Pathophysiological characterization of asthma transitions across adolescence

BACKGROUND: Adolescence is a period of change, which coincides with disease remission in a significant proportion of subjects with childhood asthma. There is incomplete understanding of the changing characteristics underlying different adolescent asthma transitions. We undertook pathophysiological characterization of transitional adolescent asthma phenotypes in a longitudinal birth cohort.METHODS: The Isle of Wight Birth Cohort (N = 1456) was reviewed at 1, 2, 4, 10 and 18-years. Characterization included questionnaires, skin tests, spirometry, exhaled nitric oxide, bronchial challenge and (in a subset of 100 at 18-years) induced sputum. Asthma groups were "never asthma" (no asthma since birth), "persistent asthma" (asthma at age 10 and 18), "remission asthma" (asthma at age 10 but not at 18) and "adolescent-onset asthma" (asthma at age 18 but not at age 10).RESULTS: Participants whose asthma remitted during adolescence had lower bronchial reactivity (odds ratio (OR) 0.30; CI 0.10 -0.90; p = 0.03) at age 10 plus greater improvement in lung function (forced expiratory flow 25-75% gain: 1.7 L; 1.0-2.9; p = 0.04) compared to persistent asthma by age 18. Male sex (0.3; 0.1-0.7; p &lt; 0.01) and lower acetaminophen use (0.4; 0.2-0.8; p &lt; 0.01) independently favoured asthma remission, when compared to persistent asthma. Asthma remission had a lower total sputum cell count compared to never asthma (31.5 [25-75 centiles] 12.9-40.4) vs. 47.0 (19.5-181.3); p = 0.03). Sputum examination in adolescent-onset asthma showed eosinophilic airway inflammation (3.0%, 0.7-6.6), not seen in persistent asthma (1.0%, 0-3.9), while remission group had the lowest sputum eosinophil count (0.3%, 0-1.4) and lowest eosinophils/neutrophils ratio of 0.0 (Interquartile range: 0.1).CONCLUSION: Asthma remission during adolescence is associated with lower initial BHR and greater gain in small airways function, while adolescent-onset asthma is primarily eosinophilic.</p

Symptoms and management of cow's milk allergy: perception and evidence

IntroductionThe diagnosis and management of cow's milk allergy (CMA) is a topic of debate and controversy. Our aim was to compare the opinions of expert groups from the Middle East (n = 14) and the European Society of Paediatric Gastroenterology, Hepatology and Nutrition (ESPGHAN) (n = 13).MethodsThese Expert groups voted on statements that were developed by the ESPGHAN group and published in a recent position paper. The voting outcome was compared.ResultsOverall, there was consensus amongst both groups of experts. Experts agreed that symptoms of crying, irritability and colic, as single manifestation, are not suggestive of CMA. They agreed that amino-acid based formula (AAF) should be reserved for severe cases (e.g., malnutrition and anaphylaxis) and that there is insufficient evidence to recommend a step-down approach. There was no unanimous consensus on the statement that a cow's milk based extensively hydrolysed formula (eHF) should be the first choice as a diagnostic elimination diet in mild/moderate cases. Although the statements regarding the role for hydrolysed rice formula as a diagnostic and therapeutic elimination diet were accepted, 3/27 disagreed. The votes regarding soy formula highlight the differences in opinion in the role of soy protein in CMA dietary treatment. Generally, soy-based formula is seldom available in the Middle-East region. All ESPGHAN experts agreed that there is insufficient evidence that the addition of probiotics, prebiotics and synbiotics increase the efficacy of elimination diets regarding CMA symptoms (despite other benefits such as decrease of infections and antibiotic intake), whereas 3/14 of the Middle East group thought there was sufficient evidence.DiscussionDifferences in voting are related to geographical, cultural and other conditions, such as cost and availability. This emphasizes the need to develop region-specific guidelines considering social and cultural conditions, and to perform further research in this area

Automatic documents summarization using ontology based methodologies

When humans summarize a document they usually read the text first, understand it then attempt to write a summary. In essence, these processes require at least some basic level of background knowledge by the reader. The least of which would be the Natural Language the text is written in. In this thesis, an attempt is made to bridge the gap of machines understanding by proposing a framework backed with knowledge repositories constructed by humans and containing real human concepts. I use WordNet, a hierarchically-structured repository that was created by linguistic experts and is rich in its explicitly defined lexical relations. With WordNet, algorithms for computing the semantic similarity between terms were proposed and implemented. These algorithms were especially useful when applied to the application of Automatic Documents Summarization as shown with the obtained evaluation results. I also use Wikipedia, the largest encyclopedia to date. Because of its openness and structure, three problems had to be handled in this thesis: Extracting knowledge and features from Wikipedia, enriching the representation of text documents with the extracted features, and using them in the application of Automatic Summarization. When applying the features extractor to a summarization system, competitive evaluation results were obtained

A schema exploration approach for document-oriented data using unsupervised techniques

For more than 40 years, relational data was the dominant force in the world ofstoring and managing data. However, the complex and strict data model provided by the relational data then started to lose some ground, especially when the data requirements of applications frequently change, thus requiring a flexible data model. Contemporary applications favour less strict data models that could be described as semi-structured or unstructured. These kinds of data are gaining popularity among database developers. For instance, the amount of document oriented data available on the Web is rising as time passes. A great portion of this data comes from Web APIs. Although document-oriented data is widely used, it cannot be easily consumed or analysed if it lacks description (i.e., metadata) or schemas that explain their internal structure. Schemas are used to make datasets more understandable and easier to query and analyse.Based on our literature review, we found out that current initiatives and tools available for JSON documents in particular, do not provide comprehensive summaries that explore the internal structure of the documents. Our research builds on current work on JSON schema inference by addressing specified research gaps related to extracting accurate and explicit summaries of the internal structure of the JSON documents. This research aims to provide structural summaries to help in understanding JSON documents by explicitly extracting schemas and analysing their attributes. Our approach firstly infers all attributes from a JSON dataset and then applies a clustering algorithm to the documents in order to identify unique schemas; finally, based on the resulting schemas from the clustering process, we apply statistical analysis on the attributes to generate useful summaries by detecting common and schema-specific attributes. The approach is proved and evaluated through real-world and synthetic data collected from the Web in different domains