Biomimetic materials are used for creating microsystems to control cell growth spatially and elicit specific cellular responses by combining complex biomolecules with nanostructured surfaces. Intercellular cell-to-cell and cell-to-extracellular matrix (ECM) interactions in biomimetic materials have demonstrated potential in the development of drug discovery platforms and regeneration medicine. In this study, we developed a biomimetic nanostructured matrix by using various ECM molecular layers to create a biomimetic and biocompatible environment for realizing neuronal guidance in neural regeneration medicine. Silicon-based substrates possessing nanostructures were modified using different ECM proteins and peptides to develop a biomimetic and biocompatible environment for studying neural behaviors in adhesion, proliferation, and differentiation. The substrates were flat glass, flat silicon wafers (FWs), and nanorod-structured wafers prepared using wet etching. The three substrates were then functionalized using laminin-1 peptide, PA22-2-contained active isoleucine-lysine-valine-alanine-valine (IKVAV) peptide, and poly-d-lysine (PDL), separately. When PC12 cells were cultured and differentiated on the modified substrates, the cells were able to elongate the neurites on the glass and FW, which was coated with three types of peptide. More differentiated neurons were observed on the nanorod-structured wafers coated with laminin than on those coated with IKVAV or PDL. For achieving directional guidance of neurite outgrowth, substrates exhibiting a grating pattern of nanorods were partially collapsed by the pulling force of water, leaving few nanorods, which support the net form of laminin on the surface. Furthermore, we fabricated the topological nanostructure-patterned wafer coated with laminin and successfully manipulated the extension and direction of neurites by using more than 80 μm of a single soma. This approach demonstrates potential as a facile and efficient method for guiding the direction of single axons and for enhancing neurite outgrowth in studies on nerve regenerative medicine.
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http://dx.doi.org/10.1016/j.ijpharm.2013.08.006 | DOI Listing |
Sci Rep
December 2024
Faculty of Dental Medicine and Oral Health Sciences, McGill University, Montreal, Canada.
Accurate diagnosis of oral lesions, early indicators of oral cancer, is a complex clinical challenge. Recent advances in deep learning have demonstrated potential in supporting clinical decisions. This paper introduces a deep learning model for classifying oral lesions, focusing on accuracy, interpretability, and reducing dataset bias.
View Article and Find Full Text PDFNetw Neurosci
December 2024
Department of Biomedical Engineering, Faculty of Engineering and Information Technology, The University of Melbourne, Melbourne, VIC, Australia.
Connectome generative models, otherwise known as generative network models, provide insight into the wiring principles underpinning brain network organization. While these models can approximate numerous statistical properties of empirical networks, they typically fail to explicitly characterize an important contributor to brain organization-axonal growth. Emulating the chemoaffinity-guided axonal growth, we provide a novel generative model in which axons dynamically steer the direction of propagation based on distance-dependent chemoattractive forces acting on their growth cones.
View Article and Find Full Text PDFSci Rep
December 2024
School of Marxism, China University of Political Science and Law (CUPL), Beijing, 100091, China.
To improve students' understanding of physical education teaching concepts and help teachers analyze students' cognitive patterns, the study proposes an association learning-based method for understanding physical education teaching concepts using deep learning algorithms, which extracts image features related to teaching concepts using convolutional neural networks. Moreover, a neurocognitive diagnostic model based on hypergraph convolution is constructed to mine the data of students' long-term learning sequences and identify students' cognitive outcomes. The findings revealed that the highest accuracy of the association graph convolutional neural network was 0.
View Article and Find Full Text PDFWorld Neurosurg
December 2024
Department of Pathology, Huashan Hospital, Fudan University, Shanghai 200040, China.
Background: The presence of isocitrate dehydrogenase (IDH) mutations and 1p/19q codeletion significantly influences the diagnosis and prognosis of patients with lower-grade gliomas (LGGs). The ability to predict these molecular signatures preoperatively can inform surgical strategies. This study sought to establish an interpretable imaging feature set for predicting molecular signatures and overall survival in LGGs.
View Article and Find Full Text PDFJ Pers Med
December 2024
Division of Neurotology and Skull Base Surgery, Department of Otolaryngology-Head and Neck Surgery, University of California, Irvine, CA 92697, USA.
This study aimed to develop a machine learning (ML) algorithm that can predict unplanned reoperations and surgical/medical complications after vestibular schwannoma (VS) surgery. All pre- and peri-operative variables available in the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) database (n = 110), except those directly related to our outcome variables, were used as input variables. A deep neural network model consisting of seven layers was developed using the Keras open-source library, with a 70:30 breakdown for training and testing.
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