525 research outputs found
An open label randomised clinical trial of atorvastatin in combination with IFN beta-1a in active MS patients: 12 months results.
No full textSubcutaneous interferon β-1a may protect against cognitive impairment in patients with relapsing-remitting multiple sclerosis: 5-year follow-up of the COGIMUS study
No full textOBJECTIVE:
To assess the effects of subcutaneous (sc) interferon (IFN) -1a on cognition over 5 years in mildly disabled patients with relapsing-remitting multiple sclerosis (RRMS).
METHODS:
Patients aged 18-50 years with RRMS (Expanded Disability Status Scale score ≤4.0) who had completed the 3-year COGIMUS study underwent standardized magnetic resonance imaging, neurological examination, and neuropsychological testing at years 4 and 5. Predictors of cognitive impairment at year 5 were identified using multivariate analysis.
RESULTS:
Of 331 patients who completed the 3-year COGIMUS study, 265 participated in the 2-year extension study, 201 of whom (75.8%; sc IFN β-1a three times weekly: 44 µg, n = 108; 22 µg, n = 93) completed 5 years' follow-up. The proportion of patients with cognitive impairment in the study population overall remained stable between baseline (18.0%) and year 5 (22.6%). The proportion of patients with cognitive impairment also remained stable in both treatment groups between baseline and year 5, and between year 3 and year 5. However, a significantly higher proportion of men than women had cognitive impairment at year 5 (26.5% vs 14.4%, p = 0.046). Treatment with the 22 versus 44 µg dose was predictive of cognitive impairment at year 5 (hazard ratio 0.68; 95% confidence interval 0.48-0.97).
CONCLUSIONS:
This study suggests that sc IFN β-1a dose-dependently stabilizes or delays cognitive impairment over a 5-year period in most patients with mild RRMS. Women seem to be more protected against developing cognitive impairment, which may indicate greater response to therapy or the inherently better prognosis associated with female sex in MS
Randomized placebo-controlled trial of mitoxantrone in relapsing-remitting multiple sclerosis: 24-month clinical and MRI outcome.
No full textInvestigation of the reactivity of AlCl3 and CoCl2 toward molten alkali-metal nitrates in order to synthesize CoAl2O4
No full textCobalt aluminate CoAl2O4 powder, constituted of nano-sized crystallites, is prepared, involving the reactivity of AlCl3 and CoCl2 with molten alkali-metal nitrates. The reaction at 450 °C for 2 h leads to a mixture of spinel oxide Co3O4 and amorphous γ-Al2O3. It is transformed into the spinel oxide CoAl2O4 by heating at 1000 °C. The powders are mainly characterized by XRD, FTIR, ICP, electron microscopy and diffraction, X-EDS and diffuse reflection. Their properties are compared to those of powders obtained by solid state reactions of a mechanical mixture of chlorides or oxides submitted to the same thermal treatment
BENDAMUSTINE AND ALEMTUZUMAB (BEN CAM) COMBINATION IN RELAPSED AND REFRACTORY CHRONIC LYMPHOCYTIC LEUKAEMIA (CLL): PRELIMINARY REPORT OF THE ITALIAN TRIAL
No full textRandomized placebo-controlled trial of mitoxantrone in relapsing-remitting multiple sclerosis: 24-month clinical and MRI outcome.
No full text
Safety of the first dose of fingolimod for multiple sclerosis: results of an open-label clinical trial.
No full textSlow Cortical Potential BCI Classification Using Sparse Variational Bayesian Logistic Regression with Automatic Relevance Determination
No full textDetecting P300 slow-cortical ERPs poses a considerable challenge in signal processing due to the complex and non-stationary characteristics of a single-trial EEG signal. EEG-based neurofeedback training is a possible strategy to improve the social abilities in Autism-Spectrum Disorder (ASD) subjects. This paper presents a BCI P300 ERPs based protocol optimization used for the enhancement of joint-attention skills in ASD subjects, using a robust logistic regression with Automatic Relevance Determination based on full Variational Bayesian inference (VB-ARD). The performance of the proposed approach was investigated utilizing the IFMBE 2019 Scientific Challenge Competition dataset, which consisted of 15 ASD subjects who underwent a total of 7 BCI sessions spread over 4 months. The results showed that the proposed VB-ARD approach eliminates irrelevant channels and features effectively, producing a robust sparse model with 81.5 ± 12.0% accuracy in relatively short modeling computational time 19.3 ± 1.4 s, and it outperforms the standard regularized logistic regression in terms of accuracy and speed needed to produce the BCI model. This paper demonstrated the effectiveness of the probabilistic approach using Bayesian inference for the production of a robust BCI model. Considering the good classification accuracy over sessions and fast modeling time the proposed method could be a useful tool used for the BCI based protocol for the improvement of joint-attention ability in ASD subjects
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