• Asian Pacific Journal of Cancer Prevention
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• v.14 no.1
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• pp.351-354
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• 2013

Objective: To assess the practical utility of pleural fluid carbonic anhydrase XII (CAXII) quantification for differential diagnosis of effusions. Materials and Methods: Fluid was collected prospectively from fifty patients presenting with lymphocytic pleural effusions for investigation and CAXII was quantified by ELISA. Results: Pleural fluid CAXII concentrations were significantly higher in lung cancer patients (n=30) than in tuberculous controls (n=20). The sensitivity and specificity of this biomarker were 60%and 75%, respectively. CAXII measurement was not inferior to cytological examination in the diagnosis and exclusion of pleural effusions from lung cancer patitents (sensitivity 60% vs. 57%; specificity 75% vs. 100%; positive predictive value 77%; negative predictive value 54%). In patients with negative cytology, it offered a sensitivity of 54%. Conclusions: Pleural fluid CAXII is elevated in pleural effusions from lung cancer patients. Measurement of CAXII may be used in the future as a valuable adjunct to cytology in the diagnostic assessment of patients with pleural effusions related to lung cancer, especially when cytological examination is inconclusive.

• 대한생식의학회:학술대회논문집
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• 2006.06a
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• pp.156.1-156.1
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• 2006

• Journal of Life Science
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• v.30 no.12
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• pp.1118-1127
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• 2020

Alzheimer's disease causes progressive neuronal loss that leads to cognitive disturbances. It is not currently curable, and there is no way to stop its progression. However, since medical treatment for Alzheimer's disease is most effective in the early stages, early detection can provide the best chance for symptom management. Biomarkers for the diagnosis of Alzheimer's disease include amyloid β (Aβ) deposition, pathologic tau, and neurodegeneration. Aβ deposition and phosphorylated tau can be detected by cerebrospinal fluid (CSF) analysis or positron emission tomography (PET). However, CSF sampling is quite invasive, and PET analysis needs specialized and expensive equipment. During the last decades, blood-based biomarker analysis has been studied to develop fast and minimally invasive biomarker analysis method. And one of the remarkable findings is the involvement of platelets as a primary source of Aβ in plasma. Aβ can be transported across the blood - brain barrier, creating an equilibrium of Aβ levels between the brain and blood under normal condition. Interestingly, a number of clinical studies have unequivocally demonstrated that plasma Aβ42/Aβ40 ratios are reduced in mild cognitive impairment and Alzheimer's disease. Together, these recent findings may lead to the development of a fast and minimally invasive early diagnostic approach to Alzheimer's disease. In this review, we summarize recent advances in the biomarkers of Alzheimer's disease, especially the involvement of platelets as a source of peripheral Aβ production and its potential as a blood-based biomarker.

• Biomedical Science Letters
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• v.21 no.1
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• pp.1-8
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• 2015

Alzheimer's disease is a common form of dementia occurring among the elderly population and can be identified by symptoms such as cognition impairments, memory loss and neuronal dysfunction. Alzheimer's disease was found to be caused by the deposition of $\beta$-amyloid plaques and neurofibrillary tangles. In addition, mutation in the APP (Amyloid precursor protein), Presenilin 1 (PSEN1) and Presenilin 2 (PSEN2) genes were also found to contribute to Alzheimer's disease. Since the potential conformational diagnosis of Alzheimer's disease requires histopathological tests on brain through autopsy, potential early diagnosis still remains challenging. In recent years, several researches have proposed the use of biomarkers for early diagnosis. In cerebrospinal fluid (CSF), $\beta$-amyloid(1-42), phosphorylated-tau and total tau were suggested to be effective biomarkers for Alzheimer's disease diagnosis. However, a single biomarker might not be sufficient for potential diagnosis of Alzheimer's disease. Thus, the use of RNA interference (RNAi) through microRNAs (miRNAs) has been proposed by several researchers for simultaneous analysis of several biomarkers using microarray technology. These miRNA based biomarkers can be analysed from both blood and CSF, but miRNAs from blood are advantageous over CSF as they are non-invasive and simple for collection. Moreover, the RNAi based therapeutics by siRNA (short interference RNA) or shRNA (short hairpin RNA) have also been proposed to be effective in the treatment of Alzheimer's disease. This review describes the promising application of RNAi technology in therapeutics and as a biomarker for both Alzheimer's disease diagnosis and treatment.

• Journal of Gastric Cancer
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• v.24 no.1
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• pp.4-28
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• 2024

Liquid biopsy, a minimally invasive procedure that causes minimal pain and complication risks to patients, has been extensively studied for cancer diagnosis and treatment. Moreover, it facilitates comprehensive quantification and serial assessment of the whole-body tumor burden. Several biosources obtained through liquid biopsy have been studied as important biomarkers for establishing early diagnosis, monitoring minimal residual disease, and predicting the prognosis and response to treatment in patients with cancer. Although the clinical application of liquid biopsy in gastric cancer is not as robust as that in other cancers, biomarker studies using liquid biopsy are being actively conducted in patients with gastric cancer. Herein, we aimed to review the role of various biosources that can be obtained from patients with gastric cancer through liquid biopsies, such as blood, saliva, gastric juice, urine, stool, peritoneal lavage fluid, and ascites, by dividing them into cellular and acellular components. In addition, we reviewed previous studies on the diagnostic, prognostic, and predictive biomarkers for gastric cancer using liquid biopsy and discussed the limitations of liquid biopsy and the challenges to overcome these limitations in patients with gastric cancer.

