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Radiovirotherapy for the treatment of refractory tumors
No full textCancer remains a major public health challenge and entities such as glioblastoma (GBM) and pancreatic ductal adenocarcinoma (PDAC) are particularly difficult to treat with the currently available therapeutics: Immune privileged locations, a challenging tumor microenvironment and often-occurring treatment resistances underline the need for novel therapeutic strategies. Radiotherapy is a cornerstone of cancer management and has benefited immensely from technological advances. Nevertheless, dose-limiting toxicities and relapse due to treatment resistance are frequent. A promising approach to increase treatment efficacy is the combination with immunotherapies. Oncolytic viruses, such as the vaccine strain of measles virus (MeV), are one such immunotherapeutic approach: MeV has a natural cancer tropism, lyses tumor cells and induces an anti-tumor immune response. However, MeV monotherapies have shown limited therapeutic efficacy in solid tumors. Preclinical data indicates that combining MeV with radiotherapy can produce synergistic effects and a favorable (innate) immune activation, although interferon (IFN)-mediated antiviral responses can also restrict MeV replication. I thus hypothesized that the addition of a second oncolytic vector, parvovirus, known to suppress the IFN response, could further enhance treatment efficacy. Through RNA sequencing, I characterized the combination of MeV and radiotherapy and observed distinct immune induction patterns in the combination. I then identified candidate cell lines from a panel of GBM and PDAC cell lines with the desired intact IFN signaling capacity, showing attenuation of MeV replication. In these models, I assessed cytotoxicity, synergy and the potential mechanisms of dual (PV + MeV) and triple (radiation + PV + MeV) therapy. Dual virotherapy produced additive cytotoxic effects alongside PV-mediated IFN suppression. While MeV replication was unaffected, its transgene expression was markedly reduced during co-infection. Triple radiovirotherapy demonstrated enhanced cytotoxicity and synergy in GBM cells at specific dose combinations, accompanied by modest IFN dampening and an increase in the immunogenic cell death (ICD) marker calreticulin. I additionally employed a heterotypic spheroid model, where the MeV-mediated IFN response was reduced when combined with certain PV doses, but cytotoxicity was not enhanced. On the contrary, triple combinations showed an antagonistic pattern. Finally, I generated and characterized murine cell lines expressing the MeV entry receptor for future in vivo evaluation.
Overall, I performed a comprehensive analysis of (triple) radiovirotherapy. The variable treatment efficacy reflects the complexity of analyzing advanced combination approaches in vitro. Nevertheless, synergistic combinations were identified, suggesting a potential therapeutic benefit for selected cancer patients suffering from refractory cancers such as PDAC and GBM
Untersuchung der Selbstmanagementfähigkeit depressiver Patienten
No full textDie vorliegende Arbeit untersucht an einer klinischen Stichprobe die Frage, ob ein Zusammenhang
zwischen der individuellen Selbstmanagementfähigkeit depressiver Patienten* und ihrer depressiven
Symptomschwere besteht.
In der untersuchten Stichprobe, die stationär behandelte Patienten* mit einer Depression nach ICD-10-Kriterien umfasste, bestand zum Zeitpunkt der Klinikaufnahme eine
positive Korrelation zwischen der Selbstmanagementfähigkeit der Patienten* und der Depressivität.
Darüber hinaus zeigte sich eine inverse Korrelation von vier der fünf Dimensionen des Selbstmanagements nach Wehmeier (Erkenntnis, Beziehung, Planen, Entscheiden, Handeln) mit der Depressivität der Patienten* zum Zeitpunkt der Klinikaufnahme.
Daneben konnte für den Zeitpunkt der Klinikaufnahme ein Unterschied hinsichtlich der Selbstmanagementfähigkeit der Patienten* in Abhängigkeit von dem Schweregrad der vorliegenden Depression nachgewiesen werden. Die Selbstmanagementfähigkeit derjenigen Patienten*, bei denen
ein leichtes depressives Syndrom bestand, war zum Zeitpunkt der Klinikaufnahme höher als die Selbstmanagementfähigkeit von Patienten*, bei denen ein schweres depressives Syndrom bestand.
Während der Behandlung in der Klinik kam es zu einem Anstieg der Selbstmanagementfähigkeit der Patienten*. Dabei war die Veränderung der Selbstmanagementfähigkeit nicht von den
soziodemografischen Variablen Geschlecht oder Alter abhängig.
Die Veränderung der Selbstmanagementfähigkeit zwischen Klinikaufnahme (Erstbefragung) und Entlassung beziehungsweise der Zeit unmittelbar nach der Entlassung (Zweitbefragung) erwies sich als ein signifikanter Prädiktor für die Veränderung der Depressivität zwischen Erst- und
Zweitbefragung. Je stärker sich die Selbstmanagementfähigkeit der Patienten* im beschriebenen
Zeitraum verbesserte, desto größer war die Abnahme der Depressivität.
