Electroporation-based techniques are known for their potential to temporarily increase cells membrane permeability by controlled electric fields for transfer of non-permeant molecules; these techniques evolved in many useful biomedical applications. Current research in this domain addresses both experimental and computational analysis in a complementary manner. Numerical simulations, considering realistic cell shapes and field exposure conditions can complete the experimental investigations by opening insights and providing quantitative data. Our approach here provides cell models for EP simulations, based on experimental acquisition of images in a holographic microscopy setup and digital reconstruction of phase images of living attached B16F10 murine melanoma cells. A procedure to process and import phase images in dedicated finite element software COMSOL Multiphysics is described in detail. Based on such realistically shaped computational domains, the electric field problem is successively defined and solved under time-harmonic electric excitation, uniformly applied; the frequency dependent dielectric properties are set accordingly. Induced transmembrane voltage distribution is the representative numerical output of the analysis shown here for different exposure conditions (membrane regions under stress, dielectric properties, field frequency), aiming to evaluate their potential efficiency on electroporation.
The histopathological diagnosis in malignances requests well trained specialists and multi-step operational procedures for sample preparation. Faster and more objective evaluation protocols should be implemented to give support to the pathologists. The Quantitative Phase Imaging based methods are biological-proved to be efficient in revealing important characteristics of the living structures without any labeling. These can be further exploited for an automatic evaluation of complex tissues. Using an off-axis Digital Holographic Microscopy setup, biopsies of two histological origins: cerebral (grade II glioma and grade IV glioblastoma) and colonic malignancies (dysplastic and malignant colonic adenomatous polyps), were investigated. Various parameters of quantitative phase shift maps (QPMs) were computed (mean, variance, median, kurtosis, skewness, energy, entropy). The possibility of automatic discrimination of tumor tissues having different structural complexity and presenting various malignancy grades was evaluated using supervised machine learning algorithms. The analysis of phase shift maps has successfully discriminated between levels of malignancy with high statistical confidence in the case of gliomas. Moreover an algorithm with the ability to classify the tissue biopsies in different malignant stages using parameters based on QPMs has been implemented on glioma tissues having a high level of homogeneity. In case of colonic polyps, the heterogeneity of the multilayered tissue demanded QPMs analysis to be performed on selected area of interest even though some statistical differences were obtained for global evaluation of phase shift distributions. In case of colonic polyps, for a good accuracy of classification algorithm a larger library of QPMs is under construction.
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