Rationale: Some patients with coronavirus disease 2019 (COVID-19) rapidly develop respiratory failure or even die, underscoring the need for early identification of patients at elevated risk of severe illness. This study aims to quantify pneumonia lesions by computed tomography (CT) in the early days to predict progression to severe illness in a cohort of COVID-19 patients. Methods: This retrospective cohort study included confirmed COVID-19 patients. Three quantitative CT features of pneumonia lesions were automatically calculated using artificial intelligence algorithms, representing the percentages of ground-glass opacity volume (PGV), semi-consolidation volume (PSV), and consolidation volume (PCV) in both lungs. CT features, acute physiology and chronic health evaluation II (APACHE-II) score, neutrophil-to-lymphocyte ratio (NLR), and d-dimer, on day 0 (hospital admission) and day 4, were collected to predict the occurrence of severe illness within a 28-day follow-up using both logistic regression and Cox proportional hazard models. Results: We included 134 patients, of whom 19 (14.2%) developed any severe illness. CT features on day 0 and day 4, as well as their changes from day 0 to day 4, showed predictive capability. Changes in CT features from day 0 to day 4 performed the best in the prediction (area under the receiver operating characteristic curve = 0.93, 95% confidence interval [CI] 0.87~0.99; C-index=0.88, 95% CI 0.81~0.95). The hazard ratios of PGV and PCV were 1.39 (95% CI 1.05~1.84, P=0.023) and 1.67 (95% CI 1.17~2.38, P=0.005), respectively. CT features, adjusted for age and gender, on day 4 and in terms of changes from day 0 to day 4 outperformed APACHE-II, NLR, and d-dimer. Conclusions: CT quantification of pneumonia lesions can early and non-invasively predict the progression to severe illness, providing a promising prognostic indicator for clinical management of COVID-19.
Non-small-cell lung cancer (NSCLC) is one of the deadliest cancers in the world. Circular RNA (circRNA) has been shown to participate in oncogenesis regulation, including lung cancer. Although the involvement of circRNAs in lung cancer has been reported, the regulatory mechanisms of circRNAs in NSCLC remain poorly understood. Thus, the present study aims at investigating the role of circARHGAP10 in NSCLC progression, which has been observed to be significantly upregulated in both NSCLC tissues and cell lines with profile analysis. A higher expression of circARHGAP10 also leads to a poor prognosis in NSCLC patients with fluorescence in situ hybridization (FISH). Both in vitro and in vivo experiments found that the downregulation of circARHGAP10 suppressed glycometabolism by decreasing GLUT1 expression. Silencing cir-cARHGAP10 also suppressed proliferation and metastasis by targeting the miR-150-5p/GLUT1 axis in NSCLC, which was confirmed with a luciferase reporter assay. Overexpression of GLUT1 or downregulation miR-150-5p will recover NSCLC cell proliferation and metastasis after a knockdown of cir-cARHGAP10. Taken together, these findings demonstrate that circARHGAP10 suppresses NSCLC progression by acting as a miR-150-5p sponge to promote GLUT1 expression. Thus, circARHGAP10 may be a potential target for NSCLC treatment.
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