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Usage Information

The complex landscape of microRNAs in articular cartilage: biology, pathology, and therapeutic targets
Helal Endisha, Jason Rockel, Igor Jurisica, Mohit Kapoor
Helal Endisha, Jason Rockel, Igor Jurisica, Mohit Kapoor
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The complex landscape of microRNAs in articular cartilage: biology, pathology, and therapeutic targets

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Abstract

The disabling degenerative disease osteoarthritis (OA) is prevalent among the global population. Articular cartilage degeneration is a central feature of OA; therefore, a better understanding of the mechanisms that maintain cartilage homeostasis is vital for developing effective therapeutic interventions. MicroRNAs (miRs) modulate cell signaling pathways and various processes in articular cartilage via posttranscriptional repression of target genes. As dysregulated miRs frequently alter the homeostasis of articular cartilage, modulating select miRs presents a potential therapeutic opportunity for OA. Here, we review key miRs that have been shown to modulate cartilage-protective or -destructive mechanisms and signaling pathways. Additionally, we use an integrative computational biology approach to provide insight into predicted miR gene targets that may contribute to OA pathogenesis, and highlight the complexity of miR signaling in OA by generating both unique and overlapping gene targets of miRs that mediate protective or destructive effects. Early OA detection would enable effective prevention; thus, miRs are being explored as diagnostic biomarkers. We discuss these ongoing efforts and the applicability of miR mimics and antisense inhibitors as potential OA therapeutics.

Authors

Helal Endisha, Jason Rockel, Igor Jurisica, Mohit Kapoor

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Usage data is cumulative from January 2025 through January 2026.

Usage JCI PMC
Text version 754 69
PDF 146 20
Figure 190 1
Supplemental data 47 1
Citation downloads 120 0
Totals 1,257 91
Total Views 1,348
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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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