Next Issue
Volume 4, March
Previous Issue
Volume 4, September

Table of Contents

Microarrays, Volume 4, Issue 4 (December 2015) , Pages 432-713

  • Issues are regarded as officially published after their release is announced to the table of contents alert mailing list.
  • You may sign up for e-mail alerts to receive table of contents of newly released issues.
  • PDF is the official format for papers published in both, html and pdf forms. To view the papers in pdf format, click on the "PDF Full-text" link, and use the free Adobe Readerexternal link to open them.
Order results
Result details
Select all
Export citation of selected articles as:
Open AccessReview
Assessing Bacterial Interactions Using Carbohydrate-Based Microarrays
Microarrays 2015, 4(4), 690-713; https://doi.org/10.3390/microarrays4040690 - 10 Dec 2015
Cited by 5 | Viewed by 2288
Abstract
Carbohydrates play a crucial role in host-microorganism interactions and many host glycoconjugates are receptors or co-receptors for microbial binding. Host glycosylation varies with species and location in the body, and this contributes to species specificity and tropism of commensal and pathogenic bacteria. Additionally, [...] Read more.
Carbohydrates play a crucial role in host-microorganism interactions and many host glycoconjugates are receptors or co-receptors for microbial binding. Host glycosylation varies with species and location in the body, and this contributes to species specificity and tropism of commensal and pathogenic bacteria. Additionally, bacterial glycosylation is often the first bacterial molecular species encountered and responded to by the host system. Accordingly, characterising and identifying the exact structures involved in these critical interactions is an important priority in deciphering microbial pathogenesis. Carbohydrate-based microarray platforms have been an underused tool for screening bacterial interactions with specific carbohydrate structures, but they are growing in popularity in recent years. In this review, we discuss carbohydrate-based microarrays that have been profiled with whole bacteria, recombinantly expressed adhesins or serum antibodies. Three main types of carbohydrate-based microarray platform are considered; (i) conventional carbohydrate or glycan microarrays; (ii) whole mucin microarrays; and (iii) microarrays constructed from bacterial polysaccharides or their components. Determining the nature of the interactions between bacteria and host can help clarify the molecular mechanisms of carbohydrate-mediated interactions in microbial pathogenesis, infectious disease and host immune response and may lead to new strategies to boost therapeutic treatments. Full article
(This article belongs to the Special Issue Carbohydrate Microarrays)
Show Figures

Figure 1

Open AccessArticle
Mining the Dynamic Genome: A Method for Identifying Multiple Disease Signatures Using Quantitative RNA Expression Analysis of a Single Blood Sample
Microarrays 2015, 4(4), 671-689; https://doi.org/10.3390/microarrays4040671 - 10 Dec 2015
Cited by 1 | Viewed by 2158
Abstract
Background: Blood has advantages over tissue samples as a diagnostic tool, and blood mRNA transcriptomics is an exciting research field. To realize the full potential of blood transcriptomic investigations requires improved methods for gene expression measurement and data interpretation able to detect biological [...] Read more.
Background: Blood has advantages over tissue samples as a diagnostic tool, and blood mRNA transcriptomics is an exciting research field. To realize the full potential of blood transcriptomic investigations requires improved methods for gene expression measurement and data interpretation able to detect biological signatures within the “noisy” variability of whole blood. Methods: We demonstrate collection tube bias compensation during the process of identifying a liver cancer-specific gene signature. The candidate probe set list of liver cancer was filtered, based on previous repeatability performance obtained from technical replicates. We built a prediction model using differential pairs to reduce the impact of confounding factors. We compared prediction performance on an independent test set against prediction on an alternative model derived by Weka. The method was applied to an independent set of 157 blood samples collected in PAXgene tubes. Results: The model discriminated liver cancer equally well in both EDTA and PAXgene collected samples, whereas the Weka-derived model (using default settings) was not able to compensate for collection tube bias. Cross-validation results show our procedure predicted membership of each sample within the disease groups and healthy controls. Conclusion: Our versatile method for blood transcriptomic investigation overcomes several limitations hampering research in blood-based gene tests. Full article
Show Figures

