Objectives Rapid and accurate diagnosis of coronavirus disease 2019 is critical during the epidemic. We aim to identify differences in CT imaging and clinical manifestations between pneumonia patients with and without COVID-19, and to develop and validate a diagnostic model for COVID-19 based on radiological semantic and clinical features alone. Methods A consecutive cohort of 70 COVID-19 and 66 non-COVID-19 pneumonia patients were retrospectively recruited from five institutions. Patients were divided into primary (n = 98) and validation (n = 38) cohorts. The chi-square test, Student's t test, and Kruskal-Wallis H test were performed, comparing 1745 lesions and 67 features in the two groups. Three models were constructed using radiological semantic and clinical features through multivariate logistic regression. Diagnostic efficacies of developed models were quantified by receiver operating characteristic curve. Clinical usage was evaluated by decision curve analysis and nomogram. Results Eighteen radiological semantic features and seventeen clinical features were identified to be significantly different. Besides ground-glass opacities (p = 0.032) and consolidation (p = 0.001) in the lung periphery, the lesion size (1-3 cm) is also significant for the diagnosis of COVID-19 (p = 0.027). Lung score presents no significant difference (p = 0.417). Three diagnostic models achieved an area under the curve value as high as 0.986 (95% CI 0.966~1.000). The clinical and radiological semantic models provided a better diagnostic performance and more considerable net benefits. Conclusions Based on CT imaging and clinical manifestations alone, the pneumonia patients with and without COVID-19 can be distinguished. A model composed of radiological semantic and clinical features has an excellent performance for the diagnosis of COVID-19.Xiaofeng Chen and Yanyan Tang contributed equally to this work.Electronic supplementary material The online version of this article (https://doi.org/10.1007/s00330-020-06829-2) contains supplementary material, which is available to authorized users. Key Points• Based on CT imaging and clinical manifestations alone, the pneumonia patients with and without COVID-19 can be distinguished. • A diagnostic model for COVID-19 was developed and validated using radiological semantic and clinical features, which had an area under the curve value of 0.986 (95% CI 0.966~1.000) and 0.936 (95% CI 0.866~1.000) in the primary and validation cohorts, respectively.

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In this study, we aimed to use 3T magnetic resonance imaging (MRI), which is clinically available, to determine the extracellular pH (pHe) of liver tumors and prospectively evaluate the ability of chemical exchange saturation transfer (CEST) MRI to distinguish between benign and malignant liver tumors. Methods: Different radiofrequency irradiation schemes were assessed for ioversol-based pH measurements at 3T. CEST effects were quantified in vitro using the asymmetric magnetization transfer ratio (MTRasym) at 4.3 ppm from the corrected Z spectrum. Generalized ratiometric analysis was conducted by rationing resolved ioversol CEST effects at 4.3 ppm at a flip angle of 60 and 350°. Fifteen patients recently diagnosed with hepatic carcinoma and five patients diagnosed with hepatic hemangioma [1 male; mean age, 48.6 (range, 37-59) years] were assessed. Results: By conducting dual-power CEST MRI, the pH of solutions was determined to be 6.0-7.2 at 3T in vitro. In vivo, ioversol signal intensities in the tumor region showed that the extracellular pH in hepatic carcinoma was acidic(mean ± standard deviation, 6.66 ± 0.19), whereas the extracellular pH was more physiologically neutral in hemangioma (mean ± standard deviation, 7.34 ± 0.09).The lesion size was similar between CEST pH MRI and T2-weighted imaging. Conclusion: dual-power CEST MRI can detect extracellular pH in human liver tumors and can provide molecular-level diagnostic tools for differentiating benign and malignant liver tumors at 3T.

The present study used metabolomics, transcriptomics, protein analysis by immunoblots, chemical inhibition and gene knockout mice to determine the effects of celastrol on cholestasis and its mechanisms. Samples from patients were used to validate our findings, and the data supported the conclusions that celastrol protected against cholestatic liver injury through modulation of the SIRT1 and FXR. Graphical Abstract Highlights• 1. Celastrol alleviated cholestatic liver injury.• 2. Celastrol inhibited the decrease of SIRT1 induced by deoxycholic acid.• 3. SIRT1-FXR signaling pathway mediated the effect of celastrol.Celastrol, derived from the roots of the Tripterygium Wilfordi, shows a striking effect on obesity. In the present study, the role of celastrol in cholestasis was investigated using metabolomics and transcriptomics. Celastrol treatment significantly alleviated cholestatic liver injury in mice induced by ␣-naphthyl isothiocyanate (ANIT) and thioacetamide (TAA). Celastrol was found to activate sirtuin 1 (SIRT1), increase farnesoid X receptor (FXR) signaling and inhibit nuclear factor-kappa B and P53 signaling. The protective role of celastrol in cholestatic liver injury was diminished in mice on co-administration of SIRT1 inhibitors. Further, the effects of celastrol on cholestatic liver injury were dramatically decreased in Fxr-null mice, suggesting that the SIRT1-FXR signaling pathway mediates the protective effects of celastrol. These observations demonstrated a novel role for celastrol in protecting against cholestatic liver injury through modulation of the SIRT1 and FXR.