Quercetin attenuates brain apoptosis in mice with chronic unpredictable mild stress-induced depression.

BACKGROUND: Depression is a common psychiatric disorder with limited effective treatments. Research suggests that depression involves apoptosis mechanisms. Quercetin (QUE) has been reported to have anti-apoptotic activities. In this study, we aimed to investigate the effects and mechanisms of QUE in chronic unpredictable mild stress (CUMS)-induced depression. METHODS: After establishing mouse models of CUMS-induced depression, the mice were randomly assigned into four groups: control, CUMS, CUMS+QUE, and CUMS+Fluoxetine (FLX). The body weight of the mice was measured during the study. Then, depression-associated behaviors were evaluated using the sucrose preference test (SPT), novelty suppressed feeding test (NSFT), forced swim test (FST) and tail suspension test (TST). Apoptosis in the hippocampus and prefrontal cortex was determined using flow cytometry. Bcl-2 and Nrf2 protein expressions in the hippocampus and prefrontal cortex were also detected. Furthermore, Western blot was used to measure the protein levels of p-ERK, ERK, p-CREB, CREB, and Nrf2 in brain tissues. RESULTS: QUE or FLX administration increased the body weight of the CUMS mice. Behavioral tests indicated that CUMS mice developed a state of depression, but QUE or FLX treatment improved their depression-associated behaviors. Meanwhile, QUE or FLX treatment decreased apoptosis in the hippocampus and prefrontal cortex. Furthermore, the decreased Nrf2 protein expression, ERK and CREB phosphorylation in CUMS group were enhanced by QUE or FLX administration. CONCLUSION: QUE could attenuate brain apoptosis in mice with CUMS-induced depression, and the mechanism may be related to the ERK/Nrf2 pathway, indicating that QUE could be a potential treatment for depression.

Quercetin mitigates depression-like behavior via the suppression of neuroinflammation and oxidative damage in corticosterone-induced mice.

Depression is a clinically common and easily overlooked mental disease. Quercetin is a flavonoid compound, which has anti-inflammatory and antioxidant roles. Previous reports presented the anti-depressant role of quercetin. Nevertheless, the latent mechanism of the anti-depressant function of quercetin is blurry. This research aimed to probe its effects on corticosterone (CORT)-induced depression-like behaviors and explore the underlying mechanism. A depression model was established by subcutaneous injection of CORT (20 mg/kg). Thereafter, CORT-treated mice were given 40 mg/kg and 80 mg/kg of quercetin by gavage. This study found that quercetin mitigated depression-like behaviors, as evidenced by increased the number of line crossings, swimming time, and time spent in open arm and reduced thigmotaxis time in CORT-challenged mice in open field test and decreased immobility time as well as the swimming and climbing time in forced swim test and increased number of head dips, time spent and entries in open arm elevated plus maze test. Also, quercetin exerted anti-inflammatory and anti-oxidation effects in hippocampus and prefrontal cortex of CORT-induced mice. Additionally, quercetin alleviated the pathological injury of the liver tissue and weakened alkaline phosphatase (ALP) and alanine aminotransferase (ALT) concentrations of the serum in CORT-induced mice. Quercetin also suppressed Caspase-3 content but advanced vascular endothelial growth factor (VEGF) and brain derived neurotrophic factor (BDNF) contents in hippocampus of CORT-treated mice. Based on these results, quercetin mitigated CORT-induced depression-like behaviors, and the mechanism was partly related to the repression of neuroinflammation and oxidative damage.

PKCε inhibition prevents ischemia­induced dendritic spine impairment in cultured primary neurons.

Brain ischemia is an independent risk factor for Alzheimer's disease (AD); however, the mechanisms underlining ischemic stroke and AD remain unclear. The present study aimed to investigate the function of the ε isoform of protein kinase C (PKCε) in brain ischemia-induced dendritic spine dysfunction to elucidate how brain ischemia causes AD. In the present study, primary hippocampus and cortical neurons were cultured while an oxygen-glucose deprivation (OGD) model was used to simulate brain ischemia. In the OGD cell model, in vitro kinase activity assay was performed to investigate whether the PKCε kinase activity changed after OGD treatment. Confocal microscopy was performed to investigate whether inhibiting PKCε kinase activity protects dendritic spine morphology and function. G-LISA was used to investigate whether small GTPases worked downstream of PKCε. The results showed that PKCε kinase activity was significantly increased following OGD treatment in primary neurons, leading to dendritic spine dysfunction. Pre-treatment with PKCε-inhibiting peptide, which blocks PKCε activity, significantly rescued dendritic spine function following OGD treatment. Furthermore, PKCε could activate Ras homolog gene family member A (RhoA) as a downstream molecule, which mediated OGD-induced dendritic spine morphology changes and caused dendritic spine dysfunction. In conclusion, the present study demonstrated that the PKCε/RhoA signalling pathway is a novel mechanism mediating brain ischemia-induced dendritic spine dysfunction. Developing therapeutic targets for this pathway may protect against and prevent brain ischemia-induced cognitive impairment and AD.

Introduction to Deep Learning in Clinical Neuroscience.

