Anti-ICAM3 monoclonal antibody, clone Bu65 [FITC]

Background & AimsHepatopulmonary syndrome is a complication of chronic liver disease resulting in increased morbidity and mortality. It is caused by intrapulmonary vascular dilations and arteriovenous connections with devastating influence on gas exchange. The pathogenesis is not completely understood but evidence mounts for angiogenesis. Aims of this study were to identify angiogenic factors in serum of patients with hepatopulmonary syndrome and to study the possibility to predict its presence by these factors. MethodsMultiplex assays were used to measure the concentration of angiogenic factors in patients with (n=30) and without hepatopulmonary syndrome (n=30). Diagnosis was based on the presence of gas exchange abnormality and intrapulmonary vasodilations according to published guidelines. ResultsPatients with and without hepatopulmonary syndrome had similar MELD scores (median: 11.2 vs. 11.6; P=0.7), Child-Pugh score (P=0.7) and PaCO2 values (median: 35 vs. 37; P=0.06). PaO2 and P(A-a) O-2 gradient were significantly different (respectively median of 80 vs. 86, P=0.02; and 24 vs. 16, P=0.004). Based on area under the curve (AUC) data and P-values, the best predictors were vascular cell adhesion molecule 1 (VCAM1) (AUC=0.932; P<0.001) and intercellular adhesion molecule 3 (ICAM3) (AUC=0.741; P=0.003). Combining these factors results in an AUC of 0.99 (after cross-validation still 0.99). ConclusionsVCAM1 and ICAM3 might be promising biomarkers for predicting hepatopulmonary syndrome. Combining these factors results in an AUC of 0.99 and a negative predictive value of 100%. Determining the concentration of these biomarkers might be a screening method to detect hepatopulmonary syndrome. The use of these biomarkers should be validated in larger groups of patients.

Protein is an important part of the organism. Proteins must interact with others to perform its functions properly. One of the interactions between proteins is the interactions between HIV-1 proteins and human proteins. Although HIV-1 and human proteins interact, we need to do depth analysis because some of the HIV-1 proteins do not interact with human proteins. Bicluster is the method which used to observe this interaction. Bicluster can groups interactions by rows and columns, so we can analyze it easier. The local search framework based on pairs operation algorithm called POLS algorithm. POLS algorithm is one of many algorithms to find a bicluster, it uses a balanced biclique approach. The algorithm is good for binary data because the initial step of the algorithm is to find local bicluster. The purpose of finding local bicluster is to make sure whether a bicluster can be found or not. In this paper, we use the POLS algorithm to find local bicluster on data interactions protein between HIV-1 and human. We divided the data into two types. The first data is HIV positive and the second is HIV negative. In HIV positive, the local bicluster consists of protein asp, envelope surface glycoprotein gp120, BECN1, and IFNG. In HIV negative, we found the local bicluster consist of protein envelope surface glycoprotein gp120, envelope surface glycoprotein gp160, ICAM1, and ICAM3.