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Usage Information

Animal models of sepsis and sepsis-induced kidney injury
Kent Doi, Asada Leelahavanichkul, Peter S.T. Yuen, Robert A. Star
Kent Doi, Asada Leelahavanichkul, Peter S.T. Yuen, Robert A. Star
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Science in Medicine

Animal models of sepsis and sepsis-induced kidney injury

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Abstract

Sepsis is characterized by a severe inflammatory response to infection, and its complications, including acute kidney injury, can be fatal. Animal models that correctly mimic human disease are extremely valuable because they hasten the development of clinically useful therapeutics. Too often, however, animal models do not properly mimic human disease. In this Review, we outline a bedside-to-bench-to-bedside approach that has resulted in improved animal models for the study of sepsis — a complex disease for which preventive and therapeutic strategies are unfortunately lacking. We also highlight a few of the promising avenues for therapeutic advances and biomarkers for sepsis and sepsis-induced acute kidney injury. Finally, we review how the study of drug targets and biomarkers are affected by and in turn have influenced these evolving animal models.

Authors

Kent Doi, Asada Leelahavanichkul, Peter S.T. Yuen, Robert A. Star

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Usage data is cumulative from June 2025 through June 2026.

Usage JCI PMC
Text version 3,278 530
PDF 269 113
Figure 266 2
Table 478 0
Supplemental data 100 11
Citation downloads 152 0
Totals 4,543 656
Total Views 5,199
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Usage information is collected from two different sources: this site (JCI) and Pubmed Central (PMC). JCI information (compiled daily) shows human readership based on methods we employ to screen out robotic usage. PMC information (aggregated monthly) is also similarly screened of robotic usage.

Various methods are used to distinguish robotic usage. For example, Google automatically scans articles to add to its search index and identifies itself as robotic; other services might not clearly identify themselves as robotic, or they are new or unknown as robotic. Because this activity can be misinterpreted as human readership, data may be re-processed periodically to reflect an improved understanding of robotic activity. Because of these factors, readers should consider usage information illustrative but subject to change.

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ISSN: 0021-9738 (print), 1558-8238 (online)

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