Abstract
Living cells possess properties that enable them to withstand the physiological environment as well as mechanical stimuli occurring within and outside the body. Any deviation from these properties will undermine the physical integrity of the cells as well as their biological functions. Thus, a quantitative study in single cell mechanics needs to be conducted. In this paper we will examine fluid flow and Neo-Hookean deformation. Particularly, a mechanical model to describe the cellular adhesion with detachment is proposed. Restricting the interest on the contact surface and elaborating again the computational results, it is possible to develop our idea about to reproduce the phases coexistence in the adhesion strip. Subsequently, a number of simulations have been carried out, involving a number of human cells with different mechanical properties. All the collected data have been used in order to train and test a suitable Artificial Neural Network (ANN) in order to classify the kind of cell. Obtained results assure good performances of the implemented classifier, with very interesting applications.
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Cacciola, M., Fiasché, M., Megali, G., Morabito, F.C., Versaci, M. (2009). A Neural Network Based Classification of Human Blood Cells in a Multiphysic Framework. In: Köppen, M., Kasabov, N., Coghill, G. (eds) Advances in Neuro-Information Processing. ICONIP 2008. Lecture Notes in Computer Science, vol 5507. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03040-6_88
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DOI: https://doi.org/10.1007/978-3-642-03040-6_88
Publisher Name: Springer, Berlin, Heidelberg
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