Cellular communication can be mediated by the exchange of biological information, mainly in the form of proteins and RNAs. This can occur when extracellular vesicles, such as exosomes, secreted by a ...donor cell are internalized by an acceptor cell. Exosomes bear specific repertoires of proteins and RNAs, indicating the existence of mechanisms that control the sorting of molecules into them. Knowledge about loadings and processes and mechanisms of cargo sorting of exosomes is essential to shed light on the physiological and pathological functions of these vesicles as well as on clinical applications involving their use and/or analysis. In this review, we will discuss the molecular mechanisms associated with exosome secretion and their specific cargo sorting, with special attention to the sorting of RNAs and proteins, and thus the outcome and the emerging therapeutic opportunities of the communication between the exosome-producer and recipient cells.
Breast cancer incidence and mortality rates have been consistently high among women. The use of diverse therapeutic strategies, including chemotherapy, endocrine therapy, targeted therapy, and ...immunotherapy, has improved breast cancer prognosis. However, drug resistance has become a tremendous obstacle in overcoming breast cancer recurrence and metastasis. It is known that mitochondria play an important role in carcinoma cell growth, invasion and apoptosis. Recent studies have explored the involvement of mitochondrial metabolism in breast cancer prognosis. Here, we will provide an overview of studies that investigated mitochondrial metabolism pathways in breast cancer treatment resistance, and discuss the application prospects of agents targeting mitochondrial pathways against drug-resistant breast cancer.
Cesium carbonate-catalyzed oxidation of substituted phenylsilanes (ArSiH3) in N,N-dimethylformamide (DMF) at room temperature for the efficient synthesis of polyhedral oligomeric silsesquioxanes ...(POSS) was described. This protocol allowed the rapid and selective access to several types of new POSS cages in modest to good yields under nonaqueous conditions. Depending on the bulkiness of the substituents on the phenyl rings, hexa- (T6), octa- (T8), and dodecaphenylsilsesquioxanes (T12) can be selectively obtained. With the more bulky 2-(2′,4′,6′-trimethylphenyl)phenyl group, the cyclic tetrasiloxane (D4) bearing four hydroxyl groups was isolated. Mechanism studies disclosed that the initial step involved the Cs2CO3-catalyzed hydrosilylation of DMF with a hydrosilane to generate a siloxymethylamine intermediate followed by the dehydrocarbonative cross-coupling of the hydrosilane with the siloxymethylamine.
Effective Pattern Discovery for Text Mining Zhong, Ning; Li, Yuefeng; Wu, Sheng-Tang
IEEE transactions on knowledge and data engineering,
2012-Jan., 2012-01-00, 20120101, Letnik:
24, Številka:
1
Journal Article
Recenzirano
Odprti dostop
Many data mining techniques have been proposed for mining useful patterns in text documents. However, how to effectively use and update discovered patterns is still an open research issue, especially ...in the domain of text mining. Since most existing text mining methods adopted term-based approaches, they all suffer from the problems of polysemy and synonymy. Over the years, people have often held the hypothesis that pattern (or phrase)-based approaches should perform better than the term-based ones, but many experiments do not support this hypothesis. This paper presents an innovative and effective pattern discovery technique which includes the processes of pattern deploying and pattern evolving, to improve the effectiveness of using and updating discovered patterns for finding relevant and interesting information. Substantial experiments on RCV1 data collection and TREC topics demonstrate that the proposed solution achieves encouraging performance.
How exosomal microRNAs (miRNAs) derived from macrophages contribute to the development of drug resistance in the context of the hypoxic tumor microenvironment in epithelial ovarian cancer (EOC) ...remains poorly understood.
The miRNA levels were detected by qRT-PCR. Protein levels of HIF-1α, CD163 and PTEN-PI3K/AKT pathway were assessed by Western blot (WB) and Immunohistochemistry (IHC). Exosomes were isolated, and then confirmed by Transmission electron microscopy (TEM), Nanoparticle Tracking Analysis (NTA) and WB. Internalization of macrophages-secreted exosomes in EOC cells was detected by Confocal microscope. Subsequently, Dual-luciferase reporter assay verified PTEN was the target of miR-223. Gain- and loss-of-function experiments, rescue experiments, and SKOV3 xenograft models were performed to uncover the underlying mechanisms of miR-223 and PTEN-PI3K/AKT pathway, as well as the exosomal miR-223 in inducing multidrug resistance in vitro and in vivo.
Here, we showed hypoxic EOC cells triggered macrophages recruitment and induced macrophages into a tumor-associated macrophage (TAM)-like phenotype; exosomes derived from hypoxic macrophages enhanced the malignant phenotype of EOC cells, miR-223 was enriched in exosomes released from macrophages under hypoxia, which could be transferred to the co-cultivated EOC cells, accompanied by enhanced drug resistant of EOC cells. Besides, results from a functional assay revealed that exosomal miR-223 derived from macrophages promoted the drug resistance of EOC cells via the PTEN-PI3K/AKT pathway both in vivo and in vitro. Furthermore, patients with high HIF-1a expression had statistically higher CD163+ cell infiltration and intertumoral levels of miR-223. Finally, circulating exosomal miR-223 levels were closely related to the recurrence of EOC.
These data indicate a unique role of exosomal miR-223 in the cross-talk between macrophages and EOC cells in chemotherapy resistance, through a novel exosomal miR-223/PTEN-PI3K/AKT signaling pathway.
