A scheme combining feature fusion and hybrid deep learning models for epileptic seizure detection and prediction.

Epilepsy is one of the most well-known neurological disorders globally, leading to individuals experiencing sudden seizures and significantly impacting their quality of life. Hence, there is an urgent necessity for an efficient method to detect and predict seizures in order to mitigate the risks faced by epilepsy patients. In this paper, a new method for seizure detection and prediction is proposed, which is based on multi-class feature fusion and the convolutional neural network-gated recurrent unit-attention mechanism (CNN-GRU-AM) model. Initially, the Electroencephalography (EEG) signal undergoes wavelet decomposition through the Discrete Wavelet Transform (DWT), resulting in six subbands. Subsequently, time-frequency domain and nonlinear features are extracted from each subband. Finally, the CNN-GRU-AM further extracts features and performs classification. The CHB-MIT dataset is used to validate the proposed approach. The results of tenfold cross validation show that our method achieved a sensitivity of 99.24% and 95.47%, specificity of 99.51% and 94.93%, accuracy of 99.35% and 95.16%, and an AUC of 99.34% and 95.15% in seizure detection and prediction tasks, respectively. The results show that the method proposed in this paper can effectively achieve high-precision detection and prediction of seizures, so as to remind patients and doctors to take timely protective measures.

Characteristics of tandem repeat inheritance and sympathetic nerve involvement in GAA-FGF14 ataxia.

BACKGROUND: Intronic GAA repeat expansion ([GAA] ≥250) in FGF14 is associated with the late-onset neurodegenerative disorder, spinocerebellar ataxia 27B (SCA27B, GAA-FGF14 ataxia). We aim to determine the prevalence of the GAA repeat expansion in FGF14 in Chinese populations presenting late-onset cerebellar ataxia (LOCA) and evaluate the characteristics of tandem repeat inheritance, radiological features and sympathetic nerve involvement. METHODS: GAA-FGF14 repeat expansion was screened in an undiagnosed LOCA cohort (n = 664) and variations in repeat-length were analyzed in families of confirmed GAA-FGF14 ataxia patients. Brain magnetic resonance imaging (MRI) was used to evaluate the radiological feature in GAA-FGF14 ataxia patients. Clinical examinations and sympathetic skin response (SSR) recordings in GAA-FGF14 patients (n = 16) were used to quantify sympathetic nerve involvement. RESULTS: Two unrelated probands (2/664) were identified. Genetic screening for GAA-FGF14 repeat expansion was performed in 39 family members, 16 of whom were genetically diagnosed with GAA-FGF14 ataxia. Familial screening revealed expansion of GAA repeats in maternal transmissions, but contraction upon paternal transmission. Brain MRI showed slight to moderate cerebellar atrophy. SSR amplitude was lower in GAA-FGF14 patients in pre-symptomatic stage compared to healthy controls, and further decreased in the symptomatic stage. CONCLUSIONS: GAA-FGF14 ataxia was rare among Chinese LOCA cases. Parental gender appears to affect variability in GAA repeat number between generations. Reduced SSR amplitude is a prominent feature in GAA-FGF14 patients, even in the pre-symptomatic stage.

A robust approach for multi-type classification of brain tumor using deep feature fusion.

Brain tumors can be classified into many different types based on their shape, texture, and location. Accurate diagnosis of brain tumor types can help doctors to develop appropriate treatment plans to save patients' lives. Therefore, it is very crucial to improve the accuracy of this classification system for brain tumors to assist doctors in their treatment. We propose a deep feature fusion method based on convolutional neural networks to enhance the accuracy and robustness of brain tumor classification while mitigating the risk of over-fitting. Firstly, the extracted features of three pre-trained models including ResNet101, DenseNet121, and EfficientNetB0 are adjusted to ensure that the shape of extracted features for the three models is the same. Secondly, the three models are fine-tuned to extract features from brain tumor images. Thirdly, pairwise summation of the extracted features is carried out to achieve feature fusion. Finally, classification of brain tumors based on fused features is performed. The public datasets including Figshare (Dataset 1) and Kaggle (Dataset 2) are used to verify the reliability of the proposed method. Experimental results demonstrate that the fusion method of ResNet101 and DenseNet121 features achieves the best performance, which achieves classification accuracy of 99.18 and 97.24% in Figshare dataset and Kaggle dataset, respectively.

