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Letteratura scientifica selezionata sul tema "Microarrays"

Autore: Grafiati

Pubblicato: 4 giugno 2021

Ultima modifica: 5 luglio 2025

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Articoli di riviste sul tema "Microarrays"

Aparna, G. M., and Kishore K. R. Tetala. "Recent Progress in Development and Application of DNA, Protein, Peptide, Glycan, Antibody, and Aptamer Microarrays." Biomolecules 13, no. 4 (March 27, 2023): 602. http://dx.doi.org/10.3390/biom13040602.

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Microarrays are one of the trailblazing technologies of the last two decades and have displayed their importance in all the associated fields of biology. They are widely explored to screen, identify, and gain insights on the characteristics traits of biomolecules (individually or in complex solutions). A wide variety of biomolecule-based microarrays (DNA microarrays, protein microarrays, glycan microarrays, antibody microarrays, peptide microarrays, and aptamer microarrays) are either commercially available or fabricated in-house by researchers to explore diverse substrates, surface coating, immobilization techniques, and detection strategies. The aim of this review is to explore the development of biomolecule-based microarray applications since 2018 onwards. Here, we have covered a different array of printing strategies, substrate surface modification, biomolecule immobilization strategies, detection techniques, and biomolecule-based microarray applications. The period of 2018–2022 focused on using biomolecule-based microarrays for the identification of biomarkers, detection of viruses, differentiation of multiple pathogens, etc. A few potential future applications of microarrays could be for personalized medicine, vaccine candidate screening, toxin screening, pathogen identification, and posttranslational modifications.

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Paredes, Carlos J., Ryan S. Senger, Iwona S. Spath, Jacob R. Borden, Ryan Sillers, and Eleftherios T. Papoutsakis. "A General Framework for Designing and Validating Oligomer-Based DNA Microarrays and Its Application to Clostridium acetobutylicum." Applied and Environmental Microbiology 73, no. 14 (May 25, 2007): 4631–38. http://dx.doi.org/10.1128/aem.00144-07.

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ABSTRACT While DNA microarray analysis is widely accepted as an essential tool for modern biology, its use still eludes many researchers for several reasons, especially when microarrays are not commercially available. In that case, the design, construction, and use of microarrays for a sequenced organism constitute substantial, time-consuming, and expensive tasks. Recently, it has become possible to construct custom microarrays using industrial manufacturing processes, which offer several advantages, including speed of manufacturing, quality control, no up-front setup costs, and need-based microarray ordering. Here, we describe a strategy for designing and validating DNA microarrays manufactured using a commercial process. The 22K microarrays for the solvent producer Clostridium acetobutylicum ATCC 824 are based on in situ-synthesized 60-mers employing the Agilent technology. The strategy involves designing a large library of possible oligomer probes for each target (i.e., gene or DNA sequence) and experimentally testing and selecting the best probes for each target. The degenerate C. acetobutylicum strain M5 lacking the pSOL1 megaplasmid (with 178 annotated open reading frames [genes]) was used to estimate the level of probe cross-hybridization in the new microarrays and to establish the minimum intensity for a gene to be considered expressed. Results obtained using this microarray design were consistent with previously reported results from spotted cDNA-based microarrays. The proposed strategy is applicable to any sequenced organism.

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Chiodi, Elisa, Allison M. Marn, Matthew T. Geib, and M. Selim Ünlü. "The Role of Surface Chemistry in the Efficacy of Protein and DNA Microarrays for Label-Free Detection: An Overview." Polymers 13, no. 7 (March 26, 2021): 1026. http://dx.doi.org/10.3390/polym13071026.

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The importance of microarrays in diagnostics and medicine has drastically increased in the last few years. Nevertheless, the efficiency of a microarray-based assay intrinsically depends on the density and functionality of the biorecognition elements immobilized onto each sensor spot. Recently, researchers have put effort into developing new functionalization strategies and technologies which provide efficient immobilization and stability of any sort of molecule. Here, we present an overview of the most widely used methods of surface functionalization of microarray substrates, as well as the most recent advances in the field, and compare their performance in terms of optimal immobilization of the bioreceptor molecules. We focus on label-free microarrays and, in particular, we aim to describe the impact of surface chemistry on two types of microarray-based sensors: microarrays for single particle imaging and for label-free measurements of binding kinetics. Both protein and DNA microarrays are taken into consideration, and the effect of different polymeric coatings on the molecules’ functionalities is critically analyzed.

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Handley, Daniel, Nicoleta Serban, David G. Peters, and Clark Glymour. "Concerns About Unreliable Data from Spotted cDNA Microarrays Due to Cross-Hybridization and Sequence Errors." Statistical Applications in Genetics and Molecular Biology 3, no. 1 (January 6, 2004): 1–2. http://dx.doi.org/10.2202/1544-6115.1091.

