Exosomal ZEB1 Derived from Neural Stem Cells Reduces Inflammation Injury in OGD/R-Treated Microglia via the GPR30-TLR4-NF-κB Axis.

Ischemic stroke (IS) is the most common type of stroke and the second leading cause of death overall. Neural stem cells play protective roles in IS, but the underlying mechanism remains to be determined. Neural stem cells (NSC) were obtained from the fetal brain tissue of C57BL/6J mice. NSC-derived exosomes (NSC-Exos) were identified in the conditioned medium. Internalization of NSC-Exos was analyzed by fluorescence microscopy. In vitro microglia ischemic stroke injury model was induced using oxygen glucose deprivation/re-oxygenation (OGD/R) method. Cell viability and inflammation were analyzed by MTT, qPCR, ELISA and Western blotting assay. Interaction between ZEB1 and the promoter of GPR30 was verified by luciferase assay and chromatin immunoprecipitation. NSC-Exos prevented OGD/R-mediated inhibition of cell survival and the production of inflammatory cytokines in microglia cells. NSC-Exos increased ZEB1 expression in OGD/R-treated microglia. Down-regulation of ZEB1 expression in NSC-Exos abolished NSC-Exos' protective effects on OGD/R-treated microglia. ZEB1 bound to the promoter region of GPR30 and promoted its expression. Inhibiting GPR30 reversed NSC-Exos effects on cell viability and inflammation injury in OGD/R-treated microglia. Our study demonstrated that NSC exerted cytoprotective roles through release of exosomal ZEB1,which transcriptionally upregulated GPR30 expression, resulting in a reduction in TLR4/NF-κB pathway-induced inflammation. These findings shed light on NSC-Exos' cytoprotective mechanism and highlighted its potential application in the treatment of IS.

OIP5-AS1 Inhibits Oxidative Stress and Inflammation in Ischemic Stroke Through miR-155-5p/IRF2BP2 Axis.

BACKGROUND: Ischemic stroke is a very dangerous disease with high incidence, fatality and disability rate in human beings. Massive evidence has indicated that oxidative stress and inflammation are intimately correlated with progression of ischemic stroke. Additionally, LncRNAs were reported to be involved in ischemic stroke. Here, we aim to explore the effects and molecular mechanism of lncRNA OIP5-AS1 on oxidative stress and inflammation in ischemic stroke. METHODS: HMC3 and SH-SY5Y cells were under the condition of oxygen-glucose deprivation/reoxygenation (OGD/R) treatment to establish cell models of ischemic stroke. Commercial kits were employed to detect the indicators of oxidative stress including ROS, MDA and SOD. The expression of OIP5-AS1, miR-155-5p and IRF2BP2 mRNA was determined using RT-qPCR. The protein levels of inflammatory factors including TNF-α, IL-1ß and IL-6 and IRF2BP2 were assessed by western blot and/or ELISA. Luciferase activity assay was employed to validate their correlations among OIP5-AS1, miR-155-5p and IRF2BP2. RESULTS: In OGD/R-induced HMC3 and SH-SY5Y cells, the expression of OIP5-AS1 and IRF2BP2 was reduced while miR-155-5p was elevated. OGD/R induction promoted oxidative stress and inflammatory response in HMC3 and SH-SY5Y cells, while OIP5-AS1 or IRF2BP2 sufficiency as well as miR-155-5p inhibitor attenuated OGD/R-induced these influences. In addition, IRF2BP2 knockdown abolished the suppressive impacts of OIP5-AS1 overexpression on oxidative stress and inflammatory response in OGD/R-induced HMC3 and SH-SY5Y cells. Mechanistically, OIP5-AS1 enhanced IRF2BP2 expression via sponging miR-155-5p. CONCLUSION: OIP5-AS1 suppressed oxidative stress and inflammatory response to alleviate cell injury caused by OGD/R induction in HMC3 and SH-SY5Y cells through regulating miR-155-5p/IRF2BP2 axis, which might offer novel targeted molecules for ischemic stroke therapy.