EFF_D_SVM: a robust multi-type brain tumor classification system.

Brain tumors are one of the most threatening diseases to human health. Accurate identification of the type of brain tumor is essential for patients and doctors. An automated brain tumor diagnosis system based on Magnetic Resonance Imaging (MRI) can help doctors to identify the type of tumor and reduce their workload, so it is vital to improve the performance of such systems. Due to the challenge of collecting sufficient data on brain tumors, utilizing pre-trained Convolutional Neural Network (CNN) models for brain tumors classification is a feasible approach. The study proposes a novel brain tumor classification system, called EFF_D_SVM, which is developed on the basic of pre-trained EfficientNetB0 model. Firstly, a new feature extraction module EFF_D was proposed, in which the classification layer of EfficientNetB0 was replaced with two dropout layers and two dense layers. Secondly, the EFF_D model was fine-tuned using Softmax, and then features of brain tumor images were extracted using the fine-tuned EFF_D. Finally, the features were classified using Support Vector Machine (SVM). In order to verify the effectiveness of the proposed brain tumor classification system, a series of comparative experiments were carried out. Moreover, to understand the extracted features of the brain tumor images, Grad-CAM technology was used to visualize the proposed model. Furthermore, cross-validation was conducted to verify the robustness of the proposed model. The evaluation metrics including accuracy, F1-score, recall, and precision were used to evaluate proposed system performance. The experimental results indicate that the proposed model is superior to other state-of-the-art models.

The key roles of reactive oxygen species in microglial inflammatory activation: Regulation by endogenous antioxidant system and exogenous sulfur-containing compounds.

Aberrant innate immunity in the brain has been implicated in the pathogenesis of several central nervous system (CNS) disorders, including Alzheimer's disease, Huntington's disease, Parkinson's disease, stroke, amyotrophic lateral sclerosis, and depression. Except for extraparenchymal CNS-associated macrophages, which predominantly afford protection against peripheral invading pathogens, it has been reported that microglia, a population of macrophage-like cells governing CNS immune defense in nearly all neurological diseases, are the main CNS resident immune cells. Although microglia have been recognized as the most important source of reactive oxygen species (ROS) in the CNS, ROS also may underlie microglial functions, especially M1 polarization, by modulating redox-sensitive signaling pathways. Recently, endogenous antioxidant systems, including glutathione, hydrogen sulfide, superoxide dismutase, and methionine sulfoxide reductase A, were found to be involved in regulating microglia-mediated neuroinflammation. A series of natural sulfur-containing compounds, including S-adenosyl methionine, S-methyl-L-cysteine, sulforaphane, DMS, and S-alk(enyl)-l-cysteine sulfoxide, modulating endogenous antioxidant systems have been discovered. We have summarized the current knowledge on the involvement of endogenous antioxidant systems in regulating microglial inflammatory activation and the effects of sulfur-containing compounds on endogenous antioxidant systems. Finally, we discuss the possibilities associated with compounds targeting the endogenous antioxidant system to treat neuroinflammation-associated diseases.

Menstrually-related migraine shapes the structural similarity network integration of brain.

Menstrually-related migraine (MM) is a primary migraine in women of reproductive age. The underlying neural mechanism of MM was still unclear. In this study, we aimed to reveal the case-control differences in network integration and segregation for the morphometric similarity network of MM. Thirty-six patients with MM and 29 healthy females were recruited and underwent MRI scanning. The morphometric features were extracted in each region to construct the single-subject interareal cortical connection using morphometric similarity. The network topology characteristics, in terms of integration and segregation, were analyzed. Our results revealed that, in the absence of morphology differences, disrupted cortical network integration was found in MM patients compared to controls. The patients with MM showed a decreased global efficiency and increased characteristic path length compared to healthy controls. Regional efficiency analysis revealed the decreased efficiency in the left precentral gyrus and bilateral superior temporal gyrus contributed to the decreased network integration. The increased nodal degree centrality in the right pars triangularis was positively associated with the attack frequency in MM. Our results suggested MM would reorganize the morphology in the pain-related brain regions and reduce the parallel information processing capacity of the brain.

