Migrasomal autophagosomes relieve endoplasmic reticulum stress in glioblastoma cells.

BACKGROUND: Glioblastoma (GBM) is more difficult to treat than other intractable adult tumors. The main reason that GBM is so difficult to treat is that it is highly infiltrative. Migrasomes are newly discovered membrane structures observed in migrating cells. Thus, they can be generated from GBM cells that have the ability to migrate along the brain parenchyma. However, the function of migrasomes has not yet been elucidated in GBM cells. RESULTS: Here, we describe the composition and function of migrasomes generated along with GBM cell migration. Proteomic analysis revealed that LC3B-positive autophagosomes were abundant in the migrasomes of GBM cells. An increased number of migrasomes was observed following treatment with chloroquine (CQ) or inhibition of the expression of STX17 and SNAP29, which are involved in autophagosome/lysosome fusion. Furthermore, depletion of ITGA5 or TSPAN4 did not relieve endoplasmic reticulum (ER) stress in cells, resulting in cell death. CONCLUSIONS: Taken together, our study suggests that increasing the number of autophagosomes, through inhibition of autophagosome/lysosome fusion, generates migrasomes that have the capacity to alleviate cellular stress.

Retraction fibers produced by fibronectin-integrin α5ß1 interaction promote motility of brain tumor cells.

Glioblastoma (GBM) is a refractory disease that has a highly infiltrative characteristic. Over the past decade, GBM perivascular niche (PVN) has been described as a route of dissemination. Here, we investigated that trailed membrane structures, namely retraction fibers (RFs), are formed by perivascular extracellular matrix (ECM) proteins. By using the anatomical GBM database, we validated that the ECM-related genes were highly expressed in the cells within the PVN where fibronectin (FN) induced RF formation. By disrupting candidates of FN-binding integrins, integrin α5ß1 was identified as the main regulator of RF formation. De novo RFs were produced at the trailing edge, and focal adhesions were actively localized in RFs, indicating that adhesive force makes RFs remain at the bottom surface. Furthermore, we observed that GBM cells more frequently migrated along the residual RFs formed by preceding cells in microfluidic channels in comparison to those in the channels without RFs, suggesting that the infiltrative characteristics GBM could be attributed to RFs formed by the preceding cells in concert with chemoattractant cues. Altogether, we demonstrated that shedding membrane structures of GBM cells are maintained by FN-integrin α5ß1 interaction and promoted their motility .

Hemp-Derived Nanovesicles Protect Leaky Gut and Liver Injury in Dextran Sodium Sulfate-Induced Colitis.

Hemp (Cannabis sativa L.) is used for medicinal purposes owing to its anti-inflammatory and antioxidant activities. We evaluated the protective effect of nanovesicles isolated from hemp plant parts (root, seed, hemp sprout, and leaf) in dextran sulfate sodium (DSS)-induced colitis in mice. The particle sizes of root-derived nanovesicles (RNVs), seed-derived nanovesicles (SNVs), hemp sprout-derived nanovesicles (HSNVs), and leaf-derived nanovesicles (LNVs) were within the range of 100-200 nm as measured by nanoparticle tracking analysis. Acute colitis was induced in C57BL/N mice by 5% DSS in water provided for 7 days. RNVs were administered orally once a day, leading to the recovery of both the small intestine and colon lengths. RNVs, SNVs, and HSNVs restored the tight (ZO-1, claudin-4, occludin) and adherent junctions (E-cadherin and α-tubulin) in DSS-induced small intestine and colon injury. Additionally, RNVs markedly reduced NF-κB activation and oxidative stress proteins in DSS-induced small intestine and colon injury. Tight junction protein expression and epithelial cell permeability were elevated in RNV-, SNV-, and HSNV-treated T84 colon cells exposed to 2% DSS. Interestedly, RNVs, SNVs, HSNVs, and LNVs reduced ALT activity and liver regeneration marker proteins in DSS-induced liver injury. These results showed for the first time that hemp-derived nanovesicles (HNVs) exhibited a protective effect on DSS-induced gut leaky and liver injury through the gut-liver axis by inhibiting oxidative stress marker proteins.

