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潜在的口腔微生物标记物用于使用机器学习模型对克罗恩病和性结肠炎的差分诊断

Sang-Bum Kang¹, Hyeonwoo Kim², Sangsoo Kim²

¹Department of Internal Medicine, College of Medicine, Daejeon St. Mary's Hospital, The Catholic University of Korea, Daejeon 34943, Republic of Korea.

|July 29, 2023

概括

口腔微生物组的改变与炎症性肠病 (IBD) 有关. 这项研究在IBD患者中发现了明显的唾液微生物模式,有助于区分克罗恩病 (CD) 和性结肠炎 (UC).

科学领域:

微生物学微生物学
胃肠病学胃肠病学
基因组学就是基因组学.

背景情况:

肠道微生物组失调与炎症性肠道疾病 (IBD) 有关.
口腔微生物群在IBD病原和诊断中的作用尚不清楚.
在临床上,区分克罗恩病 (CD) 和性结肠炎 (UC) 是非常重要的.

研究的目的:

在IBD患者中识别独特的口腔微生物组模式.
探索潜在的唾液微生物生物标志物,以区分CD和UC.
评估机器学习模型在分类IBD亚型中的准确性.

主要方法:

潜在的队列研究,包括IBD患者 (UC,CD) 和健康对照 (HC).
使用16S rRNA基因测序进行口腔微生物群分析.
机器学习模型 (sPLS-DA) 用于分类和差异化.

主要成果:

与HC相比,IBD患者观察到的微生物丰富度减少.
在区分IBD和HC方面取得了高精度 (精度:0.908,AUC:0.966).
在区分CD和UC时获得了良好的精度 (精度:0.846,AUC:0.923).

结论:

独特的口腔微生物组形状与IBD有关.
唾液微生物模式显示出潜在的IBD亚型的诊断标志物.
机器学习有效地利用口腔微生物群数据进行IBD分类.

关键词:

16S rRNA 测序的测序方法 IBD IBD IBD IBD IBD IBD IBD IBD IBD IBD IBD IBD IBD IBD IBD 这种疾病叫做dysbiosis.机器学习是机器学习.口腔微生物组是口腔的微生物组.sPLS-DA 的时间.

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