Background Glioblastoma (GBM) is an aggressive cancer with limited therapeutic options. Investigating the mechanisms underlying temozolomide (TMZ) resistance and enhancing its sensitivity remain critical for improving GBM treatment outcomes. Ubiquitin-conjugating enzyme E2S (UBE2S) has been implicated in various cancers; however, its role in TMZ resistance in GBM remains unclear. Methods After UBE2S knockdown, cell viability, apoptosis, and DNA damage were measured in TMZ-treated GBM cells. Immunoprecipitation coupled with mass spectrometry was employed to identify a protein complex involving UBE2S and phosphoglycerate mutase 1 (PGAM1). Co-immunoprecipitation and ubiquitination assays were conducted to examine the interactions among UBE2S, PGAM1, and Otubain-2 (OTUB2). In vivo, a GBM mouse model was used to evaluate the impact of UBE2S knockdown on TMZ efficacy. Results UBE2S was found to be overexpressed in GBM cells, where it interacts with PGAM1 and OTUB2 to inhibit PGAM1 degradation via K48-linked deubiquitylation. This interaction increased PGAM1 protein levels, promoting DNA repair and reducing apoptosis, thereby decreasing the sensitivity of GBM cells to TMZ. Conclusion UBE2S plays a critical role in TMZ resistance by stabilizing PGAM1 protein levels through its interaction with OTUB2. Targeting UBE2S represents a promising therapeutic strategy to enhance TMZ efficacy and overcome chemotherapy resistance in GBM.

IntroductionTemporal lobe epilepsy (TLE) represents a significant neurological disorder with complex genetic underpinnings. This study aimed to develop an interpretable deep learning diagnostic model for TLE and identify disease-associated markers.MethodsUsing RNA-seq and microarray data from 287 samples collected from eight GEO datasets, we constructed multiple machine learning algorithms including Deep Neural Networks (DNN), Extreme Gradient Boosting (XGBoost), Random Forest (RF), Logistic Regression (LR), and K-Nearest Neighbors (KNN) to distinguish TLE from normal. SHapley Additive exPlanations (SHAP) and Kolmogorov-Arnold Networks (KAN) were employed to interpret the model and identify key genes associated with TLE pathogenesis.ResultsAfter comparative analysis, a Deep Neural Network (DNN) model with 10 optimized genetic features achieved perfect diagnostic performance (AUC = 1.000, accuracy = 1.000). SHAP interpretation identified DEPDC5, STXBP1, GABRG2, SLC2A1, and LGI1 as the most significant TLE-associated genes. The KAN model revealed complex nonlinear relationships between these genes and TLE status, providing mathematical expressions that capture their contributions. To facilitate clinical application, we developed an online diagnostic platform that delivers interpretable predictions based on gene expression values.DiscussionThis study advances our understanding of TLE pathogenesis and provides a transparent, interpretable diagnostic model, which combines with traditional diagnostic methods may significantly improve the accuracy of TLE diagnosis, serving as a supplementary tool for clinical assessment.

Advancing the understanding of temporal lobe epilepsy (TLE) requires sophisticated analytical tools. In this study, we introduce a hybrid model, namely the HMM-Wavformer, aiming at identifying the phasic brain activity patterns during seizures on Stereo-electroencephalography (SEEG) records. The model is composed of a wavelet packet decomposition (WPD) based signal processing module, an embedding module for spatial feature extraction, and a Transformer module to weigh the time-series frequency importance. The model is trained with a downstream seizure detection task on the HUP-iEEG dataset, demonstrating an accuracy of 92.75%. Frequency analysis identifies the most sensitive bands in TLE seizure detection. The Hidden Markov Model (HMM) is applied for the time-series analysis, categorizing the seizures into three ictal phases. Complementary analyses using power spectra and brain networks pinpoint biomarkers for each phase. The analysis results indicate that, the HMM-Wavformer model is able to effectively depict the neural dynamics of TLE seizures, aligning with prior medical studies, and provide a more detailed description of the staged characteristics of these seizures.

