Maternal smoking, consumption of alcohol, and caffeinated beverages during pregnancy and the risk of childhood brain tumors: a meta-analysis of observational studies.

BACKGROUND: We conducted this meta-analysis to investigate the potential association between maternal smoking, alcohol and caffeinated beverages consumption during pregnancy and the risk of childhood brain tumors (CBTs). METHODS: A thorough search was carried out on PubMed, Embase, Web of Science, Cochrane Library, and China National Knowledge Internet to identify pertinent articles. Fixed or random effects model was applied to meta-analyze the data. RESULTS: The results suggested a borderline statistically significant increased risk of CBTs associated with maternal smoking during pregnancy (OR 1.04, 95% CI 0.99-1.09). We found that passive smoking (OR 1.12, 95% CI 1.03-1.20), rather than active smoking (OR 1.00, 95% CI 0.93-1.07), led to an increased risk of CBTs. The results suggested a higher risk in 0-1 year old children (OR 1.21, 95% CI 0.94-1.56), followed by 0-4 years old children (OR 1.12, 95% CI 0.97-1.28) and 5-9 years old children (OR 1.11, 95% CI 0.95-1.29). This meta-analysis found no significant association between maternal alcohol consumption during pregnancy and CBTs risk (OR 1.00, 95% CI 0.80-1.24). An increased risk of CBTs was found to be associated with maternal consumption of caffeinated beverages (OR 1.16, 95% CI 1.07-1.26) during pregnancy, especially coffee (OR 1.18, 95% CI 1.00-1.38). CONCLUSIONS: Maternal passive smoking, consumption of caffeinated beverages during pregnancy should be considered as risk factors for CBTs, especially glioma. More prospective cohort studies are warranted to provide a higher level of evidence.

Motion states identification of underwater glider based on complex networks and graph convolutional networks.

Underwater glider (UG) plays an important role in ocean observation and exploration for a more efficient and deeper understanding of complex ocean environment. Timely identifying the motion states of UG is conducive for timely attitude adjustment and detection of potential anomalies, thereby improving the working reliability of UG. Combining limited penetrable visibility graph (LPVG) and graph convolutional networks (GCN) with self-attention mechanisms, we propose a novel method for motion states identification of UG, which is called as visibility graph and self-attention mechanism-based graph convolutional network (VGSA-GCN). Based on the actual sea trial data of UG, we chose the attitude angle signals of motion states related sensors collected by the control system of UG as the research object and constructed complex networks based on the LPVG method from pitch angle, roll angle, and heading angle data in diving and climbing states. Then, we build a self-attention mechanism-based GCN framework and classify the graphs under different motion states constructed by a complex network. Compared with support vector machines, convolutional neural network, and GCN without self-attention pooling layer, the proposed VGSA-GCN method can more accurately distinguish the diving and climbing states of UG. Subsequently, we analyze the variation of the transitivity coefficient corresponding to these two motion states. The results suggest that the coordination of the various sensors in the attitude adjustment unit during diving becomes closer and more efficient, which corresponds to the higher network measure of the diving state compared to the climbing state.

RGD and Scutellarin Conjugate (WK001) Targeting Platelet Glycoprotein IIb/IIIa Receptor Protects from Myocardial Ischemia/Reperfusion Injury: Synthesis, Characterization, and Bioactivity Evaluation.

Myocardial ischemia/reperfusion (MI/R) injury is an unresolved clinical challenge. The blockade of binding fibrinogen by glycoprotein IIb/IIIa (GPIIb-IIIa) inhibitors has become a new therapeutic approach against MI/R injury. In this study, we modified the RGD structure to combine with scutellarin and synthesized a novel peptide, scutellarin-HomoArg-Gly-Asp-Trp-NH2 (WK001). Herein, reported experimental and docking evidence indicates that WK001 provides immediate and potent platelet inhibition, with stronger inhibition of platelet aggregation than eptifibatide and scutellarin. In particular, it is administered intravenously to prevent thrombus formation and attenuate myocardial fibrosis progression in vivo. Therefore, WK001 could be developed as an antiplatelet drug to treat thrombosis-associated diseases, such as stroke and myocardial infarction.

