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Lianzhou Yang,Yuchen Cai,Dongsheng Zhang,Jian Sun,Chenyu Xu,Wenli Zhao,Wenqi Jiang,Chunhua Pan 한국유방암학회 2018 Journal of breast cancer Vol.21 No.4
Purpose: Immune suppression is common in patients with advanced breast cancer but the mechanisms underlying this phenomenon have not been sufficiently studied. In this study, we aimed to identify B7 family members that were able to predict the immune status of patients, and which may serve as potential targets for the treatment of breast cancer. We also aimed to identify microRNAs that may regulate the expression of B7 family members. Methods: The Cancer Genome Atlas data from 1,092 patients with breast cancer, including gene expression, microRNA expression and survival data, were used for statistical and survival analyses. Polymerase chain reaction and Western blot were used to measure messenger RNA and protein expression, respectively. Luciferase assay was used to investigate direct microRNA target. Results: Bioinformatic analysis predicted that microRNA (miR)-93, miR-195, miR-497, and miR-340 are potential regulators of the immune evasion of breast cancer cells, and that they exert this function by targeting CD274, PDCD1LG2, and NCR3LG1. We chose CD274 for further investigations. We found that miR-195, miR-497, and CD274 expression levels were inversely correlated in MDA-MB-231 cells, and miR-195 and miR-497 expressions mimic inhibited CD274 expression in vitro. Mechanistic investigations demonstrated that miR-195 and miR-497 directly target CD274 3´ untranslated region. Conclusion: Our data indicated that the level of B7 family members can predict the prognosis of breast cancer patients, and miR-195/miR-497 regulate CD274 expression in triple negative breast cancer. This regulation may further influence tumor progression and the immune tolerance mechanism in breast cancer and may be able to predict the effect of immunotherapy on patients.
박지혜,김성민,Justin Y. Jeon,김연욱,이현철,Dongsheng Cai,Eleftheria Maratos-Flier 한국체육학회 2007 International journal of human movement science Vol.1 No.1
The purpose of this study is to investigate the effects of voluntary exercise on obesity-associated insulin resistance in mice. First, mice were fed either high fat diet (HFD) or chow diet for 6 weeks and subsequently divided into either an exercise (EHFD) or a no exercise group (N-HFD) for an additional 13 weeks. Mouse were housed individually and E-HFD group had continuous access to a running wheel. During the 13-week observation period, E-HFD mice gained only 1.8±0.7 g, compared to 6.2±0.4 g for the N-HFD group. Glucose and insulin tolerance tests showed expected impaired glucose tolerance and insulin resistance in N-HFD. These abnormalities were reversed by voluntary exercise. Increases in plasma cholesterol, non-esterified fatty acids and liver triglycerides levels seen in the N-HFD group were also partially reversed by voluntary exercise in the E-HFD group. Similarly, expression and activity of three key transcriptional mediators of the inflammatory cascade, TNF-α , IKKβ and NF-kB were increased in white adipose tissue from N-HFD compared to chow-fed controls whereas voluntary exercise decreased TNF-α , IKKβ expression and NF-kB activity to levels similar to those seen in chow-fed mice. In conclusion, voluntary exercise leads to weight reduction secondary to increased physical activity. This is in turn associated with deceased adipocyte inflammation and improved insulin sensitivity.
An Anti-Inflammatory Approach to Treating Diabetes and Atherosclerosis Using Salicylate
Steven E. Shoelson,Ju Ho Youn,Giulio Romeo,Tanya Ignjatovic,Dongsheng Cai,Minsheng Yuan,Nicky Konstantoupolos,Laura Herrero,Myrlene Staten,Vivan Fonseca,Allison Goldfine,Jongsoon Lee 한국식품영양과학회 2014 한국식품영양과학회 학술대회발표집 Vol.2014 No.10
Xing Yankai,Zhang Guangdou,Wang Baolu,Li Jian,Bamisile Olusola,Cai Dongsheng,Huang Qi 대한전기학회 2024 Journal of Electrical Engineering & Technology Vol.19 No.2
The fast response and low inertia characteristics of converter-based generation (CBG) lead to a new stability issue that limits renewable energy development. This is due to the fact that, using traditional control theory as basic, there is no unifed and efective way to linearize the electronic device and set the parameters in the existing analysis method. In order to optimize the parameters tuning process of the converter control, a benchmark model is adopted in this paper, and the linearization model is updated by selecting suitable variables and detailed with considering the infuence of resistance of the converter. Based on this model, the stability margin related to the parameters of the system is analyzed. Furthermore, to consider more dynamics and make the converter controller focus on the topologies and scenarios, the dynamic response of a disturbance in the grid is selected as the iteration state to design a controller using Deep Reinforcement Learning (DRL). That is, the cascaded voltage and current control along with droop control are tuned by Deep Deterministic Policy Gradient (DDPG) algorithm in the linearized model. The validation tests are carried out on the nonlinear model in Simulink test and real-time platform. They indicate that the proposed linearization and tuning method provides more accuracy and stability for the power system in various grid conditions.