• International Journal of Contents
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• v.14 no.3
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• pp.39-45
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• 2018

Purpose: Food Allergen Immunotherapy (Oral) is a form of immunotherapy administered to patients who are allergic to foods such as egg, milk, and peanut. The food allergen is orally administered to the patient in an escalating dose for desensitization or tolerance development. The safety and effectiveness of the therapy were assessed using a systematic literature review and meta-analysis. Methods: For a literature search, 8 national databases and a number of international databases including Ovid-MEDLINE, Ovid-EMBASE, and Cochrane Library were used; and 13 articles (all from international databases) were selected. The target of Food Allergen Immunotherapy (Oral) included patients with food allergy, and the intervention was food allergen immunotherapy without limiting the food type. The safety and effectiveness of Food Allergen Immunotherapy (Oral) were assessed by reviewing all the articles reporting on the therapy. The control group received standard therapies including aversion therapy, no treatment, anti-histamine treatment, and placebo. Safety was assessed through the incidence of complication and emergency medication. Effectiveness was assessed based on therapy success rate, symptomatic improvement, and quality of life. Results: Although Food Allergen Immunotherapy (Oral) was shown to have successful desensitization in patients with food allergy, the safety of the technique has not yet reached an acceptable level; the possible reason is due to the high rate of complication and frequency of emergency medication. Also, each study employed varying protocols while relying on a small number of participants and a short monitoring period. Conclusion: The results of assessment suggest that the level of evidence from current literature review is low and further research is necessitated for the verification of the safety and effectiveness of the therapy (Grade of Recommendation: A; Level of Technology: II-b).

• Genomics & Informatics
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• v.15 no.1
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• pp.19-27
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• 2017

Understanding complex relationships among heterogeneous biological data is one of the fundamental goals in biology. In most cases, diverse biological data are stored in relational databases, such as MySQL and Oracle, which store data in multiple tables and then infer relationships by multiple-join statements. Recently, a new type of database, called the graph-based database, was developed to natively represent various kinds of complex relationships, and it is widely used among computer science communities and IT industries. Here, we demonstrate the feasibility of using a graph-based database for complex biological relationships by comparing the performance between MySQL and Neo4j, one of the most widely used graph databases. We collected various biological data (protein-protein interaction, drug-target, gene-disease, etc.) from several existing sources, removed duplicate and redundant data, and finally constructed a graph database containing 114,550 nodes and 82,674,321 relationships. When we tested the query execution performance of MySQL versus Neo4j, we found that Neo4j outperformed MySQL in all cases. While Neo4j exhibited a very fast response for various queries, MySQL exhibited latent or unfinished responses for complex queries with multiple-join statements. These results show that using graph-based databases, such as Neo4j, is an efficient way to store complex biological relationships. Moreover, querying a graph database in diverse ways has the potential to reveal novel relationships among heterogeneous biological data.

• Journal of Korea Multimedia Society
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• v.7 no.11
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• pp.1505-1514
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• 2004

Sequence matching in time-series databases is an operation that finds the data sequences whose changing patterns are similar to that of a query sequence. Typically, sequence matching hires a multi-dimensional index for its efficient processing. In order to alleviate the dimensionality curse problem of the multi-dimensional index in high-dimensional cases, the previous methods for sequence matching apply the Discrete Fourier Transform(DFT) to data sequences, and take only the first two or three DFT coefficients as organizing attributes of the multi-dimensional index. This paper first points out the problems in such simple methods taking the firs two or three coefficients, and proposes a novel solution to construct the optimal multi -dimensional index. The proposed method analyzes the characteristics of a target database, and identifies the organizing attributes having the best discrimination power based on the analysis. It also determines the optimal number of organizing attributes for efficient sequence matching by using a cost model. To show the effectiveness of the proposed method, we perform a series of experiments. The results show that the Proposed method outperforms the previous ones significantly.

