• Journal of Microbiology and Biotechnology
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• v.21 no.2
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• pp.162-169
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• 2011

A dynamic model of lactic acid fermentation using Lactococcus lactis was constructed, and a metabolic flux analysis (MFA) and metabolic control analysis (MCA) were performed to reveal an intensive metabolic understanding of lactic acid bacteria (LAB). The parameter estimation was conducted with COPASI software to construct a more accurate metabolic model. The experimental data used in the parameter estimation were obtained from an LC-MS/MS analysis and time-course simulation study. The MFA results were a reasonable explanation of the experimental data. Through the parameter estimation, the metabolic system of lactic acid bacteria can be thoroughly understood through comparisons with the original parameters. The coefficients derived from the MCA indicated that the reaction rate of L-lactate dehydrogenase was activated by fructose 1,6-bisphosphate and pyruvate, and pyruvate appeared to be a stronger activator of L-lactate dehydrogenase than fructose 1,6-bisphosphate. Additionally, pyruvate acted as an inhibitor to pyruvate kinase and the phosphotransferase system. Glucose 6-phosphate and phosphoenolpyruvate showed activation effects on pyruvate kinase. Hexose transporter was the strongest effector on the flux through L-lactate dehydrogenase. The concentration control coefficient (CCC) showed similar results to the flux control coefficient (FCC).

• 한국생물공학회:학술대회논문집
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• 2000.04a
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• pp.181-184
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• 2000

The cooling rate of a bioreactor was measured to estimate the heat generation by microbial cultivation production. The estimated heat production was calculated from the varying temperature of cooling water. It was used for monitoring growth and specific metabolic events for microbial cultivations. Metabolic heat measured was also adopted for a control parameter for fed-batch cultivation.

• Journal of Microbiology and Biotechnology
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• v.14 no.6
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• pp.1163-1169
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• 2004

An unstructured mathematical model is presented for lactic acid fermentation based on the energy balance. The proposed model reflects the energy metabolic state and then predicts the cell growth, lactic acid production, and glucose consumption rates by relating the above rates with the energy metabolic rate. Fermentation experiments were conducted under various initial lactic acid concentrations of 0, 30, 50, 70, and 90 g/l. Also, a genetic algorithm was used for further optimization of the model parameters and included the operations of coding, initialization, hybridization, mutation, decoding, fitness calculation, selection, and reproduction exerted on individuals (or chromosomes) in a population. The simulation results showed a good fit between the model prediction and the experimental data. The genetic algorithm proved to be useful for model parameter optimization, suggesting wider applications in the field of biological engineering.

• Korean Journal of Radiology
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• v.23 no.10
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• pp.986-997
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• 2022

Objective: Whether metabolic redistribution occurs in patients with white matter hyperintensities (WMHs) on magnetic resonance imaging (MRI) is unknown. This study aimed 1) to propose a measure of the brain metabolic network for an individual patient and preliminarily apply it to identify impaired metabolic networks in patients with WMHs, and 2) to explore the clinical and imaging features of metabolic redistribution in patients with WMHs. Materials and Methods: This study included 50 patients with WMHs and 70 healthy controls (HCs) who underwent ¹⁸F-fluorodeoxyglucose-positron emission tomography/MRI. Various global property parameters according to graph theory and an individual parameter of brain metabolic network called "individual contribution index" were obtained. Parameter values were compared between the WMH and HC groups. The performance of the parameters in discriminating between the two groups was assessed using the area under the receiver operating characteristic curve (AUC). The correlation between the individual contribution index and Fazekas score was assessed, and the interaction between age and individual contribution index was determined. A generalized linear model was fitted with the individual contribution index as the dependent variable and the mean standardized uptake value (SUV_mean) of nodes in the whole-brain network or seven classic functional networks as independent variables to determine their association. Results: The means ± standard deviations of the individual contribution index were (0.697 ± 10.9) × 10^-3 and (0.0967 ± 0.0545) × 10^-3 in the WMH and HC groups, respectively (p < 0.001). The AUC of the individual contribution index was 0.864 (95% confidence interval, 0.785-0.943). A positive correlation was identified between the individual contribution index and the Fazekas scores in patients with WMHs (r = 0.57, p < 0.001). Age and individual contribution index demonstrated a significant interaction effect on the Fazekas score. A significant direct association was observed between the individual contribution index and the SUV_mean of the limbic network (p < 0.001). Conclusion: The individual contribution index may demonstrate the redistribution of the brain metabolic network in patients with WMHs.

