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: Viral bronchiolitis is a common clinical syndrome aff ecting infants and young children. Concern about its associated morbidity and cost has led to a large body of research that has been summarised in systematic reviews and integrated into clinical practice guidelines in several countries. The evidence and guideline recommendations consistently support a clinical diagnosis with the limited role for diagnostic testing for children presenting with the typical clinical syndrome of viral upper respiratory infection progressing to the lower respiratory tract. Management is largely supportive, focusing on maintaining oxygenation and hydration of the patient. Evidence suggests no benefi t from bronchodilator or corticosteroid use in infants with a fi rst episode of bronchiolitis. Evidence for other treatments such as hypertonic saline is evolving but not clearly defi ned yet. For infants with severe disease, the insuffi cient available data suggest a role for high-fl ow nasal cannula and continuous positive airway pressure use in a monitored setting to prevent respiratory failure. 
 Title: Viral bronchiolitis

Output: True


Input: Consider Input: Paragraph: A significant number of biosafety level 2 (BSL-2) laboratories have been established in many countries for studies of various types of pathogenic agents and other infectious biological materials. The harmonized management of biological risks in such diverse laboratories thus appears as a real challenge. Zhejiang Province in China has taken the initiative to establish a comprehensively integrated laboratory biosafety management system called SINS (Standardization, Informatization, Normalization and Systematization). The SINS model system has been introduced and adopted in 1,721 BSL-2 laboratories in Zhejiang Province, and thus lead to an increase in the number of biosafety committees from 20% to more than 95% from 2007 to 2018, and the number of biosafety laboratory managers who knows biosafety-related laws and regulations increase from 52.7% to 83.7% from 2009 to 2017. Such achievements indicate that the successful implementation of SINS model has increased the effective control of biological risks in BSL-2 laboratories of the Zhejiang Province. SINS model and its main effects on leading the improvement of laboratory biosafety management was presented in this review. The SINS model helps to strengthen laboratory biosafety and thus effectively reduces occurrence of biosafety-related incidences. This model can potentially be used by other regions or countries where harmonized biosafety management system is still under-developing. 
 Title: SINS model in the management of biosafety level 2 laboratories: exploration and practice-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)


Output: False