• 대한방사성의약품학회지
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• 제2권2호
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• pp.63-68
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• 2016

Peptides have been developed for in vivo imaging probes against to the specific biomarker in the biological process of living systems. Peptide based imaging probes have been applied to identify and detect their active sites using imaging modalities, such as PET, SPECT and MRI. Especially, tumor receptor imaging with the peptides has been widely used to specific tumor detection. This review discusses the targeting peptides that have been successfully characterized for tumor diagnosis by receptor imaging.

• 한국의학물리학회지:의학물리
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• 제30권2호
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• pp.39-48
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• 2019

Deep learning has been applied to various medical data. In particular, current deep learning models exhibit remarkable performance at specific tasks, sometimes offering higher accuracy than that of experts for discriminating specific diseases from medical images. The current status of deep learning applications to molecular imaging can be divided into a few subtypes in terms of their purposes: differential diagnostic classification, enhancement of image acquisition, and image-based quantification. As functional and pathophysiologic information is key to molecular imaging, this review will emphasize the need for accurate biomarker acquisition by deep learning in molecular imaging. Furthermore, this review addresses practical issues that include clinical validation, data distribution, labeling issues, and harmonization to achieve clinically feasible deep learning models. Eventually, deep learning will enhance the role of theranostics, which aims at precision targeting of pathophysiology by maximizing molecular imaging functional information.

• Mass Spectrometry Letters
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• 제10권3호
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• pp.71-78
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• 2019

For several decades, cancer has been the primary cause of mortality worldwide. New diagnosis and regimens have been developed to improve the chemotherapeutic efficacy and the quality of life of the patients. However, cancer tissues are complex and difficult to assess. Understanding the various properties of the tumor and its environment is crucial for cancer and pharmaceutical research. Several analytical techniques have been providing new insights into cancer research. Recently, matrix-assisted laser desorption ionization (MALDI)-mass spectrometry imaging (MSI), an advanced analytical technique, has been applied to translational research. Proteomic and lipidomic profiling obtained by MALDI-MSI has been critical for biomarker discovery and for monitoring heterogenous tumor tissues. In this review, we discuss technical approaches, benefits and recent applications of MALDI-MSI as a valuable tool in cancer research, namely for diagnosis, therapy, prognosis.

• Korean Journal of Radiology
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• 제25권2호
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• pp.214-215
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• 2024

• Korean Journal of Radiology
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• 제23권6호
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• pp.574-580
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• 2022

• BMB Reports
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• 제57권3호
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• pp.155-160
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• 2024

Lung cancer carries one of the highest mortality rates among all cancers. It is often diagnosed at more advanced stages with limited treatment options compared to other malignancies. This study focuses on calnexin as a potential biomarker for diagnosis and treatment of lung cancer. Calnexin, a molecular chaperone integral to N-linked glycoprotein synthesis, has shown some associations with cancer. However, targeted therapeutic or diagnostic methods using calnexin have been proposed. Through 1D-LCMSMS, we identified calnexin as a biomarker for lung cancer and substantiated its expression in human lung cancer cell membranes using Western blotting, flow cytometry, and immunocytochemistry. Anti-calnexin antibodies exhibited complement-dependent cytotoxicity to lung cancer cell lines, resulting in a notable reduction in tumor growth in a subcutaneous xenograft model. Additionally, we verified the feasibility of labeling tumors through in vivo imaging using antibodies against calnexin. Furthermore, exosomal detection of calnexin suggested the potential utility of liquid biopsy for diagnostic purposes. In conclusion, this study establishes calnexin as a promising target for antibody-based lung cancer diagnosis and therapy, unlocking novel avenues for early detection and treatment.

• BMB Reports
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• 제56권3호
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• pp.190-195
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• 2023

We propose a novel blood biomarker detection method that uses miRNA super-resolution imaging to enable the early diagnosis of Alzheimer's disease (AD). Here, we report a single-molecule detection method for visualizing disease-specific miRNA in tissue from an AD mice model, and peripheral blood mononuclear cells (PBMCs) from AD patients. Using optimized Magnified Analysis of Proteome (MAPs), we confirmed that five miRNAs contribute to neurodegenerative disease in the brain hippocampi of 5XFAD and wild-type mice. We also assessed PBMCs isolated from the whole blood of AD patients and a healthy control group, and subsequently analyzed those samples using miRNA super-resolution imaging. We detected more miR-200a-3p expression in the cornu ammonis 1 and dentate gyrus regions of 3 month-old 5XFAD mice than in wild-type mice. Additionally, miRNA super-resolution imaging of blood provides AD diagnosis platform for studying miRNA regulation inside cells at the single molecule level. Our results present a potential liquid biopsy method that could improve the diagnosis of early stage AD and other diseases.

