Background Dry weight assessment in hemodialysis (HD) remains a challenge. The aim of the study was to investigate the prevalence of subclinical pulmonary congestion using lung ultrasound (LUS) in maintenance HD patients with no clinical or bioimpedance signs of hyperhydration. The correlation between B-lines Score (BLS) and brain natriuretic peptide (BNP) was also evaluated. Methods Twenty-four HD patients underwent LUS and BNP dosage at the end of the mid-week HD session, monthly for 6 months . LUS was considered as positive when BLS was >15. Hospitalizations and cardiovascular events were also evaluated in relation to the BLS. Results LUS+ patients at baseline were 16 (67%), whereas 11 (46%) showed LUS + in at least 50% of the measurements (rLUS+ patients). Only the rLUS+ patients had a higher number of cardiovascular events [p=0.019, OR: 7.4 (CI 95%. 1.32-39.8)] and hospitalizations [p=0.034, OR 5.5 (CI 95% 1.22- 24.89)]. A BNP level of 165 pg/ml was identified as cut-off value for predicting pulmonary congestion, defined by BLS >15. Conclusion Prevalence of pulmonary congestion as assessed by LUS and persistent or recurrent BLS >15 were quite prevalent findings in euvolemic HD patients. In the patients defined as rLUS+, a higher rate of cardiovascular events and hospital admissions was registered. BNP serum levels > 165 pg/ml resulted predictive of pulmonary congestion at LUS. In the dialysis care, regular LUS examination should be reasonably included among the methods useful to detect subclinical lung congestion and to adjust patients’ dry weight.

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The real-time environmental surveillance of large areas requires the ability to dislocate sensor networks. Generally, the probability of the occurrence of a pollution event depends on the burden of possible sources operating in the areas to be monitored. This implies a challenge for devising optimal real-time dislocation of wireless sensor networks. This challenge involves both hardware solutions and algorithms optimizing the displacements of mobile sensor networks in large areas with a vast number of sources of pollutant factors based mainly on diffusion mechanisms. In this paper, we present theoretical and simulated results inherent to a Voronoi partition approach for the optimized dislocation of a set of mobile wireless sensors with circular (radial) sensing power on large areas. The optimal deployment was found to be a variation of the generalized centroidal Voronoi configuration, where the Voronoi configuration is event-driven, and the centroid set of the corresponding generalized Voronoi cells changes as a function of the pollution event. The initial localization of the pollution events is simulated with a Poisson distribution. Our results could improve the possibility of reducing the costs for real-time surveillance of large areas, and other environmental monitoring when wireless sensor networks are involved.

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