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This paper summarizes our initial efforts to reconstruct the urinary bladder from endoscopic images acquired in the clinical routine. We found that up to now, only very few attempts have been reported which achieve a true 3D reconstruction of the human bladder. One promising approach which yields a geometric reconstruction up to scale from a monocular stream of images is highlighted and our initial results obtained from adapting the method for its use in clinical cystoscopy are presented.

An acoustofluidic trap is used for accurate 3D cell proliferation and cell function analysis in levitation. The prototype trap can be integrated with any microscope setup, allowing continuous perfusion experiments with temperature and flow control under optical inspection. To describe the trap function, we present a mathematical and FEM-based COMSOL model for the acoustic mode that defines the nodal position of trapped objects in the spherical cavity aligned with the microscope field of view and depth of field. Continuous perfusion experiments were conducted in sterile conditions over 55 h with a K562 cell line, allowing for deterministic monitoring. The acoustofluidic platform allows for rational in vitro cell testing imitating in vivo conditions such as cell function tests or cell-cell interactions.

Cell culture assays for therapeutic drug screening today are fully automated. Vitality of the cells is monitored by different sensors. For such a system, we propose a new reader unit, which is capable of reading two different fluorescent sensors and electrical impedance in 24-well-plates. Main goals are to reduce cost, complexity and size while achieving a similar performance as the existing reader unit. To achieve this, measurement electronics and signal paths for frequency domain fluorescence and bio-impedance measurement are combined. Central component is an integrated circuit for impedance spectroscopy. A new compact and economic optical setup is developed to read two different sensor spots on the bottom of the well. Measurement errors introduced by different components like DFT leakage, and frequency dependent signal delays are evaluated and compensated. A set of commercially available fluorescence sensor spots is used to verify the read out performance. The results are usable, with noise slightly higher than commercial readers. To verify the impedance measurement accuracy, measurements of known resistances are conducted. In the relevant impedance and frequency range for biological applications a suitable accuracy is achieved. Due to the higher sampling rate of the new reader, the higher noise can be reduced through averaging. The new system is significantly smaller and cheaper to manufacture than commercially available devices., (Copyright © 2018 Elsevier B.V. All rights reserved.)

Goal: This contribution points out the need for well-defined and documented data processing protocols in microphysiometry, an evolving field of label-free cell assays. The sensitivity of the obtained cell metabolic rates toward different routines of raw data processing is evaluated., Methods: A standard microphysiometric experiment structured in discrete measurement intervals was performed on a platform with a pH- and O 2 -sensor readout. It is evaluated using three different data evaluation protocols, based on A) fast Fourier transformation of such dynamics, B) linear regression (LIN) of pH(t) and O 2 (t) dynamics, and C) numerical simulation (SIM) with a subsequent fitting of dynamics for parameter estimation., Results: We propose a sequence of well documented steps for an organized processing of raw sensor data. Figures of merit for the quality of raw data and the performance of data processing are provided. To estimate metabolic rates, a reaction-diffusion modeling approach is recommended if the necessary model input parameters such as the distribution of the active biomass, sensor response time, and material properties are available., Conclusion: The information about cellular metabolic activity contained by measured sensor data dynamics is superimposed by manifold sources of error. Careful consideration of data processing is necessary to eliminate these errors as much as possible and to avoid an incorrect interpretation of data.