Paclitaxel liposomes exert radio-sensitization effect on breast cancer cell line SK-BR-3 by regulating expressions of apoptotic proteins

Purpose: To study the radio-sanitization effect of paclitaxel liposomes on breast cancer cells, SK-BR-3.Methods: Breast cancer cell line SK-BR-3 was cultured and made into a cell suspension. Four groups of cells were used: control and radiotherapy groups, paclitaxel group, and paclitaxel liposome + radiotherapy group (combination group). The growth inhibitory effects of the different treatments on SKBR-3 cells were determined with CCK-8 method. Apoptosis in each group was evaluated with flow cytometry, while Western blotting was employed to assay Bcl-2, Caspase-3 and Bax protein levels.Results: There were marked inhibitory effect on growth of SK-BR-3 cells in drug, radiotherapy and combination groups, relative to control, while apoptosis was greater in combination group than in drug and radiotherapy groups (p < 0.05). The Bcl-2 protein level was higher in radiotherapy, drug and combination groups than in control group, while Caspase-3 and Bax proteins were markedly higher thancontrol values (p < 0.05).Conclusion: Paclitaxel liposomes exert radio-sensitization effect on SK-BR-3 by regulating the levels of apoptotic proteins. This provides a basis for research and development of new drugs. Keywords: Paclitaxel liposomes, Apoptotic proteins, Breast cancer, SK-BR-3, Radiotherapy, Sensitization, Bcl-2, Caspase-3, Ba

Uncertainty Quantification by Convolutional Neural Network Gaussian Process Regression with Image and Numerical Data

Uncertainty Quantification (UQ) plays a critical role in engineering analysis and design. Regression is commonly employed to construct surrogate models to replace expensive simulation models for UQ. Classical regression methods suffer from the curse of dimensionality, especially when image data and numerical data coexist, which makes UQ computationally unaffordable. In this work, we propose a Convolutional Neural Network (CNN) based framework, which accommodates both image and numerical data. We first transform numerical data into images and then combine them with existing image data. The combined images are fed to CNN for regression. To obtain the model uncertainty, we integrate CNN with Gaussian Process (GP), which results in the mixed network CNN-GP. The simulation results show that CNN-GP can build accurate surrogate models for UQ with mixed data and that CNN-GP can also provide the uncertainty associated with the model prediction

Active learning with generalized sliced inverse regression for high-dimensional reliability analysis

It is computationally expensive to predict reliability using physical models at the design stage if many random input variables exist. This work introduces a dimension reduction technique based on generalized sliced inverse regression (GSIR) to mitigate the curse of dimensionality. The proposed high dimensional reliability method enables active learning to integrate GSIR, Gaussian Process (GP) modeling, and Importance Sampling (IS), resulting in an accurate reliability prediction at a reduced computational cost. The new method consists of three core steps, 1) identification of the importance sampling region, 2) dimension reduction by GSIR to produce a sufficient predictor, and 3) construction of a GP model for the true response with respect to the sufficient predictor in the reduced-dimension space. High accuracy and efficiency are achieved with active learning that is iteratively executed with the above three steps by adding new training points one by one in the region with a high chance of failure

High-Dimensional Reliability Method Accounting for Important and Unimportant Input Variables

Reliability analysis is a core element in engineering design and can be performed with physical models (limit-state functions). Reliability analysis becomes computationally expensive when the dimensionality of input random variables is high. This work develops a high-dimensional reliability analysis method through a new dimension reduction strategy so that the contributions of unimportant input variables are also accommodated after dimension reduction. Dimension reduction is performed with the first iteration of the first-order reliability method (FORM), which identifies important and unimportant input variables. Then a higher order reliability analysis is performed in the reduced space of only important input variables. The reliability obtained in the reduced space is then integrated with the contributions of unimportant input variables, resulting in the final reliability prediction that accounts for both types of input variables. Consequently, the new reliability method is more accurate than the traditional method which fixes unimportant input variables at their means. The accuracy is demonstrated by three examples

Mean velocity and temperature profiles in turbulent Rayleigh-Bénard convection at low Prandtl numbers

We report a direct numerical simulation (DNS) study of the mean velocity and temperature profiles in turbulent Rayleigh-Bénard convection (RBC) at low Prandtl numbers (Pr). The numerical study is conducted in a vertical thin disk with Pr varied in the range 0.17 ≤ Pr ≤ 4.4 and the Rayleigh number (Ra) varied in the range 5 × 10^8 ≤ Ra ≤ 1 × 10^10. By varying Pr from 4.4 to 0.17, we find a sharp change of flow patterns for the large-scale circulation (LSC) from a rigid-body rotation to a near-wall turbulent jet. We numerically examine the mean velocity equation in the bulk region and find that the mean horizontal velocity profile u(z) can be determined by a balance equation between the mean convection and turbulent diffusion with a constant turbulent viscosity νt. This balance equation admits a self-similarity jet solution, which fits the DNS data well. In the boundary-layer region, we find that both the mean temperature profile T(z) and u(z) can be determined by a balance equation between the molecular diffusion and turbulent diffusion. Within the viscous boundary layer, both u(z) and T(z) can be solved analytically and the analytical results agree well with the DNS data. Our careful characterisation of the mean velocity and temperature profiles in low-Pr RBC provides a further understanding of the intricate interplay between the LSC, plume emission and boundary-layer dynamics, and pinpoints the physical mechanism for the emergence of a pronounced LSC in low-Pr RBC

Effects of Fungicide Propiconazole on the Yeast-Like Symbiotes in Brown Planthopper (BPH, Nilaparvata lugens Stål) and Its Role in Controlling BPH Infestation

Yeast-like symbiotes (YLS), harbored in the abdomen fat-body cells of the rice brown planthopper (BPH), Nilaparvata lugens Stål (Hemiptera: Delphacidae), are vital to the growth and reproduction of their host. It is feasible to manipulate BPH infestation on rice by inhibiting YLS using fungicide. In this study, the fungicide propiconazole was injected into the hemolymph of BPH thorax via microinjection to investigate its effect on YLS, especially the dominant species, Hypomyces chrysospermus, and their host BPH. Propiconazole markedly reduced the total number of YLS and H. chrysospermus in BPH hemolymph and fat body, thereby leading to an obvious higher mortality and lower fecundity of BPH than the negative control (PBS, phosphate buffer solution). After microinjecting propiconazole, the survival rate of BPH nymphs at the 5th instar was significantly lower than that obtained after PBS treatment. Eight days after propiconazole microinjection, the BPH survival rate dropped to 40%, only half of BPH survival rate treated with PBS microinjection. For female adults (1-day-old), there were significant differences in the survival rates between BPHs treated with propiconazole and those treated with PBS at days 5–8. The fecundity of BPH decreased significantly by microinjecting propiconazole and averaged only 229 eggs per female, which was 20% less than that of the negative control. Furthermore, we reared BPH on the susceptible variety TN1 sprayed with propiconazole to prove the feasibility manipulating field occurrence of BPH by inhibiting YLS using fungicides. The number of YLS and H. chrysospermus in BPH obviously declined. Subsequently, the survival rate and fecundity of BPH significantly decreased after feeding on rice treated with propiconazole. Meanwhile, the propiconazole residue was detected in the hemolymph and gut of BPH by HPLC analysis within 1 day of feeding. Inhibiting YLS using fungicides was a novel and effective way to control BPH infestation