Diagnosis of Bacterial Plant Diseases via a Nitroreductase-Activated Fluorescent Sensor.

Plant diseases caused by bacteria have become one of the serious problems that threaten human food security, which led to the remarkable reduction of agricultural yields and economic loss. Nitroreductase (NTR), as an important biomarker, is highly expressed in bacteria, and the level of NTR is closely related to the progression of pathogen infection. Therefore, the design of small-molecule fluorescent sensors targeting NTR is of great significance for the detection and diagnosis of plant pathogenic bacteria. In this study, a new fluorescent sensor targeting NTR was discovered and then successfully applied to the imaging of zebrafish and pathogenic bacteria. Most importantly, the developed sensor achieved the real-time diagnosis of Brassica napus L. infected with bacteria, which provides a promising tool for examining the temporal and spatial infection of plant pathogens in precision agriculture.

A Multifrequency Brain Network-Based Deep Learning Framework for Motor Imagery Decoding.

Motor imagery (MI) is an important part of brain-computer interface (BCI) research, which could decode the subject's intention and help remodel the neural system of stroke patients. Therefore, accurate decoding of electroencephalography- (EEG-) based motion imagination has received a lot of attention, especially in the research of rehabilitation training. We propose a novel multifrequency brain network-based deep learning framework for motor imagery decoding. Firstly, a multifrequency brain network is constructed from the multichannel MI-related EEG signals, and each layer corresponds to a specific brain frequency band. The structure of the multifrequency brain network matches the activity profile of the brain properly, which combines the information of channel and multifrequency. The filter bank common spatial pattern (FBCSP) algorithm filters the MI-based EEG signals in the spatial domain to extract features. Further, a multilayer convolutional network model is designed to distinguish different MI tasks accurately, which allows extracting and exploiting the topology in the multifrequency brain network. We use the public BCI competition IV dataset 2a and the public BCI competition III dataset IIIa to evaluate our framework and get state-of-the-art results in the first dataset, i.e., the average accuracy is 83.83% and the value of kappa is 0.784 for the BCI competition IV dataset 2a, and the accuracy is 89.45% and the value of kappa is 0.859 for the BCI competition III dataset IIIa. All these results demonstrate that our framework can classify different MI tasks from multichannel EEG signals effectively and show great potential in the study of remodelling the neural system of stroke patients.

Circ-U2AF1 promotes human glioma via derepressing neuro-oncological ventral antigen 2 by sponging hsa-miR-7-5p.

The prognosis for human glioma, a malignant tumor of the central nervous system, is poor due to its rapid growth, genetic heterogeneity, and inadequate understanding of its underlying molecular mechanisms. Circular RNAs composed of exonic sequences, represent an understudied form of noncoding RNAs (ncRNAs) that was discovered more than a decade ago, function as microRNA sponges. We aimed to assess the relationship between circ-U2AF1 (CircRNA ID: hsa_circ_0061868) and hsa-mir-7-5p and examine their effects on proliferation, apoptosis, and the metastatic phenotype of glioma cells regulated by neuro-oncological ventral antigen 2 (NOVA2). We found that the expression levels of circ-U2AF1 and NOVA2 were upregulated, while hsa-miR-7-5p was downregulated in human glioma tissues and glioma cell lines. Our data and bioinformatic analysis indicated the association of these molecules with glioma grade, a positive correlation between circ-U2AF1 and NOVA2 expression levels and a negative correlation of hsa-miR-7-5p with both circ-U2AF1 and NOVA2, respectively. In addition, silencing of circ-U2AF1 expression resulted in increased hsa-miR-7-5p expression and decreased NOVA2 expression both in vitro and in vivo. Luciferase assay confirmed hsa-miR-7-5p as a direct target of circ-U2AF1 and NOVA2 as a direct target of hsa-miR-7-5p. Functionally, silencing of circ-U2AF1 inhibits glioma development by repressing NOVA2 via upregulating hsa-miR-7-5p both in vitro and in vivo. Thus, we assumed that circ-U2AF1 promotes glioma malignancy via derepressing NOVA2 by sponging hsa-miR-7-5p. Taken together, we suggest that circ-U2AF1 can be a prognostic biomarker and the circ-U2AF1/hsa-miR-7-5p/NOVA2 regulatory pathway may be a novel therapeutic target for treating gliomas.