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We discuss our concerns regarding the reliability of data generated by spotted cDNA microarrays. Two types of error we highlight are cross-hybridization artifact due to sequence homologies and sequence errors in the cDNA used for spotting on microarrays. We feel that statisticians who analyze microarray data should be aware of these sources of unreliability intrinsic to cDNA microarray design and use.

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Fesseha, Haben, and Hiwot Tilahun. "Principles and Applications of Deoxyribonucleic Acid Microarray: A Review." Pathology and Laboratory Medicine – Open Journal 3, no. 1 (March 30, 2021): 1–9. http://dx.doi.org/10.17140/plmoj-3-109.

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Deoxyribonucleic acid (DNA) microarrays are collections of DNA probes arranged on a base pair and the latest commercialized molecular diagnostic technologies that offer high throughput results, more sensitive and require less time. It is the most reliable and widely accepted tool facilitating the simultaneous identification of thousands of genetic elements even a single gene. Microarrays are powerful new tools for the investigation of global changes in gene expression profiles in cells and tissues. The different types of DNA microarray or DNA chip devices and systems are described along with their methods of fabrication and their use. The DNA microarrays assembly process is automatized and further miniaturized. DNA microarrays are used in the search of various specific genes or in gene polymorphism and expression analysis. They will be widely used to investigate the expression of various genes connected with various diseases in order to find the causes of these diseases and to enable their accurate treatment. Generally, microarray analysis is not only applied for gene expression studies, but also used in immunology, genotyping, diagnostics and sequence analysis. Additionally, microarray technology being developed and applied to new areas of proteomics, cancer research, and cellular analysis.

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Whipple, Mark Eliot, and Winston Patrick Kuo. "DNA Microarrays in Otolaryngology-Head and Neck Surgery." Otolaryngology–Head and Neck Surgery 127, no. 3 (September 2002): 196–204. http://dx.doi.org/10.1067/mhn.2002.127383.

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OBJECTIVES: Our goal was to review the technologies underlying DNA microarrays and to explore their use in otolaryngology-head and neck surgery. STUDY DESIGN: The current literature relating to microarray technology and methodology is reviewed, specifically the use of DNA microarrays to characterize gene expression. Bioinformatics involves computational and statistical methods to extract, organize, and analyze the huge amounts of data produced by microarray experiments. The means by which these techniques are being applied to otolaryngology-head and neck surgery are outlined. RESULTS: Microarray technologies are having a substantial impact on biomedical research, including many areas relevant to otolaryngology-head and neck surgery. CONCLUSIONS: DNA microarrays allow for the simultaneous investigationof thousands of individual genes in a single experiment. In the coming years, the application of these technologies to clinical medicine should allow for unprecedented methods ofdiagnosis and treatment. SIGNIFICANCE: These highly parallel experimental techniques promise to revolutionize gene discovery, disease characterization, and drug development.

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Korbelik, J., M. Cardeno, J. P. Matisic, A. C. Carraro, and C. MacAulay. "Cytology Microarrays." Analytical Cellular Pathology 29, no. 5 (January 1, 2007): 435–42. http://dx.doi.org/10.1155/2007/258297.

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The use of high throughput genetic and expression platforms are generating many candidate diagnostic markers and therapeutic targets for a wide variety of clinical conditions. Tissue microarrays can be used for the evaluation of the utility of many of these markers. However, tissue microarrays can suffer from the limitations associated with sampling and sectioning tissues. We introduce a novel microarray technique based on cell suspensions. Multiple slides can be made, all of which are equally representative of the initial sample. A robotic device was designed that can deposit 60 distinct spots of cytological material on a glass slide. Each spot of cells deposited in this manner may correspond to a unique source. Controlling the number of cells per spot, their distribution within the spot and the size of the spot can be achieved by modifying the viscosity of the cell solution or regulating the amount of fluid deposited. A fully automated analysis of quantitatively stained microarray samples has been performed to quantify the number of cells per spot, the size of spots and the DNA amount per cell in each spot. The reproducibility of these parameters was found to be high.

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Wilson, K. J., and E. de la Vega. "The potential of microarrays to assist shrimp breeding and production: a review." Australian Journal of Experimental Agriculture 45, no. 8 (2005): 901. http://dx.doi.org/10.1071/ea05060.

