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Cited by 276

The DOI matches its Crossref record; 31 of the 31 references it cites were found in a registry or an index.

Emerging Biomarkers of Acute Kidney Injury

Devarajan, Prasad·2007·Contributions to Nephrology

Abstract

BACKGROUND: Acute kidney injury (AKI) is a major clinical problem with a rising incidence and high mortality rate. The lack of early biomarkers has resulted in an unacceptable delay in initiating therapies. METHODS: Here we will update the reader on promising new blood and urinary biomarkers that have recently emerged through the application of innovative technologies such as functional genomics and proteomics to human and animal models of AKI. RESULTS: The most promising biomarkers of AKI for clinical use include a plasma panel (NGAL and cystatin C) and a urine panel (NGAL, Il-18 and KIM-1). CONCLUSIONS: As they represent tandem biomarkers, it is likely that the AKI panels will be useful for timing the initial insult and assessing the duration and severity of AKI. Based on the differential expression of the biomarkers, it is also likely that the AKI panels will distinguish between the various types and etiologies of AKI. It will be important in future studies to validate the sensitivity and specificity of these biomarker panels in clinical samples from large cohorts and from multiple clinical situations.

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Metadata

Authors
Devarajan, P.
Venue
Contributions to Nephrology
Publisher
KARGER
Published
2007
Volume
156
Pages
203-212
OpenAlex
W1540734062
Cited by
276
References
31
FWCI
61.80
Type
book chapter
Language
en
Open access
closed

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CoChat. (2026). Citation check: Emerging Biomarkers of Acute Kidney Injury. https://app.cochat.ai/papers/10.1159/000102085
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Devarajan, P. (2007). Emerging Biomarkers of Acute Kidney Injury. Contributions to Nephrology. https://doi.org/10.1159/000102085

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.