Quantitative CT-based structural alterations of segmental airways in cement dust-exposed subjects.

BACKGROUND: Dust exposure has been reported as a risk factor of pulmonary disease, leading to alterations of segmental airways and parenchymal lungs. This study aims to investigate alterations of quantitative computed tomography (QCT)-based airway structural and functional metrics due to cement-dust exposure. METHODS: To reduce confounding factors, subjects with normal spirometry without fibrosis, asthma and pneumonia histories were only selected, and a propensity score matching was applied to match age, sex, height, smoking status, and pack-years. Thus, from a larger data set (N = 609), only 41 cement dust-exposed subjects were compared with 164 non-cement dust-exposed subjects. QCT imaging metrics of airway hydraulic diameter (Dh), wall thickness (WT), and bifurcation angle (θ) were extracted at total lung capacity (TLC) and functional residual capacity (FRC), along with their deformation ratios between TLC and FRC. RESULTS: In TLC scan, dust-exposed subjects showed a decrease of Dh (airway narrowing) especially at lower-lobes (p < 0.05), an increase of WT (wall thickening) at all segmental airways (p < 0.05), and an alteration of θ at most of the central airways (p < 0.001) compared with non-dust-exposed subjects. Furthermore, dust-exposed subjects had smaller deformation ratios of WT at the segmental airways (p < 0.05) and θ at the right main bronchi and left main bronchi (p < 0.01), indicating airway stiffness. CONCLUSIONS: Dust-exposed subjects with normal spirometry demonstrated airway narrowing at lower-lobes, wall thickening at all segmental airways, a different bifurcation angle at central airways, and a loss of airway wall elasticity at lower-lobes. The airway structural alterations may indicate different airway pathophysiology due to cement dusts.

Transport and deposition of hygroscopic particles in asthmatic subjects with and without airway narrowing.

This study numerically investigates the effect of hygroscopicity on transport and deposition of particles in severe asthmatic lungs with distinct airway structures. The study human subjects were selected from two imaging-based severe asthmatic clusters with one characterized by non-constricted airways and the other by constricted airways in the lower left lobe (LLL). We compared the deposition fractions of sodium chloride (NaCl) particles with a range of aerodynamic diameters (1-8 µm) in cluster archetypes under conditions with and without hygroscopic growth. The temperature and water vapor distributions in the airways were simulated with an airway wall boundary condition that accounts for variable temperature and water vapor evaporation at the interface between the lumen and the airway surface liquid layer. On average, the deposition fraction increased by about 6% due to hygroscopic particle growth in the cluster subjects with constricted airways, while it increased by only about 0.5% in those with non-constricted airways. The effect of particle growth was most significant for particles with an initial diameter of 2 µm in the cluster subjects with constricted airways. The effect diminished with increasing particle size, especially for particles with an initial diameter larger than 4 µm. This suggests the necessity to differentiate asthmatic subjects by cluster in engineering the aerosol size for tailored treatment. Specifically, the treatment of severe asthmatic subjects who have constricted airways with inhalation aerosols may need submicron-sized hygroscopic particles to compensate for particle growth, if one targets for delivering to the peripheral region. These results could potentially inform the choice of particle size for inhalational drug delivery in a cluster-specific manner.

Imaging-based clusters in former smokers of the COPD cohort associate with clinical characteristics: the SubPopulations and intermediate outcome measures in COPD study (SPIROMICS).