An automated detection of epileptic seizures EEG using CNN classifier based on feature fusion with high accuracy.

BACKGROUND: Epilepsy is a neurological disorder that is usually detected by electroencephalogram (EEG) signals. Since manual examination of epilepsy seizures is a laborious and time-consuming process, lots of automatic epilepsy detection algorithms have been proposed. However, most of the available classification algorithms for epilepsy EEG signals adopted a single feature extraction, in turn to result in low classification accuracy. Although a small account of studies have carried out feature fusion, the computational efficiency is reduced due to too many features, because there are also some poor features that interfere with the classification results. METHODS: In order to solve the above problems, an automatic recognition method of epilepsy EEG signals based on feature fusion and selection is proposed in this paper. Firstly, the Approximate Entropy (ApEn), Fuzzy Entropy (FuzzyEn), Sample Entropy (SampEn), and Standard Deviation (STD) mixed features of the subband obtained by the Discrete Wavelet Transform (DWT) decomposition of EEG signals are extracted. Secondly, the random forest algorithm is used for feature selection. Finally, the Convolutional Neural Network (CNN) is used to classify epilepsy EEG signals. RESULTS: The empirical evaluation of the presented algorithm is performed on the benchmark Bonn EEG datasets and New Delhi datasets. In the interictal and ictal classification tasks of Bonn datasets, the proposed model achieves an accuracy of 99.9%, a sensitivity of 100%, a precision of 99.81%, and a specificity of 99.8%. For the interictal-ictal case of New Delhi datasets, the proposed model achieves a classification accuracy of 100%, a sensitivity of 100%, a specificity of 100%, and a precision of 100%. CONCLUSION: The proposed model can effectively realize the high-precision automatic detection and classification of epilepsy EEG signals. This model can provide high-precision automatic detection capability for clinical epilepsy EEG detection. We hope to provide positive implications for the prediction of seizure EEG.

GGC Repeat Expansion of RILPL1 is Associated with Oculopharyngodistal Myopathy.

OBJECTIVE: Oculopharyngodistal myopathy (OPDM) is an adult-onset neuromuscular disease characterized by progressive ptosis, dysarthria, ophthalmoplegia, and distal muscle weakness. Recent studies revealed that GGC repeat expansions in 5'-UTR of LRP12, GIPC1, and NOTCH2NLC are associated with OPDM. Despite these advances, approximately 30% of OPDM patients remain genetically undiagnosed. Herein, we aim to investigate the genetic basis for undiagnosed OPDM patients in two unrelated Chinese Han families. METHODS: Parametric linkage analysis was performed. Long-read sequencing followed by repeat-primed polymerase chain reaction and amplicon length polymerase chain reaction were used to determine the genetic cause. Targeted methylation sequencing was implemented to detect epigenetic changes. The possible pathogenesis mechanism was investigated by quantitative polymerase chain reaction, immunoblotting, RNA fluorescence in situ hybridization, and immunofluorescence staining of muscle biopsy samples. RESULTS: The disease locus was mapped to 12q24.3. Subsequently, GGC repeat expansion in the promoter region of RILPL1 was identified in six OPDM patients from two families, findings consistent with a founder effect, designated as OPDM type 4. Targeted methylation sequencing revealed hypermethylation at the RILPL1 locus in unaffected individuals with ultralong expansion. Analysis of muscle samples showed no significant differences in RILPL1 mRNA or RILPL1 protein levels between patients and controls. Public CAGE-seq data indicated that alternative transcription start sites exist upstream of the RefSeq-annotated RILPL1 transcription start site. Strand-specific RNA-seq data revealed bidirectional transcription from the RILPL1 locus. Finally, fluorescence in situ hybridization/immunofluorescence staining showed that both sense and antisense transcripts formed RNA foci, and were co-localized with hnRNPA2B1 and p62 in the intranuclear inclusions of OPDM type 4 patients. INTERPRETATION: Our findings implicate abnormal GGC repeat expansions in the promoter region of RILPL1 as a novel genetic cause for OPDM, and suggest a methylation mechanism and a potential RNA toxicity mechanism are involved in OPDM type 4 pathogenesis. ANN NEUROL 2022;92:512-526.

