Attenuation of Cigarette Smoke-Induced Emphysema in Mice by Apolipoprotein A-1 Overexpression.

Chronic inflammation, oxidative stress, and proteolysis participate primarily in the pathogenesis of chronic obstructive pulmonary disease (COPD)/emphysema. COPD is a highly prevalent smoking-related disease for which no effective therapy exists to improve the disease course. Although apolipoprotein A-1 (ApoA1) has antiinflammatory and antioxidant properties as well as cholesterol efflux potential, its role in cigarette smoke (CS)-induced emphysema has not been determined. Therefore, we investigated whether human ApoA1 transgenic (TG) mice, with conditionally induced alveolar epithelium to overexpress ApoA1, are protected against the CS-induced lung inflammatory response and development of emphysema. In this study, ApoA1 levels were significantly decreased in the lungs of patients with COPD and in the lungs of mice exposed to CS. ApoA1 TG mice did not develop emphysema when chronically exposed to CS. Compared with the control TG mice, ApoA1 overexpression attenuated lung inflammation, oxidative stress, metalloprotease activation, and apoptosis in CS-exposed mouse lungs. To explore a plausible mechanism of antiapoptotic activity of ApoA1, alveolar epithelial cells (A549) were treated with CS extract (CSE). ApoA1 prevented CSE-induced translocation of Fas and downstream death-inducing signaling complex into lipid rafts, thereby inhibiting Fas-mediated apoptosis. Taken together, the data showed that ApoA1 overexpression attenuated CS-induced lung inflammation and emphysema in mice. Augmentation of ApoA1 in the lung may have therapeutic potential in preventing smoking-related COPD/emphysema.

Screening of adolescent idiopathic scoliosis using generative adversarial network (GAN) inversion method in chest radiographs.

OBJECTIVE: Conventional computer-aided diagnosis using convolutional neural networks (CNN) has limitations in detecting sensitive changes and determining accurate decision boundaries in spectral and structural diseases such as scoliosis. We devised a new method to detect and diagnose adolescent idiopathic scoliosis in chest X-rays (CXRs) employing the latent space's discriminative ability in the generative adversarial network (GAN) and a simple multi-layer perceptron (MLP) to screen adolescent idiopathic scoliosis CXRs. MATERIALS AND METHODS: Our model was trained and validated in a two-step manner. First, we trained a GAN using CXRs with various scoliosis severities and utilized the trained network as a feature extractor using the GAN inversion method. Second, we classified each vector from the latent space using a simple MLP. RESULTS: The 2-layer MLP exhibited the best classification in the ablation study. With this model, the area under the receiver operating characteristic (AUROC) curves were 0.850 in the internal and 0.847 in the external datasets. Furthermore, when the sensitivity was fixed at 0.9, the model's specificity was 0.697 in the internal and 0.646 in the external datasets. CONCLUSION: We developed a classifier for Adolescent idiopathic scoliosis (AIS) through generative representation learning. Our model shows good AUROC under screening chest radiographs in both the internal and external datasets. Our model has learned the spectral severity of AIS, enabling it to generate normal images even when trained solely on scoliosis radiographs.

Diagnosis of Scoliosis Using Chest Radiographs with a Semi-Supervised Generative Adversarial Network.

Purpose: To develop and validate a deep learning-based screening tool for the early diagnosis of scoliosis using chest radiographs with a semi-supervised generative adversarial network (GAN). Materials and Methods: Using a semi-supervised learning framework with a GAN, a screening tool for diagnosing scoliosis was developed and validated through the chest PA radiographs of patients at two different tertiary hospitals. Our proposed method used training GAN with mild to severe scoliosis only in a semi-supervised manner, as an upstream task to learn scoliosis representations and a downstream task to perform simple classification for differentiating between normal and scoliosis states sensitively. Results: The area under the receiver operating characteristic curve, negative predictive value (NPV), positive predictive value, sensitivity, and specificity were 0.856, 0.950, 0.579, 0.985, and 0.285, respectively. Conclusion: Our deep learning-based artificial intelligence software in a semi-supervised manner achieved excellent performance in diagnosing scoliosis using the chest PA radiographs of young individuals; thus, it could be used as a screening tool with high NPV and sensitivity and reduce the burden on radiologists for diagnosing scoliosis through health screening chest radiographs.

