• Clinical Nutrition Research
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• v.12 no.2
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• pp.138-153
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• 2023

The prevalence of metabolic syndrome (MetS) and its cost are increasing due to lifestyle changes and aging. This study aimed to develop a deep neural network model for prediction and classification of MetS according to nutrient intake and other MetS-related factors. This study included 17,848 individuals aged 40-69 years from the Korea National Health and Nutrition Examination Survey (2013-2018). We set MetS (3-5 risk factors present) as the dependent variable and 52 MetS-related factors and nutrient intake variables as independent variables in a regression analysis. The analysis compared and analyzed model accuracy, precision and recall by conventional logistic regression, machine learning-based logistic regression and deep learning. The accuracy of train data was 81.2089, and the accuracy of test data was 81.1485 in a MetS classification and prediction model developed in this study. These accuracies were higher than those obtained by conventional logistic regression or machine learning-based logistic regression. Precision, recall, and F1-score also showed the high accuracy in the deep learning model. Blood alanine aminotransferase (β = 12.2035) level showed the highest regression coefficient followed by blood aspartate aminotransferase (β = 11.771) level, waist circumference (β = 10.8555), body mass index (β = 10.3842), and blood glycated hemoglobin (β = 10.1802) level. Fats (cholesterol [β = -2.0545] and saturated fatty acid [β = -2.0483]) showed high regression coefficients among nutrient intakes. The deep learning model for classification and prediction on MetS showed a higher accuracy than conventional logistic regression or machine learning-based logistic regression.

• Journal of Microbiology and Biotechnology
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• v.18 no.3
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• pp.532-538
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• 2008

For the newly isolated $H_2$-producing chemoheterotrophic bacterium Citrobacter amalonaticus Y19, anaerobic glucose metabolism was studied in batch cultivation at varying initial glucose concentrations (3.5-9.5 g/l). The carbon-mass and energy balances were determined and utilized to analyze the carbon metabolic-pathways network. The analyses revealed (a) variable production of major metabolites ($H_2$, ethanol, acetate, lactate, $CO_2$, and cell mass) depending on initial glucose levels; (b) influence of NADH regeneration on the production of acetate, lactate, and ethanol; and (c) influence of the molar production of ATP on the production of biomass. The results reported in this paper suggest how the carbon metabolic pathway(s) should be designed for optimal Hz production, especially at high glucose concentrations, such as by blocking the carbon flux via lactate dehydrogenase from the pyruvate node.

• Molecules and Cells
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• v.25 no.3
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• pp.352-357
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• 2008

Osteoprotegerin (OPG) is a soluble decoy receptor that inhibits osteoclastogenesis and is closely associated with bone resorption processes. We have designed and determined the solution structures of potent OPG analogue peptides, derived from sequences of the cysteine-rich domain of OPG. The inhibitory effects of the peptides on osteoclastogenesis are dose-dependent ($10^{-6}M-10^{-4}M$), and the activity of the linear peptide at $10^{-4}M$ is ten-fold higher than that of the cyclic OPG peptide. Both linear and cyclic peptides have a ${\beta}$-turn-like conformation and the cyclic peptide has a rigid conformation, suggesting that structural flexibility is an important factor for receptor binding. Based on structural and biochemical information about RANKL and the OPG peptides, we suggest that complex formation between the peptide and RANKL is mediated by both hydrophobic and hydrogen bonding interactions. These results provide structural insights that should aid in the design of peptidyl-mimetic inhibitors for treating metabolic bone diseases caused by abnormal osteoclast recruitment.

• Journal of Preventive Medicine and Public Health
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• v.47 no.6
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• pp.298-308
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• 2014

Objectives: To assess the current public participation in-local health policy and its implications through the analysis of policy networks in health center programs. Methods: We examined the decision-making process in sub-health center installations and the implementation process in metabolic syndrome management program cases in two districts ('gu's) of Seoul. Participants of the policy network were selected by the snowballing method and completed self-administered questionnaires. Actors, the interactions among actors, and the characteristics of the network were analyzed by Netminer. Results: The results showed that the public is not yet actively participating in the local public health policy processes of decision-making and implementation. In the decision-making process, most of the network actors were in the public sector, while the private sector was a minor actor and participated in only a limited number of issues after the major decisions were made. In the implementation process, the program was led by the health center, while other actors participated passively. Conclusions: Public participation in Korean public health policy is not yet well activated. Preliminary discussions with various stakeholders, including civil society, are needed before making important local public health policy decisions. In addition, efforts to include local institutions and residents in the implementation process with the public officials are necessary to improve the situation.