Hyperconnection and hyperperfusion of overlapping brain regions in patients with menstrual-related migraine: a multimodal neuroimaging study.

PURPOSE: Menstrual-related migraine (MRM) results in moderate to severe intensity headaches accompanied by physical and emotional disability over time in women. Neuroimaging methodologies have advanced our understanding of migraine; however, the neural mechanisms of MRM are not clearly understood. METHODS: In this study, fourteen MRM patients in the interictal phase and fifteen age- and education-matched healthy control females were recruited. Resting-state functional magnetic resonance imaging (fMRI) and pulsed arterial spin labeling (PASL) MRI were collected for both the subject groups outside of their menstrual periods. Eigenvector centrality mapping (ECM) was performed on resting-state fMRI, and the relative cerebral blood flow (relCBF) was assessed using PASL-MRI. RESULTS: MRM patients showed a significantly increased eigenvector centrality in the right medial frontal gyrus compared to healthy controls. Seed-based ECM analysis revealed that increased centrality was associated with the right medial frontal gyrus's hyperconnectivity with the left insula and the right supplementary motor area. The perfusion MRI revealed significantly increased relCBF in the hyperconnected regions. Furthermore, the hyperconnection positively correlated with the attack frequency, while the hyperperfusion showed a positive correlation with the disease duration. CONCLUSION: The results suggest that menstrual-related migraine is associated with cerebral hyperconnection and hyperperfusion in critical pain-processing brain regions. Furthermore, this elevated cerebral activity is correlated with different aspects of functional impairment in MRM patients suggesting that perfusion analysis, along with whole-brain connectivity analysis, can provide a comprehensive understanding of neural mechanisms of MRM.

[Diagnostic accuracy of recognition of stroke in the emergency room scale: a Meta-analysis].