Figure 1

Open AccessFeature PaperArticle
Cancer Biomarkers from Genome-Scale DNA Methylation: Comparison of Evolutionary and Semantic Analysis Methods
Microarrays 2015, 4(4), 647-670; https://doi.org/10.3390/microarrays4040647 - 27 Nov 2015
Cited by 4 | Viewed by 2109
Abstract
DNA methylation profiling exploits microarray technologies, thus yielding a wealth of high-volume data. Here, an intelligent framework is applied, encompassing epidemiological genome-scale DNA methylation data produced from the Illumina’s Infinium Human Methylation 450K Bead Chip platform, in an effort to correlate interesting methylation [...] Read more.
DNA methylation profiling exploits microarray technologies, thus yielding a wealth of high-volume data. Here, an intelligent framework is applied, encompassing epidemiological genome-scale DNA methylation data produced from the Illumina’s Infinium Human Methylation 450K Bead Chip platform, in an effort to correlate interesting methylation patterns with cancer predisposition and, in particular, breast cancer and B-cell lymphoma. Feature selection and classification are employed in order to select, from an initial set of ~480,000 methylation measurements at CpG sites, predictive cancer epigenetic biomarkers and assess their classification power for discriminating healthy versus cancer related classes. Feature selection exploits evolutionary algorithms or a graph-theoretic methodology which makes use of the semantics information included in the Gene Ontology (GO) tree. The selected features, corresponding to methylation of CpG sites, attained moderate-to-high classification accuracies when imported to a series of classifiers evaluated by resampling or blindfold validation. The semantics-driven selection revealed sets of CpG sites performing similarly with evolutionary selection in the classification tasks. However, gene enrichment and pathway analysis showed that it additionally provides more descriptive sets of GO terms and KEGG pathways regarding the cancer phenotypes studied here. Results support the expediency of this methodology regarding its application in epidemiological studies. Full article
(This article belongs to the Special Issue Computational Modeling and Analysis of Microarray Data: New Horizons)
Show Figures

Figure 1

Open AccessCommunication
Integrating Colon Cancer Microarray Data: Associating Locus-Specific Methylation Groups to Gene Expression-Based Classifications
Microarrays 2015, 4(4), 630-646; https://doi.org/10.3390/microarrays4040630 - 23 Nov 2015
Cited by 1 | Viewed by 2373
Abstract
Recently, considerable attention has been paid to gene expression-based classifications of colorectal cancers (CRC) and their association with patient prognosis. In addition to changes in gene expression, abnormal DNA-methylation is known to play an important role in cancer onset and development, and colon [...] Read more.
Recently, considerable attention has been paid to gene expression-based classifications of colorectal cancers (CRC) and their association with patient prognosis. In addition to changes in gene expression, abnormal DNA-methylation is known to play an important role in cancer onset and development, and colon cancer is no exception to this rule. Large-scale technologies, such as methylation microarray assays and specific sequencing of methylated DNA, have been used to determine whole genome profiles of CpG island methylation in tissue samples. In this article, publicly available microarray-based gene expression and methylation data sets are used to characterize expression subtypes with respect to locus-specific methylation. A major objective was to determine whether integration of these data types improves previously characterized subtypes, or provides evidence for additional subtypes. We used unsupervised clustering techniques to determine methylation-based subgroups, which are subsequently annotated with three published expression-based classifications, comprising from three to six subtypes. Our results showed that, while methylation profiles provide a further basis for segregation of certain (Inflammatory and Goblet-like) finer-grained expression-based subtypes, they also suggest that other finer-grained subtypes are not distinctive and can be considered as a single subtype. Full article
(This article belongs to the Special Issue Computational Modeling and Analysis of Microarray Data: New Horizons)
Show Figures

Figure 1

Open AccessArticle
A Liposome-Based Approach to the Integrated Multi-Component Antigen Microarrays
Microarrays 2015, 4(4), 618-629; https://doi.org/10.3390/microarrays4040618 - 20 Nov 2015
Viewed by 1948
Abstract
This report describes an experimental procedure for constructing integrated lipid, carbohydrate, and protein microarrays. In essence, it prints liposomes on nitrocellulose-coated micro-glass slides, a biochip substrate for spotting protein and carbohydrate microarrays, and the substances that can form liposomes (homo-liposomes) or [...] Read more.
This report describes an experimental procedure for constructing integrated lipid, carbohydrate, and protein microarrays. In essence, it prints liposomes on nitrocellulose-coated micro-glass slides, a biochip substrate for spotting protein and carbohydrate microarrays, and the substances that can form liposomes (homo-liposomes) or can be incorporated into liposomes (hetero-liposomes) are suitable for microarray construction using existing microarray spotting devices. Importantly, this technology allows simultaneous detection of serum antibody activities among the three major classes of antigens, i.e., lipids, carbohydrates, and proteins. The potential of this technology is illustrated by its use in revealing a broad-spectrum of pre-existing anti-lipid antibodies in blood circulation and monitoring the epitope spreading of autoantibody reactivities among protein, carbohydrate, and lipid antigens in experimental autoimmune encephalomyelitis (EAE). Full article
Show Figures

