8 resultados para Detectores de fibras ópticas
em Repositório da Produção Científica e Intelectual da Unicamp
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PURPOSE: To evaluate changes in retinal nerve fiber layer thickness as measured by scanning laser polarimetry (SLP) after the use of medication to reduce intraocular pressure (IOP) in glaucomatous or ocular hypertensive patients. METHODS: The authors prospectively enrolled 37 eyes of 37 patients in whom IOP was reduced by more than 25% after the use of medication. The images were obtained before and 15 to 30 days after the introduction of medication. The SLP parameters measured before and after the use of medication were compared using paired Student's t Test. RESULTS: The mean IOP was significantly reduced from 26.57±4.23 mmHg to 16.54 ±2.92 mmHg after the use of medication (p<0.05). None of the 10 SLP analyzed parameters was significantly affected by the reduction of IOP with medication (p>0.05). CONCLUSION: The retinal nerve fiber layer thickness, as measured by SLP, is not affected by the reduction of IOP with medication in patients with glaucoma or ocular hypertension.
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The aim of this work was to describe the morphology and ontogeny of P. riedelii fruits to aid in taxonomic, ecological and phylogenetic studies in Apocynaceae. Fruits were fixed in FAA, embedded in plastic resin, sectioned at 10 ìm and stained with toluidine blue, for structural analysis. The fruit of P. riedelii is a follicarium, with two follicular fruitlets. The epicarp is one-cell-layered, with trichomes and thick cuticle. The mesocarp, originating from fundamental ovary tissue, is parenchymatous with laticifers, non-lignified fibers and vascular bundles. The endocarp sensu lato is two-celllayered of crossed sclereids, originating from the inner ovary epidermis and from a single layer of parenchyma cells of fundamental ovary tissue. Follicle dehiscence is lateral and the dehiscence process involves anatomical characteristics such as a dehiscence zone with thin-walled cells, non-lignified fibers in the mesocarp and crossed sclereids in the endocarp.
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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
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Universidade Estadual de Campinas . Faculdade de Educação Física
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Universidade Estadual de Campinas . Faculdade de Educação Física
Resumo:
Universidade Estadual de Campinas. Faculdade de Educação Física