ZFAS1 is one of cardiac-specific or cardiac-related lncRNAs. This study was to explore the functional involvement of ZFAS1 and its regulatory role in AMI. In this study, the models of AMI rat and myocardial cell cultured under hypoxia were made. The expression of ZFAS1 and miR-150 of myocardial infarction tissue or cardiac myocytes was determined by quantitative real time PCR. The regulatory role of ZFAS1 on miR-150 was examined by RNA pull down assay. The effect of miR-150 or ZFAS1 expression on cell viability was analyzed by MTT assay. The relative expression of ZFAS1 in the myocardium infracted zone and border zone was significantly upregulated at 1-48 h of AMI rats, but it downregulated at 1 week and 2 weeks; miR-150 was significantly downregulated at AMI-1-48 h and upregulated at 1 and 2 weeks after model establishment. The result of RNA pull down assay indicated that ZFAS1 could interact directly with miR-150. C-reactive protein (CRP) was regulated by ZFAS1/miR-150 axis and negatively targeted by miR-150. Hypoxia caused the decrease of cell viability and the upregulation of CRP at mRNA and protein levels; whereas this upregulation could be attenuated by miR-150 mimic or si-ZFAS1 in H9C2 cells and cardiomyocytes. Knockdown of ZFAS1 or miR-150 overexpression effectively relieved AMI-induced myocardial infarction in AMI-1 week rats. The ZFAS1/miR-150 axis was involved in the molecular mechanism of AMI induced cardiomyocytes apoptosis via regulating CRP. J. Cell. Biochem. 118: 3281-3289, 2017. © 2017 Wiley Periodicals, Inc.

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Myocardial infarction (MI) is believed to be one of the most common cardiovascular diseases, and it is seriously threatening the health of people in the world. The extracellular vesicles (EVs) isolated from mesenchymal stem cells and zinc finger antisense 1 (ZFAS1) have been believed to be involved in the regulation of MI, but the mechanism has not been fully clarified. Left anterior descending artery ligation was used to establish MI animal model, hypoxia treatment was applied to establish MI cell model. CCK8, transwell, and wound healing methods were applied to measure cell proliferation, invasion, and migration. Overexpression of ZFAS1 was established via transfecting pcDNA-ZFAS1. Overexpression of ZFAS1 significantly reversed the influence of EVs on cell migration, invasion, and apoptosis. Similar effect of EVs and ZFAS1 on morphological changes of MI rat heart tissues were also observed. The activation of Akt/Nrf2/HO-1 pathway by EVs was remarkably suppressed by pcDNA-ZFAS1. Inhibitor of Akt/Nrf2/HO-1 pathway remarkably reversed the impact of EVs on the cell viability. EVs might improve MI through inhibiting ZFAS1 and promoting Akt/Nrf2/HO-1 pathway. This study might provide a new thought for the prevention and treatment of MI damage through regulating ZFAS1 or Akt/Nrf2/HO-1 pathway.

Background: A deep learning model (DLM) that enables non-invasive hypokalemia screening from an electrocardiogram (ECG) may improve the detection of this life-threatening condition. This study aimed to develop and evaluate the performance of a DLM for the detection of hypokalemia from the ECGs of emergency patients. Methods: We used a total of 9908 ECG data from emergency patients who were admitted at the Second Affiliated Hospital of Nanchang University, Jiangxi, China, from September 2017 to October 2020. The DLM was trained using 12 ECG leads (lead I, II, III, aVR, aVL, aVF, and V 1 – 6 ) to detect patients with serum potassium concentrations <3.5 mmol/L and was validated using retrospective data from the Jiangling branch of the Second Affiliated Hospital of Nanchang University. The blood draw was completed within 10 min before and after the ECG examination, and there was no new or ongoing infusion during this period. Results: We used 6904 ECGs and 1726 ECGs as development and internal validation data sets, respectively. In addition, 1278 ECGs from the Jiangling branch of the Second Affiliated Hospital of Nanchang University were used as external validation data sets. Using 12 ECG leads (leads I, II, III, aVR, aVL, aVF, and V 1 – 6 ), the area under the receiver operating characteristic curve (AUC) of the DLM was 0.80 (95% confidence interval [CI]: 0.77–0.82) for the internal validation data set. Using an optimal operating point yielded a sensitivity of 71.4% and a specificity of 77.1%. Using the same 12 ECG leads, the external validation data set resulted in an AUC for the DLM of 0.77 (95% CI: 0.75–0.79). Using an optimal operating point yielded a sensitivity of 70.0% and a specificity of 69.1%. Conclusions: In this study, using 12 ECG leads, a DLM detected hypokalemia in emergency patients with an AUC of 0.77 to 0.80. Artificial intelligence could be used to analyze an ECG to quickly screen for hypokalemia.