Digital tomosynthesis of the chest for lung nodule detection: interim sensitivity results from an ongoing NIH-sponsored trial.

The authors report interim clinical results from an ongoing NIH-sponsored trial to evaluate digital chest tomosynthesis for improving detectability of small lung nodules. Twenty-one patients undergoing computed tomography (CT) to follow up lung nodules were consented and enrolled to receive an additional digital PA chest radiograph and digital tomosynthesis exam. Tomosynthesis was performed with a commercial CsI/a-Si flat-panel detector and a custom-built tube mover. Seventy-one images were acquired in 11 s, reconstructed with the matrix inversion tomosynthesis algorithm at 5-mm plane spacing, and then averaged (seven planes) to reduce noise and low-contrast artifacts. Total exposure for tomosynthesis imaging was equivalent to that of 11 digital PA radiographs (comparable to a typical screen-film lateral radiograph or two digital lateral radiographs). CT scans (1.25-mm section thickness) were reviewed to confirm presence and location of nodules. Three chest radiologists independently reviewed tomosynthesis images and PA chest radiographs to confirm visualization of nodules identified by CT. Nodules were scored as: definitely visible, uncertain, or not visible. 175 nodules (diameter range 3.5-25.5 mm) were seen by CT and grouped according to size: < 5, 5-10, and > 10 mm. When considering as true positives only nodules that were scored definitely visible, sensitivities for all nodules by tomosynthesis and PA radiography were 70% (+/- 5%) and 22% (+/- 4%), respectively, (p < 0.0001). Digital tomosynthesis showed significantly improved sensitivity of detection of known small lung nodules in all three size groups, when compared to PA chest radiography.

A Quantitative Study of Airway Changes on Micro-CT in a Mouse Asthma Model: Comparison With Histopathological Findings.

PURPOSE: To evaluate airway changes in ovalbumin-induced asthmatic mice in terms of postmortem micro-CT images and pathological findings. METHODS: Asthma was induced in mice by intraperitoneal injection and nasal instillation of ovalbumin aluminium hydroxide into mice (experimental group, n=6), and another group of mice received intraperitoneal injection and nasal instillation of distilled phosphate-buffered saline (control group, n=6). Bronchial lumen area was measured in the main bronchial lumen of the distal third bronchial branch level (6 parts per each mouse) on axial scans of Micro-CT, using a Lucion's smart pen (semi-automated) and a curve pen (manual). Bronchial wall thickness was obtained in 4 sections (2 levels on either side) after the third bronchial branch by measuring the diameter which was perpendicular to the longitudinal axis of the main bronchus on curved Multi-planar reconstruction (MPR) images. Histologic slides were obtained from the lesion that was matched with its CT images, and bronchial wall thicknesses were determined. RESULTS: The mean bronchial lumen area was 0.196±0.072 mm(2) in the experimental group and 0.243±0.116 mm(2) in the control group; the difference was significant. Bronchial wall thickness on micro-CT images (mean, 0.119±0.01 vs. 0.108±0.013 mm) and in pathological specimens (mean, 0.066±0.011 vs. 0.041±0.009 mm) were thicker in the experimental group than in the control group; bronchial wall thickness on micro-CT images correlated well with pathological thickness (for the experimental group, r=0.712; for the control group, r=0.46). The thick bronchial wall in the experimental group demonstrated submucosal hypertrophy along with goblet cell hyperplasia and smooth muscle hyperplasia. CONCLUSIONS: The results of this study suggest that asthma may induce thickening of bronchial wall and narrowing of the lumen area on micro-CT images and that these results may significantly correlate with pathological findings.