918 resultados para high throughput


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Conventional catalyzed thermal CVD of carbon microcoils commonly suffers from poor control of the coil shape and morphology and rarely reaches the nanoscale size range. This article reports on an unconventional Ni-P alloy-catalyzed, high-throughput, highly reproducible CVD of ultra-long carbon coil-like micro- and nano-structures using acetylene precursor at relatively low process temperatures. Helical carbon microcoils with consistently uniform, circular cross-sections and a high degree of crystallinity have been synthesized at 750 °C. A further reduction of the temperature to 650 °C led to the growth of ultra-long (up to several mm) wave-like carbon nanofibers made of two nanowires with the diameters in the 100-200 nm range. The results of the XRD and Raman analysis reveal that the nanofibers feature only a slightly more disordered structure compared to the microcoils. Our results suggest that morphology and structure of the carbon coil-like micro- and nano-structures can be tailored by the appropriate alloying of the catalyst and the choice of the CVD process parameters.

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As for many other cancers, metastasis is the leading cause of death of patients with ovarian cancer. Vigorous basic and clinical research is being performed to initiate more efficacious treatment strategies to improve the poor outcome of women with this cancer. Current treatment for ovarian cancer includes advanced cyto-reductive surgery and traditional platinum and taxane combined chemotherapy. Clinical trials using novel cytotoxic reagents and tyrosine kinase inhibitors have also been progressing. In parallel, the application of robust unbiased high throughput research platforms using transcriptomic and proteomic approaches has identified that not only individual cell signalling pathways, but a network of molecular pathways, play an important role in the biology of ovarian cancer. Furthermore, intensive genomic and epigenetic analyses have also revealed single nucleotide polymorphisms associated with risk and/or aetiology of this cancer including patient response to treatment. Taken together, these approaches, that are advancing our understanding, will have an impact on the generation of new therapeutic approaches and strategies for improving the outcome and quality of life of patients with ovarian cancer in the near future.

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Human saliva harbours proteins of clinical relevance and about 30% of blood proteins are also present in saliva. This highlights that saliva can be used for clinical applications just as urine or blood. However, the translation of salivary biomarker discoveries into clinical settings is hampered by the dynamics and complexity of the salivary proteome. This review focuses on the current status of technological developments and achievements relating to approaches for unravelling the human salivary proteome. We discuss the dynamics of the salivary proteome, as well as the importance of sample preparation and processing techniques and their influence on downstream protein applications; post-translational modifications of salivary proteome and protein: protein interactions. In addition, we describe possible enrichment strategies for discerning post-translational modifications of salivary proteins, the potential utility of selected-reaction-monitoring techniques for biomarker discovery and validation, limitations to proteomics and the biomarker challenge and future perspectives. In summary, we provide recommendations for practical saliva sampling, processing and storage conditions to increase the quality of future studies in an emerging field of saliva clinical proteomics. We propose that the advent of technologies allowing sensitive and high throughput proteome-wide analyses, coupled to well-controlled study design, will allow saliva to enter clinical practice as an alternative to blood-based methods due to its simplistic nature of sampling, non-invasiveness, easy of collection and multiple collections by untrained professionals and cost-effective advantages.

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Objective There is evidence that folate metabolism has a role in migraine pathophysiology, particularly in the migraine with aura subtype. In this study we investigate whether two non-synonymous single nucleotide polymorphisms (SNPs), rs1950902 (C401T; R134K) and rs2236225 (G1958A; R653Q), in MTHDF1 are associated with migraine in an Australian case-control population. Background Increased plasma levels of homocysteine (HCy), one of the metabolites produced in the folate pathway, has been found to be a risk factor for migraine. There is also a genetic link, as a common polymorphism (C667T) that reduces the catalytic activity of MTHFR, the enzyme that catalyses the formation of HCy, is associated with an increase in risk of the migraine with aura (MA) subtype. MTHFD1 is a crucial multifunctional enzyme that catalyses three separate reactions of the folate pathway and therefore variants in MTHFD1 may also influence migraine susceptibility. Methods The R134K and R653Q variants in MTHFD1 were genotyped in an Australian cohort of 520 unrelated migraineurs (162 were diagnosed with migraine without aura [MO] and 358 with MA) and 520 matched controls. Data were analysed for association with migraine and for interaction with the MTHFR C667T polymorphism. Results We find no significant differences in genotype or allele frequencies for either SNP between migraineurs and controls, or when either MO or MA cases were compared to controls. In addition these MTHFD1 polymorphisms did not appear to influence the risk of MA conferred by the MTHFR 667T allele. Conclusions We find no evidence for association of the MTHFD1 R134K and R653Q polymorphisms with migraine in our Australian case-control population. However, as folate metabolism appears to be important in migraine, particularly with respect to the aura component, future studies using high throughput methods to expand the number of SNPs in folate-related genes genotyped and investigation of interactions between SNPs may be justified.

