In a recent paper, we demonstrated an optical deep neural network with a real living piece of brain tumor (a 3D “tumour model”). We think this is the first example of a hybrid living/photonic hardware: a sort of artificially intelligent device performing optical functions and detecting tumour morphodynamics (including the effect of chemotherapy)
Abstract: The new era of artificial intelligence demands large-scale ultrafast hardware for machine learning. Optical artificial neural networks process classical and quantum information at the speed of light, and are compatible with silicon technology, but lack scalability and need expensive manufacturing of many computational layers. New paradigms, as reservoir computing and the extreme learning machine, suggest that disordered and biological materials may realize artificial neural networks with thousands of computational nodes trained only at the input and at the readout. Here we employ biological complex systems, i.e., living three-dimensional tumour brain models, and demonstrate a random neural network (RNN) trained to detect tumour morphodynamics via image transmission. The RNN, with the tumour spheroid 19 as a three-dimensional deep computational reservoir, performs programmed optical functions and detects cancer morphodynamics from laser-induced hyperthermia inaccessible by optical imaging. Moreover, the RNN quantifies the effect of chemotherapy inhibiting tumour growth. We realize a non-invasive smart probe for cytotoxicity assay, which is at least one order of magnitude more sensitive with respect to conventional imaging. Our random and hybrid photonic/living system is a novel artificial machine for computing and for the real-time investigation of tumour dynamics.
Authors: D. Pierangeli, V. Palmieri, G. Marcucci, C. Moriconi, G. Perini, M. De Spirito, M. Papi, C. Conti
Graphene and Graphene oxide (GO) are capable of inducing stem cells differentiation into bone tissue with variable efficacy depending on reductive state of the material. Thus, modulation of osteogenic process and of bone mineral density distribution is theoretically possible by controlling the GO oxidative state. In this study, we laser-printed GO surfaces in order to obtain both a local photo-thermal GO reduction and the formation of nano-wrinkles along precise geometric pattern. Initially, after cells adhered on the surface, stem cells migrated and accumulated on the reduced and wrinkled surface. When the local density of the stem cells on the reduced stripes was high, cells started to proliferate and occupy the oxidized/flat area. The designed surfaces morphology guided stem cell orientation and the reduction accelerated differentiation. Furthermore the reduced sharp nano-wrinkles were able to enhance the GO antibacterial activity against Methicillin-resistant Staphylococcus aureus (MRSA), common cause of prosthetic joints infections. This strategy can offer a revolution in present and future trends of scaffolds design for regenerative medicine.
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