亲爱的研友该休息了!由于当前在线用户较少,发布求助请尽量完整的填写文献信息,科研通机器人24小时在线,伴您度过漫漫科研夜!身体可是革命的本钱,早点休息,好梦!

Colonic Microbial Abundances Predict Adenoma Formers

医学 拟杆菌 厚壁菌 胃肠病学 腺瘤 拟杆菌 微生物群 内科学 粪便 生物 病理 16S核糖体RNA 微生物学 生物信息学 遗传学 细菌
作者
Katherine M. Watson,Ivy H. Gardner,Sudarshan Anand,Kyla N. Siemens,Thomas J. Sharpton,Kristin D. Kasschau,Elizabeth N. Dewey,Robert G. Martindale,Christopher A. Gaulke,Vassiliki L. Tsikitis
出处
期刊:Annals of Surgery [Ovid Technologies (Wolters Kluwer)]
卷期号:277 (4): e817-e824 被引量:7
标识
DOI:10.1097/sla.0000000000005261
摘要

Objective: We aimed to examine associations between the oral, fecal, and mucosal microbiome communities and adenoma formation. Summary Background Data: Data are limited regarding the relationships between microbiota and preneoplastic colorectal lesions. Methods: Individuals undergoing screening colonoscopy were prospectively enrolled and divided into adenoma and nonadenoma formers. Oral, fecal, nonadenoma and adenoma-adjacent mucosa were collected along with clinical and dietary information. 16S rRNA gene libraries were generated using V4 primers. DADA2 processed sequence reads and custom R-scripts quantified microbial diversity. Linear regression identified differential taxonomy and diversity in microbial communities and machine learning identified adenoma former microbial signatures. Results: One hundred four subjects were included, 46% with adenomas. Mucosal and fecal samples were dominated by Firmicutes and Bacteroidetes whereas Firmicutes and Proteobacteria were most abundant in oral communities. Mucosal communities harbored significant microbial diversity that was not observed in fecal or oral communities. Random forest classifiers predicted adenoma formation using fecal, oral, and mucosal amplicon sequence variant (ASV) abundances. The mucosal classifier reliably diagnosed adenoma formation with an area under the curve (AUC) = 0.993 and an out-of-bag (OOB) error of 3.2%. Mucosal classifier accuracy was strongly influenced by five taxa associated with the family Lachnospiraceae, genera Bacteroides and Marvinbryantia, and Blautia obeum. In contrast, classifiers built using fecal and oral samples manifested high OOB error rates (47.3% and 51.1%, respectively) and poor diagnostic abilities (fecal and oral AUC = 0.53). Conclusion: Normal mucosa microbial abundances of adenoma formers manifest unique patterns of microbial diversity that may be predictive of adenoma formation.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
阿烨完成签到,获得积分10
5秒前
小靳完成签到,获得积分10
8秒前
图图完成签到 ,获得积分10
9秒前
11秒前
bkagyin应助科研通管家采纳,获得10
12秒前
Orange应助科研通管家采纳,获得10
12秒前
传奇3应助科研通管家采纳,获得10
12秒前
思源应助科研通管家采纳,获得10
12秒前
李爱国应助科研通管家采纳,获得10
12秒前
15秒前
Yesaniar发布了新的文献求助10
20秒前
LMY1411发布了新的文献求助10
22秒前
李文岐完成签到 ,获得积分10
23秒前
wyj完成签到,获得积分10
26秒前
27秒前
爽爽完成签到 ,获得积分10
30秒前
jyy应助威武大将军采纳,获得10
32秒前
33秒前
小武发布了新的文献求助10
34秒前
weiquanfei完成签到,获得积分20
35秒前
37秒前
weiquanfei发布了新的文献求助10
40秒前
monair完成签到 ,获得积分10
42秒前
brwen完成签到,获得积分10
43秒前
44秒前
yue完成签到,获得积分10
44秒前
yue发布了新的文献求助10
49秒前
且从容完成签到,获得积分10
50秒前
52秒前
超人不会飞完成签到,获得积分10
59秒前
冷静剑成发布了新的文献求助10
1分钟前
1分钟前
科研通AI5应助超人不会飞采纳,获得10
1分钟前
cy发布了新的文献求助10
1分钟前
Chondrite发布了新的文献求助10
1分钟前
1分钟前
江江发布了新的文献求助10
1分钟前
冷静剑成完成签到,获得积分10
1分钟前
仙方活命饮完成签到,获得积分10
1分钟前
麻酱发布了新的文献求助10
1分钟前
高分求助中
Production Logging: Theoretical and Interpretive Elements 2700
Social media impact on athlete mental health: #RealityCheck 1020
1.3μm GaAs基InAs量子点材料生长及器件应用 1000
Ensartinib (Ensacove) for Non-Small Cell Lung Cancer 1000
Unseen Mendieta: The Unpublished Works of Ana Mendieta 1000
Bacterial collagenases and their clinical applications 800
El viaje de una vida: Memorias de María Lecea 800
热门求助领域 (近24小时)
化学 材料科学 生物 医学 工程类 有机化学 生物化学 物理 纳米技术 计算机科学 内科学 化学工程 复合材料 基因 遗传学 物理化学 催化作用 量子力学 光电子学 冶金
热门帖子
关注 科研通微信公众号,转发送积分 3526437
求助须知:如何正确求助?哪些是违规求助? 3106899
关于积分的说明 9281822
捐赠科研通 2804409
什么是DOI,文献DOI怎么找? 1539435
邀请新用户注册赠送积分活动 716571
科研通“疑难数据库(出版商)”最低求助积分说明 709546