• Journal of Korean Society of Water and Wastewater
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• v.23 no.1
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• pp.15-22
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• 2009

This study reports the serial use of a 3-D hydrodynamic model, EFDC-Hydro and a dynamic water quality model WASP7.2 that are maintained by USEPA. The 48 km section of the Geum River downstream between Daechung Dam and Gongju was selected as a sample study site. Topographical information was used to accurately represent morphology of the study site and boundary conditions were derived from governmental databases including WAMIS by Ministry of Land and Ocean and WEIS by Ministry of Environment. EFDC-Hydro was successfully calibrated for observed water level and WASP was calibrated using monthly observed water quality data obtained from the above sources. It was found that the current water quality target of BOD for the Geum River-H point could not be met on monthly basis though every other tributary of the area would meet its own water quality target as assigned in Korean TMDL. This study proposed the new target BOD water quality for the Gabcheon and Mihocheon as 4.3 and 3.6 mg/l, respectively so that the Geum River-H point can meet the target. When Sejong City is constructed, it is estimated that effluent discharge limit of BOD must be less than 4.5 mg/l to meet water quality of the point. This study shows that it is possible to carry out more precise modeling considering both water movement and water kinetics by using EFDC and WASP simultaneously.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.2
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• pp.937-943
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• 2014

Polymorphisms in miRNA binding sites have been shown to affect miRNA binding to target genes, resulting in differential mRNA and protein expression and susceptibility to common diseases. Our purpose was to predict SNPs (single nucleotide polymorphisms) within miRNA binding sites of inflammatory genes in relation to gastric cancer. A complete list of SNPs in the 3'UTR regions of all inflammatory genes associated with gastric cancer was obtained from Pubmed. miRNA target prediction databases (MirSNP, Targetscan Human 6.2, PolymiRTS 3.0, miRNASNP 2.0, and Patrocles) were used to predict miRNA target sites. There were 99 SNPs with MAF>0.05 within the miRNA binding sites of 41 genes among 72 inflammation-related genes associated with gastric cancer. NF-${\kappa}B$ and JAK-STAT are the two most important signaling pathways. 47 SNPs of 25 genes with 95 miRNAs were predicted. CCL2 and IL1F5 were found to be the shared target genes of hsa-miRNA-624-3p. Bioinformatic methods could identify a set of SNPs within miRNA binding sites of inflammatory genes, and provide data and direction for subsequent functional verification research.

• Interdisciplinary Bio Central
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• v.5 no.2
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• pp.3.1-3.7
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• 2013

Recently, the productivity of drug discovery has gradually decreased as the limitations of single-target-based drugs for various and complex diseases become exposed. To overcome these limitations, drug combinations have been proposed, and great efforts have been made to predict efficacious drug combinations by statistical methods using drug databases. However, previous methods which did not take into account biological networks are insufficient for elaborate predictions. Also, increased evidences to support the fact that drug effects are closely related to metabolic enzymes suggested the possibility for a new approach to the study drug combinations. Therefore, in this paper we suggest a novel approach for analyzing drug combinations using a metabolic network in a systematic manner. The influence of a drug on the metabolic network is described using the distance between the drug target and an enzyme. Target-enzyme distances are converted into influence scores, and from these scores we calculated the correlations between drugs. The result shows that the influence score derived from the targetenzyme distance reflects the mechanism of drug action onto the metabolic network properly. In an analysis of the correlation score distribution, efficacious drug combinations tended to have low correlation scores, and this tendency corresponded to the known properties of the drug combinations. These facts suggest that our approach is useful for prediction drug combinations with an advanced understanding of drug mechanisms.

• Journal of Pharmacopuncture
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• v.23 no.4
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• pp.262-272
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• 2020

Objectives: Whiplash injury is one of the major diseases in recent times because of increasing traffic accidents. This review aims to analyze the overall trend of studies on pharmacopuncture for whiplash injury after traffic accidents. Methods: We searched through 4 Korean electronic databases from 2001 up to October 2020 for relevant clinical studies for whiplash injury after traffic accidents, regardless of the patients' age, gender, or race. We included studies that had an intervention group receiving pharmacopuncture therapy with or without other additional treatments, and also included studies that had a control group receiving sham treatment or active treatment such as physical therapy and herbal medication. For the clinical outcomes, we did not place any restrictions on evaluation scales if they are objective metrics. Results: We included 6 randomized controlled trials (RCTs) and 10 non-randomized controlled trials (nRCTs). 10 nRCTs were divided into 4 categories that were case-control studies, case series, case report, and retrospective observational study. In RCTs, Hwangryun-haedoktang (黃蓮解毒湯) pharmacopuncture was the most frequently used. In nRCTs, Jungsong-ouhyul (中性瘀血) pharmacopuncture, and bee venom pharmacopuncture were the most frequently used. As target points of Hwangryun-haedoktang pharmacopuncture, Ashi-points, Jianjing (GB21), and Fengchi (GB20) were the most frequently used. As target points of Jungsong-ouhyul pharmacopuncture, Ashi-points were the most frequently used. And as target points of Bee venom pharmacopuncture, Fengchi (GB20) was the most frequently used. Conclusion: Hwangryun-haedoktang pharmacopuncture, bee venom pharmacopuncture, and Jungsong-ouhyul pharmacopuncture were mainly used for whiplash injury, and their usual target points were Jianjing (GB21), Fengchi (GB20), and Ashi-points. However, a high level of evidence should be conducted through studies with systematic methodology in the future.