OBJECTIVE: To systematically evaluate the diagnostic accuracy and clinical applicability of recognition of stroke in the emergency room (ROSIER) scale by systematic review and Meta-analysis. METHODS: The Chinese and English literatures concerning the diagnostic accuracy of ROSIER published from January 1st 2005 to December 31st 2018 by PubMed, Embase, Wanfang, VIP and CNKI databases were searched comprehensively and systematically. The sensitivity, specificity, and diagnostic odds ratio (DOR) of ROSIER in total population and subgroup analysis were pooled by using bivariate mixed effects model. Sensitivity analysis was used to evaluate the stability of the results. Deek funnel plot was utilized to evaluate publication bias. The clinical applicability of ROSIER was evaluated by Fagan Nomogram. RESULTS: A total of 28 studies incorporating 7 579 subjects were enrolled in this Meta-analysis. Meta-analysis in total population showed that the pooled sensitivity, specificity and DOR of ROSIER was 0.89 [95% confidence interval (95%CI) = 0.86-0.91, P = 0.00], 0.74 (95%CI = 0.67-0.80, P = 0.00) and 22.09 (95%CI = 14.86-32.82, P = 0.00), respectively. Subgroup analysis of pooled sensitivity of ROSIER showed that Asian patients was significantly higher than European patients [0.89 (95%CI = 0.86-0.92) vs. 0.74 (95%CI = 0.66-0.82), P < 0.01], prospective study was significantly higher than retrospective study [0.89 (95%CI = 0.87-0.92) vs. 0.74 (95%CI = 0.61-0.88), P < 0.05], pre-hospital emergency was significantly higher than emergency department [0.87 (95%CI = 0.80-0.94) vs. 0.85 (95%CI = 0.81-0.90), P < 0.01], study with sample size ≤ 200 was significantly higher than study with sample size > 200 [0.88 (95%CI = 0.83-0.93) vs. 0.82 (95%CI = 0.76-0.88), P < 0.05], but there was no significant difference between different evaluators or different male to female ratio subgroups. Subgroup analysis of pooled specificity of ROSIER showed that European patients was significantly higher than Asian patients [0.81 (95%CI = 0.73-0.89) vs. 0.79 (95%CI = 0.73-0.85), P < 0.05], retrospective study was significantly higher than prospective study [0.88 (95%CI = 0.78-0.97) vs. 0.79 (95%CI = 0.73-0.84), P < 0.05], pre-hospital emergency was significantly higher than emergency department [0.82 (95%CI = 0.73-0.91) vs. 0.79 (95%CI = 0.73-0.85), P < 0.01], emergency physicians was significantly higher than other medical workers [0.80 (95%CI = 0.74-0.86) vs. 0.79 (95%CI = 0.69-0.90), P < 0.05], study with sample size ≤ 200 was significantly higher than study with sample size > 200 [0.82 (95%CI = 0.76-0.89) vs. 0.78 (95%CI = 0.71-0.85), P < 0.05], but there was no significant difference between different male or female ratio subgroups. Sensitivity analysis showed that there was no significant change in pooled DOR before and after excluding each study, indicating that the results were stable. Funnel plot showed that there was a significant publication bias in the total population (P = 0.04), but there was no publication bias in the European population (P = 0.57) or the Asian population (P = 0.08). According to the results of the Fagan Nomogram, with the pretest probability of 50%, when ROSIER was positive, the probability of being diagnosed with stroke increased to 77%, and when ROSIER was negative, the probability of being diagnosed with non-stroke decreased to 13%. It was suggested that ROSIER had good applicability and high clinical diagnostic value. CONCLUSIONS: ROSIER has high diagnostic sensitivity and specificity, and has high clinical diagnostic value. It is a valid stroke identification tool which can be widely used in Asian population, pre-hospital emergency and be utilized by trained medical worker.

Diagnostic accuracy of biomarker D-dimer in patients after stroke suspected from venous thromboembolism: A diagnostic meta-analysis.

D-dimer is a promising biomarker for identification of venous thromboembolism (VTE) in patients with stroke. The purpose of our study is to evaluate the diagnostic value of D-dimer as a promising biomarker for VTE in patients after stroke. We performed an exhaustive search of leading databases including Pubmed, Embase, the Cochrane library, China National Knowledge Infrastructure (CNKI), and China Biology Medicine disc (CBM) from inception to Oct 13, 2017. We included studies written in English and Chinese. We included studies that appraised the diagnostic value of D-dimer with reference standard for VTE diagnosis in patients after stroke. We concurrently constructed a 2 × 2 table with data extracted from included studies. We identified 8 studies that included 1490 patients after stroke from our database searches. The pooled result from limited evidence showed a sensitivity of 0.85 (95% CI 0.76-0.90) and a specificity of 0.77 (95% CI 0.73-0.81). The area under the summary receiver operating characteristic curve was 0.85(95% CI 0.81-0.88). The positive likelihood ratio (LR+) and the negative likelihood ratio (LR-) were 3.8 (95% CI 3.1-4.4) and 0.20(95% CI 0.12-0.31), respectively. In patients after stroke suspected of venous thromboembolism, D-dimer is a beneficial biomarker for diagnosis of VTE. For stroke patients with low probability of VTE, a normal D-dimer test can be used to rule-out VTE. However, we do not recommend using D-dimer as the single definitive test for VTE diagnosis. We recommend diagnosing VTE using multi-branch diagnostic strategy.

The Efficacy and Safety of Pharmacological Treatments for Post-stroke Aphasia.

