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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.
Integration of Spatial and Temporal Information During Floral Induction in <i>Arabidopsis</i>
Wigge, Philip A.,Kim, Min Chul,Jaeger, Katja E.,Busch, Wolfgang,Schmid, Markus,Lohmann, Jan U.,Weigel, Detlef American Association for the Advancement of Scienc 2005 Science Vol.309 No.5737
<P>Flowering of <I>Arabidopsis</I> is regulated by several environmental and endogenous signals. An important integrator of these inputs is the <I>FLOWERING LOCUS T</I> (<I>FT</I>) gene, which encodes a small, possibly mobile protein. A primary response to floral induction is the activation of <I>FT</I> RNA expression in leaves. Because flowers form at a distant site, the shoot apex, these data suggest that <I>FT</I> primarily controls the timing of flowering. Integration of temporal and spatial information is mediated in part by the bZIP transcription factor FD, which is already expressed at the shoot apex before floral induction. A complex of FT and FD proteins in turn can activate floral identity genes such as <I>APETALA1</I> (<I>AP1</I>).</P>
Integration of Spatial and Temporal Information During Floral Induction in Arabidopsis
Philip A. Wigge,Kim, Min-Chul,Katja E. Jaeger,Wolfgang Busch,Markus Schmid,Jan U.Lohmann,Detlef Weigel Plant molecular biology and biotechnology research 2005 Plant molecular biology and biotechnology research Vol.2005 No.
Flowering of Arabidopsis is regulated by several environmental and endogenous signals. An Important integrator of these inputs is the FLOWERING LOCUST(FT) gene, which encodes a small, possibly mobile protein. A primary response to floral induction is the activation is the activation of FT RNA expression in leaves. Because flowers form at a distant site, the shoot apex, these data suggest that FT primarily controls the timing of flowering. Integration of temporal and spatial information is mediated in part by the bZIP transcription factor FD, which is already expressed at the shoot apex before floral induction. A complex of FT and FD proteins in turn can activate floral identity genes such as APETALA1(AP1).