• Food Science of Animal Resources
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• v.33 no.2
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• pp.268-275
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• 2013

This study was performed to investigate the antioxidant activity of cherry added into meat products. Water and methanol were used to extract the antioxidant compounds from cherry. Total phenolic compounds of the methanol and water extract of cherry were 2.17 g/100 g and 2.77 g/100 g, respectively. The 1,1-diphenyl-2-picrylhydrazyl radical scavenging activity of methanol extract showed similar activities to those with ascorbic acid at all concentrations (from 0.1% to 2.0%). Especially, water extract of cherry showed similar activity to those of ascorbic acid (AA), and methanol extract, when 2% of cherry extract was added. The reducing power of cherry was not comparable to those with AA, however no differences in reducing power were observed between the water and methanol extract. The iron chelating ability of cherry was observed in the range of 17.8-94.0% at both water and methanol extracts. An increased iron chelating ability was observed with increased concentration up to 2%. Iron chelating ability for water extract of cherry tended to be lower than those with methanol extract. After pork patties were manufactured with methanol extract of cherry at 0.5 and 1.0%, physicochemical properties, lipid oxidation and microbial changes of patties were measured. The addition of methanol extract of cherry reduced pH, brightness, redness, yellowness and thiobarbituric acid reactive substance (TBARS). During 14 d of storage, pH, TBARS and microbial counts were increased, while redness and yellowness values were decreased. Since the addition of methanol extract of cherry lowered TBARS during storage, it could be used as a natural antioxidant in meat products.

• Journal of the East Asian Society of Dietary Life
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• v.26 no.3
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• pp.237-249
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• 2016

In this study, the antioxidant effect of Chungkukjang supplementation against memory impairment and oxidative stress in scopolamine (2 mg/kg i.p)-injected mice was investigated. The experimental animals were divided into five groups and fed experimental diets for 12 weeks; normal diet group (C), scopolamine + normal diet group (S), scopolamine + 63.0% soybean Chungkukjang supplementation group (SS), scopolamine + 45.0% Yakkong Chungkukjang supplementation group (SY), and scopolamine + 50.0% black foods such as black rice, black sesame seeds, and sea tangle added Yakkong Chungkukjang group (SYB). For the results of food intake, body weight gain, and brain weights, levels of scopolamine-injected groups were lower than the levels of the control group. The reduced brain weight of the scopolamine-injected group (S) was regulated to control level by supplementation of three types Chungkukjang. In the oxidative stress indicator, nitric oxide and malondialdehyde levels in serum of scopolamine-injected mice were higher than those of other groups. However, supplementation of soybeans, Yakkong and black foods added Yakkong Chungkukjang was proven to regulate them. Antioxidant enzyme activities such as superoxide dismutase (SOD) and glutathione-S-transferase (GST) in serum showed no significant differences among the groups. The reduced levels of vitamin A and vitamin E in serum and brain tissue of scopolamine-injected mice were controlled by supplementation of three types of Chungkukjang. Total antioxidant capacity (TAC) of scopolamine-injected group was lower than those of other groups. However, TAC was significantly elevated by Chunggukjang supplementation. Therefore, antioxidative effects of soybeans, Yakkong, and black foods added Yakkong Chungkukjang supplementations against oxidative stress in scopolamine-injected in mice could expected.

• Journal of Intelligence and Information Systems
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• v.21 no.3
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• pp.79-99
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• 2015

