• Title/Summary/Keyword: clinical informatics

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Genomic Profiling of Liver Cancer

  • Lee, Ju-Seog
    • Genomics & Informatics
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    • v.11 no.4
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    • pp.180-185
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    • 2013
  • Development of liver cancers is driven largely by genomic alterations that deregulate signaling pathways, influencing growth and survival of cancer cells. Because of the hundreds or thousands of genomic/epigenomic alterations that have accumulated in the cancer genome, it is very challenging to find and test candidate genes driving tumor development and progression. Systematic studies of the liver cancer genome have become available in recent years. These studies have uncovered new potential driver genes, including those not previously known to be involved in the development of liver cancer. Novel approaches combining multiple datasets from patient tissues have created an unparalleled opportunity to uncover potential new therapeutic targets and prognostic/predictive biomarkers for personalized therapy that can improve clinical outcomes of the patients with liver cancer.

Identification of ERBB pathway-activated cells in triple-negative breast cancer

  • Cho, Soo Young
    • Genomics & Informatics
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    • v.17 no.1
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    • pp.3.1-3.4
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    • 2019
  • Intratumor heterogeneity within a single tumor mass is one of the hallmarks of malignancy and has been reported in various tumor types. The molecular characterization of intratumor heterogeneity in breast cancer is a significant challenge for effective treatment. Using single-cell RNA sequencing (RNA-seq) data from a public resource, an ERBB pathway activated triple-negative cell population was identified. The differential expression of three subtyping marker genes (ERBB2, ESR1, and PGR) was not changed in the bulk RNA-seq data, but the single-cell transcriptomes showed intratumor heterogeneity. This result shows that ERBB signaling is activated using an indirect route and that the molecular subtype is changed on a single-cell level. Our data propose a different view on breast cancer subtypes, clarifying much confusion in this field and contributing to precision medicine.

An Advanced Understanding of Uterine Microbial Ecology Associated with Metritis in Dairy Cows

  • Jeon, Soo Jin;Galvao, Klibs N.
    • Genomics & Informatics
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    • v.16 no.4
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    • pp.21.1-21.7
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    • 2018
  • Metritis, the inflammation of the uterus caused by polymicrobial infections, is a prevalent and costly disease to the dairy industry as it decreases milk yield, survival, and the welfare of dairy cows. Although affected cows are treated with broad-spectrum antibiotics such as ceftiofur, endometrial and ovarian function are not fully recovered, which results in subfertility and infertility. According to culture-dependent studies, uterine pathogens include Escherichia coli, Trueperella pyogenes, Fusobacterium necrophorum, and Prevotella melaninogenica. Recent studies using high-throughput sequencing observed very low relative abundance of Escherichia coli, Trueperella pyogenes, and Prevotella melaninogenica in cows with metritis. Herein, we propose that metritis is associated with a dysbiosis of the uterine microbiota, which is characterized by high abundance of Bacteroides, Porphyromonas, and Fusobacterium.

Ovarian Cancer Prognostic Prediction Model Using RNA Sequencing Data

  • Jeong, Seokho;Mok, Lydia;Kim, Se Ik;Ahn, TaeJin;Song, Yong-Sang;Park, Taesung
    • Genomics & Informatics
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    • v.16 no.4
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    • pp.32.1-32.7
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    • 2018
  • Ovarian cancer is one of the leading causes of cancer-related deaths in gynecological malignancies. Over 70% of ovarian cancer cases are high-grade serous ovarian cancers and have high death rates due to their resistance to chemotherapy. Despite advances in surgical and pharmaceutical therapies, overall survival rates are not good, and making an accurate prediction of the prognosis is not easy because of the highly heterogeneous nature of ovarian cancer. To improve the patient's prognosis through proper treatment, we present a prognostic prediction model by integrating high-dimensional RNA sequencing data with their clinical data through the following steps: gene filtration, pre-screening, gene marker selection, integrated study of selected gene markers and prediction model building. These steps of the prognostic prediction model can be applied to other types of cancer besides ovarian cancer.

Pharmacological potential of ginseng and its major component ginsenosides

  • Ratan, Zubair Ahmed;Haidere, Mohammad Faisal;Hong, Yo Han;Park, Sang Hee;Lee, Jeong-Oog;Lee, Jongsung;Cho, Jae Youl
    • Journal of Ginseng Research
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    • v.45 no.2
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    • pp.199-210
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    • 2021
  • Ginseng has been used as a traditional herb in Asian countries for thousands of years. It contains a large number of active ingredients including steroidal saponins, protopanaxadiols, and protopanaxatriols, collectively known as ginsenosides. In the last few decades, the antioxidative and anticancer effects of ginseng, in addition to its effects on improving immunity, energy and sexuality, and combating cardiovascular diseases, diabetes mellitus, and neurological diseases, have been studied in both basic and clinical research. Ginseng could be a valuable resource for future drug development; however, further higher quality evidence is required. Moreover, ginseng may have drug interactions although the available evidence suggests it is a relatively safe product. This article reviews the bioactive compounds, global distribution, and therapeutic potential of plants in the genus Panax.

