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.

Ex Input:
Please cite this article as: El-Shall N.A., Shewita R.S., Abd El-Hack M.E., AlKahtane A., Alarifi S., Alkahtani S., Abdel-Daim M.M. & Sedeik M.E., Effect of essential oils on the immune response to some viral vaccines in broiler chickens, with special reference to Newcastle disease virus, Poultry Science (2020), doi: https://doi.(Mohamed E. Abd El-Hack). 21 (1952±28.82) did not significantly (p>0.05) improve growth performance compared to 41 the non-treated birds (NC and VC) (1970±19.56 and 1904±38.66). Essential oils 42 showed an antiviral effect on vNDv in vivo (in chickens) as a preventive measure 43 (PRV) as well as some therapeutic effect (TTT) through decreasing the viral shedding 44 titres (loNC 0 ), mortality rate, and severity of clinical signs and post mortem lesions, in 45 addition to serum malondialdhyde (MDA) level. Regarding the other viruses, the EOs 46 mixture did not improve the immune response to the AI and IB vaccines but age (2108±341.05). The studied EOs mixture showed an immune stimulating response 49 to ND and IBD vaccines, antiviral effect against ND virus especially if administered 50 before the challenge, however it did not have a growth promoting effect. 51 52

Ex Output:
Journal Pre-proof Effect of essential oils on the immune response to some viral vaccines in broiler chickens, with special reference to Newcastle disease virus Effect of essential oils on the immune response to some viral vaccines in broiler 3 chickens, with special reference to Newcastle disease virus 4


Ex 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.

Ex 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


Ex Input:
To develop early diagnostic reagents, effective vaccines, and even drugs against SARS-associated coronavirus (SARS-CoV), the human single fold single-chain antibody fragments, (scFv) libraries I + J (Tomlinson I + J) were used to identify novel scFvs, which can specifically bind to SARS-CoV. Interestingly, two scFvs (B5 and B9) exhibited higher binding specificity to SARS-CoV with the OD 450 value 0.608 and 0.545, respectively, and their coding sequences shared the identical sequence composed of V H gene (351 bp) and V L gene (327 bp), so the two scFvs were uniformly named as SA59B and chosen for further analysis. SA59B scFv was expressed in soluble form in Escherichia coli HB2151 and purified by immobilized metal affinity chromatography. The soluble 30 kDa SA59B scFv-antibody was verified in SDS-PAGE and Western-blot. The purified SA59B scFv-antibody was labeled with HRP by the glutaraldehyde method, and the concentration of HRP and SA59B scFv-antibody in the SA59B-HRP solution reached 2.4 and 2.28 mg/ml, respectively. Then, the binding ability of SA59B-HRP to SARS-CoV was evaluated by ELISA with S/N of 11.6, indicating higher binding specificity between them. Finally, both the SA59B sequence specificity and its application for diagnosis, prophylaxis or therapy of SARS were discussed.

Ex Output:

Identification of single-chain antibody fragments specific against SARS-associated coronavirus from phage-displayed antibody library