Airway Branches Research Articles

Structure and position-aware graph neural network for airway labeling

We present a novel graph-based approach for labeling the anatomical branches of a given airway tree segmentation. The proposed method formulates airway labeling as a branch classification problem in the airway tree graph, where branch features are extracted using convolutional neural networks and enriched using graph neural networks. Our graph neural network is structure-aware by having each node aggregate information from its local neighbors and position-aware by encoding node positions in the graph.We evaluated the proposed method on 220 airway trees from subjects with various severity stages of Chronic Obstructive Pulmonary Disease (COPD). The results demonstrate that our approach is computationally efficient and significantly improves branch classification performance than the baseline method. The overall average accuracy of our method reaches 91.18% for labeling 18 segmental airway branches, compared to 83.83% obtained by the standard CNN method and 87.37% obtained by the existing method. Furthermore, the reader study done on an additional set of 40 subjects shows that our algorithm performs comparably to human experts in labeling segmental-airways. We published our source code at https://github.com/DIAGNijmegen/spgnn. The proposed algorithm is also publicly available at https://grand-challenge.org/algorithms/airway-anatomical-labeling/.

Airway specific deregulation of asthma-related serpins impairs tracheal architecture and oxygenation in D. melanogaster

Serine proteases are important regulators of airway epithelial homeostasis. Altered serum or cellular levels of two serpins, Scca1 and Spink5, have been described for airway diseases but their function beyond antiproteolytic activity is insufficiently understood. To close this gap, we generated fly lines with overexpression or knockdown for each gene in the airways. Overexpression of both fly homologues of Scca1 and Spink5 induced the growth of additional airway branches, with more variable results for the respective knockdowns. Dysregulation of Scca1 resulted in a general delay in fruit fly development, with increases in larval and pupal mortality following overexpression of this gene. In addition, the morphological changes in the airways were concomitant with lower tolerance to hypoxia. In conclusion, the observed structural changes of the airways evidently had a strong impact on the airway function in our model as they manifested in a lower physical fitness of the animals. We assume that this is due to insufficient tissue oxygenation. Future work will be directed at the identification of key molecular regulators following the airway-specific dysregulation of Scca1 and Spink5 expression.

Computed tomography mucus plugs and airway tree structure in patients with chronic obstructive pulmonary disease: Associations with airflow limitation, health-related independence and mortality.

Mucus plugs and underlying airway tree structure can affect airflow limitation and prognosis in patients with chronic obstructive pulmonary disease (COPD), but their relative roles are unclear. This study used two COPD cohorts to examine whether mucus plugs on computed tomography (CT) were associated with airflow limitation and clinical outcomes independent of other airway structural changes and emphysema. Based on visual CT assessment, patients with mucus plugs in 0, 1-2 and ≥3 lung segments were assigned to no-, low- and high-mucus groups. Loss of health-related independence and mortality were prospectively recorded for 3 and 10 years in the Kyoto-Himeji and Hokkaido cohorts, respectively. The percentages of the wall area of the central airways (WA%), total airway count (TAC) and emphysema were quantified on CT. Of 199 and 96 patients in the Kyoto-Himeji and Hokkaido cohorts, 34% and 30%, respectively, had high mucus scores. In both cohorts, TAC was lower in the high-mucus group than in the no-mucus group, whereas their emphysema severity did not differ. High mucus score and low TAC were independently associated with airflow limitation after adjustment for WA% and emphysema. In multivariable models adjusted for WA% and emphysema, TAC, rather than mucus score, was associated with a greater rate of loss of independence, whereas high mucus score, rather than TAC, was associated with increased mortality. Mucus plugs and lower airway branch count on CT had distinct roles in airflow limitation, health-related independence and mortality in patients with COPD.

Fuzzy Attention Neural Network to Tackle Discontinuity in Airway Segmentation.