STAT3 promotes tumour progression in glioma by inducing FOXP1 transcription.

OBJECTIVE: This paper investigated the effects of STAT3 through promoting FOXP1 transcription on proliferation, apoptosis and invasion in glioma cells. METHODS: Quantitative real-time PCR (qRT-PCR) and Western blot assay were administered to assess the mRNA and protein expression levels of STAT3 and FOXP1 in glioma tissues and cells, respectively. Luciferase reporter and Chromatin Immunoprecipitation (ChIP) assays were implemented to determine the correlation between STAT3 and FOXP1. MTT and colony formation assays were conducted to identify cell growth. Flow cytometry was run to detect the cell apoptosis rate of glioma cells. Transwell assays were conducted to reveal cell invasion ability. RESULTS: The mRNA and protein expression levels of STAT3 were highly expressed in glioma tissues and cells. After cells transfected with siRNA of STAT3, both STAT3 and FOXP1 were simultaneously downregulated. STAT3 directly regulated FOXP1 transcription. STAT3 promoted cell proliferation, inhibited cell apoptosis and enhanced cell invasion through promoting FOXP1 transcription in glioma cells. CONCLUSION: In summary, STAT3 gene was a transcriptional regulator of FOXP1. Depleted STAT3 restrained cell proliferation and invasion, promoted cell apoptosis in glioma cells. This molecular mechanism between STAT3 and FOXP1 can serve as a therapeutic target for glioma treatment.

Identification of novel prognostic signature of recurrent low-grade glioma.

The prognosis of patients with recurrent low-grade glioma (rLGG) varies greatly. Some patients can survive more than 10 years after recurrence, while other patients have less than 1 year of survival. In order to identify the related risk factors affecting the prognosis of rLGG patients, we performed a series of bioinformatics analyses on RNA-sequencing data of rLGG based on the CGGA database, and finally constructed a 12-genes prognostic signature, dividing all the rLGG patients into high- and low-risk subgroups. The result showed an excellent predictive effect in both the training cohort and the validation cohort using LASSO-COX regression. Moreover, multivariate COX analysis identified 4 independent prognostic factors of rLGG, and among them, ZCWPW1 is identified as a high-value protective factor. In all, this prognostic model displayed robust predictive capability for the overall survival (OS) of rLGG patients, providing a new monitoring method for rLGG, and the 4 independent prognostic factors, especially ZCWPW1, can be potential targets for rLGG, bringing new possibilities for the treatment of rLGG patients.

Self-supervised contrastive learning for EEG-based cross-subject motor imagery recognition.

Objective. The extensive application of electroencephalography (EEG) in brain-computer interfaces (BCIs) can be attributed to its non-invasive nature and capability to offer high-resolution data. The acquisition of EEG signals is a straightforward process, but the datasets associated with these signals frequently exhibit data scarcity and require substantial resources for proper labeling. Furthermore, there is a significant limitation in the generalization performance of EEG models due to the substantial inter-individual variability observed in EEG signals.Approach. To address these issues, we propose a novel self-supervised contrastive learning framework for decoding motor imagery (MI) signals in cross-subject scenarios. Specifically, we design an encoder combining convolutional neural network and attention mechanism. In the contrastive learning training stage, the network undergoes training with the pretext task of data augmentation to minimize the distance between pairs of homologous transformations while simultaneously maximizing the distance between pairs of heterologous transformations. It enhances the amount of data utilized for training and improves the network's ability to extract deep features from original signals without relying on the true labels of the data.Main results. To evaluate our framework's efficacy, we conduct extensive experiments on three public MI datasets: BCI IV IIa, BCI IV IIb, and HGD datasets. The proposed method achieves cross-subject classification accuracies of 67.32%, 82.34%, and 81.13%on the three datasets, demonstrating superior performance compared to existing methods.Significance. Therefore, this method has great promise for improving the performance of cross-subject transfer learning in MI-based BCI systems.