BACKGROUND: Aphasia is a common complication after stroke, and traditional speech and language therapy (SLT) has a limited effect on post-stroke aphasia (PSA). While there has been an increasing number of controlled clinical trials on the efficacy of drugs in the treatment of PSA, there have been very few systematic reviews on the efficacy and safety of pharmacological treatments in people with PSA. OBJECTIVE: To evaluate the efficacy and safety of pharmacological interventions for PSA. METHODS: The Cochrane Central Register of Controlled Trials (CENTRA), PubMed, Embase, Chinese Journal Full-text Database (CJFD), China Biology Medicine disc (CBMdisc), Wanfang Data and VIP Information System were searched for randomized controlled trials about pharmacological treatments for PSA. Literature screening using the inclusion and exclusion criteria, data extraction and methodological quality assessment of the included studies were completed by two independent reviewers. Methodological quality was considered high for modified Jadad quality scale scores of 4 to 7. RevMan 5.3 software was used to conduct a meta-analysis of high-quality studies. RESULTS: Fifteen studies (578 participants) satisfied the eligibility criteria for this systematic review. Five trials (277 participants) assessed donepezil, four studies (124 participants) assessed memantine, three studies (72 participants) assessed bromocriptine, one trial (45 patients) evaluated galantamine, one study (21 patients) evaluated amphetamine, and one trial (39 patients) evaluated levodopa. The systematic review showed that donepezil achieved remarkable results in terms of the aphasia quotient (AQ) (SMD 0.82, 95% CI 0.48-1.17, P < 0.00001), repetition ability (SMD 0. 81, 95% CI 0.57-1.06, P < 0.00001), naming ability (SMD 0.56, 95% CI 0.29-0. 84, P < 0.00001), auditory comprehension (SMD 0.85, 95% CI 0.58-1. 13, P< 0.00001) and oral expression (SMD 0.90, 95% CI 0.54-1.26, P < 0.00001). Memantine showed no pronounced improvement in auditory comprehension (SMD 0.35, 95% CI -0.05-0.74, P = 0.09) but did improve the AQ (SMD 0.57, 95% CI 0.09-1.06, P = 0. 02), naming ability (SMD 0.81, 95% CI 0.38-1.25, P = 0.0002), spontaneous speech (SMD 0.76, 95% CI 0. 39- 1.13, P < 0.0001), and repetition ability (SMD 0.37, 95% CI 0.01-0.73, P = 0.04). Bromocriptine showed pronounced improvement in naming ability (SMD -0.20, 95% CI- 0.67-0.26, P = 0.39), verbal fluency (SMD 0.02, 95% CI 0.53-0.56, P = 0.95), and repetition ability (SMD 0.29, 95% CI -0.23-0. 81, P = 0.28). There is limited and inconclusive evidence for galantamine, amphetamine and levodopa. CONCLUSION: Current evidence suggests that drugs, such as donepezil and memantine, can improve the prognosis of PSA. Donepezil has a significant effect in improving the ability of auditory comprehension, naming, repetition and oral expression. Memantine has a significant effect in improving the ability of naming, spontaneous speech and repetition. Bromocriptine showed no significant improvements in the treatment of aphasia after stroke. Data regarding galantamine, amphetamine and levodopa in the treatment of aphasia after stroke are limited and inconclusive.

Effect of Notch1 gene on remyelination in multiple sclerosis in mouse models of acute demyelination.

