190 research outputs found
Predicting performance of constant flow depth filtration using constant pressure filtration data
This paper describes a method of predicting constant flow filtration capacities using constant pressure datasets collected during the purification of several monoclonal antibodies through depth filtration. The method required characterisation of the fouling mechanism occurring in constant pressure filtration processes by evaluating the best fit of each of the classic and combined theoretical fouling models. The optimised coefficients of the various models were correlated with the corresponding capacities achieved during constant flow operation at the specific pressures performed during constant pressure operation for each centrate. Of the classic and combined fouling models investigated, the Cake-Adsorption fouling model was found to best describe the fouling mechanisms observed for each centrate at the various different pressures investigated. A linear regression model was generated with these coefficients and was shown to predict accurately the capacities at constant flow operation at each pressure. This model was subsequently validated using an additional centrate and accurately predicted the constant flow capacities at three different pressures (0.69, 1.03 and 1.38 bar). The model used the optimised Cake-Adsorption model coefficients that best described the flux decline during constant pressure operation. The proposed method of predicting depth filtration performance proved to be faster than the traditional approach whilst requiring significantly less material, making it particularly attractive for early process development activities
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Loss of the tumor suppressor, Tp53, enhances the androgen receptor-mediated oncogenic transformation and tumor development in the mouse prostate.
Recent genome analysis of human prostate cancers demonstrated that both AR gene amplification and TP53 mutation are among the most frequently observed alterations in advanced prostate cancer. However, the biological role of these dual genetic alterations in prostate tumorigenesis is largely unknown. In addition, there are no biologically relevant models that can be used to assess the molecular mechanisms for these genetic abnormalities. Here, we report a novel mouse model, in which elevated transgenic AR expression and Trp53 deletion occur simultaneously in mouse prostatic epithelium to mimic human prostate cancer cells. These compound mice developed an earlier onset of high-grade prostatic intraepithelial neoplasia and accelerated prostate tumors in comparison with mice harboring only the AR transgene. Histological analysis showed prostatic sarcomatoid and basaloid carcinomas with massive squamous differentiation in the above compound mice. RNA-sequencing analyses identified a robust enrichment of the signature genes for human prostatic basal cell carcinomas in the above prostate tumors. Master regulator analysis revealed SOX2 as a transcriptional regulator in prostatic basal cell tumors. Elevated expression of SOX2 and its downstream target genes were detected in prostatic tumors of the compound mice. Chromatin immunoprecipitation analyses implicate a coregulatory role of AR and SOX2 in the expression of prostatic basal cell signature genes. Our data demonstrate a critical role of SOX2 in prostate tumorigenesis and provide mechanistic insight into prostate tumor aggressiveness and progression mediated by aberrant AR and p53 signaling pathways
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