Figure 1

Open AccessReview
An Overview of NCA-Based Algorithms for Transcriptional Regulatory Network Inference
Microarrays 2015, 4(4), 596-617; https://doi.org/10.3390/microarrays4040596 - 16 Nov 2015
Cited by 5 | Viewed by 2212
Abstract
In systems biology, the regulation of gene expressions involves a complex network of regulators. Transcription factors (TFs) represent an important component of this network: they are proteins that control which genes are turned on or off in the genome by binding to specific [...] Read more.
In systems biology, the regulation of gene expressions involves a complex network of regulators. Transcription factors (TFs) represent an important component of this network: they are proteins that control which genes are turned on or off in the genome by binding to specific DNA sequences. Transcription regulatory networks (TRNs) describe gene expressions as a function of regulatory inputs specified by interactions between proteins and DNA. A complete understanding of TRNs helps to predict a variety of biological processes and to diagnose, characterize and eventually develop more efficient therapies. Recent advances in biological high-throughput technologies, such as DNA microarray data and next-generation sequence (NGS) data, have made the inference of transcription factor activities (TFAs) and TF-gene regulations possible. Network component analysis (NCA) represents an efficient computational framework for TRN inference from the information provided by microarrays, ChIP-on-chip and the prior information about TF-gene regulation. However, NCA suffers from several shortcomings. Recently, several algorithms based on the NCA framework have been proposed to overcome these shortcomings. This paper first overviews the computational principles behind NCA, and then, it surveys the state-of-the-art NCA-based algorithms proposed in the literature for TRN reconstruction. Full article
(This article belongs to the Special Issue Computational Modeling and Analysis of Microarray Data: New Horizons)
Show Figures

Figure 1

Open AccessReview
Efficient SNP Discovery by Combining Microarray and Lab-on-a-Chip Data for Animal Breeding and Selection
Microarrays 2015, 4(4), 570-595; https://doi.org/10.3390/microarrays4040570 - 16 Nov 2015
Cited by 6 | Viewed by 3567
Abstract
The genetic markers associated with economic traits have been widely explored for animal breeding. Among these markers, single-nucleotide polymorphism (SNPs) are gradually becoming a prevalent and effective evaluation tool. Since SNPs only focus on the genetic sequences of interest, it thereby reduces the [...] Read more.
The genetic markers associated with economic traits have been widely explored for animal breeding. Among these markers, single-nucleotide polymorphism (SNPs) are gradually becoming a prevalent and effective evaluation tool. Since SNPs only focus on the genetic sequences of interest, it thereby reduces the evaluation time and cost. Compared to traditional approaches, SNP genotyping techniques incorporate informative genetic background, improve the breeding prediction accuracy and acquiesce breeding quality on the farm. This article therefore reviews the typical procedures of animal breeding using SNPs and the current status of related techniques. The associated SNP information and genotyping techniques, including microarray and Lab-on-a-Chip based platforms, along with their potential are highlighted. Examples in pig and poultry with different SNP loci linked to high economic trait values are given. The recommendations for utilizing SNP genotyping in nimal breeding are summarized. Full article
(This article belongs to the Special Issue SNP Array)
Show Figures

