@article{WinterAndelovicKampfetal.2021,
  author    = {Winter, Patrick M. and Andelovic, Kristina and Kampf, Thomas and Hansmann, Jan and Jakob, Peter Michael and Bauer, Wolfgang Rudolf and Zernecke, Alma and Herold, Volker},
  title     = {Simultaneous measurements of 3D wall shear stress and pulse wave velocity in the murine aortic arch},
  series = {Journal of Cardiovascular Magnetic Resonance},
  volume    = {23},
  journal   = {Journal of Cardiovascular Magnetic Resonance},
  number    = {1},
  doi       = {10.1186/s12968-021-00725-4},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-259152},
  pages     = {34},
  year      = {2021},
  abstract  = {Purpose Wall shear stress (WSS) and pulse wave velocity (PWV) are important parameters to characterize blood flow in the vessel wall. Their quantification with flow-sensitive phase-contrast (PC) cardiovascular magnetic resonance (CMR), however, is time-consuming. Furthermore, the measurement of WSS requires high spatial resolution, whereas high temporal resolution is necessary for PWV measurements. For these reasons, PWV and WSS are challenging to measure in one CMR session, making it difficult to directly compare these parameters. By using a retrospective approach with a flexible reconstruction framework, we here aimed to simultaneously assess both PWV and WSS in the murine aortic arch from the same 4D flow measurement. Methods Flow was measured in the aortic arch of 18-week-old wildtype (n = 5) and ApoE\(^{-/-}\) mice (n = 5) with a self-navigated radial 4D-PC-CMR sequence. Retrospective data analysis was used to reconstruct the same dataset either at low spatial and high temporal resolution (PWV analysis) or high spatial and low temporal resolution (WSS analysis). To assess WSS, the aortic lumen was labeled by semi-automatically segmenting the reconstruction with high spatial resolution. WSS was determined from the spatial velocity gradients at the lumen surface. For calculation of the PWV, segmentation data was interpolated along the temporal dimension. Subsequently, PWV was quantified from the through-plane flow data using the multiple-points transit-time method. Reconstructions with varying frame rates and spatial resolutions were performed to investigate the influence of spatiotemporal resolution on the PWV and WSS quantification. Results 4D flow measurements were conducted in an acquisition time of only 35 min. Increased peak flow and peak WSS values and lower errors in PWV estimation were observed in the reconstructions with high temporal resolution. Aortic PWV was significantly increased in ApoE\(^{-/-}\) mice compared to the control group (1.7 ± 0.2 versus 2.6 ± 0.2 m/s, p < 0.001). Mean WSS magnitude values averaged over the aortic arch were (1.17 ± 0.07) N/m\(^2\) in wildtype mice and (1.27 ± 0.10) N/m\(^2\) in ApoE\(^{-/-}\) mice. Conclusion The post processing algorithm using the flexible reconstruction framework developed in this study permitted quantification of global PWV and 3D-WSS in a single acquisition. The possibility to assess both parameters in only 35 min will markedly improve the analyses and information content of in vivo measurements.},
  language  = {en}
}
@article{AlizadehradKruegerEngstleretal.2015,
  author    = {Alizadehrad, Davod and Kr{\"u}ger, Timothy and Engstler, Markus and Stark, Holger},
  title     = {Simulating the complex cell design of Trypanosoma brucei and its motility},
  series = {PLOS Computational Biology},
  volume    = {11},
  journal   = {PLOS Computational Biology},
  number    = {1},
  doi       = {10.1371/journal.pcbi.1003967},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-144610},
  pages     = {e1003967},
  year      = {2015},
  abstract  = {The flagellate Trypanosoma brucei, which causes the sleeping sickness when infecting a mammalian host, goes through an intricate life cycle. It has a rather complex propulsion mechanism and swims in diverse microenvironments. These continuously exert selective pressure, to which the trypanosome adjusts with its architecture and behavior. As a result, the trypanosome assumes a diversity of complex morphotypes during its life cycle. However, although cell biology has detailed form and function of most of them, experimental data on the dynamic behavior and development of most morphotypes is lacking. Here we show that simulation science can predict intermediate cell designs by conducting specific and controlled modifications of an accurate, nature-inspired cell model, which we developed using information from live cell analyses. The cell models account for several important characteristics of the real trypanosomal morphotypes, such as the geometry and elastic properties of the cell body, and their swimming mechanism using an eukaryotic flagellum. We introduce an elastic network model for the cell body, including bending rigidity and simulate swimming in a fluid environment, using the mesoscale simulation technique called multi-particle collision dynamics. The in silico trypanosome of the bloodstream form displays the characteristic in vivo rotational and translational motility pattern that is crucial for survival and virulence in the vertebrate host. Moreover, our model accurately simulates the trypanosome's tumbling and backward motion. We show that the distinctive course of the attached flagellum around the cell body is one important aspect to produce the observed swimming behavior in a viscous fluid, and also required to reach the maximal swimming velocity. Changing details of the flagellar attachment generates less efficient swimmers. We also simulate different morphotypes that occur during the parasite's development in the tsetse fly, and predict a flagellar course we have not been able to measure in experiments so far.},
  language  = {en}
}