Background Circulating long noncoding RNA (lncRNA) plays a vital role in clinical disease diagnosis and prognosis. Here, we evaluate the role of a lncRNA, named growth arrest specific 5 (GAS5), in atrial fibrillation (AF). Methods Expression of GAS5 was measured by qRT‐PCR. Diagnostic and prognostic values of GAS5 were assessed by the receiver operating characteristics curve (ROC), Kaplan–Meier (KM) and Cox regression analyses. Results A total of 173 participants were enrolled in this study. Circulating GAS5 expression was significantly down‐regulated in AF patients. This change occurred prior to enlargement of the left atrial volume and was strongly associated with AF progression, which demonstrates the potential use of GAS5 as an early biomarker. The area under the ROC curve (AUC) was 0.858 (95% CI 0.789‐0.926, P < .001). Seventy of the 85 AF patients received radiofrequency catheter ablation (RFCA), and 22 (31.4%) had relapsed by the 1‐year follow‐up. The KM analysis (log‐rank test, P = .031) and multivariable Cox analysis (HR = 0.127, 95% CI 0.026‐0.616; P = .01) revealed that GAS5 has a role in predicting recurrence after RFCA. Conclusion Circulating lncRNA GAS5 is a potential biomarker for AF diagnosis and prognosis. Down‐regulation of GAS5 occurs prior to left atrial enlargement and can be used for the prognosis of AF progression and recurrence.

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Fundus blood vessel image segmentation plays an important role in the diagnosis and treatment of diseases and is the basis of computer-aided diagnosis. Feature information from the retinal blood vessel image is relatively complicated, and the existing algorithms are sometimes difficult to perform effective segmentation with. Aiming at the problems of low accuracy and low sensitivity of the existing segmentation methods, an improved U-shaped neural network (MRU-NET) segmentation method for retinal vessels was proposed. Firstly, the image enhancement algorithm and random segmentation method are used to solve the problems of low contrast and insufficient image data of the original image. Moreover, smaller image blocks after random segmentation are helpful to reduce the complexity of the U-shaped neural network model; secondly, the residual learning is introduced into the encoder and decoder to improve the efficiency of feature use and to reduce information loss, and a feature fusion module is introduced between the encoder and decoder to extract image features with different granularities; and finally, a feature balancing module is added to the skip connections to resolve the semantic gap between low-dimensional features in the encoder and high-dimensional features in decoder. Experimental results show that our method has better accuracy and sensitivity on the DRIVE and STARE datasets (accuracy (ACC) = 0.9611, sensitivity (SE) = 0.8613; STARE: ACC = 0.9662, SE = 0.7887) than some of the state-of-the-art methods.

The association of the CYP2J2 G-50T polymorphism with coronary artery disease has been explored, but the results remain controversial. Thus, a meta-analysis was conducted to provide a comprehensive estimate of this association. We selected ten articles encompassing 12 independent case-control studies with 7063 cases and 10,453 controls for this meta-analysis. Overall, we found significant associations between the CYP2J2 G-50T polymorphism and coronary artery disease risk in three genetic models (allele model: odds ratio (OR) = 1.19, 95% confidence interval (CI) = 1.05–1.34; homozygote model: OR = 2.25, 95% CI = 1.27–4.01; recessive model: OR = 2.17, 95% CI = 1.22–3.86). In these three genetic models, a significant association was observed in Caucasians but not in Asians when the data were stratified by ethnicity. However, no significant associations were found between the CYP2J2 polymorphism G-50T and coronary artery disease risk in heterozygote model and dominant model. In conclusion, our meta-analysis suggested that the CYP2J2 G-50T polymorphism was associated with coronary artery disease risk in the allele, homozygote and recessive models in Caucasians.