• Journal of Biomedical Engineering Research
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• v.19 no.5
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• pp.455-468
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• 1998

For the objective interpretation of cerebral metabolic patterns in epilepsy patients, we developed computer-aided classifier using artificial neural network. We studied interictal brain FDG PET scans of 257 epilepsy patients who were diagnosed as normal(n=64), L TLE (n=112), or R TLE (n=81) by visual interpretation. Automatically segmented volume of interest (VOI) was used to reliably extract the features representing patterns of cerebral metabolism. All images were spatially normalized to MNI standard PET template and smoothed with 16mm FWHM Gaussian kernel using SPM96. Mean count in cerebral region was normalized. The VOls for 34 cerebral regions were previously defined on the standard template and 17 different counts of mirrored regions to hemispheric midline were extracted from spatially normalized images. A three-layer feed-forward error back-propagation neural network classifier with 7 input nodes and 3 output nodes was used. The network was trained to interpret metabolic patterns and produce identical diagnoses with those of expert viewers. The performance of the neural network was optimized by testing with 5~40 nodes in hidden layer. Randomly selected 40 images from each group were used to train the network and the remainders were used to test the learned network. The optimized neural network gave a maximum agreement rate of 80.3% with expert viewers. It used 20 hidden nodes and was trained for 1508 epochs. Also, neural network gave agreement rates of 75~80% with 10 or 30 nodes in hidden layer. We conclude that artificial neural network performed as well as human experts and could be potentially useful as clinical decision support tool for the localization of epileptogenic zones.

• Horticultural Science & Technology
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• v.32 no.5
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• pp.684-693
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• 2014

Abiotic stress conditions such as cold, drought, and salinity trigger physiological and morphological changes and yield loss in plants. Hence, plants adapt to adverse environments by developing tolerance through complex regulation of genes related to various metabolic processes. This study was conducted to construct a coexpression network for multidirectional analysis of salt-stress response genes in Brassica rapa (Chinese cabbage). To construct the coexpression network, we collected KBGP-24K microarray data from the B. rapa EST and microarray database (BrEMD) and performed time-based expression analyses of B. rapa plants. The constructed coexpression network model showed 1,853 nodes, 5,740 edges, and 142 connected components (correlation coefficient > 0.85). On the basis of the significantly expressed genes in the network, we concluded that the development of salt tolerance is closely related to the activation of $Na^+$ transport by reactive oxygen species signaling and the accumulation of proline in Chinese cabbage.

• Journal of Microbiology and Biotechnology
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• v.15 no.3
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• pp.551-559
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• 2005

The recent rapid increase in genomic data related to many microorganisms and the development of computational tools to accurately analyze large amounts of data have enabled us to design several kinds of simulation approaches for the complex behaviors of cells. Among these approaches, dFBA (dynamic flux balance analysis), which utilizes FBA, differential equations, and regulatory events, has correctly predicted cellular behaviors under given environmental conditions. However, until now, dFBA has centered on substrate concentration, cell growth, and gene on/off, but a detailed hierarchical structure of a regulatory network has not been taken into account. The use of Boolean rules for regulatory events in dFBA has limited the representation of interactions between specific regulatory proteins and genes and the whole transcriptional regulation mechanism with environmental change. In this paper, we adopted the operon as the basic structure, constructed a hierarchical structure for a regulatory network with defined fundamental symbols, and introduced a weight between symbols in order to solve the above problems. Finally, the total control mechanism of regulatory elements (operons, genes, effectors, etc.) with time was simulated through the linkage of dFBA with regulatory network modeling. The lac operon, trp operon, and tna operon in the central metabolic network of E. coli were chosen as the basic models for control patterns. The suggested modeling method in this study can be adopted as a basic framework to describe other transcriptional regulations, and provide biologists and engineers with useful information on transcriptional regulation mechanisms under extracellular environmental change.

