Current Issue : January - March Volume : 2019 Issue Number : 1 Articles : 6 Articles
In metabolomics data, like other -omics data, normalization is an important\npart of the data processing. The goal of normalization is to reduce the variation\nfrom non-biological sources (such as instrument batch effects), while\nmaintaining the biological variation. Many normalization techniques make\nadjustments to each sample. One common method is to adjust each sample\nby its Total Ion Current (TIC), i.e. for each feature in the sample, divide its\nintensity value by the total for the sample. Because many of the assumptions\nof these methods are dubious in metabolomics data sets, we compare these\nmethods to two methods that make adjustments separately for each metabolite,\nrather than for each sample. These two methods are the following: 1) for\neach metabolite, divide its value by the median level in bridge samples\n(BRDG); 2) for each metabolite divide its value by the median across the experimental\nsamples (MED). These methods were assessed by comparing the\ncorrelation of the normalized values to the values from targeted assays for a\nsubset of metabolites in a large human plasma data set. The BRDG and MED\nnormalization techniques greatly outperformed the other methods, which often\nperformed worse than performing no normalization at all....
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Since most of the bodyâ??s extracellular matrix (ECM) is composed of collagen and elastin,\nwe believe the choice of these materials is key for the future and promise of tissue engineering. Once it\nis known how elastin content of ECM guides cellular behavior (in 2D or 3D), one will be able to harness\nthe power of collagen-elastin microenvironments to design and engineer stimuli-responsive tissues.\nMoreover, the implementation of such matrices to promote endothelial-mesenchymal transition\nof primary endothelial cells constitutes a powerful tool to engineer 3D tissues. Here, we design\na 3D collagen-elastin scaffold to mimic the native ECM of heart valves, by providing the strength\nof collagen layers, as well as elasticity. Valve interstitial cells (VICs) were encapsulated in the\ncollagen-elastin hydrogels and valve endothelial cells (VECs) cultured onto the surface to create\nan in vitro 3D VEC-VIC co-culture. Over a seven-day period, VICs had stable expression levels of integrin-- and F-actin and continuously proliferated, while cell morphology changed to more\nelongated. VECs maintained endothelial phenotype up to day five, as indicated by low expression\nof F-actin and integrin--, while transformed VECs accounted for less than 7% of the total VECs in\nculture. On day seven, over 20% VECs were transformed to mesenchymal phenotype, indicated by\nincreased actin filaments and higher expression of integrin These findings demonstrate that our\n3D collagen-elastin scaffolds provided a novel tool to study cell-cell or cell-matrix interactions in vitro,\npromoting advances in the current knowledge of valvular endothelial cell mesenchymal transition....
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High-frequency oscillations (HFOs) in the electroencephalogram (EEG) are thought to be a promising marker for epileptogenicity.\nA number of automated detection algorithms have been developed for reliable analysis of invasively recorded HFOs.\nHowever, invasive recordings are not widely applicable since they bear risks and costs, and the harm of the surgical intervention of\nimplantation needs to be weighted against the informational benefits of the invasive examination. In contrast, scalp EEG is widely\navailable at low costs and does not bear any risks. However, the detection of HFOs on the scalp represents a challenge that was\ntaken on so far mostly via visual detection. Visual detection of HFOs is, in turn, highly time-consuming and subjective. In this\nreview, we discuss that automated detection algorithms for detection of HFOs on the scalp are highly warranted because the\navailable algorithms were all developed for invasively recorded EEG and do not perform satisfactorily in scalp EEG because of the\nlow signal-to-noise ratio and numerous artefacts as well as physiological activity that obscures the tiny phenomena in the highfrequency\nrange....
Background: The area of application of electronic stethoscopes in medical diagnostics\ncovers the scope of usability of the acoustic stethoscopes, from which they have\nevolved and which they could potentially replace. However, the principle of operation\nof these two groups of diagnostic devices is substantially different. Thus, an important\nquestion arises, regarding the differences in parameters of the transmitted sound and\ntheir potential diagnostic consequences in clinical practice.\nMethods: In order to answer this question, heart auscultation signals are recorded\nusing various stethoscopes and divided into short fragments based on the analysis of\nthe synchronized recordings of electrocardiogram signals. Next, a dedicated algorithm\nis used to extract representative datasets for each case, which are then analyzed for\ntheir acoustic parameters. Four different electronic stethoscopes were investigated,\ntogether with an acoustic stethoscope as a reference point.\nResults: The determined acoustic characteristics of the considered stethoscopes differ\nsignificantly between each other.\nConclusions: The differences in sound transmitted by various stethoscope models\nmay translate into significant differences in quality of the obtained diagnosis. It is also\npointed out, that the terminology and application guidelines regarding the electronic\nstethoscopes are misleading and should be changed....
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