• Nuclear Medicine and Molecular Imaging
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• 제41권2호
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• pp.132-140
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• 2007

Parkinson's disease is a common neurodegenerative disorder that is mainly caused by dopaminergic neuron loss in the substantia nigra. Several radiopharmaceutics have been developed to evaluate the integrity of dopaminergic neuronal system. In vivo PET and SPECT imaging of presynaptic dopamine imaing are already applied to Parkinson's disease and other parkinsonism, and can demonstrate the dopaminergic dysfunction. This review summarized the use of the presynaptic dopaminergic imaging in PD as biomarkers in evaluation of disease progression as well as in diagnosis of PD.

• Nuclear Medicine and Molecular Imaging
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• 제41권4호
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• pp.299-308
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• 2007

본 연구의 목적은 의사결정트리를 생성하는 생물정보학 프로그램을 개발하고, 이를 갑상선유두암 혈청의 질량분석자료로 시험해 보는 것이다. 대상 및 방법: C4.5를 커스터마이징하여 의사결정트리 분석을 수행할 수 있는 'Protein analysis'라는 프로그램을 개발하였다 61개의 혈청시료(갑상선유두암 27, 자가면역성 갑상선염 17, 대조군 17)를 일정 기간 동안 순차적으로 냉동한 후 실온에서 일시에 해동하여 분석에 사용하였다. 모든 시료는 탈지질화 과정을 거쳐 준비한 후, 2종류의 단백질칩(CM10, IMAC3)에 각각 60개, 50개 시료를 적용하였다. 갑상선유두암의 특징적인 단백질 패턴을 찾기 위해 질량분석기를 이용하여 단백질칩을 분석했다. 'Protein analysis' 프로그램을 이용하여 단백질분포 자료로부터 의사결정트리를 작성하고, 생체표지자 후보물질을 검출하였다. CM10칩에서 발견된 생체표지자 후보물질을 무작위 표본추출 방법을 이용하여 검증하였다. 결과: 단백질분포 자료의 훈련과 검증이 가능한 의사결정트리 프로그램이 개발되었으며, 이 프로그램은 트리 구조와 노드 정보, 트리 구성 과정을 표시하는 3개의 창으로 구성되었다. CM10칩을 이용한 분석에서 총 113개의 단백질 피크 중 23개가 3그룹 간에 유의한 차이가 있었으며, IMAC3는 41개의 단백질 피크 중 8개가 3그룹 간에 유의한 차이가 있었다. 3그룹 분석에서 의사결정트리는 CM10칩과 IMAE3의 단백질분포 자료로부터 각각 60개와 50개의 시료를 높은 정확도로 분류하였으며(오차율 = 각각 3.3%, 2.0%), 각각 4개와 7개의 생체표지자 후보물질을 검출하였다. 암시료와 비암시료를 구분하는 2그룹 분석 에서, 의사결정트리는 모든 암시료를 정확히 구분하였으며(모두 오차율 = 0%), CM10칩을 이용한 분석에서는 단일 노드를 사용하고, IMAC3칩을 이용한 분석에서는 여러 개의 노드를 사용하였다. CM10칩의 단백질 분포자료를 5번의 무작위 추출에 의해 시행한 검증에서 암시료와 비암시료를 구분하는데 높은 정확도를 보였으나(정확도 = 98%, 54/55), 3그룹을 구분할 때는 중등도의 정확도를 보였다(정확도 = 65%, 36/55). 결론: 우리가 개발한 프로그램은 질량분석 자료로부터 성공적으로 의사결정트리를 생성하고, 생체표지자 후보물질을 검출할 수 있었다. 따라서 이 프로그램은 혈청 시료를 이용한 생체표지자 발굴 및 갑상선유두암의 추적관찰에 유용하게 사용될 수 있을 것이다.

• 생물정신의학
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• 제18권2호
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• pp.72-79
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• 2011

Recent advances in brain imaging research are remarkable. Among them, many results from a variety of neuroimaging modalities in Alzheimer's dementia accompanied by the development and growing of imaging techniques have been presented in the research field. In this review we are focused on the imaging biomarkers for the Alzheimer's dementia to investigate the pathophysiologic mechanism. Future research on biomarkers for Alzheimer's dementia will provide more diverse and complex mechanisms or hypotheses than have been proposed in the current hypothesis about the pathogenesis of Alzheimer's dementia.