• Journal of Society of Preventive Korean Medicine
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• v.21 no.2
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• pp.1-13
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• 2017

Objectives : This paper serves to explore current trends of systems biology in Traditional Chinese Medicine (TCM) and examine how it may influence the Traditional Korean medicine. Methods : Literature review method was collectively used to classify Introduction to systems biology, diagnosis and syndrome classification of systems biology in TCM perspective, physiotherapy including acupuncture, herbs and formula functions, TCM systems biology, and directions of academic development. Results : The term 'Systems biology' is coined as a combination of systems science and biology. It is a field of study that tries to understand living organism by establishing a theory based on an ideal model that analyzes and predicts the desired output with understanding of interrelationships and dynamics between variables. Systems biology has an integrated and multi-dimensional nature that observes the interaction among the elements constructing the network. The current state of systems biology in TCM is categorized into 4 parts: diagnosis and syndrome, physical therapy, herbs and formulas and academic development of TCM systems biology and its technology. Diagnosis and syndrome field is focusing on developing TCM into personalized medicine by clarifying Kidney yin deficiency patterns and metabolic differences among five patterns of diabetes and analyzing plasma metabolism and biomarkers of coronary heart disease patients. In the field of physical therapy such as acupuncture and moxibustion, researchers discovered the effect of stimulating acupoint ST40 on gene expression and the effects of acupuncture on treating functional dyspepsia and acute ischemic stroke. Herbs and formulas were analyzed with TCM network pharmacology. The therapeutic mechanisms of Si Wu Tang and its series formulas are explained by identifying potential active substances, targets and mechanism of action, including metabolic pathways of amino acid and fatty acid. For the academic development of TCM systems biology and its technology, it is necessary to integrate massive database, integrate pharmacokinetics and pharmacodynamics, as well as systems biology. It is also essential to establish a platform to maximize herbal treatment through accumulation of research data and diseases-specific, or drug-specific network combined with clinical experiences, and identify functions and roles of molecules in herbs and conduct animal-based studies within TCM frame. So far, few literature reviews exist for systems biology in traditional Korean medicine and they merely re-examine known efficacies of simple substances, herbs and formulas. For the future, it is necessary to identify specific mechanisms of working agents and targets to maximize the effects of traditional medicine modalities. Conclusions : Systems biology is widely accepted and studied in TCM and already advanced into a field known as 'TCM systems biology', which calls for the study of incorporating TCM and systems biology. It is time for traditional Korean medicine to acknowledge the importance of systems biology and present scientific basis of traditional medicine and establish the principles of diagnosis, prevention and treatment of diseases. By doing so, traditional Korean medicine would be innovated and further developed into a personalized medicine.

• Journal of Microbiology and Biotechnology
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• v.16 no.6
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• pp.911-920
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• 2006

[ $CD8^+$ ] T Iymphocytes with the cytotoxic activity and capability to release various cytokines are the major players in immune responses against viral infection and cancer. To identify the proteins specific to resting or activated human CD8$^+$ T cells, human CD8$^+$ T cells were activated with anti-CD3+anti-CD28 mAb in the presence of IL-2. The solubilized proteins from resting and activated human CD8$^+$ T cells were separated by high-resolution two-dimensional polyacrylamide gel electrophoresis, and their proteomes were analyzed. Proteomic analysis of resting and activated T cells resulted in identification of 35 proteins with the altered expression. Mass spectrometry coupled with Profound and SWISS-PROT database analysis revealed that these identified proteins are to be functionally associated with cell proliferation, metabolic pathways, antigen presentation, and intracellular signal transduction pathways. We also identified six unknown proteins predicted from genomic DNA sequences specific to resting or activated CD8$^+$ T cells. Protein network studies and functional characterization of these novel proteins may provide new insight into the signaling transduction pathway of CD8$^+$ T cell activation.

• Journal of Microbiology and Biotechnology
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• v.16 no.6
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• pp.993-998
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• 2006

We earlier suggested a hierarchical regulatory network using defined modeling symbols and weights in order to improve the flux balance analysis (FBA) with regulatory events that were represented by if-then rules and Boolean logic. In the present study, the simulation results of the models, which were developed and improved from the previou model by incorporating a hierarchical regulatory network into the FBA, were compared with the experimental outcome of an aerobic batch growth of E. coli on glucose and tryptophan. From the experimental result, a diauxic growth curve was observed, reflecting growth resumption, when tryptophan was used as an alternativee after the supply of glucose was exhausted. The model parameters, the initial concentration of substrates (0.92 mM glucose and 1 mM tryptophan), cell density (0.0086 g biomass/1), the maximal uptake rates of substrates (5.4 mmol glucose/g DCW h and 1.32 mmol tryptophan/g DCW h), and lag time (0.32 h) were derived from the experimental data for more accurate prediction. The simulation results agreed with the experimental outcome of the temporal profiles of cell density and glucose, and tryptophan concentrations.