Figure 1

Open AccessReview
SNPs Array Karyotyping in Non-Hodgkin Lymphoma
Microarrays 2015, 4(4), 551-569; https://doi.org/10.3390/microarrays4040551 - 12 Nov 2015
Cited by 1 | Viewed by 1917
Abstract
The traditional methods for detection of chromosomal aberrations, which included cytogenetic or gene candidate solutions, suffered from low sensitivity or the need for previous knowledge of the target regions of the genome. With the advent of single nucleotide polymorphism (SNP) arrays, genome screening [...] Read more.
The traditional methods for detection of chromosomal aberrations, which included cytogenetic or gene candidate solutions, suffered from low sensitivity or the need for previous knowledge of the target regions of the genome. With the advent of single nucleotide polymorphism (SNP) arrays, genome screening at global level in order to find chromosomal aberrations like copy number variants, DNA amplifications, deletions, and also loss of heterozygosity became feasible. In this review, we present an update of the knowledge, gained by SNPs arrays, of the genomic complexity of the most important subtypes of non-Hodgkin lymphomas. Full article
(This article belongs to the Special Issue SNP Array)
Open AccessArticle
Evaluating the Effect of Cell Culture on Gene Expression in Primary Tissue Samples Using Microfluidic-Based Single Cell Transcriptional Analysis
Microarrays 2015, 4(4), 540-550; https://doi.org/10.3390/microarrays4040540 - 04 Nov 2015
Cited by 9 | Viewed by 2496
Abstract
Significant transcriptional heterogeneity is an inherent property of complex tissues such as tumors and healing wounds. Traditional methods of high-throughput analysis rely on pooling gene expression data from hundreds of thousands of cells and reporting a population-wide average that is unable to capture [...] Read more.
Significant transcriptional heterogeneity is an inherent property of complex tissues such as tumors and healing wounds. Traditional methods of high-throughput analysis rely on pooling gene expression data from hundreds of thousands of cells and reporting a population-wide average that is unable to capture differences within distinct cell subsets. Recent advances in microfluidic technology have permitted the development of large-scale single cell analytic methods that overcome this limitation. The increased granularity afforded by such approaches allows us to answer the critical question of whether expansion in cell culture significantly alters the transcriptional characteristics of cells isolated from primary tissue. Here we examine an established population of human adipose-derived stem cells (ASCs) using a novel, microfluidic-based method for high-throughput transcriptional interrogation, coupled with advanced bioinformatic analysis, to evaluate the dynamics of single cell gene expression among primary, passage 0, and passage 1 stem cells. We find significant differences in the transcriptional profiles of cells from each group, as well as a considerable shift in subpopulation dynamics as those subgroups better able to adhere and proliferate under these culture conditions gradually emerge as dominant. Taken together, these findings reinforce the importance of using primary or very early passage cells in future studies. Full article
(This article belongs to the Special Issue Microfluidics Technology)
Show Figures

Figure 1

Open AccessReview
Analysis of Reverse Phase Protein Array Data: From Experimental Design towards Targeted Biomarker Discovery
Microarrays 2015, 4(4), 520-539; https://doi.org/10.3390/microarrays4040520 - 03 Nov 2015
Cited by 8 | Viewed by 3549
Abstract
Mastering the systematic analysis of tumor tissues on a large scale has long been a technical challenge for proteomics. In 2001, reverse phase protein arrays (RPPA) were added to the repertoire of existing immunoassays, which, for the first time, allowed a profiling of [...] Read more.
Mastering the systematic analysis of tumor tissues on a large scale has long been a technical challenge for proteomics. In 2001, reverse phase protein arrays (RPPA) were added to the repertoire of existing immunoassays, which, for the first time, allowed a profiling of minute amounts of tumor lysates even after microdissection. A characteristic feature of RPPA is its outstanding sample capacity permitting the analysis of thousands of samples in parallel as a routine task. Until today, the RPPA approach has matured to a robust and highly sensitive high-throughput platform, which is ideally suited for biomarker discovery. Concomitant with technical advancements, new bioinformatic tools were developed for data normalization and data analysis as outlined in detail in this review. Furthermore, biomarker signatures obtained by different RPPA screens were compared with another or with that obtained by other proteomic formats, if possible. Options for overcoming the downside of RPPA, which is the need to steadily validate new antibody batches, will be discussed. Finally, a debate on using RPPA to advance personalized medicine will conclude this article. Full article
(This article belongs to the Special Issue Antibody Microarrays in Clinical Proteomics)
Show Figures

