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

LASER SCANNER TECHNOLOGY, GROUND-PENETRATING RADAR AND AUGMENTED REALITY FOR THE SURVEY AND RECOVERY OF ARTISTIC, ARCHAEOLOGICAL AND CULTURAL HERITAGE

In this study, using technologies such as laser scanner and GPR it was desired to see their potential in the cultural heritage. Also with regard to the processing part we are compared the results obtained by the various commercial software and algorithms developed and implemented in Matlab. Moreover, Virtual Reality and Augmented Reality allow integrating the real world with historical-artistic information, laser scanners and georadar (GPR) data and virtual objects, virtually enriching it with multimedia elements, graphic and textual information accessible through smartphones and tablets

LASER SCANNER TECHNOLOGY, GROUND-PENETRATING RADAR AND AUGMENTED REALITY FOR THE SURVEY AND RECOVERY OF ARTISTIC, ARCHAEOLOGICAL AND CULTURAL HERITAGE

In this study, using technologies such as laser scanner and GPR it was desired to see their potential in the cultural heritage. Also with regard to the processing part we are compared the results obtained by the various commercial software and algorithms developed and implemented in Matlab. Moreover, Virtual Reality and Augmented Reality allow integrating the real world with historical-artistic information, laser scanners and georadar (GPR) data and virtual objects, virtually enriching it with multimedia elements, graphic and textual information accessible through smartphones and tablets

The predictive role of serum and bronchoalveolar lavage cytokines and adhesion molecules for acute respiratory distress syndrome development and outcome

BACKGROUND: The predictive role of many cytokines and adhesion molecules has not been studied systematically in acute respiratory distress syndrome (ARDS). METHODS: We measured prospectively tumour necrosis factor alpha (TNF-α), interleukin (IL)-1, soluble vascular adhesion molecule-1 (VCAM-1) and soluble intercellular adhesion molecule-1 (ICAM-1) in serum and bronchoalveolar lavage fluid (BALF) within 2 hours following admission, in 65 patients. The patients were divided into: those fulfilling the criteria for ARDS (n = 23, group A), those who were pre-ARDS and who developed ARDS within 24 hours (n = 14, group B), and those on pre-ARDS but who never developed ARDS (n = 28, group C). RESULTS: All the measured molecules were only found at higher levels in the serum of patients that died either with or without ARDS (P < 0.05 – P < 0.0001). Patients at risk exhibited a good negative predictive value (NPV) of the measured molecules for ARDS development both in their serum (89 to 95%) and BALF (86 to 92%) levels. In contrast to BALF, serum levels of IL-1 and adhesion molecules exhibited a good NPV (68 to 96%), sensitivity (60 to 88%) and survival specificity (74 to 96%) in all groups. All molecules in serum and BALF IL-1 were correlated with the APACHE II (P < 0.05 – P < 0.0001). Serum and BALF IL-1 as well as BALF TNF-α were negatively correlated to PaO(2)/FiO(2) (all P < 0.05). CONCLUSIONS: The studied molecules have good NPV for ARDS development both in serum and BALF. Serum rather than BALF levels seem to be related to outcome

GBJOF: Gradient Boosting Integrated with Jaya Algorithm to Optimize the Features in Malware Analysis

Malware analysis is used to identify suspicious file transferring in the network. It can be identified efficiently by using the reverse engineering hybrid approach. Implementing a hybrid approach depends on the feature selection because the dataset contains static and dynamic parameters. The given dataset contains 85 attributes with 10 different class labels. Since it has high dimensional and multi-classification data, existing approaches of ML could be more efficient in reducing the features. The model combines the enhanced JAYA genetic algorithm with a gradient boosting technique to identify the efficiency and a smaller number of features. Many existing approaches for feature selection either implement correlation analysis or wrapper techniques. The major disadvantages of these issues are that they are facing fitting problems with a very small number of features. With the Usage of the genetic approach, this paper has achieved 95% accuracy with 12 features, approximately 7% greater than ML approaches