Cerebral Hemodynamics and Vascular Reactivity in Mild and Severe Ischemic Rodent Middle Cerebral Artery Occlusion Stroke Models

심정은,조아름,이소희,방오영,허채정,전길자,서민아,강복만,이영미 한국뇌신경과학회 2016 Experimental Neurobiology Vol.25 No.3

Ischemia can cause decreased cerebral neurovascular coupling, leading to a failure in the autoregulation of cerebral blood flow. This study aims to investigate the effect of varying degrees of ischemia on cerebral hemodynamic reactivity using in vivo real-time optical imaging. We utilized direct cortical stimulation to elicit hyper-excitable neuronal activation, which leads to induced hemodynamic changes in both the normal and middle cerebral artery occlusion (MCAO) ischemic stroke groups. Hemodynamic measurements from optical imaging accurately predict the severity of occlusion in mild and severe MCAO animals. There is neither an increase in cerebral blood volume nor in vessel reactivity in the ipsilateral hemisphere (I.H) of animals with severe MCAO. The pial artery in the contralateral hemisphere (C.H) of the severe MCAO group reacted more slowly than both hemispheres in the normal and mild MCAO groups. In addition, the arterial reactivity of the I.H in the mild MCAO animals was faster than the normal animals. Furthermore, artery reactivity is tightly correlated with histological and behavioral results in the MCAO ischemic group. Thus, in vivo optical imaging may offer a simple and useful tool to assess the degree of ischemia and to understand how cerebral hemodynamics and vascular reactivity are affected by ischemia.

Network-assisted approaches for human disease research

심정은,이인석 한국통합생물학회 2015 Animal cells and systems Vol.19 No.4

Multiple genes and their interactions are involved in most human diseases. This pathway-centric view of human pathology is beginning to guide our approaches to disease research. Analytical algorithms describing human gene networks have been developed for three major tasks in disease research: (i) disease gene prioritization, (ii) disease module discovery, and (iii) stratification of complex diseases. To understand the underlying biology of human diseases, identification of disease genes and disease pathways is crucial. The functional interdependence between genes for disease progression has been identified by their connections in gene networks, which enables prediction of novel disease genes based on their connections to known disease genes. Disease modules can be identified by subnetworks that are enriched for patientspecific activated or mutated genes. Network biology also facilitates the subtyping of complex diseases such as cancer, which is a prerequisite for developing personalized medicinal therapies. In this review, we discuss network-assisted approaches in human disease research, with particular focus on the three major tasks. Network biology will provide powerful research platforms to dissect and interpret disease genomics data in the future.

장기보상으로서 Stock Option 도입이 경영자 위험행동과 기업의 위험·성과 관계에 미치는 영향

심정은,조남신,김영곤 한국전략경영학회 2003 한국전략경영학회 학술대회발표논문집 Vol.- No.-

탐구중심 환경교육을 위한 전기전도도의 환경교육적 가치

심정은,이두곤 한국환경교육학회 2008 한국환경교육학회 학술대회 자료집 Vol.- No.1

패션트렌드에 나타난 매니쉬와 페미닌

심정은 한국패션뷰티학회 2007 한국패션뷰티학회지 Vol.5 No.2

초등 교과서의 환경교육적 내용 분석 및 초등 환경교육 체계화를 위한 학교 교육과정 개발 방안 : 환경교육을 위한 환경학의 관점에서

심정은,이두곤 한국환경교육학회 2009 한국환경교육학회 학술대회 자료집 Vol.- No.1

Effect of the size of the bony access window and the collagen barrier over the window in sinus floor elevation: a preclinical investigation in a rabbit sinus model

심정은,김상엽,홍지연,신승일,정종혁,임현창 대한치주과학회 2022 Journal of Periodontal & Implant Science Vol.52 No.4

Purpose: The aim of this study was to investigate the effect of (1) the size of the bony access window and (2) collagen membrane coverage over the window in sinus floor elevation in a rabbit sinus model. Methods: Small bony access windows (SW; ø 2.8 mm) were made in 6 rabbits and large windows (LW; ø 6 mm) in 6 other rabbits. Both sinuses in each rabbit were allocated to groups with or without coverage of a collagen membrane (CM) on the window, resulting in 4 groups: SW, LW, SW+CM, and LW+CM. After 4 weeks of healing, micro-computed tomographic, histologic, and histomorphometric analyses were performed. Results: Bony healing in the window area was incomplete in all groups, but most bone graft particles were well confined in the augmented cavity. Histologically, the pattern of new bone formation was similar in all groups. Histomorphometrically, the percentage of newly formed bone was greater in the groups with CM than in the groups without CM, and in the groups with SW than in the groups with LW (12.92%±6.40% in the SW+CM group, 4.21%±7.73% in the SW group, 10.45%±4.81% in the LW+CM group, 11.77%±3.83% in the LW group). The above differences were not statistically significant (P>0.05). Conclusions: The combination of a small bony access window and the use of a collagen membrane over the window favored new bone formation compared to other groups, but this result should be further investigated due to the limitations of the present animal model.