BACKGROUND: Quantitative computed tomographic (QCT) imaging-based metrics enable to quantify smoking induced disease alterations and to identify imaging-based clusters for current smokers. We aimed to derive clinically meaningful sub-groups of former smokers using dimensional reduction and clustering methods to develop a new way of COPD phenotyping. METHODS: An imaging-based cluster analysis was performed for 406 former smokers with a comprehensive set of imaging metrics including 75 imaging-based metrics. They consisted of structural and functional variables at 10 segmental and 5 lobar locations. The structural variables included lung shape, branching angle, airway-circularity, airway-wall-thickness, airway diameter; the functional variables included regional ventilation, emphysema percentage, functional small airway disease percentage, Jacobian (volume change), anisotropic deformation index (directional preference in volume change), and tissue fractions at inspiration and expiration. RESULTS: We derived four distinct imaging-based clusters as possible phenotypes with the sizes of 100, 80, 141, and 85, respectively. Cluster 1 subjects were asymptomatic and showed relatively normal airway structure and lung function except airway wall thickening and moderate emphysema. Cluster 2 subjects populated with obese females showed an increase of tissue fraction at inspiration, minimal emphysema, and the lowest progression rate of emphysema. Cluster 3 subjects populated with older males showed small airway narrowing and a decreased tissue fraction at expiration, both indicating air-trapping. Cluster 4 subjects populated with lean males were likely to be severe COPD subjects showing the highest progression rate of emphysema. CONCLUSIONS: QCT imaging-based metrics for former smokers allow for the derivation of statistically stable clusters associated with unique clinical characteristics. This approach helps better categorization of COPD sub-populations; suggesting possible quantitative structural and functional phenotypes.

Imaging-based clusters in current smokers of the COPD cohort associate with clinical characteristics: the SubPopulations and Intermediate Outcome Measures in COPD Study (SPIROMICS).

BACKGROUND: Classification of COPD is usually based on the severity of airflow, which may not sensitively differentiate subpopulations. Using a multiscale imaging-based cluster analysis (MICA), we aim to identify subpopulations for current smokers with COPD. METHODS: Among the SPIROMICS subjects, we analyzed computed tomography images at total lung capacity (TLC) and residual volume (RV) of 284 current smokers. Functional variables were derived from registration of TLC and RV images, e.g. functional small airways disease (fSAD%). Structural variables were assessed at TLC images, e.g. emphysema and airway wall thickness and diameter. We employed an unsupervised method for clustering. RESULTS: Four clusters were identified. Cluster 1 had relatively normal airway structures; Cluster 2 had an increase of fSAD% and wall thickness; Cluster 3 exhibited a further increase of fSAD% but a decrease of wall thickness and airway diameter; Cluster 4 had a significant increase of fSAD% and emphysema. Clinically, Cluster 1 showed normal FEV1/FVC and low exacerbations. Cluster 4 showed relatively low FEV1/FVC and high exacerbations. While Cluster 2 and Cluster 3 showed similar exacerbations, Cluster 2 had the highest BMI among all clusters. CONCLUSIONS: Association of imaging-based clusters with existing clinical metrics suggests the sensitivity of MICA in differentiating subpopulations.

Contributions of Kinetic Energy and Viscous Dissipation to Airway Resistance in Pulmonary Inspiratory and Expiratory Airflows in Successive Symmetric Airway Models With Various Bifurcation Angles.

The aim of this study was to investigate and quantify contributions of kinetic energy and viscous dissipation to airway resistance during inspiration and expiration at various flow rates in airway models of different bifurcation angles. We employed symmetric airway models up to the 20th generation with the following five different bifurcation angles at a tracheal flow rate of 20 L/min: 15 deg, 25 deg, 35 deg, 45 deg, and 55 deg. Thus, a total of ten computational fluid dynamics (CFD) simulations for both inspiration and expiration were conducted. Furthermore, we performed additional four simulations with tracheal flow rate values of 10 and 40 L/min for a bifurcation angle of 35 deg to study the effect of flow rate on inspiration and expiration. Using an energy balance equation, we quantified contributions of the pressure drop associated with kinetic energy and viscous dissipation. Kinetic energy was found to be a key variable that explained the differences in airway resistance on inspiration and expiration. The total pressure drop and airway resistance were larger during expiration than inspiration, whereas wall shear stress and viscous dissipation were larger during inspiration than expiration. The dimensional analysis demonstrated that the coefficients of kinetic energy and viscous dissipation were strongly correlated with generation number. In addition, the viscous dissipation coefficient was significantly correlated with bifurcation angle and tracheal flow rate. We performed multiple linear regressions to determine the coefficients of kinetic energy and viscous dissipation, which could be utilized to better estimate the pressure drop in broader ranges of successive bifurcation structures.

