In this task, you're given a paragraph from the research paper and your task is to generate a suitable title for the research paper based on the given paper. Under 100 words is a good title length.
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Question: 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.

Answer: Accuracy of using automated methods for detecting adverse events from electronic health record data: a research protocol


Question: The Sec7 domain ADP-ribosylation factor (Arf) guanine nucleotide exchange factors (GEFs) are found in all eukaryotes, and are involved in membrane remodeling processes throughout the cell. This review is focused on members of the GBF/Gea and BIG/Sec7 subfamilies of Arf GEFs, all of which use the class I Arf proteins (Arf1-3) as substrates, and play a fundamental role in traYcking in the endoplasmic reticulum (ER)-Golgi and endosomal membrane systems. Members of the GBF/Gea and BIG/Sec7 subfamilies are large proteins on the order of 200 kDa, and they possess multiple homology domains. Phylogenetic analyses indicate that both of these subfamilies of Arf GEFs have members in at least Wve out of the six eukaryotic supergroups, and hence were likely present very early in eukaryotic evolution. The homology domains of the large Arf1 GEFs play important functional roles, and are involved in interactions with numerous protein partners. The large Arf1 GEFs have been implicated in several human diseases. They are crucial host factors for the replication of several viral pathogens, including poliovirus, coxsackievirus, mouse hepatitis coronavirus, and hepatitis C virus. Mutations in the BIG2 Arf1 GEF have been linked to autosomal recessive periventricular heterotopia, a disorder of neuronal migration that leads to severe malformation of the cerebral cortex. Understanding the roles of the Arf1 GEFs in membrane dynamics is crucial to a full understanding of traYcking in the secretory and endosomal pathways, which in turn will provide essential insights into human diseases that arise from misregulation of these pathways.

Answer: Large Arf1 guanine nucleotide exchange factors: evolution, domain structure, and roles in membrane traYcking and human disease


Question: Multiple sclerosis (MS) is an inflammatory, demyelinating, central nervous system disease mediated by myelin-specific T cells. Environmental triggers that cause a breakdown of myelinspecific T cell tolerance are unknown. We found that CD8 + myelin basic protein (MBP)-specific T cell tolerance can be broken and autoimmunity induced by infection with a virus that does not express MBP cross-reactive epitopes and does not depend on bystander activation. Instead, the virus activated dual T cell receptor (TCR)-expressing T cells capable of recognizing both MBP and viral antigens. These results demonstrate the importance of dual TCR T cells in autoimmunity and suggest a mechanism by which a ubiquitous viral infection could trigger autoimmunity in a subset of infected individuals, as hypothesized in the etiology of MS. Multiple sclerosis (MS) is an inflammatory, demyelinating disease of the central nervous system (CNS) that affects more than one million people worldwide. It is believed to be an autoimmune disease in which exposure of genetically predisposed individuals to environmental factors triggers a breakdown in T cell tolerance to myelin antigens. The specific types of myelin-specific T cells that contribute to the pathogenesis of MS are not known. Most studies have focused on the pathogenic role of myelin-specific CD4 + T cells because of the relatively strong association of MS susceptibility with major histocompatibility complex (MHC) class II alleles. In addition, CD4 + T cells are the primary effector T cells in experimental autoimmune encephalomyelitis (EAE), a widely used animal model of MS. However, there has been increasing recognition of the potential importance of CD8 + T cells in the pathogenesis of MS. CD8 + T cells typically outnumber CD4 + T cells in acute and chronic lesions in MS patients, and the CD8 + T cell subset exhibits more evidence of antigen-driven activation compared to CD4 + T cells in the CNS and blood of MS patients1, 2. The frequency of CNS antigen-specific CD8 + , but not CD4 + , using Power SYBR Green PCR Master Mix (Applied Biosystems). Tcra V α gene primers and a C α primer have been previously described50. Reactions were run in duplicate, and samples were normalized to the internal β actin control. All P values were calculated with a two-tailed Student's t-test.

Answer:
Viral Infection Triggers Central Nervous System Autoimmunity Via Activation of Dual TCR-Expressing CD8 + T Cells HHS Public Access Author manuscript