Canadian respiratory journal最新文献

Adenosine deaminase 2 (ADA₂) is reported as a novel diagnostic biomarker for tuberculous pleural effusion (TPE) in many studies. This meta-analysis was conducted to systematically evaluate the general diagnostic performance of pleural ADA₂ in TPE. After searching for relevant studies that investigated the diagnostic performance of pleural ADA₂ in TPE in several databases, we assessed and selected eligible studies to calculate pooled parameters by STATA 16.0 software. A final set of thirteen studies entirely met the inclusion standards and were used to calculate pooled parameters in our meta-analysis. Among them, there were nine English studies and four Chinese studies. The pooled parameters of pleural ADA₂ in diagnosing TPE were summarized as follows: sensitivity, 0.91 (95% CI: 0.86-0.95); specificity, 0.93 (95% CI: 0.92-0.95); positive likelihood ratio, 13.9 (95% CI: 10.6-18.3); negative likelihood ratio, 0.09 (95% CI:0.06-0.16); diagnostic odds ratio, 147 (95% CI: 76-284); and the area under the curve, 0.95 (95% CI: 0.93-0.97). Pleural ADA₂ is a reliable indicator with excellent accuracy in TPE diagnosis. However, we need to combine pleural ADA₂ with diverse examinations to diagnose TPE in clinical practice.

Objective: To investigate the predictive factors of residual pulmonary opacity on midterm follow-up CT scans in patients hospitalized with COVID-19 pneumonia.

Materials and methods: This prospective study was conducted in a tertiary referral university hospital in Iran, from March 2020 to December 2020. Patients hospitalized due to novel coronavirus pneumonia with bilateral pulmonary involvement in the first CT scan were included and underwent an 8-week follow-up CT scan. Pulmonary involvement (PI) severity was assessed using a 25-scale semiquantitative scoring system. Density of opacities was recorded using the Hounsfield unit (HU).

Results: The chest CT scans of 50 participants (mean age = 54.4 ± 14.2 years, 72% male) were reviewed, among whom 8 (16%) had residual findings on follow-up CT scans. The most common residual findings were faint ground-glass opacities (GGOs) (14%); fibrotic-like changes were observed in 2 (4%) patients. Demographic findings, underlying disease, and laboratory findings did not show significant association with remaining pulmonary opacities. The total PI score was significantly higher in participants with remaining parenchymal involvement (14.5 ± 6.5 versus 10.2 ± 3.7; P=0.02). On admission, the HU of patients with remaining opacities was significantly higher (-239.8 ± 107.6 versus -344.0 ± 157.4; P=0.01). Remaining pulmonary findings were more frequently detected in patients who had received antivirals, steroid pulse, or IVIG treatments (P=0.02, 0.02, and 0.001, respectively). Only the PI score remained statistically significant in multivariate logistic regression with 88.1% accuracy (OR = 1.2 [1.01-1.53]; P=0.03).

Conclusion: Pulmonary opacities are more likely to persist in midterm follow-up CT scans in patients with severe initial pulmonary involvement.

Background and aims: Chest X-ray (CXR) is indispensable to the assessment of severity, diagnosis, and management of pneumonia. Deep learning is an artificial intelligence (AI) technology that has been applied to the interpretation of medical images. This study investigated the feasibility of classifying fatal pneumonia based on CXR images using deep learning models on publicly available platforms.

Methods: CXR images of patients with pneumonia at diagnosis were labeled as fatal or nonfatal based on medical records. We applied CXR images from 1031 patients with nonfatal pneumonia and 243 patients with fatal pneumonia for training and self-evaluation of the deep learning models. All labeled CXR images were randomly allocated to the training, validation, and test datasets of deep learning models. Data augmentation techniques were not used in this study. We created two deep learning models using two publicly available platforms.

