Objective The outbreak of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-COV-2) has caused more than 26 million cases of Corona virus disease (COVID-19) in the world so far. To control the spread of the disease, screening large numbers of suspected cases for appropriate quarantine and treatment are a priority. Pathogenic laboratory testing is typically the gold standard, but it bears the burden of significant false negativity, adding to the urgent need of alternative diagnostic methods to combat the disease. Based on COVID-19 radiographic changes in CT images, this study hypothesized that artificial intelligence methods might be able to extract specific graphical features of COVID-19 and provide a clinical diagnosis ahead of the pathogenic test, thus saving critical time for disease control. Methods We collected 1065 CT images of pathogen-confirmed COVID-19 cases along with those previously diagnosed with typical viral pneumonia. We modified the inception transfer-learning model to establish the algorithm, followed by internal and external validation. Results The internal validation achieved a total accuracy of 89.5% with a specificity of 0.88 and sensitivity of 0.87. The external testing dataset showed a total accuracy of 79.3% with a specificity of 0.83 and sensitivity of 0.67. In addition, in 54 COVID-19 images, the first two nucleic acid test results were negative, and 46 were predicted as COVID-19 positive by the algorithm, with an accuracy of 85.2%. Conclusion These results demonstrate the proof-of-principle for using artificial intelligence to extract radiological features for timely and accurate COVID-19 diagnosis. Key Points • The study evaluated the diagnostic performance of a deep learning algorithm using CT images to screen for COVID-19 during the influenza season. • As a screening method, our model achieved a relatively high sensitivity on internal and external CT image datasets. • The model was used to distinguish between COVID-19 and other typical viral pneumonia, both of which have quite similar radiologic characteristics.

To control the spread of Corona Virus Disease , screening large numbers of suspected cases for appropriate quarantine and treatment is a priority.Pathogenic laboratory testing is the diagnostic gold standard but it is time consuming with significant false negative results. Fast and accurate diagnostic methods are urgently needed to combat the disease. Based on COVID-19 radiographical changes in CT images, we aimed to develop a deep learning method that could extract COVID-19's graphical features in order to provide a clinical diagnosis ahead of the pathogenic test, thus saving critical time for disease control.Methods：We collected 1,119 CT images of pathogen-confirmed COVID-19 cases along with those previously diagnosed with typical viral pneumonia. We modified the Inception transfer-learning model to establish the algorithm, followed by internal and external validation. Results:The internal validation achieved a total accuracy of 89.5% with specificity of 0.88 and sensitivity of 0.87. The external testing dataset showed a total accuracy of 79.3% with specificity of 0.83 and sensitivity of 0.67. In addition, in 54 COVID-19 images that first two nucleic acid test results were negative, 46 were predicted as COVID-19 positive by the algorithm, with the accuracy of 85.2%. Conclusion:These results demonstrate the proof-of-principle for using artificial intelligence to extract radiological features for timely and accurate COVID-19 diagnosis.

No abstract

No abstract

Highlights d ADORA1 inhibition promotes immune escape by regulating tumor PD-L1 via ATF3 d ADORA1 or ATF3 screening may be used to assess PD-1 mAb therapy efficacy d Combination of an ADORA1 antagonist and a PD-1 mAb provides therapeutic benefit

Ghrelin, also called “the hunger hormone”, is a gastric peptide hormone that regulates food intake, body weight, as well as taste sensation, reward, cognition, learning and memory. One unique feature of ghrelin is its acylation, primarily with an octanoic acid, which is essential for its binding and activation of the ghrelin receptor, a G protein-coupled receptor. The multifaceted roles of ghrelin make ghrelin receptor a highly attractive drug target for growth retardation, obesity, and metabolic disorders. Here we present two cryo-electron microscopy structures of Gq-coupled ghrelin receptor bound to ghrelin and a synthetic agonist, GHRP-6. Analysis of these two structures reveals a unique binding pocket for the octanoyl group, which guides the correct positioning of the peptide to initiate the receptor activation. Together with mutational and functional data, our structures define the rules for recognition of the acylated peptide hormone and activation of ghrelin receptor, and provide structural templates to facilitate drug design targeting ghrelin receptor.

BackgroundAnti-obesity drugs are widely used to prevent the complications of obesity, however, the effects of anti-obesity drugs on cardiovascular risk factors are unclear at the present time. We carried out a comprehensively systematic review and meta-analysis to assess the effects of anti-obesity drugs on cardiovascular risk factors.Methodology and Principal FindingsWe systematically searched Medline, EmBase, the Cochrane Central Register of Controlled Trials, reference lists of articles and proceedings of major meetings for relevant literatures. We included randomized placebo-controlled trials that reported the effects of anti-obesity drugs on cardiovascular risk factors compared to placebo. Overall, orlistat produced a reduction of 2.39 kg (95%CI-3.34 to −1.45) for weight, a reduction of 0.27 mmol/L (95%CI: −0.36 to −0.17) for total cholesterol, a reduction of 0.21 mmol/L (95%CI: −0.30 to −0.12) for LDL, a reduction of 0.12 mmol/L (95%CI: −0.20 to −0.04) for fasting glucose, 1.85 mmHg reduction (95%CI: −3.30 to −0.40) for SBP, and a reduction of 1.49 mmHg (95%CI: −2.39 to −0.58) for DBP. Sibutramine only showed effects on weight loss and triglycerides reduction with statistical significances. Rimonabant was associated with statistically significant effects on weight loss, SBP reduction and DBP reduction. No other significantly different effects were identified between anti-obesity therapy and placebo.Conclusion/SignificanceWe identified that anti-obesity therapy was associated with a decrease of weight regardless of the type of the drug. Orlistat and rimonabant could lead to an improvement on cardiovascular risk factors. However, Sibutramine may have a direct effect on cardiovascular risk factors.

The role of CDX2 in trophectoderm (TE) cells has been extensively studied, yet the results are contradictory and species specific. Here, CDX2 expression and function were explored in early porcine embryos. Notably, siRNA-mediated gene knockdown and lentivirusmediated TE-specific gene regulation demonstrated that CDX2 is essential for the maintenance of blastocyst integrity by regulating the BMP4-mediated blastocyst niche and classic protein kinase C (PKC)-mediated TE polarity in mammalian embryos. Mechanistically, CDX2-depleted porcine embryos stalled at the blastocyst stage and exhibited apoptosis and inactive cell proliferation, possibly resulting from BMP4 downregulation. Moreover, TE cells in CDX2-depleted blastocysts displayed defective F-actin apical organization associated with downregulation of PKCα (PRKCA). Collectively, these results provide further insight into the functional diversity of CDX2 in early mammalian embryos.