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Institut
- Graduate School of Life Sciences (131)
- Theodor-Boveri-Institut für Biowissenschaften (87)
- Institut für Informatik (47)
- Institut für Psychologie (46)
- Medizinische Klinik und Poliklinik I (44)
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- Neurologische Klinik und Poliklinik (39)
- Klinik und Poliklinik für Anästhesiologie (ab 2004) (33)
- Lehrstuhl für Orthopädie (32)
Schriftenreihe
Sonstige beteiligte Institutionen
- Helmholtz Institute for RNA-based Infection Research (HIRI) (2)
- Interdisziplinäres Zentrum für Klinische Forschung (IZKF) (2)
- Klinische Studienzentrale (Universitätsklinikum) (2)
- Naturalis Biodiversity Centre (2)
- Rudolf Virchow Center for Integrative and Translational Bioimaging, University of Würzburg (2)
- Akademie der Wissenschaften und der Literatur, Mainz (1)
- Anthropology Department University of Tennessee, Knoxville (1)
- Bungando Medical Centre, Mwanza, Tanzania (1)
- CHC Würzburg (Comprehensive Hearing Center) (1)
- CIBSS Centre for Integrative Biological Signalling Studies, University of Freiburg (1)
Environmental issues have emerged especially since humans burned fossil fuels, which led to air pollution and climate change that harm the environment. These issues’ substantial consequences evoked strong efforts towards assessing the state of our environment.
Various environmental machine learning (ML) tasks aid these efforts. These tasks concern environmental data but are common ML tasks otherwise, i.e., datasets are split (training, validatition, test), hyperparameters are optimized on validation data, and test set metrics measure a model’s generalizability. This work focuses on the following environmental ML tasks: Regarding air pollution, land use regression (LUR) estimates air pollutant concentrations at locations where no measurements are available based on measured locations and each location’s land use (e.g., industry, streets). For LUR, this work uses data from London (modeled) and Zurich (measured). Concerning climate change, a common ML task is model output statistics (MOS), where a climate model’s output for a study area is altered to better fit Earth observations and provide more accurate climate data. This work uses the regional climate model (RCM) REMO and Earth observations from the E-OBS dataset for MOS. Another task regarding climate is grain size distribution interpolation where soil properties at locations without measurements are estimated based on the few measured locations. This can provide climate models with soil information, that is important for hydrology. For this task, data from Lower Franconia is used.
Such environmental ML tasks commonly have a number of properties: (i) geospatiality, i.e., their data refers to locations relative to the Earth’s surface. (ii) The environmental variables to estimate or predict are usually continuous. (iii) Data can be imbalanced due to relatively rare extreme events (e.g., extreme precipitation). (iv) Multiple related potential target variables can be available per location, since measurement devices often contain different sensors. (v) Labels are spatially often only sparsely available since conducting measurements at all locations of interest is usually infeasible. These properties present challenges but also opportunities when designing ML methods for such tasks.
In the past, environmental ML tasks have been tackled with conventional ML methods, such as linear regression or random forests (RFs). However, the field of ML has made tremendous leaps beyond these classic models through deep learning (DL). In DL, models use multiple layers of neurons, producing increasingly higher-level feature representations with growing layer depth. DL has made previously infeasible ML tasks feasible, improved the performance for many tasks in comparison to existing ML models significantly, and eliminated the need for manual feature engineering in some domains due to its ability to learn features from raw data. To harness these advantages for environmental domains it is promising to develop novel DL methods for environmental ML tasks.
This thesis presents methods for dealing with special challenges and exploiting opportunities inherent to environmental ML tasks in conjunction with DL. To this end, the proposed methods explore the following techniques: (i) Convolutions as in convolutional neural networks (CNNs) to exploit reoccurring spatial patterns in geospatial data. (ii) Posing the problems as regression tasks to estimate the continuous variables. (iii) Density-based weighting to improve estimation performance for rare and extreme events. (iv) Multi-task learning to make use of multiple related target variables. (v) Semi–supervised learning to cope with label sparsity. Using these techniques, this thesis considers four research questions: (i) Can air pollution be estimated without manual feature engineering? This is answered positively by the introduction of the CNN-based LUR model MapLUR as well as the off-the-shelf LUR solution OpenLUR. (ii) Can colocated pollution data improve spatial air pollution models? Multi-task learning for LUR is developed for this, showing potential for improvements with colocated data. (iii) Can DL models improve the quality of climate model outputs? The proposed DL climate MOS architecture ConvMOS demonstrates this. Additionally, semi-supervised training of multilayer perceptrons (MLPs) for grain size distribution interpolation is presented, which can provide improved input data. (iv) Can DL models be taught to better estimate climate extremes? To this end, density-based weighting for imbalanced regression (DenseLoss) is proposed and applied to the DL architecture ConvMOS, improving climate extremes estimation. These methods show how especially DL techniques can be developed for environmental ML tasks with their special characteristics in mind. This allows for better models than previously possible with conventional ML, leading to more accurate assessment and better understanding of the state of our environment.
