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Huntington’s disease (HD) is caused by a polyglutamine (polyQ) domain that is expanded beyond a critical threshold near the N-terminus of the huntingtin (htt) protein, directly leading to htt aggregation. While full-length htt is a large (on the order of ∼350 kDa) protein, it is proteolyzed into a variety of N-terminal fragments that accumulate in oligomers, fibrils, and larger aggregates. It is clear that polyQ length is a key determinant of htt aggregation and toxicity. However, the flanking sequences around the polyQ domain, such as the first 17 amino acids on the N terminus (Nt17), influence aggregation, aggregate stability, influence other important biochemical properties of the protein and ultimately its role in pathogenesis. Here, we review the impact of Nt17 on htt aggregation mechanisms and kinetics, structural properties of Nt17 in both monomeric and aggregate forms, the potential role of posttranslational modifications (PTMs) that occur in Nt17 in HD, and the function of Nt17 as a membrane targeting domain.

Background and Aims: Currently, less than 20% of patients at very high-risk achieve ESC/EAS dyslipidemia guideline-recommended LDL-C target levels in Europe. "Jena auf Ziel-JaZ" is a prospective cohort study in which early combination therapy with atorvastatin 80 mg and ezetimibe 10 mg was initiated on admission in patients with ST-elevation myocardial infarction (STEMI) and lipid-lowering therapy was escalated during follow-up with bempedoic acid and PCSK9 inhibitors to achieve recommended LDL-C targets in all patients. Moreover, we evaluated side-effects of lipid-lowering therapy., Methods: Patients admitted with STEMI at Jena University Hospital were started on atorvastatin 80 mg and ezetimibe 10 mg on admission. Patients were followed for EAS/ESC LDL-C target achievement during follow-up., Results: A total of 85 consecutive patients were enrolled in the study. On discharge, 32.9% achieved LDL-C targets on atorvastatin 80 mg and ezetimibe 10 mg. After 4-6 weeks, 80% of all patients on atorvastatin 80 mg and ezetimibe started at the index event were on ESC/EAS LDL-C targets. In 20%, combined lipid-lowering therapy was escalated with either bempedoic acid or PCSK9 inhibitors. All patients achieved LDL-C levels of or below 55 mg/dL during follow-up on triple lipid-lowering therapy. Combined lipid-lowering therapy was well-tolerated with rare side effects., Conclusions: Early combination therapy with a high-intensity statin and ezetimibe and escalation of lipid-lowering therapy with either bempedoic acid or PCSK9 inhibitors gets potentially all patients with STEMI on recommended ESC/EAS LDL-C targets without significant side effects., (© 2023. The Author(s).)

Poor air quality is still a threat for human health in many parts of the world. In order to assess measures for emission reductions and improved air quality, three-dimensional atmospheric chemistry transport modeling systems are used in numerous research institutions and public authorities. These models need accurate emission data in appropriate spatial and temporal resolution as input. This paper reviews the most widely used emission inventories on global and regional scales and looks into the methods used to make the inventory data model ready. Shortcomings of using standard temporal profiles for each emission sector are discussed, and new methods to improve the spatiotemporal distribution of the emissions are presented. These methods are often neither top-down nor bottom-up approaches but can be seen as hybrid methods that use detailed information about the emission process to derive spatially varying temporal emission profiles. These profiles are subsequently used to distribute bulk emissions such as national totals on appropriate grids. The wide area of natural emissions is also summarized, and the calculation methods are described. Almost all types of natural emissions depend on meteorological information, which is why they are highly variable in time and space and frequently calculated within the chemistry transport models themselves. The paper closes with an outlook for new ways to improve model ready emission data, for example, by using external databases about road traffic flow or satellite data to determine actual land use or leaf area. In a world where emission patterns change rapidly, it seems appropriate to use new types of statistical and observational data to create detailed emission data sets and keep emission inventories up-to-date., Implications: Emission data are probably the most important input for chemistry transport model (CTM) systems. They need to be provided in high spatial and temporal resolution and on a grid that is in agreement with the CTM grid. Simple methods to distribute the emissions in time and space need to be replaced by sophisticated emission models in order to improve the CTM results. New methods, e.g., for ammonia emissions, provide grid cell-dependent temporal profiles. In the future, large data fields from traffic observations or satellite observations could be used for more detailed emission data.