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1. Damaged lung gas exchange function of discharged COVID-19 patients detected by hyperpolarized ¹²⁹ Xe MRI

   https://doi.org/10.1126/sciadv.abc8180  

   Li, Haidong; Zhao, Xiuchao; Wang, Yujin; Lou, Xin; Chen, Shizhen; Deng, He; Shi, Lei; Xie, Junshuai; Tang, Dazhong; Zhao, Jianping; et al (January 2021, Science Advances)

   null (Ed.)

   The recovery process of COVID-19 patients is unclear. Some recovered patients complain of continued shortness of breath. Vasculopathy has been reported in COVID-19, stressing the importance of probing pulmonary microstructure and function at the alveolar-capillary interface. While computed tomography (CT) detects structural abnormalities, little is known about the impact of disease on lung function. 129 Xe magnetic resonance imaging (MRI) is a technique uniquely capable of assessing ventilation, microstructure, and gas exchange. Using 129 Xe MRI, we found that COVID-19 patients show a higher rate of ventilation defects (5.9% versus 3.7%), unchanged microstructure, and longer gas-blood exchange time (43.5 ms versus 32.5 ms) compared with healthy individuals. These findings suggest that regional ventilation and alveolar airspace dimensions are relatively normal around the time of discharge, while gas-blood exchange function is diminished. This study establishes the feasibility of localized lung function measurements in COVID-19 patients and their potential usefulness as a supplement to structural imaging. 

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2. Chronic lung diseases are associated with gene expression programs favoring SARS-CoV-2 entry and severity

   https://doi.org/10.1038/s41467-021-24467-0  

   Bui, Linh T.; Winters, Nichelle I.; Chung, Mei-I; Joseph, Chitra; Gutierrez, Austin J.; Habermann, Arun C.; Adams, Taylor S.; Schupp, Jonas C.; Poli, Sergio; Peter, Lance M.; et al (December 2021, Nature Communications)

   null (Ed.)

   Abstract Patients with chronic lung disease (CLD) have an increased risk for severe coronavirus disease-19 (COVID-19) and poor outcomes. Here, we analyze the transcriptomes of 611,398 single cells isolated from healthy and CLD lungs to identify molecular characteristics of lung cells that may account for worse COVID-19 outcomes in patients with chronic lung diseases. We observe a similar cellular distribution and relative expression of SARS-CoV-2 entry factors in control and CLD lungs. CLD AT2 cells express higher levels of genes linked directly to the efficiency of viral replication and the innate immune response. Additionally, we identify basal differences in inflammatory gene expression programs that highlight how CLD alters the inflammatory microenvironment encountered upon viral exposure to the peripheral lung. Our study indicates that CLD is accompanied by changes in cell-type-specific gene expression programs that prime the lung epithelium for and influence the innate and adaptive immune responses to SARS-CoV-2 infection. 

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3. Microscale distribution and dynamic surface tension of pulmonary surfactant normalize the recruitment of asymmetric bifurcating airways

   https://doi.org/10.1152/japplphysiol.00543.2016  

   Yamaguchi, Eiichiro; Nolan, Liam P.; Gaver, Donald P. (May 2017, Journal of Applied Physiology)

   We investigate the influence of bifurcation geometry, asymmetry of daughter airways, surfactant distribution, and physicochemical properties on the uniformity of airway recruitment of asymmetric bifurcating airways. To do so, we developed microfluidic idealized in vitro models of bifurcating airways, through which we can independently evaluate the impact of carina location and daughter airway width and length. We explore the uniformity of recruitment and its relationship to the dynamic surface tension of the lining fluid and relate this behavior to the hydraulic (P Hyd ) and capillary (P Cap ) pressure drops. These studies demonstrate the extraordinary importance of P Cap in stabilizing reopening, even in highly asymmetric systems. The dynamic surface tension of pulmonary surfactant is integral to this stability because it modulates P Cap in a velocity-dependent manner. Furthermore, the surfactant distribution at the propagating interface can have a very large influence on recruitment stability by focusing surfactant preferentially to specific daughter airways. This implies that modification of the surfactant distribution through novel modes of ventilation could be useful in inducing uniformly recruited lungs, aiding in gas exchange, and reducing ventilator-induced lung injury. NEW & NOTEWORTHY The dynamic surface tension of pulmonary surfactant is integral to the uniformity of asymmetric bifurcation airway recruitments because it modulates capillary pressure drop in a velocity-dependent manner. Also, the surfactant distribution at the propagating interface can have a very large influence on recruitment stability by focusing surfactant preferentially to specific daughter airways. This implies that modification of the surfactant distribution through novel modes of ventilation could be useful in inducing uniformly recruited lungs, reducing ventilator-induced lung injury. 