• Herbal Formula Science
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• v.32 no.1
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• pp.11-28
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• 2024

Objectives : GunRyeong-Tang(GRT) is a traditional herbal prescription that combines Oryeongsan and Sagunja-tang. This study employed network analysis methods on the components of GRT and target genes related to diabetes complications to predict the improvement effects of GRT on diabetes complications. Methods : The collection of active compounds of GRT and related target genes involved the utilization of public databases and the PubChem database. We selected diabetes complication-related genes using GeneCards and confirmed their correlation through comparative analysis with the target genes of GRT. We constructed a network using Cytoscape 3.9.1 and conducted topological analysis. To predict the mechanism, we performed functional enrichment analysis based on Gene Ontology (GO) biological processes and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Results : Through network analysis, 234 active compounds and 1361 related genes were collected from GRT. A total of 9,136 genes related to diabetes complications were collected, and 1,039 target genes overlapping with the components of GRT were identified. The core genes of this network were TP53, INS, AKT1, ALB, and EGFR. In addition, GRT significantly reduced the H9c2 cell size and the expression of myocardial hypertrophy biomarkers (ANP, BNP), which were increased by high glucose (HG). Conclusions : Through this study, we were able to predict the activity and mechanism of action of GRT on diabetes and diabetic complications, and confirmed the potential of GRT as a treatment for diabetes complications through the effect of GRT on improving myocardial hypertrophy for diabetic cardiomyopathy.

• ETRI Journal
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• v.37 no.6
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• pp.1199-1210
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• 2015

Automatic emotion recognition based on facial cues, such as facial action units (AUs), has received huge attention in the last decade due to its wide variety of applications. Current computer-based automated two-phase facial emotion recognition procedures first detect AUs from input images and then infer target emotions from the detected AUs. However, more robust AU detection and AU-to-emotion mapping methods are required to deal with the error accumulation problem inherent in the multiphase scheme. Motivated by our key observation that a single AU detector does not perform equally well for all AUs, we propose a novel two-phase facial emotion recognition framework, where the presence of AUs is detected by group decisions of multiple AU detectors and a target emotion is inferred from the combined AU detection decisions. Our emotion recognition framework consists of three major components - multiple AU detection, AU detection fusion, and AU-to-emotion mapping. The experimental results on two real-world face databases demonstrate an improved performance over the previous two-phase method using a single AU detector in terms of both AU detection accuracy and correct emotion recognition rate.

• The Korea Journal of Herbology
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• v.38 no.1
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• pp.45-53
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• 2023

Objectives : Network pharmacology-based research is one of useful tool to predict the possible efficacy and molecular mechanisms of natural materials with multi compounds-multi targeting effects. In this study, we investigated the functional underlying mechanisms of Astragalus membranaceus Bunge (AM) on its anti-obesity effects using a network pharmacology analysis. Methods : The constituents of AM were collected from public databases and its target genes were gathered from PubChem database. The target genes of AM were compared with the gene set of obesity to find the correlation. Then, the network was constructed by Cytoscape 3.9.1. and functional enrichment analysis was conducted to predict the most relevant pathway of AM. Results : The result showed that AM network contained the 707 nodes and 6867 edges, and 525 intersecting genes were exhibited between AM and obesity gene set, indicating that high correlation with the effects of AM on obesity. Based on GO biological process and KEGG Pathway, 'Response to lipid', 'Cellular response to lipid', 'Lipid metabolic process', 'Regulation of chemokine production', 'Regulation of lipase activity', 'Chemokine signaling pathway', 'Regulation of lipolysis in adipocytes' and 'PPAR signaling pathway' were predicted as functional pathways of AM on obesity. Conclusions : AM showed high relevance with the lipid metabolism related with the chemokine production and lipolysis pathways. This study could be a basis that AM has promising effects on obesity via network pharmacology analysis.