Effect of berbamine on invasion and metastasis of human liver cancer SMMC-7721 cells and its possible mechanism.

Berbamine is a bisbenzylisoquinoline alkaloid extracted from Berberis poiretii of Berberis of Berberidaceae. It has been reported that it can significantly inhibit the proliferation of a variety of malignant tumor cells, including liver cancer. However, the effect of berbamine on the invasion and metastasis of liver cancer has not been reported. The present study demonstrated that berbamine inhibited the migration and invasion of SMMC-7721 cells in a concentration-dependent manner and obviously increased the gap junction function and the expression of Cx32 in SMMC-7721 cells compared with control group. However, after silencing Cx32, berbamine had no significant effect on cell invasion and metastasis. Before silencing Cx32, the expression of PI3K and P-AKT were decreased after berbamine treated on SMMC-7721 cells for 24 h. After silencing Cx32, the expression of PI3K and P-AKT were increased in SMMC-7721 cells. The expression of PI3K and P-AKT had no significant effect after berbamine treated on SMMC-7721 cells for 24 h with silencing Cx32. In conclusion, the results of the present study suggest that berbamine could inhibit the SMMC-7721 cell migration and invasion, and its mechanism may be related to the regulation of PI3K/AKT signaling pathway by enhancing the expression of Cx32.

Characterizing functional brain networks via Spatio-Temporal Attention 4D Convolutional Neural Networks (STA-4DCNNs).

Characterizing individualized spatio-temporal patterns of functional brain networks (FBNs) via functional magnetic resonance imaging (fMRI) provides a foundation for understanding complex brain function. Although previous studies have achieved promising performances based on either shallow or deep learning models, there is still much space to improve the accuracy of spatio-temporal pattern characterization of FBNs by optimally integrating the four-dimensional (4D) features of fMRI. In this study, we introduce a novel Spatio-Temporal Attention 4D Convolutional Neural Network (STA-4DCNN) model to characterize individualized spatio-temporal patterns of FBNs. Particularly, STA-4DCNN is composed of two subnetworks, in which the first Spatial Attention 4D CNN (SA-4DCNN) models the spatio-temporal features of 4D fMRI data and then characterizes the spatial pattern of FBNs, and the second Temporal Guided Attention Network (T-GANet) further characterizes the temporal pattern of FBNs under the guidance of the spatial pattern together with 4D fMRI data. We evaluate the proposed STA-4DCNN on seven different task fMRI and one resting state fMRI datasets from the publicly released Human Connectome Project. The experimental results demonstrate that STA-4DCNN has superior ability and generalizability in characterizing individualized spatio-temporal patterns of FBNs when compared to other state-of-the-art models. We further apply STA-4DCNN on another independent ABIDE I resting state fMRI dataset including both autism spectrum disorder (ASD) and typical developing (TD) subjects, and successfully identify abnormal spatio-temporal patterns of FBNs in ASD compared to TD. In general, STA-4DCNN provides a powerful tool for FBN characterization and for clinical applications on brain disease characterization at the individual level.

Echem methods and electrode types of the current in vivo electrochemical sensing.

For a long time, people have been eager to realize continuous real-time online monitoring of biological compounds. Fortunately, in vivo electrochemical biosensor technology has greatly promoted the development of biological compound detection. This article summarizes the existing in vivo electrochemical detection technologies into two categories: microdialysis (MD) and microelectrode (ME). Then we summarized and discussed the electrode surface time, pollution resistance, linearity and the number of instances of simultaneous detection and analysis, the composition and characteristics of the sensor, and finally, we also predicted and prospected the development of electrochemical technology and sensors in vivo.

Adversarial Learning Based Node-Edge Graph Attention Networks for Autism Spectrum Disorder Identification.