This study aims to explore the effects of Notch1 gene on remyelination in multiple sclerosis (MS). A mouse model of acute demyelination was successfully established and the model mice were grouped as cuprizone (CPZ) group, CPZ + small interfering RNA (siRNA)-Notch1 (siNotch1) group, and CPZ + siRNA negative control (NC) group. Meanwhile, another 3 groups (control, control + siNotch1, and control + siRNA NC) were established in normal mice. The changes of weight and maintenance time in rotating drum of mice were observed. Western blot analysis for the protein expressions related to Notch signaling pathway and oligodendrocyte (OL) differentiation in the corpus callosum of the mice. After model establishment, the weight of CPZ-induced demyelinated mice was decreased. During the repair period, the balance ability and movement of the mice was recovered, especially for those injected with siNotch1 plasmid. After model establishment, the number of myelinated axons was decreased. In comparison with the CPZ and CPZ siRNA NC groups, the CPZ + siNotch1 group had a decrease in the number of premature OLs, but increase in mature OLs, and a decrease in oligodendrocyte precursor cells and astrocytes. The expressions of proteins related to Notch signaling pathway, such as HES, Jagged-1 were decreased in the CPZ + siNotch1 group in contrast to the CPZ and CPZ + siRNA groups, but the OL-related transcription factor Sox10 was increased in the CPZ + siNotch1 group than in the CPZ + siRNA NC and CPZ groups, and Id2 was decreased. Our study provided evidence that the inhibition of Notch1 gene could accelerate remyelination in MS.

Effect of Intermittent Pneumatic Compression on Preventing Deep Vein Thrombosis Among Stroke Patients: A Systematic Review and Meta-Analysis.

BACKGROUND: Deep vein thrombosis (DVT) and subsequent pulmonary embolism (PE) are common complications of stroke. However, the effect of intermittent pneumatic compression (IPC) for patients after stroke is uncertain. OBJECTIVES: To assess the effectiveness and safety of IPC in reducing the risk of DVT, PE, and mortality in stroke patients. METHODS: We searched leading medical databases including Medline, EMBASE, Cochrane Library, Wanfang, CNKI, and CBM, from inception to June 2, 2017. Studies comparing IPC with no IPC in stroke patients were included. Agreement was measured using simple agreement and kappa statistics. The rates of PE, DVT, and mortality were compared. The results were pooled using a fixed effects model to evaluate the differences between the IPC and control groups. If there was significant heterogeneity in the pooled result, a random effect model was used. RESULTS: We identified seven randomized controlled trials that included 3,551 stroke patients. The average calculated κ for the various parameters was κ = 0.96 (0.70-1). Overall, IPC significantly reduced the incidence of DVT in stroke patients (risk ratio [RR] = 0.50; 95% confidence interval [CI 0.27, 0.94]). At the same time, IPC increased IPC-related adverse events (RR = 5.71; 95% CI [3.40, 9.58]). Though IPC was associated with a significant increase in survival by 4.5 days during 6 months of follow-up (148-152 days; 95% CI [-0.2, 9.1]), there was a mean gain of only 0.9 days (26.7-27.6 days; 95% CI [2.1, 3.9]) in quality-adjusted survival during the 6-month follow-up. Overall, sensitivity analyses did not alter these findings. LINKING EVIDENCE TO ACTION: This review provides an important basis for preventing DVT in stroke patients, especially in hemorrhagic stroke patients. IPC significantly reduces the risk of DVT and significantly improves survival in a wide variety of patients who are immobile after stroke. However, IPC does not significantly improve quality-adjusted survival. Clinicians should take functional status and quality of life into consideration when making decisions for stroke patients.

MicroRNA-107 prevents amyloid-ß-induced neurotoxicity and memory impairment in mice.

The pathogenesis of Alzheimer's disease (AD) has still not been fully elucidated, however it is thought that the build up of amyloid plaque at least partially causes the symptoms of AD. MicroRNAs (miRNAs) are endogenous non­coding small RNA molecules that regulate the expression and degradation of proteins. The present study induced symptoms of AD in mice via intraventricular injection of amyloid­ß 1­42 (Aß1­42), which decreased levels of miR­107. However, miR­107 levels increased following administration of miR­107 mimic, a double­stranded RNA molecule designed to imitate the native miRNA. Intraventricular injection of Aß1­42 aggregates led to spatial memory impairments, inhibited hippocampal long­term potentiation (LTP) and resulted in the loss of pyramidal cells in the CA1 region of the hippocampus. The miR­107 mimic reversed the impairments of spatial memory and LTP and the loss of pyramidal neurons caused by Aß neurotoxicity. Furthermore, the miR­107 mimic reversed the Aß­induced increase in Aß1­42 and phosphorylated Tau levels. Critically, Aß1­42 injection decreased levels of brain­derived neurotrophic factor and reduced the phosphorylation of tyrosine receptor kinase B and protein kinase B; these changes were reversed following treatment with the miR­107 mimic. Collectively, these results demonstrated that miR­107 may be a potential target for the treatment of AD.

miR-146a negatively regulates the induction of proinflammatory cytokines in response to Japanese encephalitis virus infection in microglial cells.

