Neuroscience
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Glucose uptake in neurons depends on their cellular/physiological activity and the extracellular concentration of glucose around the cell. High concentration of extra-cellular glucose, as under hyperglycemic conditions or pathological condition in diabetes, may persist for extended periods of time in neurons and trigger cellular damage by altering voltage-gated sodium channels (VGSCs), the exact mechanism of which remains unclear. Therefore, we hypothesized that high glucose may directly affect kinetics of the VGSCs in the dorsal root ganglion (DRG) neurons. ⋯ Steady-state fast inactivation of INa was shifted in the hyperpolarizing direction whereas voltage-dependent activation was shifted in the rightward direction. Diabetic rats treated with lidocaine and tetracaine (3 mg/kg, i.p.) significantly improved thermal hyperalgesia, mechanical allodynia and motor nerve conduction velocity with a significant inhibition of TTX-R INa density as compared to the diabetic control. These results suggest that HG exposure increases the TTX-R Na(+) channel activity sensitive to Na(+) channel blockers, lidocaine and tetracaine.
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Rapid automatized naming (RAN) has been established to be a strong predictor of reading. Yet, the neural correlates underlying the RAN-reading relationship remain unknown. Thus, the purpose of this study was to determine: (a) the extent to which RAN and reading activate similar brain regions (within subjects), (b) whether RAN and reading are directly related in the shared activity network outlined in (a), and (c) to what extent RAN neural activation predicts behavioral reading performance. ⋯ Further, we found a unique relationship between in-scanner reading response time and RAN PSC in the left inferior frontal gyrus. Taken together, these findings support the notion that RAN and reading activate similar neural networks. However, the relationship between RAN and reading is primarily driven by commonalities in the motor-sequencing/articulatory processes.
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The clinical differential diagnosis between the Parkinson variant of multiple system atrophy (MSA-P) and Parkinson's disease (PD) is difficult in early stages. To identify objective markers for differential diagnosis, we combined the novel tract-based spatial statistics (TBSS) and region of interest (ROI) analyses for the first time to investigate three groups (15 MSA-P, 20 PD patients and 20 controls) with diffusion tensor imaging data. By TBSS, we performed pairwise comparisons of fractional anisotropy (FA), mean diffusivity, radial diffusivity (RD) and axial diffusivity maps. ⋯ FA/RD values in bilateral corticospinal tract (CST) and left anterior thalamic radiation (ATR) in MSA-P were significantly different from PD or controls, and significantly correlated with clinical data. These findings indicated that the abnormalities of left ATR and bilateral CST were specific for MSA-P relative to PD or controls, and seemed to be promising for differential diagnosis. Furthermore, it may be useful for severity assessment of MSA-P.
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The development of a hyperexcitable neuronal network is thought to be a critical event in epilepsy. Thrombospondins (TSPs) regulate synaptogenesis by binding the neuronal α2δ subunit of the voltage-gated calcium channel. TSPs regulate synapse formation during development and in the mature brain following injury. ⋯ Here we report similar findings, TSP1, and TSP1/2 KO mice have low levels of CACNA2D2 mRNA expression and α2δ-1/2 receptor level in the cortex, and are more susceptible to seizures. CACNA2D2 mutations in mice and humans can cause epilepsy. Our data suggest TSP1 in particular may control CACNA2D2 levels and could be a modifier of seizure susceptibility.
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Alzheimer's disease (AD) is a progressive neurodegenerative disorder and the most common type of dementia among older people. The number of patients with AD will grow rapidly each year and AD is the fifth leading cause of death for those aged 65 and older. In recent years, one of the main challenges for medical investigators has been the early diagnosis of patients with AD because an early diagnosis can provide greater opportunities for patients to be eligible for more clinical trials and they will have enough time to plan for future, medical and financial decisions. ⋯ We selected 50 percent of the MRIs randomly for use in training the classifiers, 25 percent for validation and we used 25 percent for the testing phase. The technique proposed here yielded the best overall classification results between AD and MCI (accuracy 94.88%, sensitivity 94.18%, and specificity 95.55%), and for pairs of the MCI and HC (accuracy 95.59%, sensitivity 95.89% and specificity 95.34%). These results were achieved using maximum order 30 of PZM and the pattern recognition network with the scaled conjugate gradient (SCG) back-propagation training algorithm as a classifier.