• Fisheries and Aquatic Sciences
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• v.5 no.4
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• pp.263-270
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• 2002

The Pacific oysters, Crassostrea gigas, were stressed with different concentrations of benzo(a) pyrene and depurated to determine the hemocyte lysosomal membrane stability and hydrolytic enzymatic activity as a biomarker candidate to the chemical, using NRR (neutral red retention) and API ZYM System, respectively. The membrane damage measured as NRR decrease was significant with the increase of chemical concentration and exposure time (P<0.05), providing a possible tool for biomarker. Interestingly, the control showed intrinsic stress probably due to captive life in the laboratory, and a recovering trend was also found during the depuration. The benzo(a)pyrene-exposed oysters showed increased enzyme activities in alkaline phosphatase, esterase (C4), acid phosphatase, naphthol-AS-BI-phospho­hydrolase, $\beta$-galactosidase, $\beta$-glucuronidase, and N-acetyl- $\beta$-glucosaminidase. Of them, only two enzymes, acid phosphatase and alkaline phosphatase, showed some potential available for the generation of enzymatic biomarker in the oyster. The results are suggestive of the potential availability of the cellular and enzymatic properties as a biomarker. However, considering that a robust biomarker should be insensitive to natural stress coming from normal physiological variation, but sensitive to pollutants, a concept of intrinsic stress the animal possesses should be taken into consideration. This reflects the necessity of further research on the intrinsic stress affecting the cellular and enzymatic properties of the chemical­stressed oysters prior to using the data as a biomarker.

• BMB Reports
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• v.44 no.12
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• pp.765-771
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• 2011

Cancer treatment has been stratified by companion biomarker tests that serve to provide information on the genetic status of cancer patients and to identify patients who can be expected to respond to a given treatment. This stratification guarantees better efficiency and safety during treatment. Cancer patients, however, marginally benefit from the current companion biomarker-aided treatment regimens, presumably because companion biomarker tests are dependent solely on the mutation status of several genes status quo. In the true sense of the term, "personalized medicine", cancer patients are deemed to be identified individually by their molecular signatures, which are not necessarily confined to genetic mutations. Glycosylation is tremendously dynamic and shows alterations in cancer. Evidence is accumulating that aberrant glycosylation contributes to the development and progression of cancer, holding the promise for use of glycosylation status as a companion biomarker in cancer treatment. There are, however, several challenges derived from the lack of a reliable detection system for aberrant glycosylation, and a limited library of aberrant glycosylation. The challenges should be addressed if glycosylation status is to be used as a companion biomarker in cancer treatment and contribute to the fulfillment of personalized medicine.

• Proceedings of the Korea Society of Environmental Toocicology Conference
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• 1998.06a
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• pp.85-93
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• 1998

• Ocean and Polar Research
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• v.30 no.1
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• pp.109-117
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• 2008

Application of biomarkers for assessing marine environmental health risk is a relatively new field. According to the National Research Council and the World Health Organization, biomarkers can be divided into three classes: biomarkers of exposure, biomarkers of effect, and biomarkers of susceptibility. In order to assess exposure to or effect of the environmental pollutants on marine ecosystem, the following set of biomarkers can be examined: detoxification, oxidative stress, biotransformation products, stress responses, apoptosis, physiological metabolisms, neuromuscular responses, reproductions, steroid hormones, antioxidants, genetic modifications. Since early 1990s, several biomarker research groups have developed health indices of marine organisms to be used for assessing the state of the marine environment. Biomarker indices can be used to interpret data obtained from monitoring biological effects. In this review, we will summarize Health assessment Index, Biomarker Index, Bioeffect Assessment Index and Generalized Linear Model. Measurements of biomarker responses and development of biomarker index in marine organisms from contaminated sites offer great a lot of information, which can be used in environmental monitoring programs, designed for various aspects of ecosystem risk assessment.

• Communications for Statistical Applications and Methods
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• v.14 no.1
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• pp.169-182
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• 2007

Receive operating characteristic (ROC) approach can be employed to rank candidate genes from a microarray experiment, in particular, for the biomarker development with the purpose of population screening of a cancer. In the cancer microarray experiment based on n patients the researcher often wants to compare the tumor tissue with the normal tissue within the same individual using a common reference RNA. Ideally, this experiment produces n pairs of microarray data. However, it is often the case that there are missing values either in the normal or tumor tissue data. Practically, we have $n_1$ pairs of complete observations, $n_2$ "normal only" and $n_3$ "tumor only" data for the microarray. We refer to this data set as a mixed data set. We develop a ROC approach on the mixed data set to rank candidate genes for the biomarker development for the colorectal cancer screening. It turns out that the correlation between two ranks in terms of ROC and t statistics based on the top 50 genes of ROC rank is less than 0.6. This result indicates that employing a right approach of ranking candidate genes for the biomarker development is important for the allocation of resources.

