Neuroscience
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Motor variability is an intrinsic feature of human beings that has been associated with the ability for learning and adaptation to specific tasks. The purpose of this review is to examine whether there is a possible direct relationship between individuals' initial variability in their ability for learning and adaptation in motor tasks. Eighteen articles examined the relationship between initial motor variability and the ability for learning or adaptation. ⋯ While in error-based task associations were reported with both greater amount variability and more complexity temporal structure. Nevertheless, bias in initial performance related to the amount of variability was found, so the temporal structure of initial variability seems to be a better indicator of improvement in this type of task. Further research is needed for further research to better understand the potential relationship between initial motor variability and the ability for learning or adaptation in motor tasks.
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Neurodegenerative diseases (ND) are complex diseases of still unknown etiology. Lately, long non-coding RNAs (lncRNAs) have become increasingly popular and implicated in several pathologies as they have several roles and appear to be involved in all biological processes such as cell signaling and cycle control as well as translation and transcription. MEG3 is one of these and acts by binding proteins or directly or competitively binding miRNAs. ⋯ This review examines the current state of knowledge concerning the level of expression and the regulatory function of MEG3 in relation to several NDs. In addition, we examined the relation of MEG3 with neurotrophic factors such as Tumor growth factor β (TGFβ) and its possible mechanism of action. A comprehensive and in-depth analysis of the role of MEG3 in ND could give a clearer picture about the initiation of the process of neuronal death and help develop an alternative therapy that targets MEG3.
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Parkinson's disease (PD) is the second most common central neurodegenerative disease in the world after Alzheimer's disease (AD), which mainly occurs in middle-aged and elderly people, and is increasing with the aging of the population. With the increasing incidence of PD, it is particularly important to explore its pathology and provide effective interventions and treatments. The pathogenesis of PD involves a variety of factors such as genetics, environment, and age, and is not yet fully understood. ⋯ Currently, all treatments for PD are symptomatic and there is no radical cure. This paper reviews existing traditional and emerging treatments for PD to provide a theoretical basis for the in-depth study of PD pathogenesis and therapeutic approaches. Meanwhile, the application of gene editing and delivery, stem cell transplantation, immunotherapy and multi-target therapy laid the foundation for the development of safer, more effective and more comprehensive treatments for PD.
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Review
Multifaceted roles of DLG3/SAP102 in neurophysiology, neurological disorders and tumorigenesis.
DLG3, also known as Synapse-associated protein 102 (SAP102), is essential for the organization and plasticity of excitatory synapses within the central nervous system (CNS). It plays a critical role in clustering and moving key components necessary for learning and memory processes. Mutations in the DLG3 gene, which result in truncated SAP102 proteins, have been associated with a range of neurological disorders, including X-linked intellectual disability (XLID), autism spectrum disorders (ASD), and schizophrenia, all of which can disrupt synaptic structure and cognitive functions. ⋯ Moreover, SAP102 has been demonstrated to regulate tumor-induced bone pain through activating NMDA receptors. These findings highlight SAP102 as a promising therapeutic target for both neurological disorders and cancer. Therefore, further investigation into the regulatory roles of SAP102 in neural development and disease may lead to novel therapeutic approaches for treating synaptic disorders and managing cancer progression.
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Multicenter Study
Deep learning-based segmentation of acute ischemic stroke MRI lesions and recurrence prediction within 1 year after discharge: A multicenter study.
To explore the performance of deep learning-based segmentation of infarcted lesions in the brain magnetic resonance imaging (MRI) of patients with acute ischemic stroke (AIS) and the recurrence prediction value of radiomics within 1 year after discharge as well as to develop a model incorporating radiomics features and clinical factors to accurately predict AIS recurrence. ⋯ The MRA-UNet model can effectively improve the segmentation accuracy of MRI. The model, which was established by combining radiomics features and clinical factors, held some value for predicting AIS recurrence within 1 year.