Neuroscience
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Myelination is the process by which oligodendrocytes ensheathe axons to form myelin sheaths. Myelination is a crucial aspect of brain development and is closely associated with central nervous system abnormalities. However, previous studies have found that advanced maternal age might affect the myelination of offspring, potentially through the pathway of disrupting DNA methylation levels in the offspring's hippocampus. ⋯ The demethylation level of oligodendrocyte progenitor cells was detected by immunofluorescence co-expression of OLIG2 and DNA hydroxylase ten-eleven translocation 1 (TET1), TET2, and TET3. Our study found that advanced maternal age could impair myelination in the hippocampus and corpus callosum of offspring. Ascorbic acid intervention may induce TET1 and TET2-mediated hydroxymethylation to ameliorate myelination disorders, promote myelination and maturation, and reverse the effects of advanced maternal age on offspring.
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Review Meta Analysis
The safety and efficacy of stem cell therapy for diabetic peripheral neuropathy in animal studies: A systematic review and meta-analysis.
Diabetic peripheral neuropathy (DPN) is the most common form of diabetic neuropathy, representing 75% of cases and posing a substantial public health challenge. Emerging evidence from animal studies indicates that stem cell therapy holds significant promise as a potential treatment for diabetic neuropathy. Nevertheless, a comprehensive evaluation of the safety and efficacy of stem cell therapy for DPN in animal studies remains outstanding. ⋯ The stem cell subgroup analysis showed that dental pulp stem cells had the greatest effects across all parameters, while bone marrow mononuclear cells had strong biochemical responses. Stem cell therapy demonstrates promising efficacy in ameliorating neuropathic symptoms in DPN animal models. Human patient studies and targeted treatment procedures for specific neuropathic disorders are advocated to improve therapeutic outcomes.
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Bayesian brain theory, a computational framework grounded in the principles of Predictive Processing (PP), proposes a mechanistic account of how beliefs are formed and updated. This theory assumes that the brain encodes a generative model of its environment, made up of probabilistic beliefs organized in networks, from which it generates predictions about future sensory inputs. The difference between predictions and sensory signals produces prediction errors, which are used to update belief networks. In this article, we introduce the fundamental principles of Bayesian brain theory, and show how the brain dynamics of prediction are associated with the generation and evolution of beliefs.
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Review
Exploring the cellular and molecular basis of nerve growth factor in cerebral ischemia recovery.
Vascular obstruction often causes inadequate oxygen and nutrient supply to the brain. This deficiency results in cerebral ischemic injury, which significantly impairs neurological function. This review aimed to explore the neuroprotective and regenerative effects of nerve growth factor (NGF) in cerebral ischemic injury. ⋯ Moreover, the mechanisms of NGF in the acute and recovery phases, along with the strategies to enhance its therapeutic effects using delivery systems (such as intranasal administration, nanovesicles, and gene therapy) were also summarized. Although NGF shows great potential for clinical application, its delivery efficiency and long-term safety still need more research and improvements. Future research should focus on exploring the specific action mechanism of NGF, optimizing the delivery strategy, and evaluating its long-term efficacy and safety to facilitate its clinical transformation in cerebral ischemic stroke.
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The diagnosis and analysis of major depressive disorder (MDD) faces some intractable challenges such as dataset limitations and clinical variability. Resting-state functional magnetic resonance imaging (Rs-fMRI) can reflect the fluctuation data of brain activity in a resting state, which can find the interrelationships, functional connections, and network characteristics among brain regions of the patients. In this paper, a brain functional connectivity matrix is constructed using Pearson correlation based on the characteristics of multi-site Rs-fMRI data and brain atlas, and an adaptive propagation operator graph convolutional network (APO-GCN) model is designed. ⋯ The experimental results on Rs-fMRI data from 1601 participants (830 MDD and 771 HC) and 16 sites of REST-meta-MDD project show that the APO-GCN achieved a classification accuracy of 91.8%, outperforming those of the state-of-the-art classifier methods. The classification process is driven by multiple significant brain regions, and our method further reveals functional connectivity abnormalities between these brain regions, which are important biomarkers of classification. It is worth noting that the brain regions identified by the classifier and the networks involved are consistent with existing research results, which suggest that the pathogenesis of depression may be related to dysfunction of multiple brain networks.