The use of deep learning (DL) is rapidly increasing in clinical neuroscience. The term denotes models with multiple sequential layers of learning algorithms, architecturally similar to neural networks of the brain. We provide examples of DL in analyzing MRI data and discuss potential applications and methodological caveats.Important aspects are data pre-processing, volumetric segmentation, and specific task-performing DL methods, such as CNNs and AEs. Additionally, GAN-expansion and domain mapping are useful DL techniques for generating artificial data and combining several smaller datasets.We present results of DL-based segmentation and accuracy in predicting glioma subtypes based on MRI features. Dice scores range from 0.77 to 0.89. In mixed glioma cohorts, IDH mutation can be predicted with a sensitivity of 0.98 and specificity of 0.97. Results in test cohorts have shown improvements of 5-7% in accuracy, following GAN-expansion of data and domain mapping of smaller datasets.The provided DL examples are promising, although not yet in clinical practice. DL has demonstrated usefulness in data augmentation and for overcoming data variability. DL methods should be further studied, developed, and validated for broader clinical use. Ultimately, DL models can serve as effective decision support systems, and are especially well-suited for time-consuming, detail-focused, and data-ample tasks.

Deep semi-supervised learning for brain tumor classification.

BACKGROUND: This paper addresses issues of brain tumor, glioma, classification from four modalities of Magnetic Resonance Image (MRI) scans (i.e., T1 weighted MRI, T1 weighted MRI with contrast-enhanced, T2 weighted MRI and FLAIR). Currently, many available glioma datasets often contain some unlabeled brain scans, and many datasets are moderate in size. METHODS: We propose to exploit deep semi-supervised learning to make full use of the unlabeled data. Deep CNN features were incorporated into a new graph-based semi-supervised learning framework for learning the labels of the unlabeled data, where a new 3D-2D consistent constraint is added to make consistent classifications for the 2D slices from the same 3D brain scan. A deep-learning classifier is then trained to classify different glioma types using both labeled and unlabeled data with estimated labels. To alleviate the overfitting caused by moderate-size datasets, synthetic MRIs generated by Generative Adversarial Networks (GANs) are added in the training of CNNs. RESULTS: The proposed scheme has been tested on two glioma datasets, TCGA dataset for IDH-mutation prediction (molecular-based glioma subtype classification) and MICCAI dataset for glioma grading. Our results have shown good performance (with test accuracies 86.53% on TCGA dataset and 90.70% on MICCAI dataset). CONCLUSIONS: The proposed scheme is effective for glioma IDH-mutation prediction and glioma grading, and its performance is comparable to the state-of-the-art.

Co-Saliency-Enhanced Deep Recurrent Convolutional Networks for Human Fall Detection in E-Healthcare.

This paper addresses the issue of fall detection from videos for e-healthcare and assisted-living. Instead of using conventional hand-crafted features from videos, we propose a fall detection scheme based on co-saliency-enhanced recurrent convolutional network (RCN) architecture for fall detection from videos. In the proposed scheme, a deep learning method RCN is realized by a set of Convolutional Neural Networks (CNNs) in segment-levels followed by a Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), to handle the time-dependent video frames. The co-saliency-based method enhances salient human activity regions hence further improves the deep learning performance. The main contributions of the paper include: (a) propose a recurrent convolutional network (RCN) architecture that is dedicated to the tasks of human fall detection in videos; (b) integrate a co-saliency enhancement to the deep learning scheme for further improving the deep learning performance; (c) extensive empirical tests for performance analysis and evaluation under different network settings and data partitioning. Experiments using the proposed scheme were conducted on an open dataset containing multicamera videos from different view angles, results have shown very good performance (test accuracy 98.96%). Comparisons with two existing methods have provided further support to the proposed scheme.

Deep Learning and Multi-Sensor Fusion for Glioma Classification Using Multistream 2D Convolutional Networks.

This paper addresses issues of brain tumor, glioma, grading from multi-sensor images. Different types of scanners (or sensors) like enhanced T1-MRI, T2-MRI and FLAIR, show different contrast and are sensitive to different brain tissues and fluid regions. Most existing works use 3D brain images from single sensor. In this paper, we propose a novel multistream deep Convolutional Neural Network (CNN) architecture that extracts and fuses the features from multiple sensors for glioma tumor grading/subcategory grading. The main contributions of the paper are: (a) propose a novel multistream deep CNN architecture for glioma grading; (b) apply sensor fusion from T1-MRI, T2-MRI and/or FLAIR for enhancing performance through feature aggregation; (c) mitigate overfitting by using 2D brain image slices in combination with 2D image augmentation. Two datasets were used for our experiments, one for classifying low/high grade gliomas, another for classifying glioma with/without 1p19q codeletion. Experiments using the proposed scheme have shown good results (with test accuracy of 90.87% for former case, and 89.39 % for the latter case). Comparisons with several existing methods have provided further support to the proposed scheme. keywords: brain tumor classification, glioma, 1p19q codeletion, glioma grading, deep learning, multi-stream convolutional neural networks, sensor fusion, T1-MR image, T2-MR image, FLAIR.