Xuworm5709

The possible mechanisms of baicalein for PD are regulating neurotransmitters, adjusting enzyme activity, antioxidation, anti-inflammatory, inhibiting protein aggregation, restorating mitochondrial dysfunction, inhibiting apoptosis, and autophagy. In conclusion, these findings preliminarily demonstrated that baicalein exerts potential neuroprotective effects through multiple signaling pathways in animal models of PD.We previously reported that the levels of astrocyte-derived interleukin-17A (IL-17A) increased both in the peri-infarct region and cerebrospinal fluid (CSF) of mice with 1-h middle cerebral artery (MCA) occlusion/12-h reperfusion (1-h MCAO/R 12 h)-induced ischemic stroke. However, the effects of IL-17A neutralization on the neurological outcome of mice with ischemic stroke and its underlying molecular mechanism are unclear. In this study, we found that the intracerebroventricular injection of IL-17A-neutralizing monoclonal antibody (mAb; 2.0 μg) could reduce the infarct volume, alleviate neuron loss, and improve the neurological outcomes of mice with 1-h MCAO/R 24-h- or 3-day-induced ischemic-stroke mice. The IL-17A neutralization could also significantly inhibit the increase of pro-caspase-3 cleavage through caspase-12-dependent cell apoptosis, as well as preventing the decrease of antiapoptotic factor B-cell lymphoma 2 (Bcl-2) and the increase of proapoptotic Bcl-2-associated X protein (Bax) in the peri-infarct region of mice following ischemic stroke. In addition, we confirmed that the recombinant mouse (rm) IL-17A could significantly aggravate 1-h oxygen-glucose deprivation/24-h reoxygenation (1-h OGD/R 24 h)-induced ischemic injuries in cortical neurons in a dose-dependent manner, and the rmIL-17A could also exacerbate neuronal apoptosis through caspase-12 (not caspase-8 or caspase-9)-dependent pathway. These results suggest that IL-17A neutralization could improve the neurological outcome of mice with ischemic stroke through inhibiting caspase-12-dependent neuronal apoptosis.Objectives The aim of this study was to evaluate the validity of brief cognitive screening (BCS) tools designed to diagnose mild cognitive impairment (MCI) or dementia in Spanish-speaking individuals over the age of 50 years from Latin America (LA). Methods A systematic search of titles and abstracts in Medline, Biomed Central, Embase, Scopus, Scirus, PsycINFO, LILACS, and SciELO was conducted. Inclusion criteria were papers written in English or Spanish involving samples from Spanish-speaking Latin American individuals published until 2018. Standard procedures were applied for reviewing the literature. The data related to the study sample, methodology, and procedures applied, as well as the performance obtained with the corresponding BCS, were collected and systematized. Results Thirteen of 211 articles met the inclusion criteria. The studies primarily involved memory clinic-based samples, with the exception of two studies from an adult day-care center, one from a primary care clinic, and one from a communitigenous populations are required.Mild cognitive impairment (MCI) is a clinical state with a high risk of conversion to Alzheimer's Disease (AD). Since there is no effective treatment for AD, it is extremely important to diagnose MCI as early as possible, as this makes it possible to delay its progression toward AD. However, it's challenging to identify early MCI (EMCI) because there are only mild changes in the brain structures of patients compared with a normal control (NC). To extract remarkable features for these mild changes, in this paper, a multi-modality diagnosis approach based on deep learning is presented. Firstly, we propose to use structure MRI and diffusion tensor imaging (DTI) images as the multi-modality data to identify EMCI. SW100 Then, a convolutional neural network based on transfer learning technique is developed to extract features of the multi-modality data, where an L1-norm is introduced to reduce the feature dimensionality and retrieve essential features for the identification. At last, the classifier produces 94.2% accuracy for EMCI vs. NC on an ADNI dataset. Experimental results show that multi-modality data can provide more useful information to distinguish EMCI from NC compared with single modality data, and the proposed method can improve classification performance, which is beneficial to early intervention of AD. In addition, it is found that DTI image can act as an important biomarker for EMCI from the point of view of a clinical diagnosis.This paper describes an improved brain-inspired simultaneous localization and mapping (RatSLAM) that extracts visual features from saliency maps using a frequency-tuned (FT) model. In the traditional RatSLAM algorithm, the visual template feature is organized as a one-dimensional vector whose values only depend on pixel intensity; therefore, this feature is susceptible to changes in illumination intensity. In contrast to this approach, which directly generates visual templates from raw RGB images, we propose an FT model that converts RGB images into saliency maps to obtain visual templates. The visual templates extracted from the saliency maps contain more of the feature information contained within the original images. Our experimental results demonstrate that the accuracy of loop closure detection was improved, as measured by the number of loop closures detected by our method compared with the traditional RatSLAM system. We additionally verified that the proposed FT model-based visual templates improve the robustness of familiar visual scene identification by RatSLAM.Parkinson's disease (PD) is a neurodegenerative disorder caused by loss of dopaminergic neurons in substantia nigra pars compacta (SNc). Although the exact cause of cell death is not clear, the hypothesis that metabolic deficiency is a key factor has been gaining attention in recent years. In the present study, we investigated this hypothesis using a multi-scale computational model of the subsystem of the basal ganglia comprising the subthalamic nucleus (STN), globus pallidus externa (GPe), and SNc. The proposed model is a multiscale model in that interaction among the three nuclei are simulated using more abstract Izhikevich neuron models, while the molecular pathways involved in cell death of SNc neurons are simulated in terms of detailed chemical kinetics. Simulation results obtained from the proposed model showed that energy deficiencies occurring at cellular and network levels could precipitate the excitotoxic loss of SNc neurons in PD. At the subcellular level, the models show how calcium elevation leads to apoptosis of SNc neurons.