Robust Graph Regularized NMF with Dissimilarity and Similarity Constraints for ScRNA-seq Data Clustering.

The notable progress in single-cell RNA sequencing (ScRNA-seq) technology is beneficial to accurately discover the heterogeneity and diversity of cells. Clustering is an extremely important step during the ScRNA-seq data analysis. However, it cannot achieve satisfactory performances by directly clustering ScRNA-seq data due to its high dimensionality and noise. To address these issues, we propose a novel ScRNA-seq data representation model, termed Robust Graph regularized Non-Negative Matrix Factorization with Dissimilarity and Similarity constraints (RGNMF-DS), for ScRNA-seq data clustering. To accurately characterize the structure information of the labeled samples and the unlabeled samples, respectively, the proposed RGNMF-DS model adopts a couple of complementary regularizers (i.e., similarity and dissimilar regularizers) to guide matrix decomposition. In addition, we construct a graph regularizer to discover the local geometric structure hidden in ScRNA-seq data. Moreover, we adopt the l2,1-norm to measure the reconstruction error and thereby effectively improve the robustness of the proposed RGNMF-DS model to the noises. Experimental results on several ScRNA-seq datasets have demonstrated that our proposed RGNMF-DS model outperforms other state-of-the-art competitors in clustering.

LncRNA CEBPA-AS1 knockdown prevents neuronal apoptosis against oxygen glucose deprivation/reoxygenation by regulating the miR-455/GPER1 axis.

Ischemic stroke (IS) is a common nervous system disease, which is a major cause of disability and death in the world. In present study, we demonstrated a regulatory mechanism of CCAAT/enhancer binding protein-alpha antisense 1 (CEBPA-AS1) in oxygen glucose deprivation/reoxygenation (OGD/R)-induced SH-SY5Y cells, with a focus on neuronal apoptosis. CEBPA-AS1, miR-455, and GPER1 expressions were evaluated by using qRT-PCR and Western blotting. The binding relationship among CEBPA-AS1, miR-455, and GPER1 was determined by a dual luciferase reporter assay. Neuronal viability and apoptosis were examined using MTT and flow cytometry assays, followed by determination of apoptosis-related factors (caspase 3, caspase 8, caspase 9, Bax, and Bcl-2). CEBPA-AS1 and GPER1 levels were upregulated, and miR-455 level was downregulated in the cell model of OGD/R induced. CEBPA-AS1 knockdown increased SH-SY5Y viability and reduced OGD/R-induced apoptosis. CEBPA-AS1 could act as a sponge of miR-455, and CEBPA-AS1 knockdown was found to elevate miR-455 expression. miR-455 overexpression also promoted SH-SY5Y cell viability and rescued them from OGD/R-induced apoptosis by binding to GPER1. GPER1 overexpression or miR-455 inhibition reversed the anti-apoptotic effect of CEBPA-AS1 knockdown. These findings suggest a regulatory network of CEBPA-AS1/miR-455/GPER1 that mediates neuronal cell apoptosis in the OGD model, providing a better understanding of pathogenic mechanisms after IS.

Mir-139-5p inhibits glioma cell proliferation and progression by targeting GABRA1.

