A microbiota-based perspective on urinary stone disease: insights from 16S rRNA sequencing and machine learning models
This study reveals that different types of stone are characterized by distinct compositions of microbiota. Machine learning models based on microbiota and clinical data can predict urinary stone types noninvasively. This provides novel insights into the microecological mechanisms of urinary stones and opens up new avenues for clinical diagnosis.
Authors
- Aoyu Yang
- Xiancheng Li (ORCID: https://orcid.org/0000-0003-0553-0062)
- Ziyi Zhang (ORCID: https://orcid.org/0000-0002-2175-4931)
- Yufeng Liu (ORCID: https://orcid.org/0000-0002-2733-7733)
- Wei Wang (ORCID: https://orcid.org/0000-0003-2380-5314)
- Chen Shen (ORCID: https://orcid.org/0000-0001-9686-8952)
Institutions
- Dalian Medical University (CN)
- Second Affiliated Hospital of Dalian Medical University (CN)
Publication Details
- Journal
- Frontiers in Cellular and Infection Microbiology
- Published
- 2025-10-23
- DOI
- https://doi.org/10.3389/fcimb.2025.1623429
- Citations
- 1
- Primary Topic
- Kidney Stones and Urolithiasis Treatments
- Type
- article
- Field-Weighted Citation Impact
- 1.52