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The shrimp aquaculture industry is a relatively new livestock industry, having developed over the past 30 years. Thus, it is poised to take advantage of new technologies from the outset of selective breeding programs. This contrasts with long established livestock industries, where there are already highly specialised breeds. This review focuses specifically on the potential application of microarrays to shrimp breeding. Potential applications of microarrays in selective breeding programs are summarised. Microarrays can be used as a rapid means to generate molecular markers for genetic linkage mapping, and genetic maps have been constructed for yeast, Arabidopsis and barley using microarray technology. Microarrays can also be used in the hunt for candidate genes affecting particular traits, leading to development of perfect markers for these traits (i.e. causative mutations). However, this requires that microarray analysis be combined with genetic linkage mapping, and that substantial genomic information is available for the species in question. A novel application of microarrays is to treat gene expression as a quantitative trait in itself and to combine this with linkage mapping to identify quantitative trait loci controlling the levels of gene expression; this approach may identify higher level regulatory genes in specific pathways. Finally, patterns of gene expression observed using microarrays may themselves be treated as phenotypic traits in selection programs (e.g. a particular pattern of gene expression might be indicative of a disease tolerant individual). Microarrays are now being developed for a number of shrimp species in laboratories around the world, primarily with a focus on identifying genes involved in the immune response. However, at present, there is no central repository of shrimp genomic information, which limits the rate at which shrimp genomic research can be progressed. The application of microarrays to shrimp breeding will be extremely limited until there is a shared repository of genomic information for shrimp, and the collective will and resources to develop comprehensive genomic tools for shrimp.

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Trost, Brett, Catherine A. Moir, Zoe E. Gillespie, Anthony Kusalik, Jennifer A. Mitchell, and Christopher H. Eskiw. "Concordance between RNA-sequencing data and DNA microarray data in transcriptome analysis of proliferative and quiescent fibroblasts." Royal Society Open Science 2, no. 9 (September 2015): 150402. http://dx.doi.org/10.1098/rsos.150402.

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DNA microarrays and RNA sequencing (RNA-seq) are major technologies for performing high-throughput analysis of transcript abundance. Recently, concerns have been raised regarding the concordance of data derived from the two techniques. Using cDNA libraries derived from normal human foreskin fibroblasts, we measured changes in transcript abundance as cells transitioned from proliferative growth to quiescence using both DNA microarrays and RNA-seq. The internal reproducibility of the RNA-seq data was greater than that of the microarray data. Correlations between the RNA-seq data and the individual microarrays were low, but correlations between the RNA-seq values and the geometric mean of the microarray values were moderate. The two technologies had good agreement when considering probes with the largest (both positive and negative) fold change (FC) values. An independent technique, quantitative reverse-transcription PCR (qRT-PCR), was used to measure the FC of 76 genes between proliferative and quiescent samples, and a higher correlation was observed between the qRT-PCR data and the RNA-seq data than between the qRT-PCR data and the microarray data.

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Raczynski, Lech, Krzysztof Wozniak, Tymon Rubel, and Krzysztof Zaremba. "Application of Density Based Clustering to Microarray Data Analysis." International Journal of Electronics and Telecommunications 56, no. 3 (September 1, 2010): 281–86. http://dx.doi.org/10.2478/v10177-010-0037-9.

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Application of Density Based Clustering to Microarray Data AnalysisIn just a few years, gene expression microarrays have rapidly become a standard experimental tool in the biological and medical research. Microarray experiments are being increasingly carried out to address the wide range of problems, including the cluster analysis. The estimation of the number of clusters in datasets is one of the main problems of clustering microarrays. As a supplement to the existing methods we suggest the use of a density based clustering technique DBSCAN that automatically defines the number of clusters. The DBSCAN and other existing methods were compared using the microarray data from two datasets used for diagnosis of leukemia and lung cancer.

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Tesi sul tema "Microarrays"

Pernagallo, Salvatore. "Biocompatible polymer microarrays for cellular high-content screening." Thesis, University of Edinburgh, 2010. http://hdl.handle.net/1842/7571.

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The global aim of this thesis was to study the use of microarray technology for the screening and identification of biocompatible polymers, to understand physiological phenomena, and the design of biomaterials, implant surfaces and tissue-engineering scaffolds. This work was based upon the polymer microarray platform developed by the Bradley group. Polymer microarrays were successfully applied to find the best polymer supports for: (i) mouse fibroblast cells and used to evaluate cell biocompatibility and cell morphology. Fourteen polyurethanes demonstrated significant cellular adhesion. (ii) Analysis of the adhesion of human erythroleukaemic K562 suspension cells onto biomaterials with particular families of polyurethanes and polyacrylates identified. A DNA microarray study (to access the global gene expression profiles upon cellular binding) demonstrated that interactions between cells and some polyacrylates induced a number of transcriptomic changes. These results suggested that, during these interactions, a chain of cellular changes is triggered, most notably resulting in the downregulation of membrane receptors and ligands. (iii) Identification of polymers with potential applications in the field of stem cell biology. Polymers were identified that showed attachment, promotion and stabilisation of hepatocyte-like cells. A polyurethane support (PU-134) was pinpointed, which significantly improved both hepatocyte-like cell function and “lifespan”. A second project investigated biomaterials that promoted adhesion, growth and function of endothelial progenitor cells. A new polymer matrix was identified which contained the necessary signals to promote endothelial phenotype and function. This has potential application in the creation of blood vessels and the endothelialisation of artificial vessel prostheses and stent coatings for improving angioplasty therapy. (iv) The study of bacterial adhesion, focusing on the adhesion of food-borne pathogenic bacterium Salmonella enterica serovar typhimurium, strain SL1344, and the commensal bacterium Escherichia coli, strain W3110. Several polymers were found to support selective bacterial enrichment, as well as others that minimised bacterial adhesion.