Glioma is an extremely aggressive malignant neoplasm of the central nervous system. MicroRNA (miRNA) are known to bind to specific target mRNA to regulate post-transcriptional gene expression and are, therefore, currently regarded as promising biomarkers for glioma diagnosis and prognosis. The aim of the present study was to examine the pathogenesis and potential molecular markers of glioma by comparing the differential expression of miRNA and mRNA between glioma tissue and peritumor brain tissue. We explored the impact of screened core miRNA and mRNA on cell proliferation, invasion, and migration of glioma. An miRNA expression profile dataset (GSE90603) and a transcriptome profile dataset (GSE90598) were downloaded from combined miRNA-mRNA microarray chips in the Gene Expression Omnibus (GEO) database. Overall, 59 differentially expressed miRNAs (DEMs) and 419 differentially expressed genes (DEGs) were identified using the R limma software package. FunRich software was used to predict DEM target genes and miRNA-gene pairs, and Perl software was used to find overlapping genes between DEGs and DEM target genes. There were 129 overlapping genes regulated by nine miRNAs between target genes of the DEMs and DEGs. The Chinese Glioma Genome Atlas(CGGA) was analyzed in order to identify miRNAs with diagnostic and prognostic significance. MiR-139-5p, miR-137, and miR-338-3p were validated to be significantly linked to prognosis in glioma patients. Finally, we validated that miR-139-5p affected glioma malignant biological behavior via targeting gamma-aminobutyric acid A receptor alpha 1(GABRA1) through rescue experiments. Low miR-139-5p expression was correlated with survival probability and World Health Organization (WHO) grade. MiR-139-5p overexpression inhibited cell proliferation, migration, and invasion of glioma in vitro. GABRA1 was identified as a functional downstream target of miR-139-5p. Decreased GABRA1 expression was related to similar biological roles as miR-139-5p overexpression while upregulation of GABRA1 effectively reversed the inhibition effects of miR-139-5p. These results demonstrate a novel axis for miR-139-5p/GABRA1 in glioma progression and provide potential prognostic predictors and therapeutic target for glioma patients.

A Clustering Method of Case-Involved News by Combining Topic Network and Multi-Head Attention Mechanism.

Finding the news of same case from the large numbers of case-involved news is an important basis for public opinion analysis. Existing text clustering methods usually based on topic models which only use topic and case infomation as the global features of documents, so distinguishing between different cases with similar types remains a challenge. The contents of documents contain rich local features. Taking into account the internal features of news, the information of cases and the contributions provided by different topics, we propose a clustering method of case-involved news, which combines topic network and multi-head attention mechanism. Using case information and topic information to construct a topic network, then extracting the global features by graph convolution network, thus realizing the combination of case information and topic information. At the same time, the local features are extracted by multi-head attention mechanism. Finally, the fusion of global features and local features is realized by variational auto-encoder, and the learned latent representations are used for clustering. The experiments show that the proposed method significantly outperforms the state-of-the-art unsupervised clustering methods.

Intelligent Intraoperative Haptic-AR Navigation for COVID-19 Lung Biopsy Using Deep Hybrid Model.

A novel intelligent navigation technique for accurate image-guided COVID-19 lung biopsy is addressed, which systematically combines augmented reality (AR), customized haptic-enabled surgical tools, and deep neural network to achieve customized surgical navigation. Clinic data from 341 COVID-19 positive patients, with 1598 negative control group, have collected for the model synergy and evaluation. Biomechanics force data from the experiment are applied a WPD-CNN-LSTM (WCL) to learn a new patient-specific COVID-19 surgical model, and the ResNet was employed for the intraoperative force classification. To boost the user immersion and promote the user experience, intro-operational guiding images have combined with the haptic-AR navigational view. Furthermore, a 3-D user interface (3DUI), including all requisite surgical details, was developed with a real-time response guaranteed. Twenty-four thoracic surgeons were invited to the objective and subjective experiments for performance evaluation. The root-mean-square error results of our proposed WCL model is 0.0128, and the classification accuracy is 97%, which demonstrated that the innovative AR with deep learning (DL) intelligent model outperforms the existing perception navigation techniques with significantly higher performance. This article shows a novel framework in the interventional surgical integration for COVID-19 and opens the new research about the integration of AR, haptic rendering, and deep learning for surgical navigation.

[Terahertz Spectrum Features Extraction Based on Kernel Optimization Relevance Vector Machine].

