Chronic obstructive pulmonary disease (COPD) is a disease in which airflow obstruction in the lung makes it difficult for patients to breathe. Although COPD occurs predominantly in smokers, there are still deficits in our understanding of the additional risk factors in smokers. To gain a deeper understanding of the COPD molecular signatures, we used Sparse Multiple Canonical Correlation Network (SmCCNet), a recently developed tool that uses sparse multiple canonical correlation analysis, to integrate proteomic and metabolomic data from the blood of 1008 participants of the COPDGene study to identify novel protein–metabolite networks associated with lung function and emphysema. Our aim was to integrate -omic data through SmCCNet to build interpretable networks that could assist in the discovery of novel biomarkers that may have been overlooked in alternative biomarker discovery methods. We found a protein–metabolite network consisting of 13 proteins and 7 metabolites which had a −0.34 correlation (p-value = 2.5 × 10−28) to lung function. We also found a network of 13 proteins and 10 metabolites that had a −0.27 correlation (p-value = 2.6 × 10−17) to percent emphysema. Protein–metabolite networks can provide additional information on the progression of COPD that complements single biomarker or single -omic analyses.
Sulfur mustard (SM) is a chemical warfare agent. When inhaled, SM causes significant injury to the respiratory tract. Although the mechanism involved in acute airway injury after SM inhalation has been well described previously, the mechanism of SM's contribution to distal lung vascular injury is not well understood. We hypothesized that acute inhalation of vaporized SM causes activated systemic coagulation with subsequent pulmonary vascular thrombi formation after SM inhalation exposure. Sprague Dawley rats inhaled SM ethanolic vapor (3.8 mg/kg). Barium/gelatin CT pulmonary angiograms were performed to assess for pulmonary vascular thrombi burden. Lung immunohistochemistry was performed for common procoagulant markers including fibrin(ogen), von Willebrand factor, and CD42d in control and SM-exposed lungs. Additionally, systemic levels of d-dimer and platelet aggregometry after adenosine diphosphate- and thrombin-stimulation were measured in plasma after SM exposure. In SM-exposed lungs, chest CT angiography demonstrated a significant decrease in the distal pulmonary vessel density assessed at 6 h postexposure. Immunohistochemistry also demonstrated increased intravascular fibrin(ogen), vascular von Willebrand factor, and platelet CD42d in the distal pulmonary vessels (<200 µm diameter). Circulating d-dimer levels were significantly increased (p < .001) at 6, 9, and 12 h after SM inhalation versus controls. Platelet aggregation was also increased in both adenosine diphosphate - (p < .01) and thrombin- (p < .001) stimulated platelet-rich plasma after SM inhalation. Significant pulmonary vascular thrombi formation was evident in distal pulmonary arterioles following SM inhalation in rats assessed by CT angiography and immunohistochemistry. Enhanced systemic platelet aggregation and activated systemic coagulation with subsequent thrombi formation likely contributed to pulmonary vessel occlusion.
Lung and airway shapes were different between children with NEHI and control children in this cohort. Children with NEHI had an increased anteroposterior diameter of their lungs that might be useful in the diagnostic criteria.
The widespread use of machine learning algorithms in radiomics has led to a proliferation of flexible prognostic models for clinical outcomes. However, a limitation of these techniques is their black-box nature, which prevents the ability for increased mechanistic phenomenological understanding. In this article, we develop an inferential framework for estimating causal effects with radiomics data. A new challenge is that the exposure of interest is latent so that new estimation procedures are needed. We leverage a multivariate version of partial least squares for causal effect estimation. The methodology is illustrated with applications to two radiomics datasets, one in osteosarcoma and one in glioblastoma.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.