Neuroscience
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Astrocytes experience significant metabolic shifts in the "sensitive period" of neurological function recovery following cerebral ischemia. However, the changes in astrocyte lipid metabolism and their implications for neurological recovery remain unknown. In the present study, we employed a mouse middle cerebral artery occlusion model to investigate the changes in de novo lipogenesis and interleukin-33 (IL-33) production in astrocytes and elucidate their role in blood-brain barrier (BBB) repair in the subacute phase of cerebral ischemia. ⋯ Inhibition of lipogenesis in astrocytes decreased IL-33 production in the peri-infarct area, deteriorated BBB damage and interfered with neurological recovery. In addition, supplementation of IL-33 alleviated BBB destruction and improved neurological recovery worsened by lipogenesis inhibition. These findings indicate that astrocyte lipogenesis increases the production of IL-33 in the peri-infarct area, which promotes BBB repair in the subacute phase of cerebral ischemia injury and improves long-term functional recovery.
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We explored the phenomenon of unintentional force drifts in the absence of visual feedback. Based on the idea of direct force control with internal models and on the idea of indirect force control with referent coordinates to the involved muscle groups, contrasting predictions were drawn for changes in the drift magnitude when acting against external spring loads. Fifteen young subjects performed typical accurate force production tasks by pressing with the Index finger at 20% of maximal voluntary contraction (MVC) in isometric conditions and while acting against one of the three external springs with different stiffness. ⋯ We view these observations as strong arguments in favor of the theory of control with spatial referent coordinates. In particular, force drifts were likely consequences of drifts of referent coordinates to both agonist and antagonist muscles. The lack of drift effects on both perception-to-report and perception-to-act fit the scheme of kinesthetic perception based on the interaction of efferent (referent coordinate) and afferent processes.
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Microglia serve as resident immune cells in the brain, responding to insults and pathological developments. They have also been implicated in shaping synaptic development and regulation. The present study examined microglial cell density in a number of brain regions across select postnatal (P) ages along with the effects of valproic acid (VPA) on microglia density. ⋯ Finally, animals treated with VPA at P60 exhibited decreased microglia density in the hippocampus only. These results suggest rapid VPA-induced increases in microglial cell density in a developmentally-regulated fashion which differs across distinct brain areas. Furthermore, in the context of prior reports that early VPA causes excitotoxic damage, the present findings suggest early VPA exposure may provide a model for studying altered microglial responses to early toxicant challenge.
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Review
Lactate supply from astrocytes to neurons and its role in ischemic stroke-induced neurodegeneration.
Glucose transported to the brain is metabolized to lactate in astrocytes and supplied to neuronal cells via a monocarboxylic acid transporter (MCT). Lactate is used in neuronal cells for various functions, including learning and memory formation. Furthermore, lactate can block stroke-induced neurodegeneration. ⋯ These findings suggest that the lack of lactate supply may strongly contribute to hypoxia-induced neurodegeneration. Furthermore, diminished lactate supply from astrocytes could facilitate stroke-induced neurodegeneration. Therefore, astrocyte-derived lactate may contribute to stroke prevention.
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The unpredictability of epileptic seizures is one of the most problematic aspects of the field of epilepsy. Methods or devices capable of detecting seizures minutes before they occur may help prevent injury or even death and significantly improve the quality of life. Machine learning (ML) is an emerging technology that can markedly enhance algorithm performance by interpreting data. ⋯ While ML has good prospects with regard to detecting epileptic seizures via EEG signals, many clinicians are still unfamiliar with this field. This work briefly summarizes the history and recent significant progress made in this field and clarifies the essential components of the automatic seizure detection system using ML methodologies for clinicians. This review also proposes how neurologists can actively contribute to ensure improvements in seizure prediction using EEG-based ML.