• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.26 no.4
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• pp.396-403
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• 2016

Objectives: The study is to evaluate biological monitoring and risk assessment for epichlorohydrin-bisphenol A resin exposed from waterproofing or finishing work in the apartment building construction. Methods: Subjected workers were working on spray-painting and waterproofing work for 8 hours per day every 20 days. The urine samples were collected at the end of 20 days working period. For urinary bisphenol A as metabolite from epichlorohydrin-bisphenol A exposure, urine samples were analyzed with liquid chromatography mass-mass spectrometry(HPLC-MS/MS). Results: Geometric means of urinary bisphenol A(BPA) with no hydrolysis and with enzymic hydrolysis(BPA-EH) in the workers were $1.10{\mu}g/L$ and $2.90{\mu}g/L$. BPA-EH was 4 times higher than that of control group. The factors for working period and ages did not affect the variation of BPA and BPA-EH. The levels for BPA and BPA-EH were not higher than 95th percentile for exposure on human-life environment. Conclusions: The BPA and BPA-EH were therefore effective biological markers for epichlorohydrin-bisphenol A exposure workers, but not seem to hazardous exposure level. Waterproofing work in construction workshop is required to measuring work environment and health care management for the workers.

• Bulletin of the Korean Chemical Society
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• v.24 no.3
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• pp.285-290
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• 2003

An analytical method for the simultaneous determination of trace Ge, As and Se in biological samples by inductively coupled plasma/mass spectrometry has been investigated. The effects of added organic gas into the coolant argon gas on the analyte signal were studied to improve the detection limit, accuracy and precision. The addition of a small amount of methane (10 mL/min.) into the coolant gas channel improved the ionization of Ge, As and Se. The analytical sensitivity of the proposed Ar/CH₄system was superior by at least two-fold to that of the conventional Ar method. In the present method, the detection limits obtained for Ge, As and Se were 0.014, 0.012 and 0.064 ㎍/L, respectively. The analytical reliability of the proposed method was evaluated by analyzing the certified standard reference materials (SRM). Recoveries of 99.9% for Ge, 103% for As, 96.5% for Se were obtained for NIST SRM of freeze dried urine sample. The proposed method was also applied to the biological samples.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.10
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• pp.830-836
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• 2005

Application of microarray technologies which monitor simultaneously the expression pattern of thousands of individual genes in different biological systems results in a tremendous increase of the amount of available gene expression data and have provided new insights into gene expression during drug development, within disease processes, and across species. There is a great need of data mining methods allowing straightforward interpretation, visualization and analysis of the relevant information contained in gene expression profiles. Specially, classifying biological samples into known classes or phenotypes is an important practical application for microarray gene expression profiles. Gene expression profiles obtained from tissue samples of patients thus allowcancer classification. In this research, molecular classification of microarray gene expression data is applied for multi-class cancer using computational biology such gene selection, principal component analysis and fuzzy clustering. The proposed method was applied to microarray data from leukemia patients; specifically, it was used to interpret the gene expression pattern and analyze the leukemia subtype whose expression profiles correlated with four cases of acute leukemia gene expression. A basic understanding of the microarray data analysis is also introduced.

• Journal of Pharmaceutical Investigation
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• v.37 no.6
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• pp.377-395
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• 2007

Near infrared(NIR) spectral data have been used for the noninvasive analysis of various biological samples. Nonetheless, absorption bands of NIR region are overlapped extensively. It is very difficult to select the proper wavelengths of spectral data, which give the best PCR(principal component regression) models for the analysis of constituents of biological samples. The NIR data were used after polynomial smoothing and differentiation of 1st order, using Savitzky-Golay filters. To find the best PCR models, all-possible combinations of available principal components from the given NIR spectral data were derived by in-house programs written in MATLAB codes. All of the extensively generated PCR models were compared in terms of SEC(standard error of calibration), $R^2$, SEP(standard error of prediction) and SECP(standard error of calibration and prediction) to find the best combination of principal components of the initial PCR models. The initial PCR models were found by SEC or Malinowski's indicator function and a priori selection of spectral points were examined in terms of correlation coefficients between NIR data at each wavelength and corresponding concentrations. For the test of the developed program, aqueous solutions of BSA(bovine serum albumin) and glucose were prepared and analyzed. As a result, the best PCR models were found using a priori selection of spectral points and the final model selection by SEP or SECP.

• Proceedings of the Korean Society of Applied Pharmacology
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• 2007.11a
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• pp.19-27
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• 2007

An important potential of metabolomics-based approach is the possibility to develop fingerprints of diseases or cellular responses to classes of compounds with known common biological effect. Such fingerprints have the potential to allow classification of disease states or compounds, to provide mechanistic information on cellular perturbations and pathways and to identify biomarkers specific for disease severity and drug efficacy. Metabolic profiles of biological fluids contain a vast array of endogenous metabolites. Changes in those profiles resulting from perturbations of the system can be observed using analytical techniques, such as NMR and MS. $^1H$ NMR was used to generate a molecular fingerprint of serum or urinary sample, and then pattern recognition technique was applied to identity molecular signatures associated with the specific diseases or drug efficiency. Several metabolites that differentiate disease samples from the control were thoroughly characterized by NMR spectroscopy. We investigated the metabolic changes in human normal and clinical samples using $^1H$ NMR. Spectral data were applied to targeted profiling and spectral binning method, and then multivariate statistical data analysis (MVDA) was used to examine in detail the modulation of small molecule candidate biomarkers. We show that targeted profiling produces robust models, generates accurate metabolite concentration data, and provides data that can be used to help understand metabolic differences between healthy and disease population. Such metabolic signatures could provide diagnostic markers for a disease state or biomarkers for drug response phenotypes.

