EEG-based emotion classification using spiking neural networks

A novel method of using the spiking neural networks (SNNs) and the electroencephalograph (EEG) processing techniques to recognize emotion states is proposed in this paper. Three algorithms including discrete wavelet transform (DWT), variance and fast Fourier transform (FFT) are employed to extract the EEG signals, which are further taken by the SNN for the emotion classification. Two datasets, i.e., DEAP and SEED, are used to validate the proposed method. For the former dataset, the emotional states include arousal, valence, dominance and liking where each state is denoted as either high or low status. For the latter dataset, the emotional states are divided into three categories (negative, positive and neutral). Experimental results show that by using the variance data processing technique and SNN, the emotion states of arousal, valence, dominance and liking can be classified with accuracies of 74%, 78%, 80% and 86.27% for the DEAP dataset, and an overall accuracy is 96.67% for the SEED dataset, which outperform the FFT and DWT processing methods. In the meantime, this work achieves a better emotion classification performance than the benchmarking approaches, and also demonstrates the advantages of using SNN for the emotion state classifications

EEG-based emotion classification using a deep neural network and sparse autoencoder

Emotion classification based on brain–computer interface (BCI) systems is an appealing research topic. Recently, deep learning has been employed for the emotion classifications of BCI systems and compared to traditional classification methods improved results have been obtained. In this paper, a novel deep neural network is proposed for emotion classification using EEG systems, which combines the Convolutional Neural Network (CNN), Sparse Autoencoder (SAE), and Deep Neural Network (DNN) together. In the proposed network, the features extracted by the CNN are first sent to SAE for encoding and decoding. Then the data with reduced redundancy are used as the input features of a DNN for classification task. The public datasets of DEAP and SEED are used for testing. Experimental results show that the proposed network is more effective than conventional CNN methods on the emotion recognitions. For the DEAP dataset, the highest recognition accuracies of 89.49% and 92.86% are achieved for valence and arousal, respectively. For the SEED dataset, however, the best recognition accuracy reaches 96.77%. By combining the CNN, SAE, and DNN and training them separately, the proposed network is shown as an efficient method with a faster convergence than the conventional CNN

Molecular Mechanism Underlying Lymphatic Metastasis in Pancreatic Cancer

As the most challenging human malignancies, pancreatic cancer is characterized by its insidious symptoms, low rate of surgical resection, high risk of local invasion, metastasis and recurrence, and overall dismal prognosis. Lymphatic metastasis, above all, is recognized as an early adverse event in progression of pancreatic cancer and has been described to be an independent poor prognostic factor. It should be noted that the occurrence of lymphatic metastasis is not a casual or stochastic but an ineluctable and designed event. Increasing evidences suggest that metastasis-initiating cells (MICs) and the microenvironments may act as a double-reed style in this crime. However, the exact mechanisms on how they function synergistically for this dismal clinical course remain largely elusive. Therefore, a better understanding of its molecular and cellular mechanisms involved in pancreatic lymphatic metastasis is urgently required. In this review, we will summarize the latest advances on lymphatic metastasis in pancreatic cancer

The TGFβ2‐Snail1‐miRNATGFβ2 Circuitry is Critical for the Development of Aggressive Functions in Breast Cancer

Abstract There have been contradictory reports on the biological role of transforming growth factor‐βs (TGFβs) in breast cancer (BC), especially with regard to their ability to promote epithelial‐mesenchymal transition (EMT). Here, we show that TGFβ2 is preferentially expressed in mesenchymal‐like BCs and maintains the EMT phenotype, correlating with cancer stem cell‐like characteristics, growth, metastasis and chemo‐resistance and predicting worse clinical outcomes. However, this is only true in ERα− BC. In ERα+ luminal‐type BC, estrogen receptor interacts with p‐Smads to block TGFβ signalling. Furthermore, we also identify a microRNAs (miRNAs) signature (miRNAsTGFβ2) that is weakened in TGFβ2‐overexpressing BC cells. We discover that TGFβ2‐Snail1 recruits enhancer of zeste homolog‐2 to convert miRNAsTGFβ2 promoters from an active to repressive chromatin configuration and then repress miRNAsTGFβ2 transcription, forming a negative feedback loop. On the other hand, miRNAsTGFβ2 overexpression reverses the mesenchymal‐like traits in agreement with the inhibition of TGFβ2‐Snail1 signalling in BC cells. These findings clarify the roles of TGFβ2 in BC and suggest novel therapeutic strategies based on the TGFβ2‐Snail1‐miRNAsTGFβ2 loop for a subset type of human BCs