Airway segmentation is crucial for the examination, diagnosis, and prognosis of lung diseases, while its manual delineation is unduly burdensome. To alleviate this time-consuming and potentially subjective manual procedure, researchers have proposed methods to automatically segment airways from computerized tomography (CT) images. However, some small-sized airway branches (e.g., bronchus and terminal bronchioles) significantly aggravate the difficulty of automatic segmentation by machine learning models. In particular, the variance of voxel values and the severe data imbalance in airway branches make the computational module prone to discontinuous and false-negative predictions, especially for cohorts with different lung diseases. The attention mechanism has shown the capacity to segment complex structures, while fuzzy logic can reduce the uncertainty in feature representations. Therefore, the integration of deep attention networks and fuzzy theory, given by the fuzzy attention layer, should be an escalated solution for better generalization and robustness. This article presents an efficient method for airway segmentation, comprising a novel fuzzy attention neural network (FANN) and a comprehensive loss function to enhance the spatial continuity of airway segmentation. The deep fuzzy set is formulated by a set of voxels in the feature map and a learnable Gaussian membership function. Different from the existing attention mechanism, the proposed channel-specific fuzzy attention addresses the issue of heterogeneous features in different channels. Furthermore, a novel evaluation metric is proposed to assess both the continuity and completeness of airway structures. The efficiency, generalization, and robustness of the proposed method have been proved by training on normal lung disease while testing on datasets of lung cancer, COVID-19, and pulmonary fibrosis.

BranchLabelNet: Anatomical Human Airway Labeling Approach using a Dividing-and-Grouping Multi-Label Classification.

Anatomical airway labeling is crucial for precisely identifying airways displaying symptoms such as constriction, increased wall thickness, and modified branching patterns, facilitating the diagnosis and treatment of pulmonary ailments. This study introduces an innovative airway labeling methodology, BranchLabelNet, which accounts for the fractal nature of airways and inherent hierarchical branch nomenclature. In developing this methodology, branch-related parameters, including position vectors, generation levels, branch lengths, areas, perimeters, and more, are extracted from a dataset of 1000 chest computed tomography (CT) images. To effectively manage this intricate branch data, we employ an n-ary tree structure that captures the complicated relationships within the airway tree. Subsequently, we employ a divide-and-group deep learning approach for multi-label classification, streamlining the anatomical airway branch labeling process. Additionally, we address the challenge of class imbalance in the dataset by incorporating the Tomek Links algorithm to maintain model reliability and accuracy. Our proposed airway labeling method provides robust branch designations and achieves an impressive average classification accuracy of 95.94% across fivefold cross-validation. This approach is adaptable for addressing similar complexities in general multi-label classification problems within biomedical systems.

An end-to-end multi-scale airway segmentation framework based on pulmonary CT image

Objective. Automatic and accurate airway segmentation is necessary for lung disease diagnosis. The complex tree-like structures leads to gaps in the different generations of the airway tree, and thus airway segmentation is also considered to be a multi-scale problem. In recent years, convolutional neural networks have facilitated the development of medical image segmentation. In particular, 2D CNNs and 3D CNNs can extract different scale features. Hence, we propose a two-stage and 2D + 3D framework for multi-scale airway tree segmentation. Approach. In stage 1, we use a 2D full airway SegNet(2D FA-SegNet) to segment the complete airway tree. Multi-scale atros spatial pyramid and Atros Residual Skip connection modules are inserted to extract different scales feature. We designed a hard sample selection strategy to increase the proportion of intrapulmonary airway samples in stage 2. 3D airway RefineNet (3D ARNet) as stage 2 takes the results of stage 1 as a priori information. Spatial information extracted by 3D convolutional kernel compensates for the loss of in 2D FA-SegNet. Furthermore, we added false positive losses and false negative losses to improve the segmentation performance of airway branches within the lungs. Main results. We performed data enhancement on the publicly available dataset of ISICDM 2020 Challenge 3, and on which evaluated our method. Comprehensive experiments show that the proposed method has the highest dice similarity coefficient (DSC) of 0.931, and IoU of 0.871 for the whole airway tree and DSC of 0.699, and IoU of 0.543 for the intrapulmonary bronchi tree. In addition, 3D ARNet proposed in this paper cascaded with other state-of-the-art methods to increase detected tree length rate by up to 46.33% and detected tree branch rate by up to 42.97%. Significance. The quantitative and qualitative evaluation results show that our proposed method performs well in segmenting the airway at different scales.

A simple and economical method for unbiasedly quantifying the alveolar size of entire mouse lung tissue utilizing Hematoxylin and Eosin Staining

Alveolar, the smallest structural and functional units within the respiratory system, play a crucial role in maintaining lung function. Alveolar damage is a typical pathological hallmark of respiratory diseases. Nevertheless, there is currently no simple, rapid, economical, and unbiased method for quantifying alveolar size for entire lung tissue. Here, firstly, we conducted lung sample slicing based on the size, shape, and distribution of airway branches of different lobes. Next, we performed HE staining on different slices. Then, we provided an unbiased quantification of alveolar size using free software ImageJ. Through this protocol, we demonstrated that C57Bl/6 mice exhibit varying alveolar sizes among different lobes. Collectively, we provided a simple and unbiased method for a more comprehensive quantification of alveolar size in mice, which holds promise for a broader range of respiratory research using mouse models.

