Methods in molecular biology
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Rapid diagnostic methods for fungal infections are long awaited and are expected to improve outcomes through early initiation of targeted antifungal therapy. T2Candida panel is a novel qualitative diagnostic platform that was recently approved by the US Food and Drug Administration (FDA) for diagnosis of candidemia with a mean time to species identification of less than 5 h. ⋯ By combining magnetic resonance with molecular diagnostics, T2Candida panel amplifies DNA and detects the amplified product by amplicon-induced agglomeration of supermagnetic particles and T2 Magnetic Resonance (T2MR) measurement. Here we describe the materials and methods needed to diagnose candidemia with the T2Candida panel.
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In various biomedical applications that collect, handle, and manipulate data, the amounts of data tend to build up and venture into the range identified as bigdata. In such occurrences, a design decision has to be taken as to what type of database would be used to handle this data. ⋯ However, it still has paramount importance to understand the interrelation that exists between biomedical big data and relational databases. This chapter will review the pros and cons of using relational databases to store biomedical big data that previous researches have discussed and used.
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The activated partial thromboplastin time (APTT) is a useful global assay for the assessment of the contact factor pathway of hemostasis and its inhibitors. The test is usually performed on fully automated analyzers using commercially prepared reagents. The three main clinical areas of interest are detection of factor deficiencies, detection of lupus anticoagulants and in the monitoring of therapy with unfractionated heparin. Methods are described here for assessing APTT reagents for their sensitivity to clotting time prolongation in each of these areas of interest.
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In this chapter we describe the workflow we use for labeled quantitative proteomics analysis using tandem mass tags (TMT) starting with the sample preparation and ending with the multivariate analysis of the resulting data. We detail the step-by-step process from sample processing, labeling, fractionation, and data processing using Proteome Discoverer through to data analysis and interpretation in the context of a multi-run experiment. The final analysis and data interpretation rely on an R package we call TMTPrepPro, which are deployed on a local GenePattern server, and used for generating various outputs which are also outlined herein.
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A true and accurate bottom-up global proteomic measurement will only be achieved when all proteins in a sample can be digested efficiently and at least some peptides recovered on which to base an estimate of abundance. Integral membrane proteins make up around one-third of the proteome and require specialized protocols if they are to be successfully solubilized for efficient digestion by the enzymes used in bottom-up proteomics. ⋯ A subset of peptides is purified by reverse-phase solid-phase extraction and fractionated by strong-cation exchange prior to nano-liquid chromatography with data-dependent tandem mass spectrometry. For quantitative proteomics experiments a protocol is described for stable-isotope coding of peptides using dimethylation of primary amines allowing for three-way sample multiplexing.