Time to first relapse.

<p>Beneath the plot patients at risk and number of events (in brackets) by treatment were reported for each interval of 6 months. Abbreviations: AZA, azathioprine; IFN, interferon.</p

Non-inferiority of the effect AZA vs. IFN on new T2 lesions over 2 years.

<p>One-sided 99% CI (upper-limit, U_L = 1.63), and one-sided 95% CI (U_L = 1.45), of the effect of AZA vs. IFNs as for annualized new T2 lesion rate ratio (RR_AZA/IFN), compared with the pre-established non-inferiority margin (M = 1.84), representing an effect of AZA vs. IFNs equivalent to the 73% of the effect of IFNs vs placebo. Abbreviations: AZA, azathioprine; IFN, interferon; PY, person-years; RR, rate ratio.</p

Adverse Events.

<p>Abbreviations: AZA, azathioprine; IFN, interferon; PY, person-years.</p>1<p>P-values for AZA vs. IFN comparison were obtained through χ² test with one degree of freedom for rate comparison, discontinued interventions due to adverse events, seriousness of adverse event, and correlation of event with treatment.</p>2<p>All 95% CI were estimated using the exact method.</p>3<p>Liver enzymes, thyroid function and bilirubin level.</p>4<p>The sum does not add up to the total because of some missing values.</p>5<p>Seriousness judged by the treating neurologist. SAEs classified according to the National Cancer Institute Common Terminology Criteria for AE <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0113371#pone.0113371-CTC1" target="_blank">[21]</a> are reported in Table S1 in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0113371#pone.0113371.s004" target="_blank">File S1</a>.</p><p>Adverse Events.</p

Primary clinical outcome over 2 years: non-inferiority of effect of AZA vs. IFN, represented as annualized relapse rate ratio (RR_AZA/IFN) compared with the pre-established non-inferiority margin M ( = 1.23) and with a margin M₁ = 1.0.

<p>One-sided 99% CI of the 0.67 ratio (upper-limit, U_L = 1.12), represents an effect of AZA vs. IFNs equivalent to at least 75% of the effect of IFNs vs. Placebo. One-sided 95% CI of the same ratio (U_L = 0.96), represents an effect of AZA vs. IFNs equivalent to at least 100% of the effect of IFNs vs. Placebo. Abbreviations: AZA, azathioprine; IFN, interferon; PY, person-years; RR, rate ratio.</p

Secondary clinical outcomes.

<p>Abbreviations: AZA, azathioprine; IFN, interferon.</p>1<p>P-values for AZA vs. IFN comparison were obtained through χ² test with one degree of freedom for rate comparison, χ² test with two degrees of freedom for number of patients with relapses, Fisher's exact test for patients with no confirmed disability progression, and t-test for change in EDSS score.</p>2<p>The analyses were adjusted for number of relapses during the previous two years, baseline EDSS score, and duration of disease from symptom onset.</p>3<p>The analyses were based on 56 AZA and 50 IFN patients respectively, because of some missing values.</p>4<p>A confirmed disability progression was defined as an increase of no less than one point of the EDSS score confirmed at least after six months; 95% CI were estimated through the exact method. All the patients, with the exception of two (who did not report a disability progression), had a baseline EDSS score between 1 and 5.</p>5<p>Adjusted for baseline EDSS score.</p><p>Secondary clinical outcomes.</p

Flow-chart: patient allocation and follow-up.

<p>Abbreviations: AZA, azathioprine; IFN, interferon; ITT, intention to treat; PP, per-protocol. ¹One missing CRF at month 12.</p

Baseline characteristics of the patients.

<p>Abbreviations: AZA, azathioprine; EDSS, Expanded Disability Status Scale; IFN, interferon; SD, standard deviation.</p>1<p>P-values for AZA vs. IFN comparison were obtained through: χ² test with one or two degrees of freedom for sex, number of patients with previous histories of AZA/IFN treatment, number of patients with relapses with concomitant disease and with Gd+ lesions; t-test for age; Mann-Whitney test for duration of disease, number of relapses, EDSS score, number of Gd+ lesions and T2 lesion load.</p>2<p>Protocol violations.</p>3<p>Scores on the EDSS range from 0 to 10, with higher scores indicating greater degree of disability.</p>4<p>The sum does not add up to the total because of some missing values.</p><p>Baseline characteristics of the patients.</p

MRI outcomes. New brain lesions.

<p>Abbreviations: AZA, azathioprine; IFN, interferon.</p>1<p>P-values for AZA vs. IFN comparison were obtained through χ² test with one degree of freedom for rate comparison, χ² test with two degrees of freedom for number of patients with lesions, and Mann-Whitney test for Gd+ lesion number.</p><p>MRI outcomes. New brain lesions.</p

Carbon Nanotube Scaffolds Instruct Human Dendritic Cells: Modulating Immune Responses by Contacts at the Nanoscale

Nanomaterials interact with cells and modify their function and biology. Manufacturing this ability can provide tissue-engineering scaffolds with nanostructures able to influence tissue growth and performance. Carbon nanotube compatibility with biomolecules motivated ongoing interest in the development of biosensors and devices including such materials. More recently, carbon nanotubes have been applied in several areas of nerve tissue engineering to study cell behavior or to instruct the growth and organization of neural networks. To gather further knowledge on the true potential of future constructs, in particular to assess their immune-modulatory action, we evaluate carbon nanotubes interactions with human dendritic cells (DCs). DCs are professional antigen-presenting cells and their behavior can predict immune responses triggered by adhesion-dependent signaling. Here, we incorporate DC cultures to carbon nanotubes and we show by phenotype, microscopy, and transcriptional analysis that in vitro differentiated and activated DCs show when interfaced to carbon nanotubes a lower immunogenic profile

Carbon Nanotube Scaffolds Instruct Human Dendritic Cells: Modulating Immune Responses by Contacts at the Nanoscale

Nanomaterials interact with cells and modify their function and biology. Manufacturing this ability can provide tissue-engineering scaffolds with nanostructures able to influence tissue growth and performance. Carbon nanotube compatibility with biomolecules motivated ongoing interest in the development of biosensors and devices including such materials. More recently, carbon nanotubes have been applied in several areas of nerve tissue engineering to study cell behavior or to instruct the growth and organization of neural networks. To gather further knowledge on the true potential of future constructs, in particular to assess their immune-modulatory action, we evaluate carbon nanotubes interactions with human dendritic cells (DCs). DCs are professional antigen-presenting cells and their behavior can predict immune responses triggered by adhesion-dependent signaling. Here, we incorporate DC cultures to carbon nanotubes and we show by phenotype, microscopy, and transcriptional analysis that in vitro differentiated and activated DCs show when interfaced to carbon nanotubes a lower immunogenic profile