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".

[EX Q]: 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
[EX A]: False

[EX Q]: Paragraph: • The M2 protein of influenza A is a proton-gated proton channel that is required for viral replication • Current influenza strains are resistant to licensed M2 antivirals, so new therapies that target M2 are needed • The structure and function of M2, rise of drug resistance mutations, and current antiviral strategies are discussed The ongoing threat of seasonal and pandemic influenza to human health requires antivirals that can effectively supplement existing vaccination strategies. The M2 protein of influenza A virus (IAV) is a proton-gated, proton-selective ion channel that is required for virus replication and is an established antiviral target. While licensed adamantane-based M2 antivirals have been historically used, M2 mutations that confer major adamantane resistance are now so prevalent in circulating virus strains that these drugs are no longer recommended. Here we review the current understanding of IAV M2 structure and function, mechanisms of inhibition, the rise of drug resistance mutations, and ongoing efforts to develop new antivirals that target resistant forms of M2. 
 Title: Journal Pre-proof Put a cork in it: Plugging the M2 viral ion channel to sink influenza Put a cork in it: Plugging the M2 viral ion channel to sink influenza
[EX A]: False

[EX Q]: Paragraph: The serum antibodies to severe acute respiratory syndrome (SARS) coronavirus of 18 SARS patients were checked at 1 month and every 3 months after disease onset. All of them except one, who missed blood sampling at 1 month, tested positive for the immunoglobulin G (IgG) antibody at 1 month. Fifteen out of 17 tested positive for the IgM antibody at 1 month. The serum IgM antibody of most patients became undetectable within 6 months after the onset of SARS. The IgG antibody of all 17 patients, whose serum was checked 1 year after disease onset, remained positive. 
 Title: Longitudinal Analysis of Severe Acute Respiratory Syndrome (SARS) Coronavirus-Specific Antibody in SARS Patients
[EX A]:
False