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".

Q: Paragraph: The next influenza pandemic may be imminent. Because antiviral agents and vaccines will be unavailable to people in most countries, we need to determine whether other agents could offer clinical benefits. Influenza is associated with an increase in acute cardiovascular diseases, and influenza viruses induce proinflammatory cytokines. Statins are cardioprotective and have anti-inflammatory and immunomodulatory effects, and they thus might benefit patients with influenza. This hypothesis should be evaluated by using administrative databases to search for reduced rates of hospitalization and death due to influenzarelated conditions among people taking statins. These studies should be followed by laboratory studies of statins in animal and cell-based models of influenza virus infection and, later, by clinical trials. Positive results from such studies would provide physicians in all countries with something to offer patients for treatment and prophylaxis of pandemic influenza. Generic statins will be widely distributed and inexpensive. They might be the only agents that could alter the course of a global pandemic. 
 Title: Pandemic Influenza: A Potential Role for Statins in Treatment and Prophylaxis

A: True
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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

A: False
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Q: Paragraph: Using 11 overlapping synthetic peptides covering more than 95% of the amino acid sequence of capsid protein of rubella virus, 7 CD4+ T cell clones (Rl 0, Rl 1, R18, A2, Al 0, Al 1, and Al 2) isolated from 2 rubella seropositive donors reacted strongly to rubella capsid peptides C6 (residues 119-l 521, C9 (residues 205-233), or Cl 1 (residues 255-280), respectively, in both proliferation and cytotoxicity assay. Truncated peptides C6E (residues 125-l 39), C9B (residues 205-216), and Cl 1 E (residues 260-272) were shown to be involved directly to the T cell determinants of C6, C9, and Cl 1, respectively. Genetic restriction of these T cell clones was analyzed by using human cell lines with various HLA-DR phenotypes as targets and/or antigen-presenting cells in cytotoxicity assay and/or proliferation assays. The results indicated that the recognition of peptide C6 byT cell clones (Rl 1 and R18) was associated with DRw9 molecule, while the HLA restriction element of the responses of other T cell clones (A2 and Al 1, Al 0, and Al 2) that reacted with peptide C9 or Cl 1 was DR4 molecule. However, there may be a cross-recognition by the T cell clone (Al 2) between DRl and DR4 subtypes. 
 Title: Characterization of the Specificity and Genetic Restriction of Human CD4+ Cytotoxic T Cell Clones Reactive to Capsid Antigen of Rubella Virus

A:
False
****