The prediction of bankruptcy has been extensively studied in the accounting and finance field. It can have an important impact on lending decisions and the profitability of financial institutions in terms of risk management. Many researchers have focused on constructing a more robust bankruptcy prediction model. Early studies primarily used statistical techniques such as multiple discriminant analysis (MDA) and logit analysis for bankruptcy prediction. However, many studies have demonstrated that artificial intelligence (AI) approaches, such as artificial neural networks (ANN), decision trees, case-based reasoning (CBR), and support vector machine (SVM), have been outperforming statistical techniques since 1990s for business classification problems because statistical methods have some rigid assumptions in their application. In previous studies on corporate bankruptcy, many researchers have focused on developing a bankruptcy prediction model using financial ratios. However, there are few studies that suggest the specific types of bankruptcy. Previous bankruptcy prediction models have generally been interested in predicting whether or not firms will become bankrupt. Most of the studies on bankruptcy types have focused on reviewing the previous literature or performing a case study. Thus, this study develops a model using data mining techniques for predicting the specific types of bankruptcy as well as the occurrence of bankruptcy in Korean small- and medium-sized construction firms in terms of profitability, stability, and activity index. Thus, firms will be able to prevent it from occurring in advance. We propose a hybrid approach using two artificial neural networks (ANNs) for the prediction of bankruptcy types. The first is a back-propagation neural network (BPN) model using supervised learning for bankruptcy prediction and the second is a self-organizing map (SOM) model using unsupervised learning to classify bankruptcy data into several types. Based on the constructed model, we predict the bankruptcy of companies by applying the BPN model to a validation set that was not utilized in the development of the model. This allows for identifying the specific types of bankruptcy by using bankruptcy data predicted by the BPN model. We calculated the average of selected input variables through statistical test for each cluster to interpret characteristics of the derived clusters in the SOM model. Each cluster represents bankruptcy type classified through data of bankruptcy firms, and input variables indicate financial ratios in interpreting the meaning of each cluster. The experimental result shows that each of five bankruptcy types has different characteristics according to financial ratios. Type 1 (severe bankruptcy) has inferior financial statements except for EBITDA (earnings before interest, taxes, depreciation, and amortization) to sales based on the clustering results. Type 2 (lack of stability) has a low quick ratio, low stockholder's equity to total assets, and high total borrowings to total assets. Type 3 (lack of activity) has a slightly low total asset turnover and fixed asset turnover. Type 4 (lack of profitability) has low retained earnings to total assets and EBITDA to sales which represent the indices of profitability. Type 5 (recoverable bankruptcy) includes firms that have a relatively good financial condition as compared to other bankruptcy types even though they are bankrupt. Based on the findings, researchers and practitioners engaged in the credit evaluation field can obtain more useful information about the types of corporate bankruptcy. In this paper, we utilized the financial ratios of firms to classify bankruptcy types. It is important to select the input variables that correctly predict bankruptcy and meaningfully classify the type of bankruptcy. In a further study, we will include non-financial factors such as size, industry, and age of the firms. Thus, we can obtain realistic clustering results for bankruptcy types by combining qualitative factors and reflecting the domain knowledge of experts.

• Korean Journal of Veterinary Research
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• v.36 no.4
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• pp.795-807
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• 1996

