Sinomenine Suppresses Development of Hepatocellular Carcinoma Cells via Inhibiting MARCH1 and AMPK/STAT3 Signaling Pathway

Promotion of apoptosis and suppression of proliferation in tumor cells are popular strategies for developing anticancer drugs. Sinomenine (SIN), a plant-derived alkaloid, displays antitumor activity. However, the mechanism of action of SIN against hepatocellular carcinoma (HCC) is unclear. Herein, several molecular technologies, such as Western Blotting, qRT-PCR, flow cytometry, and gene knockdown were applied to explore the role and mechanism of action of SIN in the treatment of HCC. It was found that SIN arrests HCC cell cycle at G0/G1 phase, induces apoptosis, and suppresses proliferation of HCC cells via down-regulating the expression of membrane-associated RING-CH finger protein 1 (MARCH1). Moreover, SIN induces cell death and growth inhibition through AMPK/STAT3 signaling pathway. MARCH1 expression was silenced by siRNA to explore its involvement in the regulation of AMPK/STAT3 signaling pathway. Silencing MARCH1 caused down-regulation of phosphorylation of AMPK, STAT3 and decreased cell viability and function. Our results suggested that SIN inhibits proliferation and promotes apoptosis of HCC cells by MARCH1-mediated AMPK/STAT3 signaling pathway. This study provides new support for SIN as a clinical anticancer drug and illustrates that targeting MARCH1 could be a novel treatment strategy in developing anticancer therapeutics

Multivariate Pattern Classification of Facial Expressions Based on Large-Scale Functional Connectivity

It is an important question how human beings achieve efficient recognition of others’ facial expressions in cognitive neuroscience, and it has been identified that specific cortical regions show preferential activation to facial expressions in previous studies. However, the potential contributions of the connectivity patterns in the processing of facial expressions remained unclear. The present functional magnetic resonance imaging (fMRI) study explored whether facial expressions could be decoded from the functional connectivity (FC) patterns using multivariate pattern analysis combined with machine learning algorithms (fcMVPA). We employed a block design experiment and collected neural activities while participants viewed facial expressions of six basic emotions (anger, disgust, fear, joy, sadness, and surprise). Both static and dynamic expression stimuli were included in our study. A behavioral experiment after scanning confirmed the validity of the facial stimuli presented during the fMRI experiment with classification accuracies and emotional intensities. We obtained whole-brain FC patterns for each facial expression and found that both static and dynamic facial expressions could be successfully decoded from the FC patterns. Moreover, we identified the expression-discriminative networks for the static and dynamic facial expressions, which span beyond the conventional face-selective areas. Overall, these results reveal that large-scale FC patterns may also contain rich expression information to accurately decode facial expressions, suggesting a novel mechanism, which includes general interactions between distributed brain regions, and that contributes to the human facial expression recognition

Application of LC-MS-based metabolomics method in differentiating septic survivors from non-survivors

Septic shock is the most severe form of sepsis, which is still one of the leading causes of death in the intensive care unit (ICU). Even though early prognosis and diagnosis are known to be indispensable for reaching an optimistic outcome, pathogenic complexities and the lack of specific treatment make it difficult to predict the outcome individually. In the present study, serum samples from surviving and non-surviving septic shock patients were drawn before clinical intervention at admission. Metabolic profiles of all the samples were analyzed by liquid chromatography-mass spectrometry (LC-MS)-based metabolomics. One thousand four hundred nineteen peaks in positive mode and 1878 peaks in negative mode were retained with their relative standard deviation (RSD) below 30 %, in which 187 metabolites were initially identified by retention time and database in the light of the exact molecular mass. Differences between samples from the survivors and the non-survivors were investigated using multivariate and univariate analysis. Finally, 43 significantly varied metabolites were found in the comparison between survivors and non-survivors. Concretely, metabolites in the tricarboxylic acid (TCA) cycle, amino acids, and several energy metabolism-related metabolites were up-regulated in the non-survivors, whereas those in the urea cycle and fatty acids were generally down-regulated. Metabolites such as lysine, alanine, and methionine did not present significant changes in the comparison. Six metabolites were further defined as primary discriminators differentiating the survivors from the non-survivors at the early stage of septic shock. Our findings reveal that LC-MS-based metabolomics is a useful tool for studying septic shock