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Stephens, Nathan W. "A comparison of genetic microarray analyses : a mixed models approach versus the significance analysis of microarrays /." Diss., CLICK HERE for online access, 2006. http://contentdm.lib.byu.edu/ETD/image/etd1604.pdf.

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Marsden, David Michael. "3D small-molecule microarrays." Thesis, University of Cambridge, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.611660.

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Ooi, Siew Loon. "Yeast genetics of microarrays." Available to US Hopkins community, 2002. http://wwwlib.umi.com/dissertations/dlnow/3080738.

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Dvergsten, Erik C. "A Weighted Gene Co-expression Network Analysis for Streptococcus sanguinis Microarray Experiments." VCU Scholars Compass, 2016. http://scholarscompass.vcu.edu/etd/4430.

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Streptococcus sanguinis is a gram-positive, non-motile bacterium native to human mouths. It is the primary cause of endocarditis and is also responsible for tooth decay. Two-component systems (TCSs) are commonly found in bacteria. In response to environmental signals, TCSs may regulate the expression of virulence factor genes. Gene co-expression networks are exploratory tools used to analyze system-level gene functionality. A gene co-expression network consists of gene expression profiles represented as nodes and gene connections, which occur if two genes are significantly co-expressed. An adjacency function transforms the similarity matrix containing co-expression similarities into the adjacency matrix containing connection strengths. Gene modules were determined from the connection strengths, and various network connectivity measures were calculated. S. sanguinis gene expression profile data was loaded for 2272 genes and 14 samples with 3 replicates each. The soft thresholding power β=6 was chosen to maximize R2 while maintaining a high mean number of connections. Nine modules were found. Possible meta-modules were found to be: Module 1: Blue & Green, Module 2: Pink, Module 3: Yellow, Brown & Red, Module 4: Black, Module 5: Magenta & Turquoise. The absolute value of module membership was found to be highly positively correlated with intramodular connectivity. Each of the nine modules were examined. Two methods (intramodular connectivity and TOM-based connectivity followed by network mapping) for identifying candidate hub genes were performed. Most modules provided similar results between the two methods. Similar rankings between the two methods can be considered equivalent and both can be used to detect candidate hub genes. Gene ontology information was unavailable to help select a module of interest. This network analysis would help researchers create new research hypotheses and design experiments for validation of candidate hub genes in biologically important modules.

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Harness, Denise. "A Comparison of Unsupervised Methods for DNA Microarray Leukemia Data." Digital Commons @ East Tennessee State University, 2018. https://dc.etsu.edu/asrf/2018/schedule/106.

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Advancements in DNA microarray data sequencing have created the need for sophisticated machine learning algorithms and feature selection methods. Probabilistic graphical models, in particular, have been used to identify whether microarrays or genes cluster together in groups of individuals having a similar diagnosis. These clusters of genes are informative, but can be misleading when every gene is used in the calculation. First feature reduction techniques are explored, however the size and nature of the data prevents traditional techniques from working efficiently. Our method is to use the partial correlations between the features to create a precision matrix and predict which associations between genes are most important to predicting Leukemia diagnosis. This technique reduces the number of genes to a fraction of the original. In this approach, partial correlations are then extended into a spectral clustering approach. In particular, a variety of different Laplacian matrices are generated from the network of connections between features, and each implies a graphical network model of gene interconnectivity. Various edge and vertex weighted Laplacians are considered and compared against each other in a probabilistic graphical modeling approach. The resulting multivariate Gaussian distributed clusters are subsequently analyzed to determine which genes are activated in a patient with Leukemia. Finally, the results of this are compared against other feature engineering approaches to assess its accuracy on the Leukemia data set. The initial results show the partial correlation approach of feature selection predicts the diagnosis of a Leukemia patient with almost the same accuracy as using a machine learning algorithm on the full set of genes. More calculations of the precision matrix are needed to ensure the set of most important genes is correct. Additionally more machine learning algorithms will be implemented using the full and reduced data sets to further validate the current prediction accuracy of the partial correlation method.

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Stephens, Nathan Wallace. "A Comparison of Microarray Analyses: A Mixed Models Approach Versus the Significance Analysis of Microarrays." BYU ScholarsArchive, 2006. https://scholarsarchive.byu.edu/etd/1115.