• Kidney Research and Clinical Practice
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• v.36 no.1
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• pp.48-57
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• 2017

Background: Hepatic steatosis measured with controlled attenuation parameter (CAP) using transient elastography predicts metabolic syndrome in the general population. We investigated whether CAP predicted metabolic syndrome in chronic kidney disease patients. Methods: CAP was measured with transient elastography in 465 predialysis chronic kidney disease patients (mean age, 57.5 years). Results: The median CAP value was 239 (202-274) dB/m. In 195 (41.9%) patients with metabolic syndrome, diabetes mellitus was more prevalent (105 [53.8%] vs. 71 [26.3%], P < 0.001), with significantly increased urine albumin-to-creatinine ratio (184 [38-706] vs. 56 [16-408] mg/g Cr, P = 0.003), high sensitivity C-reactive protein levels (5.4 [1.4-28.2] vs. 1.7 [0.6-9.9] mg/L, P < 0.001), and CAP (248 [210-302] vs. 226 [196-259] dB/m, P < 0.001). In multiple linear regression analysis, CAP was independently related to body mass index (${\beta}=0.742$, P < 0.001), triglyceride levels (${\beta}=2.034$, P < 0.001), estimated glomerular filtration rate (${\beta}=0.316$, P = 0.001), serum albumin (${\beta}=1.386$, P < 0.001), alanine aminotransferase (${\beta}=0.064$, P = 0.029), and total bilirubin (${\beta}=-0.881$, P = 0.009). In multiple logistic regression analysis, increased CAP was independently associated with increased metabolic syndrome risk (per 10 dB/m increase; odds ratio, 1.093; 95% confidence interval, 1.009-1.183; P = 0.029) even after adjusting for multiple confounding factors. Conclusion: Increased CAP measured with transient elastography significantly correlated with and could predict increased metabolic syndrome risk in chronic kidney disease patients.

• Journal of Veterinary Clinics
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• v.10 no.1
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• pp.65-94
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• 1993

This study examined metabolic profiles of 1349 Holstein cows from 91 commercial herds. Thirteen parameters which are consisted of twelve blood components and body condition score were examined and their mean values. standard deviations and standard limits, which are 80% confidential limits, in each lactational stage were reported. The variations of each parameter affected by season, individual milk yield, adjusted corrected milk yield of herd. and lactation number were also reported. A model of metabolic profile test applicable to this country where the average number of cows in a herd is small as to be fifteen is designed. Metabolic profiles as reflected in each parameter were discussed in relation to adequacy of dietary intake for production, milk production, reproductive performance, and diseases, and the possible measure to improve productivity of dairy cows were proposed. Much of the variation in parameters was due to differences between herds, and less to differences between seasons, differences between individual milk yield, and differences between lactational stages. As the average herd size in this country is small, it is believed that all the cows in a herd must be sampled, and the individual result of each parameter was compared with the standard limit for each lactational stage, and the percentage of cows which are outside the standard limits in a herd was calculated to use as a criteria for evaluation of the herd. Data outside the 99% confidential limits were to be deleted at first, but when the trends of the data outside the 99% confidential limits are same as the trends of the data within 99% confidential limits, the deleted data must be reviewed again, otherwise some important informations would be missed. The mean concentration of blood urea nitrogen in this study was much higher than that was reported in England, U.S.A. and Japan, and it was similar to the upper limits reported in England, U.S.A. and Japan. So it was thought that the concentration of blood urea nitrogen is improper as a criteria for protein intake. The increase of serum total protein cocentration beyond standard limits was due to increase of serum globulin concentration in most of the cows. The correlation coefficient between serum and protein and serum globulin concentration was 0.83. Serum globulin concentration was negatively related to adjusted corrected milk of herd. Serum albumin, calcium and magnessium concentrations were negatively related to adjusted corrected milk of herd, which indicate that high-producing individual or high-producing herd have not taken sufficient protein/amino acids, calcium and magnessium. Packed cell volume was negatively related to adjusted corrected milk of the herd, and the trend was same In each lactational stage. The correlation coefficient between serum and packed cell volume was 0.16 and the correlation was very weak. Blood glucose concentration was lowest in early lactational stage, which indicates negative energy balance in early lactational stage. Blood glucose concentration was negatively related to adjusted corrected milk of herd from peak to late lactational stage, which indicates negative energy balance during the period in high-producing individuals or high-producing herds. Correlation coefficient between serum aspartate aminotransferase activity and serum ${\gamma}$-glutamyltransferase activity was 0.41, and this indicates that serum ${\gamma}$-glutamyltransferase should be included as a parameter of metabolic profile test to evaluate liver function. Body condition score of dairy cows in this country was lower than that of Japan in every lactational stages, and the magnitude of increase in body condition score during middle and late lactational stages was small. Metabolic profile can not be evaluated with solely nutritional intake. When an individual or large percentage of cows in a herd have adnormal values In parameters of metabolic profile test, veterinary clinician and nutritionist should cooperate so as to diagnose diseases and to calculate the e of no운ents simultaneously.

• Clinical and Experimental Pediatrics
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• v.52 no.3
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• pp.370-375
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• 2009

Purpose : The risk of metabolic syndrome (MS) and cardiovascular disease in Turner syndrome (TS) patients is high. We analyzed metabolic factors in adults with TS and evaluated the metabolic risk of insulin resistance. Methods : Forty-three adults with TS were enrolled. The frequency of MS and the values of the metabolic factors were analyzed. Patients were divided into insulin resistant and non-resistant groups according to values of homeostasis model assessment of insulin resistance (HOMA-IR). The correlations of HOMA-IR with metabolic parameters were analyzed. Results : The frequency of MS was 7% and those of each metabolic parameter were as follows: insulin resistance, 16.3%; central obesity, 15.4%; hypertriglyceridemia, 2.3%; low HDL cholesterol, 9.3%; hypertension, 36.8%. The insulin-resistant group had significantly higher values of body mass index (BMI), waist circumference (WC), fasting plasma glucose (FPG), HOMA-IR, and systolic blood pressure (SBP) than the non-resistant group (P<0.05). HOMA-IR showed a significantly positive correlation with BMI, WC, FPG, and SBP and showed a negative correlation with HDL cholesterol. Conclusion : This study suggests that adults with TS have a high risk of metabolic syndrome, and insulin resistance is correlated with metabolic factors. Therefore, TS patients should have their metabolic parameters monitored regularly to minimize metabolic complications and prevent cardiovascular diseases.

