Assessment of Different Niosome Formulations for Optogenetic Applications: Morphological and Electrophysiological Effects.

Gene therapy and optogenetics are becoming promising tools for treating several nervous system pathologies. Currently, most of these approaches use viral vectors to transport the genetic material inside the cells, but viruses present some potential risks, such as marked immunogenicity, insertional mutagenesis, and limited insert gene size. In this framework, non-viral nanoparticles, such as niosomes, are emerging as possible alternative tools to deliver genetic material, avoiding the aforementioned problems. To determine their suitability as vectors for optogenetic therapies in this work, we tested three different niosome formulations combined with three optogenetic plasmids in rat cortical neurons in vitro. All niosomes tested successfully expressed optogenetic channels, which were dependent on the ratio of niosome to plasmid, with higher concentrations yielding higher expression rates. However, we found changes in the dendritic morphology and electrophysiological properties of transfected cells, especially when we used higher concentrations of niosomes. Our results highlight the potential use of niosomes for optogenetic applications and suggest that special care must be taken to achieve an optimal balance of niosomes and nucleic acids to achieve the therapeutic effects envisioned by these technologies.

Machine Learning Method for Functional Assessment of Retinal Models.

Challenges in the field of retinal prostheses motivate the development of retinal models to accurately simulate Retinal Ganglion Cells (RGCs) responses. The goal of retinal prostheses is to enable blind individuals to solve complex, reallife visual tasks. In this paper, we introduce the functional assessment (FA) of retinal models, which describes the concept of evaluating the performance of retinal models on visual understanding tasks. We present a machine learning method for FA: we feed traditional machine learning classifiers with RGC responses generated by retinal models, to solve object and digit recognition tasks (CIFAR-10, MNIST, Fashion MNIST, Imagenette). We examined critical FA aspects, including how the performance of FA depends on the task, how to optimally feed RGC responses to the classifiers and how the number of output neurons correlates with the model's accuracy. To increase the number of output neurons, we manipulated input images - by splitting and then feeding them to the retinal model and we found that image splitting does not significantly improve the model's accuracy. We also show that differences in the structure of datasets result in largely divergent performance of the retinal model (MNIST and Fashion MNIST exceeded 80% accuracy, while CIFAR-10 and Imagenette achieved ∼40%). Furthermore, retinal models which perform better in standard evaluation, i.e. more accurately predict RGC response, perform better in FA as well. However, unlike standard evaluation, FA results can be straightforwardly interpreted in the context of comparing the quality of visual perception.