BACKGROUND:Rapid proliferation is a hallmark of glioblastoma multiforme (GBM) and a major contributor to its recurrence. Aberrant ubiquitination has been implicated in various diseases, including cancer. In our preliminary studies, we identified Ubiquitin-conjugating enzyme E2S (UBE2S) as a potential glioma biomarker, exhibiting close associations with glioma grade and protein phosphatase 1, regulatory subunit 105 (Ki67) expression levels. However, the underlying molecular mechanisms remained elusive. NF-κB is an important signaling pathway that promotes GBM proliferation. Direct intervention targeting NF-κB has not yielded the expected results, prompting the exploration of new molecules for regulating NF-κB as a new direction. METHODS:This study employed methods including yeast two-hybrid and immunoprecipitation to uncover the interaction between UBE2S and A kinase interacting protein 1 (AKIP1). Laser confocal microscopy was used to observe the localization of UBE2S and AKIP1. Dual luciferase reporter genes were utilized to observe the activation of NF-κB. RESULTS:Our findings demonstrate that UBE2S deficiency significantly impedes GBM progression, both in vitro and in vivo. Mechanistically, UBE2S plays a crucial role in recruiting Ubiquitin Specific Peptidase 15 (USP15), facilitating the removal of K11-linked ubiquitination on AKIP1. This action enhances AKIP1 stability within the GBM context. The resulting increase in AKIP1 levels further augments nuclear factor kappa-B (NF-κB) transcriptional activity, leading to the upregulation of downstream genes regulated by the NF-κB pathway, thereby promoting GBM progression. CONCLUSIONS:In summary, our findings reveal the role of the UBE2S/AKIP1-NF-κB axis in regulating GBM progression and provide novel evidence supporting UBE2S as a potential drug target for GBM.

Chimeric antigen receptor (CAR) T cells have shown significant efficacy in hematological diseases. However, CAR T therapy has demonstrated limited efficacy in solid tumors, including glioblastoma (GBM). One of the most important reasons is the immunosuppressive tumor microenvironment (TME), which promotes tumor growth and suppresses immune cells used to eliminate tumor cells. The human transforming growth factor β (TGF-β) plays a crucial role in forming the suppressive GBM TME and driving the suppression of the anti-GBM response. To mitigate TGF-β-mediated suppressive activity, we combined a dominant-negative TGF-β receptor II (dnTGFβRII) with our previous bicistronic CART-EGFR-IL13Rα2 construct, currently being evaluated in a clinical trial, to generate CART-EGFR-IL13Rα2-dnTGFβRII, a tri-modular construct we are developing for clinical application. We hypothesized that this approach would more effectively subvert resistance mechanisms observed with GBM. Our data suggest that CART-EGFR-IL13Rα2-dnTGFβRII significantly augments T cell proliferation, enhances functional responses, and improves the fitness of bystander cells, particularly by decreasing the TGF-β concentration in a TGF-β-rich TME. In addition, in vivo studies validate the safety and efficacy of the dnTGFβRII cooperating with CARs in targeting and eradicating GBM in an NSG mouse model.

The localization of seizure onset zones from long term electrophysiological data is essential of epilepsy surgery, which demands significant effort and clinical knowledge to analysis long term electrophysiological data. In this study we proposed a Graph Neural Network (GNN)-based technique for automated localization of seizure onset zones in prolonged electrophysiological data. Initially, we established a Graph Convolutional Networks (GCN) model for epileptic stereo electroencephalography (SEEG) data classification. Then we used a GNN Explanation method to extract the magnitude of classification contribution from electrodes in the epilepsy SEEG data. The electrodes with most contribution would be considered as part of the seizure onset zone. The results revealed that the proposed method successfully identifies the primary seizure onset zones and estimates the impact of seizure foci on the other zones. This method applies graph neural networks explanation and offers novel perspectives for understanding the mechanisms of epilepsy.

