@phdthesis{Wilhelm2018,
  author    = {Wilhelm, Christian},
  title     = {Die Rolle von Chronophin bei Schlaganfall-induziertem Funktionsverlust der Blut-Hirn-Schranke},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-163877},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2018},
  abstract  = {Der isch{\"a}mische Schlaganfall ist mit einer j{\"a}hrlichen Inzidenz von 200/100 000 Einwohnern die h{\"a}ufigste Gef{\"a}ßerkrankung in Deutschland. Atherothrombose, arterielle Hypertonie und Embolien unterschiedlichen Ursprungs sind die wesentlichen Ursachen des isch{\"a}mischen Schlaganfalls. Die neurologischen Defizite nach einem Schlaganfall resultieren aus einem gest{\"o}rten zerebralen Blutfluss und somit einer insuffizienten Sauerstoffversorgung. Zus{\"a}tzlich ist die {\"O}dembildung, welche von einer gesteigerten Permeabilit{\"a}t der Blut-Hirn-Schranke verursacht wird, am neuronalen Zelltod beteiligt. Chronophin ist eine Aktinzytoskelett-regulierende Serin-Phosphatase. In einem isch{\"a}mischen Schlaganfall-Modell konnte im Rahmen dieser Arbeit gezeigt werden, dass der globale Verlust von Chronophin zu einer vermehrten {\"O}dembildung und einem aggravierten neurologischen Zustand der M{\"a}use im Vergleich zu wildtypischen Kontrollen f{\"u}hrte. Hirnlysate von wildtypischen M{\"a}usen zeigten verringerte Chronophin-Level in der vom Schlaganfall betroffenen Hemisph{\"a}re. Jedoch konnten initiale immunhistochemische und zellbiologische Untersuchungen weder Chronophin-abh{\"a}ngige Ver{\"a}nderungen der Blut-Hirn-Schranke feststellen noch einen zerebralen Zelltyp identifizieren, der f{\"u}r den sch{\"u}tzenden Effekt von Chronophin verantwortlich ist. Diese Ergebnisse weisen auf einen komplexen, vielzelligen Mechanismus hin, dem die sch{\"u}tzende Rolle von Chronophin im isch{\"a}mischen Schlaganfall unterliegt. Die Entschl{\"u}sselung dieses Mechanismus ist Aufgabe k{\"u}nftiger Untersuchungen.},
  subject      = {Schlaganfall},
  language  = {de}
}
@article{PfitznerMayNuechter2018,
  author    = {Pfitzner, Christian and May, Stefan and N{\"u}chter, Andreas},
  title     = {Body weight estimation for dose-finding and health monitoring of lying, standing and walking patients based on RGB-D data},
  series = {Sensors},
  volume    = {18},
  journal   = {Sensors},
  number    = {5},
  doi       = {10.3390/s18051311},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-176642},
  pages     = {1311},
  year      = {2018},
  abstract  = {This paper describes the estimation of the body weight of a person in front of an RGB-D camera. A survey of different methods for body weight estimation based on depth sensors is given. First, an estimation of people standing in front of a camera is presented. Second, an approach based on a stream of depth images is used to obtain the body weight of a person walking towards a sensor. The algorithm first extracts features from a point cloud and forwards them to an artificial neural network (ANN) to obtain an estimation of body weight. Besides the algorithm for the estimation, this paper further presents an open-access dataset based on measurements from a trauma room in a hospital as well as data from visitors of a public event. In total, the dataset contains 439 measurements. The article illustrates the efficiency of the approach with experiments with persons lying down in a hospital, standing persons, and walking persons. Applicable scenarios for the presented algorithm are body weight-related dosing of emergency patients.},
  language  = {en}
}
@phdthesis{Zaum2018,
  author    = {Zaum, Sebastian},
  title     = {Die hCMEC/D3-Zelllinie als humanes in-vitro-Modell der Blut-Hirn-Schranke im isch{\"a}mischen Schlaganfall},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-166499},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2018},