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4. Early prediction of level-of-care requirements in patients with COVID-19

   https://doi.org/10.7554/eLife.60519  

   Hao, Boran; Sotudian, Shahabeddin; Wang, Taiyao; Xu, Tingting; Hu, Yang; Gaitanidis, Apostolos; Breen, Kerry; Velmahos, George C; Paschalidis, Ioannis Ch (October 2020, eLife)

   null (Ed.)

   The new coronavirus (now named SARS-CoV-2) causing the disease pandemic in 2019 (COVID-19), has so far infected over 35 million people worldwide and killed more than 1 million. Most people with COVID-19 have no symptoms or only mild symptoms. But some become seriously ill and need hospitalization. The sickest are admitted to an Intensive Care Unit (ICU) and may need mechanical ventilation to help them breath. Being able to predict which patients with COVID-19 will become severely ill could help hospitals around the world manage the huge influx of patients caused by the pandemic and save lives. Now, Hao, Sotudian, Wang, Xu et al. show that computer models using artificial intelligence technology can help predict which COVID-19 patients will be hospitalized, admitted to the ICU, or need mechanical ventilation. Using data of 2,566 COVID-19 patients from five Massachusetts hospitals, Hao et al. created three separate models that can predict hospitalization, ICU admission, and the need for mechanical ventilation with more than 86% accuracy, based on patient characteristics, clinical symptoms, laboratory results and chest x-rays. Hao et al. found that the patients’ vital signs, age, obesity, difficulty breathing, and underlying diseases like diabetes, were the strongest predictors of the need for hospitalization. Being male, having diabetes, cloudy chest x-rays, and certain laboratory results were the most important risk factors for intensive care treatment and mechanical ventilation. Laboratory results suggesting tissue damage, severe inflammation or oxygen deprivation in the body's tissues were important warning signs of severe disease. The results provide a more detailed picture of the patients who are likely to suffer from severe forms of COVID-19. Using the predictive models may help physicians identify patients who appear okay but need closer monitoring and more aggressive treatment. The models may also help policy makers decide who needs workplace accommodations such as being allowed to work from home, which individuals may benefit from more frequent testing, and who should be prioritized for vaccination when a vaccine becomes available. 

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5. A scalable 3D tissue culture pipeline to enable functional therapeutic screening for pulmonary fibrosis

   https://doi.org/10.1063/5.0054967  

   Cummins, Katherine_A; Bitterman, Peter_B; Tschumperlin, Daniel_J; Wood, David_K (November 2021, APL Bioengineering)

   Idiopathic pulmonary fibrosis (IPF) is a lethal lung disease targeting the alveolar gas exchange apparatus, leading to death by asphyxiation. IPF progresses on a tissue scale through aberrant matrix remodeling, enhanced cell contraction, and subsequent microenvironment densification. Although two pharmaceuticals modestly slow progression, IPF patient survival averages less than 5 years. A major impediment to therapeutic development is the lack of high-fidelity models that account for the fibrotic microenvironment. Our goal is to create a three-dimensional (3D) platform to enable lung fibrosis studies and recapitulate IPF tissue features. We demonstrate that normal lung fibroblasts encapsulated in collagen microspheres can be pushed toward an activated phenotype, treated with FDA-approved therapies, and their fibrotic function quantified using imaging assays (extracellular matrix deposition, contractile protein expression, and microenvironment compaction). Highlighting the system's utility, we further show that fibroblasts isolated from IPF patient lungs maintain fibrotic phenotypes and manifest reduced fibrotic function when treated with epigenetic modifiers. Our system enables enhanced screening due to improved predictability and fidelity compared to 2D systems combined with superior tractability and throughput compared to 3D systems. 

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