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.

Example Input: BALB/c male mice were mated with either Swiss-Webster or MF1 females to produce first generation cross-bred offspring. Hybridoma cell lines, from the fusion of P3-NS1-Ag4/1 myeloma cells with spleen cells sensitised to the porcine coronavirus causing transmissible gastroenteritis, were injected intraperitoneally into these mice to produce ascitic fluid containing monoclonal antibodies. Mice of 11 weeks of age weighing between 26 and 34 g were used. The volume of ascites produced by mice injected with four of the five hybrid cell lines tested was greater in the cross-bred offspring than in the BALB/c parent. The fifth cell line gave comparable volumes in the MF1 cross-breed and BALB/c parent but a lesser volume in the Swiss-Webster cross-breed. The antibody titres of the ascites as determined by virus neutralisation, radioimmune and indirect immune fluorescence assays, did not differ significantly between mouse types. The ability to use all offspring from a litter of cross-bred mice, irrespective of sex, and the increased volume of ascitic fluid formed in each mouse, permits fewer animals to be used for the production of ascites in these strains, thereby offering considerable economic and ethical advantages over the use of BALB/c mice.
Example Output: Comparison of ascites production for monoclonal antibodies in BALB/c and BALB/c-derived cross-bred mice

Example Input: 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.
Example Output: 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

Example Input: Purpose-Hospital-acquired infections with highly resistant organisms are an important problem among critically ill patients. Control of these organisms has largely focused within individual hospitals. We examine the extent to which transfers of critically ill patients could be a vector for wide spread of highly resistant organisms, and compare the efficiency of different approaches to targeting infection control resources. analyzed the network of interhospital transfers of intensive care unit patients in 2005 U.S. Medicare data and 2004-2006 Pennsylvania all-payer data. We simulated spread of highly resistant hospital-acquired infections by randomly choosing a single hospital to develop a highly resistant organism and following spread of infection or colonization throughout the network under varying strategies of infection control and varying levels of infectivity. Results-Critical care transfers could spread a highly resistant organism between any two U.S. hospitals in a median of three years. Hospitals varied substantially in their importance to limiting potential spread. Targeting resources to a small subset of hospitals based on their position in the transfer network was 16 times more efficient than distributing infection control resources uniformly. Within any set of targeted hospitals, the best strategy for infection control heavily concentrated resources at a few particularly important hospitals, regardless of level of infectivity. Conclusions-Critical care transfers provide a plausible vector for wide-spread dissemination of highly resistant hospital-acquired microorganisms. Infection control efforts can be made more efficient by selectively targeting hospitals most important for transmission.
Example Output:
Assistant Professor of Internal Medicine, Pulmonary & Critical Care Medicine, 3A23 300 NIB, SPC 5419, 300 North Ingalls