The use of digital media by children and young people offers opportunities for communication, collaboration, and participation. However, to prepare them for the risks and challenges of media usage, promoting digital competencies of students and teachers is an indispensable goal for educational institutions. To meet this requirement, teacher education must be opened to innovative pedagogical concepts for initial teacher education that considers new technologies in a reflective, action-oriented way to promote competencies. Therefore, this work aims to promote the technological pedagogical content knowledge (TPACK) of prospective teachers that enables the purposeful integration of social virtual reality (social VR) into the classroom. Consequently, a pedagogical concept is developed and evaluated in an iterative research and development process following the design- based research approach (DBR) through four consecutive studies. The first study involved an analysis of the requirements of teachers and students for the effective use of social VR in the classroom. The second study examined how prospective teachers perceive teaching and learning activities within two theory-driven scenarios in social VR. The third study investigated the development of Technological Pedagogical Content Knowledge (TPACK) among students in social VR compared to video-based communication. Finally, the fourth study measured the development of TPACK in social VR using epistemic network analysis, finding that social VR can be an effective tool for teacher education, emphasizing the importance of authentic contexts and practical experiences for effective teaching in social VR. In the concluding chapter, appropriate implications for teacher education research and practice are derived from findings. For example, that a deeper understanding of TPACK as metacognitive awareness could enhance teacher education for media integration. It also highlights the need for digital literacy in seminars that address new technologies, emphasizing the importance of considering moral values and sustainability when using VR.
Mittels einer fünfstufigen Synthese wurde das 2,2´-Ditetracen als Modellsystem zur Erforschung von singlet fission-Prozessen hergestellt. Die Synthese wurde mit einer Gesamtausbeute von 21 % durchgeführt, wobei der Schlüsselschritt, die Kopplung der beiden Monomere, durch eine Suzuki-Kopplung erfolgte. Das gewünschte Produkt konnte nach gründlicher Reinigung mittels Gradientensublimation als leuchtend rote Einkristalle erhalten werden. Während die Emissionsspektren der Einzelmoleküle nahezu identisch sind, zeigen Untersuchungen mittels Photolumineszenzspektroskopie eine Rotverschiebung im Emissionsspektrum des Dimer-Einkristalls im Vergleich zum Einkristall des Tetracen-Monomers. Durch theoretische Berechnung konnte die Absenkung des S1-Zustands des Dimers im Kristall erklärt werden, wodurch die Energiebedingung für singlet fission (2 E(T1) ≤ E(S1)) nicht mehr erfüllt ist.
Weiterhin wurden mehrere mit Alkylgruppen und Vinylgruppen substituierte Tetracenderivate synthetisiert und diese mittels optischer und elektrochemischer Methoden auf ihre Eigenschaften hin untersucht. Es wurde bei allen synthetisierten Derivaten eine Rotverschiebung der Hauptbanden im Absorptionsspektrum beobachtet, was durch einen kleineren HOMO-LUMO-Abstand im Vergleich zum nicht substituierten Tetracen erklärt wird. Es wurde zudem eine erhöhte Stabilität dieser Derivate gegenüber Umwelteinflüssen wie Licht und Sauerstoff, die die Bildung von Endoperoxiden und Dimeren zur Folge haben, festgestellt. Dies kann auf sterische Effekte sowie die Stabilisierung des biradikalischen Zustands dieser Moleküle durch Hyperkonjugation und Resonanzeffekte zurückgeführt werden.
