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: In February 2014, health officials from around the world announced the Global Health Security Agenda, a critical effort to strengthen national and global systems to prevent, detect, and respond to infectious disease threats and to foster stronger collaboration across borders. With its increasing global roles and broad range of regulatory responsibilities in ensuring the availability, safety, and security of medical and food products, the US Food and Drug Administration (FDA) is engaged in a range of efforts in support of global health security. This article provides an overview of FDA's global health security roles, focusing on its responsibilities related to the development and use of medical countermeasures (MCMs) for preventing, detecting, and responding to global infectious disease and other public health emergency threats. The article also discusses several areas-antimicrobial resistance, food safety, and supply chain integrity-in which FDA's global health security roles continue to evolve and extend beyond MCMs and, in some cases, beyond traditional infectious disease threats. Brooke Courtney, JD, MPH, is Senior Regulatory Counsel, Office of Counterterrorism and Emerging Threats, Office of the Chief Scientist, Office of the Commissioner; Katherine C. Bond, ScD, is Director, Office
Title: Regulatory Underpinnings of Global Health Security: FDA's Roles in Preventing, Detecting, and Responding to Global Health Threats

Output: True

Input: Consider Input: Paragraph: Background: Adverse events are associated with significant morbidity, mortality and cost in hospitalized patients. Measuring adverse events is necessary for quality improvement, but current detection methods are inaccurate, untimely and expensive. The advent of electronic health records and the development of automated methods for encoding and classifying electronic narrative data, such as natural language processing, offer an opportunity to identify potentially better methods. The objective of this study is to determine the accuracy of using automated methods for detecting three highly prevalent adverse events: a) hospital-acquired pneumonia, b) catheter-associated bloodstream infections, and c) in-hospital falls. Methods/design: This validation study will be conducted at two large Canadian academic health centres: the McGill University Health Centre (MUHC) and The Ottawa Hospital (TOH). The study population consists of all medical, surgical and intensive care unit patients admitted to these centres between 2008 and 2014. An automated detection algorithm will be developed and validated for each of the three adverse events using electronic data extracted from multiple clinical databases. A random sample of MUHC patients will be used to develop the automated detection algorithms (cohort 1, development set). The accuracy of these algorithms will be assessed using chart review as the reference standard. Then, receiver operating characteristic curves will be used to identify optimal cut points for each of the data sources. Multivariate logistic regression and the areas under curve (AUC) will be used to identify the optimal combination of data sources that maximize the accuracy of adverse event detection. The most accurate algorithms will then be validated on a second random sample of MUHC patients (cohort 1, validation set), and accuracy will be measured using chart review as the reference standard. The most accurate algorithms validated at the MUHC will then be applied to TOH data (cohort 2), and their accuracy will be assessed using a reference standard assessment of the medical chart. Discussion: There is a need for more accurate, timely and efficient measures of adverse events in acute care hospitals. This is a critical requirement for evaluating the effectiveness of preventive interventions and for tracking progress in patient safety through time.
Title: Accuracy of using automated methods for detecting adverse events from electronic health record data: a research protocol

Output: True

Input: Consider Input: Paragraph: Background: Swine-origin virus infection spreading widely could cause significant economic loss to porcine industry. Novel antiviral agents need to be developed to control this situation. Methods: In this study, we evaluated the activities of five broad-spectrum antimicrobial peptides (AMPs) against several important swine-origin pathogenic viruses by TCID 50 assay. Plaque reduction assay and cell apoptosis assay were also used to test the activity of the peptides. Protection effect of piscidin against pseudorabies virus (PRV) was also examined in mouse model. Results: Piscidin (piscidin 1), caerin (caerin 1.1) and maculatin (maculatin 1.1) could inhibit PRV by direct interaction with the virus particles in a dose-dependent manner and they could also protect the cells from PRV-induced apoptosis. Among the peptides tested, piscidin showed the strongest activity against PRV. Moreover, in vivo assay showed that piscidin can reduce the mortality of mice infected with PRV. Conclusion: In vitro and in vivo experiments indicate that piscidin has antiviral activity against PRV.
Title: Antiviral activity of Piscidin 1 against pseudorabies virus both in vitro and in vivo
Output: False