A rapid, affordable, and reliable method for profiling microbiome biomarkers from fecal images

IF 4.6 2区综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES

iScience Pub Date : 2024-11-08 DOI:10.1016/j.isci.2024.111310

Donghyeok Lee , Annemiek Maaskant , Huy Ngo , Roy C. Montijn , Jaco Bakker , Jan A.M. Langermans , Evgeni Levin

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Abstract

Human and veterinary healthcare professionals are interested in utilizing the gut-microbiome as a target to diagnose, treat, and prevent (gastrointestinal) diseases. However, the current microbiome analysis techniques are expensive and time-consuming, and data interpretation requires the expertise of specialists. Therefore, we explored the development and application of artificial intelligence technology for rapid, affordable, and reliable microbiome profiling in rhesus macaques (Macaca mulatta). Tailor-made learning algorithms were created by integrating digital images of fecal samples with corresponding whole-genome sequenced microbial profiles. These algorithms were trained to identify alpha-diversity (Shannon index), key microbial markers, and fecal consistency from the digital images of fecal smears. A binary classification strategy was applied to distinguish between samples with high and low diversity and presence or absence of selected bacterial genera. Our results revealed a successful proof of concept for “high and low” prediction of diversity, fecal consistency, and “present or absent” for selected bacterial genera.

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快速、经济、可靠的粪便图像微生物组生物标记分析方法

人类和兽医保健专业人员对利用肠道微生物组作为诊断、治疗和预防（胃肠道）疾病的目标很感兴趣。然而，目前的微生物组分析技术既昂贵又耗时，而且数据解读需要专家的专业知识。因此，我们探索了人工智能技术的开发和应用，以实现快速、经济、可靠的猕猴（Macaca mulatta）微生物组分析。通过整合粪便样本的数字图像和相应的全基因组测序微生物图谱，我们创建了量身定制的学习算法。对这些算法进行了训练，以便从粪便涂片的数字图像中识别阿尔法多样性（香农指数）、关键微生物标记物和粪便一致性。采用二元分类策略来区分样本的多样性高低和所选细菌属的存在与否。我们的研究结果表明，我们成功地验证了 "高低 "多样性预测、粪便一致性预测和选定细菌属 "存在或不存在 "预测的概念。

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来源期刊

iScience Multidisciplinary-Multidisciplinary

CiteScore

7.20

自引率

1.70%

发文量

1972

审稿时长

6 weeks

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