다차원 클러스터링 기반의 단백질 2DE 이미지에서의 자동화된 기준점 추출 방법

심정은,이원석,Shim, Jung-Eun,Lee, Won-Suk 한국정보처리학회 2005 정보처리학회논문지D Vol.12 No.5

2DE는 조직 내의 단백질을 규명하는 단백질 분리 기술이다. 그러나 2DE 이미지는 실험 조건, 스캐닝 상태와 같은 환경에 민감하게 영향을 받는다. 이러한 이미지간의 변화를 극복하기 위해서 사용자는 각각의 서로 다른 이미지에 수동으로 기준점을 입력해주어야 한다. 그러나 이 과정은 에러를 발생시키며 긴 시간을 요구하는 작업으로, 빠른 분석에 장애 요인이 된다. 따라서 본 논문에서는 기준점 프로파일에 기반 하여 기준점을 자동으로 추출하는 방법을 개발하였다. 기준점 프로파일은 이미 확인된 이미지들의 기준점들에 대한 클러스터링 방법을 통하여 생성하며, 각 클러스터의 다양한 속성을 정의한다. 새로운 이미지가 입력되면 기준점의 후보 스팟들을 대상으로 프로파일과 비교하석 기준점을 추출한다. 그리고 $A^*$알고리즘을 이용하여 기준점 선정 과정을 최적화한다. 본 논문에서는 실제 사람의 간 조직 이미지를 이용하여 기준점 추출 방법의 성능을 분석하였다 2-dimensional electrophoresis(2DE) is a separation technique to identify proteins contained in a sample. However, the image is very sensitive to its experimental conditions as well as the quality of scanning. In order to adjust the possible variation of spots in a particular image, a user should manually annotate landmark spots on each gel image to analyze the spots of different images together. However, this operation is an error-prone and tedious job. This thesis develops an automated method of extracting the landmark spots of an image based on landmark profile. The landmark profile is created by clustering the previously identified landmarks of sample images of the same type. The profile contains the various properties of clusters identified for each landmark. When the landmarks of a new image need to be fount all the candidate spots of each landmark are first identified by examining the properties of its clusters. Subsequently, all the landmark spots of the new image are collectively found by the well-known optimization algorithm $A^*$. The performance of this method is illustrated by various experiments on real 2DE images of mouse's brain-tissues.

From sequencing data to gene functions: co-functional network approaches

심정은,이탁형,이인석 한국통합생물학회 2017 Animal cells and systems Vol.21 No.2

Advanced high-throughput sequencing technology accumulated massive amount of genomics and transcriptomics data in the public databases. Due to the high technical accessibility, DNA and RNA sequencing have huge potential for the study of gene functions in most species including animals and crops. A proven analytic platform to convert sequencing data to gene functional information is co-functional network. Because all genes exert their functions through interactions with others, network analysis is a legitimate way to study gene functions. The workflow of network-based functional study is composed of three steps: (i) inferencing co-functional links, (ii) evaluating and integrating the links into genome-scale networks, and (iii) generating functional hypotheses from the networks. Co-functional links can be inferred from DNA sequencing data by using phylogenetic profiling, gene neighborhood, domain profiling, associalogs, and co-expression analysis from RNA sequencing data. The inferred links are then evaluated and integrated into a genome-scale network with aid from gold-standard co-functional links. Functional hypotheses can be generated from the network based on (i) network connectivity, (ii) network propagation, and (iii) subnetwork analysis. The functional analysis pipeline described here requires only sequencing data which can be readily available for most species by next-generation sequencing technology. Therefore, co-functional networks will greatly potentiate the use of the sequencing data for the study of genetics in any cellular organism.

데이터 큐브를 이용한 폐암 2-DE 젤 이미지에서의 예외 탐사

심정은,이원석,Shim, Jung-Eun,Lee, Won-Suk 한국정보처리학회 2008 정보처리학회논문지D Vol.15 No.5

In proteomics research, the identification of differentially expressed proteins observed under specific conditions is one of key issues. There are several ways to detect the change of a specific protein's expression level such as statistical analysis and graphical visualization. However, it is quiet difficult to handle the spot information of an individual protein manually by these methods, because there are a considerable number of proteins in a tissue sample. In this paper, using database and data mining techniques, the application plan of OLAP data cube and Discovery-driven exploration is proposed. By using data cubes, it is possible to analyze the relationship between proteins and relevant clinical information as well as analyzing the differentially expressed proteins by disease. We propose the measure and exception indicators which are suitable to analyzing protein expression level changes are proposed. In addition, we proposed the reducing method of calculating InExp in Discovery-driven exploration. We also evaluate the utility and effectiveness of the data cube and Discovery-driven exploration in the lung cancer 2-DE gel image. 단백질체학에서 특정 조건 하에서 단백질의 기능 이상 및 구조 변형 유무를 규명하고 질병 과정을 추적하는 것은 중요한 연구이다. 일반적으로 단백질의 발현량 변화 분석에는 통계적 방법이 많이 사용되고 있으며 단백질 상용 이미지 분석 소프트웨어에서 제공하는 그래픽을 이용한 방법들도 있으나, 이 방법들은 많은 조직 내에 존재하는 수많은 단백질을 수동으로 비교해야 하는 어려움이 있다. 본 논문에서는 데이터베이스와 데이터마이닝 기법을 이용하여 OLAP 데이터 큐브와 Discovery-driven 탐색의 응용 방법을 제안한다. 데이터 큐브의 특성을 이용함에 의해서, 질병에 의해 발현량이 변하는 단백질 뿐 아니라 임상적 특성과 단백질의 영향 관계를 분석하는 것이 가능하다. 데이터 큐브에서 단백질의 발현량 변화 분석에 적합한 데이터 큐브의 척도와Discovery-driven 탐색을 위한 예외 지표를 제안하고, 특히 In-exception을 계산하는데 있어서의 계산량 감소 방안을 제시한다. 실험을 통해 폐암 2-DE 데이터에서 데이터 큐브와 Discovery-driven 방법이 유용함을 보인다.