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Immune enhancement effect of an herb complex extract through the activation of natural killer cells and the regulation of cytokine levels in a cyclophosphamide-induced immunosuppression rat model

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Asian Pacific Journal of Tropical Medicine 2018年第12期11卷 653-658页

作者： Sung Min Woo Woo Rin Choi Dooly Jang Chun Sik Yi Hae Lim Kim Kyung Hyeon Kim Jong Tae Kim Won Hee Choi Seung Hee Jang Min Jeung Kim Ji Hyang Wee Yeon Ki Kim Bao Le Interdisciplinary Program of Biomodulation Myongji UniversityYonginGyeonggi 17058Republic of Korea Center for Nutraceutical and Pharmaceutical Materials Myongji UniversityYonginGyeonggi 17058Republic of Korea Teazen Co. B-606 Acrotower230 Sinim-daeroDongan-guAnyang-siGyeonggi-do 14067Republic of Korea Department of Food Science&Biotechnology Shin Ansan UniversityAnsanGyeonggi 15435Republic of Korea' Division of Bioscience and Bioinformatics Myongji UniversityYonginGyeonggi 17058Republic of Korea Department of Biotechnology Chonnam National UniversityYeosuChonnam 59626Republic of Korea

Objective: To investigate the effects of a herb complex extract(HCE) prepared from Cornus officinalis Sieb. Et Zucc., Eriobotrya japonica Lindley, and olive leaves on immune response of mouse spleen NK cells in vitro and in vivo analysis. Methods: The activity of natural killer(NK) cells was measured in splenocytes and YAC-1 cells. Mice were immunosuppressed using cyclophosphamide(5 mg/kg body weight). Three different doses of HCE(200, 400, and 800 mg/kg body weight) and red ginseng extract(800 mg/kg body weight) which was used as standard immunomodulatory herb were administered orally for 4 weeks. The body weight, dietary, water intake, organs(liver, thymus, and spleen) weight, completed blood count, and cytokines(tumor necrosis factor alpha, interferon gamma, and interleukin-2) production was measured. Results: At the maximum concentration of HCE, the activity of NK cells was increased by 48.5%. HCE increased liver, spleen, and thymus weights without altering numbers of white blood cells, lymphocytes, and neutrophils in a cyclophosphamide-induced immunosuppression rat model. However, HCE recovered the inhibited cytokine expression; HCE(800 mg/kg) increased cytokines levels. The results indicate the immune enhancement potential of this HCE. Conclusion: The HCE enhances immunity by increasing NK cell activity, regulating cytokine levels, and maintaining spleen weight. Therefore, it may be used as a potential immunity enhancer.

关键词： Immune system enhancement Cyclophosphamide Cornus officinalis Sieb Et Zucc Eriobotrya japonica Lindley Olive leaves

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BioBERT: A pre-trained biomedical language representation model for biomedical text mining

arXiv

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arXiv 2019年

作者： Lee, Jinhyuk Yoon, Wonjin Kim, Sungdong Kim, Donghyeon Kim, Sunkyu So, Chan Ho Kang, Jaewoo Department of Computer Science and Engineering Korea University Seoul02841 Korea Republic of Clova AI Research Naver Corp Seong-Nam13561 Korea Republic of Interdisciplinary Graduate Program in Bioinformatics Korea University Seoul02841 Korea Republic of

Motivation: Biomedical text mining is becoming increasingly important as the number of biomedical documents rapidly grows. With the progress in natural language processing (NLP), extracting valuable information from biomedical literature has gained popularity among researchers, and deep learning has boosted the development of effective biomedical text mining models. However, directly applying the advancements in NLP to biomedical text mining often yields unsatisfactory results due to a word distribution shift from general domain corpora to biomedical corpora. In this article, we investigate how the recently introduced pre-trained language model BERT can be adapted for biomedical corpora. Results: We introduce BioBERT (Bidirectional Encoder Representations from Transformers for Biomedical Text Mining), which is a domain-specific language representation model pre-trained on large-scale biomedical corpora. With almost the same architecture across tasks, BioBERT largely outperforms BERT and previous state-of-the-art models in a variety of biomedical text mining tasks when pre-trained on biomedical corpora. While BERT obtains performance comparable to that of previous state-of-the-art models, BioBERT significantly outperforms them on the following three representative biomedical text mining tasks: biomedical named entity recognition (0.62% F1 score improvement), biomedical relation extraction (2.80% F1 score improvement) and biomedical question answering (12.24% MRR improvement). Our analysis results show that pre-training BERT on biomedical corpora helps it to understand complex biomedical texts. Availability and implementation: We make the pre-trained weights of BioBERT freely available at https://github. com/naver/biobert-pretrained, and the source code for fine-tuning BioBERT available at https://***/dmis-lab/ biobert. Copyright © 2019, The Authors. All rights reserved.

