Immunofibrotic drivers of impaired lung function in postacute sequelae of SARS-CoV-2 infection.

BACKGROUNDIndividuals recovering from COVID-19 frequently experience persistent respiratory ailments, which are key elements of postacute sequelae of SARS-CoV-2 infection (PASC); however, little is known about the underlying biological factors that may direct lung recovery and the extent to which these are affected by COVID-19 severity.METHODSWe performed a prospective cohort study of individuals with persistent symptoms after acute COVID-19, collecting clinical data, pulmonary function tests, and plasma samples used for multiplex profiling of inflammatory, metabolic, angiogenic, and fibrotic factors.RESULTSSixty-one participants were enrolled across 2 academic medical centers at a median of 9 weeks (interquartile range, 6-10 weeks) after COVID-19 illness: n = 13 participants (21%) had mild COVID-19 and were not hospitalized, n = 30 participants (49%) were hospitalized but were considered noncritical, and n = 18 participants (30%) were hospitalized and in the intensive care unit (ICU). Fifty-three participants (85%) had lingering symptoms, most commonly dyspnea (69%) and cough (58%). Forced vital capacity (FVC), forced expiratory volume in 1 second (FEV1), and diffusing capacity for carbon monoxide (DLCO) declined as COVID-19 severity increased (P < 0.05) but these values did not correlate with respiratory symptoms. Partial least-squares discriminant analysis of plasma biomarker profiles clustered participants by past COVID-19 severity. Lipocalin-2 (LCN2), MMP-7, and HGF identified by our analysis were significantly higher in the ICU group (P < 0.05), inversely correlated with FVC and DLCO (P < 0.05), and were confirmed in a separate validation cohort (n = 53).CONCLUSIONSubjective respiratory symptoms are common after acute COVID-19 illness but do not correlate with COVID-19 severity or pulmonary function. Host response profiles reflecting neutrophil activation (LCN2), fibrosis signaling (MMP-7), and alveolar repair (HGF) track with lung impairment and may be novel therapeutic or prognostic targets.FundingNational Heart, Lung, and Blood Institute (K08HL130557 and R01HL142818), American Heart Association (Transformational Project Award), the DeLuca Foundation Award, a donation from Jack Levin to the Benign Hematology Program at Yale University, and Duke University.

Application of Machine Learning in Pulmonary Function Assessment Where Are We Now and Where Are We Going?

Analysis of pulmonary function tests (PFTs) is an area where machine learning (ML) may benefit clinicians, researchers, and the patients. PFT measures spirometry, lung volumes, and carbon monoxide diffusion capacity of the lung (DLCO). The results are usually interpreted by the clinicians using discrete numeric data according to published guidelines. PFT interpretations by clinicians, however, are known to have inter-rater variability and the inaccuracy can impact patient care. This variability may be caused by unfamiliarity of the guidelines, lack of training, inadequate understanding of lung physiology, or simply mental lapses. A rules-based automated interpretation system can recapitulate expert's pattern recognition capability and decrease errors. ML can also be used to analyze continuous data or the graphics, including the flow-volume loop, the DLCO and the nitrogen washout curves. These analyses can discover novel physiological biomarkers. In the era of wearables and telehealth, particularly with the COVID-19 pandemic restricting PFTs to be done in the clinical laboratories, ML can also be used to combine mobile spirometry results with an individual's clinical profile to deliver precision medicine. There are, however, hurdles in the development and commercialization of the ML-assisted PFT interpretation programs, including the need for high quality representative data, the existence of different formats for data acquisition and sharing in PFT software by different vendors, and the need for collaboration amongst clinicians, biomedical engineers, and information technologists. Hurdles notwithstanding, the new developments would represent significant advances that could be the future of PFT, the oldest test still in use in clinical medicine.

Human Lung Stem Cell-Based Alveolospheres Provide Insights into SARS-CoV-2-Mediated Interferon Responses and Pneumocyte Dysfunction.

Coronavirus infection causes diffuse alveolar damage leading to acute respiratory distress syndrome. The absence of ex vivo models of human alveolar epithelium is hindering an understanding of coronavirus disease 2019 (COVID-19) pathogenesis. Here, we report a feeder-free, scalable, chemically defined, and modular alveolosphere culture system for the propagation and differentiation of human alveolar type 2 cells/pneumocytes derived from primary lung tissue. Cultured pneumocytes express the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) receptor angiotensin-converting enzyme receptor type-2 (ACE2) and can be infected with virus. Transcriptome and histological analysis of infected alveolospheres mirror features of COVID-19 lungs, including emergence of interferon (IFN)-mediated inflammatory responses, loss of surfactant proteins, and apoptosis. Treatment of alveolospheres with IFNs recapitulates features of virus infection, including cell death. In contrast, alveolospheres pretreated with low-dose IFNs show a reduction in viral replication, suggesting the prophylactic effectiveness of IFNs against SARS-CoV-2. Human stem cell-based alveolospheres, thus, provide novel insights into COVID-19 pathogenesis and can serve as a model for understanding human respiratory diseases.