Investigating urban metro stations as cognitive places in cities using points of interest

The significance of urban metro stations extends beyond their roles as transport nodes in a city. Their surroundings are usually well-developed and attract a lot of human activities, which make the metro station areas important cognitive places characterized by vague boundaries and rich semantics. Current studies mainly define metro station areas based on an estimation of walking distance to the stations (e.g., 700 m) and investigate these areas from the perspectives of transportation and land use instead of as cognitive places perceived by the crowd. To fill this gap, this study proposes a novel framework for extracting and understanding the cognitive regions of urban metro stations based on points of interest (POIs). First, we extract the cognitive regions of metro stations based on co-occurrence patterns of the stations and their surrounding POIs on web pages by proposing a cohesive approach combined of spatial clustering, web page extraction, knee-point detection, and polygon generation techniques. Second, we identify the semantics of metro stations based on POI types inside the regions using the term frequency-inverse document frequency (TF-IDF) method. In total 166 metro stations along with more than one million POIs in Shenzhen, China are utilized as data sources of the case study. The results indicate that our proposed framework can well detect the place characteristics of urban metro stations, which enriches the place-based GIS research and provides a human-centric perspective for urban planning and location-based-service (LBS) applications

Effects of motor imagery based brain-computer interface on upper limb function and attention in stroke patients with hemiplegia: a randomized controlled trial

Abstract Background Seeking positive and comprehensive rehabilitation methods after stroke is an urgent problem to be solved, which is very important to improve the dysfunction of stroke. The aim of this study was to investigate the effects of motor imagery-based brain-computer interface training (MI-BCI) on upper limb function and attention in stroke patients with hemiplegia. Methods Sixty stroke patients with impairment of upper extremity function and decreased attention were randomly assigned to the control group (CR group) or the experimental group (BCI group) in a 1:1 ratio. Patients in the CR group received conventional rehabilitation. Patients in the BCI group received 20 min of MI-BCI training five times a week for 3 weeks (15 sessions) in addition to conventional rehabilitation. The primary outcome measures were the changes in Fugl-Meyer Motor Function Assessment of Upper Extremities (FMA-UE) and Attention Network Test (ANT) from baseline to 3 weeks. Results About 93% of the patients completed the allocated training. Compared with the CR group, among those in the BCI group, FMA-UE was increased by 8.0 points (95%CI, 5.0 to 10.0; P < 0.001). Alert network response time (32.4ms; 95%CI, 58.4 to 85.6; P < 0.001), orienting network response (5.6ms; 95%CI, 29.8 to 55.8; P = 0.010), and corrects number (8.0; 95%CI, 17.0 to 28.0; P < 0.001) also increased in the BCI group compared with the CR group. Additionally, the executive control network response time (− 105.9ms; 95%CI, − 68.3 to − 23.6; P = 0.002), the total average response time (− 244.8ms; 95%CI, − 155.8 to − 66.2; P = 0.002), and total time (− 122.0ms; 95%CI, − 80.0 to − 35.0; P = 0.001) were reduced in the BCI group compared with the CR group. Conclusion MI-BCI combined with conventional rehabilitation training could better enhance upper limb motor function and attention in stroke patients. This training method may be feasible and suitable for individuals with stroke. Trial registration : This study was registered in the Chinese Clinical Trial Registry with Portal Number ChiCTR2100050430(27/08/2021)

Intelligent Delay-Aware Partial Computing Task Offloading for Multi-User Industrial Internet of Things through Edge Computing