关键词： Natural language processing systems

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HATS: A hierarchical graph attention network for stock movement prediction

arXiv

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arXiv 2019年

作者： Kim, Raehyun So, Chan Ho Jeong, Minbyul Lee, Sanghoon Kim, Jinkyu Kang, Jaewoo Department of Computer Science and Engineering Korea University 145 Anam-ro Seongbuk-gu Seoul02841 Korea Republic of Interdisciplinary Graduate Program in Bioinformatics Korea University 145 Anam-ro Seongbuk-gu Seoul02841 Korea Republic of

Many researchers both in academia and industry have long been interested in the stock market. Numerous approaches were developed to accurately predict future trends in stock prices. Recently, there has been a growing interest in utilizing graph-structured data in computer science research communities. Methods that use relational data for stock market prediction have been recently proposed, but they are still in their infancy. First, the quality of collected information from different types of relations can vary considerably. No existing work has focused on the effect of using different types of relations on stock market prediction or finding an effective way to selectively aggregate information on different relation types. Furthermore, existing works have focused on only individual stock prediction which is similar to the node classification task. To address this, we propose a hierarchical attention network for stock prediction (HATS) which uses relational data for stock market prediction. Our HATS method selectively aggregates information on different relation types and adds the information to the representations of each company. Specifically, node representations are initialized with features extracted from a feature extraction module. HATS is used as a relational modeling module with initialized node representations. Then, node representations with the added information are fed into a task-specific layer. Our method is used for predicting not only individual stock prices but also market index movements, which is similar to the graph classification task. The experimental results show that performance can change depending on the relational data used. HATS which can automatically select information outperformed all the existing methods. Copyright © 2019, The Authors. All rights reserved.

关键词： Aggregates

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Impact of statins on the survival of patients with cancer: a nationwide population-based cohort study in South Korea

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Cancer Communications 2022年第2期42卷 184-187页

作者： Juhee Ahn Sungho Won Sanghun Lee Department of Public Health Science Seoul National UniversityGwanak-guSeoul 08826Republic of Korea Interdisciplinary Program of Bioinformatics Seoul National UniversityGwanak-guSeoul 08826Republic of Korea Institute of Health and Environment Seoul National UniversityGwanak-guSeoul 08826Republic of Korea Department of Bioconvergence&Engineering Dankook UniversityYongin-siGyeonggi-do 16890Republic of Korea Channing Division of Network Medicine Brigham and Women’s HospitalBostonMassachusetts 02115the United States of America

Dear Editor For over 30 years,3-hydroxy-3-methylglutaryl-coenzyme A(HMG-CoA)reductase inhibitors,statins,have been recommended worldwide as cost-effective lipid-lowering drugs for cardiovascular disease[1].Cheng et *** that lipid synthesis inhibitors are a promising anti-cancer treatment in preclinical studies and early phases of clinical trials[2].However,the association between statin use and cancer incidence or survival remains controversial[3,4].

关键词： cancer clinical drugs

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A Study of Classification Techniques Based on Spike Protein Sequences of MERS-CoV 23

A Study of Classification Techniques Based on Spike Protein ...

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Proceedings of the 2023 10th International Conference on Biomedical and bioinformatics Engineering

作者： Hayeon Kim Myeongji Cho Hyeon S. Son Laboratory of Computational Biology & Bioinformatics Graduate School of Public Health Seoul National University Republic of Korea and Institute of Health and Environment Seoul National University Republic of Korea Laboratory of Computational Biology & Bioinformatics Graduate School of Public Health Seoul National University Republic of Korea Laboratory of Computational Biology & Bioinformatics Graduate School of Public Health Seoul National University Republic of Korea Institute of Health and Environment Seoul National University Republic of Korea and Interdisciplinary Graduate Program in Bioinformatics College of Natural Science Seoul National University Republic of Korea

ISBN: (纸本)9798400708343

MERS-CoV, which belongs to the beta-coronaviruses together with SARS-CoV-2, although it has received relatively less attention by the COVID-19 pandemic, there is a sufficient possibility of new MERS-CoV lineages and variants. Previous studies have discussed the possibility of frequent recombination of MERS-CoV. We thus present a highly accurate method for the phylogenetic analysis and classification of MERS-CoV including recombinant sequences. We collected the sequences of S protein from MERS-CoV and divided them into five phylogenetic groups, of which recombinant sequences were divided into seven types. Physicochemical properties of amino acids were then calculated from the S protein sequences, and the results were used for the random forest model, Naïve Bayes classification, and k-nearest neighbor method. We also constructed several feature subsets based on the ranked amino acid properties and applied them to the random forest model. In each dataset, the amino acid physicochemical properties were ranked differently. Using this information, classification of MERS-CoV based on machine learning algorithms showed that the random forest model had the best accuracy and area under the curve compared with the k-nearest neighbor and Naïve Bayes classification methods. Several feature subsets were constructed using the correlation feature selection algorithm and applied to the random forest model. Overall, the performance of the classifier was improved compared to that when using all features. Coronaviruses including MERS-CoV continue to evolve into new forms through recombination or mutation. We thus present a method to increase the accuracy of their classification using additional information of the viral protein sequence, and confirm that a subunit consisting of optimal prominent features can improve the performance of the classifier by removing the unnecessary characteristic information.

