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The intercommunication between nerves and muscles plays an important role in the functioning of our body, and its failure leads to severe neuromuscular disorders such as spinal muscular atrophy and amyotrophic lateral sclerosis. Understanding the cellular and molecular mechanisms underlying nerve–muscle interactions and mediating their mutual influence is an integral part of strategies aimed at curing neuromuscular diseases. Here, we propose a novel ex vivo experimental model for the spinal cord (SC) and skeletal muscle interactions which for the first time utilizes only fully formed (but not yet quite functional) postnatal tissues. The model represents an organotypic co-culture comprising a longitudinal slice of the mouse postnatal SC and an extensor digitorum longus (EDL) muscle explant placed in the “damage zone” of transversally dissected longitudinal slice of the SC. Using this model, we have shown that SC tissue stimulates muscle contractions and reduces the area occupied by acetylcholine receptors on muscle surface. In turn, EDL muscles stimulate the growth of SC-derived neurites. Thus, our organotypic model allows one to assess the mutual influence of neurons and muscles in a nearly natural setting which maintains the architecture and cellular composition of intact tissues. Therefore, this model may provide an effective platform for studying molecular and cellular mechanisms linked to defective neuromuscular interactions in associated pathologies. [ABSTRACT FROM AUTHOR]

This study presents preparing and characterization of polyacrylonitrile (PAN) fibers containing various content of tetraethoxysilane (TEOS) incorporated via mutual spinning solution or emulsion using wet and mechanotropic spinning methods. It was shown that the presence of TEOS in dopes does not affect their rheological properties. The coagulation kinetics of complex PAN solution was investigated by optical methods on the solution drop. It was shown that during the interdiffusion process phase separation occurs and TEOS droplets form and move in the middle of the dope's drop. Mechanotropic spinning induces the TEOS droplets to move to the fiber periphery. The morphology and structure of the fibers obtained were investigated by scanning and transmission electron microscopy, as well as X-ray diffraction methods. It was shown that during fiber spinning stages the transformation of the TEOS drops into solid silica particles takes place as a result of hydrolytic polycondensation. This process can be characterized as the sol-gel synthesis. The formation of nano-sized (3–30 nm) silica particles proceeds without particles aggregation, but in a mode of the distribution gradient along the fiber cross-section leading to the accumulation of the silica particles either in the fiber center (wet spinning) or in the fiber periphery (mechanotropic spinning). The prepared composite fibers were carbonized and according to XRD analysis of carbon fibers, the clear peaks corresponding to SiC were observed. These findings indicate the useful role of TEOS as a precursor agent for both, silica in PAN fibers and silicon carbide in carbon fibers that has potential applications in some advanced materials with high thermal properties. [ABSTRACT FROM AUTHOR]

The intercommunication between nerves and muscles plays an important role in the functioning of our body, and its failure leads to severe neuromuscular disorders such as spinal muscular atrophy and amyotrophic lateral sclerosis. Understanding the cellular and molecular mechanisms underlying nerve-muscle interactions and mediating their mutual influence is an integral part of strategies aimed at curing neuromuscular diseases. Here, we propose a novel ex vivo experimental model for the spinal cord (SC) and skeletal muscle interactions which for the first time utilizes only fully formed (but not yet quite functional) postnatal tissues. The model represents an organotypic co-culture comprising a longitudinal slice of the mouse postnatal SC and an extensor digitorum longus (EDL) muscle explant placed in the "damage zone" of transversally dissected longitudinal slice of the SC. Using this model, we have shown that SC tissue stimulates muscle contractions and reduces the area occupied by acetylcholine receptors on muscle surface. In turn, EDL muscles stimulate the growth of SC-derived neurites. Thus, our organotypic model allows one to assess the mutual influence of neurons and muscles in a nearly natural setting which maintains the architecture and cellular composition of intact tissues. Therefore, this model may provide an effective platform for studying molecular and cellular mechanisms linked to defective neuromuscular interactions in associated pathologies., Competing Interests: Declarations. Conflict of interest: The authors declare no competing interests., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

From the very beginning, the emulation of biological principles has been the primary avenue for the development of energy-efficient artificial intelligence systems. Reservoir computing, which has a solid biological basis, is particularly appealing due to its simplicity and efficiency. So-called memristors, resistive switching elements with complex dynamics, have proved beneficial for replicating both principal parts of a reservoir computing system. However, these parts require distinct behaviors found in differing memristive structures. The development of a homogeneous memristive reservoir computing system will significantly facilitate and reduce the fabrication process cost. The following work employs the co-existence of volatile and non-volatile regimes in parylene-MoO x crossbar memristors controlled by compliance current for this aim. The stable operation of the memristors under study is confirmed by low cycle-to-cycle and device-to-device variations of the switching voltages. For the transition between the volatile and non-volatile regimes, factors such as compliance current and reading voltage along with possible intrinsic origins are discussed. The results provide a foundation for the future hardware development of a homogeneous parylene-based reservoir computing system, considering high MNIST dataset classification accuracy (∼96%).