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Raw data related to: Sample Size for Oxidative Stress and Inflammation When Treating Multiple Sclerosis with Interferon-b1a and Coenzyme Q10

<p><strong>Abstract&nbsp;</strong></p> <p>Studying multiple sclerosis (MS) and its treatments requires the use of biomarkers for&nbsp;underlying pathological mechanisms. We aim to estimate the required sample size for detecting&nbsp;variations of biomarkers of inflammation and oxidative stress. This is a post-hoc analysis on&nbsp;60 relapsing-remitting MS patients treated with Interferon-1a and Coenzyme Q10 for 3 months&nbsp;in an open-label crossover design over 6 months. At baseline and at the 3 and 6-month visits, we&nbsp;measured markers of scavenging activity, oxidative damage, and inflammation in the peripheral&nbsp;blood (180 measurements). Variations of laboratory measures (treatment eect) were estimated using&nbsp;mixed-eect linear regression models (including age, gender, disease duration, baseline expanded&nbsp;disability status scale (EDSS), and the duration of Interferon-1a treatment as covariates; creatinine&nbsp;was also included for uric acid analyses), and were used for sample size calculations. Hypothesizing&nbsp;a clinical trial aiming to detect a 70% eect in 3 months (power&nbsp;<strong>=&nbsp;</strong>80% alpha-error&nbsp;<strong>=&nbsp;</strong>5%), the sample&nbsp;size per treatment arm would be 1 for interleukin (IL)-3 and IL-5, 4 for IL-7 and IL-2R, 6 for IL-13,&nbsp;14 for IL-6, 22 for IL-8, 23 for IL-4, 25 for activation-normal T cell expressed and secreted (RANTES),&nbsp;26 for tumor necrosis factor (TNF)-, 27 for IL-1, and 29 for uric acid. Peripheral biomarkers of&nbsp;oxidative stress and inflammation could be used in proof-of-concept studies to quickly screen the&nbsp;mechanisms of action of MS treatments.</p> <p><strong>Progetto giovani ricercatori&nbsp;</strong>[GR-2016-02363725] dal titolo: &quot;Immune Tolerance, Metabolism and Multiple Sclerosis: Novel Molecular Tools to Monitor Disease Pathogenesis and Progression&quot;</p>

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28/100

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4
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4
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16
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0
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4