  abstract  = {Der Schlaganfall ist eine Krankheit mit großer Bedeutung, sowohl f{\"u}r die Betroffenen wie auch unter volkswirtschaftlichen Gesichtspunkten. In der Erforschung neuer und besserer Therapiemethoden f{\"u}r den isch{\"a}mischen Schlaganfall ist ein gutes in-vitro-Modell der Blut-Hirn-Schranke unerl{\"a}sslich, da ein Teil der Sch{\"a}digung des ZNS durch einen Zusammenbruch dieser Barriere verursacht wird. Die hCMEC/D3-Zelllinie stellt ein solches Modell dar; mit steigender Dauer der isch{\"a}mischen Stoffwechsellage zeigt sich eine Erh{\"o}hung der LDH-Konzentration als Marker f{\"u}r das Absterben der Zellen sowie ein R{\"u}ckgang der Zellvitalit{\"a}t. Zudem l{\"a}sst sich eine Entz{\"u}ndungsreaktion mit Anstieg der Marker TNF-Alpha und VEGF, sowie tendenziell auch von Interleukin 6 und Interleukin 8 beobachten, welche auch auf eine Barriereschw{\"a}chung hindeutet. Aus vorherigen Versuchen bekannte Tight junctions-Proteine wie Claudin 1 und Occludin waren in D3-Zellen unter isch{\"a}mischen Bedingungen nicht ver{\"a}ndert, Claudin 5 war in der PCR vermindert exprimiert. Die f{\"u}r die Barriereschw{\"a}chung verantwortlichen Strukturproteine m{\"u}ssen durch weitere Versuche identifiziert werden. Eine m{\"o}gliche Erh{\"o}hung der Expression des Transkriptionsfaktors ZO-1 k{\"o}nnte unter diesen Bedingungen einen Mechanismus der Barriereschw{\"a}chung darstellen. Die Expression des Glukokortikoidrezeptors war in Monokultur-Versuchen mit D3-Zellen nach Isch{\"a}mie erniedrigt. Dies stellt eine Gemeinsamkeit mit Versuchen mit Zelllinien tierischen Ursprungs dar; in diesen zeigten die Zellen durch Degradation des Glukokortikoidrezeptors ein fehlendes Ansprechen auf eine Glukokortikoid-Behandlung. In der Cokultur der D3-Zellen mit Gliomzellen der C6-Zelllinie zeigte sich jedoch eine Erh{\"o}hung der GR-Expression. Eine Cokultur kann den komplexen Aufbau der Blut-Hirn-Schranke, mit Beteiligung mehrerer Zelltypen, besser darstellen als Versuche mit nur einer Zelllinie. Die Erh{\"o}hung der GR-Expression in diesem humanen in-vitro-Modell der Blut-Hirn-Schranke steht im Gegensatz zu den in-vitro-Versuchen mit anderen Zelllinien. Dies k{\"o}nnte eine m{\"o}gliche Erkl{\"a}rung liefern, warum die Erkenntnisse aus diesen Versuchen bisher nicht zu einer Verbesserung der Evidenz der Glukokortikoid-Therapie beim isch{\"a}mischen Schlaganfall beigetragen haben. Zudem zeigt die Fluoreszenzf{\"a}rbung von D3-Zellen, dass diese auch unter Isch{\"a}mie auf Glukokortikoide reagieren.},
  subject      = {stroke},
  language  = {de}
}
@article{MalschLimanWiedmannetal.2018,
  author    = {Malsch, Carolin and Liman, Thomas and Wiedmann, Silke and Siegerink, Bob and Georgakis, Marios K. and Tiedt, Steffen and Endres, Matthias and Heuschmann, Peter U.},
  title     = {Outcome after stroke attributable to baseline factors—the PROSpective Cohort with Incident Stroke (PROSCIS)},
  series = {PLoS ONE},
  volume    = {13},
  journal   = {PLoS ONE},
  number    = {9},
  doi       = {10.1371/journal.pone.0204285},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-177342},
  pages     = {e0204285},
  year      = {2018},
  abstract  = {Background The impact of risk factors on poor outcome after ischemic stroke is well known, but estimating the amount of poor outcome attributable to single factors is challenging in presence of multimorbidity. We aim to compare population attributable risk estimates obtained from different statistical approaches regarding their consistency. We use a real-life data set from the PROSCIS study to identify predictors for mortality and functional impairment one year after first-ever ischemic stroke and quantify their contribution to poor outcome using population attributable risks. Methods The PROSpective Cohort with Incident Stroke (PROSCIS) is a prospective observational hospital-based cohort study of patients after first-ever stroke conducted independently in Berlin (PROSCIS-B) and Munich (PROSCIS-M). The association of baseline factors with poor outcome one year after stroke in PROSCIS-B was analysed using multiple logistic regression analysis and population attributable risks were calculated, which were estimated using sequential population attributable risk based on a multiple generalized additive