Terahertz spectrum is sensitive to the change of the nonlocal molecular vibration mode. Accordingly, the spectral waveform is susceptible to variety of physical and chemical factors, which will lead to peak changes, frequency shifts, and even deformation of the overall waveform. Component analysis and material identification from the correspondence between the fixed peak features and materials will prone to cause errors or mistakes. Therefore, to solve this problem, we proposed a method based on Kernel Optimization Relevance Vector Machine (KO-RVM), which extracts global graphic features to distinct from the local features extraction method. And we use Support Vector Regression (SVR) algorithm as comparison. The result shows that, when basis functions' parameters of RVM are optimized with expectation-maximization algorithm, it will be suitable for feature extraction of terahertz transmission spectrum. The spectrum can be sparsely represented, and the amount of extracted graphic features is substantially reduced. Reconstruction models based on these features are capable of retaining the overall spectral characteristics, and fitting results for each band are more consistent, while the extracted spectrum features can be used as basis of similarity measurement and the common characteristics investigation between different materials.

[Terahertz Spectroscopic Identification with Deep Belief Network].

Feature extraction and classification are the key issues of terahertz spectroscopy identification. Because many materials have no apparent absorption peaks in the terahertz band, it is difficult to extract theirs terahertz spectroscopy feature and identify. To this end, a novel of identify terahertz spectroscopy approach with Deep Belief Network (DBN) was studied in this paper, which combines the advantages of DBN and K-Nearest Neighbors (KNN) classifier. Firstly, cubic spline interpolation and S-G filter were used to normalize the eight kinds of substances (ATP, Acetylcholine Bromide, Bifenthrin, Buprofezin, Carbazole, Bleomycin, Buckminster and Cylotriphosphazene) terahertz transmission spectra in the range of 0.9-6 THz. Secondly, the DBN model was built by two restricted Boltzmann machine (RBM) and then trained layer by layer using unsupervised approach. Instead of using handmade features, the DBN was employed to learn suitable features automatically with raw input data. Finally, a KNN classifier was applied to identify the terahertz spectrum. Experimental results show that using the feature learned by DBN can identify the terahertz spectrum of different substances with the recognition rate of over 90%, which demonstrates that the proposed method can automatically extract the effective features of terahertz spectrum. Furthermore, this KNN classifier was compared with others (BP neural network, SOM neural network and RBF neural network). Comparisons showed that the recognition rate of KNN classifier is better than the other three classifiers. Using the approach that automatic extract terahertz spectrum features by DBN can greatly reduce the workload of feature extraction. This proposed method shows a promising future in the application of identifying the mass terahertz spectroscopy.

[Classification of THz Transmission Spectrum Based on Kevnel Function of Convex Combination].

In the present paper, support vector machine (SVM) based on convex combination kernel function will be used for classification of THz pulse transmission spectra. Wavelet transform is used in data pre-processing. Peaks and valleys are regarded as location features of THz pulse transmission spectra, which are injected into maximum interval features of term frequency-inverse document frequency (TF-IDF). We can conclude weight of each sampling point from the information theory. The weight represents the possibility that sampling point becomes feature. According to the situation that different terahertz-transmission spectra are lack of obvious features, we composed a SVM classification model based on convex combination kernel function. Evaluation function should be used as an evaluation method for obtaining the parameters of optimal convex combination to achieve a better accuracy. When the optimal parameter of kenal founction was determined, we should compose the model for process of classification and prediction. Compared with the single kernel function, the method can be combined with transmission spectroscopic features with classification model iteratively. Thanks to the dimensional mapping process, outstanding margin of features can be gained for the samples of different terahertz transmission spectrum. We carried out experiments using different samples The results demonstrated that the new approach is on par or superior in terms of accuracy and much better in feature fusion than SVM with single kernel function.

[An improved software design of ultrasound bone densitometer].

In order to meet the requirements of ultrasound bone density measurement, we proposed a software solution to improve the accuracy and speed of measurement of bone mineral density of the ultrasound bone densitometer. We used a high-speed USB interface chip FT232H, along with a high-speed AD converter chip to calculate speed of sound (SOS), broadband ultrasound attenuation (BUA ) and other bone density parameters in the PC software. This solution improved the accuracy of the measurement data, reduced the measurement time and increased the quality of the displayed image. It is well concluded that the new software can greatly improve the accuracy and transmission speed of bone density measurement data through a high-speed USB interface and a software data processing technology.