Verschiedene Konzepte der Röntgenmikroskopie haben sich mittlerweile im Labor etabliert und ermöglichen heute aufschlussreiche Einblicke in eine Vielzahl von Probensystemen. Der „Labormaßstab“ bezieht sich dabei auf Analysemethoden, die in Form von einem eigenständigen Gerät betrieben werden können. Insbesondere sind sie unabhängig von der Strahlerzeugung an einer Synchrotron-Großforschungseinrichtung und einem sonst kilometergroßen Elektronen-speicherring. Viele der technischen Innovationen im Labor sind dabei ein Transfer der am Synchrotron entwickelten Techniken. Andere wiederum basieren auf der konsequenten Weiterentwicklung etablierter Konzepte. Die Auflösung allein ist dabei nicht entscheidend für die spezifische Eignung eines Mikroskopiesystems im Ganzen. Ebenfalls sollte das zur Abbildung eingesetzte Energiespektrum auf das Probensystem abgestimmt sein. Zudem muss eine Tomographieanalage zusätzlich in der Lage sein, die Abbildungsleistung bei 3D-Aufnahmen zu konservieren.
Nach einem Überblick über verschiedene Techniken der Röntgenmikroskopie konzentriert sich die vorliegende Arbeit auf quellbasierte Nano-CT in Projektionsvergrößerung als vielversprechende Technologie zur Materialanalyse. Hier können höhere Photonenenergien als bei konkurrierenden Ansätzen genutzt werden, wie sie von stärker absorbierenden Proben, z. B. mit einem hohen Anteil von Metallen, zur Untersuchung benötigt werden. Das bei einem ansonsten idealen CT-Gerät auflösungs- und leistungsbegrenzende Bauteil ist die verwendete Röntgen-quelle. Durch konstruktive Innovationen sind hier die größten Leistungssprünge zu erwarten. In diesem Zuge wird erörtert, ob die Brillanz ein geeignetes Maß ist, um die Leistungsfähigkeit von Röntgenquellen zu evaluieren, welchen Schwierigkeiten die praktische Messung unterliegt und wie das die Vergleichbarkeit der Werte beeinflusst. Anhand von Monte-Carlo-Simulationen wird gezeigt, wie die Brillanz verschiedener Konstruktionen an Röntgenquellen theoretisch bestimmt und miteinander verglichen werden kann. Dies wird am Beispiel von drei modernen Konzepten von Röntgenquellen demonstriert, welche zur Mikroskopie eingesetzt werden können. Im Weiteren beschäftigt sich diese Arbeit mit den Grenzen der Leistungsfähigkeit von Transmissionsröntgenquellen. Anhand der verzahnten Simulation einer Nanofokus-Röntgenquelle auf Basis von Monte-Carlo und FEM-Methoden wird untersucht, ob etablierte Literatur¬modelle auf die modernen Quell-konstruktionen noch anwendbar sind. Aus den Simulationen wird dann ein neuer Weg abgeleitet, wie die Leistungsgrenzen für Nanofokus-Röntgenquellen bestimmt werden können und welchen Vorteil moderne strukturierte Targets dabei bieten.
Schließlich wird die Konstruktion eines neuen Nano-CT-Gerätes im Labor-maßstab auf Basis der zuvor theoretisch besprochenen Nanofokus-Röntgenquelle und Projektionsvergrößerung gezeigt, sowie auf ihre Leistungsfähigkeit validiert. Es ist spezifisch darauf konzipiert, hochauflösende Messungen an Materialsystemen in 3D zu ermöglichen, welche mit bisherigen Methoden limitiert durch mangelnde Auflösung oder Energie nicht umsetzbar waren. Daher wird die praktische Leistung des Gerätes an realen Proben und Fragestellungen aus der Material¬wissenschaft und Halbleiterprüfung validiert. Speziell die gezeigten Messungen von Fehlern in Mikrochips aus dem Automobilbereich waren in dieser Art zuvor nicht möglich.
The holy grail of structural biology is to study a protein in situ, and this goal has been fast approaching since the resolution revolution and the achievement of atomic resolution. A cell's interior is not a dilute environment, and proteins have evolved to fold and function as needed in that environment; as such, an investigation of a cellular component should ideally include the full complexity of the cellular environment. Imaging whole cells in three dimensions using electron cryotomography is the best method to accomplish this goal, but it comes with a limitation on sample thickness and produces noisy data unamenable to direct analysis. This thesis establishes a novel workflow to systematically analyse whole-cell electron cryotomography data in three dimensions and to find and identify instances of protein complexes in the data to set up a determination of their structure and identity for success. Mycoplasma pneumoniae is a very small parasitic bacterium with fewer than 700 protein-coding genes, is thin enough and small enough to be imaged in large quantities by electron cryotomography, and can grow directly on the grids used for imaging, making it ideal for exploratory studies in structural proteomics. As part of the workflow, a methodology for training deep-learning-based particle-picking models is established.
