934 resultados para CDNA MICROARRAY


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Usher syndrome, a combination of retinitis pigmentosa (RP) and sensorineural hearing loss with or without vestibular dysfunction, displays a high degree of clinical and genetic heterogeneity. Three clinical subtypes can be distinguished, based on the age of onset and severity of the hearing impairment, and the presence or absence of vestibular abnormalities. Thus far, eight genes have been implicated in the syndrome, together comprising 347 protein-coding exons.

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A novel multiplexed immunoassay for the analysis of phycotoxins in shellfish samples has been developed. Therefore, a regenerable chemiluminescence (CL) microarray was established which is able to analyze automatically three different phycotoxins (domoic acid (DA), okadaic acid (OA) and saxitoxin (STX)) in parallel on the analysis platform MCR3. As a test format an indirect competitive immunoassay format was applied. These phycotoxins were directly immobilized on an epoxy-activated PEG chip surface. The parallel analysis was enabled by the simultaneous addition of all analytes and specific antibodies on one microarray chip. After the competitive reaction, the CL signal was recorded by a CCD camera. Due to the ability to regenerate the toxin microarray, internal calibrations of phycotoxins in parallel were performed using the same microarray chip, which was suitable for 25 consecutive measurements. For the three target phycotoxins multi-analyte calibration curves were generated. In extracted shellfish matrix, the determined LODs for DA, OA and STX with values of 0.5±0.3 µg L(-1), 1.0±0.6 µg L(-1), and 0.4±0.2 µg L(-1) were slightly lower than in PBS buffer. For determination of toxin recoveries, the observed signal loss in the regeneration was corrected. After applying mathematical corrections spiked shellfish samples were quantified with recoveries for DA, OA, and STX of 86.2%, 102.5%, and 61.6%, respectively, in 20 min. This is the first demonstration of an antibody based phycotoxin microarray.

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Paralytic Shellfish Poisoning (PSP) is a serious human illness caused by ingestion of seafood enriched with paralytic shellfish toxins (PSTs). PSTs are neurotoxic compounds produced by marine dinoflagellates, specifically by Alexandrium spp., Gymnodinium catenatum and Pyrodinium bahamense. Every year, massive monitoring of PSTs and their producers is undertaken worldwide to avoid PSP incidences. Here we developed a sensitive, hydrolysis probe-based quantitative PCR (qPCR) assay to detect a gene essential for PST synthesis across different dinoflagellate species and genera and tested it on cDNA generated from environmental samples spiked with Alexandrium minutum or Alexandrium fundyense cells. The assay was then applied to two environmental sample series from Norway and Spain and the results were complemented with cell counts, LSU-based microarray data and toxin measurements (enzyme-linked immunosorbent assay (ELISA) and surface plasmon resonance (SPR) biosensor method). The overall agreement between the results of the qPCR assay and the complementary data was good. The assay reliably detected sxtA transcripts from Alexandrium spp. and G. catenatum, even though Alexandrium spp. cell concentrations were mostly so low that they could not be quantified microscopically. Agreement between the novel assay and toxin measurements or cell counts was generally good; the few inconsistencies observed were most likely due to disparate residence times of sxtA transcripts and PSTs in seawater, or, in the case of cell counts, to dissimilar sxtA4 transcript numbers per cell in different dinoflagellate strains or species. © 2013 Elsevier B.V.

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Blooms of Alexandrium occur annually during the summer months in the North Channel of Cork Harbour on the south coast of Ireland. This study monitored an extensive bloom of the toxin producing Alexandrium minutum during the summer of 2011 with the use of the MIDTAL (Microarrays for the Detection of Toxic Algae) microarray and a prototype multiplex surface plasmon resonance (multi SPR) biosensor. Microarray signal intensities and toxin results from three testing platforms of the prototype multi SPR biosensor, commercial (CER) enzyme-linked immunosorbent assay (ELISA) and high performance liquid chromatography (HPLC) were compared against light microscopy counts. The main aim was to demonstrate the use of these methodologies to support national monitoring agencies by providing a faster and more accurate means of identifying and quantifying the harmful phytoplankton community and their toxins in natural water samples. Both the microarray signals and multi SPR biosensor results followed a significant trend with light microscopy results and both techniques indicated detection limits of <4000 cells of A. minutum in natural seawater samples.

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A novel multiplex microarray has been developed for the detection of five groups of harmful algal and cyanobacterial toxins found in marine, brackish, and freshwater environments including domoic acid (DA), okadaic acid (OA, and analogues), saxitoxin (STX, and analogues), cylindrospermopsin (CYN) and microcystins (MC, and analogues). The sensitivity and specificity were determined and feasibility to be used as a screening tool investigated. Results for algal/cyanobacterial cultures (n = 12) and seawater samples (n = 33) were compared to conventional analytical methods, such as high performance liquid chromatography (HPLC) and liquid chromatography tandem mass spectrometry (LC-MS/MS). Detection limits for the 15 min assay were 0.37, 0.44, 0.05, 0.08, and 0.40 ng/mL for DA, OA, STX, CYN, and MC, respectively. The correlation of data obtained from the microarray compared to conventional analysis for the 12 cultures was r(2) = 0.83. Analysis of seawater samples showed that 82, 82, 70, 82, and 12% of samples were positive (>IC20) compared to 67, 55, 36, 0, and 0% for DA, OA, STX, CYN, and MC, respectively, for conventional analytical methods. The discrepancies in results can be attributed to the enhanced sensitivity and cross-reactivity profiles of the antibodies in the MBio microarray. The feasibility of the microarray as a rapid, easy to use, and highly sensitive screening tool has been illustrated for the five-plex detection of biotoxins. The research demonstrates an early warning screening assay to support national monitoring agencies by providing a faster and more accurate means of identifying and quantifying harmful toxins in water samples.

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The introduction of microarray technology to the scientific and medical communities has dramatically changed the way in which we now address basic biomedical questions. Expression profiling using microarrays facilitates an experimental approach where alterations in the transcript level of entire transcriptomes can be simultaneously assayed in response to defined stimuli. We have used microarray analysis to identify downstream transcriptional targets of the BRCA1 (Breast Cancer 1) tumour-suppressor gene as a means of defining its function. BRCA1 has been implicated in the predisposition to early onset breast and ovarian cancer and while its exact function remains to be defined, roles in DNA repair, cell-cycle control and transcriptional regulation have been implied. In the current study we have generated cell lines with tetracycline-regulated, inducible expression of BRCA1 as a tool to identify genes, which might represent important effectors of BRCA1 function. Oligonucleotide array-based expression profiling identified a number of genes that were upregulated at various times following inducible expression of BRCA1 including the DNA damage-responsive gene GADD45 (Growth Arrest after DNA Damage). Identified targets were confirmed by Northern blot analysis and their functional significance as BRCA1 targets examined.

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This paper investigates the gene selection problem for microarray data with small samples and variant correlation. Most existing algorithms usually require expensive computational effort, especially under thousands of gene conditions. The main objective of this paper is to effectively select the most informative genes from microarray data, while making the computational expenses affordable. This is achieved by proposing a novel forward gene selection algorithm (FGSA). To overcome the small samples' problem, the augmented data technique is firstly employed to produce an augmented data set. Taking inspiration from other gene selection methods, the L2-norm penalty is then introduced into the recently proposed fast regression algorithm to achieve the group selection ability. Finally, by defining a proper regression context, the proposed method can be fast implemented in the software, which significantly reduces computational burden. Both computational complexity analysis and simulation results confirm the effectiveness of the proposed algorithm in comparison with other approaches