Blood eosinophil percentage as a predictor of response to inhaled corticosteroid in bronchiectasis

Abstract Introduction The role of inhaled corticosteroid (ICS) among patients with bronchiectasis remains controversial. There is limited evidence of using baseline eosinophil count (absolute and percentage) as a marker to predict the role of ICS among patients with bronchiectasis. Methods A retrospective case–control study was conducted in a major regional hospital and tertiary respiratory referral centre in Hong Kong, including 140 Chinese patients with noncystic fibrosis (CF) bronchiectasis, to investigate the exacerbation risks of bronchiectasis among ICS users and nonusers with different baseline eosinophil counts. Results ICS user had significantly lower risk to develop bronchiectasis exacerbation with adjusted odds ratio (OR) of 0.461 (95% confidence interval [CI] 0.225–0.945, p‐value 0.035). Univariate logistic regression was performed for different cut‐offs of blood eosinophil count (by percentage) from 2% to 4% (with a 0.5% grid each time). Baseline eosinophil 3.5% was found to be the best cut‐off among all with adjusted OR of 0.138 (95% CI = 0.023–0.822, p‐value = 0.030). Conclusion Baseline eosinophil count of 3.5% might serve as a marker to predict the benefits of ICS on exacerbation risk among patients with non‐CF bronchiectasis

NaviAirway: a Bronchiole-sensitive Deep Learning-based Airway Segmentation Pipeline

Airway segmentation is essential for chest CT image analysis. However, it remains a challenging task because of the intrinsic complex tree-like structure and imbalanced sizes of airway branches. Current deep learning-based methods focus on model structure design while the potential of training strategy and loss function have not been fully explored. Therefore, we present a simple yet effective airway segmentation pipeline, denoted NaviAirway, which finds finer bronchioles with a bronchiole-sensitive loss function and a human-vision-inspired iterative training strategy. Experimental results show that NaviAirway outperforms existing methods, particularly in identification of higher generation bronchioles and robustness to new CT scans. Besides, NaviAirway is general. It can be combined with different backbone models and significantly improve their performance. Moreover, we propose two new metrics (Branch Detected and Tree-length Detected) for a more comprehensive and fairer evaluation of deep learning-based airway segmentation approaches. NaviAirway can generate airway roadmap for Navigation Bronchoscopy and can also be applied to other scenarios when segmenting fine and long tubular structures in biomedical images. The code is publicly available on https://github.com/AntonotnaWang/NaviAirway

Description and performance of track and primary-vertex reconstruction with the CMS tracker

A description is provided of the software algorithms developed for the CMS tracker both for reconstructing charged-particle trajectories in proton-proton interactions and for using the resulting tracks to estimate the positions of the LHC luminous region and individual primary-interaction vertices. Despite the very hostile environment at the LHC, the performance obtained with these algorithms is found to be excellent. For tbar t events under typical 2011 pileup conditions, the average track-reconstruction efficiency for promptly-produced charged particles with transverse momenta of p(T) > 0.9GeV is 94% for pseudorapidities of |η| < 0.9 and 85% for 0.9 < |η| < 2.5. The inefficiency is caused mainly by hadrons that undergo nuclear interactions in the tracker material. For isolated muons, the corresponding efficiencies are essentially 100%. For isolated muons of p(T) = 100GeV emitted at |η| < 1.4, the resolutions are approximately 2.8% in p(T), and respectively, 10μm and 30μm in the transverse and longitudinal impact parameters. The position resolution achieved for reconstructed primary vertices that correspond to interesting pp collisions is 10–12μm in each of the three spatial dimensions. The tracking and vertexing software is fast and flexible, and easily adaptable to other functions, such as fast tracking for the trigger, or dedicated tracking for electrons that takes into account bremsstrahlung