Neurons derived from P19 embryonic carcinoma cells as a platform for biosensor applications - optimisation and characterisation

P19 is a mouse-derived embryonal carcinoma cell line capable of differentiation toward ectodermal, mesodermal and endodermal lineages and could thus be differentiated into neurons. Different culture conditions were tested to optimise and increase the efficiency of neuronal differentiation since the population of P19-derived neurons was reported to be heterogeneous with respect to the morphology and neurotransmitters they synthesise. P19-derived neurons were cultured on microelectrode arrays as cell aggregates and as dissociated cells. Improved neuronal maturation was shown by the presence of microtubule associated protein 2, neurofilament and synaptophysin formation when initiation of neuronal differentiation was prolonged. High initial cell density cultures and coating of surfaces with polyethylenimine-laminin further improved neuronal maturation of differentiated P19 cells. Increased spontaneous activities of the P19-derived neurons were correspondingly recorded. Two to three hours recordings were performed between 17 and 25 days when extracellular signals were stabilised. It was found that P19-derived neurons developed network properties as partially synchronised network activities. P19-derived neurons appeared to give inhomogenous response to the 2 major neurotransmitters, -aminobutyric acid (GABA) and glutamate. The P19-derived neuronal networks obtained from optimised protocol in this thesis were predominantly GABAergic. The reproducible long term extracellular recordings performed showed that neurons derived from P19 embryonal carcinoma cells could be applied as a model for cell based biosensor in corporation with microelectrode arrays.

Development and optimisation of novel 68 Ga-folates - towards early diagnosis of ovarian cancer

Der Folsäure-basierte Radiotracer Etarfolatide (99mTc-EC 20) hat in der Vergangenheit sehr vielversprechende Ergebnisse im Bereich der frühzeitigen Diagnostik von Ovarialkarzinomen gezeigt. Einzelphotonen-Emissionscomputertomographie (SPECT) erlaubt dabei eine Visualisierung der Krankheit in einem sehr frühen Stadium – ermöglicht wird dies durch Folsäure, welche als Target Vektor dient. Um das erfolgreiche Prinzip der Radiofolate auf die Positronen-Emissionstomographie (PET) zu übertragen, welche eine noch höhere räumliche Auflösung ermöglicht, wurden in den letzten fünf Jahren bereits 18F-folate entwickelt. Deren hepatobiliären Exkretionsmuster, verursacht durch die relativ hohe Lipophilie der Strukturen, entsprachen jedoch nicht den Anforderungen. Eine optimierte Bioverteilung der Tracer in vivo kann durch eine generelle Erhöhung der Polarität erfolgen. Die Kombination aus einem polaren 68Ga-Komplex mit Folsäure als Target Vektor stellte den Fokus dieses Projektes dar. Ziel war die Entwicklung eines Radiofolates mit der Tendenz einer raschen renalen Ausscheidung und verringerter hepatobiliärer Anreicherung. Dazu wurde Folsäure regiospezifisch über ihre y-Säure an verschiedene bifunktionelle Chelatoren (BFCs) gekoppelt. Vier verschiedene Reaktionstypen wurden gewählt und durchgeführt: Cu-katalysierte sowie Cu-freie Click Reaktion, Amindbindung und Thioharnstoff Bildung. Es wurden sechs verschiedene Derivate erhalten und mit 68Ga radiomarkiert.

Contributions to exact approaches in combinatorial optimization

The focus of this thesis is to contribute to the development of new, exact solution approaches to different combinatorial optimization problems. In particular, we derive dedicated algorithms for a special class of Traveling Tournament Problems (TTPs), the Dial-A-Ride Problem (DARP), and the Vehicle Routing Problem with Time Windows and Temporal Synchronized Pickup and Delivery (VRPTWTSPD). Furthermore, we extend the concept of using dual-optimal inequalities for stabilized Column Generation (CG) and detail its application to improved CG algorithms for the cutting stock problem, the bin packing problem, the vertex coloring problem, and the bin packing problem with conflicts. In all approaches, we make use of some knowledge about the structure of the problem at hand to individualize and enhance existing algorithms. Specifically, we utilize knowledge about the input data (TTP), problem-specific constraints (DARP and VRPTWTSPD), and the dual solution space (stabilized CG). Extensive computational results proving the usefulness of the proposed methods are reported.