This study was conducted to identify the effects of caffeine on the lipid and protein components or blood chemistry levels of the serum as well as the total homogenate, mitochondrial and microsomal fraction of the rat(Sprague-Dawley, female) liver. Acute test were conducted to determine those effects. The acute test was conducted by dividing rats into 7 groups according to the time lapsed after a single oral administration of 100mg/kg caffeine(that is control, 2hrs, 4hrs, 8hrs, 24hrs, 48hrs and 72hrs lapsed group). The concentrations of glucose, urea nitrogen, uric acid, creatinine, total protein, albumin, A/G ratio, triglyceride, total cholesterol, HDL-cholesterol, free fatty acid, phospholipid as well as the activities of alanine aminotransferase(ALT), aspartate aminotransferase(AST) and alkaline phosphatase(ALP) were measured in the serum of each experimental groups. The concentrations of the carbonyl group, malondialdehyde(MDA) and the patterns of the SDS-PAGE(Sodium Dodecyl Sulfate-Polyacrylamide Gel Electrophoresis) were analyzed to determine the oxidative damages and metabolic changes on the lipid and protein components in the serum, and total homogenate, mitochondrial and microsomal fractions of the rat liver. The results obtained from this study were summarized as follows; 1. The concentrations of serum glucose were significantly higher(p<0.01) between 4(143.0mg/dl) and 8hrs(138.0mg/dl) in comparison to that of the control(101.1mg/dl) after a single oral administration of caffeine(100mg/kg). While on the other, there were no significant differences in the concentrations of urea nitrogen, uric acid, creatinine, total protein, albumin and albumin/globulin(A/G) ratio in comparison to those of the control. 2. The concentrations of total cholesterol and HDL-cholesterol in serum were significantly higher(p<0.01) between 4(77.4mg/dl, total cholesterol) and 8hrs(64.7mg/dl, HDL-cholesterol) in comparison to those of the control(62.8, 46.7mg/dl) after a single oral administration of caffeine(100mg/kg). On the other hand, the concentrations of triglyceride in serum were significantly lower(p<0.01) after 8hrs(38.8mg/dl) in comparison to that of the control(66.5mg/dl). 3. The activities of AST in serum was significantly higher(p<0.05) from 2hrs(149U/L) to 8hrs(178U/L) in comparison to the control(112U/L) after a single oral administration of caffeine(100mg/kg). The activities of ALT in serum were significantly higher(p<0.01) at 4(45.5U/L), 24(49.3U/L), 48(46.8U/L) and 72 hrs(42.3U/L) in comparison to that of the control(39.7U/L) after a single oral administration of caffeine(100mg/kg). On the other hand, there were no significant differences in the activities of ALP in comparison to that of the control. 4. The concentrations of free fatty acid in serum were significantly higher(p<0.01) at 8hrs(65.0mg/dl) in comparison to that of the control(37.6mg/dl) after a single oral administration of caffeine(100mg/kg). However, there were no significant differences in the concentrations of carbonyl group and malondialdehyde within serum, and liver homogenate, mitochondrial and microsomal fractions in comparison to that of the control. 5. The patterns of SDS-PAGE in serum, mitochondrial and microsomal fraction of the liver showed no significant differences.

• Journal of Life Science
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• v.26 no.2
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• pp.147-154
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• 2016

Osteosarcoma (OS) is the most common and malignant bone tumors. Although many types of resection surgery and experimental agents were developed, median survival and clinical prognosis are poorly investigated. Recently, several researches have reported that Eucheuma cottonii has potent as protective effects of coal dust-induced lung damage via inhibition of malondialdehyde (MDA) and oxidative stress in bronchoalveolar lavage fluids (BALF). However, anti-cancer effects and specific molecular mechanism of extract from Eucheuma cottonii (EE) has not been clearly studied yet. This study evaluated that anti-cancer potential of EE in human osteosarcoma Saos-2 cells. EE indicated cytotoxicity on Saos-2 cells in a dose-dependent manner. Morphological degradation and nucleic condensation were also observed under the EE treatment. However, it did not significantly affect on non-cancerous kidney HEK-293 cells under the same concentration which is shown cytotoxicity on Saos-2 cells. The phosphorylation of Fas-Associated Death Domain (FADD) and expression of cleaved caspase-8, -7 and -3 were upregulated in a dose-dependent manner. In immunofluorescence staining, expression level of Fas and cleaved PARP were upregulated by EE treatment. Furthermore, treatment of EE induces upregulation of sub G1 phase by flow cytometry analysis. The results demonstrated that EE has a therapeutic potential against osteosarcoma via FADD mediated caspase cascade apoptosis signal pathway.

• Korean Journal of Food Science and Technology
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• v.48 no.1
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• pp.77-85
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• 2016

In this study, 4-week-old rats were fed bread supplemented with Terminalia chebula (TC), Plantago asiatica (PA), Linder obtusiloba (LO), and Capsosiphon fulvescens (CF) ethanol extracts, to determine the decrease in blood glucose levels, as well as the anti-inflammatory and lipid-enhancing effects. Previous studies have demonstrated the antioxidative effects of these ethanol extracts. After sacrifice, the liver tissue, whole blood, and serum samples were collected for biochemical analysis. The results showed a significant decrease in blood glucose level, lipid peroxidation, malondialdehyde (MDA) level, HbA1c level, total cholesterol, and low-density lipoprotein (LDL)-cholesterol (p<0.05) and an increase in high-density lipoprotein (HDL)-cholesterol level in rats fed bread supplemented with LO and CF ethanol extracts (p<0.05). Therefore, the results of this study demonstrate that bread supplemented with LO and CF ethanol extracts can potentially affect the blood glucose level and lead to lipid enhancement.