Results: The first model showed an area under the precision-recall curve of 0.929 with a sensitivity of 50.0% and a specificity of 92.4% for classifying fatal pneumonia. We evaluated the performance of our deep learning models using sensitivity, specificity, PPV, negative predictive value (NPV), accuracy, and F1 score. Using the external validation test dataset of 100 CXR images, the sensitivity, specificity, accuracy, and F1 score were 68.0%, 86.0%, 77.0%, and 74.7%, respectively. In the original dataset, the performance of the second model showed a sensitivity, specificity, and accuracy of 39.6%, 92.8%, and 82.7%, respectively, while external validation showed values of 38.0%, 92.0%, and 65.0%, respectively. The F1 score was 52.1%. These results were comparable to those obtained by respiratory physicians and residents.

Conclusions: The deep learning models yielded good accuracy in classifying fatal pneumonia. By further improving the performance, AI could assist physicians in the severity assessment of patients with pneumonia.

Background: Cigarette smoke is assumed to cause the loss of airway wall structure in chronic obstructive pulmonary disease (COPD) by reducing airway smooth muscle cell (ASMC) function. It also modifies mTOR activity, microRNA (miR)-101-3p expression, and mitochondria function. Here, the link between miR-101-3p and mTOR-regulated mitochondria integrity and ASMC deterioration was assessed.

Methods: Disease-specific miR-101-3p expression was determined by RT-PCR in primary ASMC (non-COPD smokers: n = 6; COPD: n = 8; healthy: n = 6). The regulatory effect of miR-101-3p modification on mTOR expression, mitochondrial fragmentation, and remodeling properties (α-SMA, fibronectin, MTCO2, and p70S6 kinase) was assessed in ASMC (healthy nonsmokers: n = 3; COPD: n = 3) by Western blotting and immunofluorescence microscopy. MiR-101-3p was modified by specific mimics or inhibitors, in ASMC stimulated with TNF-α (10 ng/ml) or cigarette smoke extract (CSE).

Results: MiR-101-3p expression was significantly higher in ASMC of COPD patients, compared to ASMC of healthy or active smokers. MiR-101-3p expression was increased by TNF-α or CSE. TNF-α or miR-101-3p deteriorated ASMC and mitochondria, while decreasing mTOR signaling, α-SMA, fibronectin, and MTCO2. MiR-101-3p inhibition reduced ASMC deterioration and mitochondrial fragmentation.

Conclusion: Constitutive high miR-101-3p expression characterizes COPD-ASMC, causing increased mitochondrial fragmentation and ASMC deterioration. Thus, reactivation mTOR or blocking miR-101-3p presents a potential new strategy for COPD therapy.

Background: Asthma airway remodeling is closely related to the abnormal migration of human airway smooth muscle cells (ASMCs), and vascular endothelial growth factor (VEGF) is involved in the pathophysiological process of asthma. This study aimed to investigate the effect of VEGF on ASMC migration through in vitro cell experiments and to intervene in ASMC migration with different asthma drugs and signaling pathway inhibitors to provide a basis for screening effective drugs for airway remodeling.

Methods: The effect of VEGF on the proliferation of ASMCs was detected by the CCK-8 method, and the effect of VEGF on the migration of ASMCs was proven by scratch and transwell assays. Different asthma drugs and signaling pathway inhibitors were used to interfere with the migration of ASMCs. The number of migrating cells was compared between the intervention and nonintervention groups.

Results: Our results showed that VEGF induction enhanced ASMC migration; pretreatment with the commonly used asthma drugs (salbutamol, budesonide, and ipratropium bromide) significantly attenuated VEGF-induced ASMC migration; and inhibitors SB203580, LY294002, and Y27632 blocked the VEGF-induced activation of p38 MAPK, PI3K, and ROCK signaling pathway targets in ASMCs and inhibited migration.

Conclusion: This study shows that the current commonly used asthma drugs salbutamol, budesonide, and ipratropium have potential value in the treatment of airway remodeling, and the p38 MAPK, PI3K, and ROCK signaling pathway targets are involved in the VEGF-induced ASMC migration process. Signaling pathway inhibitor drugs may be a new way to treat asthma-induced airway remodeling in asthma patients in the future. However, the related mechanism and safety profile still need further research.