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Usage Information

Photoacoustic imaging of kidney fibrosis for assessing pretransplant organ quality
Eno Hysi, Xiaolin He, Muhannad N. Fadhel, Tianzhou Zhang, Adriana Krizova, Michael Ordon, Monica Farcas, Kenneth T. Pace, Victoria Mintsopoulos, Warren L. Lee, Michael C. Kolios, Darren A. Yuen
Eno Hysi, Xiaolin He, Muhannad N. Fadhel, Tianzhou Zhang, Adriana Krizova, Michael Ordon, Monica Farcas, Kenneth T. Pace, Victoria Mintsopoulos, Warren L. Lee, Michael C. Kolios, Darren A. Yuen
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Resource and Technical Advance Nephrology

Photoacoustic imaging of kidney fibrosis for assessing pretransplant organ quality

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Abstract

Roughly 10% of the world’s population has chronic kidney disease (CKD). In its advanced stages, CKD greatly increases the risk of hospitalization and death. Although kidney transplantation has revolutionized the care of advanced CKD, clinicians have limited ways of assessing donor kidney quality. Thus, optimal donor kidney–recipient matching cannot be performed, meaning that some patients receive damaged kidneys that function poorly. Fibrosis is a form of chronic damage often present in donor kidneys, and it is an important predictor of future renal function. Currently, no safe, easy-to-perform technique exists that accurately quantifies renal fibrosis. We describe a potentially novel photoacoustic (PA) imaging technique that directly images collagen, the principal component of fibrotic tissue. PA imaging noninvasively quantifies whole kidney fibrotic burden in mice, and cortical fibrosis in pig and human kidneys, with outstanding accuracy and speed. Remarkably, 3-dimensional PA imaging exhibited sufficiently high resolution to capture intrarenal variations in collagen content. We further show that PA imaging can be performed in a setting that mimics human kidney transplantation, suggesting the potential for rapid clinical translation. Taken together, our data suggest that PA collagen imaging is a major advance in fibrosis quantification that could have widespread preclinical and clinical impact.

Authors

Eno Hysi, Xiaolin He, Muhannad N. Fadhel, Tianzhou Zhang, Adriana Krizova, Michael Ordon, Monica Farcas, Kenneth T. Pace, Victoria Mintsopoulos, Warren L. Lee, Michael C. Kolios, Darren A. Yuen

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Usage data is cumulative from January 2025 through January 2026.

Usage JCI PMC
Text version 1,044 374
PDF 140 108
Figure 491 11
Supplemental data 77 23
Citation downloads 115 0
Totals 1,867 516
Total Views 2,383
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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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