Interactive attack-defense for generalized person re-identification.

Generalized Person Re-Identification (GReID) aims to develop a model capable of robust generalization across unseen target domains, even with training on a limited set of observed domains. Recently, methods based on the Attack-Defense mechanism are emerging as a prevailing technology to this issue, which treats domain transformation as a type of attack and enhances the model's generalization performance on the target domain by equipping it with a defense module. However, a significant limitation of most existing approaches is their inability to effectively model complex domain transformations, largely due to the separation of attack and defense components. To overcome this limitation, we introduce an innovative Interactive Attack-Defense (IAD) mechanism for GReID. The core of IAD is the interactive learning of two models: an attack model and a defense model. The attack model dynamically generates directional attack information responsive to the current state of the defense model, while the defense model is designed to derive generalizable representations by utilizing a variety of attack samples. The training approach involves a dual process: for the attack model, the aim is to increase the challenge for the defense model in countering the attack; conversely, for the defense model, the focus is on minimizing the effects instigated by the attack model. This interactive framework allows for mutual learning between attack and defense, creating a synergistic learning environment. Our diverse experiments across datasets confirm IAD's effectiveness, consistently surpassing current state-of-the-art methods, and using MSMT17 as the target domain in different protocols resulted in a notable 13.4% improvement in GReID task average Rank-1 accuracy. Code is available at: https://github.com/lhf12278/IAD.

A two-stage fine-tuning method for low-resource cross-lingual summarization.

Cross-lingual summarization (CLS) is the task of condensing lengthy source language text into a concise summary in a target language. This presents a dual challenge, demanding both cross-language semantic understanding (i.e., semantic alignment) and effective information compression capabilities. Traditionally, researchers have tackled these challenges using two types of methods: pipeline methods (e.g., translate-then-summarize) and end-to-end methods. The former is intuitive but prone to error propagation, particularly for low-resource languages. The later has shown an impressive performance, due to multilingual pre-trained models (mPTMs). However, mPTMs (e.g., mBART) are primarily trained on resource-rich languages, thereby limiting their semantic alignment capabilities for low-resource languages. To address these issues, this paper integrates the intuitiveness of pipeline methods and the effectiveness of mPTMs, and then proposes a two-stage fine-tuning method for low-resource cross-lingual summarization (TFLCLS). In the first stage, by recognizing the deficiency in the semantic alignment for low-resource languages in mPTMs, a semantic alignment fine-tuning method is employed to enhance the mPTMs' understanding of such languages. In the second stage, while considering that mPTMs are not originally tailored for information compression and CLS demands the model to simultaneously align and compress, an adaptive joint fine-tuning method is introduced. This method further enhances the semantic alignment and information compression abilities of mPTMs that were trained in the first stage. To evaluate the performance of TFLCLS, a low-resource CLS dataset, named Vi2ZhLow, is constructed from scratch; moreover, two additional low-resource CLS datasets, En2ZhLow and Zh2EnLow, are synthesized from widely used large-scale CLS datasets. Experimental results show that TFCLS outperforms state-of-the-art methods by 18.88%, 12.71% and 16.91% in ROUGE-2 on the three datasets, respectively, even when limited with only 5,000 training samples.

Reinforced GNNs for Multiple Instance Learning.

