In DPN patients, DRG volume was negatively correlated with the neuropathy disability score (r = -0.43; 95%CI = -0.66 to -0.14; p = 0.02), a measure of neuropathy severity. DRG volume showed negative correlations with triglycerides (r = -0.40; 95%CI = -0.57 to -0.19; p = 0.006), and LDL cholesterol (r = -0.33; 95%CI = -0.51 to -0.11; p = 0.04). There was a strong positive correlation of normalized MR signal intensity (SI) with the neuropathy symptom score in the subgroup of patients with painful DPN (r = 0.80; 95%CI = 0.46 to 0.93; p = 0.005). DRG SI was positively correlated with HbA1c levels (r = 0.30; 95%CI = 0.09 to 0.50; p = 0.03) and the triglyceride/HDL ratio (r = 0.40; 95%CI = 0.19 to 0.57; p = 0.007). In this first in vivo study, we found DRG morphological degeneration and signal increase in correlation with neuropathy severity. This elucidates the potential importance of MR-based DRG assessments in studying structural and functional changes in DPN.There is a lack of multi-session P300 datasets for Brain-Computer Interfaces (BCI). Publicly available datasets are usually limited by small number of participants with few BCI sessions. In this sense, the lack of large, comprehensive datasets with various individuals and multiple sessions has limited advances in the development of more effective data processing and analysis methods for BCI systems. This is particularly evident to explore the feasibility of deep learning methods that require large datasets. Here we present the BCIAUT-P300 dataset, containing 15 autism spectrum disorder individuals undergoing 7 sessions of P300-based BCI joint-attention training, for a total of 105 sessions. The dataset was used for the 2019 IFMBE Scientific Challenge organized during MEDICON 2019 where, in two phases, teams from all over the world tried to achieve the best possible object-detection accuracy based on the P300 signals. This paper presents the characteristics of the dataset and the approaches followed by the 9 finalist teams during the competition. The winner obtained an average accuracy of 92.3% with a convolutional neural network based on EEGNet. The dataset is now publicly released and stands as a benchmark for future P300-based BCI algorithms based on multiple session data.Mild cognitive impairment (MCI) is generally regarded as a prodromal stage of Alzheimer’s disease (AD). In coping with the challenges caused by AD, we analyzed resting-state functional magnetic resonance imaging data of 82 MCI subjects and 93 normal controls (NCs). The alteration of brain functional network in MCI was investigated on three scales, including global metrics, nodal characteristics, and modular properties. The results supported the existence of small worldness, hubs, and community structure in the brain functional networks of both groups. Compared with NCs, the network altered in MCI over all the three scales. In scale I, we found significantly decreased characteristic path length and increased global efficiency in MCI. Moreover, altered global network metrics were associated with cognitive level evaluated by neuropsychological assessments. In scale II, the nodal betweenness centrality of some global hubs, such as the right Crus II of cerebellar hemisphere (CERCRU2.R) and fusiform gyrus (FFG.R), changed significantly and associated with the severity and cognitive impairment in MCI. In scale III, although anatomically adjacent regions tended to be clustered into the same module regardless of group, discrepancies existed in the composition of modules in both groups, with a prominent separation of the cerebellum and a less localized organization of community structure in MCI compared with NC. Taking advantages of random forest approach, we achieved an accuracy of 91.4% to discriminate MCI patients from NCs by integrating cognitive assessments and network analysis. The importance of the used features fed into the classifier further validated the nodal characteristics of CERCRU2.R and FFG.R could be potential biomarkers in the identification of MCI. In conclusion, the present study demonstrated that the brain functional connectome data altered at the stage of MCI and could assist the automatic diagnosis of MCI patients.

Moyamoya disease (MMD) is an important cause of stroke in children and young adults in Asia. selleck chemicals To date, diagnosis remains challenging due to varying clinical manifestations and unknown pathogenesis. The study aims to identify cerebrospinal fluid (CSF) exosomal microRNAs (exomiRs) that can serve as a novel diagnostic biomarker for diagnosis and assess its clinical applications.

CSF samples were taken from 31 MMD patients and 31 healthy controls. Initial screening of miRNA expression was performed on samples pooled from MMD patients and controls using microarray and validated using quantitative reverse transcription polymerase chain reaction (qRT-PCR). The diagnostic accuracy of the potential exosomal miRNAs was evaluated using receiver operating characteristic curve analyses in an independent patient cohort. The potential pathways regulated by the miRNAs was also determined using bioinformatics analysis.

The microarray results demonstrated that six exomiRs were dysregulated in the MMD patients compared to the controls. Using qRT-PCR, we validated four of the miRNAs (miR-3679-5p, miR-6165, miR-6760-5p, and miR-574-5p) as a biomarker for MMD diagnosis. The four exomiRs showed enhanced sensitivity (75%) and specificity (93.75%) in terms of differentiating MMD patients from healthy subjects [area under the curve (AUC) = 0.9453]. Pathway enrichment analysis for potential targets of six exomiRs identified proteins involved in cell adhesion and junction formation in the brain.

We identified a novel and highly sensitive exomiRs signature for MMD detection and explored its potential targets using bioinformatics analysis.

We identified a novel and highly sensitive exomiRs signature for MMD detection and explored its potential targets using bioinformatics analysis.Insulin is a hormone typically associated with pancreatic release and blood sugar regulation. The brain was long thought to be “insulin-independent,” but research has shown that insulin receptors (IR) are expressed on neurons, microglia and astrocytes, among other cells. The effects of insulin on cells within the central nervous system are varied, and can include both metabolic and non-metabolic functions. Emerging data suggests that insulin can improve neuronal survival or recovery after trauma or during neurodegenerative diseases. Further, data suggests a strong anti-inflammatory component of insulin, which may also play a role in both neurotrauma and neurodegeneration. As a result, administration of exogenous insulin, either via systemic or intranasal routes, is an increasing area of focus in research in neurotrauma and neurodegenerative disorders. This review will explore the literature to date on the role of insulin in neurotrauma and neurodegeneration, with a focus on traumatic brain injury (TBI), spinal cord injury (SCI), Alzheimer’s disease (AD) and Parkinson’s disease (PD).