Development of new methods for optical non-linear microscopy to be applied to biophysics and medical physics in-vivo. In this field the Biophotonics group is developing two-photon excitation fluorescence imaging microscopy and second harmonic generation microscopy. One of the main application aims to the study of the motion of lymphocytes in lymph nodes in order to model the immune response of mice. This work is being carried out in collaboration with the Biotechnology group of our University.
The formation of images through opaque media has applications ranging from engineering to electronics and medicine and biotechnology. The wave front of light is strongly modified by the inhomogeneities of the medium in which it propagates over a wide range of spatial frequencies. In order to correct these effects, various programmable reflective or diffractive elements such as deformable mirrors or spatial light modulators can be used. The purpose of the proposed theses is to couple these technologies, partly borrowed from observational astronomy, to optical microscopy techniques for the study of tissues both in vitro and in vivo.
Pozzi et al. J. of Biomedical Optics, 19(6), 067007 (2014) https://doi.org/10.1117/1.JBO.19.6.067007
Photo- polymerization can be used to fabricate hydrogels for cell culturing and tissue engineering. In particular we develop protein based photo-resists to fabricate, by means of lasers or UV LEDs, microstructures with high axial ratio (3 to 5) and small size (from 30 μm to 50 μm). We employ both UV sources (385 nm) or near infrared pulsed (femtosecond pulse width) lasers to obtain high resolution writing. The laser is scanned on the resist to induce polymerization or a mask is used in front of the UV LED. These methods are used to fabricate microstructures that can host cells for cell culturing or be implanted in vivo for tissue engineering.
The physics of complex systems, from biophysics to Environmental Physics, requires the analysis of a considerable amount of data that depend on numerous parameters. There are therefore numerous areas in which Machine Learning and Artificial Intelligence methods can be used advantageously to organize the data obtained and to try to formulate chemical-physical models. In addition to this, the attempt to numerically simulate complex systems, such as the immune system, must go through the use of active learning methods. The theses offered in this area range from the development of correction systems for images taken in the optical range (UV-NIR) both in tissue microscopy and in environmental monitoring and X-ray or PET images, to the development of active learning methods for the analysis of numerical simulations of the immune system.