1.Evaluation of esophagogastric variceal bleeding using multi-slice CT portal venography
Yanghong YU ; Shan DING ; Kexue DENG
Acta Universitatis Medicinalis Anhui 2013;(11):1376-1379
Objective To discuss the relationship between the diameter of portal vein and esophagogastric variceal bleeding and the severity of liver cirrhosis by CT portal venography ( CTPV). To analyze the occurence about esophagogastric variceal bleeding under in different liver cirrhosis degree. Methods 60 patients of portal hyperten-sion with liver cirrhosis and 15 healthy volunteers (controls). According to Child-Pugh classification, 60 patients were divided into Child-Pugh A,B and C groups,According to the patients whether the esophagogastric variceal bleeding or not, it was divided into two groups that esophagogastric variceal bleeding (EVB) and no EVB. All of patients underwent with 64-slice spiral CT. Image post-processing techniques such as MIP, VR, MPR and SSD were applied to measuring the diameters of portal venous system vessels and depict the portosystemic collaterals of portal venous system. Results The diameters of the right branch of portal vein and super mesenteric vein were no statisti-cal significance between bleeding group and no bleeding group. The rest parameters of portal system in EVB group are all larger than those of no EVB group(P<0.05). Age and gender in two groups had no statistic significance. All diameters of portal system in cirrhotic group were all larger than those of control group(P<0.05). In different liver function,there are differences in each groups of diameter. The bleeding rate of different groups according to he-patic function showed statistical significance(P <0.05), higher the degree of liver cirrhosis, higher the bleeding rate. Conclusion The diameters of portal system in EVB group are larger than no EVB. All diameters of portal sys-tem in cirrhotic group are all larger than those of control group. There is difference the diameter of vascular in differ-ent hepatic function. Different degree of liver cirrhosis can predict the esophagogastric variceal bleeding.
2.Release of arachidonic acid metabolites from blood by cultivation of human amniotic fluid with oneself blood
Jian YANG ; Yanghong YU ; Fengqing ZHOU ; Mei ZHONG
Chinese Journal of Pathophysiology 2000;0(12):-
AIM: To investigate the effect of human amniotic fluid on the release of thromboxane A 2 (TXA 2), prostaglandin I 2 (PGI 2) and Leukotriene C 4(LTC 4) from blood cells. METHODS: 1 mL human amniotic fluid and 10 mL oneself blood collected from 38-41 weeks with cesarean section were cultured at 37℃ for 30 min, and then centrifuged. The supernatants were taken and stored at -70℃. TXB 2 and 6-Keto-PGF 1? of the superntants were determined by radioimmunoassay and LTC 4 by enzyme immunoassay. RESULTS: It was found that the levels of TXB 2 and LTC 4 in blood were elevated from (63.5?52.0) ng/L and (40.1?39.2) ng/L to (189.1?102.0) ng/L and (293.5?206.1) ng/L respectively (P0.05).CONCLUSION: Amniotic fluid might stimulate the release of TXA 2 and LTC 4 from blood, it might affect the balance of TXA 2 and PGI 2 in blood, which might play an important role in the pathogenesis of amniotic fluid embolism.
3.Screening and bioinformatics analysis of differentially expressed genes in hyperplastic scar
Yanghong HU ; Yangliu HU ; Dewu LIU ; Jianxing YU ; Deming LIU
Journal of Southern Medical University 2014;(7):939-944
Objective To screen differentially expressed genes in hyperplastic scar to explore the pathogenesis of hyperplastic scar and identify new therapeutic targets. Methods Three pairs of surgical specimens of hyperplastic scar and adjacent normal skin tissues were collected to investigate the differentially expressed genes in hyperplastic scar using Agilent gene oligonucletide microarray and clustering analysis. DAVID Bioinformatics Resources6.7 was used for GO analysis and pathway analysis. Results and Conlcusion Distinctly different gene expression profiles were found between hyperplastic scar tissues and normal skin tissues. Compared with normal skin tissue, hyperplastic scar tissues showed 3142 up-regulated and 2984 down-regulated genes by two folds and 28 up-regulated and 44 down-regulated genes by 5 folds after repeating the experiment once; after repeating the experiment twice, 3004 genes were found up-regulated and 3038 down-regulated by 2 folds and 25 up-regulated and 38 down-regulated by 5 folds in hyperplastic scars. In all the 3 specimens, 1920 genes were up-regulated and 1912 down-regulated by 2 folds and 18 up-regulated and 29 down-regulated by 5 folds. The dysregulated genes in hyperplastic scar were involved in cell cycles, cell proliferation, immune response and cell adhesion (CDKN1C, CDKN2A, CTNNA3, COL6A3, and HOXB4) and in signaling pathway of focal adhesion, TGF-beta signaling pathway, p53 signaling pathway, cell cycle, and tumor-associated pathways (TGFβ1, CDKN1C, CDKN2A, CDC14A , ITGB6, and EGF).