As a proof of principle, a dataset of whole-cell Mycoplasma pneumoniae tomograms is used with this workflow to characterize a novel membrane-associated complex observed in the data. Ultimately, 25431 such particles are picked from 353 tomograms and refined to a density map with a resolution of 11 Å. Making good use of orthogonal datasets to filter search space and verify results, structures were predicted for candidate proteins and checked for suitable fit in the density map. In the end, with this approach, nine proteins were found to be part of the complex, which appears to be associated with chaperone activity and interact with translocon machinery.
Visual proteomics refers to the ultimate potential of in situ electron cryotomography: the comprehensive interpretation of tomograms. The workflow presented here is demonstrated to help in reaching that potential.
Small bacterial regulatory RNAs (sRNAs) have been implicated in the regulation of numerous metabolic pathways. In most of these studies, sRNA-dependent regulation of mRNAs or proteins of enzymes in metabolic pathways has been predicted to affect the metabolism of these bacteria. However, only in a very few cases has the role in metabolism been demonstrated. Here, we performed a combined transcriptome and metabolome analysis to define the regulon of the sibling sRNAs NgncR_162 and NgncR_163 (NgncR_162/163) and their impact on the metabolism of Neisseria gonorrhoeae. These sRNAs have been reported to control genes of the citric acid and methylcitric acid cycles by posttranscriptional negative regulation. By transcriptome analysis, we now expand the NgncR_162/163 regulon by several new members and provide evidence that the sibling sRNAs act as both negative and positive regulators of target gene expression. Newly identified NgncR_162/163 targets are mostly involved in transport processes, especially in the uptake of glycine, phenylalanine, and branched-chain amino acids. NgncR_162/163 also play key roles in the control of serine-glycine metabolism and, hence, probably affect biosyntheses of nucleotides, vitamins, and other amino acids via the supply of one-carbon (C\(_1\)) units. Indeed, these roles were confirmed by metabolomics and metabolic flux analysis, which revealed a bipartite metabolic network with glucose degradation for the supply of anabolic pathways and the usage of amino acids via the citric acid cycle for energy metabolism. Thus, by combined deep RNA sequencing (RNA-seq) and metabolomics, we significantly extended the regulon of NgncR_162/163 and demonstrated the role of NgncR_162/163 in the regulation of central metabolic pathways of the gonococcus.
The signal modelling framework JimenaE simulates dynamically Boolean networks. In contrast to SQUAD, there is systematic and not just heuristic calculation of all system states. These specific features are not present in CellNetAnalyzer and BoolNet. JimenaE is an expert extension of Jimena, with new optimized code, network conversion into different formats, rapid convergence both for system state calculation as well as for all three network centralities. It allows higher accuracy in determining network states and allows to dissect networks and identification of network control type and amount for each protein with high accuracy. Biological examples demonstrate this: (i) High plasticity of mesenchymal stromal cells for differentiation into chondrocytes, osteoblasts and adipocytes and differentiation-specific network control focusses on wnt-, TGF-beta and PPAR-gamma signaling. JimenaE allows to study individual proteins, removal or adding interactions (or autocrine loops) and accurately quantifies effects as well as number of system states. (ii) Dynamical modelling of cell–cell interactions of plant Arapidopsis thaliana against Pseudomonas syringae DC3000: We analyze for the first time the pathogen perspective and its interaction with the host. We next provide a detailed analysis on how plant hormonal regulation stimulates specific proteins and who and which protein has which type and amount of network control including a detailed heatmap of the A.thaliana response distinguishing between two states of the immune response. (iii) In an immune response network of dendritic cells confronted with Aspergillus fumigatus, JimenaE calculates now accurately the specific values for centralities and protein-specific network control including chemokine and pattern recognition receptors.
