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".

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
True

Paragraph: The open reading frame 3 of the severe acute respiratory syndrome coronavirus (SARS-CoV) genome encodes a predicted protein 3a, consisting of 274 amino acids, that lacks any significant similarities to any known protein. We generated specific antibodies against SARS protein 3a by using a synthetic peptide (P2) corresponding to amino acids 261-274 of the putative protein. Anti-P2 antibodies and the sera from SARS patients could specifically detect the recombinant SARS protein 3a expressed in Escherichia coli and in Vero E6 cells. Expression of SARS protein 3a was detected at 8-12 h after infection and reached a higher level after $24 h in SARS-CoV-infected Vero E6 cells. Protein 3a was also detected in the alveolar lining pneumocytes and some intra-alveolar cells of a SARS-CoVinfected patient's lung specimen. Recombinant protein 3a expressed in Vero E6 cells and protein 3a in the SARS-CoVinfected cells was distributed over the cytoplasm in a fine punctate pattern with partly concentrated staining in the Golgi apparatus. Our study demonstrates that SARS-CoV indeed expresses a novel protein 3a, which is present only in SARS-CoV and not in other known CoVs. 
 Title: Identification of a novel protein 3a from severe acute respiratory syndrome coronavirus
False

Paragraph: The apical and basolateral surfaces of airway epithelial cells demonstrate directional responses to pathogen exposure in vivo. Thus, ideal in vitro models for examining cellular responses to respiratory pathogens polarize, forming apical and basolateral surfaces. One such model is differentiated normal human bronchial epithelial cells (NHBE). However, this system requires lung tissue samples, expertise isolating and culturing epithelial cells from tissue, and time to generate an air-liquid interface culture. Calu-3 cells, derived from a human bronchial adenocarcinoma, are an alternative model for examining the response of proximal airway epithelial cells to respiratory insult Safety Measures: Perform all procedures in a biosafety cabinet using sterile culture technique. 
 Title: Establishing a Liquid-covered Culture of Polarized Human Airway Epithelial Calu-3 Cells to Study Host Cell Response to Respiratory Pathogens In vitro

False