Draft genome of Semisulcospira libertina, a species of freshwater snail

  • Gim, Jeong-An;Baek, Kyung-Wan;Hah, Young-Sool;Choo, Ho Jin;Kim, Ji-Seok;Yoo, Jun-Il
    • Genomics & Informatics
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    • v.19 no.3
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    • pp.32.1-32.10
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    • 2021
  • Semisulcospira libertina, a species of freshwater snail, is widespread in East Asia. It is important as a food source. Additionally, it is a vector of clonorchiasis, paragonimiasis, metagonimiasis, and other parasites. Although S. libertina has ecological, commercial, and clinical importance, its whole-genome has not been reported yet. Here, we revealed the genome of S. libertina through de novo assembly. We assembled the whole-genome of S. libertina and determined its transcriptome for the first time using Illumina NovaSeq 6000 platform. According to the k-mer analysis, the genome size of S. libertina was estimated to be 3.04 Gb. Using RepeatMasker, a total of 53.68% of repeats were identified in the genome assembly. Genome data of S. libertina reported in this study will be useful for identification and conservation of S. libertina in East Asia.

The nature of triple-negative breast cancer classification and antitumoral strategies

  • Kim, Songmi;Kim, Dong Hee;Lee, Wooseok;Lee, Yong-Moon;Choi, Song-Yi;Han, Kyudong
    • Genomics & Informatics
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    • v.18 no.4
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    • pp.35.1-35.7
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    • 2020
  • Identifying the patterns of gene expression in breast cancers is essential to understanding their pathophysiology and developing anticancer drugs. Breast cancer is a heterogeneous disease with different subtypes determined by distinct biological features. Luminal breast cancer is characterized by a relatively high expression of estrogen receptor (ER) and progesterone receptor (PR) genes, which are expressed in breast luminal cells. In ~25% of invasive breast cancers, human epidermal growth factor receptor 2 (HER2) is overexpressed; these cancers are categorized as the HER2 type. Triple-negative breast cancer (TNBC), in which the cancer cells do not express ER/PR or HER2, shows highly aggressive clinical outcomes. TNBC can be further classified into specific subtypes according to genomic mutations and cancer immunogenicity. Herein, we discuss the brief history of TNBC classification and its implications for promising treatments.

MODIFIED GEOMETRIC DISTRIBUTION OF ORDER k AND ITS APPLICATIONS

  • JUNGTAEK OH;KYEONG EUN LEE
    • Journal of applied mathematics & informatics
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    • v.42 no.3
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    • pp.709-723
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    • 2024
  • We study the distributions of waiting times in variations of the geometric distribution of order k. Variation imposes length on the runs of successes and failures. We study two types of waiting time random variables. First, we consider the waiting time for a run of k consecutive successes the first time no sequence of consecutive k failures occurs prior, denoted by T(k). Next, we consider the waiting time for a run of k consecutive failures the first time no sequence of k consecutive successes occurred prior, denoted by J(k). In addition, we study the distribution of the weighted average. The exact formulae of the probability mass function, mean, and variance of distributions are also obtained.

Radiomics in Breast Imaging from Techniques to Clinical Applications: A Review

  • Seung-Hak Lee;Hyunjin Park;Eun Sook Ko
    • Korean Journal of Radiology
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    • v.21 no.7
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    • pp.779-792
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    • 2020
  • Recent advances in computer technology have generated a new area of research known as radiomics. Radiomics is defined as the high throughput extraction and analysis of quantitative features from imaging data. Radiomic features provide information on the gray-scale patterns, inter-pixel relationships, as well as shape and spectral properties of radiological images. Moreover, these features can be used to develop computational models that may serve as a tool for personalized diagnosis and treatment guidance. Although radiomics is becoming popular and widely used in oncology, many problems such as overfitting and reproducibility issues remain unresolved. In this review, we will outline the steps of radiomics used for oncology, specifically addressing applications for breast cancer patients and focusing on technical issues.

Bauhinia rufescens, Ocimum basilicum and Salvadora persica: a review of their chemical compounds and properties for antimicrobial, antioxidant and cytotoxicity

  • Abdel-razakh Hissein Hassan;Gaymary George Bakari;Cheol-Ho Pan;Abubakar Shaaban Hoza
    • Journal of Applied Biological Chemistry
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    • v.66
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    • pp.179-185
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    • 2023
  • Bauhinia rufescens, Ocimum basilicum and Salvadora persica are well known plants used in African traditional medicine, especially in Chadian traditional medicine. They are mostly used in the treatment of infectious diseases, inflammatory diseases, fever, and so on. Studies using various in vitro and in vivo bioassay techniques support the scientific rationale for most of these usages. In this review, ethnobotanical uses, chemistry of natural products, and pharmacological and clinical data for these plants are presented.