Darüber hinaus erwiesen sich die Veränderung der Selbstmanagementfähigkeit zwischen Erst- und
Zweitbefragung und die Depressivität bei Klinikaufnahme als signifikante Prädiktoren für die Depressivität zum Zweitbefragungszeitpunkt. Je stärker sich die Selbstmanagementfähigkeit der Patienten* zwischen Erst- und Zweitbefragung verbesserte, desto geringer war ihre Depressivität zum Zeitpunkt der Zweitbefragung. Eine höhere Depressivität bei Klinikaufnahme ging einher mit einer
höheren Depressivität zum Zweitbefragungszeitpunkt. Im errechneten Regressionsmodell spielte die
Veränderung der Selbstmanagementfähigkeit zwischen Erst- und Zweitbefragung eine größere Rolle für die Depressivität zum Zweitbefragungszeitpunkt als die Depressivität bei Klinikaufnahme.
Zusammenfassend deuten die Ergebnisse der vorliegenden Arbeit auf einen Zusammenhang zwischen der individuellen Selbstmanagementfähigkeit und der Symptomschwere depressiver Patienten*. Die Erkenntnisse dieser Arbeit lassen die Vermutung zu, dass die Selbstmanagementfähigkeit eine
wichtige Ressource in der Behandlung depressiver Störungen ist. Eine verstärkte Einbeziehung selbstmanagementfördernder Interventionen in klinische, ambulante und psychotherapeutische Behandlungsstrategien depressiver Störungen erscheint daher vielversprechend. Zukünftige
Untersuchungen auf diesem Gebiet könnten zeigen, ob die verstärkte Integration selbstmanagementfördernder
Interventionen in bestehende Behandlungsstrategien die Behandlungsergebnisse bei depressiven Störungen verbessert.
Anm.: Anstelle einer geschlechtsneutralen Formulierung wurde in der vorliegenden Arbeit auf die Kurzform mit Genderstern (*) zurückgegriffen. Dieser Asterisk im Wort symbolisiert auch den Einschluss derjenigen Personen, die sich weder als Frau noch als Mann definieren, wie etwa Personen mit dem
dritten Geschlechtseintrag divers und weitere Geschlechtsidentitäten (LGBTQIA+)
Liberalitas legibus et aequitate subnixa: Rechtserwerb durch kaiserliche Eigentumszuweisung in der Spätantike
No full textDie Binnenhaftung im Rahmen der Leitung der Geschäfte bei der Societas Europaea: Eine rechtsvergleichende Betrachtung der deutschen und der liechtensteinischen Rechtslage
No full textConsensus approach for assessing and resolving uncertainty in genome-scale metabolic models: advancing systems-level understanding of microbial metabolism
No full textGenome-scale metabolic models (GEMs) are an important methodology in systems biology:
they play a major role in investigating microbial metabolism and predicting responses to
perturbations by representing bacterial metabolism as a whole system.
GEMs can be
automatically reconstructed from bacterial genomes with computational tools that employ
distinct methodological approaches. My initial analyses of GEMs reconstructed by different
tools for a small set of diverse gut microbial species revealed that automated reconstruction
pipelines often produce GEMs with different structures and predictive behaviour, even for the
same organism. Because individual models can excel at different tasks and capture distinct
metabolic capabilities, I hypothesised that combining them can increase confidence in network
content and improve performance. In this thesis, I present a consensus approach implemented
in GEMsembler, a Python package for cross-tool model comparison and assembly of consensus
models from any subset of input GEMs. Alongside the consensus strategy, GEMsembler offers
broad analysis functionality, including detection and visualisation of biosynthetic pathways,
growth evaluation, and an agreement-driven curation workflow. In a use-case study, consensus
models curated with this workflow, combining four automatically reconstructed GEMs for
Lactiplantibacillus plantarum and Escherichia coli, outperform gold-standard models on
auxotrophy and gene-essentiality benchmarks.
Moreover, enabled by GEMs comparison,
optimising gene–protein–reaction (GPR) rule combinations derived from input and consensus
models improves gene-essentiality predictions even for manually curated gold-standard models.
