Background: Targeting endoplasmic reticulum (ER) stress with melatonin has been proven helpful for cerebral ischemic/reperfusion (CI/R) damage, while the mechanism remains unclear. In current study, we investigated whether melatonin could ameliorate ER stress in CI/R injury through sirtuin 2 (SIRT2). Methods: Male SD rats were underwent middle cerebral artery occlusion and reperfusion (MCAO-R) surgery. Melatonin was treated 30 min before MCAO-R. Results: Melatonin (20 mg/kg) notably improved MCAO-R-induced cerebral neurologic impairment and infarct volume. Melatonin reversed MCAO-R induced upregulation of SIRT2 and activation of ER stress (reduced phosphorylated protein kinase-like ER kinase (PERK) and phosphorylated eukaryotic initiation factor 2α). Consistently, in OGD/R-treated HT22 cells, melatonin also significantly alleviated ER stress and SIRT2 expression. Further Co-immunoprecipitation and co-immunofluorescence studies revealed that melatonin enhanced heat shock factor 1 (HSF 1)acetylation. Inhibiting of Sirt2 by siRNA also increased HSF1 acetylation in OGD/R-treated cells. Melatonin significantly inhibited PERK activator (CCT020312)-induced ER stress, while CCT020312 had no influence on SIRT2 and HSF1 acetylation. Conclusion: Our findings elucidated that SIRT2/HSF1/PERK pathway is essential for melatonin-alleviated CI/R injury, providing a novel molecular mechanism.

Background: Atrial fibrillation (AF) is closely related to atherosclerosis (AS), but the common mechanism of the two remains unclear, This study aims to further explore the common hub genes and molecular pathways, to elucidate the common mechanisms of AF and AS. Methods: AF (GSE41177) and AS (GSE28829) data sets were downloaded from the gene expression Synthesis (GEO) database to search for the co-expressed differential genes (EDGs) of AF and AS, and to analyze the enrichment function of common DEGs. The protein-protein Interaction (PPI) network was created using the (STRING) database with Cytoscape software, and the plug-in cytoHubba was used to select hub genes. The central gene was verified in GSE14905 (AF) and GSE100927 (AS), and the enrichment function of the hub gene was analyzed. In four data sets, GSE41177, GSE28829, GSE14905, and GSE100927, subject manipulation characteristic curves were used to evaluate the availability of hub genes. Results: A total of 42 common DEGs (37 up-regulated genes and 5 down-regulated genes) were selected for analysis. The PPI network was constructed, and 15 key genes of PPI were identified through cytoHubba, and 9 key genes were finally verified, namely NCF2, C1QC, ITGB2, HLA-DRA, TYROBP, VSIG4, FCER1G, LAPTM5, and C1QB. Finally, the ROC curve was used to verify the effectiveness of key genes. In the result table, 9 hub genes had strong diagnostic values. Conclusions: In our study, we conducted gene differential expression analysis, functional enrichment analysis, and PPI analysis for DEGs in AF and AS, identified key genes in AF and AS, provided potential biomarkers for the identification of AF and AS, revealed the common pathogenesis of AF and AS, and provided new ideas for the treatment of AF combined with AS.

Aiming at the problems of extensive data semantic differences and complex data analysis and processing caused by data fragmentation, semi-structure, and information heterogeneity in the information interaction of node devices under the edge computing framework, this paper proposes an information model to load the state data of the node and develops a corresponding parser for the information model, which reduces the heterogeneity of node data and ensures that the data can be uniformly connected to the edge computing platform. Through the device monitoring platform, the running status of various devices can be viewed in real-time, which proves the feasibility and effectiveness of the proposed information model and parser.

Background: An association between fasting blood glucose (FBG) and gensini scores has been reported. However, no studies have investigated the relationship between ST elevation myocardial infarction and FBG. The purpose of this study was to investigate the association between FBG and coronary artery disease severity score (Gensini score) in patients with ST-segment elevation (STEMI) myocardial infarction. Methods: In a retrospective analysis of 464 enrolled patients, we used minimum absolute contraction and selection operator (lasso) regression analysis to screen for covariates; In multiple regression analyses, we used gensini scores as the dependent variable, glucose as the independent variable, Fasting blood glucose was divided into hypoglycemic group 5.30mmol/L (2.69-6.15), medium glycemic group 7.11mmol/L (6.19-8.61) and hyperglycemic group 10.70mmol/L (8.64-14.81)，and selected variables as covariates to adjust and observe the true association between glucose and gensini scores. Considering that there is not necessarily a linear relationship between blood glucose and Guernsini score in the real world, we used curve fitting to observe the changing trend of blood glucose and Guernsini score. Results: When confounding factors are not adjusted, The gensini scores in the medium-glycemic and high-glycemic groups had significant clinical significance (the medium-glycemic group, 95%CI: -140162--0.213, P=0.044; Hyperglycemia group, 95%CI: 5.295-19.221, P=0.0006;) The relationship between fasting blood glucose and Guernsini score is U-shaped but non-linear. Using minimum absolute contraction and selection operator (LASSO) regression to select variables, Variables included neutrophils, hemoglobin, platelets, albumin, low-density lipoprotein, D-dimer, urea nitrogen, glucose, white blood cells, systolic blood pressure, heart rate, age, dm, culprit vessel, history of myocardial infarction, Killip grade, number of stents, creatinine, uric acid, total cholesterol,CTNI, CKMB, left ventricular end-diastolic diameter, left atrial diameter. After adjusting for confounder factors, only the hyperglycemic group had significant clinical significance in Gensini score (95%CI: -16.95--2.71, P=0.015). Curve fitting analysis showed that fasting glucose and Gensini score presented a U-shaped but non-linear relationship in patients diagnosed with or without diabetes. Conclusions: The severity of coronary stenosis in patients with ST-segment elevation myocardial infarction is influenced by either excessively high or excessively low fasting glucose concentration,Whether people with or without diabetes.