Insights from 3D modeling and fluid dynamics in COVID-19 pneumonia.

We address the lack of research regarding aerodynamic events behind respiratory distress at COVID-19. The use of chest CT enables quantification of pneumonia extent; however, there is a paucity of data regarding the impact of airflow changes. We reviewed 31 COVID-19 patients who were admitted in March 2020 with varying severity of pulmonary disease. Lung volumes were segmented and measured on CT images and patient-specific models of the lungs were created. Incompressible, laminar, and three-dimensional Navier-Stokes equations were used for the fluid dynamics (CFD) analyses of ten patients (five mild, five pneumonia). Of 31 patients, 17 were female, 18 had pneumonia, and 2 were deceased. Effective lung volume decreased in the general group, but the involvement of the right lung was prominent in dyspnea patients. CFD analyses revealed that the mass flow distribution was significantly distorted in pneumonia cases with diminished flow rate towards the right lung. In addition, the distribution of flow parameters showed mild group had less airway resistance with higher velocity (1.228 m/s vs 1.572 m/s) and higher static pressure values at airway branches (1.5112 Pa vs 1.3024 Pa). Therefore, we conclude that airway resistance and mass flow rate distribution are as important as the radiological involvement degree in defining the disease severity.

Two-stage contextual transformer-based convolutional neural network for airway extraction from CT images

Accurate airway segmentation from computed tomography (CT) images is critical for planning navigation bronchoscopy and realizing a quantitative assessment of airway-related chronic obstructive pulmonary disease (COPD). Existing methods face difficulty in airway segmentation, particularly for the small branches of the airway. These difficulties arise due to the constraints of limited labeling and failure to meet clinical use requirements in COPD. We propose a two-stage framework with a novel 3D contextual transformer for segmenting the overall airway and small airway branches using CT images. The method consists of two training stages sharing the same modified 3D U-Net network. The novel 3D contextual transformer block is integrated into both the encoder and decoder path of the network to effectively capture contextual and long-range information. In the first training stage, the proposed network segments the overall airway with the overall airway mask. To improve the performance of the segmentation result, we generate the intrapulmonary airway branch label, and train the network to focus on producing small airway branches in the second training stage. Extensive experiments were performed on in-house and multiple public datasets. Quantitative and qualitative analyses demonstrate that our proposed method extracts significantly more branches and longer lengths of the airway tree while accomplishing state-of-the-art airway segmentation performance. The code is available at https://github.com/zhaozsq/airway_segmentation.

Physiological impacts of computed tomography airway dysanapsis, fractal dimension, and branch count in asymptomatic never smokers.

Dysanapsis, a mismatch between airway tree caliber and lung size, contributes to a large variation in lung function on spirometry in healthy subjects. However, it remains unclear whether other morphological features of the airway tree could be associated with the variation in lung function independent of dysanapsis. This study used lung cancer screening chest computed tomography (CT) and spirometry data from asymptomatic never smokers. Dysanapsis and the complexity of airway tree geometry were quantified on CT by measuring airway to lung ratio (ALR) and airway fractal dimension (AFD). Moreover, total airway count (TAC), ratio of airway luminal surface area to volume (SA/V), longitudinal tapering and irregularity of the radius of the internal lumen from the central to peripheral airways (Tapering index and Irregularity index) were quantified. In 431 asymptomatic never smokers without a history of lung diseases, lower ALR was associated with lower forced expiratory volume in 1 s (FEV1) and FEV1/forced vital capacity (FEV1/FVC). The associations of ALR with AFD and TAC (r = 0.41 and 0.13) were weaker than the association between TAC and AFD (r = 0.64). In multivariable models adjusted for age, sex, height, and mean lung density, lower AFD and TAC were associated with lower FEV1 and FEV1/FVC independent of ALR, whereas SA/V and Tapering index were not. These results suggest that the smaller airway tree relative to a given lung size and the lower complexity of airway tree shape, including lower branch count, are independently associated with lower lung function in healthy subjects.NEW & NOTEWORTHY This study showed that fractal dimension and total airway count of the airway tree on computed tomography are associated with lung function on spirometry independent of a smaller airway for a given lung size (dysanapsis) in asymptomatic never smokers without a history of lung diseases. In addition to dysanapsis, the morphometric complexity of the airway tree and the airway branch count may cause a substantial variation of lung function in these subjects.

