Use of novel structural features to identify urinary biomarkers during acute kidney injury that predict progression to chronic kidney disease
- Location
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Deutsche Nationalbibliothek Frankfurt am Main
- Extent
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Online-Ressource, 1 online resource.
- Language
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Englisch
- Bibliographic citation
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Use of novel structural features to identify urinary biomarkers during acute kidney injury that predict progression to chronic kidney disease ; volume:24 ; number:1 ; day:19 ; month:6 ; year:2023 ; pages:1-13 ; date:12.2023
BMC nephrology ; 24, Heft 1 (19.6.2023), 1-13, 12.2023
- Creator
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Charlton, Jennifer R.
Li, Teng
Wu, Teresa
deRonde, Kimberly
Xu, Yanzhe
Baldelomar, Edwin J.
Bennett, Kevin M.
- Contributor
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SpringerLink (Online service)
- DOI
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10.1186/s12882-023-03196-0
- URN
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urn:nbn:de:101:1-2023092208335077654554
- Rights
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
-
14.08.2025, 10:58 AM CEST
Data provider
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Associated
- Charlton, Jennifer R.
- Li, Teng
- Wu, Teresa
- deRonde, Kimberly
- Xu, Yanzhe
- Baldelomar, Edwin J.
- Bennett, Kevin M.
- SpringerLink (Online service)
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Tongue trail to kidney
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Machine learning models to predict end-stage kidney disease in chronic kidney disease stage 4
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Development and validation of a risk index to predict kidney graft survival: the kidney transplant risk index
Urinary Biomarkers TIMP-2 and IGFBP7 Early Predict Acute Kidney Injury after Major Surgery
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New urinary biomarkers for diabetic kidney disease
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