Search

We aimed to investigate the role of miR-17 in cardiac matrix remodeling following myocardial infarction (MI). Using real-time PCR, we quantified endogenous miR-17 in infarcted mouse hearts. Compared with related microRNAs, miR-17 was up-regulated most dramatically: 3.7-fold and 2.4-fold in the infarct region 3 and 7 d post-MI, respectively, and 2.4-fold in the border zone at d 3 compared to sham control (P<0.01). Chimeric luciferase reporter constructs were cloned for miR-17 target validation. miR-17 targeted the 3'-UTR of TIMP2 and the protein coding region of TIMP1. The miR-17 mimic decreased TIMP2 (P<0.01) and TIMP1 (P<0.05) protein expression compared with the scrambled control. Inhibition of endogenous miR-17 by in vivo antagomir delivery enhanced TIMP2 (P<0.01) and TIMP1 (P<0.05) protein expression compared to the mismatch group, decreased MMP9 activity (P<0.05), reduced infarct size as early as 7 d post-MI (P<0.05), and improved cardiac function (fractional shortening and fractional area contraction, P<0.05) at d 21 and 28 post-MI. Transgenic mice overexpressing miR-17 in the heart confirmed the deleterious role of miR-17 in matrix modulation. Our study suggests that miR-17 participates in the regulation of cardiac matrix remodeling and provides a novel therapeutic approach using miR-17 inhibitors to prevent remodeling and heart failure after MI. [ABSTRACT FROM AUTHOR]

Location-based services (LBSs) play a very important role in pervasive computing environment, and QoS (quality of service) is one of the key evaluations for LBS. To maintain high QoS, the traditional approaches rely on accurate and continuous localization. However, the energy consumption of the mobile device under this situation is often too high for practical applications. Thus, it seems that the energy consumption and QoS become two conflicting factors in LBS systems. In this article, a new adaptive goal-aware computing framework (Adaware) is proposed to solve this contradiction. We show that the QoS of LBS can be evaluated by recognizing user goals. We design new algorithms to mine user goals from discontinuous location data to reduce the energy consumption while keeping a high QoS at the same time. More specifically, Adaware employs an accelerometer to implement motion-based localization, which greatly reduces the unnecessary energy consumption on Wi-Fi scanning compared to the original continuous localization methods. Then based on the estimated discontiguous critical point traces which have been postprocessed by our proposed Localization Confident Coefficient filter method, a novel N-gram goal inference algorithm is used to predict the accurate goal. The experimental results in real-world wireless network environments validate the effectiveness of our framework. We can get 80% QoS under 70% location estimation accuracy within 10 meters and 30% energy saving compared to continuous Wi-Fi scanning. [ABSTRACT FROM AUTHOR]