Co-exposure of decabromodiphenyl ethane and cadmium increases toxicity to earthworms: Enrichment, oxidative stress, damage and molecular binding mechanisms.

The increase of electronic waste worldwide has resulted in the exacerbation of combined decabromodiphenyl ethane (DBDPE) and cadmium (Cd) pollution in soil, posing a serious threat to the safety of soil organisms. However, whether combined exposure increases toxicity remains unclear. Therefore, this study primarily investigated the toxic effects of DBDPE and Cd on earthworms at the individual, tissue, and cellular levels under single and combined exposure. The results showed that the combined exposure significantly increased the enrichment of Cd in earthworms by 50.32-90.42 %. The toxicity to earthworms increased with co-exposure, primarily resulting in enhanced oxidative stress, inhibition of growth and reproduction, intensified intestinal and epidermal damage, and amplified coelomocyte apoptosis. PLS-PM analysis revealed a significant and direct relationship between the accumulation of target pollutants in earthworms and oxidative stress, damage, as well as growth and reproduction of earthworms. Furthermore, IBR analysis indicated that SOD and POD were sensitive biomarkers in earthworms. Molecular docking elucidated that DBDPE and Cd induced oxidative stress responses in earthworms through the alteration of the conformation of the two enzymes. This study enhances understanding of the mechanisms behind the toxicity of combined pollution and provides important insights for assessing e-waste contaminated soils.

Comparison of the neural differentiation potential of human mesenchymal stem cells from amniotic fluid and adult bone marrow.

Human mesenchymal stem cells (MSCs) are considered a promising tool for cell-based therapies of nervous system diseases. Bone marrow (BM) has been the traditional source of MSCs (BM-MSCs). However, there are some limitations for their clinical use, such as the decline in cell number and differentiation potential with age. Recently, amniotic fluid (AF)-derived MSCs (AF-MSCs) have been shown to express embryonic and adult stem cell markers, and can differentiate into cells of all three germ layers. In this study, we isolated AF-MSCs from second-trimester AF by limiting dilution and compared their proliferative capacity, multipotency, neural differentiation ability, and secretion of neurotrophins to those of BM-MSCs. AF-MSCs showed a higher proliferative capacity and more rapidly formed and expanded neurospheres compared to those of BM-MSCs. Both immunocytochemical and quantitative real-time PCR analyses demonstrated that AF-MSCs showed higher expression of neural stemness markers than those of BM-MSCs following neural stem cell (NSC) differentiation. Furthermore, the levels of brain-derived growth factor and nerve growth factor secreted by AF-MSCs in the culture medium were higher than those of BM-MSCs. In addition, AF-MSCs maintained a normal karyotype in long-term cultures after NSC differentiation and were not tumorigenic in vivo. Our findings suggest that AF-MSCs are a promising and safe alternative to BM-MSCs for therapy of nervous system diseases.

Vasculogenic mimicry is a prognostic factor for postoperative survival in patients with glioblastoma.