Proteomic Profiling of Hypoplastic Lungs Suggests an Underlying Inflammatory Response in the Pathogenesis of Abnormal Lung Development in Congenital Diaphragmatic Hernia.

The pathogenesis of lung hypoplasia in congenital diaphragmatic hernia (CDH), a common birth defect, is poorly understood. The diaphragmatic defect can be repaired surgically, but the abnormal lung development contributes to a high mortality in these patients. To understand the underlying pathobiology, we compared the proteomic profiles of fetal rat lungs at the alveolar stage (E21) that were either exposed to nitrofen in utero (CDH lungs, n=5) or exposed to vehicle only (non-CDH control lungs, n=5). Pathway analysis of proteomic datasets showed significant enrichment in inflammatory response proteins associated with cytokine signaling and Epstein Barr Virus in nitrofen CDH lungs. Among the 218 significantly altered proteins between CDH and non-CDH control lungs were Tenascin C, CREBBP, LYN, and STAT3. We showed that Tenascin C was decreased around the distal airway branches in nitrofen rat lungs and human CDH lungs, obtained from stillborn fetuses that did not receive pre- or postnatal treatment. In contrast, STAT3 was significantly increased in the airway epithelium of nitrofen lungs at E21. STAT3 inhibition after direct nitrofen exposure to fetal rat lung explants (E14.5) partially rescued the hypoplastic lung phenotype ex vivo by increasing peripheral lung budding. Moreover, we demonstrated that several STAT3-associated cytokines (IL-15, IL-9, andIL-2) are increased in fetal tracheal aspirates of CDH survivors compared with nonsurvivors after fetoscopic endoluminal tracheal occlusion. With our unbiased proteomics approach, we showed for the first time that downstream inflammatory processes are likely involved in the pathogenesis of abnormal lung development in CDH.

Abstract 2202: Contiguous airway sampling for basal cell isolation

Abstract Introduction: Airway basal cells isolated from histologically normal epithelium in current and former smokers demonstrate a significantly higher mutation burden than those in never-smokers. However, a proportion of basal cells with lower (“near normal”) mutation burden are observed, which show preferential expansion in response to smoking cessation(1). We believe this expansion represents a “cancer protective” process, reducing the risk of future malignancy. However, mechanisms determining the dynamic balance of high versus low mutant basal cells throughout the bronchial tree are unclear. We have developed protocols that enable systematic scanning of airway epithelium in resected lung specimens, through which we aim to elucidate the mutation burden and spatial clonal architecture across contiguous regions of normal epithelium through the bronchial tree. Study design: 5-10 current or former smokers undergoing curative lobectomy for peripheral lung cancer with histologically normal airways will be consented to the study. To date, two participants have donated their samples. Fresh lobectomies were collected at the point of resection and kept on wet ice until pathological examination. All samples undergo examination within 1 hour of resection. The lobe is orientated, and the tumour and segmental bronchi identified. A sterile metal cannula is inserted through the airway lumen and sterile pathology scissors are used to carefully isolate the airways from the surrounding parenchyma. Upon macro-dissection, airway branches are immediately placed into transport media containing antibiotics. Samples are gently washed using transport media to remove residual blood, then sectioned into 1 cm3 segments, which are orientated, embedded and frozen in room-temperature Optimal Cutting Temperature compound. An 8 μM section is cut and stained with haematoxylin and eosin from each block immediately after a “full-face” tissue confirmation is observed.These will be used as guiding slides for laser capture microdissection of the regions of epithelium prior to downstream sequencing pathway. Conclusion: We have developed a pathological approach in which we can define somatic mutations in normal epithelium along the bronchial tree. We will use these data to determine basal cell clonal relationships and interpret these in a spatial context, creating a more detailed map of the smoking-induced changes that occur in the airway epithelium and how these may contribute to early carcinogenesis.

