Oral Contraceptive Use and Clinical Outcomes in Patients with Multiple Sclerosis

Experimental and clinical data suggest a role of sex steroids in the pathogenesis of multiple sclerosis (MS). Scant information is available about the potential effect of oral contraceptive (OC) use on the prognosis of the disease. We aimed to evaluate this. The study population consisted of 132 women with relapsing-remitting MS before receiving disease modifying treatment and a mean disease duration 6.2 (SD 5.1) years. Three groups of patients were distinguished according to their OC behavior: [1] never-users, patients who never used OC [2] past-users, patients who stopped OC use before disease onset, and [3] after-users, those who used these drugs after disease onset. Multiple linear and logistic regression models were used to analyze the association between oral contraceptive use and annualized relapse rates, disability accumulation and severity of the disease. After-user patients had lower Expanded Disability Status Scale (EDSS) and Multiple Sclerosis Severity Score (MSSS) values than never users (p<0.001 and p=0.002, respectively) and past users (p=0.010 and p=0.002, respectively). These patients were also more likely to have a benign disease course (MSSS<2.5) than never and past users together (OR: 4.52, 95%CI: 2.13-9.56, p<0.001). This effect remained significant after adjustment for confounders, including smoking and childbirths (OR: 2.97, 95%CI: 1.24, 6.54, p=0.011 and for MSSS β: -1.04; 95% C.I. -1.78, -0.30, p=0.006). These results suggest that OC use in women with relapsing-remitting MS is possible associated with a milder disabling disease course.

Potential risk factors for multiple sclerosis in Rio de Janeiro: a case-control study.

PURPOSE. To evaluate potential risk factors for the development of multiple sclerosis in Brazilian patients. METHOD. A case control study was carried out in 81 patients enrolled at the Department of Neurology of the Hospital da Lagoa in Rio de Janeiro, and 81 paired controls. A standardized questionnaire on demographic, social and cultural variables, and medical and family history was used. Statistical analysis was performed using descriptive statistics and conditional logistic regression models with the SPSS for Windows software program. RESULTS. Having standard vaccinations (vaccinations specified by the Brazilian government) (OR=16.2; 95% CI=2.3-115.2), smoking (OR=7.6; 95% CI=2.1-28.2), being single (OR=4.7; 95% CI=1.4-15.6) and eating animal brain (OR=3.4; 95% CI=1.2-9.8) increased the risk of developing MS. CONCLUSIONS. RESULTS of this study may contribute towards better awareness of the epidemiological characteristics of Brazilian patients with multiple sclerosis.

Uncertainty Quantification Of A Semi-Supervised Support Vector Regression Reservoir Model

Uncertainty quantification of petroleum reservoir models is one of the present challenges, which is usually approached with a wide range of geostatistical tools linked with statistical optimisation or/and inference algorithms. Recent advances in machine learning offer a novel approach to model spatial distribution of petrophysical properties in complex reservoirs alternative to geostatistics. The approach is based of semisupervised learning, which handles both ?labelled? observed data and ?unlabelled? data, which have no measured value but describe prior knowledge and other relevant data in forms of manifolds in the input space where the modelled property is continuous. Proposed semi-supervised Support Vector Regression (SVR) model has demonstrated its capability to represent realistic geological features and describe stochastic variability and non-uniqueness of spatial properties. On the other hand, it is able to capture and preserve key spatial dependencies such as connectivity of high permeability geo-bodies, which is often difficult in contemporary petroleum reservoir studies. Semi-supervised SVR as a data driven algorithm is designed to integrate various kind of conditioning information and learn dependences from it. The semi-supervised SVR model is able to balance signal/noise levels and control the prior belief in available data. In this work, stochastic semi-supervised SVR geomodel is integrated into Bayesian framework to quantify uncertainty of reservoir production with multiple models fitted to past dynamic observations (production history). Multiple history matched models are obtained using stochastic sampling and/or MCMC-based inference algorithms, which evaluate posterior probability distribution. Uncertainty of the model is described by posterior probability of the model parameters that represent key geological properties: spatial correlation size, continuity strength, smoothness/variability of spatial property distribution. The developed approach is illustrated with a fluvial reservoir case. The resulting probabilistic production forecasts are described by uncertainty envelopes. The paper compares the performance of the models with different combinations of unknown parameters and discusses sensitivity issues.