8 Papers
73 Citations
P Wolf is an academic researcher from Technische Universität München. The author has contributed to research in topics: Data processing & Cell. The author has an hindex of 5, co-authored 8 publications.
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Papers
Sensor-based cell and tissue screening for personalized cancer chemotherapy
Regina Kleinhans,Martin Brischwein,Pei Wang,B. Becker,Franz Demmel,T. Schwarzenberger,Marlies Zottmann,P Wolf,Axel Niendorf,Bernhard Wolf +9 more
TL;DR: A novel high-content platform is described monitoring human mamma carcinoma explants in real time and label-free before, during and after an ex vivo modeled chemotherapy.
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Impedance sensor technology for cell-based assays in the framework of a high-content screening system
T. Schwarzenberger,P Wolf,Martin Brischwein,R. Kleinhans,Franz Demmel,A Lechner,B. Becker,Bernhard Wolf +7 more
TL;DR: The development of miniaturized electronics for impedance measurements and its system integration as a modular unit is developed and it is shown how sensor electrodes were optimized by impedance matching such that spectroscopy and raw data analysis become feasible for every culture well.
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Automated platform for sensor-based monitoring and controlled assays of living cells and tissues
P Wolf,Martin Brischwein,R. Kleinhans,Franz Demmel,T. Schwarzenberger,Cornelia Pfister,Bernhard Wolf +6 more
TL;DR: A novel measuring platform was developed, which combines automated assay processing with label-free high-content measuring and real-time monitoring of multiple metabolic and morphologic parameters of living cells or tissues, and overcomes problems of endpoint tests.
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Determination of dynamic doxorubicin-EC50 value in an automated high-content workstation for cellular assays
TL;DR: A novel automated high-content workstation was utilized and a dynamic, time-resolved EC50 characteristic for different time points was pointed out, proving the workstation is a powerful tool to record in vitro kinetic data of pharmacologic effects in vital cells in an automated experimental run.
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Data Processing in Cellular Microphysiometry
TL;DR: The information about cellular metabolic activity contained by measured sensor data dynamics is superimposed by manifold sources of error and careful consideration of data processing is necessary to eliminate these errors as much as possible and to avoid an incorrect interpretation of data.
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