OBJECTIVES: The importance of the prediction of coronary heart disease (CHD) has been recognized in Korea; however, few studies have been conducted in this area. Therefore, it is necessary to develop a method for the prediction and classification of CHD in Koreans. METHODS: A model for CHD prediction must be designed according to rule-based guidelines. In this study, a fuzzy logic and decision tree (classification and regression tree [CART])-driven CHD prediction model was developed for Koreans. Datasets derived from the Korean National Health and Nutrition Examination Survey VI (KNHANES-VI) were utilized to generate the proposed model. RESULTS: The rules were generated using a decision tree technique, and fuzzy logic was applied to overcome problems associated with uncertainty in CHD prediction. CONCLUSIONS: The accuracy and receiver operating characteristic (ROC) curve values of the propose systems were 69.51% and 0.594, proving that the proposed methods were more efficient than other models.
Classification ; Coronary Disease* ; Data Mining ; Dataset ; Decision Trees* ; Fuzzy Logic* ; Heart Diseases ; Korea ; Nutrition Surveys ; ROC Curve ; Uncertainty

Sirtuins (SIRTs) are involved in multiple cellular processes including those related to aging, cancer, and a variety of cellular functions including cell cycle progression, DNA repair, and cellular proliferation. SIRTs have been shown to extend the yeast life span, although there is presently little known about SIRT expression in the organs of mice. In the present study, we were especially interested in identifying differences in SIRT expression between young mice and aged mice. Specifically, we investigated the expression of SIRT1 and SIRT3 in the kidney, lung, skin, adipose tissue, and spleens of 6-month-old and 24-month-old mice using immunohistochemical staining. Compared with that in younger mice, the expression of SIRT1 in 24-month-old rats was increased in kidney, lung, and spleen tissue, while that of SIRT3 was decreased in adipose, kidney, and lung tissue. The results of our study suggest that aging is associated with altered patterns of expression of SIRT1 and SIRT3. In addition, we noted that the expression patterns of SIRT1 and SIRT3 varied by organ. Taken together, the results of this study suggest the possibility that SIRTs may be involved in diseases associated with aging.
Adipose Tissue ; Aging ; Animals ; Cell Cycle ; Cell Proliferation ; Child, Preschool ; DNA Repair ; Humans ; Immunohistochemistry ; Infant ; Kidney ; Lung ; Mice* ; Rats ; Sirtuins ; Skin ; Spleen ; Yeasts

The human-associated microbiota is diverse, varies between individuals and body sites, and is important in human health. Microbes in human body play an essential role in immunity, health, and disease. The human microbiome has been studies using the advances of next-generation sequencing and its metagenomic applications. This has allowed investigation of the microbial composition in the human body, and identification of the functional genes expressed by this microbial community. The gut microbes have been found to be the most diverse and constitute the densest cell number in the human microbiota; thus, it has been studied more than other sites. Early results have indicated that the imbalances in gut microbiota are related to numerous disorders, such as inflammatory bowel disease, colorectal cancer, diabetes, and atopy. Clinical therapy involving modulating of the microbiota, such as fecal transplantation, has been applied, and its effects investigated in some diseases. Human microbiome studies form part of human genome projects, and understanding gleaned from studies increase the possibility of various applications including personalized medicine.
Cell Count ; Colorectal Neoplasms ; Human Body ; Human Genome Project ; Humans ; Precision Medicine ; Inflammatory Bowel Diseases ; Metagenome ; Metagenomics ; Transplants

Metagenomics has become one of the indispensable tools in microbial ecology for the last few decades, and a new revolution in metagenomic studies is now about to begin, with the help of recent advances of sequencing techniques. The massive data production and substantial cost reduction in next-generation sequencing have led to the rapid growth of metagenomic research both quantitatively and qualitatively. It is evident that metagenomics will be a standard tool for studying the diversity and function of microbes in the near future, as fingerprinting methods did previously. As the speed of data accumulation is accelerating, bioinformatic tools and associated databases for handling those datasets have become more urgent and necessary. To facilitate the bioinformatics analysis of metagenomic data, we review some recent tools and databases that are used widely in this field and give insights into the current challenges and future of metagenomics from a bioinformatics perspective.
Computational Biology ; Dermatoglyphics ; Ecology ; Handling (Psychology) ; High-Throughput Nucleotide Sequencing ; Metagenomics

BACKGROUND: Next-generation sequencing is increasingly used for taxonomic identification of pathogenic bacterial isolates. We evaluated the performance of a newly introduced whole genome-based bacterial identification system, TrueBac ID (ChunLab Inc., Seoul, Korea), using clinical isolates that were not identified by three matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) systems and 16S rRNA gene sequencing. METHODS: Thirty-six bacterial isolates were selected from a university-affiliated hospital and a commercial clinical laboratory. Species was identified by three MALDI-TOF MS systems: Bruker Biotyper MS (Bruker Daltonics, Billerica, MA, USA), VITEK MS (bioMérieux, Marcy l'Étoile, France), and ASTA MicroIDSys (ASTA Inc., Suwon, Korea). Whole genome sequencing was conducted using the Illumina MiSeq system (Illumina, San Diego, CA, USA), and genome-based identification was performed using the TrueBac ID cloud system (www.truebacid.com). RESULTS: TrueBac ID assigned 94% (34/36) of the isolates to known (N=25) or novel (N=4) species, genomospecies (N=3), or species group (N=2). The remaining two were identified at the genus level. CONCLUSIONS: TrueBac ID successfully identified the majority of isolates that MALDI-TOF MS failed to identify. Genome-based identification can be a useful tool in clinical laboratories, with its superior accuracy and database-driven operations.
Genes, rRNA ; Genome ; Gyeonggi-do ; Mass Spectrometry ; Seoul

