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Highlights
• The goal was to assess the intra- and inter-scanner reproducibility of qMRI data.
• Mean scan-rescan variations were not exceeding 2.14%.
• Mean inter-scanner model deviations were not exceeding 5.21%.
• Provided that identical acquisition sequences are used, discrepancies between qMRI data acquired with different scanner models are low.
Abstract
Background: Quantitative MRI (qMRI) techniques allow assessing cerebral tissue properties. However, previous studies on the accuracy of quantitative T1 and T2 mapping reported a scanner model bias of up to 10% for T1 and up to 23% for T2. Such differences would render multi-centre qMRI studies difficult and raise fundamental questions about the general precision of qMRI. A problem in previous studies was that different methods were used for qMRI parameter mapping or for measuring the transmitted radio frequency field B1 which is critical for qMRI techniques requiring corrections for B1 non-uniformities.
Aims: The goal was to assess the intra- and inter-scanner reproducibility of qMRI data at 3 T, using two different scanner models from the same vendor with exactly the same multiparametric acquisition protocol.
Methods: Proton density (PD), T1, T2* and T2 mapping was performed on healthy subjects and on a phantom, performing each measurement twice for each of two scanner models. Although the scanners had different hardware and software versions, identical imaging sequences were used for PD, T1 and T2* mapping, adapting the codes of an existing protocol on the older system line by line to match the software version of the newer scanner. For T2-mapping, the respective manufacturer’s sequence was used which depended on the software version. However, system-dependent corrections were carried out in this case. Reproducibility was assessed by average values in regions of interest.
Results: Mean scan-rescan variations were not exceeding 2.14%, with average values of 1.23% and 1.56% for the new and old system, respectively. Inter-scanner model deviations were not exceeding 5.21% with average values of about 2.2–3.8% for PD, 2.5–3.0% for T2*, 1.6–3.1% for T1 and 3.3–5.2% for T2.
Conclusions: Provided that identical acquisition sequences are used, discrepancies between qMRI data acquired with different scanner models are low. The level of systematic differences reported in this work may help to interpret multi-centre data.
Highlights
• This current review covers studies that have identified long non-coding RNAs in aortic aneurysm development and progression.
• We separately discuss transcripts and mechanisms of importance to thoracic as well as abdominal aortic aneurysms.
• Functional data on lncRNAs being identified are highlighted.
• Some have been studied in human as well as experimental models of the disease pathology.
Abstract
Aortic aneurysm (AA) is a complex and dangerous vascular disease, featuring progressive and irreversible vessel dilatation. AA is typically detected either by screening, or identified incidentally through imaging studies. To date, no effective pharmacological therapies have been identified for clinical AA management, and either endovascular repair or open surgery remains the only option capable of preventing aneurysm rupture. In recent years, multiple research groups have endeavored to both identify noncoding RNAs and to clarify their function in vascular diseases, including aneurysmal pathologies. Notably, the molecular roles of noncoding RNAs in AA development appear to vary significantly between thoracic aortic aneurysms (TAAs) and abdominal aortic aneurysms (AAAs). Some microRNAs (miRNA - a non-coding RNA subspecies) appear to contribute to AA pathophysiology, with some showing major potential for use as biomarkers or as therapeutic targets. Studies of long noncoding RNAs (lncRNAs) are more limited, and their specific contributions to disease development and progression largely remain unexplored. This review aims to summarize and discuss the most current data on lncRNAs and their mediation of AA pathophysiology.
Over the last decade, cases of metabolic syndrome and type II diabetes have increased exponentially. Exercise and ω-3 polyunsaturated fatty acid (PUFA)-enriched diets are usually prescribed but no therapy is effectively able to restore the impaired glucose metabolism, hypertension, and atherogenic dyslipidemia encountered by diabetic patients. PUFAs are metabolized by different enzymes into bioactive metabolites with anti- or pro-inflammatory activity. One important class of PUFA metabolizing enzymes are the cytochrome P450 (CYP) enzymes that can generate a series of bioactive products, many of which have been attributed protective/anti-inflammatory and insulin-sensitizing effects in animal models. PUFA epoxides are, however, further metabolized by the soluble epoxide hydrolase (sEH) to fatty acid diols. The biological actions of the latter are less well understood but while low concentrations may be biologically important, higher concentrations of diols derived from linoleic acid and docosahexaenoic acid have been linked with inflammation. One potential application for sEH inhibitors is in the treatment of diabetic retinopathy where sEH expression and activity is elevated as are levels of a diol of docosahexaenoic acid that can induce the destabilization of the retina vasculature.
Loosely-bound objects such as light nuclei are copiously produced in proton-proton and nuclear collisions at the Large Hadron Collider (LHC), despite the fact that typical energy scales in such collisions exceed the binding energy of the objects by orders of magnitude. In this review we summarise the experimental observations, put them into context of previous studies at lower energies, and discuss the underlying physics. Most of the data discussed here were taken by the ALICE Collaboration during LHC Run1, which started in 2009 and ended in 2013. Specifically we focus on the production of (anti-)nuclei and (anti-)hypernuclei. Also included are searches for exotic objects like the H-dibaryon, a possible uuddss hexaquark state, or also a possible bound state of a Λ hyperon and a neutron. Furthermore, the study of hyperon-nucleon and hyperon-hyperon interactions through measurements of correlations are briefly discussed, especially in connection with the possible existence of loosely-bound states composed of these baryons. In addition, some results in the strange and charmed hadron sector are presented, to show the capabilities for future measurements on loosely-bound objects in this direction. Finally, perspectives are given for measurements in the currently ongoing Run2 period of the LHC and in the future LHC Run3.
The core of neutron stars consists of extremely dense matter at relatively low temperatures. In such an environment the appearance of exotic strongly interacting particles beyond nucleons appears quite natural. In this context we consider hybrid stars that, in addition to nucleons and hyperons, also contain quarks as further degrees of freedom. We investigate the impact of quarks on the properties of these compact stars. In addition, we discuss new constraints on such objects arising from the recently measured gravitational wave signal of two merging neutron stars.
We present a systematic study on the influence of spatial correlations between the proton constituents, in our case gluonic hot spots, their size and their number on the symmetric cumulant SC(2,3), at the eccentricity level, within a Monte Carlo Glauber framework [J.L. Albacete, H. Petersen, A. Soto-Ontoso, Symmetric cumulants as a probe of the proton substructure at LHC energies, Phys. Lett. B778 (2018) 128–136. arXiv:1707.05592, doi:10.1016/j.physletb.2018.01.011]. When modeling the proton as composed by 3 gluonic hot spots, the most common assumption in the literature, we find that the inclusion of spatial correlations is indispensable to reproduce the negative sign of SC(2,3) in the highest centrality bins as dictated by data. Further, the subtle interplay between the different scales of the problem is discussed. To conclude, the possibility of feeding a 2+1D viscous hydrodynamic simulation with our entropy profiles is exposed.