Genomics and proteomics: role in the management of multiple sclerosis

Epidemiological studies and neuro-imaging have provided important insights into the natural course and prognostic factors of multiple sclerosis (MS), but our ability to predict different courses of the disease, and especially its response to treatment, is still very limited. Pharmacogenetic, pharmacogenomic and proteomic studies aim to assess gene and protein function in disease and promise to help to fill this important gap in our knowledge. Such studies may increase our understanding of disease mechanisms and responses to therapeutic compounds. Large-scale transcriptional expression profiling can be performed using gene chip microarrays; this technology allows screening for differentially expressed genes without having well-defined underlying hypotheses ("discovery-driven research”). To complement the technique, real time reverse transcription and polymerase chain reaction (RT-PCR) can be used for more targeted profiling and provides quantitative data on pre-selected genes. However, to maximise their clinical utility, expression profiling results need to be combined with well-documented clinical and imaging data. Two forthcoming studies will investigate the long-term effects of early treatment with interferon beta-1b (IFNβ) on the course of MS. The BENEFIT (BEtaseron®/Betaferon® in Newly Emerging MS for Initial Treatment) study will incorporate pharmacogenetic and pharmacogenomic analyses to determine the genetic elements controlling treatment response. BEST-PGx (Betaferon®/Betaseron® in Early relapsing-remitting MS Surveillance Trial—Pharmacogenomics) is an exploratory 2-year study that will investigate the value of RNA expression profiling and pharmacogenetics in predicting treatment response to IFNβ in patients with early relapsing MS. The main goal of BEST-PGx is the identification of differences in gene expression profiles of patients showing differential treatment responses. In addition, this study may reveal new information relevant to the mechanism of action of interferon treatment in MS and also to differences in the underlying pathology of the immune system. These data may help us approach the goal of a really "individualised therapy” with increased efficacy, reduced adverse drug reactions and more efficient use of healthcare resource

Characterisation of MS phenotypes across the age span using a novel data set integrating 34 clinical trials (NO.MS cohort): age is a key contributor to presentation

Background: The Oxford Big Data Institute, multiple sclerosis (MS) physicians and Novartis aim to address unresolved questions in MS with a novel comprehensive clinical trial data set. Objective: The objective of this study is to describe the Novartis–Oxford MS (NO.MS) data set and to explore the relationships between age, disease activity and disease worsening across MS phenotypes. Methods: We report key characteristics of NO.MS. We modelled MS lesion formation, relapse frequency, brain volume change and disability worsening cross-sectionally, as a function of patients’ baseline age, using phase III study data (≈8000 patients). Results: NO.MS contains data of ≈35,000 patients (>200,000 brain images from ≈10,000 patients), with >10 years follow-up. (1) Focal disease activity is highest in paediatric patients and decreases with age, (2) brain volume loss is similar across age and phenotypes and (3) the youngest patients have the lowest likelihood (<25%) of disability worsening over 2 years while risk is higher (25%–75%) in older, disabled or progressive MS patients. Young patients benefit most from treatment. Conclusion: NO.MS will illuminate questions related to MS characterisation, progression and prognosis. Age modulates relapse frequency and, thus, the phenotypic presentation of MS. Disease worsening across all phenotypes is mediated by age and appears to some extent be independent from new focal inflammatory activity

Siponimod vs placebo in active secondary progressive multiple sclerosis: a post hoc analysis from the phase 3 EXPAND study.

