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".

Example Input: Paragraph: Ebola virus is the causative agent of Ebola virus disease in humans. The lethality of Ebola virus infection is about 50%, supporting the urgent need to develop anti-Ebola drugs. Glycoprotein (GP) is the only surface protein of the Ebola virus, which is functionally critical for the virus to attach and enter the host cells, and is a promising target for anti-Ebola virus drug development. In this study, using the recombinant HIV-1/Ebola pseudovirus platform we previously established, we evaluated a small molecule library containing various quinoline compounds for anti-Ebola virus entry inhibitors. Some of the quinoline compounds specifically inhibited the entry of the Ebola virus. Among them, compound SYL1712 was the most potent Ebola virus entry inhibitor with an IC 50 of~1 M. The binding of SYL1712 to the vial glycoprotein was computationally modeled and was predicted to interact with specific residues of GP. We used the time of the addition assay to show that compound SYL1712 blocks Ebola GP-mediated entry. Finally, consistent with being an Ebola virus entry inhibitor, compound SYL1712 inhibited infectious Ebola virus replication in tissue culture under biosafety level 4 containment, with an IC 50 of 2 M. In conclusion, we identified several related molecules with a diaryl-quinoline scaffold as potential anti-EBOV entry inhibitors, which can be further optimized for anti-Ebola drug development.
Title: Identification of Diaryl-Quinoline Compounds as Entry Inhibitors of Ebola Virus
Example Output: True

Example Input: Paragraph: Background: Detection of respiratory viruses by time-resolved fluoroimmunoassay based on monoclonal antibodies were developed in our laboratories in the late 1980s and they have been successfully used in daily diagnosis for more than seven years. Later, similar Biotin-EIAs were developed but the sensitivities were unsatisfactory. Objectives: Further optimization of monoclonal Biotin-EIAs and comparison of the optimized assays with TR-FIAs. Study design: Variations in test format, diluents, incubation times and temperatures, and different monoclonal antibodies were tested, and the final comparisons were made with TR-FIA using stored nasopharyngeal aspirates. Results: The improvements in Biotin-EIA featured four changes which increased sensitivity in the assay: (a) test diluent contained diethylenetriamino-pentaacetic acid; (b) antigen and biotinylated detector antibody were added simultaneously; (c) reaction time was extended from 1 h at 37C to overnight at 4C; (d) from the thirteen monoclonal antibodies used in TR-FIA, ten were optimal also in Biotin-EIA, but in the parainfluenza 1 and 2 assays other monoclonals proved more sensitive. Out of 257 originally positive specimens tested in the comparison studies, 192 (74.7%) were again positive and 54 (21.0%) were negative in both assays; nine were negative in TR-FIA but positive in Biotin-EIA, while two specimens were negative in Biotin-EIA but positive in TR-FIA. The overall agreement between the two assays was 95.7%.
Title: Clinical and Diagnostic Virology Comparison of monoclonal biotin-avidin enzyme immunoassay and monoclonal time-resolved fluoroimmunoassay in detection of respiratory virus antigens
Example Output: False

Example Input: Paragraph: Under acute perturbations from the outside, cells can trigger self-defensive mechanisms to fight against genome stress. To investigate the cellular response to continuous ion radiation (IR), a dynamic model for p53 stress response networks at the cellular level is proposed. The model can successfully be used to simulate the dynamic processes of double-strand breaks (DSBs) generation and their repair, switch-like ataxia telangiectasia mutated (ATM) activation, oscillations occurring in the p53-MDM2 feedback loop, as well as toxins elimination triggered by p53 stress response networks. Especially, the model can predict the plausible outcomes of cellular response under different IR dose regimes.
Title: Dynamic modeling of cellular response to DNA damage based on p53 stress response networks
Example Output:
False