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.

Input: Consider Input: Background: Influenza is a zoonotic disease that infects millions of people each year resulting in hundreds of thousands of deaths, and in turn devastating pandemics. Influenza is caused by influenza viruses, including influenza A virus (IAV). There are many subtypes of IAV but only a few seem to be able to adapt to humans and to cause disease. In 2013, an H7N9 IAV subtype emerged in China that does not cause clinical symptoms in its chicken host but leads to severe infections when transmitted into humans. Since 2013, there have been six epidemic waves of H7N9 with 1567 laboratory-confirmed human infections and 615 deaths. Pathogenicity of IAV is complex, but a crucial feature contributing to virulence is the activation of the hemagglutinin (HA) fusion protein by host proteases that triggers membrane fusion and leads to subsequent virus propagation. : 293T, VERO, and MDCK cells were used to conduct Western blot analysis, immunofluorescence assays, and pseudoparticle and live virus infections, and to evaluate H7N9 HA cleavage-activation. Results/Conclusions: We show that human matriptase/ST 14 is able to cleave H7N9 HA. Cleavage of H7N9 HA expressed in cell culture results in fusogenic HA and syncytia formation. In infection studies with viral pseudoparticles carrying matriptase/ ST 14-activated H7N9 HA, we observed a high infectivity of cells. Finally, human matriptase/ST 14 also activated H7N9 live virus which resulted in high infectivity. Our data demonstrate that human matriptase/ST 14 is a likely candidate protease to promote H7N9 infections in humans.

Output: Human matriptase/ST 14 proteolytically cleaves H7N9 hemagglutinin and facilitates the activation of influenza A/Shanghai/2/2013 virus in cell culture


Input: Consider Input: Motivation: Deep sequencing of clinical samples is now an established tool for the detection of infectious pathogens, with direct medical applications. The large amount of data generated produces an opportunity to detect species even at very low levels, provided that computational tools can effectively profile the relevant metagenomic communities. Data interpretation is complicated by the fact that short sequencing reads can match multiple organisms and by the lack of completeness of existing databases, in particular for viral pathogens. Here we present metaMix, a Bayesian mixture model framework for resolving complex metagenomic mixtures. We show that the use of parallel Monte Carlo Markov chains for the exploration of the species space enables the identification of the set of species most likely to contribute to the mixture. Results: We demonstrate the greater accuracy of metaMix compared with relevant methods, particularly for profiling complex communities consisting of several related species. We designed metaMix specifically for the analysis of deep transcriptome sequencing datasets, with a focus on viral pathogen detection; however, the principles are generally applicable to all types of metagenomic mixtures. Availability and implementation: metaMix is implemented as a user friendly R package, freely available on CRAN: http://cran.r-project.org/web/packages/metaMix Contact: sofia.morfopoulou.10@ucl.ac.uk Supplementary information: Supplementary data are available at Bionformatics online.

Output: Bayesian mixture analysis for metagenomic community profiling


Input: Consider Input: Purpose: Despite their controversial role, corticosteroids (CS) are frequently administered to patients with H1N1 virus infection with severe respiratory failure secondary to viral pneumonia. We hypothesized that invasive pulmonary aspergillosis (IPA) is a frequent complication in critically ill patients with H1N1 virus infection and that CS may contribute to this complication. Methods: We retrospectively selected all adult patients with confirmed H1N1 virus infection admitted to the intensive care unit (ICU) of two tertiary care hospitals from September 2009 to March 2011. Differences in baseline factors, risk factors, and outcome parameters were studied between patients with and without IPA. Results: Of 40 critically ill patients with confirmed H1N1, 9 (23 %) developed IPA 3 days after ICU admission. Five patients had proven and four had probable IPA. Significantly more IPA patients received CS within 7 days before ICU admission (78 versus 23 %, p = 0.002). IPA patients also received significantly higher doses of CS before ICU admission [hydrocortisone equivalent 800 (360-2,635) versus 0 (0-0) mg, p = 0.005]. On multivariate analysis, use of CS before ICU admission was independently associated with IPA [odds ratio (OR) 14.4 (2.0-101.6), p = 0.007]. Conclusions: IPA was diagnosed in 23 % of critically ill patients with H1N1 virus infection after a median of 3 days after ICU admission. Our data suggest that use of CS 7 days before ICU admission is an independent risk factor for fungal superinfection. These findings may have consequences for clinical practice as they point out the need for increased awareness of IPA, especially in those critically ill H1N1 patients already receiving CS.


Output: Invasive pulmonary aspergillosis is a frequent complication of critically ill H1N1 patients: a retrospective study