Literature DB >> 23271065

Analysis of the human intestinal microbiota from 92 volunteers after ingestion of identical meals.

J S Jin1, M Touyama, R Kibe, Y Tanaka, Y Benno, T Kobayashi, M Shimakawa, T Maruo, T Toda, I Matsuda, H Tagami, M Matsumoto, G Seo, O Chonan, Y Benno.   

Abstract

The intestinal microbiota composition of 92 volunteers living in Japan was identified following the consumption of 'identical meals' (1,879 kcal/day) for 3 days. When faecal samples were analysed by terminal restriction fragment length polymorphism with several primer-restriction enzyme systems and then clustered, the patterns could be divided into 2 clusters. Contribution tests and partition modelling showed that OTU211 of the 35f-MspI system and OTU237 of the 35f-AluI system were key factors in the distribution of these groups. However, significant differences among these groups in terms of body mass index and age were not observed.

Entities:  

Mesh:

Year:  2013        PMID: 23271065     DOI: 10.3920/BM2012.0045

Source DB:  PubMed          Journal:  Benef Microbes        ISSN: 1876-2883            Impact factor:   4.205


  7 in total

1.  Characterization of gut microbiota profiles in coronary artery disease patients using data mining analysis of terminal restriction fragment length polymorphism: gut microbiota could be a diagnostic marker of coronary artery disease.

Authors:  Takuo Emoto; Tomoya Yamashita; Toshio Kobayashi; Naoto Sasaki; Yushi Hirota; Tomohiro Hayashi; Anna So; Kazuyuki Kasahara; Keiko Yodoi; Takuya Matsumoto; Taiji Mizoguchi; Wataru Ogawa; Ken-Ichi Hirata
Journal:  Heart Vessels       Date:  2016-04-28       Impact factor: 2.037

2.  Applying Data Mining to Classify Age by Intestinal Microbiota in 92 Healthy Men Using a Combination of Several Restriction Enzymes for T-RFLP Experiments.

Authors:  Toshio Kobayashi; Takako Osaki; Shinya Oikawa
Journal:  Biosci Microbiota Food Health       Date:  2014-04-29

3.  Comprehensive analysis of the fecal microbiota of healthy Japanese adults reveals a new bacterial lineage associated with a phenotype characterized by a high frequency of bowel movements and a lean body type.

Authors:  Kaihei Oki; Mutsumi Toyama; Taihei Banno; Osamu Chonan; Yoshimi Benno; Koichi Watanabe
Journal:  BMC Microbiol       Date:  2016-11-28       Impact factor: 3.605

4.  Identification of Heavy Smokers through Their Intestinal Microbiota by Data Mining Analysis.

Authors:  Toshio Kobayashi; Kenji Fujiwara
Journal:  Biosci Microbiota Food Health       Date:  2013-04-27

5.  Comparison of the accuracy and mechanism of data mining identification of the intestinal microbiota with 7 restriction enzymes.

Authors:  Toshio Kobayashi; Kenji Fujiwara
Journal:  Biosci Microbiota Food Health       Date:  2013-10-30

6.  Identification of Human Intestinal Microbiota of 92 Men by Data Mining for 5 Characteristics, i.e., Age, BMI, Smoking Habit, Cessation Period of Previous Smokers and Drinking Habit.

Authors:  Toshio Kobayashi; Jong-Sik Jin; Ryoko Kibe; Mutsumi Touyama; Yoshiki Tanaka; Yoshiko Benno; Kenji Fujiwara; Masaki Shimakawa; Toshiya Maruo; Toshiya Toda; Isao Matsuda; Hiroyuki Tagami; Mitsuharu Matsumoto; Genichirou Seo; Naoki Sato; Osamu Chounan; Yoshimi Benno
Journal:  Biosci Microbiota Food Health       Date:  2013-05-15

7.  Technical Aspects of Nominal Partitions on Accuracy of Data Mining Classification of Intestinal Microbiota - Comparison between 7 Restriction Enzymes.

Authors:  Toshio Kobayashi; Kenji Fujiwara
Journal:  Biosci Microbiota Food Health       Date:  2014-05-16
  7 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.