Low Dose Theophylline Showed an Inhibitory Effect on the Production of IL-6 and IL-8 in Primary Lung Fibroblast from Patients with COPD

Chronic obstructive pulmonary disease (COPD) is characterized by the abnormal and chronic lung inflammation. We hypothesized that lung fibroblasts could contribute to the local inflammation and investigated whether low dose theophylline had a beneficial effect on fibroblasts inflammation. Subjects undergoing lobectomy for bronchial carcinoma were enrolled and divided into COPD and control groups according to spirometry. Primary human lung fibroblasts were cultured from peripheral lung tissue distant to tumor tissue. There was a significant increase in both the mRNA expressions and protein levels for IL-6 and IL-8 in fibroblasts in COPD group, and the values were negatively correlated with lung function (P < 0.05). For COPD fibroblasts, the protein levels of IL-6 and IL-8 decreased from 993.0 ± 738.9 pg/mL to 650.1 ± 421.9 pg/mL (P = 0.014) and from 703.1 ± 278.0 pg/mL to 492.0 ± 214.9 pg/mL (P = 0.001), respectively, with 5 μg/mL theophylline treatment. In addition, theophylline at the dose of 5 μg/mL reduced the increased production of IL-6 and IL-8 induced by 1 μg/mL LPS in primary human lung fibroblasts. Our data suggest that lung fibroblasts participate in the chronic inflammation in COPD by releasing IL-6 and IL-8, and low dose theophylline can alleviate the proinflammatory mediators' production by fibroblasts

An Attention-based Collaboration Framework for Multi-View Network Representation Learning

Learning distributed node representations in networks has been attracting increasing attention recently due to its effectiveness in a variety of applications. Existing approaches usually study networks with a single type of proximity between nodes, which defines a single view of a network. However, in reality there usually exists multiple types of proximities between nodes, yielding networks with multiple views. This paper studies learning node representations for networks with multiple views, which aims to infer robust node representations across different views. We propose a multi-view representation learning approach, which promotes the collaboration of different views and lets them vote for the robust representations. During the voting process, an attention mechanism is introduced, which enables each node to focus on the most informative views. Experimental results on real-world networks show that the proposed approach outperforms existing state-of-the-art approaches for network representation learning with a single view and other competitive approaches with multiple views.Comment: CIKM 201