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Handling a large number of geometric constraints brings a big challenge to the surrogate-based aerodynamic shape optimization (ASO) driven by computational fluid dynamics (CFD). It is not feasible to calculate the geometric constraint functions directly during the sub-optimization of a surrogate-based optimization, as the geometric constraint functions are to be evaluated thousands of times for each updating cycle and the total cost of a number of cycles can be prohibitive. This article proposes an efficient method of handling geometric constraints within the framework of a surrogate-based optimization to address this problem. The core idea is to use the Kreisselmeier- Steinhauser (KS) method to aggregate all geometric constraints into one that can be approximated by a cheap surrogate model, in order to avoid the large computational cost associated with tremendous calculation of geometric constraint values. The proposed method is verified by an analytical test case. Then, the proposed method is demonstrated and compared with the methods of building surrogate models of all geometric constraints and calculating all geometric constraints directly during each sub-optimization by drag minimizations of NACA0012 air foil and ONERA M6 wing in transonic flows. To investigate the ability of the proposed method for handling various geometric constraints, drag minimization of CRM wing in viscous transonic flow driven by CFD is performed. Results show that the proposed method can dramatically improve the optimization efficiency of ASO with the number of geometric constraints ranging from 15 to 1429 and the number of types of geometric constraints up to 3, which offers great potential for handling a larger number and more types of geometric constraints. [ABSTRACT FROM AUTHOR]

This study assessed the clinical efficacy of the neoadjuvant chemotherapy TAC scheme in treatment of patients with locally advanced breast cancer, and the value of the level of Ras association domain family 1A (RASSF1A) gene methylation and the Wnt inhibitory factor (WIF)-1 gene in tissue and serum of patients in clinical outcome prediction. In total, 126 patients were consecutively selected to receive TAC scheme (docetaxel, pirarubicin/epirubicin and cyclophosphamide) for at least four cycles with the total effective rate. The incidence of complications, progression-free survival and survival rate were recorded. Tumor tissues and peripheral blood samples collected in this study was used to detect methylation positive rate of RASSF1A and WIF-1 by methylation-specific PCR method and the relative level of expression of RASSF1A and WIF-1 mRNA by reverse transcription PCR method. Of the 126 patients, there were 18 cases with complete response (CR), 32 cases with partial response (PR), 50 cases with stable disease (SD), and 26 cases with disease progression (PD) with a total effective rate of 79.37%. Comparison on baseline data of effective group and ineffective group showed no difference (P>0.05), and comparison on adverse reactions occurrence showed no difference (P>0.05). Progression-free survival of the effective group was prolonged with a significant increase in survival rate (P<0.05). Positive rates of RASSF1A methylation and WIF-1 in tissue and serum of the patients in the effective group were significantly lower than those in the ineffective group, but the mRNA of RASSF1A and WIF-mRNA was significantly higher than the ineffective group (P<0.05). The sensitivity of clinical outcome prediction using tissue RASSF1A methylation was 67.0%, the specificity 15.4%, positive predictive value 69.0% and negative predictive value 31.0%. The above-mentioned indexes of tissue WIF-1 were 76.0, 31.4, 72.2 and 27.8, respectively. The indexes of serum RASSF1A were 85.0, 50.0, 76.2 and 23.8%, respectively, and the indexes of serum WIF-1 were 94.0, 75.0, 81.0 and 19.0%, respectively. The receiver operating characteristic curve analysis suggested that the accuracy of clinical outcome prediction using tissue RASSF1A mRNA level was 0.812. The sensitivity 85.2%, the specificity 76.3% and the critical value 0.4256. These indexes of tissue WIF-1 were 0.833, 86.7%, 75.4% and 0.3562 for CR, PR, SD and PD, respectively. These indexes of serum RASSF1A were 0.864, 88.3%, 77.4% and 0.2564, respectively, and for serum WIF-1 were 0.882, 89.4%, 73.5% and 0.1562, respectively. In conclusion, the detection of RASSF1A and WIF-1 gene methylation and level of mRNA expression in tissue and serum of patients with locally advanced breast cancer has an important application value in predicting clinical efficacy of neoadjuvant chemotherapy of the TAC scheme. [ABSTRACT FROM AUTHOR]

The aim of the present study was to investigate the effects of RNA interference with prostaglandin-endoperoxide synthase 2 (COX-2) gene on the proliferation and apoptosis of breast cancer MCF-7 cells, as well as the underlying mechanism. The present study constructed the eukaryotic expression vector of the targeted COX-2 gene, transfected the MCF-7 cells and screened the stably expressed clone. Changes in the COX-2 gene expression in breast cancer MCF-7 cells prior to and following transfection were examined; the proliferation and apoptosis of MCF-7 cells were analyzed. Furthermore, changes in the protein levels of survivin, B-cell lymphoma 2 (Bcl-2) and Bcl-2-associated X (Bax) genes were detected. RNA interference mediated by a lentiviral expression vector significantly decreased the protein expression levels of the COX-2 gene, and therefore, the proliferation and growth of breast cancer MCF-7 cells was significantly suppressed and the apoptotic rate increased. Of note, the mRNA and protein expression levels of survivin and Bcl-2 decreased, while those of Bax increased following COX-2 silencing. RNA interference markedly deactivated the COX-2 gene, suppressed the proliferation of breast cancer MCF-7 cells, and, to a certain extent, enhanced the induced spontaneous apoptosis, which is regulated by the Bax gene. These results provided evidence for the potential applications of RNA interference of the targeted COX-2 gene in gene therapy for the treatment of breast cancer. [ABSTRACT FROM AUTHOR]