Development of a novel 3D nanofibre co-culture model for characterisation of neural cell degeneration

Neurodegenerative diseases are prolonged and progressive. In general, they affect individuals in the latter stages of their lives. The pathology of neural cell degeneration is widely researched, and most current therapeutic strategies aim to delay its progression by either promoting neuronal regeneration, resurrecting the lost brain function with stimulation electrodes or by cell replacement therapies. Neuronal and glial cells are researched to better understand the physiological condition and to find a cure for degenerative diseases. In current in vitro techniques, cells dissociated from their natural three-dimensional (3D) tissue and cultured on flat surfaces present a significant drawback in drug discovery and cell therapy research. Communication and various signal transduction between neuronal and glial cells in the in vivo system are purely based on a dynamic convoluted systematic network constructed and expanded in a 3D manner. Recently, electrospun 3D nanofibre scaffolds have gained attention among researchers for their ability to mimic the natural 3D microenvironment that cells inhabit. Developing an in vitro system which emulates the in vivo 3D habitat of neural cells has been a significant challenge. This thesis reports on the advantages of using novel 3D suspended nanofibre membrane technology to create better models for both drug discovery and therapeutic implants. In this study, we focused on developing a suitable sterile nanofibre porous membrane using Polyacrylonitrile (PAN), and Jeffamine® ED-2003 modified polyacrylonitrile (PJ) to provide favourable conditions for neuronal and glial proliferation, differentiation and survival. The study was designed, engineered and optimised three different state-of-the-art fully suspended nanofibre models that are highly multi-functional and suitable for investigating several diseases and chronic conditions. We have characterised the growth and survival of human SH-SY5Y neuroblastoma, human U-87MG glioblastoma, human ReNcell CX neural progenitor and primary neural cells from E18 rat hippocampal tissue on both PAN and PJ nanofibre scaffolds. Our investigations and chronic studies have shown extended survival of cells on a scaffold in comparison to these cells cultured on the base of cell adherent tissue culture plates (TCP). Differentiation cell culture trials have demonstrated that both PAN and PJ are capable of supporting cell differentiation and immunofluorescence and western blot analysis has shown elevated levels of key differentiation marker proteins on the cells cultured on the suspended scaffolds compared to TCP. Our findings indicate that the new 3D suspended nanofibre scaffolds support improved growth, survival and differentiation of both cell populations as well as increasing the sensitivity of the cells to Toxins when compared to the sensitivity of cells growth of the base of TCPs. Moreover, chronic exposures to Toxins using the novel co-culture scaffold model has shown prolonged neuronal survival in the presence of astrocytes. Together, our findings suggest the potential for the 3D nanofibre approach to improve in vitro therapeutic studies and our co-culture system, which creates a better mimic, should lead to a reduced number of animals used for pharmaceutical development and in the screening of compounds to find neuroprotective compounds to prevent degeneration of neural cells

Spatiotemporal interaction residual networks with pseudo3d for video action recognition

10.3390/s20113126Sensors (Switzerland)2011312

Spatiotemporal Interaction Residual Networks with Pseudo3D for Video Action Recognition

Action recognition is a significant and challenging topic in the field of sensor and computer vision. Two-stream convolutional neural networks (CNNs) and 3D CNNs are two mainstream deep learning architectures for video action recognition. To combine them into one framework to further improve performance, we proposed a novel deep network, named the spatiotemporal interaction residual network with pseudo3D (STINP). The STINP possesses three advantages. First, the STINP consists of two branches constructed based on residual networks (ResNets) to simultaneously learn the spatial and temporal information of the video. Second, the STINP integrates the pseudo3D block into residual units for building the spatial branch, which ensures that the spatial branch can not only learn the appearance feature of the objects and scene in the video, but also capture the potential interaction information among the consecutive frames. Finally, the STINP adopts a simple but effective multiplication operation to fuse the spatial branch and temporal branch, which guarantees that the learned spatial and temporal representation can interact with each other during the entire process of training the STINP. Experiments were implemented on two classic action recognition datasets, UCF101 and HMDB51. The experimental results show that our proposed STINP can provide better performance for video recognition than other state-of-the-art algorithms