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Weak cell-surface adhesion of cell lines to tissue culture surfaces is a common problem and presents technical limitations to the design of experiments. To overcome this problem, various surface coating protocols have been developed. However, a comparative and precise real-time measurement of their impact on cell behavior has not been conducted. The prostate cancer cell line LNCaP, derived from a patient lymph node metastasis, is a commonly used model system in prostate cancer research. However, the cells’ characteristically weak attachment to the surface of tissue culture vessels and cover slips has impeded their manipulation and analysis and use in high throughput screening. To improve the adherence of LNCaP cells to the culture surface, we compared different coating reagents (poly-L-lysine, poly-L-ornithine, collagen type IV, fibronectin, and laminin) and culturing conditions and analyzed their impact on cell proliferation, adhesion, morphology, mobility and gene expression using real-time technologies. The results showed that fibronectin, poly-L-lysine and poly-L-ornithine improved LNCaP cells adherence and provoked cell morphology alterations, such as increase of nuclear and cellular area. These coating reagents also induced a higher expression of F-actin and reduced cell mobility. In contrast, laminin and collagen type IV did not improve adherence but promoted cell aggregation and affected cell morphology. Cells cultured in the presence of laminin displayed higher mobility than control cells. All the coating conditions significantly affected cell viability; however, they did not affect the expression of androgen receptor-regulated genes. Our comparative findings provide important insight for the selection of the ideal coating reagent and culture conditions for the cancer cell lines with respect to their effect on proliferation rate, attachment, morphology, migration, transcriptional response and cellular cytoskeleton arrangement.

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Sortase A is a membrane enzyme responsible for the anchoring of surface-exposed proteins to the cell wall envelope of Gram-positive bacteria. As a well-studied member of the sortase subfamily catalysing the cell wall anchoring of important virulence factors to the surface of staphylococci, enterococci and streptococci, sortase A plays a critical role in Gram-positive bacterial pathogenesis. It is thus considered a promising target for the development of new anti-infective drugs that aim to interfere with important Gram-positive virulence mechanisms, such as adhesion to host tissues, evasion of host defences, and biofilm formation. The additional properties of sortase A as an enzyme that is not required for Gram-positive bacterial growth or viability and is conveniently located on the cell membrane making it more accessible to inhibitor targeting, constitute additional reasons reinforcing the view that sortase A is an ideal target for anti-virulence drug development. Many inhibitors of sortase A have been identified to date using high-throughput or in silico screening of compound libraries (synthetic or natural), and while many have proved useful tools for probing the action model of the enzyme, several are also promising candidates for the development into potent inhibitors. This review is focused on the most promising sortase A inhibitor compounds that are currently in development as leads towards a new class of anti-infective drugs that are urgently needed to help combat the alarming increase in antimicrobial resistance.

