• Journal of the Korea Society of Computer and Information
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• v.29 no.2
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• pp.109-118
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• 2024

With the increasing number of social media users worldwide, cases of social media being abused to perpetrate various crimes are increasing. Specifically, drug distribution through social media is emerging as a serious social problem. Using social media channels, the curiosity of teenagers regarding drugs is stimulated through clever marketing. Further, social media easily facilitates drug purchases due to the high accessibility of drug sellers and consumers. Among various social media platforms, we focused on Instagram, which is the most used social media platform by young adults aged 19 to 24 years in South Korea. We collected four types of information, including profile photos, introductions, posts in the form of images, and posts in the form of texts on Instagram; then, we analyzed the similarity among each type of collected information. The profile photos and posts in the form of image were analyzed for similarity based on the SSIM(Structural Simplicity Index Measure), while introductions and posts in the form of text were analyzed for similarity using Jaccard and Cosine similarity techniques. Through the similarity analysis, the similarity among various accounts for each collected information type was measured, and accounts with similarity above the significance level were determined as the same drug sales account. By performing logistic regression analysis on the aforementioned information types, we confirmed that except posts in image form, profile photos, introductions, and posts in the text form were valid information for tracking the same drug sales account.

• Journal of Biomedical Engineering Research
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• v.43 no.4
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• pp.230-240
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• 2022

Drug regeneration technology is an efficient strategy than the existing new drug development process, which requires large costs and time by using drugs that have already been proven safe. In this study, we recognize the importance of the new drug regeneration aspect of new drug development and research in predicting functional similarities through the basic molecular structure that forms drugs. We test four string-based algorithms by using SMILES data and searching for their similarities. And by using the ATC codes, pair them with functional similarities, which we compare and validate to select the optimal model. We confirmed that the higher the molecular structure similarity, the higher the ATC code matching rate. We suggest the possibility of additional potency of random drugs, which can be predicted through data that give information on drugs with high molecular similarities. This model has the advantage of being a great combination with additional data, so we look forward to using this model in future research.

• Journal of the Korea Society of Computer and Information
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• v.21 no.3
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• pp.65-71
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• 2016

To reduce the expenses for development a novel drug, systems biology has been studied actively. Target prediction, a part of systems biology, contributes to finding a new purpose for FDA(Food and Drug Administration) approved drugs and development novel drugs. In this paper, we propose a classification model for predicting novel target genes based on relation between target genes and disease related genes. After collecting known target genes from TTD(Therapeutic Target Database) and disease related genes from OMIM(Online Mendelian Inheritance in Man), we analyzed the effect of target genes on disease related genes based on PPI(Protein-Protein Interactions) network. We focused on the distinguishing characteristics between known target genes and random target genes, and used the characteristics as features for building a classifier. Because our model is constructed using information about only a disease and its known targets, the model can be applied to unusual diseases without similar drugs and diseases, while existing models for finding new drug-disease associations are based on drug-drug similarity and disease-disease similarity. We validated accuracy of the model using LOOCV of ten times and the AUCs were 0.74 on Alzheimer's disease and 0.71 on Breast cancer.

• International Journal of Computer Science & Network Security
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• v.21 no.6
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• pp.319-328
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• 2021

Parallel administration of numerous drugs increases Drug-Drug Interaction (DDI) because one drug might affect the activity of other drugs. DDI causes negative or positive impacts on therapeutic output. So there is a need to discover DDI to enhance the safety of consuming drugs. Though there are several DDI system exist to predict an interaction but nowadays it becomes impossible to maintain with a large number of biomedical texts which is getting increased rapidly. Mostly the existing DDI system address classification issues, and especially rely on handcrafted features, and some features which are based on particular domain tools. The objective of this paper to predict DDI in a way to avoid adverse effects caused by the consumed drugs, to predict similarities among the drug, Drug pair similarity calculation is performed. The best optimal weight is obtained with the support of KHA. LSTM function with weight obtained from KHA and makes bets prediction of DDI. Our methodology depends on (LSTM-KHA) for the detection of DDI. Similarities among the drugs are measured with the help of drug pair similarity calculation. KHA is used to find the best optimal weight which is used by LSTM to predict DDI. The experimental result was conducted on three kinds of dataset DS1 (CYP), DS2 (NCYP), and DS3 taken from the DrugBank database. To evaluate the performance of proposed work in terms of performance metrics like accuracy, recall, precision, F-measures, AUPR, AUC, and AUROC. Experimental results express that the proposed method outperforms other existing methods for predicting DDI. LSTMKHA produces reasonable performance metrics when compared to the existing DDI prediction model.

• Journal of Microbiology and Biotechnology
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• v.32 no.12
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• pp.1605-1614
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• 2022

The strains associated with foodborne Salmonella enterica Thompson outbreaks in Korea have not been identified. Therefore, we characterized S. Thompson strains isolated from chocolate cakes linked to foodborne outbreaks in Korea. A total of 56 strains were isolated from preserved cake products, products in the supply chain distribution, the manufacturer's apparatus, and egg white liquid products used for cream preparation. Subsequently, serological typing, pathogenic gene-targeted polymerase chain reaction (PCR), pulsed-field gel electrophoresis (PFGE), and whole-genome multi-locus sequence typing (wgMLST) were performed to characterize these isolates. The antigen formula of all isolates was 7:k:1,5, namely Salmonella enterica subsp. enterica Serovar Thompson. All 56 isolates harbored invA, his, hin, and stn, and were negative for sefA and spvC based on gene-targeted PCR analyses. Based on PFGE results, these isolates were classified into one group based on the same SP6X01.011 pattern with 100% similarity. We selected 19 strains based on the region and sample type, which were subjected to wgMLST. Although the examined strains showed 100% similarity, they were classified into seven clusters based on allelic differences. According to our findings, the cause of these outbreaks was chocolate cake manufactured with egg white liquid contaminated with the same Salmonella Thompson. Additionally, comparative analysis of wgMLST on domestic isolates of S. Thompson from the three outbreaks showed genetic similarities of over 99.6%. Based on the results, the PFGE and wgMLST combination can provide highly resolved phylogeny and reliable evidence during Salmonella outbreak investigations.