Coarse-to-fine airway segmentation using multi information fusion network and CNN-based region growing

Background and Objectives: Automatic airway segmentation from chest computed tomography (CT) scans plays an important role in pulmonary disease diagnosis and computer-assisted therapy. However, low contrast at peripheral branches and complex tree-like structures remain as two mainly challenges for airway segmentation. Recent research has illustrated that deep learning methods perform well in segmentation tasks. Motivated by these works, a coarse-to-fine segmentation framework is proposed to obtain a complete airway tree.Methods: Our framework segments the overall airway and small branches via the multi-information fusion convolution neural network (Mif-CNN) and the CNN-based region growing, respectively. In Mif-CNN, atrous spatial pyramid pooling (ASPP) is integrated into a u-shaped network, and it can expend the receptive field and capture multi-scale information. Meanwhile, boundary and location information are incorporated into semantic information. These information are fused to help Mif-CNN utilize additional context knowledge and useful features. To improve the performance of the segmentation result, the CNN-based region growing method is designed to focus on obtaining small branches. A voxel classification network (VCN), which can entirely capture the rich information around each voxel, is applied to classify the voxels into airway and non-airway. In addition, a shape reconstruction method is used to refine the airway tree.Results: We evaluate our method on a private dataset and a public dataset from EXACT09. Compared with the segmentation results from other methods, our method demonstrated promising accuracy in complete airway tree segmentation. In the private dataset, the Dice similarity coefficient (DSC), Intersection over Union (IoU), false positive rate (FPR), and sensitivity are 93.5%, 87.8%, 0.015%, and 90.8%, respectively. In the public dataset, the DSC, IoU, FPR, and sensitivity are 95.8%, 91.9%, 0.053% and 96.6%, respectively.Conclusion: The proposed Mif-CNN and CNN-based region growing method segment the airway tree accurately and efficiently in CT scans. Experimental results also demonstrate that the framework is ready for application in computer-aided diagnosis systems for lung disease and other related works.

Automatic triangulated mesh generation of pulmonary airways from segmented lung 3DCTs for computational fluid dynamics.

Computational fluid dynamics (CFD) of lung airflow during normal and pathophysiological breathing provides insight into regional pulmonary ventilation. By integrating CFD methods with 4D lung imaging workflows, regions of normal pulmonary function can be spared during treatment planning. To facilitate the use of CFD simulations in a clinical setup, a robust, automated, and CFD-compliant airway mesh generation technique is necessary. We define a CFD-compliant airway mesh to be devoid of blockages of airflow and leaks in the airway path, both of which are caused by airway meshing errors that occur when using conventional meshing techniques. We present an algorithm to create a CFD-compliant airway mesh in an automated manner. Beginning with a medial skeleton of the airway segmentation, the branches were tracked, and 3D points at which bifurcations occur were identified. Airway branches and bifurcation features were isolated to allow for automated and careful meshing that considered their anatomical nature. We present the meshing results from three state-of-the-art tools and compare them with the meshes generated by our algorithm. The results show that fully CFD-compliant meshes were automatically generated for an ideal geometry and patient-specific CT scans. Using an open-source smoothed-particle hydrodynamics CFD implementation, we compared the airflow using our approach and conventionally generated airway meshes. Our meshing algorithm was able to successfully generate a CFD-compliant mesh from pre-segmented lung CT scans, providing an automatic meshing approach that enables interventional CFD simulations to guide lung procedures such as radiotherapy or lung volume reduction surgery.

Conserved Mechanisms in the Formation of the Airways and Alveoli of the Lung.

Branching is an intrinsic property of respiratory epithelium that can be induced and modified by signals emerging from the mesenchyme. However, during stereotypic branching morphogenesis of the airway, the relatively thick upper respiratory epithelium extrudes through a mesenchymal orifice to form a new branch, whereas during alveologenesis the relatively thin lower respiratory epithelium extrudes to form sacs or bubbles. Thus, both branching morphogenesis of the upper airway and alveolarization in the lower airway seem to rely on the same fundamental physical process: epithelial extrusion through an orifice. Here I propose that it is the orientation and relative stiffness of the orifice boundary that determines the stereotypy of upper airway branching as well as the orientation of individual alveolar components of the gas exchange surface. The previously accepted dogma of the process of alveologenesis, largely based on 2D microscopy, is that alveoli arise by erection of finger-like interalveolar septae to form septal clefts that subdivide pre-existing saccules, a process for which the contractile properties of specialized alveolar myofibroblasts are necessary. Here I suggest that airway tip splitting and stereotypical side domain branching are actually conserved processes, but modified somewhat by evolution to achieve both airway tip splitting and side branching of the upper airway epithelium, as well as alveologenesis. Viewed in 3D it is clear that alveolar “septal tips” are in fact ring or purse string structures containing elastin and collagen that only appear as finger like projections in cross section. Therefore, I propose that airway branch orifices as well as alveolar mouth rings serve to delineate and stabilize the budding of both airway and alveolar epithelium, from the tips and sides of upper airways as well as from the sides and tips of alveolar ducts. Certainly, in the case of alveoli arising laterally and with radial symmetry from the sides of alveolar ducts, the mouth of each alveolus remains within the plane of the side of the ductal lumen. This suggests that the thin epithelium lining these lateral alveolar duct buds may extrude or “pop out” from the duct lumen through rings rather like soap or gum bubbles, whereas the thicker upper airway epithelium extrudes through a ring like toothpaste from a tube to form a new branch.

