• Journal of Radiopharmaceuticals and Molecular Probes
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• v.2 no.2
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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.

• Progress in Medical Physics
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• v.30 no.2
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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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• v.10 no.3
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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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• v.25 no.2
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• pp.214-215
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• 2024

• Korean Journal of Radiology
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• v.23 no.6
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• pp.574-580
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• 2022

• BMB Reports
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• v.57 no.3
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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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• v.56 no.3
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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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• v.41 no.2
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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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• v.41 no.4
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• pp.299-308
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• 2007

Purpose: The aim of this study was to develop a bioinformatics software and to test it in serum samples of papillary thyroid cancer using mass spectrometry (SELDI-TOF-MS). Materials and Methods: Development of 'Protein analysis' software performing decision tree analysis was done by customizing C4.5. Sixty-one serum samples from 27 papillary thyroid cancer, 17 autoimmune thyroiditis, 17 controls were applied to 2 types of protein chips, CM10 (weak cation exchange) and IMAC3 (metal binding - Cu). Mass spectrometry was performed to reveal the protein expression profiles. Decision trees were generated using 'Protein analysis' software, and automatically detected biomarker candidates. Validation analysis was performed for CM10 chip by random sampling. Results: Decision tree software, which can perform training and validation from profiling data, was developed. For CM10 and IMAC3 chips, 23 of 113 and 8 of 41 protein peaks were significantly different among 3 groups (p<0.05), respectively. Decision tree correctly classified 3 groups with an error rate of 3.3% for CM10 and 2.0% for IMAC3, and 4 and 7 biomarker candidates were detected respectively. In 2 group comparisons, all cancer samples were correctly discriminated from non-cancer samples (error rate = 0%) for CM10 by single node and for IMAC3 by multiple nodes. Validation results from 5 test sets revealed SELDI-TOF-MS and decision tree correctly differentiated cancers from non-cancers (54/55, 98%), while predictability was moderate in 3 group classification (36/55, 65%). Conclusion: Our in-house software was able to successfully build decision trees and detect biomarker candidates, therefore it could be useful for biomarker discovery and clinical follow up of papillary thyroid cancer.

• Korean Journal of Biological Psychiatry
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• v.18 no.2
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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.