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: Infrared thermography (IRT), one of the most valuable tools, is used for noncontact, noninvasive, and rapid monitoring of body temperature; this has been used for mass screening of febrile travelers at places such as airport quarantine stations for over 10 years after the 2003 severe acute respiratory syndrome (SARS) outbreak. The usefulness of thermography for mass screening has been evaluated in many recent studies; its sensitivity varies from 40 to 89.4% under various circumstances. In this chapter, we perform IRT evaluations for detecting febrile international travelers entering Japan at Nagoya Airport, immediately after the SARS epidemic, . The correlation of body surface temperature measured via thermography with the axillary temperature was significant. Through IRT, febrile individuals were detected with good accuracy and the detection accuracy was improved by corroborating surveillance with self-reporting questionnaires. However, there are several limitations associated with the use of IRT for fever screening. For instance, taking antifebrile medications results in rapid modification of the body temperature and directly affects the efficiency of IRT. To solve this unreliability and obtain higher accuracy in mass screening, we have developed a novel infection screening system using multisensor data, i.e., heart and respiration rates are determined by microwave radar in noncontact manner and facial skin temperature is monitored through IRT. The detection accuracy of the system improved, which is notably higher compared to the conventional screening method using only IRT.
Title: Applications of Infrared Thermography for Noncontact and Noninvasive Mass Screening of Febrile International Travelers at Airport Quarantine Stations

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: It has been previously demonstrated that the generation of measles virus (MV)-specific cytotoxicity (CTL) is reduced in patients with multiple sclerosis (MS). By contrast, CTL specific for influenza virus (FLU) and mumps virus is normal. It is uncertain if reduced CTL is limited to MV in MS patients, or if reduced CTL may be found to other viruses as well. Since MV-specific CTL is predominantly restricted by HLA class II molecules, while FLU-specific and mumps-specific CTL have large HLA class I-restricted components, reduced MV-specific CTL may reflect a broader reduction in HLA class II-restricted CTL in patients with MS. To examine this question we studied the generation of CTL specific for herpes simplex virus type I (HSV). HSV-specific CTL, like MV-specific CTL is predominantly restricted by HLA class II molecules. We found that patients with MS had reduced generation of CTL to both MV and HSV. Most, but not all patients who had reduced generation of CTL to one virus also had a similar impairment with respect to the second virus. Some patients, however, had a reduction in the generation of CTL only to MV or to HSV. These findings extend our earlier observations regarding reduced MV-specific CTL in patients with MS to a second HLA class II-restricted virus, HSV. Such a reduction may reflect discrete impairments in immune function to separate viruses, possibly those that are associated with viral persistence, or may reflect a more generalized defect in HLA class II-restricted CTL.
Title: Multiple sclerosis patients have reduced HLA class II-restricted cytotoxic responses specific for both measles and herpes virus

SOLUTION:
False