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Usage Information

Diabetic retinopathy: current understanding, mechanisms, and treatment strategies
Elia J. Duh, … , Jennifer K. Sun, Alan W. Stitt
Elia J. Duh, … , Jennifer K. Sun, Alan W. Stitt
Published July 20, 2017
Citation Information: JCI Insight. 2017;2(14):e93751. https://doi.org/10.1172/jci.insight.93751.
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Diabetic retinopathy: current understanding, mechanisms, and treatment strategies

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Abstract

Diabetic retinopathy (DR) causes significant visual loss on a global scale. Treatments for the vision-threatening complications of diabetic macular edema (DME) and proliferative diabetic retinopathy (PDR) have greatly improved over the past decade. However, additional therapeutic options are needed that take into account pathology associated with vascular, glial, and neuronal components of the diabetic retina. Recent work indicates that diabetes markedly impacts the retinal neurovascular unit and its interdependent vascular, neuronal, glial, and immune cells. This knowledge is leading to identification of new targets and therapeutic strategies for preventing or reversing retinal neuronal dysfunction, vascular leakage, ischemia, and pathologic angiogenesis. These advances, together with approaches embracing the potential of preventative or regenerative medicine, could provide the means to better manage DR, including treatment at earlier stages and more precise tailoring of treatments based on individual patient variations.

Authors

Elia J. Duh, Jennifer K. Sun, Alan W. Stitt

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Usage data is cumulative from August 2024 through August 2025.

Usage JCI PMC
Text version 4,503 3,588
PDF 369 574
Figure 551 13
Citation downloads 167 0
Totals 5,590 4,175
Total Views 9,765
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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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