Enterococcus faecalis and Enterococcus faecium are major nosocomial pathogens. Despite their relevance to public health and their role in the development of bacterial antibiotic resistance, relatively little is known about gene regulation in these species. RNA–protein complexes serve crucial functions in all cellular processes associated with gene expression, including post-transcriptional control mediated by small regulatory RNAs (sRNAs). Here, we present a new resource for the study of enterococcal RNA biology, employing the Grad-seq technique to comprehensively predict complexes formed by RNA and proteins in E. faecalis V583 and E. faecium AUS0004. Analysis of the generated global RNA and protein sedimentation profiles led to the identification of RNA–protein complexes and putative novel sRNAs. Validating our data sets, we observe well-established cellular RNA–protein complexes such as the 6S RNA–RNA polymerase complex, suggesting that 6S RNA-mediated global control of transcription is conserved in enterococci. Focusing on the largely uncharacterized RNA-binding protein KhpB, we use the RIP-seq technique to predict that KhpB interacts with sRNAs, tRNAs, and untranslated regions of mRNAs, and might be involved in the processing of specific tRNAs. Collectively, these datasets provide departure points for in-depth studies of the cellular interactome of enterococci that should facilitate functional discovery in these and related Gram-positive species. Our data are available to the community through a user-friendly Grad-seq browser that allows interactive searches of the sedimentation profiles (https://resources.helmholtz-hiri.de/gradseqef/).
The present cumulative dissertation summarizes three clinical studies, which examine
subgroups of patients within the fibromyalgia syndrome (FMS). FMS entails chronic pain and
associated symptoms, and its pathophysiology is incompletely understood (1). Previous studies
show that there is a subgroup of patients with FMS with objective histological pathology of the
small nerve fibers of the peripheral nervous system (PNS). Another subgroup of FMS patients
does not show any signs of pathological changes of the small nerve fibers. The aim of this
dissertation was to compare FMS patients with healthy controls, and these two FMS subgroups
for differences in the central nervous system (CNS) in order to explore possible interactions
between PNS and the CNS. Regarding the CNS, differences of FMS patients with healthy
controls have already been found in studies with small sample sizes, but no subgroups have yet
been identified. Another aim of this thesis was to test whether the subgroups show a different
response to different classes of pain medication. The methods used in this thesis are structural
and functional magnetic resonance imaging (MRI), magnetic resonance diffusion imaging and
magnetic resonance spectroscopy. For the evaluation of clinical symptoms, we used
standardized questionnaires. The subgroups with and without pathologies of the PNS were
determined by skin biopsies of the right thigh and lower leg based on the intraepidermal nerve
fiber density (IENFD) of the small nerve fibers.
1) In the first MRI study, 43 female patients with the diagnosis of FMS and 40 healthy
control subjects, matched in age and body mass index, were examined with different MRI
sequences. Cortical thickness was investigated by structural T1 imaging, white matter integrity
by diffusion tensor imaging and functional connectivity within neuronal networks by functional
resting state MRI. Compared to the controls, FMS patients had a lower cortical volume in
bilateral frontotemporoparietal regions and the left insula, but a higher cortical volume in the
left pericalcarine cortex. Compared to the subgroup without PNS pathology, the subgroup with
PNS pathology had lower cortical volume in both pericalcarine cortices. Diffusion tensor
imaging revealed an increased fractional anisotropy (FA) of FMS patients in corticospinal
pathways such as the corona radiata, but also in regions of the limbic systems such as the fornix
and cingulum. Subgroup comparison again revealed lower mean FA values of the posterior
thalamic radiation and the posterior limb of the left internal capsule in the subgroup with PNS
pathology. In the functional connectivity analysis FMS patients, compared to controls, showed
a hypoconnectivity between the right median frontal gyrus and the posterior cerebellum and
the right crus cerebellum, respectively. In the subgroup comparisons, the subgroup with PNS
pathology showed a hyperconnectivity between both inferior frontal gyri, the right posterior
parietal cortex and the right angular gyrus. In summary, these results show that differences in
brain morphology and functional connectivity exist between FMS patients with and without
PNS pathology. These differences were not associated with symptom duration or severity and,
in some cases, have not yet been described in the context of FMS. The differences in brain
morphology and connectivity between subgroups could also lead to a differential response to
treatment with centrally acting drugs. Further imaging studies with FMS patients should take
into account this heterogeneity of FMS patient cohorts.
2) Following the results from the first MRI study, drug therapies of FMS patients and
their treatment response were compared between PNS subgroups. As there is no licensed drug
for FMS in Europe, the German S3 guideline recommends amitriptyline, duloxetine and
pregabalin for temporary use. In order to examine the current drug use in FMS patients in
Germany on a cross-sectional basis, 156 patients with FMS were systematically interviewed.
