Allergic asthma exhaled breath metabolome: a challenge for comprehensive two-dimensional gas chromatography

Allergic asthma represents an important public health issue, most common in the paediatric population, characterized by airway inflammation that may lead to changes in volatiles secreted via the lungs. Thus, exhaled breath has potential to be a matrix with relevant metabolomic information to characterize this disease. Progress in biochemistry, health sciences and related areas depends on instrumental advances, and a high throughput and sensitive equipment such as comprehensive two-dimensional gas chromatography–time of flight mass spectrometry (GC × GC–ToFMS) was considered. GC × GC–ToFMS application in the analysis of the exhaled breath of 32 children with allergic asthma, from which 10 had also allergic rhinitis, and 27 control children allowed the identification of several hundreds of compounds belonging to different chemical families. Multivariate analysis, using Partial Least Squares-Discriminant Analysis in tandem with Monte Carlo Cross Validation was performed to assess the predictive power and to help the interpretation of recovered compounds possibly linked to oxidative stress, inflammation processes or other cellular processes that may characterize asthma. The results suggest that the model is robust, considering the high classification rate, sensitivity, and specificity. A pattern of six compounds belonging to the alkanes characterized the asthmatic population: nonane, 2,2,4,6,6-pentamethylheptane, decane, 3,6-dimethyldecane, dodecane, and tetradecane. To explore future clinical applications, and considering the future role of molecular-based methodologies, a compound set was established to rapid access of information from exhaled breath, reducing the time of data processing, and thus, becoming more expedite method for the clinical purposes.

Using Neo4J geospatial data storage and integration

Online geographic-databases have been growing increasingly as they have become a crucial source of information for both social networks and safety-critical systems. Since the quality of such applications is largely related to the richness and completeness of their data, it becomes imperative to develop adaptable and persistent storage systems, able to make use of several sources of information as well as enabling the fastest possible response from them. This work will create a shared and extensible geographic model, able to retrieve and store information from the major spatial sources available. A geographic-based system also has very high requirements in terms of scalability, computational power and domain complexity, causing several difficulties for a traditional relational database as the number of results increases. NoSQL systems provide valuable advantages for this scenario, in particular graph databases which are capable of modeling vast amounts of inter-connected data while providing a very substantial increase of performance for several spatial requests, such as finding shortestpath routes and performing relationship lookups with high concurrency. In this work, we will analyze the current state of geographic information systems and develop a unified geographic model, named GeoPlace Explorer (GE). GE is able to import and store spatial data from several online sources at a symbolic level in both a relational and a graph databases, where several stress tests were performed in order to find the advantages and disadvantages of each database paradigm.