A previous report has confirmed the existence and clinical significance of vasculogenic mimicry (VM) in glioma. However, its conclusions about the negative clinical significance of VM in glioblastoma are based on a small group of patients and, thus, might be unconvincing. The aim of the present study was to reevaluate the clinical significance of VM in glioblastoma. Patients were classified as VM-positive or VM-negative according to CD34 and periodic acid-Schiff staining. The association between VM and the clinical characteristics of the patients was analyzed. Univariate and multivariate analyses were carried out to identify the independent prognostic factors for overall survival using the Cox regression hazard model. Survival times were estimated using the Kaplan-Meier method and compared using the log-rank test. Of all 86 glioblastomas, 23 were found to have VM. The presence of VM in glioblastoma was not associated with gender, age, Karnofsky performance status, hydrocephalus, tumor burden, microvessel density, tumor relapse, or the extent of tumor resection. The univariate and multivariate analyses revealed that VM is an independent prognostic factor for overall survival. The median survival time for patients with VM was 11.17 months compared with 16.10 months for those without VM (P = 0.017). In addition to VM, an age of 65 years or older, a KPS of 60 or less, a large tumor burden are significant prognostic factors for patient survival. Our data suggest that VM might be an independent adverse prognostic factor in newly diagnosed GBM, further prospective studies are needed to answer this question.

A Brain Network Analysis-Based Double Way Deep Neural Network for Emotion Recognition.

Constructing reliable and effective models to recognize human emotional states has become an important issue in recent years. In this article, we propose a double way deep residual neural network combined with brain network analysis, which enables the classification of multiple emotional states. To begin with, we transform the emotional EEG signals into five frequency bands by wavelet transform and construct brain networks by inter-channel correlation coefficients. These brain networks are then fed into a subsequent deep neural network block which contains several modules with residual connection and enhanced by channel attention mechanism and spatial attention mechanism. In the second way of the model, we feed the emotional EEG signals directly into another deep neural network block to extract temporal features. At the end of the two ways, the features are concatenated for classification. To verify the effectiveness of our proposed model, we carried out a series of experiments to collect emotional EEG from eight subjects. The average accuracy of the proposed model on our emotional dataset is 94.57%. In addition, the evaluation results on public databases SEED and SEED-IV are 94.55% and 78.91%, respectively, demonstrating the superiority of our model in emotion recognition tasks.

EEG-Based Motor Imagery Recognition Framework via Multisubject Dynamic Transfer and Iterative Self-Training.

A robust decoding model that can efficiently deal with the subject and period variation is urgently needed to apply the brain-computer interface (BCI) system. The performance of most electroencephalogram (EEG) decoding models depends on the characteristics of specific subjects and periods, which require calibration and training with annotated data prior to application. However, this situation will become unacceptable as it would be difficult for subjects to collect data for an extended period, especially in the rehabilitation process of disability based on motor imagery (MI). To address this issue, we propose an unsupervised domain adaptation framework called iterative self-training multisubject domain adaptation (ISMDA) that focuses on the offline MI task. First, the feature extractor is purposefully designed to map the EEG to a latent space of discriminative representations. Second, the attention module based on dynamic transfer matches the source domain and target domain samples with a higher coincidence degree in latent space. Then, an independent classifier oriented to the target domain is employed in the first stage of the iterative training process to cluster the samples of the target domain through similarity. Finally, a pseudolabel algorithm based on certainty and confidence is employed in the second stage of the iterative training process to adequately calibrate the error between prediction and empirical probabilities. To evaluate the effectiveness of the model, extensive testing has been performed on three publicly available MI datasets, the BCI IV IIa, the High gamma dataset, and Kwon et al. datasets. The proposed method achieved 69.51%, 82.38%, and 90.98% cross-subject classification accuracy on the three datasets, which outperforms the current state-of-the-art offline algorithms. Meanwhile, all results demonstrated that the proposed method could address the main challenges of the offline MI paradigm.

Toxicity of decabromodiphenyl ethane on lettuce: Evaluation through growth, oxidative defense, microstructure, and metabolism.