Low-frequency Intrapulmonary Percussive Ventilation Increases Aerosol Penetration in a 2-Compartment Physical Model of Fibrotic Lung Disease

In patients with fibrotic pulmonary disease such as idiopathic pulmonary fibrosis (IPF), inhaled aerosols deposit mostly in the les affected region of the lung, resulting in suboptimal pharmacokinetics of airway-delivered treatments. Refinement of aerosol delivery technique requires new models to simulate the major alterations of lung physiology associated with IPF, i.e. heterogeneously reduced lung compliance and increased airway caliber. A novel physical model of the respiratory system was constructed to simulate aerosol drug delivery in spontaneously breathing (negative pressure ventilation) IPF patients. The model comprises upper (Alberta idealized throat) and lower airway (plastic tubing) models and branches into two compartments (Michigan lung model) which differ in compliance and caliber of conducting airway. Aerosol penetration and deposition (using NaF as a model aerosol) in the model was studied in different conditions. The model was able to reproduce the heterogeneous, compliance-dependent reduction in ventilation and aerosol penetration seen in the fibrotic lung regions in IPF. Addition of intrapulmonary percussive ventilation (IPV) induces a 2-3 fold increase in aerosol penetration in the low-complaince/high airway caliber compartment of the model, demonstrating the responsiveness of the model to therapeutic intervention. Thanks to the model, we observed that reduced compliance of target lung regions is a critical determinant of aerosol penetration, and thus presumably of aerosol deposition. This finding underscores the need for techniques specifically tailored to enhance delivery of inhaled aerosols to low compliance regions of lungs. Low-frequency IPV (≤ 3 Hz) pulses may represent an attractive option to increase aerosol deposition in fibrotic lung regions.

Evaluation of the Use of Fiberoptic Bronchoscopy Following Rigid Bronchoscopy in Foreign Body Aspiration

Background: Foreign body aspiration (FBA) is a life-threatening problem that can be lethal in some cases. It usually occurs in children between 1–3 years old. Bronchoscopy is the best way for recognition, treatment and management of this problem. We aimed to evaluate the use of fiberoptic bronchoscopy after rigid bronchoscopy in foreign body aspiration in pediatric population. Materials and Methods: 275 pediatric patients with a history of choking were entered to this cross-sectional study, from August 2015 to September 2018. The study was done in a Dr. Sheikh hospital, Mashhad, Iran. The age and gender of the patients were recorded individually. Rigid bronchoscopy was done for all patients. After that, fiberoptic bronchoscopy was performed immediately or 72 hours after the first procedure based on the patients' symptoms, time and personnel conditions. Data were analyzed by the SPSS software version 16.0. Results: Among 275 patients, 175 cases (63.6%) were male. The patients' age was 3 months to 15 years old. In rigid bronchoscopy, 85 cases had no foreign body and in 190 cases, foreign body was found. In 43 cases fiberoptic bronchoscopy found a residual organic material in spite of negative rigid bronchoscopy.The most residual organic material pertained to 0-3 year age group and the cases with residue in males were more than females. There was a significant difference between FBA and age and gender (p <0.001). Conclusion: FBA in airway branches may not be seen by rigid bronchoscope due to the limited access to the distal branches. We suggest fiberoptic bronchoscopy as a complementary management along with rigid bronchoscopy to investigate distal branches, especially if the foreign body is soft in material.

Airway Telomere Length in Lung Transplant Recipients.