• Bulletin of the Korean Chemical Society
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• v.34 no.4
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• pp.1212-1220
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• 2013

The human coagulation factor XI (FXI) is a serine protease that plays a significant role in blocking of the blood coagulation cascade as an attractive antithrombotic target. Selective inhibition of FXIa (an activated form of factor XI) disrupts the intrinsic coagulation pathway without affecting the extrinsic pathway or other coagulation factors such as FXa, FIIa, FVIIa. Furthermore, targeting the FXIa might significantly reduce the bleeding side effects and improve the safety index. This paper reports on a docking-based three dimensional quantitative structure activity relationship (3D-QSAR) study of the potent FXIa inhibitors, the chloro-phenyl tetrazole scaffold series, using comparative molecular field analysis (CoMFA) and comparative molecular similarity analysis (CoMSIA) methods. Due to the characterization of FXIa binding site, we classified the alignment of the known FXIa inhibitors into two groups according to the docked pose: S1-S2-S4 and S1-S1'-S2'. Consequently, highly predictive 3D-QSAR models of our result will provide insight for designing new potent FXIa inhibitors.

• Journal of Food Hygiene and Safety
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• v.29 no.3
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• pp.228-233
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• 2014

This study was performed to examine antimicrobial resistance of Enterococcus spp. strains from retail raw meat and fish products purchased in 2012. 43 Enterococcus spp. strains were isolated from a total of 207 samples (beef, pork, chicken, fish) with contamination rate of 20.8%. The isolated strains were identified as E. faecalis (22 strains), E. gallinarum, E. hirae (5 strains), E. avium (4 strains), E. faecium (3 strains), E. duram, E. casseliflavus (2 strains). Susceptibility to 10 antibiotics was tested, and the highest resistance was observed to tetracycline. And antimicrobial resistance rates were presented below 20% with most of the other antimicrobial agents. The isolated Enterococci from chicken showed higher resistance also to ciprofloxacin and erythromycin, not only to tetracycline, compared to the isolated Enterococci from beef, pork and fish. Sixteen isolates (37.2%) were sensitive to all antibiotics. Four isolates (9.3%) were resistant to 3 or more antibiotics. Vancomycin-resistant enterococci (VRE) was not identified. According to the results of genetic similarity pattern analysis via PFGE and rep-PCR, Enterococci strains showed different patterns from these collected in 2011. This indicates that there is no genetic similarity among all the strains.

• Journal of Pharmaceutical Investigation
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• v.40 no.5
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• pp.297-304
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• 2010

The present work focuses on preparation of olanzapine, orally dispersing tablets by direct compression method. Effect of super disintegrant crospovidone, disintegration time, drug content on in vitro release has been studied. A factorial design was employed in formulating a prompt dispersible tablet. The selected independent variables crospovidone and fmelt showed significant effect on dependent variables i.e. disintegration time and percent drug dissolved. Disintegration time and percent drug dissolved decreased with increase in the level of crospovidone. The similarity factor $f_2$ was found to be 97.48 for the developed formulation indicating the release was similar to that of the marketed formulation. Pharmacokinetics of olanzapine after single-dose oral administration of orally disintegrating tablet in normal volunteers were evaluated and the results showed that PK parameters (Cmax, Tmax, AUC) of the designed ODT matrix were similar to those of commercial product, Zyprexa Zydis$^{(R)}$ as a reference.

• Journal of the Korea Society of Computer and Information
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• v.21 no.1
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• pp.115-123
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• 2016

In this study, we collect various side effect pairs which are appeared frequently at many drugs, and select side effect pairs that have higher severity. For every selected side effect pair, we extract common genetic networks which are shared by side effects' genes and drugs' target genes based on PPI(Protein-Protein Interaction) network. For this work, firstly, we gather drug related data, side effect data and PPI data. Secondly, for extracting common genetic network, we find shortest paths between drug target genes and side effect genes based on PPI network, and integrate these shortest paths. Thirdly, we develop a classification model which uses this common genetic network as a classifier. We calculate similarity score between the common genetic network and genetic network of a drug for classifying the drug. Lastly, we validate our classification model by means of AUC(Area Under the Curve) value.

• Genomics & Informatics
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• v.20 no.4
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• pp.47.1-47.13
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• 2022

Klebsiella pneumoniae is a gram-negative bacterium that is known for causing infection in nosocomial settings. As reported by the World Health Organization, carbapenem-resistant Enterobacteriaceae, a category that includes K. pneumoniae, are classified as an urgent threat, and the greatest concern is that these bacterial pathogens may acquire genetic traits that make them resistant towards antibiotics. The last class of antibiotics, carbapenems, are not able to combat these bacterial pathogens, allowing them to clonally expand antibiotic-resistant strains. Most antibiotics target essential pathways of bacterial cells; however, these targets are no longer susceptible to antibiotics. Hence, in our study, we focused on a hypothetical protein in K. pneumoniae that contains a DNA methylation protein domain, suggesting a new potential site as a drug target. DNA methylation regulates the attenuation of bacterial virulence. We integrated computational-aided drug design by using a bioinformatics approach to perform subtractive genomics, virtual screening, and fingerprint similarity search. We identified a new potential drug, koenimbine, which could be a novel antibiotic.