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Summary: Obstructive sleep apnea (OSA) is a common sleep disordered breathing diseases that characterized by chronic intermittent hypoxia (CIH). This work aimed to explore the role of circ-CIMIRC in CIH-induced myocardial injury. CIH aggravated myocardial tissue damage in rats. Circ_CIMIRC overexpression promoted apoptosis and reduced the colocalization of Tom20 and Parkin and mitophagy in CIH-treated H9c2 cells. Additionally, FbxL4 interacted with PINK1, FbxL4 silencing reduced PINK1 ubiquitination in H9c2 cells. Two major ubiquitination sites (K319 and K433) were responsible for ubiquitination of PINK1. Circ_CIMIRC promoted FbxL4-mediated ubiquitination and degradation of PINK1. Furthermore, circ_CIMIRC inhibition alleviated the pathological damage, fibrosis and apoptosis of myocardial tissues, reduced oxidative stress in CIH rats. In conclusion, circ_CIMIRC silencing repressed FbxL4-mediated ubiquitination and degradation of PINK1 and then enhanced PINK1/Parkin-mediated mitophagy, thereby alleviating myocardial damage in CIH rats. Thus, circ_CIMIRC may be a potential strategy to alleviate CIH-induced myocardial damage.

Abstract With the rapid development of the social economy, consumer demand is evolving towards diversification. To satisfy market demand, enterprises tend to improve competitiveness by providing differentiated products. How to price differentiated products becomes a hot topic. Traditionally, customers' preferences are assumed to be independent and identically distributed. With a known distribution, companies can easily make pricing decisions for differentiated products. However, such an assumption may be invalid in practice, especially for rapidly updating products. In this paper, a dynamic pricing policy for differentiated products with incomplete information is developed. An adaptive multi‐armed bandit algorithm based on reinforcement learning is proposed to balance exploration and exploitation. Numerical examples show that the frequency of price adjustment affects the total profit significantly. Specifically, the more chances to adjust the price, the higher the total profit. Furthermore, experiments show that the dynamic pricing policy proposed in this paper outperforms other algorithms, such as Softmax and UCB1.

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