Multiple instance learning (MIL) trains models from bags of instances, where each bag contains multiple instances, and only bag-level labels are available for supervision. The application of graph neural networks (GNNs) in capturing intrabag topology effectively improves MIL. Existing GNNs usually require filtering low-confidence edges among instances and adapting graph neural architectures to new bag structures. However, such asynchronous adjustments to structure and architecture are tedious and ignore their correlations. To tackle these issues, we propose a reinforced GNN framework for MIL (RGMIL), pioneering the exploitation of multiagent deep reinforcement learning (MADRL) in MIL tasks. MADRL enables the flexible definition or extension of factors that influence bag graphs or GNNs and provides synchronous control over them. Moreover, MADRL explores structure-to-architecture correlations while automating adjustments. Experimental results on multiple MIL datasets demonstrate that RGMIL achieves the best performance with excellent explainability. The code and data are available at https://github.com/RingBDStack/RGMIL.

METTL3 mediates m6A modification of lncRNA CRNDE to promote ATG10 expression and improve brain ischemia/reperfusion injury through YTHDC1.

BACKGROUND: Ischemia/reperfusion (I/R) injury is a severe brain disorder with currently limited effective treatments. This study aims to explore the role of N6-methyladenosine (m6A) modification and associated regulatory factors in I/R to identify potential therapeutic targets. METHODS: We utilized a middle cerebral artery occlusion (MCAO) rat model and SH-SY5Y cells subjected to oxygen-glucose deprivation/reoxygenation (OGD/R) to assess m6A levels and investigate the impact of METTL3 overexpression on long non-coding RNA (lncRNA) CRNDE expression. The effects of silencing lncRNA CRNDE on the interaction between YTHDC1 and ATG10 mRNA, as well as the stability of ATG10 mRNA, were evaluated. Additionally, apoptosis rates, pro-inflammatory and anti-inflammatory factor levels, ATG10 expression, and autophagic activity were analyzed to determine the effects of METTL3. The reverse effects of YTHDC1 overexpression were also examined. RESULTS: MCAO rats and OGD/R-treated SH-SY5Y cells exhibited reduced m6A levels. METTL3 overexpression significantly inhibited lncRNA CRNDE expression. Silencing lncRNA CRNDE mitigated OGD/R-induced apoptosis and inflammation in SH-SY5Y cells, while enhancing autophagy and stabilizing ATG10 mRNA. METTL3 overexpression decreased cell apoptosis, reduced the levels of pro-inflammatory cytokines TNF-α, IL-1ß, IL-6, and increased IL-10 secretion. Furthermore, METTL3 overexpression upregulated ATG10 expression and promoted autophagy. Conversely, lncRNA CRNDE overexpression negated these effects. CONCLUSION: The inhibition of lncRNA CRNDE affects the interaction between YTHDC1 and ATG10 mRNA and stabilizes ATG10 mRNA, mediated by METTL3 overexpression. These findings suggest that targeting lncRNA CRNDE to reduce apoptosis, inhibit inflammation, increase ATG10 expression, and enhance autophagy could offer new therapeutic strategies for I/R injury.

IL-1ß-induced mesenchymal stem cell-derived exosomes inhibit neuronal ferroptosis in intracerebral hemorrhage through the HSPA5/GPX4 axis.

BACKGROUND: Neuronal cell ferroptosis following intracerebral hemorrhage (ICH) is a crucial factor contributing to the poor prognosis of ICH patients. The objective of this investigation was to investigate the molecular mechanism of IL-1ß-induced mesenchymal stem cell-derived exosomes (IL-1ß-Exo) in mitigating ICH injury. METHODS: Exo and IL-1ß-Exo were obtained and identified. Hemin was used to induce an ICH model, and an ICH mouse model was established using Collagenase. Exo and IL-1ß-Exo interventions were conducted to study their impact and molecular mechanisms on neuronal ferroptosis in ICH. RESULTS: Vesicular structure Exo and IL-1ß-Exo, with an average particle size of 141.7 ± 38.8 nm and 138.8 ± 37.5 nm, respectively, showed high expression of CD63, CD9 and CD81 could be taken up by SH-SY5Y cells. These Exos reversed Hemin-induced abnormalities in neuronal cells, including elevated iron, Fe2+, ROS, MDA, 4-HNE, and decreased SOD, GSH-Px, GSH, FTH1 levels, and cell vitality. The RNA content of IL-1ß-Exo was linked to its ability to reduce iron accumulation. There was an interaction between HSPA5 and GPX4. Exo and IL-1ß-Exo reversed Hemin-induced downregulation of HSPA5 and GPX4 expression. Overexpression and knockdown of HSPA5 respectively potentiate or counteract the impacts of Exo and IL-1ß-Exo. IL-1ß-Exo was more effective than Exo. These findings were further validated in ICH mice. Moreover, both Exo and IL-1ß-Exo reduced the modified neurological severity score and brain water content, as well as alleviated pathological damage in ICH mice. CONCLUSION: IL-1ß-Exo inhibited neuronal ferroptosis in ICH through the HSPA5/GPX4 axis.