GEMsembler also helps explain model performance by highlighting relevant metabolic pathways
and, together with the consensus principle, supports the assessment of network uncertainty and
informs the design of targeted experiments to resolve it. Finally, I apply the consensus approach
to de novo reconstruction of two of the most abundant human gut bacteria, Bacteroides uniformis
and Phocaeicola vulgatus, yielding first-iteration curated models that reproduce their growth
and major metabolic phenotypes. In agreement with experimental data, the B. uniformis model
secretes more lactate and malate but grows less, whereas the P. vulgatus model secretes less of
these metabolites, grows more, and also secretes succinate. Together, these results show that the
consensus approach facilitates building metabolic models that are more accurate, concise, and
biologically informed, advancing systems-level understanding of microbial metabolism
Photobiomodulation of blue light irradiation on human keratinocytes, fibroblasts, and endothelial cells involved in wound healing and angiogenesis
No full textBackground: Blue light irradiation (BLI) has been widely reported to induce photobiomodulation (PBM) across different cell types. Based on the experimental basis reported, we further
investigate its effects on cell types involved in wound healing and angiogenesis, with various
light doses at continuous irradiation mode.
Methods: In terms of short-term (from 0 to 2 h) irradiation, cellular responses of immortalized
human keratinocytes (HaCaTs), normal human dermal fibroblasts (NHDFs), and human umbilical vein endothelial cells (HUVECs) after light treatment at 450 nm were analyzed by kinetic assays on cell viability, proliferation, ATP quantification, migration assay, and apoptosis
assay. The level of gene expression and potential mechanisms of photobiomodulation were
analyzed by transcriptomic and bioinformatic analyses. Additionally, cellular responses after
long-term irradiation, which was over 2 h, and sequential light treatments with irradiance at
23 and 10 mW/cm2 were investigated by XTT and ATP. Moreover, more influencing factors
were assessed by comparison of cell viability after altering cell culture conditions, including
medium irradiation, medium refreshment, and the existence of phenol red.
Results: A biphasic effect was observed on HaCaTs, NHDFs, and HUVECs. 4.5 J/cm2 irradiation stimulated cell viability, proliferation, and migration. mRNA sequencing indicated involvement of transforming growth factor beta (TGF-β), ErbB, and vascular endothelial growth
factor (VEGF) pathways after the low-fuence irradiation. High-fluence (18 J/cm2
) irradiation
inhibited these cellular activities by downregulating DNA replication, the cell cycle, and mismatch repair pathways. The biological effect of 4.5 J/cm2 were further verified to stimulate
cell lines after 2 h irradiation at an irradiance of 23 mW/cm2 by XTT and ATP quantification.
However, after extending the corresponding irradiation time up to 5 h, cell viability decreased
continuously. Irradiation only on medium could induce changes in cell viability, and medium
refreshment after irradiation could eliminate the changes in cell viability. No significant difference was observed in cell viability after irradiation on phenol red and phenol red-free medium.
Conclusions: (1) HaCaTs, NHDFs, and HUVECs exhibited a dose-dependent pattern after
BLI. Meanwhile, cell-type-specific responses followed by BLI were obvious. These findings
broaden the view of PBM following BL irradiation of these three cell types, thereby promoting
their potential application in wound healing and angiogenesis. Our data on low-fluence BL at
450 nm indicates clinical potential for a novel modality in wound therapy. (2) Overexposure
under BLI not only led to severe inhibitions on cell growth but also ended up with cytotoxicity
after longer time of irradiation. (3)The interaction between photons and components of the
medium (riboflavin) potentially caused photosensitization, which resulted in the generation of
reactive oxygen species (ROS) and influenced cellular responses
Vergleichende Analyse des Einflusses der Stimmungsfrequenz (443 Hz vs 432 Hz) auf hämodynamische Parameter bei Patient*innen mit Krebs
No full textBackground: This study investigated whether a sound intervention tuned to 432 Hz (Hz) yields differential effects on cardiovascular parameters and psychological outcomes compared to 443 Hz, which is the concert pitch in German professional orchestras.
Methods: Using a randomized cross-over design, patients with cancer were recruited to receive both a 15-minute sound intervention with a body monochord tuned to 432–443 Hz. Before ( pre ) and after ( post ) intervention, cardiovascular parameters were measured using the VascAssist2.0. In addition, visual analogue scales (VAS) for emotional well-being, anxiety, stress, pain and sadness were also assessed pre and post intervention.
Results: 43 patients (8 male, 35 female) with a median age of 61 years (range 35–86) were included. Both interventions led to a significant reduction in heart rate with a more pronounced effect for 432 Hz (median reduction − 3 bpm (432 Hz) vs. median reduction − 1 bpm (443 Hz), p = 0.04). While heart rate variability was increased exclusively by 432 Hz (median increase + 3 ms, p = 0.01), both vascular resistance (median reduction − 5%, p = 0.008) and stiffness (median reduction %, p = 0.04) were significantly reduced by 432 Hz, which was not observed at 443 Hz. Nevertheless, these effects were not significantly different compared to 443 Hz. On the other hand, 432 Hz led to a reduced pulse wave velocity (median reduction − 0.5 m/s, p < 0.001), which was also significantly different compared to 443 Hz ( p < 0.001). Improvement in VAS was observed for both groups, with significant increases in emotional well-being and reduction in fatigue, anxiety and stress for both intervention timepoints, although the majority showed no increased VAS scores even before the intervention (median values 0 for anxiety and stress).
