Cell-based therapy in lung regenerative medicine

Abstract Chronic lung diseases are becoming a leading cause of death worldwide. There are few effective treatments for those patients and less choices to prevent the exacerbation or even reverse the progress of the diseases. Over the past decade, cell-based therapies using stem cells to regenerate lung tissue have experienced a rapid growth in a variety of animal models for distinct lung diseases. This novel approach offers great promise for the treatment of several devastating and incurable lung diseases, including emphysema, idiopathic pulmonary fibrosis, pulmonary hypertension, and the acute respiratory distress syndrome. In this review, we provide a concise summary of the current knowledge on the attributes of endogenous lung epithelial stem/progenitor cells (EpiSPCs), mesenchymal stem cells (MSCs) and endothelial progenitor cells (EPCs) in both animal models and translational studies. We also describe the promise and challenges of tissue bioengineering in lung regenerative medicine. The therapeutic potential of MSCs is further discussed in IPF and chronic obstructive pulmonary diseases (COPD).http://deepblue.lib.umich.edu/bitstream/2027.42/109470/1/40340_2013_Article_11.pd

NCAGC: A Neighborhood Contrast Framework for Attributed Graph Clustering

Attributed graph clustering is one of the most fundamental tasks among graph learning field, the goal of which is to group nodes with similar representations into the same cluster without human annotations. Recent studies based on graph contrastive learning method have achieved remarkable results when exploit graph-structured data. However, most existing methods 1) do not directly address the clustering task, since the representation learning and clustering process are separated; 2) depend too much on data augmentation, which greatly limits the capability of contrastive learning; 3) ignore the contrastive message for clustering tasks, which adversely degenerate the clustering results. In this paper, we propose a Neighborhood Contrast Framework for Attributed Graph Clustering, namely NCAGC, seeking for conquering the aforementioned limitations. Specifically, by leveraging the Neighborhood Contrast Module, the representation of neighbor nodes will be 'push closer' and become clustering-oriented with the neighborhood contrast loss. Moreover, a Contrastive Self-Expression Module is built by minimizing the node representation before and after the self-expression layer to constraint the learning of self-expression matrix. All the modules of NCAGC are optimized in a unified framework, so the learned node representation contains clustering-oriented messages. Extensive experimental results on four attributed graph datasets demonstrate the promising performance of NCAGC compared with 16 state-of-the-art clustering methods. The code is available at https://github.com/wangtong627/NCAGC

Regulatory Effect of Polysaccharides from Antrodia cinnamomea in Submerged Fermentation on Gut Microbiota in Mice with Antibiotic-Associated Diarrhea

In order to study the effect of polysaccharides produced by Antrodia cinnamomea in submerged fermentation on the intestinal flora of mice and, more broadly, to develop the potential and application value of A. cinnamomea in the field of functional food, we extracted and characterized intracellular polysaccharides (AIPS) and exopolysaccharides (AEPS) from the submerged cultured mycelia and broth of Antrodia cinnamomea. It was found that AIPS and AEPS were predominantly composed of glucose, galactose and mannose. Their average molecular masses were 3.52 × 106 and 4.16 × 105 Da, respectively. AEPS contained a pyran ring, while AIPS had (–C≡C–H) and (C–O) functional groups. Both AIPS and AEPS had strong digestive resistance as demonstrated by their resistance to α-amylase digestion and simulated gastric digestion. Intragastrically administered AIPS and AEPS significantly increased the relative abundance of some beneficial microorganisms (such as Lactobacillus) in the intestine of mice with lincomycin-caused diarrhea, and significantly reduced the relative abundance of some harmful microorganisms (such as Enterococcus, Staphylococcus, Parasutterella and Shigella) (P < 0.05), AEPS being more significantly better than AIPS. This study can provide a new idea and basis for the development of new multifunctional prebiotics

Cytotoxicity of Air Pollutant 9,10-Phenanthrenequinone: Role of Reactive Oxygen Species and Redox Signaling

Atmospheric pollution has been a principal topic recently in the scientific and political community due to its role and impact on human and ecological health. 9,10-phenanthrenequinone (9,10-PQ) is a quinone molecule found in air pollution abundantly in the diesel exhaust particles (DEP). This compound has studied extensively and has been shown to develop cytotoxic effects both in vitro and in vivo. 9, 10-PQ has been proposed to play a critical role in the development of cytotoxicity via generation of reactive oxygen species (ROS) through redox cycling. This compound also reduces expression of glutathione (GSH), which is critical in Phase II detoxification reactions. Understanding the underlying cellular mechanisms involved in cytotoxicity can allow for the development of therapeutics designed to target specific molecules significantly involved in the 9,10-PQ-induced ROS toxicity. This review highlights the developments in the understanding of the cytotoxic effects of 9, 10-PQ with special emphasis on the possible mechanisms involved

Selective Sensing and Access Strategy to Maximize Throughput in Cognitive Radio Sensor Network

This paper presents a selective spectrum sensing and access strategy in a cognitive radio sensor network (CRSN), in order to maximize the throughput of secondary user (SU) system. An SU senses multiple channels simultaneously via wideband spectrum sensing. To maximize the throughput and reduce the sensing energy consumption, not all of the channels are sensed. The SU selects some channels for spectrum sensing and accesses these channels based on the sensing results. The unselected channels are accessed directly with low transmission power. A selection making algorithm based on partially observable Markov decision process (POMDP) theory is proposed, to make the SU determine which channels are selected for sensing, how long the sensing time, and the transmission powers of channels. An optimal policy and a myopic policy are proposed to solve the proposed POMDP problem. Moreover, an optimization problem is proposed to solve the synchronism problem among the selected channels. Numerical results show that the proposed selective spectrum sensing and access strategy improves the system performance efficiently