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".

Q: Paragraph: Ebolavirus, a member of the Filoviridae family of negative-sense RNA viruses, causes severe haemorrhagic fever leading up to 90% lethality. Ebolavirus matrix protein VP40 is involved in the virus assembly and budding process. The RNA binding pocket of VP40 is considered as the drug target site for structure based drug design. High Throughput Virtual Screening and molecular docking studies were employed to find the suitable inhibitors against VP40. Ten compounds showing good glide score and glide energy as well as interaction with specific amino acid residues were short listed as drug leads. These small molecule inhibitors could be potent inhibitors for VP40 matrix protein by blocking virus assembly and budding process. ISSN 0973-2063 (online) 0973-8894 (print) Bioinformation 9(6): 286-292 (2013) 287 ©️ 2013 Biomedical Informatics replication [23]. C-terminal domain of VP40 is required for membrane association [24, 25]. open access ISSN 0973-2063 (online) 0973-8894 (print) Bioinformation 9(6): 286-292 (2013) 289
Title: Hypothesis High-throughput virtual screening and docking studies of matrix protein vp40 of ebola virus

A: False
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Q: 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

A: True
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Q: Paragraph: Retroviruses encode their genetic information with RNA molecules, and have a high genomic recombination rate which allows them to mutate more rapidly, thereby posting a higher risk to humans. One important way to help combat a pandemic of viral infectious diseases is early detection before large-scale outbreaks occur. The polymerase chain reaction (PCR) and reverse transcription-PCR (RT-PCR) have been used to identify precisely different strains of some very closely related pathogens. However, isolation and detection of viral RNA in the field are difficult due to the unstable nature of viral RNA molecules. Consequently, performing in-the-field nucleic acid analysis to monitor the spread of viruses is financially and technologically challenging in remote and underdeveloped regions that are high-risk areas for outbreaks. A simplified rapid viral RNA extraction method is reported to meet the requirements for in-the-field viral RNA extraction and detection. The ability of this device to perform viral RNA extraction with subsequent RT-PCR detection of retrovirus is demonstrated. This inexpensive device has the potential to be distributed on a large scale to underdeveloped regions for early detection of retrovirus, with the possibility of reducing viral pandemic events.
Title: Viral RNA extraction for in-the-field analysis

A:
True
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