Quantitative computed tomographic imaging-based clustering differentiates asthmatic subgroups with distinctive clinical phenotypes.

BACKGROUND: Imaging variables, including airway diameter, wall thickness, and air trapping, have been found to be important metrics when differentiating patients with severe asthma from those with nonsevere asthma and healthy subjects. OBJECTIVE: The objective of this study was to identify imaging-based clusters and to explore the association of the clusters with existing clinical metrics. METHODS: We performed an imaging-based cluster analysis using quantitative computed tomography-based structural and functional variables extracted from the respective inspiration and expiration scans of 248 asthmatic patients. The imaging-based metrics included a broader set of multiscale variables, such as inspiratory airway dimension, expiratory air trapping, and registration-based lung deformation (inspiration vs expiration). Asthma subgroups derived from a clustering method were associated with subject demographics, questionnaire results, medication history, and biomarker variables. RESULTS: Cluster 1 was composed of younger patients with early-onset nonsevere asthma and reversible airflow obstruction and normal airway structure. Cluster 2 was composed of patients with a mix of patients with nonsevere and severe asthma with marginal inflammation who exhibited airway luminal narrowing without wall thickening. Clusters 3 and 4 were dominated by patients with severe asthma. Cluster 3 patients were obese female patients with reversible airflow obstruction who exhibited airway wall thickening without airway narrowing. Cluster 4 patients were late-onset older male subjects with persistent airflow obstruction who exhibited significant air trapping and reduced regional deformation. Cluster 3 and 4 patients also showed decreased lymphocyte and increased neutrophil counts, respectively. CONCLUSIONS: Four image-based clusters were identified and shown to be correlated with clinical characteristics. Such clustering serves to differentiate asthma subgroups that can be used as a basis for the development of new therapies.

A cell-autonomous positive-signaling circuit associated with the PDGF-NO-ID4-regulatory axis in glioblastoma cells.

Most cancer-related signaling pathways sustain their active or inactive status via genetic mutations or various regulatory mechanisms. Previously, we demonstrated that platelet-derived growth factor (PDGF) activates Notch signaling through nitric oxide (NO)-signaling-driven activation of inhibitor of differentiation 4 (ID4) in glioblastoma (GBM) stem cells (GSCs) and endothelial cells in the vascular niche of GBM, leading to maintenance of GSC traits and GBM progression. Here, we determined that the PDGF-NO-ID4-signaling axis is constantly activated through a positive regulatory circuit. ID4 expression significantly increased PDGF subunit B expression in both in vitro cultures and in vivo tumor xenografts and regulated NO synthase 2 (NOS2) expression and NO production by activating PDGF signaling, as well as that of its receptor (PDGFR). Additionally, ectopic expression of PDGFRα, NOS2, or ID4 activated the PDGF-NO-ID4-signaling circuit and enhanced the self-renewal of GBM cell lines. These results suggested that the positive regulatory circuit associated with PDGF-NO-ID4 signaling plays a pivotal role in regulating the self-renewal and tumor-initiating capacity of GSCs and might provide a promising therapeutic target for GBM.

Detection of smoothly distributed spatial outliers, with applications to identifying the distribution of parenchymal hyperinflation following an airway challenge in asthmatics.

Methacholine challenge tests are used to measure changes in pulmonary function that indicate symptoms of asthma. In addition to pulmonary function tests, which measure global changes in pulmonary function, computed tomography images taken at full inspiration before and after administration of methacholine provide local air volume changes (hyper-inflation post methacholine) at individual acinar units, indicating local airway hyperresponsiveness. Some of the acini may have extreme air volume changes relative to the global average, indicating hyperresponsiveness, and those extreme values may occur in clusters. We propose a Gaussian mixture model with a spatial smoothness penalty to improve prediction of hyperresponsive locations that occur in spatial clusters. A simulation study provides evidence that the spatial smoothness penalty improves prediction under different data-generating mechanisms. We apply this method to computed tomography data from Seoul National University Hospital on five healthy and ten asthmatic subjects. Copyright © 2017 John Wiley & Sons, Ltd.