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DNA microarrays are a relatively new technology for assessing the expression levels of thousands of genes simultaneously. Researchers hope to find genes that are differentially expressed by hybridizing cDNA from known treatment sources with various genes spotted on the microarrays. The large number of tests involved in analyzing microarrays has raised new questions in multiple testing. Several approaches for identifying differentially expressed genes have been proposed. This paper considers two: (1) a mixed models approach, and (2) the Signiffcance Analysis of Microarrays.

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Brunner, Thomas. "Designing oligonucleotides for DNA microarrays /." Zürich : ETH, Eidgenössische Technische Hochschule Zürich, Department of Computer Science, 2003. http://e-collection.ethbib.ethz.ch/show?type=dipl&nr=116.

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Hartmann, Michael. "Microfluidic Methods for Protein Microarrays." Doctoral thesis, KTH, Analytisk kemi, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-26083.

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Protein microarray technology has an enormous potential for in vitro diagnostics (IVD)1. Miniaturized and parallelized immunoassays are powerful tools to measure dozens of parameters from minute amounts of sample, whilst only requiring small amounts of reagent. Protein microarrays have become well-established research tools in basic and applied research and the first diagnostic products are already released on the market. However, in order for protein microarrays to become broadly accepted tools in IVD, a number of criteria have to be fulfilled concerning robustness and automation. Robustness and automation are key demands to improve assay performance and reliability of multiplexed assays, and to minimize the time of analysis. These key demands are addressed in this thesis and novel methods and techniques concerning assay automation, array fabrication as well as performance and detection strategies related to protein microarrays are presented and discussed. In the first paper an automated assay format, based on planar protein microarrays is described and evaluated by the detection of several auto-antibodies from human serum and by quantification of matrix metalloproteases present in plasma. Diffusion-rate limited solid phase reactions were enhanced by microagitation, using the surface acoustic wave technology, resulting in a slightly increased signal-to-noise ratio. In the second paper of the thesis, a novel multiplexed immunoassay system was developed by combining a direct immunoassay with a competitive system. This set-up allows quantification of analytes present in widely varying concentrations within a single multiplex assay. In the third paper, a new concept for sample deposition is introduced, addressing contemporary problems of contact or non-contact microarrayers in protein microarray fabrication. In the fourth paper, a magnetic bead-based detection method for protein microarrays is described as a cost-effective alternative approach to the commonly used fluorescence-based confocal scanning systems. The magnetic bead-based detection could easily be performed by using an ordinary flatbed scanner. In addition, applying magnetic force to the magnetic bead-based detection approach enables to run the detection step more rapidly. Finally, in paper five, a microfluidic bead-based immunoassay for multiplexed detection of receptor tyrosine kinases in breast cancer tissue is presented. Since the assay is performed inside a capillary, the amounts of sample and reagent material could be reduced by a factor of 30 or more when compared with the current standard protein microarray assay.<br>QC 20101112

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Taylor, Michael. "Surface analysis of polymer microarrays." Thesis, University of Nottingham, 2009. http://eprints.nottingham.ac.uk/10717/.