Glutamine deprivation induces ferroptosis in pancreatic cancer cells

Ferroptosis is a type of programmed cell death closely related to amino acid metabolism. Pancreatic cancer cells have a strong dependence on glutamine, which serves as a carbon and nitrogen substrate to sustain rapid growth. Glutamine also aids in self-protection mechanisms. However, the effect of glutamine on ferroptosis in pancreatic cancer remains largely unknown. Here, we aim to explore the association between ferroptosis and glutamine deprivation in pancreatic cancer. The growth of pancreatic cancer cells in culture media with or without glutamine is evaluated using Cell Counting Kit-8. Reactive oxygen species (ROS) are measured by 2′,7′-dichlorodihydrofluorescein diacetate staining. Ferroptosis is assessed by BODIPY-C11 dye using confocal microscopy and flow cytometry. Amino acid concentrations are measured using ultrahigh-performance liquid chromatography-tandem mass spectrometry. Isotope-labelled metabolic flux analysis is performed to track the metabolic flow of glutamine. Additionally, RNA sequencing is performed to analyse the genetic alterations. Glutamine deprivation inhibits pancreatic cancer growth and induces ferroptosis both in vitro and in vivo. Additionally, glutamine decreases ROS formation via glutathione production in pancreatic cancer cells. Interestingly, glutamine inhibitors (diazooxonorleucine and azaserine) promotes ROS formation and ferroptosis in pancreatic cancer cells. Furthermore, ferrostatin, a ferroptosis inhibitor, rescues ferroptosis in pancreatic cancer cells. Glutamine deprivation leads to changes in molecular pathways, including cytokine-cytokine receptor interaction pathways ( CCL5, CCR4, LTA, CXCR4, IL-6R, and IL-7R). Thus, exogenous glutamine is required for the detoxification of ROS in pancreatic cancer cells, thereby preventing ferroptosis

Somatic Genetic Variation in Solid Pseudopapillary Tumor of the Pancreas by Whole Exome Sequencing

Solid pseudopapillary tumor of the pancreas (SPT) is a rare pancreatic disease with a unique clinical manifestation. Although CTNNB1 gene mutations had been universally reported, genetic variation profiles of SPT are largely unidentified. We conducted whole exome sequencing in nine SPT patients to probe the SPT-specific insertions and deletions (indels) and single nucleotide polymorphisms (SNPs). In total, 54 SNPs and 41 indels of prominent variations were demonstrated through parallel exome sequencing. We detected that CTNNB1 mutations presented throughout all patients studied (100%), and a higher count of SNPs was particularly detected in patients with older age, larger tumor, and metastatic disease. By aggregating 95 detected variation events and viewing the interconnections among each of the genes with variations, CTNNB1 was identified as the core portion in the network, which might collaborate with other events such as variations of USP9X, EP400, HTT, MED12, and PKD1 to regulate tumorigenesis. Pathway analysis showed that the events involved in other cancers had the potential to influence the progression of the SNPs count. Our study revealed an insight into the variation of the gene encoding region underlying solid-pseudopapillary neoplasm tumorigenesis. The detection of these variations might partly reflect the potential molecular mechanism

Novel agents for pancreatic ductal adenocarcinoma: emerging therapeutics and future directions

Abstract A poor prognosis of pancreatic ductal adenocarcinoma (PDAC) associated with chemoresistance has not changed for the past three decades. A multidisciplinary diagnosis followed by surgery and chemo(radiation)therapy is the main treatment approach. However, gemcitabine- and 5-fluorouracil-based therapies did not present satisfying outcomes. Novel regimens targeting pancreatic cancer cells, the tumor microenvironment, and immunosuppression are emerging. Biomarkers concerning the treatment outcome and patient selection are being discovered in preclinical or clinical studies. Combination therapies of classic chemotherapeutic drugs and novel agents or novel therapeutic combinations might bring hope to the dismal prognosis for PDAC patients

The Prognostic and Predictive Role of Epidermal Growth Factor Receptor in Surgical Resected Pancreatic Cancer

The data regarding the prognostic significance of EGFR (epidermal growth factor receptor) expression and adjuvant therapy in patients with resected pancreatic cancer are insufficient. We retrospectively investigated EGFR status in 357 resected PDAC (pancreatic duct adenocarcinoma) patients using tissue immunohistochemistry and validated the possible role of EGFR expression in predicting prognosis. The analysis was based on excluding the multiple confounding parameters. A negative association was found between overall EGFR status and postoperative survival (p = 0.986). Remarkably, adjuvant chemotherapy and radiotherapy were significantly associated with favorable postoperative survival, which prolonged median overall survival (OS) for 5.8 and 10.2 months (p = 0.009 and p = 0.006, respectively). Kaplan–Meier analysis showed that adjuvant chemotherapy correlated with an obvious survival benefit in the EGFR-positive subgroup rather than in the EGFR-negative subgroup. In the subgroup analyses, chemotherapy was highly associated with increased postoperative survival in the EGFR-positive subgroup (p = 0.002), and radiotherapy had a significant survival benefit in the EGFR-negative subgroup (p = 0.029). This study demonstrated that EGFR expression is not correlated with outcome in resected pancreatic cancer patients. Adjuvant chemotherapy and radiotherapy were significantly associated with improved survival in contrary EGFR expressing subgroup. Further studies of EGFR as a potential target for pancreatic cancer treatment are warranted