Learning Spatial-Spectral-Temporal EEG Representations with Deep Attentive-Recurrent-Convolutional Neural Networks for Pain Intensity Assessment.

Electroencephalogram (EEG)-based quantitative pain measurement is valuable in the field of clinical pain treatment, providing objective pain intensity assessment especially for nonverbal patients who are unable to self-report. At present, a key challenge in modeling pain events from EEG is to find invariant representations for intra- and inter-subject variations, where current methods based on hand-crafted features cannot provide satisfactory results. Hence, we propose a novel method based on deep learning to learn such invariant representations from multi-channel EEG signals and demonstrate its great advantages in EEG-based pain classification tasks. To begin, instead of using typical EEG analysis techniques that ignore spatial information of EEG, we convert raw EEG signals into a sequence of multi-spectral topography maps (topology-preserving EEG images). Next, inspired by various deep learning techniques applied in neuroimaging domain, a deep Attentive-Recurrent-Convolutional Neural Network (ARCNN) is proposed here to learn spatial-spectral-temporal representations from EEG images. The proposed method aims to jointly preserve the spatial-spectral-temporal structures of EEG, for learning representations with high robustness against intra-subject and inter-subject variations, making it more conducive to multi-class and subject-independent scenarios. Empirical evaluation on 4-level pain intensity assessment within the subject-independent scenario demonstrated significant improvement over baseline and state-of-the-art methods in this field. Our approach applies deep neural networks (DNNs) to pain intensity assessment for the first time and demonstrates its potential advantages in modeling pain events from EEG.

Berberine Inhibits Nod-Like Receptor Family Pyrin Domain Containing 3 Inflammasome Activation and Pyroptosis in Nonalcoholic Steatohepatitis via the ROS/TXNIP Axis.

Berberine (BBR), an isoquinoline alkaloid originating from herbal plants, has been deemed beneficial for non-alcoholic fatty liver disease. Increasing evidence has demonstrated that Nod-like receptor family pyrin domain containing 3 (NLRP3) inflammasome activation and the subsequent pyroptosis contribute to the progression of non-alcoholic steatohepatitis (NASH). However, whether BBR impacts NLRP3 inflammasome activation and pyroptosis in NASH and the potential mechanism remains unclear. In the current study, we found that BBR significantly decreased lipid accumulation, ameliorated reactive oxygen species (ROS) and lipid peroxides, Tumor necrosis factor alpha (TNF-α) expression, and phosphorylation of Nuclear factor kappa B (NF-κB) p65 both in vivo and in vitro. In particular, BBR significantly inhibited NLRP3 expression, caspase-1 activity, and the pyroptosis executor, GSDMD-N, expression. In addition, BBR displayed similar inhibitory effects on NLRP3 inflammasome and pyroptosis with a decrease in ROS levels and TXNIP expression as N-acetyl-cysteine, a ROS scavenger, did. Whereas, the inhibitory effect of BBR on ROS, TXNIP expression, NLRP3 inflammasome activation and pyroptosis could be reversed by H2O2 in AML12 cells. This study demonstrates that BBR's inhibitory effect on NLRP3 inflammasome activation and pyroptosis may be mediated by ROS/TXNIP axis in vitro for the first time. Our findings suggest BBR is a potential candidate for the treatment of NASH.

Berberine, a natural plant alkaloid, synergistically sensitizes human liver cancer cells to sorafenib.

Sorafenib resistance is one of the major factors affecting the prognosis of patients with hepatocellular carcinoma (HCC). Increasing evidence has indicated that certain traditional medicines can enhance the sensitivity of cancer cells to sorafenib. Berberine, an isoquinoline alkaloid, has been demonstrated to possess antitumor properties against various malignancies. However, the synergistic effect of the combination of berberine and sorafenib in HCC remains unknown. The aim of the present study was to determine the effects of berberine and sorafenib combination on the growth of liver cancer cells. Initially, it was observed that the combination of sorafenib and berberine exerted a synergistic inhibitory effect on the proliferation of SMMC­7721 and HepG2 cells in a dose­ and time­dependent manner by an MTS assay. Edu staining and colony formation assays also revealed that the combination of 100 µM berberine and 4 µM sorafenib exhibited a significant anti­proliferation effect on SMMC­7721 and HepG2 cells. Furthermore, western blotting assay indicated that the expressions levels of cleaved poly(ADP­ribose) polymerase and cleaved caspase­3 increased, while those of the anti­apoptotic protein B­cell lymphoma 2 and vascular endothelial growth factor decreased. To the best of our knowledge, this is the first study to demonstrate that berberine sensitized liver cancer cells to sorafenib treatment. These results suggest that berberine combined with sorafenib is able to inhibit the proliferation of liver cancer cells and induce apoptosis, which provides evidence for further clinical investigation in HCC patients with sorafenib resistance.