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Polymers have been used as biomaterials for nearly a century and have recently become the material of choice for use in tissue engineering. However, the classes of biodegradable and biocompatible polymers available for use in biomedical devices and as tissue engineering scaffolds are limited. This lack of available polymers with suitable properties could inhibit the development of biomedical devices with improved biocompatibility and hinder the growth of the fledgling tissue engineering field. Researchers in the polymer and biomaterials fields have tried to remedy this problem by applying combinatorial and high throughput methods developed in drug discovery to the search for new polymers. A recent advance has been the development of combinatorial polymer libraries printed as microarrays. This format allows the polymers to be readily screened for their cell adhesion and differentiation properties, allowing ‘hit’ materials with ideal properties to be identified. However, without knowledge of the surface properties of these novel polymers it is impossible to rationalise their biological properties. The surface characterisation of such microarrays presents numerous practical problems included small sample size, sample number and even analysis of such large amounts of data. It is the aim of this thesis to develop methods for the characterisation of the surface chemistry, wettability and protein adsorption properties of polymers in situ in microarray format and within realistic timeframes. The thesis will explore multivariate statistics in the form of PCA and PLS as methods of analysing the large amount of data acquired. The first part of this thesis describes the surface chemical analysis of a polymer microarray using ToF-SIMS and XPS. A comparison of the polymers’ surface to bulk chemistries by XPS indicated that 64 % of the polymers had a surface chemistry which differed from the bulk. This reinforces the need for characterisation of the polymers’ surface chemistries, as it is obvious that this can not be inferred from their bulk chemistries. ToF-SIMS imaging was shown to be an ideal method of studying the distribution of specific ion species across the array and to confirm that the microarray was printed in the intended layout. Principal component analysis is shown to be an ideal technique to analyse both ToF-SIMS and XPS spectral data from the arrays, allowing similarities and differences in the surface chemistry of the polymers to be easily visualised. To estimate the surface energies of the arrayed polymers it is necessary to use picolitre volume droplets to make contact angle measurements. In Chapter 4 it is shown that contact angle measurements taken from picolitre volume water droplets are equivalent to those measured from more conventional microlitre droplets. In Chapter 5 picolitre contact angle measurements are used to estimate the polar and dispersive surface energies of a polymer microarray, which has been specifically designed to exhibit a maximum range of surface energy values. The analysis shows that there is indeed great variation in the WCA and polar surface energies of the polymers, demonstrating the power of intelligently designed combinatorial libraries. To understand the chemical basis of this large range of surface energies the results are compared to surface chemical data from ToF-SIMS and XPS. Surface atomic and functional data from XPS is unable to provide any definitive explanations for the range of surface energies observed. However, information about the molecular structure of the surface from ToF-SIMS gives an insight into what surface functionalities are responsible for high and low surface energies. In Chapter 6 PLS regression is investigated further as a method for investigating surface structure-property relationships in large polymer libraries. Specifically two issues are investigated: the influence of sample number on the results obtained and the ability of PLS to make quantitative predictions. The ToF-SIMS and surface energy dataset discussed in Chapter 5 is used for this task. It is demonstrated that the results obtained from PLS models of large polymer libraries are equivalent to those obtained from much smaller datasets, in terms of the ions identified in the regression vector. Using various test sets of polymers it is shown that there is a limit to the predictive ability of PLS: specifically, as the difference between the training and test sets increases, the quality of the predictions decreases. Potential problems with data pre-processing and re-scaling are also identified. In the final experimental chapter two methods are described for investigating protein adhesion and adsorption to micro-arrayed polymers using AFM and fluorescently labelled proteins. Both methods indicate a wide range of protein adsorption properties within the group of polymers analysed. A good correlation between the two sets of data was observed which appears to validate both methods. In summary the work described in this thesis has demonstrated the feasibility of the characterisation of the surface chemistry, energetics and protein adsorption properties of a micro-arrayed polymer library within realistic time-frames. PCA and PLS have been shown to be useful tools for analysing the data obtained. It is hoped that the methods described in this thesis will allow the biological data from polymer microarrays to be rationalised using the surface properties of the polymers, allowing the design of new biomaterials.

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Più fonti

Libri sul tema "Microarrays"

Jang, Rampal B. Microarrays. New Jersey: Humana Press, 2007. http://dx.doi.org/10.1385/159745303x.

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Dill, Kilian, Robin Hui Liu, and Piotr Grodzinski, eds. Microarrays. New York, NY: Springer New York, 2009. http://dx.doi.org/10.1007/978-0-387-72719-6.

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Rampal, Jang B., ed. Microarrays. Totowa, NJ: Humana Press, 2007. http://dx.doi.org/10.1007/978-1-59745-303-5.

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Rampal, Jang B., ed. Microarrays. Totowa, NJ: Humana Press, 2007. http://dx.doi.org/10.1007/978-1-59745-304-2.

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Muller, Hans-Joachim. Microarrays. Burlington, MA: Elsevier Academic Press, 2005.

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B, Rampal Jang, and Rampal Jang B, eds. Microarrays. 2nd ed. Totowa, N.J: Humana, 2007.

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Thomas, Roeder, ed. Microarrays. Burlington, MA: Elsevier Academic Press, 2006.

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Kilcoyne, Michelle, and Jared Q. Gerlach, eds. Glycan Microarrays. New York, NY: Springer US, 2022. http://dx.doi.org/10.1007/978-1-0716-2148-6.

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Cretich, Marina, and Alessandro Gori, eds. Peptide Microarrays. New York, NY: Springer US, 2023. http://dx.doi.org/10.1007/978-1-0716-2732-7.

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Khademhosseini, Ali, Kahp-Yang Suh, and Mohammed Zourob, eds. Biological Microarrays. Totowa, NJ: Humana Press, 2011. http://dx.doi.org/10.1007/978-1-59745-551-0.

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Capitoli di libri sul tema "Microarrays"

Seliger, Hartmut. "Introduction." In Microarrays, 1–36. Totowa, NJ: Humana Press, 2007. http://dx.doi.org/10.1007/978-1-59745-303-5_1.

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Chou, Cheng-Chung, and Konan Peck. "Design and Fabrication of Spotted Long Oligonucleotide Microarrays for Gene Expression Analysis." In Microarrays, 213–25. Totowa, NJ: Humana Press, 2007. http://dx.doi.org/10.1007/978-1-59745-303-5_10.

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Rampal, Jang B., Peter J. Coassin, and Robert S. Matson. "Construction of In Situ Oligonucleotide Arrays on Plastic." In Microarrays, 227–46. Totowa, NJ: Humana Press, 2007. http://dx.doi.org/10.1007/978-1-59745-303-5_11.