Assessing respiratory airflow unsteadiness under different tidal respiratory frequencies using large eddy simulation method.

Unsteady respiratory airflow characteristics play a crucial role in understanding the deposition of toxic particles and inhaled aerosol drugs in the human respiratory tract. Considering the variations in respiratory flow rate and glottis motion under different respiratory frequencies, these respiratory airflow characteristics are studied by large-eddy simulations, including pressure field, power loss, modal spatial patterns, and vortex structures. Firstly, the results reveal that varying respiratory frequencies significantly affect airflow unsteadiness, turbulent evolution, and vortex structure dissipation, as they increase the complexity and butterfly effect introduced by the turbulent disturbance. Secondly, the pressure drops and flow rate at the glottis also conform to a power-law relationship considering the respiratory physiological characteristics, especially under low respiratory frequencies. Glottis motion plays different roles in energy consumption during inspiration and expiration, and its magnitude can be predicted using a polynomial function based on glottis area and respiratory flowrate under different respiratory frequencies. Finally, modal decomposition can be effectively applied to the study of respiratory flow characteristics, but we recommend separately studying the inspiration and expiration. The spatial distribution of the dominant mode characterizes the majority of respiratory flow characteristics and are influenced by respiratory frequency. Spectral entropy results indicate that glottis motion and slow breathing both delay the transitions in the upper respiratory tract during inspiration and expiration. These results confirm that the respiratory physiology characteristics under different respiratory frequencies have a significant impact on the unsteady respiratory airflow characteristics and warrant further study.

Structural and functional features of asthma participants with fixed airway obstruction using CT imaging and 1D computational fluid dynamics: A feasibility study.

Asthma with fixed airway obstruction (FAO) is associated with significant morbidity and rapid decline in lung function, making its treatment challenging. Quantitative computed tomography (QCT) along with data postprocessing is a useful tool to obtain detailed information on airway structure, parenchymal function, and computational flow features. In this study, we aim to identify the structural and functional differences between asthma with and without FAO. The FAO group was defined by a ratio of forced expiratory volume in 1 s (FEV1 ) to forced vital capacity (FVC), FEV1 /FVC <0.7. Accordingly, we obtained two sets of QCT images at inspiration and expiration of asthma subjects without (N = 24) and with FAO (N = 12). Structural and functional QCT-derived airway variables were extracted, including normalized hydraulic diameter, normalized airway wall thickness, functional small airway disease, and emphysema percentage. A one-dimensional (1D) computational fluid dynamics (CFD) model considering airway deformation was used to compare the pressure distribution between the two groups. The computational pressures showed strong correlations with the pulmonary function test (PFT)-based metrics. In conclusion, asthma participants with FAO had worse lung functions and higher-pressure drops than those without FAO.

ID1high/activin Ahigh glioblastoma cells contribute to resistance to anti-angiogenesis therapy through malformed vasculature.

Although bevacizumab (BVZ), a representative drug for anti-angiogenesis therapy (AAT), is used as a first-line treatment for patients with glioblastoma (GBM), its efficacy is notably limited. Whereas several mechanisms have been proposed to explain the acquisition of AAT resistance, the specific underlying mechanisms have yet to be sufficiently ascertained. Here, we established that inhibitor of differentiation 1 (ID1)high/activin Ahigh glioblastoma cell confers resistance to BVZ. The bipotent effect of activin A during its active phase was demonstrated to reduce vasculature dependence in tumorigenesis. In response to a temporary exposure to activin A, this cytokine was found to induce endothelial-to-mesenchymal transition via the Smad3/Slug axis, whereas prolonged exposure led to endothelial apoptosis. ID1 tumors showing resistance to BVZ were established to be characterized by a hypovascular structure, hyperpermeability, and scattered hypoxic regions. Using a GBM mouse model, we demonstrated that AAT resistance can be overcome by administering therapy based on a combination of BVZ and SB431542, a Smad2/3 inhibitor, which contributed to enhancing survival. These findings offer valuable insights that could contribute to the development of new strategies for treating AAT-resistant GBM.

