Neuroscience
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Melatonin (MT) has been reported to control and prevent Alzheimer's disease (AD) in the clinic; however, the effect and mechanism of MT on AD have not been specifically described. Therefore, the main purpose of this meta-analysis was to explore the effect and mechanism of MT on AD models by studying behavioural indicators and pathological features. Seven databases were searched and 583 articles were retrieved. ⋯ Among the pathological features, subgroup analysis found that MT may ease the symptoms of AD mainly by reducing the deposition of Aβ40 and Aβ42 in the cortex. In addition, MT exerted a superior effect on ameliorating the learning ability of senescence-related and metabolic AD models, and corrected the memory deficit of the toxin-induced AD model. The study was registered at PROSPERO (CRD42021226594).
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Because of different mechanism of electro-signaling in myelinated axons than in dendrites or unmyelinated axons, the role of the myelin needs to be reconsidered upon new premises in distinction to conventional cable model. It occurs that the latter model is inapplicable for so-called saltatory conduction in myelinated axons and the former imagination on the role of the myelin based on the cable model is confusing. ⋯ This is of particular importance for better understanding of malfunctions of neuron communication due to demyelination diseases and for the strategy of future therapy methods at paralysis and at demyelination syndromes. The new mechanism of signaling in myelinated neurons is also supported by recent advances in recognition of so-called micro-saltatory conduction in C-fibers of pain sensation, also exceeding the range of applicability of the conventional cable model.
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Accumulating evidence suggests that neuroinflammation is the main mechanism in cognitive dysfunction and that brain-derived neurotrophic factor (BDNF) is involved in learning and memory by binding to tyrosine kinase B (TrkB) receptors. Herein, we tested the roles of the BDNF-TrkB signaling pathway and its downstream cascade in lipopolysaccharide (LPS) induced cognitive dysfunction in mice. Mice were treated with LPS (0.25 mg/kg) for 7 days, and learning and memory function was evaluated by the novel object recognition test (NORT). ⋯ In the entorhinal cortex, the protein levels of BDNF, p-TrkB, Bcl-2, p-CaMK2 and p-CREB were decreased, and the protein level of Bax was increased in LPS mice. Interestingly, 7,8-DHF alleviated these disorders in LPS mice and improved learning and memory function; however, the TrkB antagonist ANA12 effectively reversed effects of 7,8-DHF. Therefore, we conclude that the BDNF-TrkB signaling pathway and its downstream cascades disorders in different regions are main mechanisms of cognitive dysfunction, and 7,8-DHF maybe useful as a new treatment for preventing or treating cognitive dysfunction induced by neuroinflammation in neurodegenerative diseases.
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Genetic analyses have linked BTBD9 to restless legs syndrome (RLS) and sleep regulation. Btbd9 knockout mice show RLS-like motor restlessness. Previously, we found hyperactivity of cerebellar Purkinje cells (PCs) in Btbd9 knockout mice, which may contribute to the motor restlessness observed. ⋯ However, Syngap1 heterozygous knockout mice showed nocturnal, instead of diurnal, motor restlessness. Our results suggest that SYNGAP1 deficiency may not contribute directly to the RLS-like motor restlessness observed in Btbd9 knockout mice. Finally, we found that PC-specific Btbd9 knockout mice exhibited deficits in motor coordination and balance similar to Btbd9 knockout mice, suggesting that the motor effect of BTBD9 in PCs is cell-autonomous.
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Electroencephalography (EEG) signals are nonlinear and non-stationary sequences that carry much information. However, physiological signals from other body regions may readily interfere with EEG signal capture, having a significant unfavorable influence on subsequent analysis. Therefore, signal denoising is a crucial step in EEG signal processing. ⋯ We compared the proposed model with a fully connected network (FCNN), the one-dimensional residual convolutional neural network (1D-ResCNN), and a recurrent neural network (RNN). The experimental results show that the proposed model can reconstruct a clear EEG waveform with a decent signal-to-noise ratio (SNR) and the relative root mean squared error (RRMSE) value. This study demonstrates the potential of BG-Attention in the pre-processing phase of EEG experiments, which has significant implications for medical technology and brain-computer interface (BCI) applications.