3 resultados para multiple sensor data

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A miniaturised gas analyser is described and evaluated based on the use of a substrate-integrated hollow waveguide (iHWG) coupled to a microsized near-infrared spectrophotometer comprising a linear variable filter and an array of InGaAs detectors. This gas sensing system was applied to analyse surrogate samples of natural fuel gas containing methane, ethane, propane and butane, quantified by using multivariate regression models based on partial least square (PLS) algorithms and Savitzky-Golay 1(st) derivative data preprocessing. The external validation of the obtained models reveals root mean square errors of prediction of 0.37, 0.36, 0.67 and 0.37% (v/v), for methane, ethane, propane and butane, respectively. The developed sensing system provides particularly rapid response times upon composition changes of the gaseous sample (approximately 2 s) due the minute volume of the iHWG-based measurement cell. The sensing system developed in this study is fully portable with a hand-held sized analyser footprint, and thus ideally suited for field analysis. Last but not least, the obtained results corroborate the potential of NIR-iHWG analysers for monitoring the quality of natural gas and petrochemical gaseous products.

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Fingolimod is a new and efficient treatment for multiple sclerosis (MS). The drug administration requires special attention to the first dose, since cardiovascular adverse events can be observed during the initial six hours of fingolimod ingestion. The present study consisted of a review of cardiovascular data on 180 patients with MS receiving the first dose of fingolimod. The rate of bradycardia in these patients was higher than that observed in clinical trials with very strict inclusion criteria for patients. There were less than 10% of cases requiring special attention, but no fatal cases. All but one patient continued the treatment after this initial dose. This is the first report on real-life administration of fingolimod to Brazilian patients with MS, and one of the few studies with these characteristics in the world.

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Multiple sclerosis, which is the most common cause of chronic neurological disability in young adults, is an inflammatory, demyelinating, and neurodegenerative disease of the CNS, which leads to the formation of multiple foci of demyelinated lesions in the white matter. The diagnosis is based currently on magnetic resonance image and evidence of dissemination in time and space. However, this could be facilitated if biomarkers were available to rule out other disorders with similar symptoms as well as to avoid cerebrospinal fluid analysis, which requires an invasive collection. Additionally, the molecular mechanisms of the disease are not completely elucidated, especially those related to the neurodegenerative aspects of the disease. The identification of biomarker candidates and molecular mechanisms of multiple sclerosis may be approached by proteomics. In the last 10 years, proteomic techniques have been applied in different biological samples (CNS tissue, cerebrospinal fluid, and blood) from multiple sclerosis patients and in its experimental model. In this review, we summarize these data, presenting their value to the current knowledge of the disease mechanisms, as well as their importance in identifying biomarkers or treatment targets.