• Clinical and Experimental Reproductive Medicine
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• v.50 no.3
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• pp.206-212
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• 2023

Objective: The aim of the present study was to evaluate the associations between hematologic parameters related to systemic inflammation and insulin resistance-associated metabolic parameters in women with polycystic ovary syndrome (PCOS). Methods: Eighty-two women between the ages of 18 and 35 years who were diagnosed with PCOS were included in this study. A 2-hour 75-g oral glucose tolerance test (OGTT) was administered to all study participants; fasting and postprandial glucose and insulin levels were measured simultaneously during the 2-hour OGTT. Hematologic parameters were derived from a standard complete blood count and a differential count of fasting-state blood samples. The correlations between hematologic parameters and insulin resistance-associated clinical and metabolic parameters were evaluated using the Spearman rank correlation and partial correlation coefficients. Hematologic parameters related to systemic inflammation were compared between the two groups, categorized by the presence or absence of insulin resistance. Results: Significant differences in the absolute neutrophil count, absolute monocyte count, platelet count, and neutrophil-lymphocyte ratio were found between the insulin-resistant group and insulin-nonresistant group. Correlation analysis found that all hematological parameters, except for the platelet-lymphocyte ratio, were associated with at least one insulin resistance-associated metabolic parameter. However, these significant correlations between hematological and metabolic parameters were attenuated after controlling for the effects of other covariates using partial correlation analysis. Conclusion: The association between hematologic parameters indicative of systemic inflammation and insulin resistance-associated metabolic parameters seems to be strongly influenced by other anthropometric covariates in women with PCOS.

• Korean Journal of Biological Psychiatry
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• v.19 no.2
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• pp.84-90
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• 2012

Objectives : The purpose of this study was to examine the changes in metabolic parameters and Positive and Negative Syndrome Scale (PANSS) scores of patients previously treated with atypical antipsychotic drugs other than paliperidone, after 8 weeks of treatment with paliperidone. Methods : Changes in body weight, body mass index, leptin, lipid levels, fasting glucose, and PANSS scores of patients who switched from other atypical antipsychotic drugs to paliperidone were measured after 8 weeks of treatment with paliperidone. We compared these results with those of patients who had not been treated with antipsychotic drugs for at least 2 weeks prior to treatment with paliperidone (antipsychotic drug-free patients). Results : The antipsychotic drug-free group (n = 9) did not show significant changes in metabolic parameters, but showed a significant improvement in total and subscale scores of PANSS. In the group that switched from other atypical antipsychotic drugs to paliperidone (n = 13), body weight, body mass index and fasting glucose level significantly increased, while total and subscale scores of PANSS significantly improved. Conclusions : Paliperidone treatment will benefit patients with schizophrenia who have been antipsychotic drug-free or who have had difficulty with other atypical antipsychotic drugs, with regard to their psychopathological state. However, if patients have been treated with other atypical antipsychotic drugs before switching to paliperidone, they could gain body weight or their fasting glucose level could increase over a short period because of a change in receptor number and sensitivity caused by the previously prescribed antipsychotic drugs, and hence, paliperidone should be prescribed with caution for these patients.

• Mass Spectrometry Letters
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• v.11 no.1
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• pp.1-5
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• 2020

In this study, some of the recently reported data processing strategies were evaluated and modified based on their capabilities and a brief workflow for data mining was redefined for Q-TOF LC-MS based untargeted metabolomics. Commercial pooled human plasma samples were used for this purpose. An ultrafiltration procedure was applied on sample preparation. Sample set was analyzed through Q-TOF LC/MS. A C18 column (Agilent Zorbax 1.8 µM, 50 × 2.1 mm) was used for chromatographic separation. Raw chromatograms were processed using XCMS - R programming language edition and Isotopologue Parameter Optimization (IPO) was used to optimize XCMS parameters. The raw XCMS table was processed using MS Excel to find reliable and reproducible peaks. Totally 1650 reliable and reproducible potential metabolite peaks were found based on the data processing procedures given in this paper. The redefined dataset was upload into MetaboAnalyst platform and the identified metabolites were matched with 86 metabolic pathways. Thus, two list were obtained and presented in this study as supplement files. The first list is to present the retention times and m/z values of detected metabolite peaks. The second list is the metabolic pathways related with the identified metabolites. The briefly described data processing strategies and dataset presented in this study could be beneficial for the researchers working on untargeted metabolomics for processing their data and validating their results.