• Microbiology and Biotechnology Letters
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• v.49 no.3
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• pp.367-373
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• 2021

The antimicrobial resistance of Staphylococcus spp. and Gram negative strains present in air samples from waste sorting facilities was assessed. Phenotypic studies have revealed a high percentage of strains of Staphylococcus spp. resistant to methicillin. Genotypically and by RT-PCR, it was found that the mecA gene usually associated with methicillin resistance was present in 8% of the Staphylococcus strains isolated. About 30% of the Gram negative strains from the same samples also displayed resistance to meropenem and 79% of these were resistant to multiple antibiotics from different classes, namely cephalosporins and β-lactams. The results suggest that in professional activities with high levels of exposure to biological agents, the quantification and identification of the microbial flora in the work environment, with the determination of the presence of potential agents displaying multi-resistances is of relevance to the risk assessment. The personal protection of workers is particularly important relevance in these cases, since many of the strains that exhibit multi-resistance are potential opportunistic agents.

• Molecules and Cells
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• v.42 no.8
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• pp.579-588
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• 2019

Gene set enrichment analysis (GSEA) is a popular tool to identify underlying biological processes in clinical samples using their gene expression phenotypes. GSEA measures the enrichment of annotated gene sets that represent biological processes for differentially expressed genes (DEGs) in clinical samples. GSEA may be suboptimal for functional gene sets; however, because DEGs from the expression dataset may not be functional genes per se but dysregulated genes perturbed by bona fide functional genes. To overcome this shortcoming, we developed network-based GSEA (NGSEA), which measures the enrichment score of functional gene sets using the expression difference of not only individual genes but also their neighbors in the functional network. We found that NGSEA outperformed GSEA in identifying pathway gene sets for matched gene expression phenotypes. We also observed that NGSEA substantially improved the ability to retrieve known anti-cancer drugs from patient-derived gene expression data using drug-target gene sets compared with another method, Connectivity Map. We also repurposed FDA-approved drugs using NGSEA and experimentally validated budesonide as a chemical with anti-cancer effects for colorectal cancer. We, therefore, expect that NGSEA will facilitate both pathway interpretation of gene expression phenotypes and anti-cancer drug repositioning. NGSEA is freely available at www.inetbio.org/ngsea.

• Journal of Applied Biological Chemistry
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• v.62 no.1
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• pp.101-107
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• 2019

The objectives of this study were to evaluate the anti-aging effects and investigate the effect of simulated gastrointestinal (GI) digestion on the anti-aging properties and intestinal permeation of the potential fish collagen hydrolysates (FCH). Therefore, procollagen synthesis, matrix metalloproteinase-1 (MMP-1) production, and Caco-2 cell permeability were analyzed before and after in vitro digestion for FCHs, low-molecular weight fractions (<1 kDa), and high molecular weight fractions (>1 kDa). After being subjected to GI digestion, the level of MMP-1 inhibition was maintained, although the procollagen production was significantly (>20%) lower with all samples. Also, the digested FCHs and their <1 kDa fraction yielded 9.1 and 13.8% increased peptide transport, respectively, compared to undigested samples. Based on the effective intestinal permeation and high digestive enzyme stability, the <1 kDa fraction of FCHs is a potential bioactive material suitable for anti-aging applications in the food and cosmetics industries.

• Analytical Science and Technology
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• v.30 no.6
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• pp.355-378
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• 2017

Because neurochemicals are related to homeostasis and cognitive and behavioral functions in human body and because they enable the diagnosis of numerous neurodegenerative disorders, there has been increasing interest in the development of analytical platforms for neurochemical profiling in biological samples. In particular, mass spectrometry (MS)-based analytical methods combined with chromatographic separation have been widely used to profile neurochemicals in metabolic pathways. However, development of delicate sample preparation procedures and highly sensitive instrumental detection is necessary considering the trace levels and chemical instabilities of neurochemicals in biological samples. Therefore, in this review, analytical trends in MS-based metabolomics approaches to neurochemicals in multiple biological samples, such as urine, blood, CSF, and biological tissues, are discussed. This paper is expected to contribute to the development of an analytical platform to discover biomarkers that will aid diagnosis, prognosis, and treatment of neurodegenerative disorders.

• Journal of Mushroom
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• v.18 no.4
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• pp.403-409
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• 2020

This study was carried out to investigate the changes in the biological activity and nutritional content of Flammulina velutipes, Grifola frondosa, and Sparassis crispa extracts after roasting treatment. Regarding biological activities, the DPPH radical scavenging activity was the highest in the extracts of air-dried G. frondosa, while the nitrite scavenging activity was significantly higher in the extracts of roasted S. crispa (p<0.05). The total polyphenol contents of F. velutipes and S. crispa were significantly increased by the roasting treatment compared with those in fresh samples (p<0.05). Regarding the amino acid composition of edible mushrooms, the content of sweet-taste amino acids, including serine (Ser) and alanine (Ala), increased in G. frondosa after roasting, whereas bitter amino acids, including decreased in roasted versus samples. Moreover, the contents of essential amino acids such as leucine (Leu), isoleucine (Ile), methionine (Met), valine (Val), and histidine (His) in F. velutipes and S. crispa were increased due to the roasting treatment (versus fresh samples). Thus, it was confirmed that the r method is effective in improving the nutritional content of edible mushrooms.