In this task, you're given a paragraph and title from the research paper. Your task is to classify whether the given title is suitable or not for the research paper based on the given paragraph. Return "True" if title is proper according to paragraph else "False".

Input: Consider Input: Paragraph: 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.
Title: 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

Output: False

Input: Consider Input: Paragraph: Danhong injection (DHI) is a widely used Chinese Materia Medica standardized product for the clinical treatment of ischemic encephalopathy and coronary heart disease. The bindings of eight natural components in DHI between bovine serum albumin (BSA) were studied by fluorescence spectroscopy technology and molecular docking. According to the results, the quenching process of salvianolic acid B and hydroxysafflor yellow A was a static quenching procedure through the analysis of quenching data by the Stern-Volmer equation, the modified Stern-Volmer equation, and the modified Scatchard equation. Meanwhile, syringin (Syr) enhanced the fluorescence of BSA, and the data were analyzed using the Lineweaver-Burk equation. Molecular docking suggested that all of these natural components bind to serum albumin at the site I location. Further competitive experiments of SaB confirmed the result of molecular docking studies duo to the displacement of warfarin by SaB. Base on these studies, we selected SaB as a research target because it presented the strongest binding ability to BSA and investigated the influence of the multi-components coexisting in DHI on the interaction between the components of the SaB-BSA binding system. The participation of these natural components in DHI affected the interaction between the components of the SaB-BSA system. Therefore, when DHI is used in mammals, SaB is released from serum albumin more quickly than it is used alone. This work would provide a new experiment basis for revealing the scientific principle of compatibility for Traditional Chinese Medicine.
Title: Interaction between the Natural Components in Danhong Injection (DHI) with Serum Albumin (SA) and the Influence of the Coexisting Multi-Components on the SaB-BSA Binding System: Fluorescence and Molecular Docking Studies

Output: False

Input: Consider Input: Paragraph: Oral administration of immunoglobulin prepared from the egg yolk of hens immunized with porcine transmissible gastroenteritis virus (TGEV) has been demonstrated to reduce piglets mortality significantly in our previous studies. In the present study, we investigated the stability of chicken yolk immunoglobulin (IgY) specific to TGEV by measuring the remaining activity by ELISA. The results showed that the IgY was stable between pH 4 and pH 11. In the incubation with pepsin at pH 4 and pH 6, about 90% and 100% of the IgY activity remained, respectively. IgY activity could remain approximately 80% at 60C for 30 min, suggesting that pasteurization can be applied to sterilizing the product. The stability of IgY at 25C and freezing-thawing treatment indicated that the IgY was easy to be conserved. These results highlight the attractive potential application of IgY as the antibodies of oral administration for treatment of TGEV infections.
Title: Preparation and physicochemical property of chicken yolk immunoglobulin (IgY) against porcine transmissible gastroenteritis virus (TGEV)
Output: False