• 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.

• Development and Reproduction
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• v.8 no.1
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• pp.1-9
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• 2004

The potential importance of proteomic approaches has been clearly demonstrated in other fields of human medical research, including liver and heart disease and certain forms of cancer. However, reproductive researches have been applied to proteomics poorly. Proteomics can be defined as the systematic analysis of proteins for their identity, quantity, and function. It could increase the predictability of early drug development and identify non-invasive biomarkers of toxicity or efficacy. Proteome analysis is most commonly accomplished by the combination of two-dimensional gel electrophoresis(2DE) and MALDI-TOF(matrix-assisted laser desorption ionization-time of flight) MS(mass spectrometry) or protein chip array and SELDI-TOF(surface-enhanced laser desorption ionization-time of flight) MS. In addition understanding the possessing knowledge of the developing biomarkers used to assess reproductive biology will also be essential components relevant to the topic of reproduction. The continued integration of proteomic and genomic data will have a fundamental impact on our understanding of the normal functioning of cells and organisms and will give insights into complex cellular processes and disease and provides new opportunities for the development of diagnostics and therapeutics. The challenge to researchers in the field of reproduction is to harness this new technology as well as others that are available to a greater extent than at present as they have considerable potential to greatly improve our understanding of the molecular aspects of reproduction both in health and disease.

• Korean Journal of Environmental Biology
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• v.21 no.2
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• pp.87-100
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• 2003

It is important to understand the potential human health implications of exposure to environmental chemicals that may act as hormonally active agents. It is necessary to have an understanding of how pharmaceutical and personal care products and other chemicals affect the ecosystem of our planet as well as human health. Endocrine disruption is defined as the ability of a chemical contaminating the workplace or the environment to interfere with homeostasis, development, reproduction, and/or behavior in a living organism or it's offspring. Certain classes of environmentally persistent chemicals such as polychlorinated biphenyls (PCBs), dioxins, furans, and some pesticides can adversely effect the endocrine systems of aquatic life and terrestrial wildlife. Research continues to support the theory of endocrine disruption. However, endocrine disruption researches have been applied to proteomics poorly. Proteomics can be defined as the systematic analysis of proteins for their identity, quantity and function. It could increase the predictability of early drug development and identify non-invasive biomarkers of tonicity or efficacy. Proteome analysis is most commonly accomplished by the combination of two-dimensional gel electrophoresis (2D/E) and MALDI-TOF mass spectrometry (MS) sr protein chip array and SELDI-TOF MS. Proteomics have an opportunity to play an important role in resolving the question of what role endocrine disruptors play in initiating human disease. Proteomics can also play an imfortant role in the evaluation of the risk assessment and use of risk management and risk communication tools required to address public health concerns related to notions of endocrine disruptors. Understanding the need for the proteomics and possessing knowledge of the developing biomakers used to abbess endocrine activity potential will he essential components relevant to the topic of endocrine disruptors.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.3
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• pp.2037-2042
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• 2013

Background: Different methods of diagnosis have been found to be inefficient in terms of screening and early diagnosis of lung cancer. Cancer cells produce proteins whose serum levels may be elevated during the early stages of cancer development. Therefore, those proteins may be recognized as potential cancer markers. The aim of this study was to differentiate healthy individuals and lung cancer cases by analyzing their serum protein profiles and evaluate the efficacy of this method in the early diagnosis of lung cancer. Materials and Methods: 170 patients with lung cancer, 53 under high risk of lung cancer, and 47 healthy people were included in our study. Proteomic analysis of the samples was performed with the SELDI-TOF-MS approach. Results: The most discriminatory peak of the high risk group was 8141. When tree classification analysis was performed between lung cancer and the healthy control group, 11547 was determined as the most discriminatory peak, with a sensitivity of 85.5%, a specificity of 89.4%, a positive predictive value (PPV) of 96.7% and a negative predictive value (NPV) of 62.7%. Conclusions: We determined three different protein peaks 11480, 11547 and 11679 were only present in the lung cancer group. The 8141 peak was found in the high-risk group, but not in the lung cancer and control groups. These peaks may prove to be markers of lung cancer which suggests that they may be used in the early diagnosis of lung cancer.

• Proceedings of the Korean Society of Crop Science Conference
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• 2004.04a
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• pp.112-113
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• 2004

• Tuberculosis and Respiratory Diseases
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• v.61 no.1
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• pp.41-45
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• 2006

Background; Several clinical scoring systems are currently being used to predict the outcome of sepsis, but they all have certain limitations. Therefore, we sought to identify the proteomic biomarkers, with wsing proteomic tools, that differed according to the outcome of sepsis patients. Methods; Upon admission to the ICU, blood samples were obtained from the 16 patients with sepsis who were enrolled in this study. Surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI -TOF MS) was used to identify the markers that could predict the outcome of sepsis. Results; We found six peaks, by using cation and anion chips, that statistically differed between those patients who died and those who survived. Conclusion; The biomarkers we found by using proteomic tools may help predict the prognosis and also plan the treatment of sepsis.

• Journal of Animal Science and Technology
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• v.65 no.2
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• pp.401-411
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• 2023

Many studies have been conducted to improve technology for semen cryopreservation in pigs. However, computer-assisted analysis of sperm motility and morphology is insufficient to predict the molecular function of frozen-thawed semen. More accurate expression patterns of boar sperm proteins may be derived using the isobaric tags for relative and absolute quantification (iTRAQ) technique. In this study, the iTRAQ-labeling system was coupled with liquid chromatography tandem-mass spectrometry (LC-MS/MS) analysis to identify differentially expressed CM10-fractionated proteins between fresh and frozen-thawed boar semen. A total of 76 protein types were identified to be differentially expressed, among which 9 and 67 proteins showed higher and lower expression in frozen-thawed than in fresh sperm samples, respectively. The classified functions of these proteins included oxidative phosphorylation, mitochondrial inner membrane and matrix, and pyruvate metabolic processes, which are involved in adenosine triphosphate (ATP) synthesis; and sperm flagellum and motile cilium, which are involved in sperm tail structure. These results suggest a possible network of biomarkers associated with survival after the cryopreservation of Duroc boar semen.