regression model, doubly robust estimation, as well as using average sequential population attributable risk. Findings were reproduced in an independent validation sample from PROSCIS-M. Results Out of 507 patients with available outcome information after 12 months in PROSCIS-B, 20.5\% suffered from poor outcome. Factors associated with poor outcome were age, pre-stroke physical disability, stroke severity (NIHSS), education, and diabetes mellitus. The order of risk factors ranked by magnitudes of population attributable risk was almost similar for all methods, but population attributable risk estimates varied markedly between the methods. In PROSCIS-M, incidence of poor outcome and distribution of baseline parameters were comparable. The multiple logistic regression model could be reproduced for all predictors, except pre-stroke physical disability. Similar to PROSCIS-B, the order of risk factors ranked by magnitudes of population attributable risk was almost similar for all methods, but magnitudes of population attributable risk differed markedly between the methods. Conclusions Ranking of risk factors by population impact is not affected by the different statistical approaches. Thus, for a rational decision on which risk factor to target in disease interventions, population attributable risk is a supportive tool. However, population attributable risk estimates are difficult to interpret and are not comparable when they origin from studies applying different methodology. The predictors for poor outcome identified in PROSCIS-B have a relevant impact on mortality and functional impairment one year after first-ever ischemic stroke.},
  language  = {en}
}
@article{KazuhinoWernerToriumietal.2018,
  author    = {Kazuhino, Koshino and Werner, Rudolf A. and Toriumi, Fuijo and Javadi, Mehrbod S. and Pomper, Martin G. and Solnes, Lilja B. and Verde, Franco and Higuchi, Takahiro and Rowe, Steven P.},
  title     = {Generative Adversarial Networks for the Creation of Realistic Artificial Brain Magnetic Resonance Images},
  series = {Tomography},
  volume    = {4},
  journal   = {Tomography},
  number    = {4},
  doi       = {10.18383/j.tom.2018.00042},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-172185},
  pages     = {159-163},
  year      = {2018},
  abstract  = {Even as medical data sets become more publicly accessible, most are restricted to specific medical conditions. Thus, data collection for machine learning approaches remains challenging, and synthetic data augmentation, such as generative adversarial networks (GAN), may overcome this hurdle. In the present quality control study, deep convolutional GAN (DCGAN)-based human brain magnetic resonance (MR) images were validated by blinded radiologists. In total, 96 T1-weighted brain images from 30 healthy individuals and 33 patients with cerebrovascular accident were included. A training data set was generated from the T1-weighted images and DCGAN was applied to generate additional artificial brain images. The likelihood that images were DCGAN-created versus acquired was evaluated by 5 radiologists (2 neuroradiologists [NRs], vs 3 non-neuroradiologists [NNRs]) in a binary fashion to identify real vs created images. Images were selected randomly from the data set (variation of created images, 40\%-60\%). None of the investigated images was rated as unknown. Of the created images, the NRs rated 45\% and 71\% as real magnetic resonance imaging images (NNRs, 24\%, 40\%, and 44\%). In contradistinction, 44\% and 70\% of the real images were rated as generated images by NRs (NNRs, 10\%, 17\%, and 27\%). The accuracy for the NRs was 0.55 and 0.30 (NNRs, 0.83, 0.72, and 0.64). DCGAN-created brain MR images are similar enough to acquired MR images so as to be indistinguishable in some cases. Such an artificial intelligence algorithm may contribute to synthetic data augmentation for "data-hungry" technologies, such as supervised machine learning approaches, in various clinical applications.},
  subject      = {Magnetresonanztomografie},
  language  = {en}
}