microRNA-29b prevents liver fibrosis by attenuating hepatic stellate cell activation and inducing apoptosis through targeting PI3K/AKT pathway

microRNA-29b (miR-29b) is known to be associated with TGF-β-mediated fibrosis, but the mechanistic action of miR-29b in liver fibrosis remains unclear and is warranted for investigation. We found that miR-29b was significantly downregulated in human and mice fibrotic liver tissues and in primary activated HSCs. miR-29b downregulation was directly mediated by Smad3 through binding to the promoter of miR-29b in hepatic stellate cell (HSC) line LX1, whilst miR-29b could in turn suppress Smad3 expression. miR-29b transduction in the liver of mice prevented CCl4 induced-fibrogenesis, concomitant with decreased expression of α-SMA, collagen I and TIMP-1. Ectopic expression of miR-29b in activated HSCs (LX-1, HSC-T6) inhibited cell viability and colony formation, and caused cell cycle arrest in G1 phase by downregulating cyclin D1 and p21cip1. Further, miR-29b induced apoptosis in HSCs mediated by caspase-9 and PARP. miR-29b inhibited its downstream effectors of PIK3R1 and AKT3 through direct targeting their 3'UTR regions. Moreover, knockdown of PIK3R1 or AKT3 suppressed α-SMA and collagen I and induced apoptosis in both HSCs and in mice. In conclusion, miR-29b prevents liver fibrogenesis by inhibiting HSC activation and inducing HSC apoptosis through inhibiting PI3K/AKT pathway. These results provide novel mechanistic insights for the anti-fibrotic effect of miR-29b.published_or_final_versio

Comparative Study of Inference Methods for Bayesian Nonnegative Matrix Factorisation

In this paper, we study the trade-offs of different inference approaches for Bayesian matrix factorisation methods, which are commonly used for predicting missing values, and for finding patterns in the data. In particular, we consider Bayesian nonnegative variants of matrix factorisation and tri-factorisation, and compare non-probabilistic inference, Gibbs sampling, variational Bayesian inference, and a maximum-a-posteriori approach. The variational approach is new for the Bayesian nonnegative models. We compare their convergence, and robustness to noise and sparsity of the data, on both synthetic and real-world datasets. Furthermore, we extend the models with the Bayesian automatic relevance determination prior, allowing the models to perform automatic model selection, and demonstrate its efficiency

Systematic investigation of gastrointestinal diseases in China (SILC): validation of survey methodology

Background: Symptom-based surveys suggest that the prevalence of gastrointestinal diseases is lower in China than in Western countries. The aim of this study was to validate a methodology for the epidemiological investigation of gastrointestinal symptoms and endoscopic findings in China. Methods: A randomized, stratified, multi-stage sampling methodology was used to select 18 000 adults aged 18-80 years from Shanghai, Beijing, Xi'an, Wuhan and Guangzhou. Participants from Shanghai were invited to provide blood samples and undergo upper gastrointestinal endoscopy. All participants completed Chinese versions of the Reflux Disease Questionnaire (RDQ) and the modified Rome II questionnaire; 20% were also invited to complete the 36-item Short Form Health Survey (SF-36) and Epworth Sleepiness Scale (ESS). The psychometric properties of the questionnaires were evaluated statistically. Results: The study was completed by 16 091 individuals (response rate: 89.4%), with 3219 (89.4% of those invited) completing the SF-36 and ESS. All 3153 participants in Shanghai provided blood samples and 1030 (32.7%) underwent endoscopy. Cronbach's alpha coefficients were 0.89, 0.89, 0.80 and 0.91, respectively, for the RDQ, modified Rome II questionnaire, ESS and SF-36, supporting internal consistency. Factor analysis supported construct validity of all questionnaire dimensions except SF-36 psychosocial dimensions. Conclusion: This population-based study has great potential to characterize the relationship between gastrointestinal symptoms and endoscopic findings in China.Xiaoyan Yan, Rui Wang, Yanfang Zhao, Xiuqiang Ma, Jiqian Fang, Hong Yan, Xiaoping Kang, Ping Yin, Yuantao Hao, Qiang Li, John Dent, Joseph Sung, Duowu Zou, Saga Johansson, Katarina Halling, Wenbin Liu and Jia H

Activation of Human Monocytes by Live Borrelia burgdorferi Generates TLR2-Dependent and -Independent Responses Which Include Induction of IFN-β

