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Background: Infective endocarditis (IE) requires multidisciplinary management. We established an endocarditis team within our hospital in 2011 and a state-wide endocarditis network with referring hospitals in 2015. We aimed to investigate their impact on perioperative outcomes., Methods: We retrospectively analyzed data from patients operated on for IE in our center between 01/2007 and 03/2018. To investigate the impact of the endocarditis network on referral latency and pre-operative complications we divided patients into two eras: before ( n = 409) and after ( n = 221) 01/2015. To investigate the impact of the endocarditis team on post-operative outcomes we conducted multivariate binary logistic regression analyses for the whole population. Kaplan-Meier estimates of 5-year survival were reported., Results: In the second era, after establishing the endocarditis network, the median time from symptoms to referral was halved (7 days (interquartile range: 2-19) vs. 15 days (interquartile range: 6-35)), and pre-operative endocarditis-related complications were reduced, i.e., stroke (14% vs. 27%, p < 0.001), heart failure (45% vs. 69%, p < 0.001), cardiac abscesses (24% vs. 34%, p = 0.018), and acute requirement of hemodialysis (8% vs. 14%, p = 0.026). In both eras, a lack of recommendations from the endocarditis team was an independent predictor for in-hospital mortality (adjusted odds ratio: 2.12, 95% CI: 1.27-3.53, p = 0.004) and post-operative stroke (adjusted odds ratio: 2.23, 95% CI: 1.12-4.39, p = 0.02), and was associated with worse 5-year survival (59% vs. 40%, log-rank < 0.001)., Conclusion: The establishment of an endocarditis network led to the earlier referral of patients with fewer pre-operative endocarditis-related complications. Adhering to endocarditis team recommendations was an independent predictor for lower post-operative stroke and in-hospital mortality, and was associated with better 5-year survival.

Shiga toxin-producing Escherichia coli (STEC) and Shigella spp./enteroinvasive E. coli (EIEC) are common diarrheagenic bacteria that cause sporadic diseases and outbreaks. Clinical manifestations vary from mild symptoms to severe complications. For microbiological diagnosis, culture confirmation of a positive stool screening PCR test is challenging because of time-consuming methods for isolation of strains, wide variety of STEC pathotypes, and increased emergence of non-classical strains with unusual serotypes. Therefore, molecular assays for the rapid identification of suspect colonies growing on selective media are very useful. In this study, the performance of the newly introduced eazyplex® EHEC assay based on loop-mediated isothermal amplification (LAMP) was evaluated using 18 representative STEC and Shigella strains and 31 isolates or positive-enrichment broths that were collected from clinical stool samples following screening by BD MAX™ EBP PCR. Results were compared to real-time PCR as a reference standard. Overall, sensitivities and specificities of the eazyplex® EHEC were as follows: 94.7% and 100% for Shiga toxin 1 (stx1), 100% and 100% for stx2, 93.3% and 97.1% for intimin (eae), 100% and 100% for enterohemolysin A (ehlyA), and 100% and 100% for invasion-associated plasmid antigen H (ipaH) as Shigella spp./EIEC target, respectively. Sample preparation for LAMP took only some minutes, and the time to result of the assay ranged from 8.5 to 13 min. This study shows that eazyplex® EHEC is a very fast and easy to perform molecular assay that provides reliable results as a culture confirmation assay for the diagnosis of STEC and Shigella spp./EIEC infections.

Here we show that designed transcription activator-like effectors (TALEs) that bind to defined areas of the interferon beta promoter are capable to induce IFN-beta expression and signaling in human cells. Importantly, TALE-mediated IFN-beta signaling occurs independently of pathogen pattern recognition but effectively prohibits viral RNA replication as demonstrated with a hepatitis C virus replicon. TALEs were thus indicated to be valuable tools in various applications addressing, for example, virus-host interactions.