Focus Affinity Perception and Super-Resolution Embedding for Multifocus Image Fusion.

Despite the fact that there is a remarkable achievement on multifocus image fusion, most of the existing methods only generate a low-resolution image if the given source images suffer from low resolution. Obviously, a naive strategy is to independently conduct image fusion and image super-resolution. However, this two-step approach would inevitably introduce and enlarge artifacts in the final result if the result from the first step meets artifacts. To address this problem, in this article, we propose a novel method to simultaneously achieve image fusion and super-resolution in one framework, avoiding step-by-step processing of fusion and super-resolution. Since a small receptive field can discriminate the focusing characteristics of pixels in detailed regions, while a large receptive field is more robust to pixels in smooth regions, a subnetwork is first proposed to compute the affinity of features under different types of receptive fields, efficiently increasing the discriminability of focused pixels. Simultaneously, in order to prevent from distortion, a gradient embedding-based super-resolution subnetwork is also proposed, in which the features from the shallow layer, the deep layer, and the gradient map are jointly taken into account, allowing us to get an upsampled image with high resolution. Compared with the existing methods, which implemented fusion and super-resolution independently, our proposed method directly achieves these two tasks in a parallel way, avoiding artifacts caused by the inferior output of image fusion or super-resolution. Experiments conducted on the real-world dataset substantiate the superiority of our proposed method compared with state of the arts.

Novel methylation-related long non-coding RNA clinical outcome prediction method: the clinical phenotype and immune infiltration research in low-grade gliomas.

Background: Recent studies have suggested that long non-coding RNAs (lncRNAs) may play crucial role in low-grade glioma; however, the underlying mechanisms linking them to epigenetic methylation remain unclear. Methods: We downloaded expression level data for regulators associated with N1 methyladenosine (m1A), 5-methyladenine (m5C), and N6 methyladenosine (m6A) (M1A/M5C/M6A) methylation from the Cancer Genome Atlas-low-grade glioma (TCGA-LGG) database. We identified the expression patterns of lncRNAs, and selected methylation-related lncRNAs using Pearson correlation coefficient>0.4. Non-negative matrix dimensionality reduction was then used to determine the expression patterns of the methylation-associated lncRNAs. We constructed a weighted gene co-expression network analysis (WGCNA) network to explore the co-expression networks between the two expression patterns. Functional enrichment of the co-expression network was performed to identify biological differences between the expression patterns of different lncRNAs. We also constructed prognostic networks based on the methylation presence in lncRNAs in low-grade gliomas. Results: We identified 44 regulators by literature review. Using a correlation coefficient greater than 0.4, we identified 2330 lncRNAs, among which 108 lncRNAs with independent prognostic values were further screened using univariate Cox regression at P< 0.05. Functional enrichment of the co-expression networks revealed that regulation of trans-synaptic signaling, modulation of chemical synaptic transmission, calmodulin binding, and SNARE binding were mostly enriched in the blue module. The calcium and CA2 signaling pathways were associated with different methylation-related long non-coding chains. Using the Least Absolute Shrinkage Selector Operator (LASSO) regression analysis, we analyzed a prognostic model containing four lncRNAs. The model's risk score was 1.12 *AC012063 + 0.74 * AC022382 + 0.32 * AL049712 + 0.16 * GSEC. Gene set variation analysis (GSVA) revealed significant differences in mismatch repair, cell cycle, WNT signaling pathway, NOTCH signaling pathway, Complement and Cascades, and cancer pathways at different GSEC expression levels. Thus, these results suggest that GSEC may be involved in the proliferation and invasion of low-grade glioma, making it a prognostic risk factor for low-grade glioma. Conclusion: Our analysis identified methylation-related lncRNAs in low-grade gliomas, providing a foundation for further research on lncRNA methylation. We found that GSEC could serve as a candidate methylation marker and a prognostic risk factor for overall survival in low-grade glioma patients. These findings shed light on the underlying mechanisms of low-grade glioma development and may facilitate the development of new treatment strategies.