• Clinical and Experimental Reproductive Medicine
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• v.44 no.2
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• pp.63-72
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• 2017

Objective: Hyperstimulation methods are broadly used for in vitro fertilization (IVF) in patients with infertility; however, the side effects associated with these therapies, such as ovarian hyperstimulation syndrome (OHSS), have not been well studied. N-glycoproteomes are subproteomes used for the remote sensing of ovarian stimulation in follicular growth. Glycoproteomic variation in human follicular fluid (hFF) has not been evaluated. In this study, we aimed to identify and quantify the glycoproteomes and N-glycoproteins (N-GPs) in natural and stimulated hFF using label-free nano-liquid chromatography/electrospray ionization-quad time-of-flight mass spectrometry. Methods: For profiling of the total proteome and glycoproteome, pooled protein samples from natural and stimulated hFF samples were selectively isolated using hydrazide chemistry to obtain the total proteomes and glycoproteomes. N-GPs were validated by the consensus sequence N-X-S/T (92.2% specificity for the N-glycomotif at p<0.05). All data were compared between natural versus hyperstimulated hFF samples. Results: We detected 41 and 44 N-GPs in the natural and stimulated hFF samples, respectively. Importantly, we identified 11 N-GPs with greater than two-fold upregulation in stimulated hFF samples compared to natural hFF samples. We also validated the novel N-GPs thyroxine-binding globulin, vitamin D-binding protein, and complement proteins C3 and C9. Conclusion: We identified and classified N-GPs in hFF to improve our understanding of follicular physiology in patients requiring assisted reproduction. Our results provided important insights into the prevention of hyperstimulation side effects, such as OHSS.

• BMB Reports
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• v.46 no.7
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• pp.338-345
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• 2013

microRNAs (miRNAs) are small non-coding RNAs that regulate gene expression by targeting the 3'-untranslated region of multiple target genes. Pathogenesis results from defects in several gene sets; therefore, disease progression could be prevented using miRNAs targeting multiple genes. Moreover, recent studies suggest that miRNAs reflect the stage of the specific disease, such as carcinogenesis. Cystic diseases, including polycystic kidney disease, polycystic liver disease, pancreatic cystic disease, and ovarian cystic disease, have common processes of cyst formation in the specific organ. Specifically, epithelial cells initiate abnormal cell proliferation and apoptosis as a result of alterations to key genes. Cysts are caused by fluid accumulation in the lumen. However, the molecular mechanisms underlying cyst formation and progression remain unclear. This review aims to introduce the key miRNAs related to cyst formation, and we suggest that miRNAs could be useful biomarkers and potential therapeutic targets in several cystic diseases.

• Molecules and Cells
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• v.41 no.3
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• pp.179-187
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• 2018

Proteomic analysis of extracellular vesicles (EVs) from biological fluid is a powerful approach to discover potential biomarkers for human diseases including cancers, as EV secreted to biological fluids are originated from the affected tissue. In order to investigate significant molecules related to the pathogenesis of bladder cancer, EVs were isolated from patient urine which was analyzed by mass spectrometry based proteomics. Comparison of the EV proteome to the whole urine proteome demonstrated an increased number of protein identification in EV. Comparative MS analyses of urinary EV from control subjects and bladder cancer patients identified a total of 1,222 proteins. Statistical analyses provided 56 proteins significantly increased in bladder cancer urine, including proteins for which expression levels varied by cancer stage (P-value < 0.05). While urine represents a valuable, non-invasive specimen for biomarker discovery in urologic cancers, there is a high degree of intra- and inter-individual variability in urine samples. The enrichment of urinary EV demonstrated its capability and applicability of providing a focused identification of biologically relevant proteins in urological diseases.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.10
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• pp.421-428
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• 2014

Like Alzheimer's disease, Parkinson's Disease(PD) is one of the most common neurodegenerative brain disorders. PD results from the deterioration of dopaminergic neurons in the brain region called the substantia nigra. Currently there is no cure for PD, but diagnosing in its early stage is important to provide treatments for relieving the symptoms and maintaining quality of life. Unlike many diagnosis methods of PD which use a single biomarker, we developed a diagnosis method that uses both biochemical biomarkers and imaging biomarkers. Our method uses ${\alpha}$-synuclein protein levels in the cerebrospinal fluid and diffusion tensor images(DTI). It achieved an accuracy over 91.3% in the 10-fold cross validation, and the best accuracy of 72% in an independent testing, which suggests a possibility for early detection of PD. We also analyzed the characteristics of the brain fiber pathways of Parkinson's disease patients and normal elderly people.

• Journal of Korea Multimedia Society
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• v.22 no.10
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• pp.1168-1177
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• 2019

In order to diagnose and prevent Alzheimer's Disease (AD), it is becoming increasingly important to develop a CAD(Computer-aided Diagnosis) system for AD diagnosis, which provides effective treatment for patients by analyzing 3D MRI images. It is essential to apply powerful deep learning algorithms in order to automatically classify stages of Alzheimer's Disease and to develop a Alzheimer's Disease support diagnosis system that has the function of detecting hippocampus and CSF(Cerebrospinal fluid) which are important biomarkers in diagnosis of Alzheimer's Disease. In this paper, for AD diagnosis, we classify a given MRI data into three categories of AD, mild cognitive impairment, and normal control according by applying 3D brain MRI image to the Faster R-CNN model and detect hippocampus and CSF in MRI image. To do this, we use the 2D MRI slice images extracted from the 3D MRI data of the Faster R-CNN, and perform the widely used majority voting algorithm on the resulting bounding box labels for classification. To verify the proposed method, we used the public ADNI data set, which is the standard brain MRI database. Experimental results show that the proposed method achieves impressive classification performance compared with other state-of-the-art methods.