Characterization of cancer/testis antigen MAGE-A11 for immunotherapy of prostate cancer

Les antigènes testiculaires du cancer sont des cibles idéales pour l’immunothérapie du cancer car ce sont des protéines immunogéniques dont l’expression est restreinte aux cellules germinales et au cancer. Le but de cette étude est d’évaluer le potentiel de MAGE-A11, un antigène testiculaire du cancer, comme cible pour développer un vaccin contre le cancer de la prostate. Pour ce faire, l’anticorps monoclonal 5C4 qui a la capacité de reconnaître la présence de MAGE-A11 dans les tissus fixés et inclus en paraffine a été produit. De plus, l’expression de MAGE-A11 a été analysée sur plusieurs lignées de cellules cancéreuses. Il a été démontré que MAGE-A11 est exprimé dans plusieurs types de cancers notamment dans le cancer du côlon et du cerveau. Finalement, nous avons identifié trois épitopes du CMH classe II HLA-DR1 dans la protéine MAGE-A11 confirmant ainsi l’immunogénicité de cet antigène et son potentiel comme cible pour l’immunothérapie du cancer.Cancer/testis antigens are ideal targets for cancer immunotherapy because of their limited expression in normal tissues, aberrant expression in malignancies and their immunogenic properties. The aim of this study was to evaluate the potential of cancer/testis antigen, MAGE-A11, as an immunotherapeutic target for development of a prostate cancer vaccine. To accomplish this, we produced the monoclonal antibody 5C4 that is capable of recognizing MAGE-A11 in formalin-fixed paraffin-embedded tissues. We also investigated the expression of MAGE-A11 in a wide variety of cancer cell lines to determine the scope of its expression in cancer. It was shown that MAGE-A11 is widely expressed in malignancies. The highest MAGE-A11 expression was observed in colon cancer and astrocytoma brain tumors. Finally, we identified three naturally processed MHC class II HLA-DR1 epitopes in MAGE-A11 protein, thus confirming its immunogenicity and its potential as a target for cancer immunotherapy

Integrating computational fluid dynamic, artificial intelligence techniques, and pore network modeling to predict relative permeability of gas condensate

Abstract The formation of gas condensate near the wellbore affects the gas liquid two-phase flow between the pores. It may occur in the path between two pores depending on the thermodynamic conditions of the single-phase gas flow, two-phase gas liquid annular flow or the closed path of condensate in throat. To model the behavior of gas condensate in a network of pores, relative permeability and naturally pressure drop should be calculated. In this study, the flow characteristics (pressure drop) between the pores at different physical and geometric conditions were obtained using computational fluid dynamic (CFD). As CFD is time-consuming, its results were transferred to an artificial neural network (ANN) model and the ANN model was trained. For calculating the pressure drop, the CFD was replaced with the ANN model. In addition, instead of utilizing empirical correlations, to compute the accurate value of condensate formed in throats' corners at every time step, the flash calculation using Esmaeilzadeh-Roshanfekr equation of state was performed, and closed throats were specified. This creates an accurate estimation of gas and condensate distribution in the pore network. Furthermore, the value of condensate that transferred to the adjacent throats was computed using Poiseuille's law. The results showed that the proposed ANN based proxy model has ability to promote the speed of calculation in gas condensate simulation, considering dynamic change of relative permeability curves as function of saturation of gas condensate. Also, it was found that the relative permeability obtained by the proposed model is in good agreement with the experimental data. By entering the fractures pattern in the network model and predicting relative permeability of gas and condensate by the proposed method, the role of fractures in the production of gas condensate in such reservoirs could be predicted. Dynamic changes due to the relative permeability of gas and condensate as function of saturation can be entered into the reservoir simulation in order to optimize inertia and positive coupling phenomena to maximized condensate production in gas condensate reservoir.</jats:p