• Journal of Aquaculture
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• v.18 no.3
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• pp.196-199
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• 2005

Farmed red sea breams, Pagrus major, were fed for 60 days with pellets containing different concentrations of benzo(a)pyrene (0, 0.2, 2, 20 mg/kg) to generate a biomarker of the chemical toxicity in the fish. The fish exposed to the chemical concentrations did not show any significant difference in the weight gain, conditioning, factor, and hepatosomatic index. However, some haematological parameters, such as glucose, calcium, magnesium, GOT (glutamic oxalate transaminase), and GPT (glutamic pyruvate transaminase) were influenced by the chemical exposure. Of them, two enzymes, GOT and GPT, increased significantly 60 days after the exposure in a way of concentration dependence (P<0.05). In the study of ecotoxicological biomarker, sensitivity to adverse environments is one of the key available factors. The fish changes in GOT and GPT were an earlier and reliable sign of the fish response against the chemical exposure, rendering the two enzymatic factors as a useful biomarker at least to benzo(a)pyrene exposure in the farming waters.

• Asian Pacific Journal of Cancer Prevention
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• v.17 no.4
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• pp.2297-2299
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• 2016

Background: Early detection of malignant transformation with expression biomarkers has significant potential to improve the survival rate of patients as such biomarkers enable prediction of progression and assess sensitivity to chemotherapy. The expression of interferon inducible transmembrane protein 1 (IFITM1) has been associated with early invasion events in several carcinomas, including head and neck cancers, and hence has been proposed as a novel candidate biomarker. As the incidence of oral squamous cell carcinoma (OSCC) is highest in the Indian population, we sought to investigate: 1) the expression pattern of IFITM1 in OSCC tissue samples obtained from Indian patients of Dravidian origin; and 2) the possibility of using IFITM1 expression as a potential biomarker. Materials and Methods: Total RNA extracted from thirty eight OSCC biopsy samples was subjected to semi-quantitative RT-PCR with IFITM1 and GAPDH specific primers. Results: Of the thirty eight OSCC samples that were analyzed, IFITM1 overexpression was identified in fifteen (39%). Seven expressed a low level, while the remainder expressed high level of IFITM1. Conclusions: The overexpression of IFITM1 in OSCC samples indicates that IFITM1 may be explored for the possibility of use as a high confidence diagnostic biomarker in oral cancers. To the best of our knowledge, this is the first time that IFITM1 overexpression is being reported in Indian OSCC samples.

• BMB Reports
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• v.45 no.6
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• pp.323-330
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• 2012

The glycome consists of all glycans (or carbohydrates) within a biological system, and modulates a wide range of important biological activities, from protein folding to cellular communications. The mining of the glycome for disease markers represents a new paradigm for biomarker discovery; however, this effort is severely complicated by the vast complexity and structural diversity of glycans. This review summarizes recent developments in analytical technology and methodology as applied to the fields of glycomics and glycoproteomics. Mass spectrometric strategies for glycan compositional profiling are described, as are potential refinements which allow structure-specific profiling. Analytical methods that can discern protein glycosylation at a specific site of modification are also discussed in detail. Biomarker discovery applications are shown at each level of analysis, highlighting the key role that glycoscience can play in helping scientists understand disease biology.

• Journal of Korea Multimedia Society
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• v.23 no.8
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• pp.940-952
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• 2020

The Alzheimer's disease (AD) is a neurodegenerative disease commonly found in the elderly individuals. It is one of the most common forms of dementia; patients with AD suffer from a degradation of cognitive abilities over time. To correctly diagnose AD, compuated-aided system equipped with automatic classification algorithm is of great importance. In this paper, we propose a novel deep learning based classification algorithm that takes advantage of MRI biomarker images including brain areas of hippocampus and cerebrospinal fluid for the purpose of improving the AD classification performance. In particular, we develop a new approach that effectively applies MRI biomarker patch images as input to 3D Deep Convolution Neural Network. To integrate multiple classification results from multiple biomarker patch images, we proposed the effective confidence score fusion that combine classification scores generated from soft-max layer. Experimental results show that AD classification performance can be considerably enhanced by using our proposed approach. Compared to the conventional AD classification approach relying on entire MRI input, our proposed method can improve AD classification performance of up to 10.57% thanks to using biomarker patch images. Moreover, the proposed method can attain better or comparable AD classification performances, compared to state-of-the-art methods.

• KSBB Journal
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• v.26 no.4
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• pp.352-356
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• 2011

The detection of biomarkers is an important issue for disease diagnosis. However, many systems are not suitable to detect the biomarker itself directly. For direct detection of biomarker proteins in human serum, a new affinity-capture method using aptamers combined with the mass spectrometry was suggested. Since signals from protein samples cannot be amplified, modified chromatin immunoprecipitation (ChIP) and subsequent cross-linking with formaldehyde between aptamers and target proteins were used not to lose the captured target proteins, which allowed us to perform a harsh washing step to remove the non-specifically bound proteins. As a model system, a thrombin aptamer was used as a bait and thrombin as a target protein. Using our modified ChIP and affinity-capture method, non-specific binding proteins on the beads decreased significantly, suggesting that our new method is efficient and can be applied to developing diagnosis systems for various biomarkers.