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Polymethacrylate monoliths, specifically poly(glycidyl methacrylate-co-ethylene dimethacrylate) or poly(GMA-co-EDMA) monoliths, are a new generation of chromatographic supports and are significantly different from conventional particle-based adsorbents, membranes, and other monolithic supports for biomolecule purification. Similar to other monoliths, polymethacrylate monoliths possess large pores which allow convective flow of mobile phase and result in high flow rates at reduced pressure drop, unlike particulate supports. The simplicity of the adsorbent synthesis, pH resistance, and the ease and flexibility of tailoring their pore size to that of the target biomolecule are the key properties which differentiate polymethacrylate monoliths from other monoliths. Polymethacrylate monoliths are endowed with reactive epoxy groups for easy functionalization (with anion-exchange, hydrophobic, and affinity ligands) and high ligand retention. In this review, the structure and performance of polymethacrylate monoliths for chromatographic purification of biomolecules are evaluated and compared to those of other supports. The development and use of polymethacrylate monoliths for research applications have grown rapidly in recent times and have enabled the achievement of high through-put biomolecule purification on semi-preparative and preparative scales.

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High-throughput plasmid DNA (pDNA) manufacture is obstructed predominantly by the performance of conventional stationary phases. For this reason, the search for new materials for fast chromatographic separation of pDNA is ongoing. A poly(glycidyl methacrylate-co-ethylene glycol dimethacrylate) (GMA-EGDMA) monolithic material was synthesised via a thermal-free radical reaction, functionalised with different amino groups from urea, 2-chloro-N,N-diethylethylamine hydrochloride (DEAE-Cl) and ammonia in order to investigate their plasmid adsorption capacities. Physical characterisation of the monolithic polymer showed a macroporous polymer having a unimodal pore size distribution pivoted at 600 nm. Chromatographic characterisation of the functionalised polymers using pUC19 plasmid isolated from E. coli DH5α-pUC19 showed a maximum plasmid adsorption capacity of 18.73 mg pDNA/mL with a dissociation constant (KD) of 0.11 mg/mL for GMA-EGDMA/DEAE-Cl polymer. Studies on ligand leaching and degradation demonstrated the stability of GMA-EGDMA/DEAE-Cl after the functionalised polymers were contacted with 1.0 M NaOH, which is a model reagent for most 'cleaning in place' (CIP) systems. However, it is the economic advantage of an adsorbent material that makes it so attractive for commercial purification purposes. Economic evaluation of the performance of the functionalised polymers on the grounds of polymer cost (PC)/mg pDNA retained endorsed the suitability of GMA-EGDMA/DEAE-Cl polymer.

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The output of a differential scanning fluorimetry (DSF) assay is a series of melt curves, which need to be interpreted to get value from the assay. An application that translates raw thermal melt curve data into more easily assimilated knowledge is described. This program, called “Meltdown,” conducts four main activities—control checks, curve normalization, outlier rejection, and melt temperature (Tm) estimation—and performs optimally in the presence of triplicate (or higher) sample data. The final output is a report that summarizes the results of a DSF experiment. The goal of Meltdown is not to replace human analysis of the raw fluorescence data but to provide a meaningful and comprehensive interpretation of the data to make this useful experimental technique accessible to inexperienced users, as well as providing a starting point for detailed analyses by more experienced users.

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Next-generation sequencing techniques have revolutionized over the last decade providing researchers with low cost, high-throughput alternatives compared to the traditional Sanger sequencing methods. These sequencing techniques have rapidly evolved from first-generation to fourth-generation with very broad applications such as unravelling the complexity of the genome, in terms of genetic variations, and having a high impact on the biological field. In this review, we discuss the transition of sequencing from the second-generation to the third- and fourth-generations, and describe some of their novel biological applications. With the advancement in technology, the earlier challenges of minimal size of the instrument, flexibility of throughput, ease of data analysis and short run times are being addressed. However, the need for prospective analysis and effectiveness to test whether the knowledge of any given new variants identified has an effect on clinical outcome may need improvement.

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Motivation Extracellular vesicles (EVs) are spherical bilayered proteolipids, harboring various bioactive molecules. Due to the complexity of the vesicular nomenclatures and components, online searches for EV-related publications and vesicular components are currently challenging. Results We present an improved version of EVpedia, a public database for EVs research. This community web portal contains a database of publications and vesicular components, identification of orthologous vesicular components, bioinformatic tools and a personalized function. EVpedia includes 6879 publications, 172 080 vesicular components from 263 high-throughput datasets, and has been accessed more than 65 000 times from more than 750 cities. In addition, about 350 members from 73 international research groups have participated in developing EVpedia. This free web-based database might serve as a useful resource to stimulate the emerging field of EV research. Availability and implementation The web site was implemented in PHP, Java, MySQL and Apache, and is freely available at http://evpedia.info.

