Balloon aortic valvuloplasty for severe aortic stenosis may reduce mitral regurgitation in mid-term follow-up.

Introduction: Mitral regurgitation (MR) is a frequent complication in patients with severe aortic stenosis (AS). Material and methods: Echocardiographic assessment of MR was performed at baseline, at 30 days and at 6 months after balloon aortic valvuloplasty (BAV). Results: Data of 271 patients were included in our final analysis, of which 21.2% (n = 85) had at least moderate MR at baseline (in 19 (22.3%) subjects MR was diagnosed as primary). Both groups showed similar severity of AS, but patients in the MR group had a greater left ventricle (LV) size (p = 0.003 for LVESD, p = 0002 for LVEDD) and slightly lower LV ejection fraction (p = 0.04). Mitral regurgitation parameters significantly improved both at 30 days and 6 months after BAV in the MR group (MR jet area: 7.2 (4.5-9.9) vs. 3.6 (2.3-7.2) cm2, and 7.2 (4.5-9.9) vs. 3.2 (2.1-6.7) cm2; %MR/left atrial area 34.5 (23.4-42.7) vs. 17.5 (9.3-29.5) and 34.5 (23.4-42.7) vs. 14.5 (8.3-24.5), p < 0.001 for all). In multivariate logistic regression analysis, the change at 30 days, from baseline, in the LVESD (OR = 1.87; 95% CI: 1.23-2.87; p < 0.001) and LVEF (OR = 0.95; 95% CI: 0.87-1.01; p < 0.001); MR jet area (OR = 2.2, 95% CI: 1.5-4.6; p < 0.001) and the presence of primary MR (OR = 3.2, 95% CI: 1.04-5.98; p < 0.001) were retained as independent predictors of significant persisting MR at 6 months. Conclusions: Balloon aortic valvuloplasty may reduce MR in mid-term follow-up. Predictors of persistent MR at 6 months after BAV included an increase of LVESD and MR jet area and decrease of LVEF at 30 days.

Responsiveness of the Sensor Network to Alarm Events Based on the Potts Model.

The paper aims to present modelling the sensor network operation based on the Potts model. The authors presented own approach based on three states in which each node can be. The change in the state of a given node depends on its current state, the impact of surrounding nodes on it, but also values of the parameters measured. Therefore, the Hamiltonian was introduced as a dependence of both exceeding the limit value of a measured parameter (corresponding to an alarm event), and the state of the battery powering a given node of a sensor. The simulations of the implemented algorithm based on the adopted model presented in the paper relate to the measurement of temperature by a network of sensors. However, this model is universal and can be applied to examine the behaviour of the sensor infrastructure performing various measurements. Moreover, it may simulate the functioning of the critical network infrastructure or sensor networks and industrial sensors supporting the functioning of Industry 4.0.