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Jang, Hannah,Kim, Sehwan,Lee, Jae Man,Oh, Yong-Seok,Park, Sang Myun,Kim, Sang Ryong Wolters Kluwer Health | Lippincott Williams Wilkin 2017 NEUROREPORT - Vol.28 No.5
<P>Although the main cause of degeneration of the nigrostriatal dopaminergic (DA) projection in Parkinson's disease (PD) is still controversial, many reports suggest that excessive inflammatory responses mediated by activated microglia can induce neurotoxicity in the nigrostriatal DA system in vivo. Montelukast, which plays an antiinflammatory role, is used to treat patients with asthma. In addition, recent studies have reported that its administration could reduce neuroinflammatory activities, showing beneficial effects against various neuropathological conditions. These results suggest that montelukast may be a useful drug to alleviate inflammatory responses in PD, even though there are no reports showing its beneficial effects against neurotoxicity in the nigrostriatal DA system. In the present study, our results showed that treatment with montelukast could protect DA neurons against 6-hydroxydopamine (6-OHDA)-induced neurotoxicity and its administration significantly attenuated the production of neurotoxic cytokines such as tumor necrosis factor-alpha (TNF alpha) and interleukin-1 beta (IL-1 beta) from activated microglia in the substantia nigra (SN) and striatum following 6-OHDA treatment. Therefore, we suggest that montelukast can be used as a potential inhibitor of microglial activation to protect DA neurons in the adult brain against PD. NeuroReport 28: 242-249 Copyright (C) 2017 Wolters Kluwer Health, Inc. All rights reserved.</P>
Path Metric 비교 기반 적응형 QRD-M MIMO 검출 기법
김봉석(Bong-seok Kim),김한나(Hannah Kim),최권휴(Kwonhue Choi) 한국통신학회 2008 韓國通信學會論文誌 Vol.33 No.6C
본 논문에서는 MIMO(Multiple-input Multiple-output) 시스템에서 실시간으로 변하고 있는 채널의 상태를 추정하여 각 layer에서 survivor path들의 개수인, M을 효율적으로 조절하는 적응형 QRD-M 기법을 제안한다. 채널상태와 상관없이 고정된 M을 사용하는 기존의 QRD-M 기법은 MLD(maximum-likelihood detection)의 성능에 근접하기 위해 correct path를 놓치지 않기 위한 큰 값의 M을 사용하여야 하므로, 큰 계산양이 요구된다. 이를 보완하기 위해 채널 행렬의 성분을 이용하여 채널 상태를 추정하여, M을 적절히 조절하는 기법이 제안되었으나 매 프레임에서 변하고 있는 채널 이득 성분만을 이용할 뿐 매 순간 바뀌고 있는 수신 잡음에 대한 정보를 이용하지 못하는 단점을 가진다. 본 논문에서는 잡음 전력 값을 측정하지 않고서도 채널 이득뿐 아니라 순간적인 수신 잡음에 대한 정보 까지 모두 반영한 채널 indicator를 이용하여 M을 더욱더 효율적으로 조절하는 QRD-M 기법을 제안한다. 채널환경이 좋은 경우에는 그렇지 못한 경우에 비해, 가장 작은 path metric 값이 다른 path의 metric 값들에 비해 확연히 작다는 사실을 이용하여, 가장 작은 값을 가지는 두 path metric의 비(ratio)를 채널상태를 추정하는 indicator로 이용하였다. 제안된 기법은 M을 적절하게 조절하므로 MLD에 근접하는 최적의 성능을 가지면서, 기존의 QRD-M 기법에 비해 계산양은 확연히 감소 시킨다. This paper proposes a new adaptive QRD-M algorithm for MIMO systems. The proposed scheme controls the number of survivor paths, M based on the channel condition at each layer. The original QRD-M algorithm used fixed M at each layer and it needs large M to achieve near-MLD (maximum-likelihood detection) performance. However, using the large M increases the computation complexity. In this paper, we further effectively control M by employing the channel indicator which includes not only the channel gain, but also instantaneous noise information without necessity of SNR measurement. We found that the ratio of the minimum path metric to the second minimum is good reliability indicator for the channel condition. By adaptively changing M based on this ratio, the proposed scheme effectively achieves near MLD performance and computation complexity of the proposed scheme is significantly smaller than the conventional QRD-M algorithms.
Jongmin Sim*,Hannah Lois Kangleon-Tan*,Ji Young You,Eun-Shin Lee,Haemin Lee,Sun Moon Yang,Min-Ki Seong,Eun Hwa Park,Seok Jin Nam,박민호,Seokwon Lee,Woo-Chan Park,Rogelio G. Kangleon Jr,Crisostomo B. Dy,S 대한외과학회 2022 Annals of Surgical Treatment and Research(ASRT) Vol.103 No.6
Purpose: Although adjuvant chemotherapy (CTx) is still recommended for high-risk patients with hormone receptorpositive and human epidermal receptor (HER)-2-negative breast cancer, recent studies found that selected patients with low disease burden may be spared from CTx and receive hormonal treatment (HT) alone. This study aims to evaluate the trends of treatment (CTx + HT vs. HT alone) in Korea and to assess the impact on overall survival (OS) according to treatment pattern. Methods: The Korean Breast Cancer Society Registry was queried (2000 to 2018) for women with pT1-2N0-1 hormone receptor-positive and HER2-negative disease who underwent surgery and adjuvant systemic treatment (CTx and HT). Clinicopathologic factors, change in pattern of treatment over time, and OS for each treatment option were analyzed. Results: A total of 40,938 women were included in the study; 20,880 (51.0%) received CTx + HT, while 20,058 (49.0%) received HT only. In recent years, there has been a steady increase in the use of HT alone, from 21.0% (2000) to 64.6% (2018). In Cox regression analysis, age, type of breast and axillary operations, T and N stages, body mass index, histologic grade, and presence of lymphovascular invasion were prognostic indicators for OS. There was no significant difference between CTx + HT and HT alone in terms of OS (P = 0.126). Conclusion: Over the years, there has been a shift from CTx + HT to HT alone without a significant difference in OS. Therefore, HT alone could be a safe treatment option in selected patients, even those with T2N1 disease.
3D CNN 기반 회전근개 파열 진단 및 활성화 맵 시각화
심응준(Eungjune Shim),김한나(Hannah Kim),김래현(Laehyun Kim),정석원(Seok-won Chung),김영준(Youngjun Kim) 대한기계학회 2017 대한기계학회 춘추학술대회 Vol.2017 No.11
When diagnosing Rotator Cuff Tear(RCT), magnetic resonance imaging(MRI) scanned 3D data is largely used. Compare to 2D-based medical image, 3D data can offer more detail information and intuitive visualization of patients condition. For example, three-dimensionally visualized MRI volume data of rotator cuff area can reveal not only presence or absence of the rupture, but also clear position and shape of it. However, only 2D-based slice images are used to diagnose RCT without taking advantage of 3D volume at the general medical field. Most of Convolutional Neural Network(CNN)-based medical image diagnosing methods are also using 2D data. We have proposed a RCT diagnosis method using 3D CNN that uses 3D information and take advantages of it to do the same task. The Voxception-Resnet(VRN) network was used to classify if volume has RCT or not. The data was preprocessed and resampled to 64x64x64 volume. and showed about 80 percent of accuracy when diagnosing test data. The 3D Class Activation Map(CAM) was also applied to visualize approximate location and shape of RCT. Our proposed method can automatically diagnose the presence of RCT using 3D data, and also visualize the activation map in 3D.This is less onerous and timeconsuming than using 2D data.