Biologically inspired simulation of livor mortis

We present a biologically motivated livor mortis simulation that is capable of modelling the colouration changes in skin caused by blood pooling after death. Our approach consists of a simulation of post mortem blood dynamics and a layered skin shader that is controlled by the haemoglobin and oxygen levels in blood. The object is represented by a layered data structure made of a triangle mesh for the skin and a tetrahedral mesh on which the blood dynamics are simulated. This allows us to simulate the skin discolouration caused by livor mortis, including early patchy appearance, fixation of hypostasis and pressure induced blanching. We demonstrate our approach on two different models and scenarios and compare the results to real world livor mortis photographic examples

Significance of Aurora B overexpression in hepatocellular carcinoma. Aurora B Overexpression in HCC

<p>Abstract</p> <p>Background</p> <p>To investigate the significance of Aurora B expression in hepatocellular carcinoma (HCC).</p> <p>Methods</p> <p>The <it>Aurora B </it>and <it>Aurora A </it>mRNA level was measured in 160 HCCs and the paired nontumorous liver tissues by reverse transcription-polymerase chain reaction. Mutations of the <it>p53 </it>and <it>β-catenin </it>genes were analyzed in 134 and 150 tumors, respectively, by direct sequencing of exon 2 to exon 11 of <it>p53 </it>and exon 3 of <it>β-catenin</it>. Anticancer effects of AZD1152-HQPA, an Aurora B kinase selective inhibitor, were examined in Huh-7 and Hep3B cell lines.</p> <p>Results</p> <p><it>Aurora B </it>was overexpressed in 98 (61%) of 160 HCCs and in all 7 HCC cell lines examined. The overexpression of <it>Aurora B </it>was associated with <it>Aurora A </it>overexpression (<it>P </it>= 0.0003) and <it>p53 </it>mutation (<it>P </it>= 0.002) and was inversely associated with <it>β</it>-<it>catenin </it>mutation (<it>P </it>= 0.002). <it>Aurora B </it>overexpression correlated with worse clinicopathologic characteristics. Multivariate analysis confirmed that <it>Aurora B </it>overexpression was an independent poor prognostic factor, despite its interaction with Aurora A overexpression and mutations of <it>p53 </it>and <it>β</it>-<it>catenin</it>. In Huh-7 and Hep3B cells, AZD1152-HQPA induced proliferation blockade, histone H3 (Ser10) dephosphorylation, cell cycle disturbance, and apoptosis.</p> <p>Conclusion</p> <p><it>Aurora B </it>overexpression is an independent molecular marker predicting tumor invasiveness and poor prognosis of HCC. Aurora B kinase selective inhibitors are potential therapeutic agents for HCC treatment.</p

Mechanism-related circulating proteins as biomarkers for clinical outcome in patients with unresectable hepatocellular carcinoma receiving sunitinib

<p>Abstract</p> <p>Background</p> <p>Several proteins that promote angiogenesis are overexpressed in hepatocellular carcinoma (HCC) and have been implicated in disease pathogenesis. Sunitinib has antiangiogenic activity and is an oral multitargeted inhibitor of vascular endothelial growth factor receptors (VEGFRs)-1, -2, and -3, platelet-derived growth factor receptors (PDGFRs)-α and -β, stem-cell factor receptor (KIT), and other tyrosine kinases. In a phase II study of sunitinib in advanced HCC, we evaluated the plasma pharmacodynamics of five proteins related to the mechanism of action of sunitinib and explored potential correlations with clinical outcome.</p> <p>Methods</p> <p>Patients with advanced HCC received a starting dose of sunitinib 50 mg/day administered orally for 4 weeks on treatment, followed by 2 weeks off treatment. Plasma samples from 37 patients were obtained at baseline and during treatment and were analyzed for vascular endothelial growth factor (VEGF)-A, VEGF-C, soluble VEGFR-2 (sVEGFR-2), soluble VEGFR-3 (sVEGFR-3), and soluble KIT (sKIT).</p> <p>Results</p> <p>At the end of the first sunitinib treatment cycle, plasma VEGF-A levels were significantly increased relative to baseline, while levels of plasma VEGF-C, sVEGFR-2, sVEGFR-3, and sKIT were significantly decreased. Changes from baseline in VEGF-A, sVEGFR-2, and sVEGFR-3, but not VEGF-C or sKIT, were partially or completely reversed during the first 2-week off-treatment period. High levels of VEGF-C at baseline were significantly associated with Response Evaluation Criteria in Solid Tumors (RECIST)-defined disease control, prolonged time to tumor progression (TTP), and prolonged overall survival (OS). Baseline VEGF-C levels were an independent predictor of TTP by multivariate analysis. Changes from baseline in VEGF-A and sKIT at cycle 1 day 14 or cycle 2 day 28, and change in VEGF-C at the end of the first off-treatment period, were significantly associated with both TTP and OS, while change in sVEGFR-2 at cycle 1 day 28 was an independent predictor of OS.</p> <p>Conclusions</p> <p>Baseline plasma VEGF-C levels predicted disease control (based on RECIST) and were positively associated with both TTP and OS in this exploratory analysis, suggesting that this VEGF family member may have utility in predicting clinical outcome in patients with HCC who receive sunitinib.</p> <p>Trial registration</p> <p>ClinicalTrials.gov: <a href="http://www.clinicaltrials.gov/ct2/show/NCT00247676">NCT00247676</a></p

Bioinformatics and molecular modeling in glycobiology

The field of glycobiology is concerned with the study of the structure, properties, and biological functions of the family of biomolecules called carbohydrates. Bioinformatics for glycobiology is a particularly challenging field, because carbohydrates exhibit a high structural diversity and their chains are often branched. Significant improvements in experimental analytical methods over recent years have led to a tremendous increase in the amount of carbohydrate structure data generated. Consequently, the availability of databases and tools to store, retrieve and analyze these data in an efficient way is of fundamental importance to progress in glycobiology. In this review, the various graphical representations and sequence formats of carbohydrates are introduced, and an overview of newly developed databases, the latest developments in sequence alignment and data mining, and tools to support experimental glycan analysis are presented. Finally, the field of structural glycoinformatics and molecular modeling of carbohydrates, glycoproteins, and protein–carbohydrate interaction are reviewed