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".

Input: Consider Input: Paragraph: Background: Porcine epidemic diarrhea virus (PEDV) is emerging as a pathogenic coronavirus that causes a huge economic burden to the swine industry. Interaction of the viral spike (S) surface glycoprotein with the host cell receptor is recognized as the first step of infection and is the main determinant of virus tropism. The mechanisms by which neutralizing antibodies inhibit PEDV have not been defined. Isolating PEDV neutralizing antibodies are crucial to identifying the receptor-binding domains of the viral spike and elucidating the mechanism of protection against PEDV infection. Methods: B cell hybridoma technique was used to generate hybridoma cells that secrete specific antibodies. E.coli prokaryotic expression system and Bac-to-Bac expression system were used to identify the target protein of each monoclonal antibody. qPCR was performed to analyze PEDV binding to Vero E6 cells with neutralizing antibody. Results: We identified 10 monoclonal antibodies using hybridoma technology. Remarkably, 4 mAbs (designed 2G8, 2B11, 3D9, 1E3) neutralized virus infection potently, of which 2B11 and 1E3 targeted the conformational epitope of the PEDV S protein. qPCR results showed that both 2B11 and 2G8 blocked virus entry into Vero cells. Conclusion: The data suggested that PEDV neutralizing antibody inhibited virus infection by binding to infectious virions, which could work as a tool to find the receptor-binding domains. 
 Title: Neutralizing antibodies against porcine epidemic diarrhea virus block virus attachment and internalization

Output: False


Input: Consider Input: Paragraph: Mannose-binding lectin (MBL) is a key soluble pathogen recognition protein of the innate immune system that binds specific mannose-containing glycans on the surfaces of microbial agents and initiates complement activation via the lectin pathway. Prior studies showed that MBL-dependent activation of the complement cascade neutralized insect cell-derived West Nile virus (WNV) in cell culture and restricted pathogenesis in mice. Here, we investigated the antiviral activity of MBL in infection by dengue virus (DENV), a related flavivirus. Using a panel of naïve sera from mouse strains deficient in different complement components, we showed that inhibition of infection by insect cell-and mammalian cell-derived DENV was primarily dependent on the lectin pathway. Human MBL also bound to DENV and neutralized infection of all four DENV serotypes through complement activation-dependent and -independent pathways. Experiments with human serum from naïve individuals with inherent variation in the levels of MBL in blood showed a direct correlation between the concentration of MBL and neutralization of DENV; samples with high levels of MBL in blood neutralized DENV more efficiently than those with lower levels. Our studies suggest that allelic variation of MBL in humans may impact complement-dependent control of DENV pathogenesis. Citation Avirutnan P, et al. 2011. Complement-mediated neutralization of dengue virus requires mannose-binding lectin. mBio 2(6):e00276-11. 
 Title: Complement-Mediated Neutralization of Dengue Virus Requires Mannose-Binding Lectin

Output: True


Input: Consider Input: Paragraph: Background: To control the spread of Corona Virus Disease , 30 screening large numbers of suspected cases for appropriate quarantine and 31 treatment is a priority. Pathogenic laboratory testing is the diagnostic gold standard 32 but it is time consuming with significant false negative results. Fast and accurate 33 diagnostic methods are urgently needed to combat the disease. Based on 34 COVID-19 radiographical changes in CT images, we aimed to develop a deep 35 learning method that could extract COVID-19's graphical features in order to provide 36 a clinical diagnosis ahead of the pathogenic test, thus saving critical time for 37 disease control. 38 Methods and Findings: We collected 1,065CT images of pathogen-confirmed 39 COVID-19 cases (325 images) along with those previously diagnosed with typical 40 viral pneumonia (740 images). We modified the Inception transfer-learning model 41 to establish the algorithm, followed by internal and external validation. The internal 42 validation achieved a total accuracy of 89.5% with specificity of 0.88 and sensitivity 43 of 0.87. The external testing dataset showed a total accuracy of 79.3% with 44 specificity of 0.83 and sensitivity of 0.67. In addition, in 54 COVID-19 images that 45 first two nucleic acid test results were negative, 46 were predicted as COVID-19 46 positive by the algorithm, with the accuracy of 85.2%. 47 Conclusion: These results demonstrate the proof-of-principle for using 48 artificial intelligence to extract radiological features for timely and accurate 49 COVID-19 diagnosis. 50 
 Title: Breast Cancer Prevention and Therapy, Ministry of Education


Output: True