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.

Human-airway surface mesh smoothing based on graph convolutional neural networks.

BACKGROUND AND OBJECTIVE: A detailed representation of the airway geometry in the respiratory system is critical for predicting precise airflow and pressure behaviors in computed tomography (CT)-image-based computational fluid dynamics (CFD). The CT-image-based geometry often contains artifacts, noise, and discontinuities due to the so-called stair step effect. Hence, an advanced surface smoothing is necessary. The existing smoothing methods based on the Laplacian operator drastically shrink airway geometries, resulting in the loss of information related to smaller branches. This study aims to introduce an unsupervised airway-mesh-smoothing learning (AMSL) method that preserves the original geometry of the three-dimensional (3D) airway for accurate CT-image-based CFD simulations. METHOD: The AMSL method jointly trains two graph convolutional neural networks (GCNNs) defined on airway meshes to filter vertex positions and face normal vectors. In addition, it regularizes a combination of loss functions such as reproducibility, smoothness and consistency of vertex positions, and normal vectors. The AMSL adopts the concept of a deep mesh prior model, and it determines the self-similarity for mesh restoration without using a large dataset for training. Images of the airways of 20 subjects were smoothed by the AMSL method, and among them, the data of two subjects were used for the CFD simulations to assess the effect of airway smoothing on flow properties. RESULTS: In 18 of 20 benchmark problems, the proposed smoothing method delivered better results compared with the conventional or state-of-the-art deep learning methods. Unlike the traditional smoothing, the AMSL successfully constructed 20 smoothed airways with airway diameters that were consistent with the original CT images. Besides, CFD simulations with the airways obtained by the AMSL method showed much smaller pressure drop and wall shear stress than the results obtained by the traditional method. CONCLUSIONS: The airway model constructed by the AMSL method reproduces branch diameters accurately without any shrinkage, especially in the case of smaller airways. The accurate estimation of airway geometry using a smoothing method is critical for estimating flow properties in CFD simulations.

Relationship of computed tomography-based measurements with symptom perception and quality of life in patients with severe asthma.

BACKGROUND: Symptom perception and quality of life (QOL) are important domains for properly managing severe asthma. This study aimed to assess the relationship between airway structural and parenchymal variables measured using chest computed tomography (CT) and subjective symptom perception and QOL in patients with severe asthma enrolled in the Korean Severe Asthma Registry. METHODS: This study used CT-based objective measurements, including airway wall thickness (WT), hydraulic diameter, functional small airway disease (fSAD), and emphysematous lung (Emph), to assess their association with subjective symptom (cough, dyspnea, wheezing, and sputum) perception measured using the visual analog scale, and QOL measured by the Severe Asthma Questionnaire (SAQ). RESULTS: A total of 94 patients with severe asthma were enrolled in this study. The WT and fSAD% were significantly positively associated with cough and dyspnea, respectively. For QOL, WT and Emph% showed significant negative associations with the SAQ. However, there was no significant association between lung function and symptom perception or between lung function and QOL. CONCLUSION: Overall, WT, fSAD%, and Emph% measured using chest CT were associated with subjective symptom perception and QOL in patients with severe asthma. This study provides a basis for clarifying the clinical correlates of imaging-derived metrics and for understanding the mechanisms of respiratory symptom perception.

Computed tomography-based imaging biomarker identifies coal workers' pneumoconiosis.

