ReviewAdvances in experimental medicine and biology2025
CircularRNA and Musculoskeletal Diseases.
Review in Advances in experimental medicine and biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
5 authors.
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Abstract
The skeletal muscle is the largest organ in the human body and plays a crucial role in the locomotion, metabolism, and homeostasis. Myogenesis is regulated by a complex molecular network and is kept finely balanced under physiological conditions. The disorder of muscular homeostasis results in musculoskeletal diseases, including myositis, dystrophy, sarcopenia, atrophy, and cachexia. These diseases severely affect the life quality of humans. In recent years, with the progress of high-throughput sequencing technology and the development of bioinformatics, a large number of noncoding RNAs (ncRNAs) have been identified in the skeletal muscle, and numerous experiments to date have shown that those ncRNAs are widely involved in multiple pathophysiological process of skeletal muscle. Circular RNAs (circRNA) are a class of noncoding RNAs formed by covalently closed loops through back-splicing and exon skipping. circRNAs have been confirmed to play a vital role in various biological functions, acting as microRNA sponges and reservoirs, as well as combining with RNA-binding proteins during the progression of myogenesis. In this chapter, we will summarize the progress of circRNA in regulating skeletal muscle and provide new information to better understand muscle diseases development and therapeutic strategy.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.