90 research outputs found

    Asthma and Wheezing in Childhood: perinatal risk factors and early detection

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    The prevalence of childhood asthma and atopic disease have increased dramatically during the end of the last century, especially in Western countries.1 Presently, asthma is the most frequent chronic disorder in childhood, with a high burden in terms of morbidity, health care costs, absenteeism from school, and reduced quality of life, despite the availability of effective and safe treatment.2 Two major challenges in the fi eld of childhood asthma, have still been insuffi ciently addressed. In this thesis we focused on both these issues

    Doing science: how to get credit for your scientific work.

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    Everyone deserves to be acknowledged for their efforts and contributions to a shared goal, and getting credit for your scientific work should be part of a natural process and should be fair and straightforward. However, credit cannot be objectively measured despite it having a big influence and, unfortunately, getting appropriate credit can occasionally be both complicated and challenging

    The development of bronchiectasis on chest computed tomography in children with cystic fibrosis: can pre-stages be identified?

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    Objective: Bronchiectasis is an important component of cystic fibrosis (CF) lung disease but little is known about its development. We aimed to study the development of bronchiectasis and identify determinants for rapid progression of bronchiectasis on chest CT. Methods: Forty-three patients with CF with at least four consecutive biennial volumetric CTs were included. Areas with bronchiectasis on the most recent CT were marked as regions of interest (ROIs). These ROIs were generated on all preceding CTs using deformable image registration. Observers indicated whether: bronchiectasis, mucus plugging, airway wall thickening, atelectasis/consolidation or normal airways were present in the ROIs. Results: We identified 362 ROIs on the most recent CT. In 187 (51.7 %) ROIs bronchiectasis was present on all preceding CTs, while 175 ROIs showed development of bronchiectasis. In 139/175 (79.4 %) no pre-stages of bronchiectasis were identified. In 36/175 (20.6 %) bronchiectatic airways the following pre-stages were identified: mucus plugging (17.7 %), airway wall thickening (1.7 %) or atelectasis/consolidation (1.1 %). Pancreatic insufficiency was more prevalent in the rapid progressors compared to the slow progressors (p = 0.05). Conclusion: Most bronchiectatic airways developed within 2 years without visible pre-stages, underlining the treacherous nature of CF lung disease. Mucus plugging was the most frequent pre-stage. Key Points: • Development of bronchiectasis in cystic fibrosis lung disease on CT.• Most bronchiectatic airways developed within 2 years without pre-stages.• The most frequently identified pre-stage was mucus plugging.• This study underlines the treacherous nature of CF lung disease

    Automatic analysis of bronchus-artery dimensions to diagnose and monitor airways disease in cystic fibrosis

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    Background:Cystic fibrosis (CF) lung disease is characterised by progressive airway wall thickening and widening. We aimed to validate an artificial intelligence-based algorithm to assess dimensions of all visible bronchus-artery (BA) pairs on chest CT scans from patients with CF.Methods:The algorithm fully automatically segments the bronchial tree; identifies bronchial generations; matches bronchi with the adjacent arteries; measures for each BA-pair bronchial outer diameter (Bout), bronchial lumen diameter (Bin), bronchial wall thickness (Bwt) and adjacent artery diameter (A); and computes Bout/A, Bin/A and Bwt/A for each BA pair from the segmental bronchi to the last visible generation. Three datasets were used to validate the automatic BA analysis. First BA analysis was executed on 23 manually annotated CT scans (11 CF, 12 control subjects) to compare automatic with manual BA-analysis outcomes. Furthermore, the BA analysis was executed on two longitudinal datasets (Copenhagen 111 CTs, ataluren 347 CTs) to assess longitudinal BA changes and compare them with manual scoring results.Results:The automatic and manual BA analysis showed no significant differences in quantifying bronchi. For the longitudinal datasets the automatic BA analysis detected 247 and 347 BA pairs/CT in the Copenhagen and ataluren dataset, respectively. A significant increase of 0.02 of Bout/A and Bin/A was detected for Copenhagen dataset over an interval of 2 years, and 0.03 of Bout/A and 0.02 of Bin/A for ataluren dataset over an interval of 48 weeks (all p<0.001). The progression of 0.01 of Bwt/A was detected only in the ataluren dataset (p<0.001). BA-analysis outcomes showed weak to strong correlations (correlation coefficient from 0.29 to 0.84) with manual scoring results for airway disease.Conclusion:The BA analysis can fully automatically analyse a large number of BA pairs on chest CTs to detect and monitor progression of bronchial wall thickening and bronchial widening in patients with CF

    The second year has been completed

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    Among many other things, the last European Respiratory Society (ERS) International Congress in Munich brought changes to the ERS Junior Members Committee (JMC). The 3-year term of JMC representatives has seen Indre Butiene, who initiated the Committee 3 years ago, finish her tenure as chair, with Anders Bjerg, respiratory epidemiologist from Gothenburg, Sweden, being elected as her replacement. Indre’s departure has also led to the election of a new representative to the ERS Education Council. We congratulate Agnes Boots from the Netherlands on her election to this important position! Also, here in Breathe, the Doing Science series has been taken over by Georgia Hardavella, UK, whose ideas will take this practical educational series to new levels in 2015. The Hot Topics section is now coordinated by Neil Saad, UK, one of many Juniors outside the JMC who have volunteered for different JMC activities

    Distinguishing Asthma Phenotypes Using Machine Learning Approaches.

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    Asthma is not a single disease, but an umbrella term for a number of distinct diseases, each of which are caused by a distinct underlying pathophysiological mechanism. These discrete disease entities are often labelled as asthma endotypes. The discovery of different asthma subtypes has moved from subjective approaches in which putative phenotypes are assigned by experts to data-driven ones which incorporate machine learning. This review focuses on the methodological developments of one such machine learning technique-latent class analysis-and how it has contributed to distinguishing asthma and wheezing subtypes in childhood. It also gives a clinical perspective, presenting the findings of studies from the past 5 years that used this approach. The identification of true asthma endotypes may be a crucial step towards understanding their distinct pathophysiological mechanisms, which could ultimately lead to more precise prevention strategies, identification of novel therapeutic targets and the development of effective personalized therapies
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