Assessment of Liver Allograft Function by Hyaluronic Acid and Endothelin Levels

Suehiro, Taketoshi; Boros, Péter; Emre, Sükrü; Sheiner, Patricia A.; Guy, Stephen R.; Schwartz, Myron; Miller, Charles M.

doi:10.1006/jsre.1997.5221

Cited by 19 publications

(15 citation statements)

References 36 publications

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“…Endothelial cells of the hepatic sinusoid selectively take up and clear the circulating HA (22), and measurement of HA concentration in blood has been used by several authors as a measure of endothelial function after LT (23,24). Our results show that, as expected, the circulating concentrations of this polysaccharide increase after the anhepatic period and decrease after reperfusion, indicating the progressive clearance of HA by the endothelium of the graft.…”

Section: Resultssupporting

confidence: 79%

“…In our study, no differences in HA concentrations were found between patients with slight and moderate primary liver dysfunction. To our knowledge, only one study has postulated that posttransplantation graft function could be predicted through plasma HA concentrations (24), but this study discriminates between 76 patients with good primary hepatic function and 9 with severely impaired function. In contrast, Karayalçin et al (23) could not find evidence for differences in markers of liver function in two groups of LT patients separated according to HA levels, results that coincide with our findings.…”

Section: Resultsmentioning

confidence: 99%

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Xanthine Oxidoreductase and Preservation Injury in Human Liver Transplantation

et al. 2004

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Section: Resultssupporting

confidence: 79%

Section: Resultsmentioning

confidence: 99%

Xanthine Oxidoreductase and Preservation Injury in Human Liver Transplantation

et al. 2004

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“…Other protein markers with very high loadings on IC2 were also RNA-binding proteins, which is also in agreement with recent studies (53,(57)(58)(59)(60)(61)(62)(63)(64)(65)(66)(67)(68)(69)(70). Kim et al (71) demonstrated that over-expression of a glycine-rich RNA-binding protein resulted in enhanced cold-shock resistance in Escherichia coli.…”

Section: Fig 2 Correlation Network Analysis Of An Integrated Metabosupporting

confidence: 89%

Integration of Metabolomic and Proteomic Phenotypes

Wienkoop

Morgenthal

Wolschin

et al. 2008

Molecular & Cellular Proteomics

142

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Statistical mining and integration of complex molecular data including metabolites, proteins, and transcripts is one of the critical goals of systems biology (Ideker, T., Galitski, T., and Hood, L. (2001) A new approach to decoding life: systems biology. Annu. Rev. Genomics Hum. Genet. 2, 343-372). A number of studies have demonstrated the parallel analysis of metabolites and large scale transcript expression. Protein analysis has been ignored in these studies, although a clear correlation between transcript and protein levels is shown only in rare cases, necessitating that actual protein levels have to be determined for protein function analysis. Here, we present an approach to investigate the combined covariance structure of metabolite and protein dynamics in a systemic response to abiotic temperature stress in Arabidopsis thaliana wild-type and a corresponding starch-deficient mutant (phosphoglucomutase-deficient). Independent component analysis revealed phenotype classification resolving genotype-dependent response effects to temperature treatment and genotype-independent general temperature compensation mechanisms. An observation is the stress-induced increase of raffinose-family-oligosaccharide levels in the absence of transitory starch storage/mobilization in temperature-treated phosphoglucomutase plants indicating that sucrose synthesis and storage in these mutant plants is sufficient to bypass the typical starch storage/mobilization pathways under abiotic stress. Eventually, sample pattern recognition and correlation network topology analysis allowed for the detection of specific metabolite-protein co-regulation and assignment of a circadian output regulated RNA-binding protein to these processes. The whole concept of high-dimensional profiling data integration from many replicates, subsequent multivariate statistics for dimensionality reduction, and covariance structure analysis is proposed to be a major strategy for revealing central responses of the biological system under study. Molecular & Cellular Proteomics 7:1725-1736, 2008.Metabolomic technologies enable the very rapid non-targeted analysis of metabolites and provide a diagnostic tool for pattern recognition of biological samples (2-5). Typical pattern recognition methods are variance discrimination algorithms such as principal components analysis (PCA) 1 or independent component analysis (ICA) (2, 6 -9). Independent component analysis is an extension of covariance analysis by looking for kurtosis thresholds or high entropy (8, 10) and thus adds a further value for biological interpretation. Variance discrimination of samples relies strongly on a high biological variability of independent biological replicate analysis (4,11,12). Recently, we demonstrated that these covariance matrixes of experimentally determined metabolite levels are connected with the elasticities of pathway reaction networks (13). Consequently, changes in the structure of these covariance networks reveal biochemical regulations (4). This was confirmed by using topology studi...

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“…The findings of our preliminary study and those of others showed that serum HA concentrations were inversely correlated with measurements of functional liver reserve and the degree of hepatic fibrosis, suggesting that measuring the HA level might be useful for monitoring liver damage or predicting postoperative complications after hepatic resection. [4][5][6][9][10][11] We also found that the serum HA level in patients with normal liver function was significantly lower than that in patients with liver injury caused by chronic hepatitis, cirrhosis, or obstructive jaundice, and that there were few posthepatectomy complications in patients with prehepatectomy normal liver function. 9 Thus, we conducted this study on patients with injured liver diseases to determine the efficiency of measuring the serum HA level before and after hepatectomy.…”

Section: Introductionmentioning

confidence: 51%