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We developed and validated a new method to create automated 3D parametric surface models of the lateral ventricles in brain MRI scans, providing an efficient approach to monitor degenerative disease in clinical studies and drug trials. First, we used a set of parameterized surfaces to represent the ventricles in four subjects' manually labeled brain MRI scans (atlases). We fluidly registered each atlas and mesh model to MRIs from 17 Alzheimer's disease (AD) patients and 13 age- and gender-matched healthy elderly control subjects, and 18 asymptomatic ApoE4-carriers and 18 age- and gender-matched non-carriers. We examined genotyped healthy subjects with the goal of detecting subtle effects of a gene that confers heightened risk for Alzheimer's disease. We averaged the meshes extracted for each 3D MR data set, and combined the automated segmentations with a radial mapping approach to localize ventricular shape differences in patients. Validation experiments comparing automated and expert manual segmentations showed that (1) the Hausdorff labeling error rapidly decreased, and (2) the power to detect disease- and gene-related alterations improved, as the number of atlases, N, was increased from 1 to 9. In surface-based statistical maps, we detected more widespread and intense anatomical deficits as we increased the number of atlases. We formulated a statistical stopping criterion to determine the optimal number of atlases to use. Healthy ApoE4-carriers and those with AD showed local ventricular abnormalities. This high-throughput method for morphometric studies further motivates the combination of genetic and neuroimaging strategies in predicting AD progression and treatment response. © 2007 Elsevier Inc. All rights reserved.

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Accurate identification of white matter structures and segmentation of fibers into tracts is important in neuroimaging and has many potential applications. Even so, it is not trivial because whole brain tractography generates hundreds of thousands of streamlines that include many false positive fibers. We developed and tested an automatic tract labeling algorithm to segment anatomically meaningful tracts from diffusion weighted images. Our multi-atlas method incorporates information from multiple hand-labeled fiber tract atlases. In validations, we showed that the method outperformed the standard ROI-based labeling using a deformable, parcellated atlas. Finally, we show a high-throughput application of the method to genetic population studies. We use the sub-voxel diffusion information from fibers in the clustered tracts based on 105-gradient HARDI scans of 86 young normal twins. The whole workflow shows promise for larger population studies in the future.

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Soil microorganisms are critical to ecosystem functioning and the maintenance of soil fertility. However, despite global increases in the inputs of nitrogen (N) and phosphorus (P) to ecosystems due to human activities, we lack a predictive understanding of how microbial communities respond to elevated nutrient inputs across environmental gradients. Here we used high-throughput sequencing of marker genes to elucidate the responses of soil fungal, archaeal, and bacterial communities using an N and P addition experiment replicated at 25 globally distributed grassland sites. We also sequenced metagenomes from a subset of the sites to determine how the functional attributes of bacterial communities change in response to elevated nutrients. Despite strong compositional differences across sites, microbial communities shifted in a consistent manner with N or P additions, and the magnitude of these shifts was related to the magnitude of plant community responses to nutrient inputs. Mycorrhizal fungi and methanogenic archaea decreased in relative abundance with nutrient additions, as did the relative abundances of oligotrophic bacterial taxa. The metagenomic data provided additional evidence for this shift in bacterial life history strategies because nutrient additions decreased the average genome sizes of the bacterial community members and elicited changes in the relative abundances of representative functional genes. Our results suggest that elevated N and P inputs lead to predictable shifts in the taxonomic and functional traits of soil microbial communities, including increases in the relative abundances of faster-growing, copiotrophic bacterial taxa, with these shifts likely to impact belowground ecosystems worldwide.

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This project aimed to identify novel genetic risk variants associated with migraine in the Norfolk Island population. Statistical analysis and bioinformatics approaches such as polygenic modeling and gene clustering methods were carried out to explore genotypic and expression data from high-throughput techniques. This project had a particular focus on hormonal genes and other genetic variants and identified a modest effect size on the migraine phenotype.