TASK DEFINITION: In this task, you're given a paragraph from the research paper and your task is to generate a suitable title for the research paper based on the given paper. Under 100 words is a good title length.
PROBLEM: Purpose: COVID-19 has become global threaten. CT acts as an important method of 60 diagnosis. However, human-based interpretation of CT imaging is time consuming. 61 More than that, substantial inter-observer-variation cannot be ignored. We aim at 62 developing a diagnostic tool for artificial intelligence (AI)-based classification of CT 63 images for recognizing COVID-19 and other common infectious diseases of the lung. 64 Experimental Design: In this study, images were retrospectively collected and 65 prospectively analyzed using machine learning. CT scan images of the lung that show 66 or do not show COVID-19 were used to train and validate a classification framework 67 based on convolutional neural network. Five conditions including COVID-19 68 pneumonia, non-COVID-19 viral pneumonia, bacterial pneumonia, pulmonary 69 tuberculosis, and normal lung were evaluated. Training and validation set of images 70 were collected from Wuhan Jin Yin-Tan Hospital whereas test set of images were 71 collected from Zhongshan Hospital Xiamen University and the fifth Hospital of 72 Wuhan. 73 Results: Accuracy, sensitivity, and specificity of the AI framework were reported. For 74 test dataset, accuracies for recognizing normal lung, COVID-19 pneumonia, 75 non-COVID-19 viral pneumonia, bacterial pneumonia, and pulmonary tuberculosis 76 were 99.4%, 98.8%, 98.5%, 98.3%, and 98.6%, respectively. For the test dataset, 77 accuracy, sensitivity, specificity, PPV, and NPV of recognizing COVID-19 were 78 98.8%, 98.2%, 98.9%, 94.5%, and 99.7%, respectively. 79 All rights reserved. No reuse allowed without permission. the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

SOLUTION: Deep Learning-Based Recognizing COVID-19 Deep Learning-Based Recognizing COVID-19 and other Common Infectious Diseases of the Lung by Chest CT 2 Scan Images

PROBLEM: Essential oil of star anise as well as phenylpropanoids and sesquiterpenes, for example, trans-anethole, eugenol, β-eudesmol, farnesol, β-caryophyllene and β-caryophyllene oxide, which are present in many essential oils, were examined for their antiviral activity against herpes simplex virus type 1 (HSV-1) in vitro. Antiviral activity was analyzed by plaque reduction assays and mode of antiviral action was determined by addition of the drugs to uninfected cells, to the virus prior to infection or to herpesvirusinfected cells. Star anise oil reduced viral infectivity by >99%, phenylpropanoids inhibited HSV infectivity by about 60-80% and sesquiterpenes suppressed herpes virus infection by 40-98%. Both, star anise essential oil and all isolated compounds exhibited anti-HSV-1 activity by direct inactivation of free virus particles in viral suspension assays. All tested drugs interacted in a dosedependent manner with herpesvirus particles, thereby inactivating viral infectivity. Star anise oil, rich in trans-anethole, revealed a high selectivity index of 160 against HSV, whereas among the isolated compounds only β-caryophyllene displayed a high selectivity index of 140. The presence of β-caryophyllene in many essential oils might contribute strongly to their antiviral ability. These results indicate that phenylpropanoids and sesquiterpenes present in essential oils contribute to their antiviral activity against HSV.

SOLUTION: Original Article Screening for Antiviral Activities of Isolated Compounds from Essential Oils

PROBLEM: Background: Since the discovery of Middle East respiratory syndrome coronavirus (MERS-CoV) in 2012, diagnostic protocols were quickly published and deployed globally. Objectives: We set out to assess the quality of MERS-CoV molecular diagnostics worldwide. Study design: Both sensitivity and specificity were assessed using 12 samples containing different viral loads of MERS-CoV or common coronaviruses (OC43, 229E, NL63, HKU1). Results: The panel was sent to more than 106 participants, of which 99 laboratories from 6 continents returned 189 panel results.Scores ranged from 100% (84 laboratories) to 33% (1 laboratory). 15% of respondents reported quantitative results, 61% semi-quantitative (Ct-values or time to positivity) and 24% reported qualitative results. The major specific technique used was real-time RT-PCR using the WHO recommended targets upE, ORF1a and ORF1b. The evaluation confirmed that RT-PCRs targeting the ORF1b are less sensitive, and therefore not advised for primary diagnostics. Conclusions: The first external quality assessment MERS-CoV panel gives a good insight in molecular diagnostic techniques and their performances for sensitive and specific detection of MERS-CoV RNA globally. Overall, all laboratories were capable of detecting MERS-CoV with some differences in sensitivity. The observation that 8% of laboratories reported false MERS-CoV positive single assay results shows room for improvement, and the importance of using confirmatory targets.

SOLUTION:
First international external quality assessment of molecular diagnostics for Mers-CoV