Englandbruce3285

Pressure overload in patients with aortic stenosis (AS) induces an adverse remodeling of the left ventricle (LV) in a sex-specific manner. We assessed whether a sex-specific miR-29b dysregulation underlies this sex-biased remodeling pattern, as has been described in liver fibrosis. We studied mice with transverse aortic constriction (TAC) and patients with AS. miR-29b was determined in the LV (mice, patients) and plasma (patients). Expression of remodeling-related markers and histological fibrosis were determined in mouse LV. Echocardiographic morpho-functional parameters were evaluated at baseline and post-TAC in mice, and preoperatively and 1 year after aortic valve replacement (AVR) in patients with AS. In mice, miR-29b LV regulation was opposite in TAC-males (down-regulation) and TAC-females (up-regulation). The subsequent changes in miR-29b targets (collagens and GSK-3β) revealed a remodeling pattern that was more fibrotic in males but more hypertrophic in females. Both systolic and diastolic cardiac functions deteriorated more in TAC-females, thus suggesting a detrimental role of miR-29b in females, but was protective in the LV under pressure overload in males. Clinically, miR-29b in controls and patients with AS reproduced most of the sexually dimorphic features observed in mice. In women with AS, the preoperative plasma expression of miR-29b paralleled the severity of hypertrophy and was a significant negative predictor of reverse remodeling after AVR; therefore, it may have potential value as a prognostic biomarker.Metformin is a substrate for plasma membrane monoamine transporters (PMAT) and organic cation transporters (OCTs); therefore, the expression of these transporters and interactions between them may affect the uptake of metformin into tumor cells and its anticancer efficacy. The aim of this study was to evaluate how chemical modification of metformin scaffold into benzene sulfonamides with halogen substituents (compounds 1-9) may affect affinity towards OCTs, cellular uptake in two breast cancer cell lines (MCF-7 and MDA-MB-231) and antiproliferative efficacy of metformin. The uptake of most sulfonamides was more efficient in MCF-7 cells than in MDA-MB-231 cells. The presence of a chlorine atom in the aromatic ring contributed to the highest uptake in MCF-7 cells. For instance, the uptake of compound 1 with o-chloro substituent in MCF-7 cells was 1.79 ± 0.79 nmol/min/mg protein, while in MDA-MB-231 cells, the uptake was considerably lower (0.005 ± 0.0005 nmol/min/mg protein). The elevated uptake of tested compounds in MCF-7 was accompanied by high antiproliferative activity, with compound 1 being the most active (IC50 = 12.6 ± 1.2 µmol/L). Further studies showed that inhibition of MCF-7 growth is associated with the induction of early and late apoptosis and cell cycle arrest at the G0/G1 phase. In summary, the chemical modification of the biguanide backbone into halogenated sulfonamides leads to improved transporter-mediated cellular uptake in MCF-7 and contributes to the greater antiproliferative potency of studied compounds through apoptosis induction and cell cycle arrest.Daily activity forecasts play an important role in the daily lives of residents in smart homes. Category forecasts and occurrence time forecasts of daily activity are two key tasks. Category forecasts of daily activity are correlated with occurrence time forecasts, however, existing research has only focused on one of the two tasks. Moreover, the performance of daily activity forecasts is low when the two tasks are performed in series. find more In this paper, a forecast model based on multi-task learning is proposed to forecast category and occurrence time of daily activity mutually and iteratively. Firstly, raw sensor events are pre-processed to form a feature space of daily activity. Secondly, a parallel multi-task learning model which combines a convolutional neural network (CNN) with bidirectional long short-term memory (Bi-LSTM) units are developed as the forecast model. Finally, five distinct datasets are used to evaluate the proposed model. The experimental results show that compared with the state-of-the-art single-task learning models, this model improves accuracy by at least 2.22%, and the metrics of NMAE, NRMSE and R2 are improved by at least 1.542%, 7.79% and 1.69%, respectively.Mobile health monitoring plays a central role in the future of cyber physical systems (CPS) for healthcare applications. Such monitoring systems need to process user data accurately. Unlike in other human-centered CPS, in healthcare CPS, the user functions in multiple roles all at the same time as an operator, an actuator, the physical environment and, most importantly, the target that needs to be monitored in the process. Therefore, mobile health CPS devices face highly dynamic settings generally, and accuracy of the machine learning models the devices employ may drop dramatically every time a change in setting happens. Novel learning architecture that specifically address challenges associated with dynamic environments are therefore needed. Using active learning and transfer learning as organizing principles, we propose a collaborative multiple-expert architecture and accompanying algorithms for the design of machine learning models that autonomously adapt to a new configuration, context, or user need. Specifically, our architecture and its constituent algorithms are designed to manage heterogeneous knowledge sources or experts with varying levels of confidence and type while minimizing adaptation cost. Additionally, our framework incorporates a mechanism for collaboration among experts to enrich their knowledge, which in turn decreases both cost and uncertainty of data labeling in future steps. We evaluate the efficacy of the architecture using two publicly available human activity datasets. We attain activity recognition accuracy of over 85 % (for the first dataset) and 92 % (for the second dataset) by labeling only 15 % of unlabeled data.Student victims of peer bullying (n = 223) in 25 coeducational Australian schools answered a questionnaire to provide accounts of how their school responded to their requests for help. In addition, respondents indicated how severely they were emotionally impacted by the bullying and whether the bullying was perpetrated by an individual or by a group. The reported outcomes from the intervention indicated that in 67% of cases the bullying stopped or was reduced. In cases where the emotional impact was reported as relatively severe, the school interventions were less successful. In addition, reportedly being bullied relatively often by groups, as distinct from individuals, was independently predictive of a less positive outcome. Among girls, but not boys, younger students reported more satisfactory outcomes. Implications are suggested for more effective interventions in cases of bullying.