instruction:
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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Four Massachusetts-type (Mass-type) strains of infectious bronchitis coronavirus (IBV) were compared genetically with the pathogenic M41 and H120 vaccine strains using the complete genomic sequences. The results revealed that strains ck/CH/LNM/091017 and ck/CH/LDL/101212 were closely related to the H120 vaccine, which suggests that they might represent re-isolations of vaccine strains or variants of vaccine strains that have resulted from the accumulated point mutations after several passages in chickens. In contrast, strains ck/CH/LHLJ/07VII and ck/CH/LHLJ/100902 had a close genetic relationship with the pathogenic M41 strain. In addition, molecular markers have been identified that distinguish between field and vaccine (or vaccine-like) Mass-type viruses, which may be able to differentiate between field and vaccine strains for diagnostic purposes. Phylogenetic analysis, and pairwise comparison of full-length genomes and the nine genes, identified the occurrence of recombination events in the genome of strain CK/VH/LHLJ/07VII, which suggests that this virus originated from recombination events between M41-and H120-like strains at the switch site located at the 3 0 end of the nucleocapsid (N) genes. To our knowledge, this is the first time that evidence for the evolution and natural recombination under field conditions between Mass-type pathogenic and vaccinal IBV strains has been documented. These findings provide insights into the emergence and evolution of the Mass-type IB coronaviruses and may help to explain the emergence of Mass-type IBV in chicken flocks all over the world.
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Comparative analysis of four Massachusetts type infectious bronchitis coronavirus genomes reveals a novel Massachusetts type strain and evidence of natural recombination in the genome


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


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The interferon-inducible transmembrane (Ifitm/Fragilis) genes encode homologous proteins that are induced by IFNs. Here, we show that IFITM proteins regulate murine CD4 + Th cell differentiation. Ifitm2 and Ifitm3 are expressed in wild-type (WT) CD4 + T cells. On activation, Ifitm3 was downregulated and Ifitm2 was upregulated. Resting Ifitm-family-deficient CD4 + T cells had higher expression of Th1-associated genes than WT and purified naive Ifitm-family-deficient CD4 + T cells differentiated more efficiently to Th1, whereas Th2 differentiation was inhibited. Ifitm-family-deficient mice, but not Ifitm3-deficient mice, were less susceptible than WT to induction of allergic airways disease, with a weaker Th2 response and less severe disease and lower Il4 but higher Ifng expression and IL-27 secretion. Thus, the Ifitm family is important in adaptive immunity, influencing Th1/Th2 polarization, and Th2 immunopathology. Keywords: Allergic airway disease r IFN-γ r Interferon-inducible transmembrane (IFITM) protein r T helper 1 (Th1) r T helper 2 (Th2) Additional supporting information may be found online in the Supporting Information section at the end of the article.
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Basic Adaptive immunity Research Article IFITM proteins drive type 2 T helper cell differentiation and exacerbate allergic airway inflammation