Predicting links in complex networks has been one of the essential topics within the realm of data mining and science discovery over the past few years. This problem remains an attempt to identify ...future, deleted, and redundant links using the existing links in a graph. Local random walk is considered to be one of the most well-known algorithms in the category of quasi-local methods. It traverses the network using the traditional random walk with a limited number of steps, randomly selecting one adjacent node in each step among the nodes which have equal importance. Then this method uses the transition probability between node pairs to calculate the similarity between them. However, in most datasets this method is not able to perform accurately in scoring remarkably similar nodes. In the present article, an efficient method is proposed for improving local random walk by encouraging random walk to move, in every step, towards the node which has a stronger influence. Therefore, the next node is selected according to the influence of the source node. To do so, using mutual information, the concept of the asymmetric mutual influence of nodes is presented. A comparison between the proposed method and other similarity-based methods (local, quasi-local, and global) has been performed, and results have been reported for 11 real-world networks. It had a higher prediction accuracy compared with other link prediction approaches.
Relevance Feature Discovery for Text Mining Yuefeng Li; Algarni, Abdulmohsen; Albathan, Mubarak ...
IEEE transactions on knowledge and data engineering,
2015-June-1, 2015-6-1, 20150601, Letnik:
27, Številka:
6
Journal Article
Recenzirano
It is a big challenge to guarantee the quality of discovered relevance features in text documents for describing user preferences because of large scale terms and data patterns. Most existing popular ...text mining and classification methods have adopted term-based approaches. However, they have all suffered from the problems of polysemy and synonymy. Over the years, there has been often held the hypothesis that pattern-based methods should perform better than term-based ones in describing user preferences; yet, how to effectively use large scale patterns remains a hard problem in text mining. To make a breakthrough in this challenging issue, this paper presents an innovative model for relevance feature discovery. It discovers both positive and negative patterns in text documents as higher level features and deploys them over low-level features (terms). It also classifies terms into categories and updates term weights based on their specificity and their distributions in patterns. Substantial experiments using this model on RCV1, TREC topics and Reuters-21578 show that the proposed model significantly outperforms both the state-of-the-art term-based methods and the pattern based methods.
► Ectopic expression of miR-137 in lung cancer cells downregulates Cdc42, Cdk6 and induced G1 cell cycle arrest. ► Cdc42 and Cdk6 are targets of miR-137. ► Downregulation of miR-137 in lung cancer ...cells can be rescued following inhibition of DNA methylation.
MicroRNAs (miRNA) have emerged as key players in carcinogenesis. Here, we investigated the role of miR-137 in the pathogenesis of lung cancer. The downregulation of miR-137 in lung cancer cells could be rescued following inhibition of DNA methylation. Ectopic expression of miR-137 in lung cancer cells significantly downregulated Cdc42, Cdk6 and induced G1 cell cycle arrest, leading to a significant decrease in cell growth in vivo and in vitro. Further, both Cdc42 and Cdk6 were confirmed as targets of miR-137.
The diffusion method is one of the main methods of community detection in complex networks. In this method, the use of the concept that diffusion within the nodes that are members of a community is ...faster than the diffusion of nodes that are not in the same community. In this way, the dense subgraph will detect the graph in the middle layer. The LPA algorithm, which mimics epidemic contagion by spreading labels, has attracted much attention in recent years as one of the most efficient algorithms in the subcategory of diffusion methods. This algorithm is one of the detection algorithms of most popular communities in recent years because of possessing some advantages including linear time order, the use of local information, and non-dependence on any parameter; however, due to the random behavior in LPA, there are some problems such as unstable and low quality resulting from larger monster communities. This algorithm is easily adaptable to attributed network. In this paper, it is supposed to propose a new version of the LPA algorithm for attributed graphs so that the detected communities solve the problems related to unstable and low quality in addition to possessing structural cohesiveness and attribute homogeneity. For this purpose, a weighted graph of the combination of node attributes and topological structure is produced from an attributed graph for nodes which have edges with each other. Also, the centrality of each node will be calculated equal to the influence of each node using Laplacian centrality, and the steps of selecting the node are being enhanced for updating as well as the mechanism of updating based on the influence of nodes. The proposed method has been compared to other primary and new attributed graph clustering algorithms for real and artificial datasets. In accordance with the results of the experiments on the proposed algorithm without parameter adjusting for different networks of density and entropy criteria, the normalized mutual information indicates that the proposed method is more efficient and precise than other state-of-the-art attributed graph clustering methods.
Plant detritus represents the major source of soil carbon (C) and nitrogen (N), and changes in its quantity can influence below-ground biogeochemical processes in forests. However, we lack a ...mechanistic understanding of how above- and belowground detrital inputs affect soil C and N in mountain forests in an arid land. Here, we explored the effects of litter and root manipulations (control (CK), doubled litter input (DL), removal of litter (NL), root exclusion (NR), and a combination of litter removal and root exclusion (NI)) on soil C and N concentrations, enzyme activity and microbial biomass during a 2-year field experiment. We found that DL had no significant effect on soil total organic carbon (SOC) and total nitrogen (TN) but significantly increased soil dissolved organic carbon (DOC), microbial biomass C, N and inorganic N as well as soil cellulase, phosphatase and peroxidase activities. Conversely, NL and NR reduced soil C and N concentrations and enzyme activities. We also found an increase in the biomass of soil bacteria, fungi and actinomycetes in the DL treatment, while NL reduced the biomass of gram-positive bacteria, gram-negative bacteria and fungi by 5.15%, 17.50% and 14.17%, respectively. The NR decreased the biomass of these three taxonomic groups by 8.97%, 22.11% and 21.36%, respectively. Correlation analysis showed that soil biotic factors (enzyme activity and microbial biomass) and abiotic factors (soil moisture content) significantly controlled the change in soil C and N concentrations (P < 0.01). In brief, we found that the short-term input of plant detritus could markedly affect the concentrations and biological characteristics of the C and N fractions in soil. The removal experiment indicated that the contribution of roots to soil nutrients is greater than that of the litter.
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