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1,775 results on '"Liu, Junjun"'

10. Research and Exploration on Cross-Border Transportation Support Development in Metropolitan Areas

Author

Liu, Yuejun, Yuan, Chunyi, Liu, Junjun, Xin, Hongxi, Lin, Rongjie, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Tan, Kay Chen, Series Editor, Wang, Wuhong, editor, Jin, Lisheng, editor, and Tan, Haiqiu, editor

Published
2024
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28. Analysis of m6A methylation expression profiles in liver tissue of high-fat diet-induced mouse models of NAFLD

Author

LIU Junjun, LU Sumei, ZHANG Bingyang, LI Yongqing, and MA Wanshan

Subjects
non-alcoholic fatty liver disease (nafld), m6a methylation modification, mrna, epitranscriptomics, Medicine
Abstract

Objective·To detect the differences in m6A methylation modification and gene expression of liver tissue mRNA in high-fat diet-induced mouse models of non-alcoholic fatty liver disease (NAFLD) using microarray technology.Methods·The NAFLD models were established in 6-8 weeks old male C57BL/6J mice fed with high-fat chow for 16 weeks (high-fat group, n=10). The basal group (n=10) was given 10% fat diet. Hematoxylin-eosin (H-E) staining was used to assess the histopathological changes in liver tissue and to determine the success of the NAFLD models. The changes of mRNA m6A methylation and expression levels in the liver tissues of the two groups were detected by using methylated RNA immunoprecipitation (MeRIP) and microarray expression profiling.Results·The livers of the mice in the basal group were bright red with few fat deposits, while the livers of the mice in the high-fat group were yellowish with diffuse infiltration and fusion of lipid droplets in the hepatocytes by H-E staining, suggesting that the high-fat diet-induced NAFLD models were successfully constructed. The results of the MeRIP-microarray showed that the m6A methylation levels of 320 genes in the livers of mice in the high-fat group were significantly altered compared with those in the basal group (P1.5), of which 108 genes were up-regulated and 212 genes were down-regulated. Genes with significant differences in m6A methylation levels between the two groups were intersected with those with differentially expressed mRNAs, and 163 genes were found to have significant differences in both m6A methylation level and mRNA expression level.Conclusion·The change in m6A modification of liver tissue mRNA in the high-fat diet-induced mouse models of NAFLD is significant and the change is associated with the gene expression of mRNA.

Published
2023
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43. Learning from Suspected Target: Bootstrapping Performance for Breast Cancer Detection in Mammography

Author

Xiao, Li, Zhu, Cheng, Liu, Junjun, Luo, Chunlong, Liu, Peifang, and Zhao, Yi

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Deep learning object detection algorithm has been widely used in medical image analysis. Currently all the object detection tasks are based on the data annotated with object classes and their bounding boxes. On the other hand, medical images such as mammography usually contain normal regions or objects that are similar to the lesion region, and may be misclassified in the testing stage if they are not taken care of. In this paper, we address such problem by introducing a novel top likelihood loss together with a new sampling procedure to select and train the suspected target regions, as well as proposing a similarity loss to further identify suspected targets from targets. Mean average precision (mAP) according to the predicted targets and specificity, sensitivity, accuracy, AUC values according to classification of patients are adopted for performance comparisons. We firstly test our proposed method on a private dense mammogram dataset. Results show that our proposed method greatly reduce the false positive rate and the specificity is increased by 0.25 on detecting mass type cancer. It is worth mention that dense breast typically has a higher risk for developing breast cancers and also are harder for cancer detection in diagnosis, and our method outperforms a reported result from performance of radiologists. Our method is also validated on the public Digital Database for Screening Mammography (DDSM) dataset, brings significant improvement on mass type cancer detection and outperforms the most state-of-the-art work.

Published
2020
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