Decabromodiphenyl ethane (DBDPE) as the most widely used novel brominated flame retardants (NBFRs), has become a ubiquitous emerging pollutant in the environment. However, its toxic effects on vegetable growth during agricultural production have not been reported. In this study, we investigated the response mechanisms of hydroponic lettuce to DBDPE accumulation, antioxidant stress, cell structure damage, and metabolic pathways after exposure to DBDPE. The concentration of DBDPE in the root of lettuce was significantly higher than that in the aboveground part. DBDPE induced oxidative stress on lettuce, which stimulated the defense of the antioxidative system of lettuce cells, and the cell structure produced slight plasma-wall separation. In terms of metabolism, metabolic pathway disorders were caused, which are mainly manifested as inhibiting amino acid biosynthesis and metabolism-related pathways, interfering with the biosyntheses of amino acids, organic acids, fatty acids, carbohydrates, and other substances, and ultimately manifested as decreased total chlorophyll content and root activity. In turn, metabolic regulation alleviated antioxidant stress. The mechanisms of the antioxidative reaction of lettuce to DBDPE were elucidated by IBR, PLS-PM analysis, and molecular docking. Our results provide a theoretical basis and research necessity for the evaluation of emerging pollutants in agricultural production and the safety of vegetables.

Paradoxical role of ß8 integrin on angiogenesis and vasculogenic mimicry in glioblastoma.

Glioblastoma multiforme (GBM) is the most aggressive and highly vascularized brain tumor with poor prognosis. Endothelial cell-dependent angiogenesis and tumor cell-dependent Vasculogenic mimicry (VM) synergistically contribute to glioma vascularization and progression. However, the mechanism underlying GBM vascularization remains unclear. In this study, GBM stem cells (GSCs) were divided into high and low ß8 integrin (ITGB8) subpopulations. Co-culture assays followed by Cell Counting Kit-8 (CCK-8), migration, Matrigel tube formation, and sprouting assays were conducted to assess the proliferative, migratory and angiogenic capacity of GBM cells and human brain microvascular endothelial cells (hBMECs). An intracranial glioma model was constructed to assess the effect of ITGB8 on tumor vascularization in vivo. Our results indicated that ITGB8 expression was elevated in GSCs and positively associated with stem cell markers in glioma tissues, and could be induced by hypoxia and p38 activation. ITGB8 in GSCs inhibited the angiogenesis of hBMECs in vitro, while it promoted the ability of network formation and expression of VM-related proteins. The orthotopic GBM model showed that ITGB8 contributed to decreased angiogenesis, meanwhile enhanced invasiveness and VM formation. Mechanistic studies indicated that ITGB8-TGFß1 axis modulates VM and epithelial-mesenchymal transition (EMT) process via Smad2/3-RhoA signaling. Together, our findings demonstrated a differential role for ITGB8 in the regulation of angiogenesis and VM formation in GBM, and suggest that pharmacological inhibition of ITGB8 may represent a promising therapeutic strategy for treatment of GBM.

Transfer Learning With Optimal Transportation and Frequency Mixup for EEG-Based Motor Imagery Recognition.

Electroencephalography-based Brain Computer Interfaces (BCIs) invariably have a degenerate performance due to the considerable individual variability. To address this problem, we develop a novel domain adaptation method with optimal transport and frequency mixup for cross-subject transfer learning in motor imagery BCIs. Specifically, the preprocessed EEG signals from source and target domain are mapped into latent space with an embedding module, where the representation distributions and label distributions across domains have a large discrepancy. We assume that there exists a non-linear coupling matrix between both domains, which can be utilized to estimate the distance of joint distributions for different domains. Depending on the optimal transport, the Wasserstein distance between source and target domains is minimized, yielding the alignment of joint distributions. Moreover, a new mixup strategy is also introduced to generalize the model, where the inputs trials are mixed in frequency domain rather than in raw space. The extensive experiments on three evaluation benchmarks are conducted to validate the proposed framework. All the results demonstrate that our method achieves a superior performance than previous state-of-the-art domain adaptation approaches.

Immortalized Mesenchymal Stem Cells: A Safe Cell Source for Cellular or Cell Membrane-Based Treatment of Glioma.