• Korean Journal of Food Science and Technology
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• v.48 no.5
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• pp.508-514
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• 2016

Effects of the ethyl acetate fraction from gomchwi (Ligularia fischeri) extract against high $glucose/H_2O_2-induced$ oxidative stress and in vitro neurodegeneration were investigated to confirm the physiological property of the extract. The ethyl acetate fraction of gomchwi extract showed the highest total phenolic contents than the other solvent fractions. An anti-hyperglycemic activity of the ethyl acetate fraction was evaluated using the ${\alpha}-glucosidase$ inhibitory assay, and the half maximal inhibitory concentration ($IC_{50}$) value for ${\alpha}-glucosidase$ was found to be $727.64{\mu}g/mL$. In addition, the ethyl acetate fraction showed excellent 2,2'-azino-bis (3-ethylbenzthiazoline-6-sulfonic acid) diammonium salt radical scavenging activity, and inhibition of malondialdehyde production. The ethyl acetate fraction also decreased intracellular reactive oxygen species, whereas neuronal cell viability against high glucose/$H_2O_2$-induced cytotoxicity was found to be increased. Finally, 3,5-dicaffeoylquinic acid as a main phenolic compound in the ethyl acetate fraction was analyzed by high-performance liquid chromatography. These results suggest that gomchwi might be a good natural source of functional materials to prevent diabetic neurodegeneration.

• Food Science of Animal Resources
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• v.30 no.3
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• pp.466-471
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• 2010

This study investigated the effects of dietary supplementation of fermented apple pomace (FAP), fermented pear pomace (FPP), fermented orange pomace (FOP), and fermented Angelica keiskei Koidz pomace (FAKP) on performance, shelf life, fatty acid composition and cholesterol in broiler chickens. A total of 600, 1-day-old male broiler chicks (Cobb strain) were randomly divided into six groups with four replicates of 30 birds each. There were five treatment groups: control (C), FAP (1.0%, T1), FPP (1.0%, T2), FOP (1.0%, T3), and FAKP (1.0%, T4). The body weight of the broiler chickens fed FAP diet was higher (1,758 g) than the other treatments. There was no difference in the thiobarbituric acid reactive substances (TBARS) in chicken meat between all groups at days 1, 3, and 5 of storage, while the FAP-supplemented group displayed lower TBARS values at day 7. There was no significant difference in fatty acid composition between the groups but the cholesterol content of chicken meat was lower than the control groups. These results suggest the possibility that FAP could be used as a functional feed to improvement the quality performance of broiler chickens.

• Journal of Nutrition and Health
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• v.44 no.1
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• pp.5-15
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• 2011

Although iron is an essential mineral, excess iron intake during pregnancy may increase oxidative stress in tissues. This study was conducted to investigate the effects of iron overload during pregnancy on iron status and oxidative stress in maternal rats. Ten week-old female Sprague-Dawley rats were mated with male rats. Non-pregnant (control) and pregnant rats were fed diets containing normal Fe (35 mg/kg diet), high Fe (350 mg/kg diet), or excess Fe (1,050 mg/kg diet) during pregnancy. Rats were sacrificed on pregnancy day 19. No significant difference in weight gain, diet intake, or litter size was observed according to iron intake levels. Furthermore, serum iron, hemoglobin, and hematocrit were not different among the rats administered the three levels of Fe both in the control and pregnant groups. However, the iron levels were lower in pregnant rats than those in the control. The liver and spleen iron contents increased significantly in the excess Fe group. An increase in liver ferritin levels with increasing iron intake was observed. Protein carbonyl content, as a marker of oxidative stress, increased significantly in liver with increasing iron intake but not malondialdehyde. Glutathione peroxidase activity in the liver of pregnant rats fed excess iron decreased significantly. Bcl-2 protein expression in the liver declined remarkably with increasing maternal iron intake in pregnant rats. Taken together, iron overload during pregnancy had little effect on hematology. However, the deposits of iron in the liver and the decline in antioxidant enzyme activity implied increased oxidative stress in tissues of the excess Fe group. These results suggest that excess iron intake during pregnancy increases oxidative stress in maternal tissues and may also affect fetal tissues.