It is widely believed that innate immune responses to Borrelia burgdorferi (Bb) are primarily triggered by the spirochete's outer membrane lipoproteins signaling through cell surface TLR1/2. We recently challenged this notion by demonstrating that phagocytosis of live Bb by peripheral blood mononuclear cells (PBMCs) elicited greater production of proinflammatory cytokines than did equivalent bacterial lysates. Using whole genome microarrays, we show herein that, compared to lysates, live spirochetes elicited a more intense and much broader transcriptional response involving genes associated with diverse cellular processes; among these were IFN-β and a number of interferon-stimulated genes (ISGs), which are not known to result from TLR2 signaling. Using isolated monocytes, we demonstrated that cell activation signals elicited by live Bb result from cell surface interactions and uptake and degradation of organisms within phagosomes. As with PBCMs, live Bb induced markedly greater transcription and secretion of TNF-α, IL-6, IL-10 and IL-1β in monocytes than did lysates. Secreted IL-18, which, like IL-1β, also requires cleavage by activated caspase-1, was generated only in response to live Bb. Pro-inflammatory cytokine production by TLR2-deficient murine macrophages was only moderately diminished in response to live Bb but was drastically impaired against lysates; TLR2 deficiency had no significant effect on uptake and degradation of spirochetes. As with PBMCs, live Bb was a much more potent inducer of IFN-β and ISGs in isolated monocytes than were lysates or a synthetic TLR2 agonist. Collectively, our results indicate that the enhanced innate immune responses of monocytes following phagocytosis of live Bb have both TLR2-dependent and -independent components and that the latter induce transcription of type I IFNs and ISGs

Optimizing multivariate performance measures from multi-view data

© 2016, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. To date, many machine learning applications have multiple views of features, and different applications require specific multivariate performance measures, such as the F-score for retrieval. However, existing multivariate performance measure optimization methods are limited to single-view data, while traditional multi-view learning methods cannot optimize multivariate performance measures directly. To fill this gap, in this paper, we propose the problem of optimizing multivariate performance measures from multi-view data, and an effective method to solve it. We propose to learn linear discriminant functions for different views, and combine them to construct an overall multivariate mapping function for multiview data. To learn the parameters of the linear discriminant functions of different views to optimize a given multivariate performance measure, we formulate an optimization problem. In this problem, we propose to minimize the complexity of the linear discriminant function of each view, promote the consistency of the responses of different views over the same data points, and minimize the upper boundary of the corresponding loss of a given multivariate performance measure. To optimize this problem, we develop an iterative cuttingplane algorithm. Experiments on four benchmark data sets show that it not only outperforms traditional single-view based multivariate performance optimization methods, but also achieves better results than ordinary multi-view learning methods

Multi-instance dictionary learning via multivariate performance measure optimization

© 2017 Elsevier Ltd The multi-instance dictionary plays a critical role in multi-instance data representation. Meanwhile, different multi-instance learning applications are evaluated by specific multivariate performance measures. For example, multi-instance ranking reports the precision and recall. It is not difficult to see that to obtain different optimal performance measures, different dictionaries are needed. This observation motives us to learn performance-optimal dictionaries for this problem. In this paper, we propose a novel joint framework for learning the multi-instance dictionary and the classifier to optimize a given multivariate performance measure, such as the F1 score and precision at rank k. We propose to represent the bags as bag-level features via the bag-instance similarity, and learn a classifier in the bag-level feature space to optimize the given performance measure. We propose to minimize the upper bound of a multivariate loss corresponding to the performance measure, the complexity of the classifier, and the complexity of the dictionary, simultaneously, with regard to both the dictionary and the classifier parameters. In this way, the dictionary learning is regularized by the performance optimization, and a performance-optimal dictionary is obtained. We develop an iterative algorithm to solve this minimization problem efficiently using a cutting-plane algorithm and a coordinate descent method. Experiments on multi-instance benchmark data sets show its advantage over both traditional multi-instance learning and performance optimization methods

The effects and mechanism of saponins of panax notoginseng on glucose metabolism in 3T3-L1 cells

This study was carried out to determine the effect of saponins of Panax notoginseng (SPN), a naturally occurring cardiovascular agent, on: (1) glucose uptake, (2) GLUT4 translocation and (3) glycogen synthesis in 3T3-L1 adipocytes. Electrospray ionization-Mass spectrometry (ESI-MS) was used to determine the structural characterization of the major active components of SPN. 3T3-L1 adipocytes were cultured and treated with 100 nM insulin alone or with 10, 50 and 100 μg/ml of SPN. [3H]2-deoxyglucose glucose uptake, GLUT4 immunofluorescence imaging and glycogen synthesis assay were carried out to determine the effects of SPN on glucose metabolism. Under insulin stimulation, SPN significantly increased glucose uptake in a dose-dependent manner; 50 μg/ml of SPN increased glucose uptake by 64% (p < 0.001). Immunofluorescence imaging and analysis have revealed that 50 and 100 μg/ml of SPN increased GLUT4 in the plasma membrane by 3-fold and 6-fold respectively (p < 0.001). Furthermore, the incorporation of D-[U-14C] glucose into glycogen was enhanced by 53% in 3T3-L1 cells treated with 100 μg/ml of SPN (p < 0.01 vs. insulin stimulation alone). SPN, a naturally occurring agent used to treat ischemic cardio-cerebral vascular disease in China, enhanced insulin-stimulated glucose uptake and glycogen synthesis in adipocytes. The results of this study indicate that SPN may have a therapeutic potential for hyperglycaemia in type 2 diabetes. © 2009 World Scientific Publishing Company & Institute for Advanced Research in Asian Science and Medicine