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Background: Coronavirus disease 2019-associated acute respiratory distress syndrome (COVID-19 ARDS) seems to differ from the “classic ARDS”, showing initial significant hypoxemia in the face of relatively preserved compliance and evolving later in a scenario of poorly compliant lungs. We tested the hypothesis that in patients with COVID-19 ARDS, the initial value of static compliance of respiratory system (Crs) (1) depends on the previous duration of the disease (i.e., the fewer days of illness, the higher the Crs and vice versa) and (2) identifies different lung patterns of time evolution and response to prone positioning. Methods: This was a single-center prospective observational study. We enrolled consecutive mechanically ventilated patients with a diagnosis of COVID-19 who met ARDS criteria, admitted to intensive care unit (ICU). Patients were divided in four groups based on quartiles of initial Crs. Relationship between Crs and the previous duration of the disease was evaluated. Respiratory parameters collected once a day and during prone positioning were compared between groups. Results: We evaluated 110 mechanically ventilated patients with a diagnosis of COVID-19 who met ARDS criteria admitted to our ICUs. Patients were divided in groups based on quartiles of initial Crs. The median initial Crs was 41 (32–47) ml/cmH2O. No association was found between the previous duration of the disease and the initial Crs. The Crs did not change significantly over time within each quartile. Positive end-expiratory pressure (PEEP) and driving pressure were respectively lower and greater in patients with lower Crs. Prone positioning significantly improved PaO2/FiO2 in the 4 groups, however it increased the Crs significantly only in patients in lower quartile of Crs. Conclusions: In our cohort, the initial Crs is not dependent on the previous duration of COVID-19 disease. Prone positioning improves oxygenation irrespective to initial Crs, but it ameliorates respiratory mecha

An error in the reporting of one affiliation. Alberto Mantovani’s is affiliated with 4. Humanitas Clinical and Research Center – IRCCS, Milan, Italy instead of 1. Postgraduate School of Respiratory Medicine, University of Milan as stated in the initial publication.

Location based queries are increasingly common in mobile applications, and the associated privacy issues have become a hot research topic in the last years. Most of the current approaches, however, do not account for the location of potentially sensitive places and for constraints on the movement of users, such as speed limits or network contraints. In this demo we present different deployment scenarios of a privacy-preserving framework for the protection of sensitive positions in real time trajectories. We assume that the sensitivity of users' positions depends on the spatial context, while the users' movement is confined to road networks and places. Further, the users are non-anonymous, as in the case of geo-social network members who agree to share their exact position whenever it does not fall within a sensitive place, e.g. a hospital. We will show that our proposal is suitable for different classes of devices and can be integrated in different kind of location based applications. © 2012 IEEE., IBM;MICS;Indian Institute of Technology;Microsoft Research;Bell Labs

This paper presents a privacy-preserving framework for the protection of sensitive positions in real time trajectories. We assume a scenario in which the sensitivity of user's positions is space-varying, and so depends on the spatial context, while the user's movement is confined to road networks and places. Typical users are the non-anonymous members of a geo-social network who agree to share their exact position whenever such position does not fall within a sensitive place, e.g. a hospital. Suspending location sharing while the user is inside a sensitive place is not an appropriate solution because the user's stopovers can be easily inferred from the user's trace. In this paper we present an extension of the semantic location cloaking model [1] originally developed for the cloaking of non-correlated positions in an unconstrained space. We investigate different algorithms for the generation of cloaked regions over the graph representing the urban setting. We also integrate methods to prevent velocity based linkage attacks. Finally we evaluate experimentally the algorithms using a real data set. © 2012 IEEE., IBM;MICS;Indian Institute of Technology;Microsoft Research;Bell Labs

This paper presents a privacy-preserving framework for the protection of sensitive positions in real time trajectories. We assume a scenario in which the sensitivity of user's positions is space-varying, and so depends on the spatial context, while the user's movement is confined to road networks and places. Typical users are the non-anonymous members of a geo-social network who agree to share their exact position whenever such position does not fall within a sensitive place, e.g. a hospital. Suspending location sharing while the user is inside a sensitive place is not an appropriate solution because the user's stopovers can be easily inferred from the user's trace. In this paper we present an extension of the semantic location cloaking model [1] originally developed for the cloaking of non-correlated positions in an unconstrained space. We investigate different algorithms for the generation of cloaked regions over the graph representing the urban setting. We also integrate methods to prevent velocity based linkage attacks. Finally we evaluate experimentally the algorithms using a real data set. © 2012 IEEE., IBM;MICS;Indian Institute of Technology;Microsoft Research;Bell Labs

Location based queries are increasingly common in mobile applications, and the associated privacy issues have become a hot research topic in the last years. Most of the current approaches, however, do not account for the location of potentially sensitive places and for constraints on the movement of users, such as speed limits or network contraints. In this demo we present different deployment scenarios of a privacy-preserving framework for the protection of sensitive positions in real time trajectories. We assume that the sensitivity of users' positions depends on the spatial context, while the users' movement is confined to road networks and places. Further, the users are non-anonymous, as in the case of geo-social network members who agree to share their exact position whenever it does not fall within a sensitive place, e.g. a hospital. We will show that our proposal is suitable for different classes of devices and can be integrated in different kind of location based applications. © 2012 IEEE., IBM;MICS;Indian Institute of Technology;Microsoft Research;Bell Labs