Graph neural networks (GNNs) have received increasing interest in the medical imaging field given their powerful graph embedding ability to characterize the non-Euclidean structure of brain networks based on magnetic resonance imaging (MRI) data. However, previous studies are largely node-centralized and ignore edge features for graph classification tasks, resulting in moderate performance of graph classification accuracy. Moreover, the generalizability of GNN model is still far from satisfactory in brain disorder [e.g., autism spectrum disorder (ASD)] identification due to considerable individual differences in symptoms among patients as well as data heterogeneity among different sites. In order to address the above limitations, this study proposes a novel adversarial learning-based node-edge graph attention network (AL-NEGAT) for ASD identification based on multimodal MRI data. First, both node and edge features are modeled based on structural and functional MRI data to leverage complementary brain information and preserved in the constructed weighted adjacent matrix for individuals through the attention mechanism in the proposed NEGAT. Second, two AL methods are employed to improve the generalizability of NEGAT. Finally, a gradient-based saliency map strategy is utilized for model interpretation to identify important brain regions and connections contributing to the classification. Experimental results based on the public Autism Brain Imaging Data Exchange I (ABIDE I) data demonstrate that the proposed framework achieves a classification accuracy of 74.7% between ASD and typical developing (TD) groups based on 1007 subjects across 17 different sites and outperforms the state-of-the-art methods, indicating satisfying classification ability and generalizability of the proposed AL-NEGAT model. Our work provides a powerful tool for brain disorder identification.

Modeling spatio-temporal patterns of holistic functional brain networks via multi-head guided attention graph neural networks (Multi-Head GAGNNs).

Mounting evidence has demonstrated that complex brain function processes are realized by the interaction of holistic functional brain networks which are spatially distributed across specific brain regions in a temporally dynamic fashion. Therefore, modeling spatio-temporal patterns of holistic functional brain networks plays an important role in understanding brain function. Compared to traditional modeling methods such as principal component analysis, independent component analysis, and sparse coding, superior performance has been achieved by recent deep learning methodologies. However, there are still two limitations of existing deep learning approaches for functional brain network modeling. They either (1) merely modeled a single targeted network and ignored holistic ones at one time, or (2) underutilized both spatial and temporal features of fMRI during network modeling, and the spatial/temporal accuracy was thus not warranted. To address these limitations, we proposed a novel Multi-Head Guided Attention Graph Neural Network (Multi-Head GAGNN) to simultaneously model both spatial and temporal patterns of holistic functional brain networks. Specifically, a spatial Multi-Head Attention Graph U-Net was first adopted to model the spatial patterns of multiple brain networks, and a temporal Multi-Head Guided Attention Network was then introduced to model the corresponding temporal patterns under the guidance of modeled spatial patterns. Based on seven task fMRI datasets from the public Human Connectome Project and resting state fMRI datasets from the public Autism Brain Imaging Data Exchange I of 1448 subjects, the proposed Multi-Head GAGNN showed superior ability and generalizability in modeling both spatial and temporal patterns of holistic functional brain networks in individual brains compared to other state-of-the-art (SOTA) models. Furthermore, the modeled spatio-temporal patterns of functional brain networks via the proposed Multi-Head GAGNN can better predict the individual cognitive behavioral measures compared to the other SOTA models. This study provided a novel and powerful tool for brain function modeling as well as for understanding the brain-cognitive behavior associations.

Anatomy-Guided Spatio-Temporal Graph Convolutional Networks (AG-STGCNs) for Modeling Functional Connectivity Between Gyri and Sulci Across Multiple Task Domains.

The cerebral cortex is folded as gyri and sulci, which provide the foundation to unveil anatomo-functional relationship of brain. Previous studies have extensively demonstrated that gyri and sulci exhibit intrinsic functional difference, which is further supported by morphological, genetic, and structural evidences. Therefore, systematically investigating the gyro-sulcal (G-S) functional difference can help deeply understand the functional mechanism of brain. By integrating functional magnetic resonance imaging (fMRI) with advanced deep learning models, recent studies have unveiled the temporal difference in functional activity between gyri and sulci. However, the potential difference of functional connectivity, which represents functional dependency between gyri and sulci, is much unknown. Moreover, the regularity and variability of the G-S functional connectivity difference across multiple task domains remains to be explored. To address the two concerns, this study developed new anatomy-guided spatio-temporal graph convolutional networks (AG-STGCNs) to investigate the regularity and variability of functional connectivity differences between gyri and sulci across multiple task domains. Based on 830 subjects with seven different task-based and one resting state fMRI (rs-fMRI) datasets from the public Human Connectome Project (HCP), we consistently found that there are significant differences of functional connectivity between gyral and sulcal regions within task domains compared with resting state (RS). Furthermore, there is considerable variability of such functional connectivity and information flow between gyri and sulci across different task domains, which are correlated with individual cognitive behaviors. Our study helps better understand the functional segregation of gyri and sulci within task domains as well as the anatomo-functional-behavioral relationship of the human brain.

