The brain is an enormously complex organ structured into various regions of layered tissue. Researchers have attempted to study the brain by modeling the architecture using two dimensional (2D) in vitro cell culturing methods. While those platforms attempt to mimic the in vivo environment, they do not truly resemble the three dimensional (3D) microstructure of neuronal tissues. Development of an accurate in vitro model of the brain remains a significant obstacle to our understanding of the functioning of the brain at the tissue or organ level. To address these obstacles, we demonstrate a new method to bioprint 3D brain-like structures consisting of discrete layers of primary neural cells encapsulated in hydrogels. Brain-like structures were constructed using a bio-ink consisting of a novel peptide-modified biopolymer, gellan gum-RGD (RGD-GG), combined with primary cortical neurons. The ink was optimized for a modified reactive printing process and developed for use in traditional cell culturing facilities without the need for extensive bioprinting equipment. Furthermore the peptide modification of the gellan gum hydrogel was found to have a profound positive effect on primary cell proliferation and network formation. The neural cell viability combined with the support of neural network formation demonstrated the cell supportive nature of the matrix. The facile ability to form discrete cell-containing layers validates the application of this novel printing technique to form complex, layered and viable 3D cell structures. These brain-like structures offer the opportunity to reproduce more accurate 3D in vitro microstructures with applications ranging from cell behavior studies to improving our understanding of brain injuries and neurodegenerative diseases.
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http://dx.doi.org/10.1016/j.biomaterials.2015.07.022 | DOI Listing |
Adv Sci (Weinh)
January 2025
College of Physics Science & Technology, School of Life Sciences, Institute of Life Science and Green Development, Key Laboratory of Brain-Like Neuromorphic Devices and Systems of Hebei Province, Hebei University, Baoding, 071002, China.
Hardware system customized toward the demands of graph neural network learning would promote efficiency and strong temporal processing for graph-structured data. However, most amorphous/polycrystalline oxides-based memristors commonly have unstable conductance regulation due to random growth of conductive filaments. And graph neural networks based on robust and epitaxial film memristors can especially improve energy efficiency due to their high endurance and ultra-low power consumption.
View Article and Find Full Text PDFNat Commun
January 2025
Key Laboratory of Brain like Neuromorphic Devices and Systems of Hebei Province, College of Physics Science and Technology, Hebei University, Baoding, Hebei, China.
Neuromorphic computing holds immense promise for developing highly efficient computational approaches. Memristor-based artificial neurons, known for due to their straightforward structure, high energy efficiency, and superior scalability, which enable them to successfully mimic biological neurons with electrical devices. However, the reliability of memristors has always been a major obstacle in neuromorphic computing.
View Article and Find Full Text PDFAngew Chem Int Ed Engl
December 2024
Department of Gynaecology and Obstetrics, Shanghai Key Laboratory of Anesthesiology and Brain Functional Modulation, Clinical Research Center for Anesthesiology and Perioperative Medicine, Translational Research Institute of Brain and Brain-Like Intelligence, Shanghai Fourth People's Hospital, School of Medicine, Tongji University, Shanghai, 200434, China.
A classical crystallization usually grows epitaxially from a crystal nucleus. Presented in this study is an unusual endotaxy growth manner of a crystalline homopolymer to form hexagonal nanosheets. The amphiphilic homopolymer, poly(3-(4-(phenyldiazenyl)phenoxy)propyl methacrylate) (PAzoPMA), is first annealed in isopropanol to afford a hexagonal nut-like structure.
View Article and Find Full Text PDFMed Phys
December 2024
Jiangsu Key Laboratory for Biomaterials and Devices, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China.
Background: Medical imaging plays a pivotal role in the real-time monitoring of patients during the diagnostic and therapeutic processes. However, in clinical scenarios, the acquisition of multi-modal imaging protocols is often impeded by a number of factors, including time and economic costs, the cooperation willingness of patients, imaging quality, and even safety concerns.
Purpose: We proposed a learning-based medical image synthesis method to simplify the acquisition of multi-contrast MRI.
ACS Macro Lett
December 2024
Department of Gynaecology and Obstetrics, Shanghai Key Laboratory of Anesthesiology and Brain Functional Modulation, Clinical Research Center for Anesthesiology and Perioperative Medicine Translational Research Institute of Brain and Brain-Like Intelligence, Shanghai Fourth People's Hospital School of Medicine, Tongji University, Shanghai 200434, China.
Nanobowls show promising potential in biomedical applications, such as bioimaging, cargo delivery, and disease theranostics, due to their unique concave structure and interior cavities. However, the lack of biodegradable nanobowls with manipulable size (especially the dent size) still exists as an obstacle for their in-depth exploration and application in biomedical fields. Herein, polypeptide-based nanobowls are successfully obtained by the self-assembly of a graft polypeptide [named TPE-P(GAAzo--GA)] via a solvent-switch method.
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