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Berry, Ian R., Carol A. Delaney, and Graham R. Taylor. "Detecting Ligated Fragments on Oligonucleotide Microarrays." In Microarrays, 247–65. Totowa, NJ: Humana Press, 2007. http://dx.doi.org/10.1007/978-1-59745-303-5_12.

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Ho-Pun-Cheung, Alexandre, Hafid Abaibou, Philippe Cleuziat, and Evelyne Lopez-Crapez. "Detection of Single-Nucleotide Polymorphisms in Cancer-Related Genes by Minisequencing on a Microelectronic DNA Chip." In Microarrays, 267–78. Totowa, NJ: Humana Press, 2007. http://dx.doi.org/10.1007/978-1-59745-303-5_13.

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Matson, Robert S., and Jang B. Rampal. "Hybridization Analysis Using Oligonucleotide Probe Arrays." In Microarrays, 279–98. Totowa, NJ: Humana Press, 2007. http://dx.doi.org/10.1007/978-1-59745-303-5_14.

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Antohe, Bogdan V., and Patrick W. Cooley. "In Situ Synthesis of Peptide Microarrays Using Ink-Jet Microdispensing." In Microarrays, 299–312. Totowa, NJ: Humana Press, 2007. http://dx.doi.org/10.1007/978-1-59745-303-5_15.

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Xu, Ming-Qun, Inca Ghosh, Samvel Kochinyan, and Luo Sun. "Intein-Mediated Peptide Arrays for Epitope Mapping and Kinase/Phosphatase Assays." In Microarrays, 313–38. Totowa, NJ: Humana Press, 2007. http://dx.doi.org/10.1007/978-1-59745-303-5_16.

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Matson, Robert S., Raymond C. Milton, Michael C. Cress, Tom S. Chan, and Jang B. Rampal. "Printing Low Density Protein Arrays in Microplates." In Microarrays, 339–61. Totowa, NJ: Humana Press, 2007. http://dx.doi.org/10.1007/978-1-59745-303-5_17.

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Zong, Yaping, Shanshan Zhang, Huang-Tsu Chen, Yunfei Zong, and Yaxian Shi. "Forward-Phase and Reverse-Phase Protein Microarray." In Microarrays, 363–73. Totowa, NJ: Humana Press, 2007. http://dx.doi.org/10.1007/978-1-59745-303-5_18.

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Atti di convegni sul tema "Microarrays"

Selcuk, Sahan Yoruc, Xilin Yang, Bijie Bai, Yijie Zhang, Yuzhu Li, Musa Aydin, Aras Firat Unal, et al. "Deep Learning-Enabled Classification of HER2 Score in Breast Cancer Using Pyramid Sampling." In CLEO: Applications and Technology, ATh1B.7. Washington, D.C.: Optica Publishing Group, 2024. http://dx.doi.org/10.1364/cleo_at.2024.ath1b.7.

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We present an automated, deep learning-based method for HER2 score classification in breast cancer, achieving 85.47% accuracy on tissue microarrays from 300 patients. This method can significantly improve the HER2 evaluation process, saving diagnostician time.

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Tsai, Chih-Yuan, Hao-Sung Chiu, Chen-Hsun Wu, Yu-Han Kung, Ting-Chun Lee, Shan-Yu Chen, Tzu-Yi Yang, and Ching-Fuh Lin. "Color-conversion Arrays with Ultra-small Pixel Sizes for Micro-LED Displays." In Frontiers in Optics, JTu4A.60. Washington, D.C.: Optica Publishing Group, 2024. https://doi.org/10.1364/fio.2024.jtu4a.60.

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We address mass transfer challenges using color-conversion technology, achieving ultrahigh-density microarrays: 11548 PPI for single color, 5774 PPI for full color, 72.98% and 65.36% conversion efficiencies for green and red, respectively, at 1.4-μm pixel size.

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Martins, Diogo, Xi Wei, Rastislav Levicky, and Yong-Ak Song. "Accelerating the Mass Transport of DNA Biomolecules Onto DNA Microarray for Enhanced Detection by Electrokinetic Concentration in a Microfluidic Chip." In ASME 2016 5th International Conference on Micro/Nanoscale Heat and Mass Transfer. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/mnhmt2016-6562.

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Morpholinos (MOs) are synthetic nucleic acids analogues with a non-charged backbone of morpholine rings. To enhance the MO-DNA hybridization assay speed, we propose the integration of a MO microarray with an ion concentration polarization (ICP) based microfluidic concentrator. The ICP concentrator collects target biomolecules from a ∼μL fluidic DNA sample and concentrates them electrokinetically into a ∼nL plug located in the vicinity of the MO probes. ICP preconcentration not only reduces the analyte diffusion length but also increases the binding reaction rate, and as a result, ICP-enhanced MO microarrays allow much faster hybridization than standard diffusion-limited MO microarrays.