关键词： MERS-CoV classification machine learning

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A Bayesian framework for incorporating exposure uncertainty into health analyses with application to air pollution and stillbirth

arXiv

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arXiv 2022年

作者： Comess, Saskia Chang, Howard H. Warren, Joshua L. Emmett Interdisciplinary Program in Environment and Resources Stanford University StanfordCA94305 United States Department of Biostatistics and Bioinformatics Emory University AtlantaGA30329 United States Department of Biostatistics Yale University New HavenCT06510 United States

Studies of the relationships between environmental exposures and adverse health outcomes often rely on a two-stage statistical modeling approach, where exposure is modeled/predicted in the first stage and used as input to a separately fit health outcome analysis in the second stage. Uncertainty in these predictions is frequently ignored, or accounted for in an overly simplistic manner, when estimating the associations of interest. Working in the Bayesian setting, we propose a flexible kernel density estimation (KDE) approach for fully utilizing posterior output from the first stage modeling/prediction to make accurate inference on the association between exposure and health in the second stage, derive the full conditional distributions needed for efficient model fitting, detail its connections with existing approaches, and compare its performance through simulation. Our KDE approach is shown to generally have improved performance across several settings and model comparison metrics. Using competing approaches, we investigate the association between lagged daily ambient fine particulate matter levels and stillbirth counts in New Jersey (2011-2015), observing an increase in risk with elevated exposure three days prior to delivery. The newly developed methods are available in the R package KDExp. © 2022, CC BY.

关键词： Uncertainty analysis

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Efficient and Fast Analysis for Detecting High Order Gene-by-Gene Interactions in a Genome-Wide Association Study

Efficient and Fast Analysis for Detecting High Order Gene-by...

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IEEE International Conference on bioinformatics and Biomedicine (BIBM)

作者： Sohee Oh Jaehoon Lee Min-Seok Kwon Kyunga Kim Taesung Park Department of Statistics Seoul National University Seoul South Korea Interdisciplinary program in Bioinformatics Seoul National University Seoul South Korea Department of Statistics Sookmyung womens University Seoul South Korea Seoul National University Gwanak-gu Seoul KR

Most common complex traits are affected by multiple genes and/or environmental factors. To understand genetic architecture of complex traits, the investigation of gene-gene and gene-environment interactions can be essential. However, conducting gene-gene interaction using genome-wide data requires exploring a huge search space and suffers from a computation burden due to high dimensionality of genetic data. To identify gene-gene interaction more efficiently, we propose a gene-based reduction method which first summarizes the gene effect by combining multiple single nucleotide polymorphism (SNP) and then performs the gene-gene interaction via the summarized gene effect. By reducing the search space from SNPs to gene, our gene-based method becomes efficient and fast for identifying gene-gene interaction in genome wide association studies. The gene-based reduction method is illustrated by hypertension data from a Korean population.

关键词： bioinformatics Genomics Hypertension Barium Diseases Logistics

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SARS-CoV-2 surveillance of wastewater in small rural communities identifies lack of vaccine coverage as influence of omicron outbreak

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Water Research 2025年 283卷 123818页

作者： Solana Narum Thibault Stalder Benjamin Ridenhour Erik R. Coats Bioinformatics and Computational Biology Graduate Program (BCB) University of Idaho 875 Perimeter Drive MS 1103 Moscow ID 83844 United States Institute for Modeling Collaboration and Innovation (IMCI) University of Idaho 875 Perimeter Drive MS 1122 Moscow ID 83844 United States Department of Biological Sciences University of Idaho 875 Perimeter Drive MS 3051 Moscow ID 83844 United States Institute for Interdisciplinary Data Sciences (IIDS) University of Idaho 875 Perimeter Drive MS 3051 Moscow ID 83844 United States Department of Mathematics and Statistical Science University of Idaho 875 Perimeter Drive MS 1103 Moscow ID 83844 United States Department of Civil and Environmental Engineering University of Idaho 875 Perimeter Drive MS 1022 Moscow ID 83844 United States

