Sepsis-associated delirium: the pro and con of C5a blockade

The intimate mechanisms of sepsis-induced delirium are unknown. Among the potential contributing factors, the breakdown of the blood–brain barrier is considered a key determinant of brain dysfunction. The complement activation is paramount to an appropriate activation of the central nervous system during stress. C3a and C5a have been extensively studied and may be involved in sepsis-induced delirium. Here we discuss the pro and con for inhibiting C5a to attenuate brain damage during sepsis. In particular, we discuss the hypothesis that C5a increased blood–brain barrier permeability amy ease the brain to mount an appropriate response to sepsis. Thus, blockade of C5a may be detrimental, resulting in an attenuated response of the stress system

Corticosteroids for community-acquired pneumonia: time to act!

The use of corticosteroids for the treatment of community-acquired pneumonia has been reported for almost 50 years. A recent systematic analysis of the relevant literature suggested that corticosteroids reduce the critical illness associated with community-acquired pneumonia. There is little doubt that a prolonged administration of a moderate dose of corticosteroids may alleviate the systemic inflammatory response and subsequent organ dysfunction in severe infection. Whether these favorable effects on morbidity may translate into better survival and quality of life needs to be addressed in additional adequately powered randomized controlled trials

SecNetworkCloudSim: An Extensible Simulation Tool for Secure Distributed Mobile Applications

Fueled by the wide interest for achieving rich-storage services with the lowest possible cost, cloud computing has emerged into a highly desired service paradigm extending well beyond Virtualization technology. The next generation of mobile cloud services is now manipulated more and more sensitive data on VM-based distributed applications. Therefore, the need to secure sensitive data over mobile cloud computing is more evident than ever. However, despite the widespread release of several cloud simulators, controlling user’s access and protecting data exchanges in distributed mobile applications over the cloud is considered a major challenge. This paper introduces a new NetworkCloudSim extension named SecNetworkCloudSim, a secure mobile simulation tool which is deliberately designed to ensure the preservation of confidential access to data hosted on mobile device and distributed cloud’s servers. Through high-level mobile users’ requests, users connect to an underlying proxy which is considered an important layer in this new simulator, where users perform secure authentication access to cloud services, allocate their tasks in secure VM-based policy, manage automatically the data confidentiality among VMs and derive high efficiency and coverage rates. Most importantly, due to the secure nature of proxy, user’s distributed tasks can be executed without alterations on different underlying proxy’s security policies. We implement a scenario of follow-up healthcare distributed application using the new extension

Building an effective and efficient background knowledge resource to enhance ontology matching

International audienceOntology matching is critical for data integration and interoperability. Original ontology matching approaches relied solely on the content of the ontologies to align. However, these approaches are less effective when equivalent concepts have dissimilar labels and are structured with different modeling views. To overcome this semantic heterogeneity, the community has turned to the use of external background knowledge resources. Several methods have been proposed to select ontologies, other than the ones to align, as background knowledge to enhance a given ontology-matching task. However, these methods return a set of complete ontologies, while, in most cases, only fragments of the returned ontologies are effective for discovering new mappings. In this article, we propose an approach to select and build a background knowledge resource with just the right concepts chosen from a set of ontologies, which improves efficiency without loss of effectiveness. The use of background knowledge in ontology matching is a double-edged sword: while it may increase recall (i.e., retrieve more correct mappings), it may lower precision (i.e., produce more incorrect mappings). Therefore, we propose two methods to select the most relevant mappings from the candidate ones: (1)~a selection based on a set of rules and (2)~a selection based on supervised machine learning. Our experiments, conducted on two Ontology Alignment Evaluation Initiative (OAEI) datasets, confirm the effectiveness and efficiency of our approach. Moreover, the F-measure values obtained with our approach are very competitive to those of the state-of-the-art matchers exploiting background knowledge resources