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: There is a need for simple and inexpensive diagnostic and screening tests for the detection of Cryptosporidium parvum infection in calves. A sucrose wet mount test and a lateral immunochromatography test were evaluated for epidemiological sensitivity and specificity, cost per test, simplicity, test time and ease of batching. Polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) of the Cryptosporidium oocyst wall protein (COWP) gene locus, with gel electrophoresis, was used as a gold standard. Cohen's kappa statistic of agreement (k) between the Ontario Veterinary College (OVC) sucrose wet mount test and COWP PCR-RFLP was 0.82, and the sensitivity and specificity of the OVC sucrose wet mount test were 88.6% and 93.8%, respectively. The sensitivity and specificity of the lateral immunochromatography test were 78.3% and 93.3%, respectively, and agreement between this test and PCR-RFLP was good (k = 0.73). There was substantial agreement between the OVC sucrose wet mount test and the lateral immunochromatography test (k = 0.84). Both tests were inexpensive and easy to use; however, the lateral immunochromatography test was faster and simpler to perform than the sucrose wet mount test, and was generally more user-friendly. These tests provide practitioners and researchers with cheap, quick and accurate methods of detecting C. parvum infection in young calves. # 
 Title: Multiattribute evaluation of two simple tests for the detection of Cryptosporidium parvum in calf faeces

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: Porcine deltacoronavirus (PDCoV) is an emerging swine coronavirus causing diarrhea and intestinal damage in nursing piglets. Previous work showed that PDCoV infection inhibits type I interferon (IFN) production. To further identify and characterize the PDCoV-encoded IFN antagonists will broaden our understanding of its pathogenesis. Nonstructural protein 15 (nsp15) encodes an endoribonuclease that is highly conserved among vertebrate nidoviruses (coronaviruses and arteriviruses) and plays a critical role in viral replication and transcription. Here, we found that PDCoV nsp15 significantly inhibits Sendai virus (SEV)-induced IFN-β production. PDCoV nsp15 disrupts the phosphorylation and nuclear translocation of NF-κB p65 subunit, but not antagonizes the activation of transcription factor IRF3. Interestingly, site-directed mutagenesis found that PDCoV nsp15 mutants (H129A, H234A, K269A) lacking endoribonuclease activity also suppress SEV-induced IFN-β production and NF-κB activation, suggesting that the endoribonuclease activity is not required for its ability to antagonize IFN-β production. Taken together, our results demonstrate that PDCoV nsp15 is an IFN antagonist and it inhibits interferon-β production via an endoribonuclease activity-independent mechanism. 
 Title: Porcine deltacoronavirus nsp15 antagonizes interferon-β production independently of its endoribonuclease activity

SOLUTION:
False