Figure 1

Open AccessArticle
Cancer–Osteoblast Interaction Reduces Sost Expression in Osteoblasts and Up-Regulates lncRNA MALAT1 in Prostate Cancer
Microarrays 2015, 4(4), 503-519; https://doi.org/10.3390/microarrays4040503 - 29 Oct 2015
Cited by 11 | Viewed by 3683
Abstract
Dynamic interaction between prostate cancer and the bone microenvironment is a major contributor to metastasis of prostate cancer to bone. In this study, we utilized an in vitro co-culture model of PC3 prostate cancer cells and osteoblasts followed by microarray based gene expression [...] Read more.
Dynamic interaction between prostate cancer and the bone microenvironment is a major contributor to metastasis of prostate cancer to bone. In this study, we utilized an in vitro co-culture model of PC3 prostate cancer cells and osteoblasts followed by microarray based gene expression profiling to identify previously unrecognized prostate cancer–bone microenvironment interactions. Factors secreted by PC3 cells resulted in the up-regulation of many genes in osteoblasts associated with bone metabolism and cancer metastasis, including Mmp13, Il-6 and Tgfb2, and down-regulation of Wnt inhibitor Sost. To determine whether altered Sost expression in the bone microenvironment has an effect on prostate cancer metastasis, we co-cultured PC3 cells with Sost knockout (SostKO) osteoblasts and wildtype (WT) osteoblasts and identified several genes differentially regulated between PC3-SostKO osteoblast co-cultures and PC3-WT osteoblast co-cultures. Co-culturing PC3 cells with WT osteoblasts up-regulated cancer-associated long noncoding RNA (lncRNA) MALAT1 in PC3 cells. MALAT1 expression was further enhanced when PC3 cells were co-cultured with SostKO osteoblasts and treatment with recombinant Sost down-regulated MALAT1 expression in these cells. Our results suggest that reduced Sost expression in the tumor microenvironment may promote bone metastasis by up-regulating MALAT1 in prostate cancer. Full article
(This article belongs to the Special Issue Microarray Gene Expression Data Analysis)
Show Figures

Figure 1

Open AccessCase Report
SNP Analysis and Whole Exome Sequencing: Their Application in the Analysis of a Consanguineous Pedigree Segregating Ataxia
Microarrays 2015, 4(4), 490-502; https://doi.org/10.3390/microarrays4040490 - 23 Oct 2015
Cited by 1 | Viewed by 2438
Abstract
Autosomal recessive cerebellar ataxia encompasses a large and heterogeneous group of neurodegenerative disorders. We employed single nucleotide polymorphism (SNP) analysis and whole exome sequencing to investigate a consanguineous Maori pedigree segregating ataxia. We identified a novel mutation in exon 10 of the SACS [...] Read more.
Autosomal recessive cerebellar ataxia encompasses a large and heterogeneous group of neurodegenerative disorders. We employed single nucleotide polymorphism (SNP) analysis and whole exome sequencing to investigate a consanguineous Maori pedigree segregating ataxia. We identified a novel mutation in exon 10 of the SACS gene: c.7962T>G p.(Tyr2654*), establishing the diagnosis of autosomal recessive spastic ataxia of Charlevoix-Saguenay (ARSACS). Our findings expand both the genetic and phenotypic spectrum of this rare disorder, and highlight the value of high-density SNP analysis and whole exome sequencing as powerful and cost-effective tools in the diagnosis of genetically heterogeneous disorders such as the hereditary ataxias. Full article
(This article belongs to the Special Issue SNP Array)
Show Figures

