BackgroundPleckstrin homology-like domain family A member 1 (PHLDA1) is a tumor suppressor gene in gastric cancer, but its role regulated by circular RNAs (circRNAs) is not known. CircRNAs are important regulators in cancer growth and progression, however, the molecular roles of circRNAs in gastric cancer are rarely known. The study was aimed to investigate the role of circRNAs in regulating PHLDA1 expression in gastric cancer.ResultsThe circRNA expression profile in the gastric cancer tissues by circRNA microarray showed that hsa_circ_0027599 (circ_0027599) was significantly down-regulated in gastric cancer patients and cells when comparing with the controls. Circ_0027599 overexpression suppressed gastric cancer cell proliferation and metastasis. By using bioinformatics tools and luciferase reporter assays, circ_0027599 was verified as a sponge of miR-101-3p.1 (miR-101) and suppressed cancer cell survival and metastasis. It was also verified that PHLDA1 was regulated by circ_0027599 in gastric cancer cells.ConclusionsThe study uncovered that PHLDA1 was regulated by circ_0027599/miR-101, which suppressed gastric cancer survival and metastasis in gastric cancer.
The automatic detection of atrial fibrillation based on electrocardiograph (ECG) signals has received wide attention both clinically and practically. It is challenging to process ECG signals with cyclical pattern, varying length and unstable quality due to noise and distortion. Besides, there has been insufficient research on separating persistent atrial fibrillation from paroxysmal atrial fibrillation, and little discussion on locating the onsets and end points of AF episodes. It is even more arduous to perform well on these two distinct but interrelated tasks, while avoiding the mistakes inherent from stage-by-stage approaches. This paper proposes the Multi-level Multi-task Attention-based Recurrent Neural Network for three-class discrimination on patients and localization of the exact timing of AF episodes. Our model captures three-level sequential features based on a hierarchical architecture utilizing Bidirectional Long and Short-Term Memory Network (Bi-LSTM) and attention layers, and accomplishes the two tasks simultaneously with a multi-head classifier. The model is designed as an end-to-end framework to enhance information interaction and reduce error accumulation. Finally, we conduct experiments on CPSC 2021 dataset and the result demonstrates the superior performance of our method, indicating the potential application of MMA-RNN to wearable mobile devices for routine AF monitoring and early diagnosis.
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