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Integrating metabolomics and machine learning with in silico analysis to identify early biomarkers and molecular interactions in sepsis-associated acute kidney injury

Crossref DOI link: https://doi.org/10.1038/s41598-026-45255-0

Published Online: 2026-03-27

Update policy: https://doi.org/10.1007/springer_crossmark_policy

Authors

Xu, Wenbo

Zhang, Zhouxing

Gu, Fuli

Ye, Tingxian

Zhang, Yuechen

Hu, Wei

Xi, Shaosong
Funding

Funding for this research was provided by:

The Construction Fund for Key Clinical Specialties of Hangzhou (HZSZDZK-ZZYXK)

Medical Science and Technology Project of Zhejiang Province (WKJ-ZJ-2315)
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Text and Data Mining valid from 2026-03-27

Version of Record valid from 2026-03-31
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Article History

Received: 30 June 2025

Accepted: 17 March 2026

First Online: 27 March 2026

Declarations

:

: The authors declare no competing interests.

: This study was approved by the Ethics Committee of Hangzhou First People’s Hospital (Ethics Approval Certificate: KY-20211105-0078-01). All studies were conducted in accordance with relevant guidelines/regulations and informed consent was obtained from all participants and/or their legal guardians.

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