Figure 1

Open AccessReview
Integrated Microfluidic Nucleic Acid Isolation, Isothermal Amplification, and Amplicon Quantification
Microarrays 2015, 4(4), 474-489; https://doi.org/10.3390/microarrays4040474 - 20 Oct 2015
Cited by 7 | Viewed by 2886
Abstract
Microfluidic components and systems for rapid (<60 min), low-cost, convenient, field-deployable sequence-specific nucleic acid-based amplification tests (NAATs) are described. A microfluidic point-of-care (POC) diagnostics test to quantify HIV viral load from blood samples serves as a representative and instructive example to discuss the [...] Read more.
Microfluidic components and systems for rapid (<60 min), low-cost, convenient, field-deployable sequence-specific nucleic acid-based amplification tests (NAATs) are described. A microfluidic point-of-care (POC) diagnostics test to quantify HIV viral load from blood samples serves as a representative and instructive example to discuss the technical issues and capabilities of “lab on a chip” NAAT devices. A portable, miniaturized POC NAAT with performance comparable to conventional PCR (polymerase-chain reaction)-based tests in clinical laboratories can be realized with a disposable, palm-sized, plastic microfluidic chip in which: (1) nucleic acids (NAs) are extracted from relatively large (~mL) volume sample lysates using an embedded porous silica glass fiber or cellulose binding phase (“membrane”) to capture sample NAs in a flow-through, filtration mode; (2) NAs captured on the membrane are isothermally (~65 °C) amplified; (3) amplicon production is monitored by real-time fluorescence detection, such as with a smartphone CCD camera serving as a low-cost detector; and (4) paraffin-encapsulated, lyophilized reagents for temperature-activated release are pre-stored in the chip. Limits of Detection (LOD) better than 103 virons/sample can be achieved. A modified chip with conduits hosting a diffusion-mode amplification process provides a simple visual indicator to readily quantify sample NA template. In addition, a companion microfluidic device for extracting plasma from whole blood without a centrifuge, generating cell-free plasma for chip-based molecular diagnostics, is described. Extensions to a myriad of related applications including, for example, food testing, cancer screening, and insect genotyping are briefly surveyed. Full article
(This article belongs to the Special Issue Microfluidics Technology)
Show Figures

Figure 1

Open AccessReview
Genomic-Wide Analysis with Microarrays in Human Oncology
Microarrays 2015, 4(4), 454-473; https://doi.org/10.3390/microarrays4040454 - 16 Oct 2015
Cited by 3 | Viewed by 1949
Abstract
DNA microarray technologies have advanced rapidly and had a profound impact on examining gene expression on a genomic scale in research. This review discusses the history and development of microarray and DNA chip devices, and specific microarrays are described along with their methods [...] Read more.
DNA microarray technologies have advanced rapidly and had a profound impact on examining gene expression on a genomic scale in research. This review discusses the history and development of microarray and DNA chip devices, and specific microarrays are described along with their methods and applications. In particular, microarrays have detected many novel cancer-related genes by comparing cancer tissues and non-cancerous tissues in oncological research. Recently, new methods have been in development, such as the double-combination array and triple-combination array, which allow more effective analysis of gene expression and epigenetic changes. Analysis of gene expression alterations in precancerous regions compared with normal regions and array analysis in drug-resistance cancer tissues are also successfully performed. Compared with next-generation sequencing, a similar method of genome analysis, several important differences distinguish these techniques and their applications. Development of novel microarray technologies is expected to contribute to further cancer research. Full article
(This article belongs to the Special Issue Diagnostic, Prognostic and Predictive Cancer Biomarkers)
Open AccessArticle
A Synthetic Kinome Microarray Data Generator
Microarrays 2015, 4(4), 432-453; https://doi.org/10.3390/microarrays4040432 - 16 Oct 2015
Viewed by 1706
Abstract
Cellular pathways involve the phosphorylation and dephosphorylation of proteins. Peptide microarrays called kinome arrays facilitate the measurement of the phosphorylation activity of hundreds of proteins in a single experiment. Analyzing the data from kinome microarrays is a multi-step process. Typically, various techniques are [...] Read more.
Cellular pathways involve the phosphorylation and dephosphorylation of proteins. Peptide microarrays called kinome arrays facilitate the measurement of the phosphorylation activity of hundreds of proteins in a single experiment. Analyzing the data from kinome microarrays is a multi-step process. Typically, various techniques are possible for a particular step, and it is necessary to compare and evaluate them. Such evaluations require data for which correct analysis results are known. Unfortunately, such kinome data is not readily available in the community. Further, there are no established techniques for creating artificial kinome datasets with known results and with the same characteristics as real kinome datasets. In this paper, a methodology for generating synthetic kinome array data is proposed. The methodology relies on actual intensity measurements from kinome microarray experiments and preserves their subtle characteristics. The utility of the methodology is demonstrated by evaluating methods for eliminating heterogeneous variance in kinome microarray data. Phosphorylation intensities from kinome microarrays often exhibit such heterogeneous variance and its presence can negatively impact downstream statistical techniques that rely on homogeneity of variance. It is shown that using the output from the proposed synthetic data generator, it is possible to critically compare two variance stabilization methods. Full article
(This article belongs to the Special Issue Peptide Microarrays)
Show Figures

Figure 1

Previous Issue
Next Issue
Back to TopTop