• Journal of Intelligence and Information Systems
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• v.22 no.2
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• pp.33-56
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• 2016

Many researchers have focused on developing bankruptcy prediction models using modeling techniques, such as statistical methods including multiple discriminant analysis (MDA) and logit analysis or artificial intelligence techniques containing artificial neural networks (ANN), decision trees, and support vector machines (SVM), to secure enhanced performance. Most of the bankruptcy prediction models in academic studies have used financial ratios as main input variables. The bankruptcy of firms is associated with firm's financial states and the external economic situation. However, the inclusion of qualitative information, such as the economic atmosphere, has not been actively discussed despite the fact that exploiting only financial ratios has some drawbacks. Accounting information, such as financial ratios, is based on past data, and it is usually determined one year before bankruptcy. Thus, a time lag exists between the point of closing financial statements and the point of credit evaluation. In addition, financial ratios do not contain environmental factors, such as external economic situations. Therefore, using only financial ratios may be insufficient in constructing a bankruptcy prediction model, because they essentially reflect past corporate internal accounting information while neglecting recent information. Thus, qualitative information must be added to the conventional bankruptcy prediction model to supplement accounting information. Due to the lack of an analytic mechanism for obtaining and processing qualitative information from various information sources, previous studies have only used qualitative information. However, recently, big data analytics, such as text mining techniques, have been drawing much attention in academia and industry, with an increasing amount of unstructured text data available on the web. A few previous studies have sought to adopt big data analytics in business prediction modeling. Nevertheless, the use of qualitative information on the web for business prediction modeling is still deemed to be in the primary stage, restricted to limited applications, such as stock prediction and movie revenue prediction applications. Thus, it is necessary to apply big data analytics techniques, such as text mining, to various business prediction problems, including credit risk evaluation. Analytic methods are required for processing qualitative information represented in unstructured text form due to the complexity of managing and processing unstructured text data. This study proposes a bankruptcy prediction model for Korean small- and medium-sized construction firms using both quantitative information, such as financial ratios, and qualitative information acquired from economic news articles. The performance of the proposed method depends on how well information types are transformed from qualitative into quantitative information that is suitable for incorporating into the bankruptcy prediction model. We employ big data analytics techniques, especially text mining, as a mechanism for processing qualitative information. The sentiment index is provided at the industry level by extracting from a large amount of text data to quantify the external economic atmosphere represented in the media. The proposed method involves keyword-based sentiment analysis using a domain-specific sentiment lexicon to extract sentiment from economic news articles. The generated sentiment lexicon is designed to represent sentiment for the construction business by considering the relationship between the occurring term and the actual situation with respect to the economic condition of the industry rather than the inherent semantics of the term. The experimental results proved that incorporating qualitative information based on big data analytics into the traditional bankruptcy prediction model based on accounting information is effective for enhancing the predictive performance. The sentiment variable extracted from economic news articles had an impact on corporate bankruptcy. In particular, a negative sentiment variable improved the accuracy of corporate bankruptcy prediction because the corporate bankruptcy of construction firms is sensitive to poor economic conditions. The bankruptcy prediction model using qualitative information based on big data analytics contributes to the field, in that it reflects not only relatively recent information but also environmental factors, such as external economic conditions.