4.Screening and bioinformatics analysis of differentially expressed genes in hyperplastic scar
Yanghong HU ; Yangliu HU ; Dewu LIU ; Jianxing YU ; Deming LIU
Journal of Southern Medical University 2014;(7):939-944
Objective To screen differentially expressed genes in hyperplastic scar to explore the pathogenesis of hyperplastic scar and identify new therapeutic targets. Methods Three pairs of surgical specimens of hyperplastic scar and adjacent normal skin tissues were collected to investigate the differentially expressed genes in hyperplastic scar using Agilent gene oligonucletide microarray and clustering analysis. DAVID Bioinformatics Resources6.7 was used for GO analysis and pathway analysis. Results and Conlcusion Distinctly different gene expression profiles were found between hyperplastic scar tissues and normal skin tissues. Compared with normal skin tissue, hyperplastic scar tissues showed 3142 up-regulated and 2984 down-regulated genes by two folds and 28 up-regulated and 44 down-regulated genes by 5 folds after repeating the experiment once; after repeating the experiment twice, 3004 genes were found up-regulated and 3038 down-regulated by 2 folds and 25 up-regulated and 38 down-regulated by 5 folds in hyperplastic scars. In all the 3 specimens, 1920 genes were up-regulated and 1912 down-regulated by 2 folds and 18 up-regulated and 29 down-regulated by 5 folds. The dysregulated genes in hyperplastic scar were involved in cell cycles, cell proliferation, immune response and cell adhesion (CDKN1C, CDKN2A, CTNNA3, COL6A3, and HOXB4) and in signaling pathway of focal adhesion, TGF-beta signaling pathway, p53 signaling pathway, cell cycle, and tumor-associated pathways (TGFβ1, CDKN1C, CDKN2A, CDC14A , ITGB6, and EGF).
5.Screening and bioinformatics analysis of differentially expressed genes in hyperplastic scar.
Yanghong HU ; Yangliu HU ; Dewu LIU ; Jianxing YU ; Deming LIU
Journal of Southern Medical University 2014;34(7):939-944
OBJECTIVETo screen differentially expressed genes in hyperplastic scar to explore the pathogenesis of hyperplastic scar and identify new therapeutic targets.
METHODSThree pairs of surgical specimens of hyperplastic scar and adjacent normal skin tissues were collected to investigate the differentially expressed genes in hyperplastic scar using Agilent gene oligonucletide microarray and clustering analysis. DAVID Bioinformatics Resources 6.7 was used for GO analysis and pathway analysis.
RESULTS AND CONCLUSIONDistinctly different gene expression profiles were found between hyperplastic scar tissues and normal skin tissues. Compared with normal skin tissue, hyperplastic scar tissues showed 3142 up-regulated and 2984 down-regulated genes by two folds and 28 up-regulated and 44 down-regulated genes by 5 folds after repeating the experiment once; after repeating the experiment twice, 3004 genes were found up-regulated and 3038 down-regulated by 2 folds and 25 up-regulated and 38 down-regulated by 5 folds in hyperplastic scars. In all the 3 specimens, 1920 genes were up-regulated and 1912 down-regulated by 2 folds and 18 up-regulated and 29 down-regulated by 5 folds. The dysregulated genes in hyperplastic scar were involved in cell cycles, cell proliferation, immune response and cell adhesion (CDKN1C, CDKN2A, CTNNA3, COL6A3, and HOXB4) and in signaling pathway of focal adhesion, TGF-beta signaling pathway, p53 signaling pathway, cell cycle, and tumor-associated pathways (TGFβ1, CDKN1C, CDKN2A, CDC14A , ITGB6, and EGF).
Cicatrix ; genetics ; Cluster Analysis ; Computational Biology ; Down-Regulation ; Gene Expression Profiling ; Humans ; Oligonucleotide Array Sequence Analysis ; Signal Transduction ; Transcriptome ; Up-Regulation