The development of Industrial Internet of Things (IIoT) and Industry 4.0 has completely changed the traditional manufacturing industry. Intelligent IIoT technology usually involves a large number of intensive computing tasks. Resource-constrained IIoT devices often cannot meet the real-time requirements of these tasks. As a promising paradigm, Mobile Edge Computing (MEC) system migrates the computation intensive tasks from resource-constrained IIoT devices to nearby MEC servers, thereby obtaining lower delay and energy consumption. However, considering the varying channel conditions as well as the distinct delay requirements for various computing tasks, it is challenging to coordinate the computing task offloading among multiple users. In this paper, we propose an autonomous partial offloading system for delay sensitive computation tasks in multi-user IIoT MEC systems. Our goal is to provide offloading services with minimum delay for better Quality of Service (QoS). Enlighten by the recent advancement of Reinforcement Learning (RL), we propose two RL based offloading strategies to automatically optimize the delay performance. Specifically, we first implement Q-learning algorithm to provide a discrete partial offloading decision. Then, to further optimize the system performance with more flexible task offloading, the offloading decisions are given as continuous based on Deep Deterministic Policy Gradient (DDPG). The simulation results show that, the Q-learning scheme reduces the delay by 23%, and the DDPG scheme reduces the delay by 30%

Optimization of large-scale pseudotargeted metabolomics method based on liquid chromatography-mass spectrometry

Liquid chromatography-mass spectrometry (LC-MS) is now a main stream technique for large-scale metabolic phenotyping to obtain a better understanding of genomic functions. However, repeatability is still an essential issue for the LC-MS based methods, and convincing strategies for long time analysis are urgently required. Our former reported pseudotargeted method which combines nontargeted and targeted analyses, is proved to be a practical approach with high-quality and information-rich data. In this study, we developed a comprehensive strategy based on the pseudotargeted analysis by integrating blank-wash, pooled quality control (QC) sample, and post-calibration for the large-scale metabolomics study. The performance of strategy was optimized from both pre- and post-acquisition sections including the selection of QC samples, insertion frequency of QC samples, and post-calibration methods. These results imply that the pseudotargeted method is rather stable and suitable for large-scale study of metabolic profiling. As a proof of concept, the proposed strategy was applied to the combination of 3 independent batches within a time span of 5 weeks, and generated about 54% of the features with coefficient of variations (CV) below 15%. Moreover, the stability and maximal capability of a single analytical batch could be extended to at least 282 injections (about 110 h) while still providing excellent stability, the CV of 63% metabolic features was less than 15%. Taken together, the improved repeatability of our strategy provides a reliable protocol for large-scale metabolomics studies. (C) 2016 Elsevier B.V. All rights reserved

Serum metabolome and targeted bile acid profiling reveals potential novel biomarkers for drug-induced liver injury

This study aims to determine the non-invasive, reliable and sensitive biochemical parameters for the diagnosis of drug-induced liver injury (DILI). Ultra-high performance liquid chromatography tandem mass spectrometry (UHPLC-MS/MS) and selected reaction monitoring (SRM) were used to profile the serum metabolome and quantify 15 targeted bile acid metabolites, respectively, in samples obtained from 38 DILI patients and 30 healthy controls. A comparison of the resulting serum metabolome profiles of the study participants revealed significant differences between DILI patients and healthy controls. Specifically, serum palmitic acid, taurochenodeoxycholic acid, glycocholic acid (GCA), and tauroursodeoxycholic acid (TUDCA) levels were significantly higher, and serum lysophosphatidylethanolamine levels were significantly lower in DILI patients vs healthy controls (P<.001). Furthermore, the SRM assay of bile acids revealed that the increase in GCA, taurocholic acid (TCA), TUDCA, glycochenodeoxycholic acid (GCDCA), glycochenodeoxycholic sulfate (GCDCS), and taurodeoxycholic acid (TDCA) corresponded to a higher degree of liver damage. These results also indicate that serum concentrations of chenodeoxycholic acid (CDCA), deoxycholic acid (DCA) and lithocholic acid (LCA) were significantly lower in patients with severe DILI, when compared to healthy controls, and that this decrease was closely correlated to the severity of liver damage. Taken together, these results demonstrate that bile acids could serve as potential biomarkers for the early diagnosis and severity of DILI