BMSC-Derived Exosomal Egr2 Ameliorates Ischemic Stroke by Directly Upregulating SIRT6 to Suppress Notch Signaling.

Exosomes generated by BMSCs contribute to functional recovery in ischemic stroke. However, the regulatory mechanism is largely unknown. Exosomes were isolated from BMSCs. Tube formation, MTT, TUNEL, and flow cytometry assays were applied to examine cell angiogenesis, viability, and apoptosis. Protein and DNA interaction was evaluated by ChIP and luciferase assays. LDH release into the culture medium was examined. Infarction area was evaluated by TTC staining. Immunofluorescence staining was applied to examine CD31 expression. A mouse model of MCAO/R was established. BMSC-derived exosomes attenuated neuronal cell damage and facilitated angiogenesis of brain endothelial cells in response to OGD/R, but these effects were abolished by the knockdown of Egr2. Egr2 directly bound to the promoter of SIRT6 to promote its expression. The incompetency of Egr2-silencing exosomes was reversed by overexpression of SIRT6. Furthermore, SIRT6 inhibited Notch signaling via suppressing Notch1. Overexpression of SIRT6 and inhibition of Notch signaling improved cell injury and angiogenesis in OGD/R-treated cells. BMSC-derived exosomal Egr2 ameliorated MCAO/R-induced brain damage via upregulating SIRT6 to suppress Notch signaling in mice. BMSC-derived exosomes ameliorate OGD/R-induced injury and MCAO/R-caused cerebral damage in mice by delivering Egr2 to promote SIRT6 expression and subsequently suppress Notch signaling. Our study provides a potential exosome-based therapy for ischemic stroke.

[miRNA-128-3p inhibits malignant behavior of glioma cells by downregulating KLHDC8A expression].

OBJECTIVE: To determine whether miRNA-128-3p regulates malignant biological behavior of glioma cells by targeting KLHDC8A. METHODS: Dual-luciferase reporter assays, qRT-PCR and Western blotting were used to verify the targeting of miRNA-128-3p to KLHDC8A. Edu assay, flow cytometry, Transwell assay and would healing assay were used to determine the effects of changes in miRNA-128-3p and KLHDC8A expression levels on malignant behavior of glioma cells. Rescue experiment was carried out to verify that miRNA-128-3p regulated glioma cell proliferation, apoptosis, invasion and migration by targeting KLHDC8A. RESULTS: The expression level of KLHDC8A was significantly increased in high-grade glioma tissue and was closely related to a poor survival outcome of the patients. Overexpression of KLHDC8A promoted glioma cell proliferation, migration and invasion, and miRNA-128-3p overexpression inhibited proliferative and metastatic capacities of glioma cells. Mechanistically, KLHDC8A expression was directly modulated by miRNA-128-3p, which, by targeting KLHDC8A, inhibited malignant behavior of glioma cells. CONCLUSION: Upregulation of miRNA-128-3p inhibits uncontrolled growth of glioma cells by negatively regulating KLHDC8A expression and its downstream effectors, suggesting that the miRNA-128-3p-KLHDC8A axis may serve as a potential prognostic indicator and a therapeutic target for developing new strategies for glioma treatment.