Siddiqui, Ahad M.

The spinal cord has a poor ability to regenerate after an injury, which may be due to cell loss, cyst formation, inflammation, and scarring. A promising approach to treating a spinal cord injury (SCI) is the use of biomaterials. We have developed a novel hydrogel scaffold fabricated from oligo(poly(ethylene glycol) fumarate) (OPF) as a 0.08 mm thick sheet containing polymer ridges and a cell-attractive surface on the other side. When the cells are cultured on OPF via chemical patterning, the cells attach, align, and deposit ECM along the direction of the pattern. Animals implanted with the rolled scaffold sheets had greater hindlimb recovery compared to that of the multichannel scaffold control, which is likely due to the greater number of axons growing across it. The immune cell number (microglia or hemopoietic cells: 50–120 cells/mm2 in all conditions), scarring (5–10% in all conditions), and ECM deposits (Laminin or Fibronectin: approximately 10–20% in all conditions) were equal in all conditions. Overall, the results suggest that the scaffold sheets promote axon outgrowth that can be guided across the scaffold, thereby promoting hindlimb recovery. This study provides a hydrogel scaffold construct that can be used in vitro for cell characterization or in vivo for future neuroprosthetics, devices, or cell and ECM delivery.

Abstract Machine learning is a powerful tool that is increasingly being used in many research areas, including neuroscience. The recent development of new algorithms and network architectures, especially in the field of deep learning, has made machine learning models more reliable and accurate and useful for the biomedical research sector. By minimizing the effort necessary to extract valuable features from datasets, they can be used to find trends in data automatically and make predictions about future data, thereby improving the reproducibility and efficiency of research. One application is the automatic evaluation of micrograph images, which is of great value in neuroscience research. While the development of novel models has enabled numerous new research applications, the barrier to use these new algorithms has also decreased by the integration of deep learning models into known applications such as microscopy image viewers. For researchers unfamiliar with machine learning algorithms, the steep learning curve can hinder the successful implementation of these methods into their workflows. This review explores the use of machine learning in neuroscience, including its potential applications and limitations, and provides some guidance on how to select a fitting framework to use in real-life research projects.

Spinal cord injury (SCI) results in cell death, demyelination, and axonal loss. The spinal cord has a limited ability to regenerate, and current clinical therapies for SCI are not effective in helping promote neurologic recovery. We have developed a novel scaffold biomaterial that is fabricated from the biodegradable hydrogel oligo(poly(ethylene glycol)fumarate) (OPF). We have previously shown that positively charged OPF scaffolds (OPF+) in an open spaced, multichannel design can be loaded with Schwann cells to support axonal generation and functional recovery following SCI. We have now developed a hybrid OPF+ biomaterial that increases the surface area available for cell attachment and that contains an aligned microarchitecture and extracellular matrix (ECM) proteins to better support axonal regeneration. OPF+ was fabricated as 0.08 mm thick sheets containing 100 μm high polymer ridges that self-assemble into a spiral shape when hydrated. Laminin, fibronectin, or collagen I coating promoted neuron attachment and axonal outgrowth on the scaffold surface. In addition, the ridges aligned axons in a longitudinal bipolar orientation. Decreasing the space between the ridges increased the number of cells and neurites aligned in the direction of the ridge. Schwann cells seeded on laminin coated OPF+ sheets aligned along the ridges over a 6-day period and could myelinate dorsal root ganglion neurons over 4 weeks. This novel scaffold design, with closer spaced ridges and Schwann cells, is a novel biomaterial construct to promote regeneration after SCI.

Evidence from preclinical and clinical research suggest that neuromodulation technologies can facilitate the sublesional spinal networks, isolated from supraspinal commands after spinal cord injury (SCI), by reestablishing the levels of excitability and enabling descending motor signals via residual connections. Herein, we evaluate available evidence that sublesional and supralesional spinal circuits could form a translesional spinal network after SCI. We further discuss evidence of translesional network reorganization after SCI in the presence of sensory inputs during motor training. In this review, we evaluate potential mechanisms that underlie translesional circuitry reorganization during neuromodulation and rehabilitation in order to enable motor functions after SCI. We discuss the potential of neuromodulation technologies to engage various components that comprise the translesional network, their functional recovery after SCI, and the implications of the concept of translesional network in development of future neuromodulation, rehabilitation, and neuroprosthetics technologies.