OBJECTIVE:Clinical studies indicated a link between DTI imaging characteristics and epilepsy, but the causality of this connection had not been established. Therefore, we employed the Mendelian randomization analysis method to determine the causal relationship between DTI imaging characteristics and epilepsy. METHOD:We used Mendelian randomization analysis to identify the causal relationship between brain structure and the risk of epilepsy. GWAS data of DTI phenotypes, focal epilepsy, and genetic generalized epilepsy (GGE) were utilized in the analysis. RESULTS:Our study found that DTI imaging phenotypes had a causal risk relationship with epilepsy. These phenotypes had a statistical impact on the risk of epilepsy seizures. There were differences in DTI phenotype causality between GGE and focal epilepsy, which were associated with the clinical phenotype differences of the two types of epilepsy. SIGNIFICANCE:Our study demonstrated that the diagnosis of subtypes could be assisted by comparing the differences in DTI phenotypes of specific brain regions. This meant that by studying the changes in brain regions before the onset of epilepsy, we might be able to intervene in epilepsy at an earlier stage. PLAIN LANGUAGE SUMMARY:Our study used Mendelian randomization to explore the causal relationship between brain structure, as seen in DTI imaging, and epilepsy. We found that specific DTI phenotypes are linked to an increased risk of epilepsy seizures, with notable differences between genetic generalized epilepsy and focal epilepsy. This suggested that analyzing DTI phenotypes could help in diagnosing and potentially intervening in epilepsy earlier by finding brain changes before seizures begin.

BACKGROUND:Temporal lobe epilepsy (TLE) and major depressive disorder (MDD) are prevalent and complex neurological disorders that affect individuals globally. Clinical and epidemiological studies indicate a significant comorbidity between TLE and MDD; however, the shared molecular mechanisms underlying this relationship remain unclear. This study aims to explore the common key genes associated with TLE and MDD through a systematic analysis of gene expression profiles, elucidate their underlying molecular pathological mechanisms, and evaluate the potential applications of these genes in diagnostic and therapeutic contexts. METHODS:Brain tissue gene expression data for TLE and MDD were obtained from the GEO database. Differentially expressed genes (DEGs), weighted gene co-expression network analysis (WGCNA), functional enrichment, and protein-protein interaction (PPI) network construction were performed to identify shared gene modules. LASSO and random forest (RF) machine learning models were used to select diagnostic candidate genes, validated through ROC curve analysis. Immune infiltration analysis explored the immune involvement of key genes, while single-cell sequencing confirmed gene expression across cell types. Potential therapeutic drugs were identified using a drug database. RESULTS:A total of 372 DEGs were identified as either up- or down-regulated between TLE and MDD, with WGCNA revealing nine shared gene modules. Seven hub genes, including HTR7 and CDHR2, demonstrated strong ROC performance. Immune infiltration analysis revealed changes in immune cell populations linked to key genes, confirmed by single-cell sequencing. Upadacitinib was identified as a potential therapeutic drug targeting these genes. CONCLUSION:This study identified shared gene expression profiles between TLE and MDD, emphasizing immune pathway-related molecular mechanisms. Immune infiltration analysis and single-cell sequencing underscored the significance of immune regulation in their comorbidity, while drug prediction highlights candidates for precision medicine, establishing a foundation for future research and therapeutic strategies.