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Zien, Alexander, Juliane Fluck, Ralf Zimmer, and Thomas Lengauer. "Microarrays." In the sixth annual international conference. New York, New York, USA: ACM Press, 2002. http://dx.doi.org/10.1145/565196.565239.

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Gruhler, Holger, Nicolaus Hey, Martin Müller, Stefan Békési, Michael Freygang, Hermann Sandmaier, and Roland Zengerle. "Topspot: A New Method for the Fabrication of Biochips." In ASME 1999 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 1999. http://dx.doi.org/10.1115/imece1999-0299.

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Abstract We present a new method for generating microarrays of liquid droplets. This is of basic importance for the fabrication of so called biochips. To generate a microarray we use a print-module containing 24 nozzles. Each nozzle is connected to one of 24 different reservoirs on the same print-module. By applying a high acceleration to the print-module it can be achieved that all of the 24 nozzles eject a small droplet at the same time. This effect is due to the inertia of the liquid inside the print-module. This new method makes the production of low and medium density biochips much faster and significantly cheaper.

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Dawson, Elliott P., James Hudson, John Steward, Philip A. Donnell, Wing W. Chan, and Richard F. Taylor. "Membrane-based microarrays." In Photonics East '99, edited by Stephanus Buettgenbach. SPIE, 1999. http://dx.doi.org/10.1117/12.370292.

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Park, Hyun Seok. "Mining a logical set of microarray data from heterogeneous multi-platform microarrays." In the 2nd international conference. New York, New York, USA: ACM Press, 2008. http://dx.doi.org/10.1145/1352793.1352911.

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Altug, Hatice. "Plasmonic Microarrays for Biology." In Bio-Optics: Design and Application. Washington, D.C.: OSA, 2013. http://dx.doi.org/10.1364/boda.2013.bw3a.2.

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ESCANDE, DENIS G. "DNA MICROARRAYS AND ARRHYTHMIAS." In Proceedings of the 31st International Congress on Electrocardiology. WORLD SCIENTIFIC, 2005. http://dx.doi.org/10.1142/9789812702234_0079.

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Carlon, Enrico. "Thermodynamics of DNA microarrays." In Stochastic Models in Biological Sciences. Warsaw: Institute of Mathematics Polish Academy of Sciences, 2008. http://dx.doi.org/10.4064/bc80-0-13.

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Rapporti di organizzazioni sul tema "Microarrays"

Beer, N., B. Baker, T. Piggott, S. Maberry, C. Hara, J. DeOtte, W. Benett, E. Mukerjee, J. Dzenitis, and E. Wheeler. Hybridization and Selective Release of DNA Microarrays. Office of Scientific and Technical Information (OSTI), November 2011. http://dx.doi.org/10.2172/1033734.

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Martin, Jennifer A., Yaroslav Chushak, Jorge C. Benavides, Joshua Hagen, and Nancy Kelley-Loughnane. DNA Microarrays for Aptamer Identification and Structural Characterization. Fort Belvoir, VA: Defense Technical Information Center, September 2012. http://dx.doi.org/10.21236/ada597207.

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Gregory Stephanopoulos. Development of DNA Microarrays for Metabolic Pathway and Bioprocess Monitoring. Office of Scientific and Technical Information (OSTI), July 2004. http://dx.doi.org/10.2172/837870.

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Rimm, David L. Spectral Analysis of Breast Cancer on Tissue Microarrays: Seeing Beyond Morphology. Fort Belvoir, VA: Defense Technical Information Center, May 2003. http://dx.doi.org/10.21236/ada417663.

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Gottardo, Raphael, Adrian E. Raftery, Ka Y. Yeung, and Roger E. Bumgarner. Bayesian Robust Inference for Differential Gene Expression in cDNA Microarrays with Multiple Samples. Fort Belvoir, VA: Defense Technical Information Center, July 2004. http://dx.doi.org/10.21236/ada478418.

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Ljungman, Mats. Use of Nascent RNA Microarrays to Study Inducible Gene Expression in Breast Cancer Cells. Fort Belvoir, VA: Defense Technical Information Center, September 2005. http://dx.doi.org/10.21236/ada443027.

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Cindy, Shi. Development of Microarrays-Based Metagenomics Technology for Monitoring Sulfate-Reducing Bacteria in Subsurface Environments. Office of Scientific and Technical Information (OSTI), July 2015. http://dx.doi.org/10.2172/1194725.

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Rimm, David L. Outcome Based Screening for Prognostic Phospho-RTK (Receptor Tyrosine Kinase) Antibodies Using Tissue Microarrays. Fort Belvoir, VA: Defense Technical Information Center, August 2002. http://dx.doi.org/10.21236/ada410085.

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Rimm, David. Outcome Based Screening for Prognostic Phospho-RTK (Receptor Tyrosine Kinase) Antibodies Using Tissue Microarrays). Fort Belvoir, VA: Defense Technical Information Center, August 2004. http://dx.doi.org/10.21236/ada430123.

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