Rationale: Deep inspirations provide physiologic protection against airway narrowing in healthy subjects, which is impaired in asthma and chronic obstructive pulmonary disease (COPD). Airway inflammation has been suggested to alter airway mechanics during deep inspiration.
Objectives: We tested the hypothesis that the number of bronchial inflammatory cells is related to deep inspiration-induced bronchodilation in asthma and COPD.
Methods: In a cross-sectional study, three modified methacholine challenges were performed in 13 patients with mild, persistent asthma, 12 patients with mild to moderate COPD, and 12 healthy control subjects.
Measurements And Main Results: After a 20-minute period of deep inspiration avoidance, inhalation of methacholine was followed by either one or five deep inspirations, or preceded by five deep inspirations. The response to deep inspiration was measured by forced oscillation technique. Inflammatory cells were counted within the lamina propria and airway smooth muscle area in bronchial biopsies of patients with asthma and COPD. The reduction in expiratory resistance by one and five deep inspirations was significantly less in asthma (mean change+/-SD: -0.5+/-0.8 and -0.9+/-1.0 cm H2O/L/s, respectively) and COPD (+0.2+/-1.1 and +0.4+/-1.0 cm H2O/L/s, respectively) as compared with healthy subjects (-1.5+/-1.3 and -2.0+/-1.2 cm H2O/L/s, respectively; p=0.05 and p=0.001, respectively). In asthma, this was related to an increase in mast cell numbers within the airway smooth muscle area (r=0.73; p=0.03), and in CD4+ lymphocytes in the lamina propria (r=0.61; p=0.04).
Conclusions: Inflammation in the airway smooth muscle bundles and submucosa of bronchial biopsies is positively associated with impaired airway mechanics during deep inspiration in asthma, but not in COPD. Clinical trial registered with www.clinicaltrials.gov (NCT OO279136).
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http://dx.doi.org/10.1164/rccm.200612-1814OC | DOI Listing |
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ISA Trans
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Department of Electronics and Telecommunication, C. V. Raman Global University, Bhubaneswar 752054, Odisha, India. Electronic address:
Early and highly accurate detection of rapidly damaging deadly disease like Acute Lymphoblastic Leukemia (ALL) is essential for providing appropriate treatment to save valuable lives. Recent development in deep learning, particularly transfer learning, is gaining a preferred trend of research in medical image processing because of their admirable performance, even with small datasets. It inspires us to develop a novel deep learning-based leukemia detection system in which an efficient and lightweight MobileNetV2 is used in conjunction with ShuffleNet to boost discrimination ability and enhance the receptive field via convolution layer succession.
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Automatic crack detection is challenging, owing to the complex and thin topologies, diversity, and background noises of cracks. Inspired by the wavelet theory, we present an instance normalization wavelet (INW) layer and embed the layer into the deep model for segmentation. The proposed layer employs prior knowledge in the wavelets to capture the crack features and filter the high-frequency noises simultaneously, accelerating the convergence of model training.
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