Increasing evidence confirms the involvement of virus infection and miRNA, such as miR-146a, in neuroinflammation-associated epilepsy. In the present study, we investigated the upregulation of miR-146a with RT-qPCR and in situ hybridization methods in a mice infection model of Japanese encephalitis virus (JEV) and in vitro. Subsequently we investigated the involvement of miR-146a in modulating JEV-induced neuroinflammation. It was demonstrated that JEV infection promoted miR-146a production in BALB/c mice brain and in cultured mouse microglial C8-B4 cells, along with pro-inflammatory cytokines, such as IL-1ß, IL-6, TNF-α, IFN-ß and IFN-α. We also found that miR-146a exerted negative regulatory effects upon IL-1ß, IL-6, TNF-α, IFN-ß and IFN-α in C8-B4 cells. Accordingly, miR-146a downregulation with a miR-146a inhibitor promoted the upregulation of IL-1ß, IL-6, TNF-α, IFN-ß and IFN-α, whereas miR-146a upregulation with miR-146a mimics reduced the upregulation of these cytokines. Moreover, miR-146a exerted no regulation upon JEV growth in C8-B4 cells. In conclusion, JEV infection upregulated miR-146a and pro-inflammatory cytokine production, in mice brain and in cultured C8-B4 cells. Furthermore, miR-146a negatively regulated the production of JEV-induced pro-inflammatory cytokines, in virus growth independent fashion, identifying miR-146a as a negative feedback regulator in JEV-induced neuroinflammation, and possibly in epilepsy.

Silencing of the long non-coding RNA NEAT1 suppresses glioma stem-like properties through modulation of the miR-107/CDK6 pathway.

Developing novel strategies against glioma remains a significant challenge. Long non-coding RNA nuclear paraspeckle assembly transcript 1 (NEAT1) significantly contributes to the progression of many human cancers, while glioma stem cells (GSCs) are believed to be responsible for tumor progression. However, whether NEAT1 alters the stem-like properties of GSC cells remains unknown. Using microbeads, CD133+ cells were isolated and were found to possess glioma stem cell properties. Using siRNA, NEAT1 was knocked down in the sorted CD133+ U87 glioma cells. We found higher NEAT1 RNA expression in CD133+ human glioma primary culture stem cells and CD133+ U87 cells via RT-PCR. Moreover, NEAT1 knockdown in the CD133+ U87 cells resulted in decreased colony formation, increased G1 cell cycle arrest and apoptosis. In addition, these effects were accompanied by miR-107 activation and inactivation of CDK6 protein. These results strongly suggest that NEAT1 knockdown suppresses stem-like properties in glioma cells by modulating the miR­107CDK6 pathway. Targeting NEAT1 may provide a novel therapeutic opportunity for developing a relapse-free treatment of glioma patients.

Inflammatory response in Parkinson's disease (Review).

Parkinson's disease (PD) is one of the most common age­related neurodegenerative diseases, which results from a number of environmental and inherited factors. PD is characterized by the slow progressive degeneration of dopaminergic (DA) neurons in the substantia nigra. The nigrostriatal DA neurons are particularly vulnerable to inflammatory attack. Neuroinflammation is an important contributor to the pathogenesis of age­related neurodegenerative disorders, such as PD, and as such anti­inflammatory agents are becoming a novel therapeutic focus. This review will discuss the current knowledge regarding inflammation and review the roles of intracellular inflammatory signaling pathways, which are specific inflammatory mediators in PD. Finally, possible therapeutic strategies are proposed, which may downregulate inflammatory processes and inhibit the progression of PD.