BACKGROUND Siponimod is a sphingosine 1-phosphate receptor modulator approved for active secondary progressive multiple sclerosis (aSPMS) in most countries; however, phase 3 EXPAND study data are from an SPMS population with/without disease activity. A need exists to characterize efficacy/safety of siponimod in aSPMS. METHODS Post hoc analysis of participants with aSPMS (≥ 1 relapse in 2 years before study and/or ≥ 1 T1 gadolinium-enhancing [Gd +] magnetic resonance imaging [MRI] lesions at baseline) receiving oral siponimod (2 mg/day) or placebo for up to 3 years in EXPAND. ENDPOINTS 3-month/6-month confirmed disability progression (3mCDP/6mCDP); 3-month confirmed ≥ 20% worsening in Timed 25-Foot Walk (T25FW); 6-month confirmed improvement/worsening in Symbol Digit Modalities Test (SDMT) scores (≥ 4-point change); T2 lesion volume (T2LV) change from baseline; number of T1 Gd + lesions baseline-month 24; number of new/enlarging (N/E) T2 lesions over all visits. RESULTS Data from 779 participants with aSPMS were analysed. Siponimod reduced risk of 3mCDP/6mCDP vs placebo (by 31%/37%, respectively; p < 0.01); there was no significant effect on T25FW. Siponimod increased likelihood of 6-month confirmed SDMT improvement vs placebo (by 62%; p = 0.007) and reduced risk of 6-month confirmed SDMT worsening (by 27%; p = 0.060). Siponimod was associated with less increase in T2LV (1316.3 vs 13.3 mm3; p < 0.0001), and fewer T1 Gd + and N/E T2 lesions than placebo (85% and 80% reductions, respectively; p < 0.0001). CONCLUSIONS In aSPMS, siponimod reduced risk of disability progression and was associated with benefits on cognition and MRI outcomes vs placebo. TRIAL REGISTRATION ClinicalTrials.gov number: NCT01665144

Usability and Acceptability of ASSESS MS: Assessment of Motor Dysfunction in Multiple Sclerosis Using Depth-Sensing Computer Vision

Background: Sensor-based recordings of human movements are becoming increasingly important for the assessment of motor symptoms in neurological disorders beyond rehabilitative purposes. ASSESS MS is a movement recording and analysis system being developed to automate the classification of motor dysfunction in patients with multiple sclerosis (MS) using depth-sensing computer vision. It aims to provide a more consistent and finer-grained measurement of motor dysfunction than currently possible. Objective: To test the usability and acceptability of ASSESS MS with health professionals and patients with MS. Methods: A prospective, mixed-methods study was carried out at 3 centers. After a 1-hour training session, a convenience sample of 12 health professionals (6 neurologists and 6 nurses) used ASSESS MS to capture recordings of standardized movements performed by 51 volunteer patients. Metrics for effectiveness, efficiency, and acceptability were defined and used to analyze data captured by ASSESS MS, video recordings of each examination, feedback questionnaires, and follow-up interviews. Results: All health professionals were able to complete recordings using ASSESS MS, achieving high levels of standardization on 3 of 4 metrics (movement performance, lateral positioning, and clear camera view but not distance positioning). Results were unaffected by patients’ level of physical or cognitive disability. ASSESS MS was perceived as easy to use by both patients and health professionals with high scores on the Likert-scale questions and positive interview commentary. ASSESS MS was highly acceptable to patients on all dimensions considered, including attitudes to future use, interaction (with health professionals), and overall perceptions of ASSESS MS. Health professionals also accepted ASSESS MS, but with greater ambivalence arising from the need to alter patient interaction styles. There was little variation in results across participating centers, and no differences between neurologists and nurses. Conclusions: In typical clinical settings, ASSESS MS is usable and acceptable to both patients and health professionals, generating data of a quality suitable for clinical analysis. An iterative design process appears to have been successful in accounting for factors that permit ASSESS MS to be used by a range of health professionals in new settings with minimal training. The study shows the potential of shifting ubiquitous sensing technologies from research into the clinic through a design approach that gives appropriate attention to the clinic environment

