7 resultados para ANALYZER

em Repositório da Produção Científica e Intelectual da Unicamp


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Silver nanoparticles have attracted considerable attention due to their beneficial properties. But toxicity issues associated with them are also rising. The reports in the past suggested health hazards of silver nanoparticles at the cellular, molecular, or whole organismal level in eukaryotes. Whereas, there is also need to examine the exposure effects of silver nanoparticle to the microbes, which are beneficial to humans as well as environment. The available literature suggests the harmful effects of physically and chemically synthesised silver nanoparticles. The toxicity of biogenically synthesized nanoparticles has been less studied than physically and chemically synthesised nanoparticles. Hence, there is a greater need to study the toxic effects of biologically synthesised silver nanoparticles in general and mycosynthesized nanoparticles in particular. In the present study, attempts have been made to assess the risk associated with the exposure of mycosynthesized silver nanoparticles on a beneficial soil microbe Pseudomonas putida. KT2440. The study demonstrates mycosynthesis of silver nanoparticles and their characterisation by UV-vis spectrophotometry, FTIR, X-ray diffraction, nanosight LM20 - a particle size distribution analyzer and TEM. Silver nanoparticles obtained herein were found to exert the hazardous effect at the concentration of 0.4μg/ml, which warrants further detailed investigations concerning toxicity.

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OBJETIVE: Investigate the cardiopulmonary responses of one strength training session in young women. METHOD: Twenty-three women aged between 18 and 29 years participated in this study. All the volunteers were submitted to the following tests: cardiopulmonary and one-repetition maximum (1-RM). The strength training protocol had emphasis on muscular hypertrophy, three sets from eight to twelve repetitions under 70% of 1-RM, with a one minute thirty-second break between sets. During the training session, the cardiopulmonary variables were measured with a metabolic gas analyzer and a telemetry module. RESULTS: The results of the oxygen consumption in the training session were from 8.43 + 1.76 ml/kg/min and of the heart rate of 108.08 + 15.26 bpm. The results of the oxygen consumption and of the heart rate in the training were lower (p < 0.01) than in the ventilatory threshold and of the oxygen consumption and the heart rate reserves. CONCLUSION: The obtained data show that the present protocol of strength training provided low overload to the cardiopulmonary system of young women.

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PURPOSE: To evaluate the sensitivity and specificity of machine learning classifiers (MLCs) for glaucoma diagnosis using Spectral Domain OCT (SD-OCT) and standard automated perimetry (SAP). METHODS: Observational cross-sectional study. Sixty two glaucoma patients and 48 healthy individuals were included. All patients underwent a complete ophthalmologic examination, achromatic standard automated perimetry (SAP) and retinal nerve fiber layer (RNFL) imaging with SD-OCT (Cirrus HD-OCT; Carl Zeiss Meditec Inc., Dublin, California). Receiver operating characteristic (ROC) curves were obtained for all SD-OCT parameters and global indices of SAP. Subsequently, the following MLCs were tested using parameters from the SD-OCT and SAP: Bagging (BAG), Naive-Bayes (NB), Multilayer Perceptron (MLP), Radial Basis Function (RBF), Random Forest (RAN), Ensemble Selection (ENS), Classification Tree (CTREE), Ada Boost M1(ADA),Support Vector Machine Linear (SVML) and Support Vector Machine Gaussian (SVMG). Areas under the receiver operating characteristic curves (aROC) obtained for isolated SAP and OCT parameters were compared with MLCs using OCT+SAP data. RESULTS: Combining OCT and SAP data, MLCs' aROCs varied from 0.777(CTREE) to 0.946 (RAN).The best OCT+SAP aROC obtained with RAN (0.946) was significantly larger the best single OCT parameter (p<0.05), but was not significantly different from the aROC obtained with the best single SAP parameter (p=0.19). CONCLUSION: Machine learning classifiers trained on OCT and SAP data can successfully discriminate between healthy and glaucomatous eyes. The combination of OCT and SAP measurements improved the diagnostic accuracy compared with OCT data alone.

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Universidade Estadual de Campinas . Faculdade de Educação Física

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Universidade Estadual de Campinas . Faculdade de Educação Física