons with radiologist-derived measurements of solid portion size. Methods: Four hundred eleven subsolid nodules (SSNs) (120 preinvasive lesions or MIAs and 291 IPAs) in 333 patients who underwent surgery between June 2010 and August 2016 were retrospectively included to develop the model (370 SSNs in 293 patients for training and 41 SSNs in 40 patients for tuning). Ninety SSNs of 2 cm or smaller (45 preinvasive lesions or MIAs and 45 IPAs) resected in 2018 formed a validation set. Six radiologists measured the solid portion of each nodule. Performances of the model and radiologists were assessed using receiver operating characteristics curve analysis. Results: The deep learning model differentiated IPA from preinvasive lesions or MIA with areas under the curve (AUCs) of 0.914, 0.956, and 0.833 for the training, tuning, and validation sets, respectively. The mean AUC of the radiologists was 0.835 in the validation set, without significant differences between radiologists and the model (p = 0.97). The sensitivity, specificity, and accuracy of the model were 71% (32/45), 87% (39/45), and 79% (71/90), respectively, whereas the corresponding values of the radiologists were 75.2% (203/270), 76.7% (207/270), and 75.9% (410/540) with a 5-mm threshold for the solid portion size. Conclusions: The performance of the model for differentiating IPA from preinvasive lesions or MIA was comparable to that of the radiologists' measurements of solid portion size