Besov Regularity of Solutions to Navier-Stokes Equations

This thesis is concerned with the regularity of solutions to Navier-Stokes and Stokes equation on domains with point singularities, namely polyhedral domains contained in R3 and general bounded Lipschitz domains in Rd, d ≥ 3 with connected boundary. The Navier-Stokes equations provide a mathematical model of the motion of a uid. These Navier-Stokes equations form the basis for the whole world of computational uid dynamics, and therefore they are considered as maybe the most important PDEs known so far. We consider the stationary (Navier-)Stokes equations. The study the Besov regularity of the solution in the scale BsƬ (LƬ (Ω))d, 1/Ƭ = s/d + 1/2 of Besov spaces. This scale is the so-called adaptivity scale. The parameter s determines the approximation order of adaptive numerical wavelet schemes and other nonlinear approximation methods when the error is measured in the L2-norm. In contrast to this the convergence order of linear schemes is determined by the classical L2-Sobolev regularity. In many papers the Besov regularity of the solution to various operator equations/partial differential equations was investigated. The proof of Besov regularity in the adaptivity scale was in many contributions performed by combining weighted Sobolev regularity results with characterizations of Besov spaces by wavelet expansions. Choosing a suitable wavelet basis the coeffcients of the wavelet expansion of the solution can be estimated by exploiting the weighted Sobolev regularity of the solution, such that a certain Besov regularity can be established. This technique was applied for the Stokes system in all papers which are part of this thesis. For achieving Besov regularity for Navier-Stokes equation we used a fixed point argument. We formulate the Navier-Stokes equation as a fixed point equation and therefore regularity results for the corresponding Stokes equation can be transferred to the non-linear case. In the first paper "Besov regularity for the Stokes and the Navier-Stokes system in polyhedral domains" we considered the stationary Stokes- and the Navier-Stokes equations in polyhedral domains. Exploiting weighted Sobolev estimates for the solution we proved that the Besov regularity of the solutions to these equations exceed their Sobolev regularity. In the second paper "Besov Regularity for the Stationary Navier-Stokes Equation on Bounded Lipschitz Domains" we have investigated the stationary (Navier-)Stokes equations on bounded Lipschitz domain. Based on weighted Sobolev estimates again we could establish a Besov regularity result for the solution to the Stokes system. By applying Banach's fixed point theorem we transferred these results to the non-linear Navier-Stokes equation. In order to apply the fixed point theorem we had to require small data and small Reynolds number

Behavioral therapy: emotion and pain, a common anatomical background

Emotion and pain are closely intertwined in the brain, as the human experience of pain includes both affective and nociceptive components. Although each of these components relies on a different system in the brain, the two systems converge on the anterior cingulate and insular cortices, which interact with the prefrontal cortex and other frontal structures to influence behavior. Both emotional and physical pain elicit activity in these common areas, and conditions that affect one system (e.g., drugs, neural plasticity) may affect the function of the other—ultimately altering the experience of pain. Changes in these areas and their connections may even contribute to the chronification of pain. This relationship should not be overlooked in the treatment of painful conditions, including headache. Nonpharmacological therapies, such as cognitive behavioral therapy, yoga, biofeedback, and meditation, that are often used for enhancing emotional regulation, are increasingly being turned to for augmenting management of migraine and pain. Because of the overlap between emotion and pain, these therapies are likely acting through similar mechanisms, and emotional cues can be sensitive indicators of treatment-related changes in patients

Autoencoder as a New Method for Maintaining Data Privacy While Analyzing Videos of Patients With Motor Dysfunction: Proof-of-Concept Study.

BACKGROUND In chronic neurological diseases, especially in multiple sclerosis (MS), clinical assessment of motor dysfunction is crucial to monitor the disease in patients. Traditional scales are not sensitive enough to detect slight changes. Video recordings of patient performance are more accurate and increase the reliability of severity ratings. When these recordings are automated, quantitative disability assessments by machine learning algorithms can be created. Creation of these algorithms involves non-health care professionals, which is a challenge for maintaining data privacy. However, autoencoders can address this issue. OBJECTIVE The aim of this proof-of-concept study was to test whether coded frame vectors of autoencoders contain relevant information for analyzing videos of the motor performance of patients with MS. METHODS In this study, 20 pre-rated videos of patients performing the finger-to-nose test were recorded. An autoencoder created encoded frame vectors from the original videos and decoded the videos again. The original and decoded videos were shown to 10 neurologists at an academic MS center in Basel, Switzerland. The neurologists tested whether the 200 videos were human-readable after decoding and rated the severity grade of each original and decoded video according to the Neurostatus-Expanded Disability Status Scale definitions of limb ataxia. Furthermore, the neurologists tested whether ratings were equivalent between the original and decoded videos. RESULTS In total, 172 of 200 (86.0%) videos were of sufficient quality to be ratable. The intrarater agreement between the original and decoded videos was 0.317 (Cohen weighted kappa). The average difference in the ratings between the original and decoded videos was 0.26, in which the original videos were rated as more severe. The interrater agreement between the original videos was 0.459 and that between the decoded videos was 0.302. The agreement was higher when no deficits or very severe deficits were present. CONCLUSIONS The vast majority of videos (172/200, 86.0%) decoded by the autoencoder contained clinically relevant information and had fair intrarater agreement with the original videos. Autoencoders are a potential method for enabling the use of patient videos while preserving data privacy, especially when non-health-care professionals are involved