Conclusion: Sound interventions tuned to 432–443 Hz exert both positive effects in cancer patients. While psychological outcomes are improved by both interventions, 432 Hz leads to a more pronounced but not significantly different effect to 443 Hz on objective cardiovascular parameters, which reflect deeper relaxation
Predictive Imaging Biomarker Discovery and Treatment Effect Estimation Using Deep Learning in Randomized Imaging Studies
No full textPersonalized medicine aims to tailor treatments to patients based on individual patient characteristics and plays an essential role for advancing healthcare and achieving better patient outcomes. As patients often respond very differently, improving personalized treatment decisions is a key challenge in this field. In clinical practice, such decisions are based on predictive biomarkers that indicate whether a patient might benefit from treatment. While established predictive biomarkers often require invasive procedures, medical imaging offers a non-invasive alternative by providing high-dimensional, spatially resolved information that could reveal patterns relevant for making treatment decisions. However, existing approaches, such as radiomics, rely on handcrafted features rather than directly estimating treatment-specific effects from imaging data.
To address the current gaps, this thesis investigates the task of discovering predictive imaging biomarkers in a data-driven way directly from images and providing treatment recommendations using pre-treatment imaging data without a separate feature extraction step.
In the first part of this thesis, the first approach for discovering predictive imaging biomarkers using deep-learning-based causal models for estimating heterogeneous treatment effects is presented. Its main contribution is an evaluation protocol for assessing identified predictive imaging biomarker candidates and for assessing model performances, which enables quantitative benchmarking and qualitative interpretation of image-based treatment effect estimation models. The proposed protocol specifically makes the important distinction between predictive and prognostic biomarkers, the latter of which can predict patient outcomes independently of treatment, by comparing predictive and prognostic effects.
In the second part, image-based treatment effect estimation methods are applied to both semi-synthetic and real clinical imaging data from a randomized phase II/III trial in glioblastoma by developing an extension of previous models for binary or continuous outcomes adapted to more clinically relevant survival outcomes. Furthermore, it investigates the impact of multimodal integration of clinical tabular data and the use of pre-trained image encoders on the resulting treatment recommendations of the proposed model and patient stratification.
The experimental results demonstrate that image-based treatment effect estimation models can identify predictive imaging biomarkers from semi-synthetic image datasets and provide interpretable insights, although the performance on real clinical data remains limited due to small sample sizes and weak treatment effect signals. Nevertheless, the findings of this work offer valuable insights into the opportunities and current limitations of image-based treatment effect estimation under realistic constraints and highlight key directions for future research. Overall, this work bridges causal inference and medical image analysis, establishing a foundation for future research on radiomics-free predictive imaging biomarker discovery and for advancing image-based methods that support personalized treatment decision-making
Towards Highly-Accelerated Hyperpolarized 13C MRSI In Vivo: Advancing Radial EPSI as a Versatile Imaging Tool
No full textHyperpolarized Carbon-13 magnetic resonance spectroscopic imaging (13C MRSI) is a powerful technique for the in vivo investigation of metabolic processes in real-time.
The greatest challenge for tailored MRSI sequences is to acquire all three spatial dimensions together with full spectral information and in multiple timesteps, while being restricted in available acquisition time through rapid decay of the hyperpolarized state (<1 minute).
In this work, advancements for radial echo-planar spectroscopic imaging (rEPSI) were implemented and optimized for various applications within the context of hyperpolarized 13C experiments in vivo on a clinical 3 T scanner.
Metabolic processes of hyperpolarized [1-13C]pyruvate were monitored in vivo with high spatio-spectral resolution in just 6 seconds. A novel readout scheme with maximal k-space homogeneity successfully suppressed blurring due to the polarization decay and boosted the temporal resolution to the subsecond regime.
Further methodological advancements enabled the acquisition of spatially-resolved 3D in vivo spectra of hyperpolarized [1,2-13C]pyruvate for the first time.
Moreover, a novel MR fingerprinting approach was developed using rEPSI for
estimation of B1+ fields, enabling a pre-scan calibration for hyperpolarized 13C MRSI.
In conclusion, this thesis presents the developed advanced rEPSI as an effective and versatile imaging tool for hyperpolarized 13C MRSI, with enormous potential for substrates of high spectral complexity like [1,2-13C]pyruvate