Main Coronary Vessel Segmentation Using Deep Learning in Smart Medical

The automatic segmentation of main vessels on X-ray angiography (XRA) images is of great importance in the smart coronary artery disease diagnosis system. However, existing methods have been developed to this task, but these methods have difficulty in recognizing the coronary artery structure in XRA images. Main vessel segmentation is still a challenging task due to the diversity and small-size region of the vessel in the XRA images. In this study, we propose a robust method for main vessel segmentation by using deep learning architectures with fully convolutional networks. Four deep learning models based on the UNet architecture are evaluated on a clinical dataset, which consists of 3200 X-ray angiography images collected from 1118 patients. Using the precision (Pre), recall (Re), and F1 score (F1) as evaluation metrics, the average Pre, Re, and F1 for main vessel segmentation in the entire experimental dataset is 0.901, 0.898, and 0.900, respectively. 89.8% of the images exhibited a high F1 score >0.8. For the main vessel segmentation in XRA images, our deep learning methods demonstrated that vessels could be segmented in real time with a more optimized implementation, to further facilitate the online diagnosis in smart medical

LncRNA SNHG1 protects the cardiac muscle cells from hypoxia/ re-oxygenation injury in vitro by targeting microRNA-21-5p and miR-30a-5p

Purpose: Cardiovascular diseases are responsible for numerous deaths globally. Long noncoding RNA SNHG1 has presented its protective role in cardiomyocytes previously. Herein, we examined the underlying molecular mechanisms of SNHG1 in cardiac muscle cells from hypoxia and re-oxygenation (H/R) in vitro.Methods: RT-qPCR measured expression of SNHG1, miR-21-5p and miR-30a-5p in rat cardiac muscle cell line HL-1 before and after H/R treatment and cell transfection, which was applied to regulate expression of SNHG1, miR-21-5p and miR-30a-5p for further use. The flow cytometry method was used to compare changes in cellular apoptosis, and cell viability was measured by CCK-8 method.Bioinformatics predicted the bindings ofSNHG1 and miR-21-5p / miR-30a-5p while the luciferase reporter assays further verified this.Results: The outcomes revealedthat SNHG1 was downregulated and meanwhile miR-21-5p / miR-30a5p was elevated that enhanced apoptosis and reduced cell viability in HL-1 cells. However, overexpressed SNHG1 inhibited cell apoptosis and increased cell viability brought by H/R. In addition, SNHG1 targeted at miR-21-5p/ miR-30a-5p, which contributed to the inter-regulation in between. Furthermore, interactive experiments revealed that upregulation of miR-21-5p/ miR-30a-5p added to the cell apoptosis which was induced by H/R and partially counteracted by the upregulation of SNHG1.Conclusion: In this study we have demonstrated the protective role of SNHG1 in the moderation of H/R-induced HL-1 apoptosis and viability through suppression of miR-21-5p/miR-30a-5p. This offers new perspective into the molecular interpretation of cardiovascular diseases such as ischemic reperfusion injury. Keywords: SNHG1; apoptosis; Hypoxia/Re-oxygenation injury;miR-21-5p; miR-30a-5p; Cardiovascular disease