Background/Aims: We aim to evaluate the differences in the microbiome of responders and non-responders, as well as predict the response to probiotic therapy, based on fecal microbiome data in patients with diarrhea-predominant irritable bowel syndrome (IBS-D). Methods: A multi-strain probiotics that contains Lactobacillus acidophilus (KCTC 11906BP), Lactobacillus plantarum (KCTC11867BP), Lactobacillus rhamnosus (KCTC 11868BP), Bifidobacterium breve (KCTC 11858BP), Bifidobacterium lactis (KCTC 11903BP), Bifidobacterium longum (KCTC 11860BP), and Streptococcus thermophilus (KCTC 11870BP) were used. Patients were categorized into probiotic and placebo groups, and fecal samples were collected from all patients before and at the end of 8 weeks of treatment. The probiotic group was further divided into responders and non-responders. Responders were defined as patients who experienced adequate relief of overall irritable bowel syndrome symptoms after probiotic therapy. Fecal microbiota were investigated using Illumina MiSeq and analyzed using the EzBioCloud 16S database and microbiome pipeline (https://www.EZbiocloud.net). Results: There was no significant difference in the alpha and beta diversity between the responder and non-responder groups. The abundances of the phylum Proteobacteria and genus Bacteroides significantly decreased after probiotic treatment. Bifidobacterium bifidum, Pediococcus acidilactici, and Enterococcus faecium showed a significantly higher abundance in the probiotic group after treatment compared to the placebo group. Enterococcus faecalis and Lactococcus lactis were identified as biomarkers of non-response to probiotics. The abundance of Fusicatenibacter saccharivorans significantly increased in the responders after treatment. Conclusions Probiotic treatment changes some composition of fecal bacteria in patients with IBS-D. E. faecalis and L. lactis may be prediction biomarkers for non-response to probiotics. Increased abundance of F. sccharivorans is correlated to symptom improvement by probiotics in patients with IBS-D.

Background: Coronavirus disease 2019 (COVID-19) outbreaks emerged at two universityaffiliated hospitals in Seoul (hospital A) and Uijeongbu City (hospital S) in the metropolitan Seoul area in March 2020. The aim of this study was to investigate epidemiological links between the outbreaks using whole genome sequencing (WGS) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Methods: Fifteen patients were enrolled in the study, including four non-outbreak (A1–A4) and three outbreak cases (A5–A7) in hospital A and eight cases (S1–S8) in hospital S. Patients' hospital stays, COVID-19 symptoms, and transfer history were reviewed. RNA samples were submitted for WGS and genome-wide single nucleotide variants and phylogenetic relationships were analyzed. Results: The index patient (A5) in hospital A was transferred from hospital S on 26 March.Patients A6 and A7 were the family caregiver and sister, respectively, of the patient who shared a room with A5 for 4 days. Prior to transfer, A5 was at the next bed to S8 in the emergency room on 25 March. Patient S6, a professional caregiver, took care of the patient in the room next to S8's room for 5 days until 22 March and then S5 for another 3 days.WGS revealed that SARS-CoV-2 in A2, A3, and A4 belong to clades V/B.2, S/A, and G/B.1, respectively, whereas that of A5–A7 and S1-S5 are of the V/B.2.1 clade and closely clustered. In particular, SARS-CoV-2 in patients A5 and S5 showed perfect identity. Conclusion WGS is a useful tool to understand epidemiology of SARS-CoV-2. It is the first study to elucidate the role of patient transfer and caregivers as links of nosocomial outbreaks of COVID-19 in multiple hospitals.

PURPOSE: The purpose of this study was to evaluate the accuracy of an active contour model for estimating the posterior ablative margin in images obtained by the fusion of real-time ultrasonography (US) and 3-dimensional (3D) US or magnetic resonance (MR) images of an experimental tumor model for radiofrequency ablation. METHODS: Chickpeas (n=12) and bovine rump meat (n=12) were used as an experimental tumor model. Grayscale 3D US and T1-weighted MR images were pre-acquired for use as reference datasets. US and MR/3D US fusion was performed for one group (n=4), and US and 3D US fusion only (n=8) was performed for the other group. Half of the models in each group were completely ablated, while the other half were incompletely ablated. Hyperechoic ablation areas were extracted using an active contour model from real-time US images, and the posterior margin of the ablation zone was estimated from the anterior margin. After the experiments, the ablated pieces of bovine rump meat were cut along the electrode path and the cut planes were photographed. The US images with the estimated posterior margin were compared with the photographs and post-ablation MR images. The extracted contours of the ablation zones from 12 US fusion videos and post-ablation MR images were also matched. RESULTS: In the four models fused under real-time US with MR/3D US, compression from the transducer and the insertion of an electrode resulted in misregistration between the real-time US and MR images, making the estimation of the ablation zones less accurate than was achieved through fusion between real-time US and 3D US. Eight of the 12 post-ablation 3D US images were graded as good when compared with the sectioned specimens, and 10 of the 12 were graded as good in a comparison with nicotinamide adenine dinucleotide staining and histopathologic results. CONCLUSION: Estimating the posterior ablative margin using an active contour model is a feasible way of predicting the ablation area, and US/3D US fusion was more accurate than US/MR fusion.
Ablation Techniques ; Catheter Ablation* ; Cicer ; Dataset ; Electrodes ; Meat ; NAD ; Shadowing (Histology) ; Transducers ; Ultrasonography*