Effects of ticagrelor on proliferation, apoptosis, and inflammatory factors of human aortic vascular smooth muscle cells through lncRNA KCNQ1OT1.

OBJECTIVE: This study was designed to determine the effects of ticagrelor on the proliferation and apoptosis of and inflammatory factors in human aortic vascular smooth muscle cells (HAVSMCs). METHODS: A total of 20 patients who were first diagnosed with coronary heart disease (CHD) from August 2020 to March 2021 and 20 healthy adults were enrolled into the study. Oxidized low-density lipoprotein (ox-LDL) and different concentrations of ticagrelor were applied in the treatment of HAVSMCs, and then the cell proliferation and apoptosis and the expression of apoptosis-related proteins, inflammatory factors, and IκBα in them were determined. RESULTS: Compared with the ox-LDL group, the OD value was significantly increased after ticagrelor treatment, and the apoptosis rate was significantly reduced (P<0.05); compared with the ox-LDL group, the B lymphoma-2 (Bcl-2) protein, IκB, KCNQ1OT1 expression in the ticagrelor group increased significantly, Bcl-2-associated X protein (Bax), monocyte chemoattractant protein-1 (MCP-1) and tumor necrosis factor-α (TNF-α) expression decreased significantly (P<0.05); The expression of serum KCNQ1OT1 in patients with coronary heart disease was significantly higher than that in healthy individuals (P<0.05). CONCLUSION: Ticagrelor may regulate the expression of lncRNA KCNQ1OT1 and up-regulate the expression of IκBα to promote proliferation and anti-apoptosis, so as to prevent ox-LDL from oxidative damage to HAVSMCs.

Effects of valproic acid on the susceptibility of human glioma stem cells for TMZ and ACNU.

To investigate the effect of valproic acid (VPA) on the susceptibility of glioma stem cells to temozolomide (TMZ) and nimustine (ACNU), the O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation and its expression of MGMT were examined. A total of 3 glioma cell populations were isolated from human glioma tissues, and immunocytochemistry was used to detect the expression of MGMT. VPA inhibition on the growth of the 3 glioma cell populations exposed to various concentrations of TMZ and ACNU was evaluated. Flow cytometry was applied to detect the apoptosis of glioma cells, and a methylation-specific polymerase chain reaction was used to identify methylation of MGMT promoter. Immunocytochemistry results indicated that MGMT was negatively expressed in the G1 population, but positively expressed in the G2 and G3 populations. Cell growth inhibition assays demonstrated that the survival rate in the VPA + TMZ or ACNU groups was decreased compared with that of the TMZ or ACNU alone groups (P<0.05). As for the apoptotic rate, those in the VPA alone group were increased compared with the control group (P<0.05), and the rates in the VPA + TMZ or ACNU groups were increased compared with TMZ or ACNU alone groups (P<0.05). The expression of MGMT remained negative in the G1 population following treatment with VPA, but MGMT expression became negative in the 2 MGMT-positive cell populations (G2 and G3) following VPA treatment. The MGMT promoter in the G1 population was partially methylated in the control group, but was fully methylated following VPA treatment, while the promoters of G2, G3 were unmethylated in the control group and became partially methylated in the VPA treatment group. Taken together, TMZ and ACNU may suppress the growth of glioma stem cells in vitro in a dose-dependent manner. VPA may enhance the inhibitory effects of various concentrations of TMZ and ACNU on the growth of MGMT-negative/positive cells, particularly on MGMT-positive cell populations. VPA itself may induce the apoptosis of glioma cells, and VPA combined with TMZ or ACNU may enhance TMZ/ACNU-induced apoptosis of glioma stem cells. Furthermore, VPA may also promote the methylation of the MGMT promoter to silence MGMT expression in glioma cells, which may be an important mechanism through which VPA enhances the efficacy of TMZ and ACNU in targeting glioma stem cells.

Build a Robust Learning Feature Descriptor by Using a New Image Visualization Method for Indoor Scenario Recognition.

In order to recognize indoor scenarios, we extract image features for detecting objects, however, computers can make some unexpected mistakes. After visualizing the histogram of oriented gradient (HOG) features, we find that the world through the eyes of a computer is indeed different from human eyes, which assists researchers to see the reasons that cause a computer to make errors. Additionally, according to the visualization, we notice that the HOG features can obtain rich texture information. However, a large amount of background interference is also introduced. In order to enhance the robustness of the HOG feature, we propose an improved method for suppressing the background interference. On the basis of the original HOG feature, we introduce a principal component analysis (PCA) to extract the principal components of the image colour information. Then, a new hybrid feature descriptor, which is named HOG-PCA (HOGP), is made by deeply fusing these two features. Finally, the HOGP is compared to the state-of-the-art HOG feature descriptor in four scenes under different illumination. In the simulation and experimental tests, the qualitative and quantitative assessments indicate that the visualizing images of the HOGP feature are close to the observation results obtained by human eyes, which is better than the original HOG feature for object detection. Furthermore, the runtime of our proposed algorithm is hardly increased in comparison to the classic HOG feature.

A Smartphone Camera-Based Indoor Positioning Algorithm of Crowded Scenarios with the Assistance of Deep CNN.

Considering the installation cost and coverage, the received signal strength indicator (RSSI)-based indoor positioning system is widely used across the world. However, the indoor positioning performance, due to the interference of wireless signals that are caused by the complex indoor environment that includes a crowded population, cannot achieve the demands of indoor location-based services. In this paper, we focus on increasing the signal strength estimation accuracy considering the population density, which is different to the other RSSI-based indoor positioning methods. Therefore, we propose a new wireless signal compensation model considering the population density, distance, and frequency. First of all, the number of individuals in an indoor crowded scenario can be calculated by our convolutional neural network (CNN)-based human detection approach. Then, the relationship between the population density and the signal attenuation is described in our model. Finally, we use the trilateral positioning principle to realize the pedestrian location. According to the simulation and tests in the crowded scenarios, the proposed model increases the accuracy of the signal strength estimation by 1.53 times compared to that without considering the human body. Therefore, the localization accuracy is less than 1.37 m, which indicates that our algorithm can improve the indoor positioning performance and is superior to other RSSI models.