Proliferation, migration and invasion of triple negative breast cancer cells are suppressed by berbamine via the PI3K/Akt/MDM2/p53 and PI3K/Akt/mTOR signaling pathways.

Breast cancer is the second most common cause of cancer-associated mortality among women worldwide, and triple negative breast cancer (TNBC) is the most aggressive subtype of breast cancer. Berbamine (BBM) is a traditional Chinese medicine used for the treatment of leukopenia without any obvious side effects. Recent reports found that BBM has anti-cancer effects. The present study aimed to investigate the effects of BBM on TNBC cell lines and the underlying molecular mechanism. MDA-MB-231 cells and MCF-7 cells, two TNBC cell lines, were treated with various concentrations of BBM. A series of bioassays including MTT, colony formation, EdU staining, apoptosis, trypan blue dye, wound healing, transwell, ELISA and western blotting assays were performed. The results showed that BBM significantly inhibited cell proliferation of MDA-MB-231 cells (P<0.05; IC50=22.72 µM) and MCF-7 cells (P<0.05; IC50=20.92 µM). BBM (20 µM) decreased the apoptosis ratio (percentage of absorbance compared with the control group) by 28.4±3.3% (P<0.05) in MDA-MB-231 cells, and 62.4±24.6% (P<0.05) in MCF-7 cells. In addition, BBM inhibited cell migration and invasion of TNBC cells. Furthermore, the expression levels of PI3K, phosphorylated-Akt/Akt, COX-2, LOX, MDM2 and mTOR were downregulated by BBM, and the expression of p53 was upregulated by BBM. These results indicated that BBM may suppress the development of TNBC via regulation of the PI3K/Akt/MDM2/p53 and PI3K/Akt/mTOR signal pathways. Therefore, BBM might be used as a drug candidate for the treatment of TNBC in the future.

DNMT1 promotes cell proliferation via methylating hMLH1 and hMSH2 promoters in EGFR-mutated non-small cell lung cancer.

Aberrant DNA methylation is a common form of epigenetic alterations and it has been proved to be closely related to many cancers, while its role in epidermal growth factor receptor (EGFR)-mutated non-small cell lung cancer (NSCLC) is not clear. This study focuses on the role of DNA methyltransferase 1 (DNMT1) in EGFR-mutated NSCLC pathogenesis. First, the expression of DNMT1 was up-regulated, while the expressions of human mutL homolog 1(hMLH1) and human mutS homolog 2 (hMSH2) were down-regulated in EGFR-mutated NSCLC patients and cell line HCC827. The results of the correlation analysis showed that DNMT1 expression was inversely correlated with the expressions of hMLH1 and hMSH2. Then, we found that DNMT1 enhanced the promoter methylation levels of hMLH1 and hMSH2, thus suppressing their expressions. DNMT1 knockdown inhibited the proliferation of HCC827 cells, while both hMLH1 knockdown and hMSH2 knockdown could eliminate its inhibitory effect on cell proliferation. In xenograft mouse models, lentiviral vector-sh-DNMT1 could significantly reduce tumor volumes, confirmed that DNMT1 inhibited tumor cell proliferation in vivo. In conclusion, DNMT1 suppressed the expressions of hMLH1 and hMSH2 via elevating their promoter methylation, thus promoting cell proliferation in EGFR-mutated NSCLC.