TASK DEFINITION: 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".
PROBLEM: Paragraph: Cytokine expression patterns of T cells can be regulated by pre-commitment to stable effector phenotypes, further modification of moderately stable phenotypes, and quantitative changes in cytokine production in response to acute signals. We showed previously that the epidermal growth factor family member Amphiregulin is expressed by T cell receptor-activated mouse CD4 T cells, particularly Th2 cells, and helps eliminate helminth infection. Here we report a detailed analysis of the regulation of Amphiregulin expression by human T cell subsets. Signaling through the T cell receptor induced Amphiregulin expression by most or all T cell subsets in human peripheral blood, including naive and memory CD4 and CD8 T cells, Th1 and Th2 in vitro T cell lines, and subsets of memory CD4 T cells expressing several different chemokine receptors and cytokines. In these different T cell types, Amphiregulin synthesis was inhibited by an antagonist of protein kinase A, a downstream component of the cAMP signaling pathway, and enhanced by ligands that increased cAMP or directly activated protein kinase A. Prostaglandin E2 and adenosine, natural ligands that stimulate adenylyl cyclase activity, also enhanced Amphiregulin synthesis while reducing synthesis of most other cytokines. Thus, in contrast to mouse T cells, Amphiregulin synthesis by human T cells is regulated more by acute signals than precommitment of T cells to a particular cytokine pattern. This may be appropriate for a cytokine more involved in repair than attack functions during most inflammatory responses. 
 Title: The Acute Environment, Rather than T Cell Subset Pre- Commitment, Regulates Expression of the Human T Cell Cytokine Amphiregulin

SOLUTION: False

PROBLEM: 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

SOLUTION: False

PROBLEM: Paragraph: CD13 is a large cell surface peptidase expressed on the monocytes and activated endothelial cells important for homing to and resolving the damaged tissue at sites of injury. We have previously shown that crosslinking of human monocytic CD13 with activating antibodies induces strong adhesion to endothelial cells in a tyrosine kinase-and microtubule-dependent manner. In the current study we examined the molecular mechanisms underlying these observations in vitro and in vivo. We found that crosslinking of CD13 on U937 monocytic cells induced phosphorylation of a number of proteins, including Src, FAK and ERK and inhibition of these abrogated CD13dependent adhesion. We found that CD13 itself was phosphorylated in a Src dependent manner, an unexpected finding as its 7 amino acid cytoplasmic tail was assumed to be inert. Furthermore, CD13 was constitutively associated with the scaffolding protein IQGAP1 and CD13 crosslinkinginduced complex formation with the actin-binding protein -actinin, linking membrane-bound CD13 to the cytoskeleton, further supporting CD13 as an inflammatory adhesion molecule. Mechanistically, mutation of the conserved CD13 cytoplasmic tyrosine to phenylalanine abrogated adhesion, Src, FAK and ERK phosphorylation and cytoskeletal alterations upon antibody crosslinking. Finally, CD13 was phosphorylated in isolated murine inflammatory peritoneal exudate cells and adoptive transfer of monocytic cell lines engineered to express the mutant CD13 were severely impaired in their ability to migrate into the inflamed peritoneum, confirming that CD13 phosphorylation is relevant to inflammatory cell trafficking in vivo. Therefore, this study identifies CD13 as a novel, direct activator of intracellular signaling pathways in pathophysiological conditions. 
 Title: Tyrosine Phosphorylation of CD13 Regulates Inflammatory Cell- Cell Adhesion and Monocyte Trafficking

SOLUTION:
True