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      • KCI등재SCOPUS

        Prediction of Patient Management in COVID-19 Using Deep Learning-Based Fully Automated Extraction of Cardiothoracic CT Metrics and Laboratory Findings

        Weikert,Thomas,Rapaka,Saikiran,Grbic,Sasa,Re,Thomas,Chaganti,Shikha,Winkel,David,J.,Anastasopoulos,Constantin,Niemann,Tilo,Wiggli,Benedikt,J.,Bremerich,Jens,Twerenbold,Raphael,Sommer,Gregor,Comaniciu 대한영상의학회 2021 Korean Journal of Radiology Vol.22 No.6

        Objective: To extract pulmonary and cardiovascular metrics from chest CTs of patients with coronavirus disease 2019 (COVID-19) using a fully automated deep learning-based approach and assess their potential to predict patient management. Materials and Methods: All initial chest CTs of patients who tested positive for severe acute respiratory syndrome coronavirus 2 at our emergency department between March 25 and April 25, 2020, were identified (n = 120). Three patient management groups were defined: group 1 (outpatient), group 2 (general ward), and group 3 (intensive care unit [ICU]). Multiple pulmonary and cardiovascular metrics were extracted from the chest CT images using deep learning. Additionally, six laboratory findings indicating inflammation and cellular damage were considered. Differences in CT metrics, laboratory findings, and demographics between the patient management groups were assessed. The potential of these parameters to predict patients' needs for intensive care (yes/no) was analyzed using logistic regression and receiver operating characteristic curves. Internal and external validity were assessed using 109 independent chest CT scans. Results: While demographic parameters alone (sex and age) were not sufficient to predict ICU management status, both CT metrics alone (including both pulmonary and cardiovascular metrics; area under the curve [AUC] = 0.88; 95% confidence interval [CI] = 0.79–0.97) and laboratory findings alone (C-reactive protein, lactate dehydrogenase, white blood cell count, and albumin; AUC = 0.86; 95% CI = 0.77–0.94) were good classifiers. Excellent performance was achieved by a combination of demographic parameters, CT metrics, and laboratory findings (AUC = 0.91; 95% CI = 0.85–0.98). Application of a model that combined both pulmonary CT metrics and demographic parameters on a dataset from another hospital indicated its external validity (AUC = 0.77; 95% CI = 0.66–0.88). Conclusion: Chest CT of patients with COVID-19 contains valuable information that can be accessed using automated image analysis. These metrics are useful for the prediction of patient management.

      • Control of lateral organ development and flowering time by the <i>Arabidopsis thaliana</i> MADS-box Gene <i>AGAMOUS-LIKE6</i>

        Koo,,Sung,C.,Bracko,,Oliver,Park,,Mi,S.,Schwab,,Rebecca,Chun,,Hyun,J.,Park,,Kyoung,M.,Seo,,Jun,S.,Grbic,,Vojislava,Balasubramanian,,Sureshkumar,Schmid,,Markus,Godard,,Franç,ois,Yun,,Dae-Jin,Lee, Blackwell Publishing Ltd 2010 The Plant journal Vol.62 No.5

        <P>Summary</P><P>MADS-domain transcription factors play pivotal roles in various developmental processes. The lack of simple loss-of-function phenotypes provides impediments to understand the biological function of some of the MADS-box transcription factors. Here we have characterized the potential role of the <I>Arabidopsis thaliana AGAMOUS-LIKE6</I> (<I>AGL6</I>) gene by fusing full-length coding sequence with transcriptional activator and repressor domains and suggest a role for <I>AGL6</I> in lateral organ development and flowering. Upon photoperiodic induction of flowering, <I>AGL6</I> becomes expressed in abaxial and proximal regions of cauline leaf primordia, as well as the cryptic bracts subtending flowers. In developing flowers, <I>AGL6</I> is detected in the proximal regions of all floral organs and in developing ovules. Converting <I>AGL6</I> into a strong activator through fusion to the VP16 domain triggers bract outgrowth, implicating <I>AGL6</I> in the development of bractless flowers in Arabidopsis<I>.</I> In addition, ectopic reproductive structures form on both bracts and flowers in <I>gAGL6::VP16</I> transgenic plants, which is dependent on B and C class homeotic genes, but independent of <I>LEAFY.</I> Overexpression of both <I>AGL6</I> and its transcriptional repressor form, <I>AGL6::EAR</I>, causes precocious flowering and terminal flower formation, suggesting that <I>AGL6</I> suppresses the function of a floral repressor.</P>

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