Neuroscience
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Parkinson's disease (PD) is a prevalent neurodegenerative disorder caused by degeneration of dopaminergic neurons, originating from the substantia nigra pars compacta, and characterized by motor symptoms such as bradykinesia, muscle rigidity, resting tremor, and postural instability, as well as non-motor symptoms such as anxiety, depression, reduced sense of smell, cognitive impairment, and visual dysfunction. Emerging evidence highlights the retina as a promising site for non-invasive exploration of PD pathology, due to its shared embryonic origin with the central nervous system. ⋯ This review provides a comprehensive synthesis of retinal dysfunctions in PD, focusing on structural and functional changes as potential biomarkers for early diagnosis and clinical assessment. By integrating findings from advanced imaging and electrophysiological studies, this review introduces novel perspectives on the correlation between retinal changes and PD pathophysiology, offering innovative approaches for early detection, disease progression monitoring, and therapeutic stratification.
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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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We investigated proprioceptive acuity for location and motion of a never seen hand-held tool (30 cm long rod) and the accuracy of movements to place tool parts in the location of remembered visual targets. Ten blindfolded right-handed subjects (5 females) reached with the tool held in the right hand to touch the tip and midpoint to the stationary and moving left index-tip, to the right and left ear lobes and to remembered visual target locations. We also tested accuracy of left hand rod reaches to the ear lobes to determine if rod dimensions and control of tool movements experienced during right hand tool use could be used to accurately localize the rod parts when held in the left hand. ⋯ The tool-tip was localized with lower mean distance errors (about 1 cm) than the tool-midpoint (5.5-6.5 cm) when reaching to touch the ear lobes with the rod in right and left hands. Right hand reaches to place the tool- tip and midpoint in remembered visual target locations were inaccurate with large overshoots of close targets and undershoots of far targets, similar to previous reports for reaching with the right hand to remembered visual targets. These results support the distalization hypothesis, that the tool endpoint becomes the effective upper limb endpoint when using the tool.
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The conversion of mild cognitive impairment (MCI) to Alzheimer's disease (AD) is related to various factors. The causal relationships among these factors remain unclear. This study aims to investigate pathways of the progression by using causal analysis and build a predictive model with high accuracy. ⋯ Our study elucidated the initiating factors and three independent pathways involved in the conversion of MCI to AD. The predictive value of each factor was clarified and a multi-predictor nomogram was established with high accuracy.
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Accurate analysis of anxiety behaviors in animal models is pivotal for advancing neuroscience research and drug discovery. This study compares the potential of DeepLabCut, ZebraLab, and machine learning models to analyze anxiety-related behaviors in adult zebrafish. Using a dataset comprising video recordings of unstressed and pre-stressed zebrafish, we extracted features such as total inactivity duration/immobility, time spent at the bottom, time spent at the top and turn angles (large and small). ⋯ The effectiveness of these machine learning models was validated and tested on independent datasets. We found that some machine learning models, such as Decision Tree and Random Forests, performed excellently to differentiate between anxious and non-anxious behavior, even in the control group, where the differences between subjects were more subtle. Our findings show that upcoming technologies, such as machine learning models, are able to effectively and accurately analyze anxiety behaviors in zebrafish and provide a cost-effective method to analyze animal behavior.