Dennismunro3711

Progesterone has been reported to inhibit the proliferation of breast cancer and osteosarcoma cells; however, its inhibitory mechanism has not yet been clarified. The aim of the present study was to clarify the effects of progesterone on apoptosis in breast cancer (MCF-7) and human osteosarcoma (MG-63) cells.

In this experimental study the cytotoxic effect of progesterone was measured in MCF-7 and MG-63 cells exposed to different concentrations of progesterone using MTT assay, and effective concentrations were identified. The expression levels of the Bax, P53 and Bcl-2 genes were evaluated by real-time PCR, and caspase-3, 8 and 9 activity levels were determined using a colorimetric method. Hoechst staining and flow cytometry were used to confirm apoptosis. The data were statistically analyzed using one-way analysis of variance (ANOVA) and independent-samples t-test.

Compared to the control group, we observed a significant increase in the expression levels of the Bax and P53 genes and the activity levels of caspase-3 and 9, and a significant decrease in the expression level of the Bcl-2 gene in MCF-7 and MG-63 treated with effective concentration of progesterone. The caspase-8 activity level did not change significantly in treated MG-63 but increased in treated MCF-7 cells. Hoechst staining and flow cytometry results confirmed apoptosis in the cells exposed to effective concentration of progesterone.

The cytotoxic effect of progesterone on breast cancer and osteosarcoma cells was mediated by apoptotic pathways. In this context, progesterone triggers the extrinsic and intrinsic apoptotic pathways in MCF-7 cells and induces the intrinsic apoptotic pathway in MG-63 cells.

The cytotoxic effect of progesterone on breast cancer and osteosarcoma cells was mediated by apoptotic pathways. check details In this context, progesterone triggers the extrinsic and intrinsic apoptotic pathways in MCF-7 cells and induces the intrinsic apoptotic pathway in MG-63 cells.

The novel coronavirus SARS-CoV-2 rapidly spread around the world, causing the disease COVID-19. To contain the virus, much hope is placed on participatory surveillance using mobile apps, such as automated digital contact tracing, but broad adoption is an important prerequisite for associated interventions to be effective. Data protection aspects are a critical factor for adoption, and privacy risks of solutions developed often need to be balanced against their functionalities. This is reflected by an intensive discussion in the public and the scientific community about privacy-preserving approaches.

Our aim is to inform the current discussions and to support the development of solutions providing an optimal balance between privacy protection and pandemic control. To this end, we present a systematic analysis of existing literature on citizen-centered surveillance solutions collecting individual-level spatial data. Our main hypothesis is that there are dependencies between the following dimensions the use s (eg, from the area of secure multiparty computing and differential privacy).

We see a large potential for future solutions supporting multiple use cases by combining different technologies (eg, Bluetooth and GPS). For this to be successful, however, adequate privacy-protection measures must be implemented. Technologies currently used in this context can probably not offer enough protection. We, therefore, recommend that future solutions should consider the use of modern privacy-enhancing techniques (eg, from the area of secure multiparty computing and differential privacy).The reconstruction of visual information from human brain activity is a very important research topic in brain decoding. Existing methods ignore the structural information underlying the brain activities and the visual features, which severely limits their performance and interpretability. Here, we propose a hierarchically structured neural decoding framework by using multitask transfer learning of deep neural network (DNN) representations and a matrix-variate Gaussian prior. Our framework consists of two stages, Voxel2Unit and Unit2Pixel. In Voxel2Unit, we decode the functional magnetic resonance imaging (fMRI) data to the intermediate features of a pretrained convolutional neural network (CNN). In Unit2Pixel, we further invert the predicted CNN features back to the visual images. Matrix-variate Gaussian prior allows us to take into account the structures between feature dimensions and between regression tasks, which are useful for improving decoding effectiveness and interpretability. This is in contrast with the existing single-output regression models that usually ignore these structures. We conduct extensive experiments on two real-world fMRI data sets, and the results show that our method can predict CNN features more accurately and reconstruct the perceived natural images and faces with higher quality.In this article, the discrete-form time-variant multi-augmented Sylvester matrix problems, including discrete-form time-variant multi-augmented Sylvester matrix equation (MASME) and discrete-form time-variant multi-augmented Sylvester matrix inequality (MASMI), are formulated first. In order to solve the above-mentioned problems, in continuous time-variant environment, aided with the Kronecker product and vectorization techniques, the multi-augmented Sylvester matrix problems are transformed into simple linear matrix problems, which can be solved by using the proposed discrete-time recurrent neural network (RNN) models. Second, the theoretical analyses and comparisons on the computational performance of the recently developed discretization formulas are presented. Based on these theoretical results, a five-instant discretization formula with superior property is leveraged to establish the corresponding discrete-time RNN (DTRNN) models for solving the discrete-form time-variant MASME and discrete-form time-variant MASMI, respectively. Note that these DTRNN models are zero stable, consistent, and convergent with satisfied precision. Furthermore, illustrative numerical experiments are given to substantiate the excellent performance of the proposed DTRNN models for solving discrete-form time-variant multi-augmented Sylvester matrix problems. In addition, an application of robot manipulator further extends the theoretical research and physical realizability of RNN methods.