Rationale: The increase in the incidence and the diagnostic limitations of pneumoconiosis have emerged as a public health concern. This study aimed to conduct a computed tomography (CT)- based quantitative analysis to understand differences in imaging results of pneumoconiosis according to disease severity. Methods: According to the International Labor Organization (ILO) guidelines, coal workers' pneumoconiosis (CWP) are classified into five categories. CT images were obtained only at full inspiration and were quantitatively evaluated for airway structural variables such as bifurcation angle (θ), hydraulic diameter (Dh), wall thickness (WT), and circularity (Cr). Parenchymal functional variables include abnormal regions (emphysema, ground-glass opacities, consolidation, semi consolidation, and fibrosis) and blood vessel volume. Through the propensity score matching method, the confounding effects were decreased. Results: Category 4 demonstrated a reduced θ in TriLUL, a thicker airway wall in both the Trachea and Bronint compared to Category 0, and a decreased Cr in Bronint. Category 4 presented with higher abnormal regions except for ground-glass opacity and a narrower pulmonary blood vessel volume. A negative correlation was found between abnormal areas with lower Hounsfield units (HU) than the normal lung and the ratio of forced expiratory volume in 1 s/forced vital capacity, with narrowed pulmonary blood vessel volume which is positively correlated with abnormal areas with upper HU than the normal lung. Conclusion: This study provided valuable insight into pneumoconiosis progression through a comparison of quantitative CT images based on severity. Furthermore, as there has been paucity of studies on the pulmonary blood vessel volume of the CWP, in this study, a correlation between reduced pulmonary blood vessel volume and regions with low HU values holds significant importance.

An unsupervised image registration method employing chest computed tomography images and deep neural networks.

BACKGROUND: Deformable image registration is crucial for multiple radiation therapy applications. Fast registration of computed tomography (CT) lung images is challenging because of the large and nonlinear deformation between inspiration and expiration. With advancements in deep learning techniques, learning-based registration methods are considered efficient alternatives to traditional methods in terms of accuracy and computational cost. METHOD: In this study, an unsupervised lung registration network (LRN) with cycle-consistent training is proposed to align two acquired CT-derived lung datasets during breath-holds at inspiratory and expiratory levels without utilizing any ground-truth registration results. Generally, the LRN model uses three loss functions: image similarity, regularization, and Jacobian determinant. Here, LRN was trained on the CT datasets of 705 subjects and tested using 10 pairs of public CT DIR-Lab datasets. Furthermore, to evaluate the effectiveness of the registration technique, target registration errors (TREs) of the LRN model were compared with those of the conventional algorithm (sum of squared tissue volume difference; SSTVD) and a state-of-the-art unsupervised registration method (VoxelMorph). RESULTS: The results showed that the LRN with an average TRE of 1.78 ± 1.56 mm outperformed VoxelMorph with an average TRE of 2.43 ± 2.43 mm, which is comparable to that of SSTVD with an average TRE of 1.66 ± 1.49 mm. In addition, estimating the displacement vector field without any folding voxel consumed less than 2 s, demonstrating the superiority of the learning-based method with respect to fiducial marker tracking and the overall soft tissue alignment with a nearly real-time speed. CONCLUSIONS: Therefore, this proposed method shows significant potential for use in time-sensitive pulmonary studies, such as lung motion tracking and image-guided surgery.

Aloe-derived nanovesicles attenuate inflammation and enhance tight junction proteins for acute colitis treatment.

Inflammatory bowel disease (IBD) is a chronic recurrent inflammatory disease of the digestive tract that causes pain and weight loss and also increases the risk of colon cancer. Inspired by the benefits of plant-derived nanovesicles and aloe, we herein report aloe-derived nanovesicles, including aloe vera-derived nanovesicles (VNVs), aloe arborescens-derived nanovesicles (ANVs), and aloe saponaria-derived nanovesicles (SNVs) and evaluate their therapeutic potential and molecular mechanisms in a dextran sulfate sodium (DSS)-induced acute experimental colitis mouse model. Aloe-derived nanovesicles not only facilitate markedly reduced DSS-induced acute colonic inflammation, but also enable the restoration of tight junction (TJ) and adherent junction (AJ) proteins to prevent gut permeability in DSS-induced acute colonic injury. These therapeutic effects are ascribed to the anti-inflammatory and anti-oxidant effects of aloe-derived nanovesicles. Therefore, aloe-derived nanovesicles are a safe treatment option for IBD.