CypA upregulation promotes self-renewal and proliferation of GICs

CypA activates Wnt/β-catenin signaling

Background: Ferroptosis is characterized by accumulation of lipid peroxides that leads to oxidative stress. In progressive rheumatoid arthritis (RA), fibroblast-like synoviocytes (FLS) suffered from oxidative stress induced by generation of excess reactive oxygen species (ROS) and survived from elevated lipid oxidation. However the phenomenon of abnormal synovial fibroblasts proliferation under ferroptotic stress remain to be explained and the effects of this event on disease progression of RA need to be investigated. Methods: FLS from RA patients (RA-FLS) were stimulated with LPS as an inflammatory model in vitro, and simultaneously treated with ferroptosis inducer Erastin/RSL3 or inhibitor ferrostatin-1. Besides, small extracellular vesicles (sEV) from the supernatant of RA-FLS culture under Erastin/RSL3 management were isolated. The degree of ferroptosis in cells were evaluated by Lipid-ROS detection via flowcytometry and ferroptosis marker protein expression determined by western bloting. The expression of core component of ESCRT-III CHMP4A and CHMP5 was determined by western bloting, and knockdown of CHMP4A was further performed to detect the influence of ESCRT-III complex on ferroptosis as well as LPS/Erastin induced sEV (LPS/Erastin-sEV) releasing. Moreover, miR-433-3p level in the isolated sEV was evaluated by RT-qPCR and interaction of miR-4333p with FOXO1/VEGF axis were evaluated. MiR-433-3p was overexpressed in synovial mesenchymal stem cells (SMSCs) via miR-433-3p mimics transfection. RA-FLS was co-cultured with human dermal microvascular endothelial cells (HDMECs). LPS/Erastin-sEV or sEV derived from miR-433-3p-overexpressing SMSCs (miR-4333p-SMSCs-sEV) were added to the co-culture system, and supernatants from co-culture without sEV were given to HDMECs. Angiogenic activity of HDMECs were identified by transwell test and endothelial tube formation analysis. Erastin-sEV and miR-433-3p-SMSCs-sEV were also administrated in collagen-induced arthritis (CIA) mouse model respectively, and progression of arthritis were evaluated. Results: Ferroptosis of RA-FLS was triggered by LPS/Erastin and accompanied with increased expression of ESCRT-III core components as well as elevated release of sEV from RA-FLS. HDMECs' migration and tube formation in vitro was significantly induced/suppressed by supernatants from co-culture under management of Erastin-sEV/miR-433-3p-SMSCs-sEV due to varied VEGF expression regulated by miR-433-3p targeting FOXO1. MiR-433-3p-SMSCs-sEV could inhibit the Erastin-sEV promoted VEGF expression and mitigated arthritis severity. Conclusion: Erastin-sEV could aggravate synovial angiogenesis and promote arthritis progression. Administration of miR-433-3p-SMSCs-sEV may be a potential novel therapeutic method as significant antagonism to Erastin-sEV for RA treatment.

目的 分析1990-2019年我国RA疾病负担变化趋势,为制定有针对性的RA防治策略提供科学依据.方法 基于2019年全球疾病负担研究(GBD 2019)数据,依据患病例数、发病例数,患病率、发病率、伤残调整寿命年(DALY)、DALY率描述1990年—2019年我国与全球RA疾病负担情况.采用Joinpoint模型分析我国与全球1990-2019年RA的年龄标化患病率、年龄标化发病率和年龄标化DALY率的平均年度变化百分比(AAPC),分析RA疾病负担的变化趋势.结果 1990年—2019年我国与全球RA标化患病率总体均呈上升的趋势,我国RA年龄标化患病率平均每年上升0.18％,差异有统计学意义(t=7.34,P=0.025);全球平均每年上升0.27％,差异有统计学意义(t=6.64,P=0.013),1990-2019年我国标化发病率呈上升趋势平均每年上升0.08％,差异有统计学意义(t=7.54,P=0.032),而全球呈下降趋势,平均每年下降0.37％,差异有统计学意义(t=-5.64,P=0.001).2019年我国RA患病例数、患病率分别为430.94万例和302.98/10万,发病例数、发病率分别为22.25万例和15.64/10万,DALY和DALY率分别为77.43万人年和54.44/10万.与1990年相比,2019年患病例数、患病率分别上升了114.17％和78.23％,发病例数、发病率分别上升了77.90％和48.05％,DALY和DALY率上升了109.05％和73.97％.1990-2019年女性患病率、发病率和DALY率均高于男性,患病率和DALY率均随年龄的增长呈上升趋势,在75岁及以上年龄组达最高.结论 1990-2019年我国RA标化发病率上升幅度高于全球,且我国发病、患病情况总体均呈上升趋势.我国RA患病、发病以及DALY情况均存在人群差异,女性和中老年人群为高危人群.

Clinicopathological characteristics

CSA inhibits Wnt/β-catenin signaling activity and GICs self-renewal

CypA overexpression enhances GIC radio-resistance

Suppl Materials and Figures

Supplementary methods and figure legends

Inverters having high voltage levels, high power density, and high integration are widely used. However, many high-frequency switch units also increase the probability of failure. Therefore, developing an accurate and stable fault diagnosis method is necessary. This paper proposes a fault diagnosis algorithm based on deep learning and the evidence reasoning (ER) rule. It not only ensures high diagnostic accuracy, but also enhances the stability of the diagnostic results. The algorithm takes the three-phase voltage source inverter as the research object and extracts the three-phase current signals with different types of faults as features. First, Convolutional and Deep Neural Network methods were utilized independently to determine a preliminary diagnosis. Second, the softmax functions of the Convolutional and Deep Neural Network outputs provided the probability distribution of the fault category, which was used as the evidence body for the ER rule to construct the fusion diagnosis. In addition, a new method of determining the reliability and the importance factors of the evidence was proposed in which the evaluation index of the deep-learning diagnosis result was applied. Finally, the final classification result was obtained using the ER rule. The proposed method can effectively enhance the accuracy and robustness compared with a single classifier.