The drugs most frequently used to treat pain in FMS were non-steroidal anti-inflammatory
drugs (NSAIDs) (28.9%), metamizole (15.4%) and amitriptyline (8.8%). Pain relief assessed by
patients on a numerical rating scale from 0-10 averaged 2.2 points for NSAIDs, 2.0 for
metamizole and 1.5 for amitriptyline. Drugs that were discontinued for lack of efficacy and not
for side effects were acetaminophen (100%), flupirtine (91.7%), selective serotonin reuptake
inhibitors (81.8%), NSAIDs (83.7%) and weak opioids (74.1%). Patients were divided into
subgroups with and without PNS pathology as determined by skin biopsies. We found no
differences in drug use and effect between the subgroups. Taken together, these results show
that many FMS patients take medication that is not in accordance with the guidelines. The
reduction of symptoms was best achieved with metamizole and NSAIDs. Further longitudinal
studies on medication in FMS are necessary to obtain clearer treatment recommendations.
3) Derived from previous pharmacological and imaging studies (with smaller case
numbers), there is a hypothesis in the FMS literature that hyperreactivity of the insular cortex
may have an impact on FMS. The hyperreactivity seems to be due to an increased concentration
of the excitatory neurotransmitter glutamate in the insular cortex of FMS patients. The
hypothesis is supported by magnetic resonance spectroscopy studies with small number of
cases, as well as results from pharmacological studies with glutamate-inhibiting medication.
Studies from animal models have also shown that an artificially induced increase in glutamate
in the insular cortex can lead to reduced skin innervation. Therefore, the aim of this study was
to compare glutamate and GABA concentrations in the insular cortex of FMS patients with
those of healthy controls using magnetic resonance imaging. There was no significant
difference of both neurotransmitters between the groups. In addition, there was no correlation
between the neurotransmitter concentrations and the severity of clinical symptoms. There
were also no differences in neurotransmitter concentrations between the subgroups with and
without PNS pathology. In conclusion, our study could not show any evidence of a correlation
of glutamate and GABA concentrations with the symptoms of FMS or the pathogenesis of
subgroups with PNS pathologies.
Die fibulare Kapselbandverletzung ist eine der häufigsten Verletzungen im Alltag und im Sport. Durch das hohe Patientenaufkommen mit finanziellen Auswirkungen entsteht eine Belastung für das Gesundheitssystem. Nicht selten wird die Verletzung bagatellisiert und endet in chronischen Folgen.
Zur Erhebung der bis dato unklaren Versorgungsrealität führten wir eine Onlinebefragung durch. Kernfrage war, ob Einheitlichkeit in der Therapie der fibularen Kapselbandverletzung herrscht. Leitende Ärzte orthopädischer/ unfallchirurgischer Kliniken sowie GFFC-Mitglieder wurden online mittels standardisierten Fragebogens gebeten, an einer Befragung teilzunehmen. Untersuchte Faktoren waren Einsatz von Bildgebung, Ottawa Ankle Rules, Immobilisation, Belastung, Rehabilitationsmaßnahmen, OP-Indikationen, operative Techniken und generelle Handlungsleitlinien.
Insgesamt 549 vollständig ausgefüllte Fragebögen wurden analysiert. Die Rückantwortquote lag bei 24,69 %. Gefragt nach der Diagnostik und Therapie unterscheiden sich die Antworten vermehrt in Abhängigkeit des jeweiligen Versorgungsstatus. Im Mittel wird die niedriggradige Verletzung mit einer Orthese oder einem Tape-/ Stützverband ruhiggestellt, die höhergradige anfangs auch mit einem Gips und im Verlauf mit einer Orthese. Drittgradig Verletzte erhalten unterstützend Unterarmgehstützen. Operiert wird bei der primären Verletzung selten. Im Falle einer OP wird in 72,5 % der Fälle arthroskopisch vorgegangen.
Anhand unserer Ergebnisse wird deutlich, dass es eine grobe Behandlungspräferenz gibt: die konservative, frühfunktionelle Therapie mit einer Orthesenversorgung für vier bis sechs Wochen. Jedoch kann man von keiner Einheitlichkeit sprechen, da sich bei Teilaspekten derselben Verletzungsschwere unterschiedliche, teils widersprüchliche Behandlungspfade ergaben. Häufig unterschieden sich die Versorgungsstufen in ihrem Vorgehen.
Als Problem sehen wir die fehlende Kenntnis, der zu dem Krankheitsbild gehörenden Leitlinie. Weitere Aufmerksamkeit und Aufklärung sind vonnöten.