Long non-coding RNAs (LncRNAs) participate in the regulation of chronic kidney disease (CKD), and acute kidney injury (AKI) is identified as an important risk factor for CKD. This study investigated the involvement of a novel LncRNA MALAT1 in regulating lipopolysaccharide (LPS)-induced cell pyroptosis and inflammation in the human renal tubular epithelial HK-2 cells. Here, the HK-2 cells were subjected to LPS (2 μg/mL) treatment to establish cellular AKI models
in vitro
, and we validated that LPS triggered NLRP3-mediated pyroptotic cell death, promoted cell apoptosis and inflammation-associated cytokines secretion to induce HK-2 cell injury. Then, a novel LncRNA MALAT1/miRNA (miRNA)-135b-5p axis was verified to rescue cell viability in LPS treated HK-2 cells by targeting NLRP3. Mechanistically, miRNA-135b-5p bound to LncRNA MALAT1, and LncRNA MALAT1 positively regulated NLRP3 through acting as RNA sponger for miRNA-135b-5p. Further gain- and loss-of-function experiments evidenced that both LncRNA MALAT1 ablation and miRNA-135b-5p overexpression reversed LPS-induced cell pyroptosis, apoptosis, and inflammation in the HK-2 cells, and the protective effects of LncRNA MALAT1 knock-down on LPS-treated HK-2 cells were abrogated by silencing miRNA-135b-5p. In general, our study firstly investigated the role of the LncRNA MALAT1/ miRNA-135b-5p/NLRP3 signaling cascade in regulating LPS-induced inflammatory death in HK-2 cells.
A Poisson distribution is commonly used as the innovation distribution for integer-valued autoregressive models, but its mean is equal to its variance, which limits flexibility, so a flexible, one-parameter, infinitely divisible Bell distribution may be a good alternative. In addition, for a parameter with a small value, the Bell distribution approaches the Poisson distribution. In this paper, we introduce a new first-order, non-negative, integer-valued autoregressive model with Bell innovations based on the binomial thinning operator. Compared with other models, the new model is not only simple but also particularly suitable for time series of counts exhibiting overdispersion. Some properties of the model are established here, such as the mean, variance, joint distribution functions, and multi-step-ahead conditional measures. Conditional least squares, Yule–Walker, and conditional maximum likelihood are used for estimating the parameters. Some simulation results are presented to access these estimates’ performances. Real data examples are provided.
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