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.

The worldwide prevalence of herpes simplex virus (HSV) and the shortage of efficient vaccines and novel therapeutic strategies against HSV are widely global concerns. The abundance on the virion and the major stimulus for the virus-neutralizing antibodies makes gD a predominant candidate for cure of HSV infection. In this study, we generated a monoclonal antibody (mAb), termed m27f, targeting to glycoprotein D (gD) of HSV-2, which also has cross-reactivity against HSV-1 gD. It has a high level of neutralizing activity against both HSV-1 and HSV-2, and binds to a highly conserved region (residues 292-297) within the pro-fusion domain of gD. It can effectively block HSV cell-to-cell spread in vitro. The pre-or post-attachment neutralization assay and syncytium formation inhibition assay revealed that m27f neutralizes HSV at the post-binding stage. Moreover, therapeutic administration of m27f completely prevented infection-related mortality of mice challenged with a lethal dose of HSV-2. Our newly identified epitope for the neutralizing antibody would facilitate studies of gD-based HSV entry or vaccine design, and m27f itself demonstrated a high potential for adaptation as a protective or therapeutic drug against HSV.
A novel glycoprotein D-specific monoclonal antibody neutralizes herpes simplex virus

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.
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

Neutralizing antibodies are a critical component in the protection or recovery from viral infections. In the absence of available vaccines or antiviral drugs for many important human viral pathogens, the identification and characterization of new human monoclonal antibodies (hmAbs) able to neutralize viruses offers the possibility for effective pre-and/or post-exposure therapeutic modalities. Such hmAbs may also help in our understanding of the virus entry process, the mechanisms of virus neutralization and in the eventual development of specific entry inhibitors, vaccines and research tools. The majority of the more recently developed antiviral hmAbs have come from the use of antibody phage-display technologies using both naive and immune libraries. Many of these agents are also enveloped viruses possessing important neutralizing determinants within their membrane-anchored envelope glycoproteins and the use of recombinant, soluble versions of these viral glycoproteins is often critical in the isolation and development of antiviral hmAbs. This chapter will detail several methods that have been successfully employed to produce, purify and characterize soluble and secreted versions of several viral envelope glycoproteins which have been successfully used as antigens to capture and isolate human phage-displayed monoclonal antibodies.
Preparation of Recombinant Viral Glycoproteins for Novel and Therapeutic Antibody Discovery