miR-2954 Inhibits PI3K Signaling and Induces Autophagy and Apoptosis in Myocardium Selenium Deficiency

Background/Aims: Selenium (Se) deficiency can lead to several cardiac diseases, including Keshan disease in humans, mulberry heart disease in pigs and cardiac injury in chickens. MicroRNAs have been a research focus in recent years and have been shown to participate in a new avenue of cell death-autophagy, which can play a significant role in several types of heart disease. Methods: MicroRNAome analysis showed that the expression of miR-2954 was increased in the myocardium of selenium-deficient chickens, and PI3K was predicted to be the target gene. The target relationship between miR-2954 and PI3K was verified with a double fluorescence enzyme assay and RNA Protein Interaction Prediction and molecular docking software. qRT-PCR and western blotting were used to detect the expression of PI3K and related pathway components in selenium-deficient chickens and miR-2954 knockout/overexpression cardiomyocytes. Results: In this study, we observed that miR-2954 overexpression led to inhibition of PI3K pathway in vivo and in vitroled to inhibition of the PI3K pathway in vivo and in vitro. Conclusion: The expression of miR-2954 was increased in selenium-deficient myocardium, whereas overexpression of miR-2954 led to autophagy and apoptosis of myocardial cells during cardiac injury through regulation of the PI3K pathway; whether this phenomenon is a self-protection mechanism of the organism or damage caused by miR-2954 requires further study. Our findings provides new insight apoptosis in cardiomyocytes; additionally, we aim to provide a new direction for the diagnosis and targeted treatment of myocardial diseases

Fingerprint Database Reconstruction Based on Robust PCA for Indoor Localization

The indoor localization method based on the Received Signal Strength (RSS) fingerprint is widely used for its high positioning accuracy and low cost. However, the propagation behavior of radio signals in an indoor environment is complicated and always leads to the existence of outliers and noises that deviate from a normal RSS value in the database. The fingerprint database containing outliers and noises will severely degrade the performance of an indoor localization system. In this paper, an approach to reconstruct the fingerprint database is proposed with the purpose of mitigating the influences of outliers. More specifically, by exploiting the spatial and temporal correlations of RSS data, the database can be transformed into a low-rank matrix. Therefore, the RPCA (Robust Principle Component Analysis) technique can be applied to recover the low-rank matrix from a noisy matrix. In addition, we propose an improved RPCA model which takes advantage of the prior knowledge of a singular value and could remove outliers and structured noise simultaneously. The experimental results show that the proposed method can eliminate outliers and structured noise efficiently