Wastewater-based epidemiology (WBE) can provide critical early warnings to aid public health, which can be particularly beneficial in rural communities with limited access to health care. Spikes of SARS-CoV-2 RNA concentration in wastewater have been used to represent infections in a community, but wastewater holds a wealth of information that has not been explored yet. The objectives of this research were to expand the use of WBE to 1) determine the dynamic of SARS-CoV-2 variants in rural communities, and 2) evaluate the relationship between community vaccination status and the outbreak of a variant. We quantified the concentration of SARS-CoV-2 RNA, as well as specific mutations that are consistent with Delta and Omicron in influent raw wastewater samples collected from wastewater treatment facilities (WWTFs) for five populations with <1000 residents and one larger population in Latah County, ID. A binomial generalized linear model using the percent of the population with protection against Omicron from the initial vaccines and the booster shot was able to predict the probability of an uptick in Omicron concentration in wastewater with an accuracy of 0.96. Evaluation of vaccination data indicate that the spike in Omicron infections in December 2021 in the studied towns was linked to low levels of population protection from the initial shots of the COVID-19 vaccine against Omicron infection and limited uptake of booster shots in these communities. Despite difficulties with applying WBE in rural regions, this study shows that beyond evaluating spikes of viral infections, WBE can be used to evaluate the effect of a population’s vaccine coverage on SARS-CoV-2 variant dynamics.

关键词： COVID-19 Delta Modeling Omicron Population coverage Prediction

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SPGP: Structure Prototype Guided Graph Pooling

arXiv

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arXiv 2022年

作者： Lee, Sangseon Lee, Dohoon Piao, Yinhua Kim, Sun Institute of Computer Technology Seoul National University Korea Republic of BK21 FOUR Intelligence Computing Seoul National University Korea Republic of Department of Computer Science and Engineering Seoul National University Korea Republic of Interdisciplinary Program in Bioinformatics Seoul National University Korea Republic of Interdisciplinary Program in Artificial Intelligence Seoul National University Korea Republic of AIGENDRUG Co. Ltd. Korea Republic of

While graph neural networks (GNNs) have been successful for node classification tasks and link prediction tasks in graph, learning graph-level representations still remains a challenge. For the graph-level representation, it is important to learn both representation of neighboring nodes, i.e., aggregation, and graph structural information. A number of graph pooling methods have been developed for this goal. However, most of the existing pooling methods utilize k-hop neighborhood without considering explicit structural information in a graph. In this paper, we propose Structure Prototype Guided Pooling (SPGP) that utilizes prior graph structures to overcome the limitation. SPGP formulates graph structures as learnable prototype vectors and computes the affinity between nodes and prototype vectors. This leads to a novel node scoring scheme that prioritizes informative nodes while encapsulating the useful structures of the graph. Our experimental results show that SPGP outperforms state-of-the-art graph pooling methods on graph classification benchmark datasets in both accuracy and scalability. Copyright © 2022, The Authors. All rights reserved.

关键词： Graph neural networks

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HBS-Tools for Hairpin Bisulfite Sequencing Data Processing and Analysis

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Advances in bioinformatics 2015年第1期2015卷 760423页

作者： Sun, Ming-An Velmurugan, Karthik Raja Keimig, David Xie, Hehuang Epigenomics and Computational Biology Lab Virginia Bioinformatics Institute Virginia Tech Blacksburg 24061 VA United States Interdisciplinary Ph.D. Program in Genomics Bioinformatics and Computational Biology Virginia Tech Blacksburg 24061 VA United States Department of Biological Sciences Virginia Tech Blacksburg 24061 VA United States

The emerging genome-wide hairpin bisulfite sequencing (hairpin-BS-Seq) technique enables the determination of the methylation pattern for DNA double strands simultaneously. Compared with traditional bisulfite sequencing (BS-Seq) techniques, hairpin-BS-Seq can determine methylation fidelity and increase mapping efficiency. However, no computational tool has been designed for the analysis of hairpin-BS-Seq data yet. Here we present HBS-tools, a set of command line based tools for the preprocessing, mapping, methylation calling, and summarizing of genome-wide hairpin-BS-Seq data. It accepts paired-end hairpin-BS-Seq reads to recover the original (pre-bisulfite-converted) sequences using global alignment and then calls the methylation statuses for cytosines on both DNA strands after mapping the original sequences to the reference genome. After applying to hairpin-BS-Seq datasets, we found that HBS-tools have a reduced mapping time and improved mapping efficiency compared with state-of-the-art mapping tools. The HBS-tools source scripts, along with user guide and testing data, are freely available for download. © 2015 Ming-an Sun et al.

关键词： Sequencing Bisulfite sequencing DNA methylation Mapping

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