IntroductionChronic lung allograft dysfunction (CLAD) represents the major impediment to long term survival following lung transplantation. Donor and recipient telomere length have been shown to associate with lung transplant outcomes, including CLAD. In this study we aimed to measure the telomere lengths of bronchial and bronchiolar airway cells in lung allografts early after transplantation and to investigate associations with CLAD and all-cause mortality.MethodsThis prospective, longitudinal study was performed at The Prince Charles Hospital, Australia. Airway cells were collected via bronchial and bronchiolar airway brushings at post-transplant bronchoscopies. The relative telomere length in airway cells was determined by quantitative PCR based on the T/S ratio. All patients were censored for CLAD and all-cause mortality in August 2020.ResultsIn total 231 bronchoscopies incorporating transbronchial brush and bronchial brush were performed in 120 patients. At the time of censoring, 43% and 35% of patients, respectively, had developed CLAD and had died. Airway bronchiolar and bronchial telomere lengths were strongly correlated (r=0.78, p<0.001), confirming conservation of telomere length with airway branch generation. Both the bronchiolar (r = -0.34, p<0.001) and bronchial (r = -0.31, p<0.001) telomere length decreased with age. Shorter airway telomere length was associated with older donor age and higher donor pack-year smoking history. Neither the bronchiolar nor the bronchial airway telomere length were associated with the development of CLAD (HR 0.39 (0.06-2.3), p=0.30; HR 0.66 (0.2-1.7), p=0.39, respectively) or all-cause mortality (HR 0.92 (0.2-4.5), p=0.92; HR 0.47 (0.1-1.9), p=0.28, respectively).ConclusionsIn this cohort, airway telomere length was associated with donor age and smoking history but was not associated with the future development of CLAD or all-cause mortality.

Use of micro‐CT to determine tracheobronchial airway geometries in three strains of mice used in inhalation toxicology as disease models

Aerosol dosimetry estimates for mouse strains used as models for human disease are not available, primarily because of the lack of tracheobronchial airway morphometry data. By using micro‐CT scans of in‐situ prepared lung casts, tracheobronchial airway morphometry for four strains of mice were obtained: Balb/c, AJ, C57BL/6, and Apoe−/−. The automated tracheobronchial airway morphometry algorithms for airway length and diameter were successfully verified against previously published manual and automated tracheobronchial airway morphometry data derived from two identical in‐situ Balb/c mouse lung casts. There was also excellent agreement in tracheobronchial airway length and diameter between the automated and manual airway data for the AJ, C57BL/6, and Apoe−/− mice. Differences in branch angle measurements were partially due to the differences in definition between the automated algorithms and manual morphometry techniques. Unlike the manual airway morphometry techniques, the automated algorithms were able to provide a value for inclination to gravity for each airway. Inclusion of an inclination to gravity angle for each airway along with airway length, diameter, and branch angle make the current automated tracheobronchial airway data suitable for use in dosimetry programs that can provide dosimetry estimates for inhaled material. The significant differences in upper tracheobronchial airways between Balb/c mice and between C57BL/6 and Apoe−/− mice highlight the need for mouse strain‐specific aerosol dosimetry estimates.

Fractal Analysis of Lung Structure in Chronic Obstructive Pulmonary Disease.

Chest CT is often used for localizing and quantitating pathologies associated with chronic obstructive pulmonary disease (COPD). While simple measurements of areas and volumes of emphysema and airway structure are common, these methods do not capture the structural complexity of the COPD lung. Since the concept of fractals has been successfully applied to evaluate complexity of the lung, this review is aimed at describing the fractal properties of airway disease, emphysema, and vascular abnormalities in COPD. An object forms a fractal if it exhibits the property of self-similarity at different length scales of evaluations. This fractal property is governed by power-law functions characterized by the fractal dimension (FD). Power-laws can also manifest in other statistical descriptors of structure such as the size distribution of emphysema clusters characterized by the power-law exponent D. Although D is not the same as FD of emphysematous clusters, it is a useful index to characterize the spatial pattern of disease progression and predict clinical outcomes in patients with COPD. The FD of the airway tree shape and the D of the size distribution of airway branches have been proposed indexes of structural assessment and clinical predictions. Simulations are also useful to understand the mechanism of disease progression. Therefore, the power-law and fractal analysis of the parenchyma and airways, especially when combined with computer simulations, could lead to a better understanding of the structural alterations during the progression of COPD and help identify subjects at a high risk of severe COPD.