目的 针对某车载油箱高周疲劳寿命难以预测问题,研究该设备在随机载荷环境下的疲劳寿命.方法 首先通过模态试验得到油箱固有频率及振型,然后利用Solidworks建立该车载油箱的仿真模型,在ANSYS Workbench软件中进行模态分析、随机振动分析、谐响应分析.最后利用ANSYS Workbench软件中的nCode SN Vibration(DesignLife)模块,在随机振动疲劳寿命频域分析法基础上,通过nCode模块中的Narrowband法进行油箱在多个加速度功率谱密度下的疲劳寿命研究.结果 该油箱在约束模态试验和仿真分析下所表现的动力学特性基本相同,油箱纵向为振动严酷方向.在已知加速度功率谱密度下,油箱疲劳寿命随低阶固有频率处功率谱密度幅值的增加而降低,但油箱薄弱部位始终保持不变.结论 建立的仿真模型准确,可为油箱优化设计及后续油箱疲劳试验提供参考.

目的 环状RNA(circRNA)是人类癌症的关键调节因子,circRNA在非小细胞肺癌(NSCLC)中的临床价值和功能研究非常有限.文章旨在评估hsa_circ_0007534对NSCLC细胞增殖、迁移和侵袭的影响及其作用机制.方法 将A549细胞接种于12孔板中(1×105个/孔),待细胞生长融合度约70％时,将细胞依次分为:NC组(不做任何处理)、NC-siRNA组(转染NC-siRNA)、hsa_circ_0007534-siRNA-3组(转染hsa_circ_0007534-siRNA-3)、hsa_circ_0007534-siRNA-3+NC-inhibitor组(转染hsa_circ_0007534-siRNA-3和NC-inhibitor)及hsa_circ_0007534-siRNA-3+miR-498-inhibitor组(转染hsa_circ_0007534-siRNA-3和miR-498-inhibitor).qRT-PCR检测中hsa_circ_0007534、miR-498的相对表达水平,CCK-8和Transwell实验检测细胞增殖、迁移和侵袭能力,免疫印迹(Western blot)检测Ki-67、E-cadherin及N-cadherin蛋白水平.裸鼠异种移植肿瘤记录肿瘤组织的重量和体积,免疫组化检测肿瘤组织中Ki-67、E-cadherin及N-cadherin的表达.结果 hsa_circ_0007534与miR-498存在靶向结合位点.与hsa_circ_0007534-WT+NC-mimics组细胞双荧光素酶活性(1.02±0.10)相比,hsa_circ_0007534-WT+miR-498-mimics组(0.46±0.08)显著降低(P<0.01).与NC-siRNA组相比,hsa_circ_0007534-siRNA-3组A549细胞中miR-498相对表达水平、E-cadherin阳性细胞率增高(P<0.05),细胞增殖活力及集落形成数量、迁移及侵袭数量、Ki-67和N-Cadherin蛋白水平、肿瘤组织重量和体积、Ki-67与N-Cadherin阳性细胞率均显著降低(P<0.05);与hsa_circ_0007534-siRNA-3+NC-inhibitor组相比,hsa_circ_0007534-siRNA-3+miR-498-inhibitor组A549细胞中miR-498相对表达水平、E-cadherin阳性细胞率降低(P<0.05),细胞增殖活力及集落形成数量、迁移及侵袭数量、Ki-67和N-Cadherin蛋白水平、肿瘤组织重量和体积、Ki-67与N-Cad-herin阳性细胞率均显著升高(P<0.05).结论 下调hsa_circ_0007534通过靶向上调miR-498抑制NSCLC细胞增殖、迁移和侵袭.