On The Distinct Molecular Architectures Of Dipping- And Spray-lbl Films Containing Lipid Vesicles.

The introduction of spraying procedures to fabricate layer-by-layer (LbL) films has brought new possibilities for the control of molecular architectures and for making the LbL technique compliant with industrial processes. In this study we show that significantly distinct architectures are produced for dipping and spray-LbL films of the same components, which included DODAB/DPPG vesicles. The films differed notably in their thickness and stratified nature. The electrical response of the two types of films to aqueous solutions containing erythrosin was also different. With multidimensional projections we showed that the impedance for the DODAB/DPPG spray-LbL film is more sensitive to changes in concentration, being therefore more promising as sensing units. Furthermore, with surface-enhanced Raman scattering (SERS) we could ascribe the high sensitivity of the LbL films to adsorption of erythrosin.

Comparative analysis of adverse events between infliximab and adalimumab in Crohn's disease management: a Brazilian single-centre experience

INTRODUCTION: Data is scarce regarding adverse events (AE) of biological therapy used in the management of Crohn's Disease (CD) among Brazilian patients. OBJECTIVES: To analyse AE prevalence and profile in patients with CD treated with Infliximab (IFX) or Adalimumab (ADA) and to verify whether there are differences between the two drugs. METHOD: Retrospective observational single-centre study of CD patients on biological therapy. Variables analysed: Demographic data, Montreal classification, biological agent administered, treatment duration, presence and type of AE and the need for treatment interruption. RESULTS: Forty-nine patients were analysed, 25 treated with ADA and 24 with IFX. The groups were homogeneous in relation to the variables studied. The average follow-up period for the group treated with ADA was 19.3 months and 21.8 months for the IFX group (p = 0.585). Overall, 40% (n = 10) of patients taking ADA had AE compared with 50% (n = 12) of IFX users (p = 0.571). There was a tendency towards higher incidence of cutaneous and infusion reactions in the IFX group and higher incidence of infections in the ADA treated group, although without significant difference. CONCLUSIONS: No difference was found in the AE prevalence and profile between ADA and IFX CD patients in the population studied.

Sensitivity and specificity of machine learning classifiers for glaucoma diagnosis using Spectral Domain OCT and standard automated perimetry

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.