Mesenchymal stem cells (MSCs) have emerged as putative therapeutic tools due to their intrinsic tumor tropism, and anti-tumor and immunoregulatory properties. The limited passage and self-differentiation abilities of MSCs in vitro hinder preclinical studies on them. In this study, we focused on the safety of immortalized mesenchymal stem cells (im-MSCs) and, for the first time, studied the feasibility of im-MSCs as candidates for the treatment of glioma. The im-MSCs were constructed by lentiviral transfection of genes. The proliferative capacity of im-MSCs and the proliferative phenotype of MSCs and MSCs co-cultured with glioma cells (U87) were measured using CCK-8 or EdU assays. After long-term culture, karyotyping of im-MSCs was conducted. The tumorigenicity of engineered MSCs was evaluated using soft agar cloning assays. Next, the engineered cells were injected into the brain of female BALB/c nude mice. Finally, the cell membranes of im-MSCs were labeled with DiO or DiR to detect their ability to be taken up by glioma cells and target in situ gliomas using the IVIS system. Engineered cells retained the immunophenotype of MSC; im-MSCs maintained the ability to differentiate into mesenchymal lineages in vitro; and im-MSCs showed stronger proliferative capacity than unengineered MSCs but without colony formation in soft agar, no tumorigenicity in the brain, and normal chromosomes. MSCs or im-MSCs co-cultured with U87 cells showed enhanced proliferation ability, but did not show malignant characteristics in vitro. Immortalized cells continued to express homing molecules. The cell membranes of im-MSCs were taken up by glioma cells and targeted in situ gliomas in vivo, suggesting that im-MSCs and their plasma membranes can be used as natural drug carriers for targeting gliomas, and providing a safe, adequate, quality-controlled, and continuous source for the treatment of gliomas based on whole-cell or cell membrane carriers.

Prrx1 promotes resistance to temozolomide by upregulating ABCC1 and inducing vasculogenic mimicry in glioma.

Gliomas are the most common primary brain tumors with dismal prognoses. Temozolomide (TMZ), the frontline therapeutic agent for gliomas, has shown limited clinical benefit primarily due to the acquired chemoresistance. Although growing evidence has suggested that the multi-drug resistance phenotype and abnormal vascular microenvironment are responsible for the intrinsic and extrinsic TMZ resistance, the molecular mechanism of TMZ resistance remains to be elucidated. In this study, we found Paired-related homeobox 1 (Prrx1) was an independent prognostic factor for the efficacy of chemotherapy-based postoperative treatment. Silencing Prrx1 markedly enhanced the TMZ-induced cytotoxicity both in vitro and in vivo. We also demonstrated that Prrx1 increased the expression of ABCC1, a member of ATP-Binding Cassette (ABC) transporter protein family, through binding to the promoter region of ABCC1 gene and initiating its transcription. Silencing ABCC1 mitigated the TMZ resistance induced by Prrx1. Furthermore, Prrx1 facilitates the formation of vasculogenic mimicry (VM), a critical extrinsic mechanism for glioma TMZ resistance. Collectively, our findings supported the critical role of Prrx1 in TMZ resistance via intrinsic and extrinsic mechanism. Targeting Prrx1 might represent a feasible strategy to overcome therapeutic resistance in glioma.

Rhythm-Dependent Multilayer Brain Network for the Detection of Driving Fatigue.

Fatigue driving has attracted a great deal of attention for its huge influence on automobile accidents. Recognizing driving fatigue provides a primary but significant way for addressing this problem. In this paper, we first conduct the simulated driving experiments to acquire the EEG signals in alert and fatigue states. Then, for multi-channel EEG signals without pre-processing, a novel rhythm-dependent multilayer brain network (RDMB network) is developed and analyzed for driving fatigue detection. We find that there exists a significant difference between alert and fatigue states from the view of network science. Further, key sub-RDMB network based on closeness centrality are extracted. We calculate six network measures from the key sub-RDMB network and construct feature vectors to classify the alert and fatigue states. The results show that our method can respectively achieve the average accuracy of 95.28% (with sample length of 5 s), 90.25% (2 s), and 87.69% (1 s), significantly higher than compared methods. All these validate the effectiveness of RDMB network for reliable driving fatigue detection via EEG.