This copy is for personal use only. To order printed copies, contact reprints@rsna.org I n P r e s s Abbreviations: AUC = area under the receiver operating characteristic curve CI = confidence interval COVID-19 = coronavirus disease 2019 COVNet = COVID-19 detection neural network CAP = community acquired pneumonia DICOM = digital imaging and communications in medicine Key Results:A deep learning method was able to identify COVID-19 on chest CT exams (area under the receiver operating characteristic curve, 0.96).A deep learning method to identify community acquired pneumonia on chest CT exams (area under the receiver operating characteristic curve, 0.95).There is overlap in the chest CT imaging findings of all viral pneumonias with other chest diseases that encourages a multidisciplinary approach to the final diagnosis used for patient treatment. Summary Statement:Deep learning detects coronavirus disease 2019 (COVID-19) and distinguish it from community acquired pneumonia and other non-pneumonic lung diseases using chest CT. I n P r e s s Abstract:Background: Coronavirus disease has widely spread all over the world since the beginning of 2020. It is desirable to develop automatic and accurate detection of COVID-19 using chest CT.Purpose: To develop a fully automatic framework to detect COVID-19 using chest CT and evaluate its performances. Materials and Methods:In this retrospective and multi-center study, a deep learning model, COVID-19 detection neural network (COVNet), was developed to extract visual features from volumetric chest CT exams for the detection of COVID-19. Community acquired pneumonia (CAP) and other non-pneumonia CT exams were included to test the robustness of the model. The datasets were collected from 6 hospitals between August 2016 and February 2020. Diagnostic performance was assessed by the area under the receiver operating characteristic curve (AUC), sensitivity and specificity. Results:The collected dataset consisted of 4356 chest CT exams from 3,322 patients. The average age is 49±15 years and there were slightly more male patients than female (1838 vs 1484; p-value=0.29). The per-exam sensitivity and specificity for detecting COVID-19 in the independent test set was 114 of 127 (90% [95% CI: 83%, 94%]) and 294 of 307 (96% [95% CI: 93%, 98%]), respectively, with an AUC of 0.96 (p-value<0.001). The per-exam sensitivity and specificity for detecting CAP in the independent test set was 87% (152 of 175) and 92% (239 of 259), respectively, with an AUC of 0.95 (95% CI: 0.93, 0.97). Conclusions:A deep learning model can accurately detect COVID-19 and differentiate it from community acquired pneumonia and other lung diseases.

Significant effort has been applied to discover and develop vehicles which can guide small interfering RNAs (siRNA) through the many barriers guarding the interior of target cells. While studies have demonstrated the potential of gene silencing in vivo, improvements in delivery efficacy are required to fulfill the broadest potential of RNA interference therapeutics. Through the combinatorial synthesis and screening of a different class of materials, a formulation has been identified that enables siRNA-directed liver gene silencing in mice at doses below 0.01 mg/kg. This formulation was also shown to specifically inhibit expression of five hepatic genes simultaneously, after a single injection. The potential of this formulation was further validated in nonhuman primates, where high levels of knockdown of the clinically relevant gene transthyretin was observed at doses as low as 0.03 mg/kg. To our knowledge, this formulation facilitates gene silencing at orders-of-magnitude lower doses than required by any previously described siRNA liver delivery system.

MDM2 is an E3 ubiquitin ligase that regulates the proteasomal degradation and activity of proteins involved in cell growth and apoptosis, including the tumor suppressors p53 and retinoblastoma and the transcription factor E2F1. Although the effect of several MDM2 targets on cardiomyocyte survival and hypertrophy has already been investigated, the role of MDM2 in these processes has not yet been established. We have, therefore, analyzed the effect of overexpression as well as inhibition of MDM2 on cardiac ischemia/ reperfusion injury and hypertrophy. Here we show that isolated cardiac myocytes overexpressing MDM2 acquired resistance to hypoxia/reoxygenation-induced cell death. Conversely, inactivation of MDM2 by a peptide inhibitor resulted in elevated p53 levels and promoted hypoxia/reoxygenation-induced apoptosis. Consistent with this, decreased expression of MDM2 in a genetic mouse model was accompanied by reduced functional recovery of the left ventricles determined with the Langendorff ex vivo model of ischemia/ reperfusion. In contrast to cell survival, cell hypertrophy induced by the ␣-agonists phenylephrine or endothelin-1 was inhibited by MDM2 overexpression. Collectively, our studies indicate that MDM2 promotes survival and attenuates hypertrophy of cardiac myocytes. This differential regulation of cell growth and cell survival is unique, because most other survival factors are prohypertrophic. MDM2, therefore, might be a potential therapeutic target to down-regulate both cell death and pathologic hypertrophy during remodeling upon cardiac infarction. In addition, our data also suggest that cancer treatments with MDM2 inhibitors to reactivate p53 may have adverse cardiac side effects by promoting cardiomyocyte death.The murine double minute 2 (mdm2) 2 gene was originally discovered as one of three genes that was overexpressed in spontaneously transformed mouse BALB/c fibroblasts (1, 2). Overexpression of the mdm2 gene product was shown to lead to transformation, which requires the binding of MDM2 to the tumor suppressor p53 (3). Through this protein-protein interaction, MDM2 inhibits the transcriptional activity of p53 (4). In addition, MDM2 also promotes the ubiquitination and proteasomal degradation of p53 by functioning as an E3 ubiquitin ligase (5, 6). p53 exerts its tumor suppressor effect by transcriptionally activating many target genes, including p21 and PUMA/Noxa, thereby promoting growth arrest both at G 1 and G 2 phases of the cell cycle and programmed cell death (apoptosis), respectively (7). Another transcriptional target of p53 is MDM2 itself, providing an autoregulatory negative feedback loop with a significant role in regulating cell cycle progression and apoptosis (7,8).The importance of the MDM2/p53 interaction has been convincingly demonstrated by in vivo studies. Mice lacking mdm2 are embryonic lethal and die before implantation as early as the blastocyst stage (9). This phenotype is completely rescued by concomitant deletion of p53 (10, 11), suggesting that the embryo lethality was ...

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